document.documentElement.clientHeight)?document.body.clientHeight:document.documentElement.clientHeight;
- }
- return clientHeight;
- }
-
- function setNativeValue(element, value) {
- const valueSetter = Object.getOwnPropertyDescriptor(element.__proto__, 'value').set;
- const prototype = Object.getPrototypeOf(element);
- const prototypeValueSetter = Object.getOwnPropertyDescriptor(prototype, 'value').set;
-
- if (valueSetter && valueSetter !== prototypeValueSetter) {
- prototypeValueSetter.call(element, value);
- } else {
- valueSetter.call(element, value);
- }
- element.dispatchEvent(new Event('input', { bubbles: true }));
- }
- function get_clear_innerHTML(innerHTML) {
- innerHTML = innerHTML.replace(/|<\\/p>|\\n/g, '');
- regexp = /\\★☆(.*?)\\☆★/;
- match = innerHTML.match(regexp);
- if (match) {
- innerHTML = match[1];
- }
- return innerHTML;
- }
- function save_conversation(chatbot) {
- var conversations = new Array();
- var conversations_clear = new Array();
- for (var i = 0; i < chatbot.children.length; i++) {
- testid_icon = '☟:'; //'user'
- if (chatbot.children[i].dataset['testid'] == 'bot') {
- testid_icon = '☝:'; //'bot'
- }
- innerHTML = chatbot.children[i].innerHTML;
- conversations.push(testid_icon + innerHTML);
- if (innerHTML.indexOf(" 100) {
- this_width = 20;
- }
- img.style.width = this_width + "%";
- img.style.height = img.offsetWidth + 'px';
- }
- function load_conversation(chatbot) {
- var json_str = localStorage.getItem('chatgpt_conversations');
- if (json_str) {
- var conversations_clear = new Array();
- conversations = JSON.parse(json_str);
- for (var i = 0; i < conversations.length; i++) {
- innerHTML = conversations[i];
- if (innerHTML.indexOf("☝:") == -1) {
- className = "message user svelte-134zwfa";
- bgcolor = "#16a34a";
- testid = "user";
- testid_icon = '☟:'; //'user'
- } else {
- className = "message bot svelte-134zwfa";
- bgcolor = "#2563eb";
- testid = "bot";
- testid_icon = '☝:'; //'bot'
- }
- var new_div = document.createElement("div");
- new_div.className = className;
- new_div.style.backgroundColor = bgcolor;
- new_div.dataset.testid = testid;
- if (innerHTML.indexOf("data:image/jpeg") >= 0) {
- new_div.style.width = "20%";
- new_div.style.padding = "0.2rem";
- new_div.onclick = function(e) {
- img_click(this);
- }
- setTimeout(function(){
- new_div.style.height = new_div.offsetWidth + 'px';
- new_div.children[0].setAttribute('style', 'max-width: none; width:100%');
- }, 10);
- }
- innerHTML = innerHTML.replace("☝:", "");
- innerHTML = innerHTML.replace("☟:", "");
- new_div.innerHTML = innerHTML;
- if (innerHTML.indexOf("null_") != -1) {
- new_div.style.display = 'none';
- }
- chatbot.appendChild(new_div);
-
- if (innerHTML.indexOf(" gradio-app').shadowRoot;
- if (!gradioEl) {
- gradioEl = document.querySelector('body > gradio-app');
- }
-
- if (typeof window['gradioEl'] === 'undefined') {
- window['gradioEl'] = gradioEl;
-
- const page1 = window['gradioEl'].querySelectorAll('#page_1')[0];
- const page2 = window['gradioEl'].querySelectorAll('#page_2')[0];
-
- page1.style.display = "none";
- page2.style.display = "block";
- window['div_count'] = 0;
- window['chat_radio_0'] = window['gradioEl'].querySelectorAll('#chat_radio')[0].querySelectorAll('input[name=radio-chat_radio]')[0];
- window['chat_radio_1'] = window['gradioEl'].querySelectorAll('#chat_radio')[0].querySelectorAll('input[name=radio-chat_radio]')[1];
- window['chat_bot'] = window['gradioEl'].querySelectorAll('#chat_bot')[0];
- window['chat_bot1'] = window['gradioEl'].querySelectorAll('#chat_bot1')[0];
- window['my_prompt'] = window['gradioEl'].querySelectorAll('#my_prompt')[0].querySelectorAll('textarea')[0];
- window['my_prompt_en'] = window['gradioEl'].querySelectorAll('#my_prompt_en')[0].querySelectorAll('textarea')[0];
- window['chat_his'] = window['gradioEl'].querySelectorAll('#chat_history')[0].querySelectorAll('textarea')[0];
- chat_row = window['gradioEl'].querySelectorAll('#chat_row')[0];
- prompt_row = window['gradioEl'].querySelectorAll('#prompt_row')[0];
- window['chat_bot1'].children[1].children[0].textContent = '';
-
- clientHeight = getClientHeight();
- if (isMobile()) {
- output_htmls = window['gradioEl'].querySelectorAll('.output-html');
- for (var i = 0; i < output_htmls.length; i++) {
- output_htmls[i].style.display = "none";
- }
- new_height = (clientHeight - 250) + 'px';
- } else {
- new_height = (clientHeight - 350) + 'px';
- }
- chat_row.style.height = new_height;
- window['chat_bot'].style.height = new_height;
- window['chat_bot'].children[1].style.height = new_height;
- window['chat_bot1'].style.height = new_height;
- window['chat_bot1'].children[1].style.height = new_height;
- window['chat_bot1'].children[0].style.top = (parseInt(window['chat_bot1'].style.height)-window['chat_bot1'].children[0].offsetHeight-2) + 'px';
-
- prompt_row.children[0].style.flex = 'auto';
- prompt_row.children[0].style.width = '100%';
- window['gradioEl'].querySelectorAll('#chat_radio')[0].style.flex = 'auto';
- window['gradioEl'].querySelectorAll('#chat_radio')[0].style.width = '100%';
- prompt_row.children[0].setAttribute('style','flex-direction: inherit; flex: 1 1 auto; width: 100%;border-color: green;border-width: 1px !important;')
- window['chat_bot1'].children[1].setAttribute('style', 'border-bottom-right-radius:0;top:unset;bottom:0;padding-left:0.1rem');
- window['gradioEl'].querySelectorAll('#btns_row')[0].children[0].setAttribute('style', 'min-width: min(10px, 100%); flex-grow: 1');
- window['gradioEl'].querySelectorAll('#btns_row')[0].children[1].setAttribute('style', 'min-width: min(10px, 100%); flex-grow: 1');
-
- load_conversation(window['chat_bot1'].children[1].children[0]);
- window['chat_bot1'].children[1].scrollTop = window['chat_bot1'].children[1].scrollHeight;
-
- window['gradioEl'].querySelectorAll('#clear-btn')[0].onclick = function(e){
- if (confirm('Clear all outputs?')==true) {
- for (var i = window['chat_bot'].children[1].children[0].children.length-1; i >= 0; i--) {
- window['chat_bot'].children[1].children[0].removeChild(window['chat_bot'].children[1].children[0].children[i]);
- }
- for (var i = window['chat_bot1'].children[1].children[0].children.length-1; i >= 0; i--) {
- window['chat_bot1'].children[1].children[0].removeChild(window['chat_bot1'].children[1].children[0].children[i]);
- }
- window['div_count'] = 0;
- save_conversation(window['chat_bot1'].children[1].children[0]);
- }
- }
-
- function set_buttons(action) {
- window['submit-btn'].disabled = action;
- window['clear-btn'].disabled = action;
- window['chat_radio_0'].disabled = action;
- window['chat_radio_1'].disabled = action;
- btn_color = 'color:#000';
- if (action) {
- btn_color = 'color:#ccc';
- }
- window['submit-btn'].setAttribute('style', btn_color);
- window['clear-btn'].setAttribute('style', btn_color);
- window['chat_radio_0'].setAttribute('style', btn_color);
- window['chat_radio_1'].setAttribute('style', btn_color);
- }
- window['prevPrompt'] = '';
- window['doCheckPrompt'] = 0;
- window['prevImgSrc'] = '';
- window['checkChange'] = function checkChange() {
- try {
- if (window['chat_radio_0'].checked) {
- dot_flashing = window['chat_bot'].children[1].children[0].querySelectorAll('.dot-flashing');
-
- if (window['chat_bot'].children[1].children[0].children.length > window['div_count'] && dot_flashing.length == 0) {
- new_len = window['chat_bot'].children[1].children[0].children.length - window['div_count'];
- for (var i = 0; i < new_len; i++) {
- new_div = window['chat_bot'].children[1].children[0].children[window['div_count'] + i].cloneNode(true);
- window['chat_bot1'].children[1].children[0].appendChild(new_div);
- }
- window['div_count'] = window['chat_bot'].children[1].children[0].children.length;
- window['chat_bot1'].children[1].scrollTop = window['chat_bot1'].children[1].scrollHeight;
- save_conversation(window['chat_bot1'].children[1].children[0]);
- }
- if (window['chat_bot'].children[0].children.length > 1) {
- set_buttons(true);
- window['chat_bot1'].children[0].textContent = window['chat_bot'].children[0].children[1].textContent;
- } else {
- set_buttons(false);
- window['chat_bot1'].children[0].textContent = '';
- }
- } else {
- img_index = 0;
- draw_prompt_en = window['my_prompt_en'].value;
- if (window['doCheckPrompt'] == 0 && window['prevPrompt'] != draw_prompt_en) {
-
- console.log('_____draw_prompt_en___[' + draw_prompt_en + ']_');
- window['doCheckPrompt'] = 1;
- window['prevPrompt'] = draw_prompt_en;
-
- tabitems = window['gradioEl'].querySelectorAll('.tabitem');
- for (var i = 0; i < tabitems.length; i++) {
- inputText = tabitems[i].children[0].children[1].children[0].querySelectorAll('input')[0];
- setNativeValue(inputText, draw_prompt_en);
- }
- setTimeout(function() {
- window['draw_prompt'] = window['my_prompt'].value;
- btns = window['gradioEl'].querySelectorAll('button');
- for (var i = 0; i < btns.length; i++) {
- if (['Generate image','Run'].includes(btns[i].innerText)) {
- btns[i].click();
- }
- }
- window['doCheckPrompt'] = 0;
- }, 10);
- }
- tabitems = window['gradioEl'].querySelectorAll('.tabitem');
- imgs = tabitems[img_index].children[0].children[1].children[1].querySelectorAll("img");
- if (imgs.length > 0) {
- if (window['prevImgSrc'] !== imgs[0].src) {
- var user_div = document.createElement("div");
- user_div.className = "message user svelte-134zwfa";
- user_div.style.backgroundColor = "#16a34a";
- user_div.dataset.testid = 'user';
- user_div.innerHTML = "作画: " + window['draw_prompt'] + "
", "").replace("
", "")
- if chat_history != '':
- chat_history_list = json.loads(chat_history)
- chat_history_list.append(f'☟:{input}')
-
- output, response_usage = openai_create(chat_history_list, model_radio)
- print(f'response_usage={response_usage}')
- return output
-
-def chat(input0, input1, chat_radio, model_radio, all_chat_history, chat_history):
- all_chat = []
- if all_chat_history != '':
- all_chat = json.loads(all_chat_history)
-
- if len(input0) == 0:
- return all_chat, json.dumps(all_chat), input0, input1
-
- if chat_radio == "Talk to chatGPT":
- response = get_response_from_openai(input0, chat_history, model_radio)
- all_chat.append((input0, response))
- return all_chat, json.dumps(all_chat), '', input1
- else:
- prompt_en = getTextTrans(input0, source='zh', target='en') + f',{random.randint(0,sys.maxsize)}'
- return all_chat, json.dumps(all_chat), input0, prompt_en
-
-def chat_radio_change(chat_radio):
- if chat_radio == "Talk to chatGPT":
- return gr.Radio.update(visible=True), gr.Text.update(visible=True)
- else:
- return gr.Radio.update(visible=False), gr.Text.update(visible=False)
-
-with gr.Blocks(title='Talk to chatGPT') as demo:
- with gr.Row(elem_id="page_0", visible=False) as page_0:
- gr.HTML("You can duplicating this space and use your own session token:
")
- with gr.Group(elem_id="page_1", visible=True) as page_1:
- with gr.Box():
- with gr.Row():
- start_button = gr.Button("Let's talk to chatGPT!", elem_id="start-btn", visible=True)
- start_button.click(fn=None, inputs=[], outputs=[], _js=start_work)
-
- with gr.Row(elem_id="page_2", visible=False) as page_2:
- with gr.Row(elem_id="chat_row"):
- chatbot = gr.Chatbot(elem_id="chat_bot", visible=False).style(color_map=("green", "blue"))
- chatbot1 = gr.Chatbot(elem_id="chat_bot1").style(color_map=("green", "blue"))
- with gr.Row(elem_id="prompt_row"):
- prompt_input0 = gr.Textbox(lines=2, label="input", elem_id="my_prompt", show_label=True)
- prompt_input1 = gr.Textbox(lines=4, label="prompt", elem_id="my_prompt_en", visible=False)
- chat_history = gr.Textbox(lines=4, label="chat_history", elem_id="chat_history", visible=False)
- all_chat_history = gr.Textbox(lines=4, label="会话上下文：", elem_id="all_chat_history", visible=False)
-
- chat_radio = gr.Radio(["Talk to chatGPT", "Text to Image"], elem_id="chat_radio",value="Talk to chatGPT", show_label=False, visible=True)
- model_radio = gr.Radio(["GPT-3.0", "GPT-3.5"], elem_id="model_radio", value="GPT-3.5",
- label='GPT model: ', show_label=True,interactive=True, visible=True)
- openai_api_key_textbox = gr.Textbox(placeholder="Paste your OpenAI API key (sk-...) and hit Enter",
- show_label=False, lines=1, type='password')
- with gr.Row(elem_id="btns_row"):
- with gr.Column(id="submit_col"):
- submit_btn = gr.Button(value = "submit",elem_id="submit-btn").style(
- margin=True,
- rounded=(True, True, True, True),
- width=100
- )
- with gr.Column(id="clear_col"):
- clear_btn = gr.Button(value = "clear outputs", elem_id="clear-btn").style(
- margin=True,
- rounded=(True, True, True, True),
- width=100
- )
- submit_btn.click(fn=chat,
- inputs=[prompt_input0, prompt_input1, chat_radio, model_radio, all_chat_history, chat_history],
- outputs=[chatbot, all_chat_history, prompt_input0, prompt_input1],
- )
- with gr.Row(elem_id='tab_img', visible=False).style(height=5):
- tab_img = gr.TabbedInterface(tab_actions, tab_titles)
-
- openai_api_key_textbox.change(set_openai_api_key,
- inputs=[openai_api_key_textbox],
- outputs=[])
- openai_api_key_textbox.submit(set_openai_api_key,
- inputs=[openai_api_key_textbox],
- outputs=[])
- chat_radio.change(fn=chat_radio_change,
- inputs=[chat_radio],
- outputs=[model_radio, openai_api_key_textbox],
- )
-
-demo.launch(debug = True)
diff --git a/spaces/Flux9665/IMS-Toucan/Layers/Convolution.py b/spaces/Flux9665/IMS-Toucan/Layers/Convolution.py
deleted file mode 100644
index e6e56e85d5908b0db5fceaea1e701d197a824d4b..0000000000000000000000000000000000000000
--- a/spaces/Flux9665/IMS-Toucan/Layers/Convolution.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
-# Northwestern Polytechnical University (Pengcheng Guo)
-# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
-# Adapted by Florian Lux 2021
-
-
-from torch import nn
-
-
-class ConvolutionModule(nn.Module):
- """
- ConvolutionModule in Conformer model.
-
- Args:
- channels (int): The number of channels of conv layers.
- kernel_size (int): Kernel size of conv layers.
-
- """
-
- def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
- super(ConvolutionModule, self).__init__()
- # kernel_size should be an odd number for 'SAME' padding
- assert (kernel_size - 1) % 2 == 0
-
- self.pointwise_conv1 = nn.Conv1d(channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=bias, )
- self.depthwise_conv = nn.Conv1d(channels, channels, kernel_size, stride=1, padding=(kernel_size - 1) // 2, groups=channels, bias=bias, )
- self.norm = nn.GroupNorm(num_groups=32, num_channels=channels)
- self.pointwise_conv2 = nn.Conv1d(channels, channels, kernel_size=1, stride=1, padding=0, bias=bias, )
- self.activation = activation
-
- def forward(self, x):
- """
- Compute convolution module.
-
- Args:
- x (torch.Tensor): Input tensor (#batch, time, channels).
-
- Returns:
- torch.Tensor: Output tensor (#batch, time, channels).
-
- """
- # exchange the temporal dimension and the feature dimension
- x = x.transpose(1, 2)
-
- # GLU mechanism
- x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
- x = nn.functional.glu(x, dim=1) # (batch, channel, dim)
-
- # 1D Depthwise Conv
- x = self.depthwise_conv(x)
- x = self.activation(self.norm(x))
-
- x = self.pointwise_conv2(x)
-
- return x.transpose(1, 2)
diff --git a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/commons.py b/spaces/FrankZxShen/vits-fast-fineturning-models-ba/commons.py
deleted file mode 100644
index db17cf0914ba6e445fe613e3ec3411b3a74b28aa..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/commons.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def intersperse(lst, item):
- result = [item] * (len(lst) * 2 + 1)
- result[1::2] = lst
- return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
- """KL(P||Q)"""
- kl = (logs_q - logs_p) - 0.5
- kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
- return kl
-
-
-def rand_gumbel(shape):
- """Sample from the Gumbel distribution, protect from overflows."""
- uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
- return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
- g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
- return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- try:
- ret[i] = x[i, :, idx_str:idx_end]
- except RuntimeError:
- print("?")
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def get_timing_signal_1d(
- length, channels, min_timescale=1.0, max_timescale=1.0e4):
- position = torch.arange(length, dtype=torch.float)
- num_timescales = channels // 2
- log_timescale_increment = (
- math.log(float(max_timescale) / float(min_timescale)) /
- (num_timescales - 1))
- inv_timescales = min_timescale * torch.exp(
- torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
- scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
- signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
- signal = F.pad(signal, [0, 0, 0, channels % 2])
- signal = signal.view(1, channels, length)
- return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def shift_1d(x):
- x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
- return x
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2,3) * mask
- return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = list(filter(lambda p: p.grad is not None, parameters))
- norm_type = float(norm_type)
- if clip_value is not None:
- clip_value = float(clip_value)
-
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item() ** norm_type
- if clip_value is not None:
- p.grad.data.clamp_(min=-clip_value, max=clip_value)
- total_norm = total_norm ** (1. / norm_type)
- return total_norm
diff --git a/spaces/Giuvyz/rvc-genshin/infer_pack/modules.py b/spaces/Giuvyz/rvc-genshin/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/Giuvyz/rvc-genshin/infer_pack/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(
- self,
- in_channels,
- hidden_channels,
- out_channels,
- kernel_size,
- n_layers,
- p_dropout,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(
- nn.Conv1d(
- in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
- )
- )
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
- for _ in range(n_layers - 1):
- self.conv_layers.append(
- nn.Conv1d(
- hidden_channels,
- hidden_channels,
- kernel_size,
- padding=kernel_size // 2,
- )
- )
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
-
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size**i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(
- nn.Conv1d(
- channels,
- channels,
- kernel_size,
- groups=channels,
- dilation=dilation,
- padding=padding,
- )
- )
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(
- self,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- p_dropout=0,
- ):
- super(WN, self).__init__()
- assert kernel_size % 2 == 1
- self.hidden_channels = hidden_channels
- self.kernel_size = (kernel_size,)
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(
- gin_channels, 2 * hidden_channels * n_layers, 1
- )
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
- for i in range(n_layers):
- dilation = dilation_rate**i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(
- hidden_channels,
- 2 * hidden_channels,
- kernel_size,
- dilation=dilation,
- padding=padding,
- )
- in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:, : self.hidden_channels, :]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:, self.hidden_channels :, :]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2]),
- )
- ),
- ]
- )
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- ]
- )
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]),
- )
- ),
- ]
- )
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels, 1))
- self.logs = nn.Parameter(torch.zeros(channels, 1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1, 2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(
- self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False,
- ):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=p_dropout,
- gin_channels=gin_channels,
- )
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels] * 2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1, 2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
- def __init__(
- self,
- in_channels,
- filter_channels,
- kernel_size,
- n_layers,
- num_bins=10,
- tail_bound=5.0,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
- self.proj = nn.Conv1d(
- filter_channels, self.half_channels * (num_bins * 3 - 1), 1
- )
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
- self.filter_channels
- )
- unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(
- x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails="linear",
- tail_bound=self.tail_bound,
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1, 2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/relax/utils.py b/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/relax/utils.py
deleted file mode 100644
index 4bd4acad4e5e7f071fb0ad98e7711b4856843464..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/relax/utils.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# Copyright 2021 DeepMind Technologies Limited
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Utils for minimization."""
-import io
-from alphafold.common import residue_constants
-from Bio import PDB
-import numpy as np
-from simtk.openmm import app as openmm_app
-from simtk.openmm.app.internal.pdbstructure import PdbStructure
-
-
-def overwrite_pdb_coordinates(pdb_str: str, pos) -> str:
- pdb_file = io.StringIO(pdb_str)
- structure = PdbStructure(pdb_file)
- topology = openmm_app.PDBFile(structure).getTopology()
- with io.StringIO() as f:
- openmm_app.PDBFile.writeFile(topology, pos, f)
- return f.getvalue()
-
-
-def overwrite_b_factors(pdb_str: str, bfactors: np.ndarray) -> str:
- """Overwrites the B-factors in pdb_str with contents of bfactors array.
-
- Args:
- pdb_str: An input PDB string.
- bfactors: A numpy array with shape [1, n_residues, 37]. We assume that the
- B-factors are per residue; i.e. that the nonzero entries are identical in
- [0, i, :].
-
- Returns:
- A new PDB string with the B-factors replaced.
- """
- if bfactors.shape[-1] != residue_constants.atom_type_num:
- raise ValueError(
- f'Invalid final dimension size for bfactors: {bfactors.shape[-1]}.')
-
- parser = PDB.PDBParser(QUIET=True)
- handle = io.StringIO(pdb_str)
- structure = parser.get_structure('', handle)
-
- curr_resid = ('', '', '')
- idx = -1
- for atom in structure.get_atoms():
- atom_resid = atom.parent.get_id()
- if atom_resid != curr_resid:
- idx += 1
- if idx >= bfactors.shape[0]:
- raise ValueError('Index into bfactors exceeds number of residues. '
- 'B-factors shape: {shape}, idx: {idx}.')
- curr_resid = atom_resid
- atom.bfactor = bfactors[idx, residue_constants.atom_order['CA']]
-
- new_pdb = io.StringIO()
- pdb_io = PDB.PDBIO()
- pdb_io.set_structure(structure)
- pdb_io.save(new_pdb)
- return new_pdb.getvalue()
-
-
-def assert_equal_nonterminal_atom_types(
- atom_mask: np.ndarray, ref_atom_mask: np.ndarray):
- """Checks that pre- and post-minimized proteins have same atom set."""
- # Ignore any terminal OXT atoms which may have been added by minimization.
- oxt = residue_constants.atom_order['OXT']
- no_oxt_mask = np.ones(shape=atom_mask.shape, dtype=np.bool)
- no_oxt_mask[..., oxt] = False
- np.testing.assert_almost_equal(ref_atom_mask[no_oxt_mask],
- atom_mask[no_oxt_mask])
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/paa/paa_r50_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/paa/paa_r50_fpn_1x_coco.py
deleted file mode 100644
index cd844108216c16801c0875723d589c5b11fb7b8d..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/paa/paa_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,70 +0,0 @@
-_base_ = [
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-model = dict(
- type='PAA',
- pretrained='torchvision://resnet50',
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- style='pytorch'),
- neck=dict(
- type='FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- start_level=1,
- add_extra_convs='on_output',
- num_outs=5),
- bbox_head=dict(
- type='PAAHead',
- reg_decoded_bbox=True,
- score_voting=True,
- topk=9,
- num_classes=80,
- in_channels=256,
- stacked_convs=4,
- feat_channels=256,
- anchor_generator=dict(
- type='AnchorGenerator',
- ratios=[1.0],
- octave_base_scale=8,
- scales_per_octave=1,
- strides=[8, 16, 32, 64, 128]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
- loss_centerness=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
- # training and testing settings
- train_cfg=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.1,
- neg_iou_thr=0.1,
- min_pos_iou=0,
- ignore_iof_thr=-1),
- allowed_border=-1,
- pos_weight=-1,
- debug=False),
- test_cfg=dict(
- nms_pre=1000,
- min_bbox_size=0,
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.6),
- max_per_img=100))
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py
deleted file mode 100644
index 75adef324877d56c157b457eecbf8446aa6b192f..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = [
- '../_base_/models/nonlocal_r50-d8.py',
- '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
- decode_head=dict(align_corners=True),
- auxiliary_head=dict(align_corners=True),
- test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/examples/FastDemo/YuyuanQA.py b/spaces/HaloMaster/chinesesummary/fengshen/examples/FastDemo/YuyuanQA.py
deleted file mode 100644
index fed2d19bc61e0735f3868e1a30a532bd19fbb4b0..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/examples/FastDemo/YuyuanQA.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import requests
-import langid
-import streamlit as st
-from translate import baiduTranslatorMedical
-from translate import baiduTranslator
-
-langid.set_languages(['en', 'zh'])
-lang_dic = {'zh': 'en', 'en': 'zh'}
-
-st.set_page_config(
- page_title="余元医疗问答",
- page_icon=":shark:",
- # layout="wide",
- initial_sidebar_state="expanded",
- menu_items={
- 'Get Help': 'https://www.extremelycoolapp.com/help',
- 'Report a bug': "https://www.extremelycoolapp.com/bug",
- 'About': "# This is a header. This is an *extremely* cool app!"
- }
-)
-st.title('Demo for MedicalQA')
-
-
-st.sidebar.header("参数配置")
-sbform = st.sidebar.form("固定参数设置")
-n_sample = sbform.slider("设置返回条数", min_value=1, max_value=10, value=3)
-text_length = sbform.slider('生成长度:', min_value=32, max_value=512, value=64, step=32)
-text_level = sbform.slider('文本多样性:', min_value=0.1, max_value=1.0, value=0.9, step=0.1)
-model_id = sbform.number_input('选择模型号:', min_value=0, max_value=13, value=13, step=1)
-trans = sbform.selectbox('选择翻译内核', ['百度通用', '医疗生物'])
-sbform.form_submit_button("配置")
-
-
-form = st.form("参数设置")
-input_text = form.text_input('请输入你的问题:', value='', placeholder='例如：糖尿病的症状有哪些？')
-if trans == '百度通用':
- translator = 'baidu_common'
-else:
- translator = 'baidu'
-if input_text:
- lang = langid.classify(input_text)[0]
- if translator == 'baidu':
- st.write('**你的问题是:**', baiduTranslatorMedical(input_text, src=lang, dest=lang_dic[lang]).text)
- else:
- st.write('**你的问题是:**', baiduTranslator(input_text, src=lang, dest=lang_dic[lang]).text)
-
-form.form_submit_button("提交")
-
-# @st.cache(suppress_st_warning=True)
-
-
-def generate_qa(input_text, n_sample, model_id='7', length=64, translator='baidu', level=0.7):
- # st.write('调用了generate函数')
- URL = 'http://192.168.190.63:6605/qa'
- data = {"text": input_text, "n_sample": n_sample, "model_id": model_id,
- "length": length, 'translator': translator, 'level': level}
- r = requests.get(URL, params=data)
- return r.text
-# my_bar = st.progress(80)
-
-
-with st.spinner('老夫正在思考中🤔...'):
- if input_text:
- results = generate_qa(input_text, n_sample, model_id=str(model_id),
- translator=translator, length=text_length, level=text_level)
- for idx, item in enumerate(eval(results), start=1):
- st.markdown(f"""
- **候选回答「{idx}」:**\n
- """)
- st.info('中文：%s' % item['fy_next_sentence'])
- st.info('英文：%s' % item['next_sentence'])
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/benchmark/dummy_model.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/benchmark/dummy_model.py
deleted file mode 100644
index ff26e4fe655d8e8d7f9942c4bd3df7cd267405fb..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/benchmark/dummy_model.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq.data import Dictionary
-from fairseq.models import (
- FairseqDecoder,
- FairseqLanguageModel,
- register_model,
- register_model_architecture,
-)
-
-
-@register_model("dummy_model")
-class DummyModel(FairseqLanguageModel):
- def __init__(self, args, encoder):
- super().__init__(encoder)
- self.args = args
-
- @staticmethod
- def add_args(parser):
- parser.add_argument("--num-layers", type=int, default=24)
- parser.add_argument("--embed-dim", type=int, default=1024)
-
- @classmethod
- def build_model(cls, args, task):
- encoder = DummyEncoder(
- num_embed=len(task.target_dictionary),
- embed_dim=args.embed_dim,
- num_layers=args.num_layers,
- )
- return cls(args, encoder)
-
- def forward(self, src_tokens, masked_tokens=None, **kwargs):
- return self.decoder(src_tokens, masked_tokens=masked_tokens)
-
-
-class DummyEncoder(FairseqDecoder):
- def __init__(self, num_embed=50000, embed_dim=1024, num_layers=24):
- super().__init__(Dictionary())
- self.embed = nn.Embedding(
- num_embeddings=num_embed, embedding_dim=embed_dim, padding_idx=0
- )
- self.layers_a = nn.ModuleList(
- [
- nn.Sequential(
- nn.LayerNorm(embed_dim),
- nn.Linear(embed_dim, 3 * embed_dim), # q, k, v input projection
- nn.Linear(3 * embed_dim, embed_dim), # skip self-attention
- nn.Linear(embed_dim, embed_dim), # output projection
- nn.Dropout(),
- )
- for i in range(num_layers)
- ]
- )
- self.layers_b = nn.ModuleList(
- [
- nn.Sequential(
- nn.LayerNorm(embed_dim),
- nn.Linear(embed_dim, 4 * embed_dim), # FFN
- nn.ReLU(),
- nn.Linear(4 * embed_dim, embed_dim), # FFN
- nn.Dropout(0.1),
- )
- for i in range(num_layers)
- ]
- )
- self.out_proj = nn.Linear(embed_dim, num_embed)
-
- def forward(self, tokens, masked_tokens=None):
- x = self.embed(tokens)
- for layer_a, layer_b in zip(self.layers_a, self.layers_b):
- x = x + layer_a(x)
- x = x + layer_b(x)
- x = self.out_proj(x)
- if masked_tokens is not None:
- x = x[masked_tokens]
- return (x,)
-
- def max_positions(self):
- return 1024
-
- def get_normalized_probs(self, net_output, log_probs, sample=None):
- logits = net_output[0].float()
- if log_probs:
- return F.log_softmax(logits, dim=-1)
- else:
- return F.softmax(logits, dim=-1)
-
-
-@register_model_architecture("dummy_model", "dummy_model")
-def base_architecture(args):
- pass
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/nat/nat_crf_transformer.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/nat/nat_crf_transformer.py
deleted file mode 100644
index d4b3cd931ceb077eb30db73df1d5d6cd714a86c2..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/nat/nat_crf_transformer.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-from fairseq.models import register_model, register_model_architecture
-from fairseq.models.nat import NATransformerModel, base_architecture
-from fairseq.modules import DynamicCRF
-
-
-@register_model("nacrf_transformer")
-class NACRFTransformerModel(NATransformerModel):
- def __init__(self, args, encoder, decoder):
- super().__init__(args, encoder, decoder)
- self.crf_layer = DynamicCRF(
- num_embedding=len(self.tgt_dict),
- low_rank=args.crf_lowrank_approx,
- beam_size=args.crf_beam_approx,
- )
-
- @property
- def allow_ensemble(self):
- return False
-
- @staticmethod
- def add_args(parser):
- NATransformerModel.add_args(parser)
- parser.add_argument(
- "--crf-lowrank-approx",
- type=int,
- help="the dimension of low-rank approximation of transition",
- )
- parser.add_argument(
- "--crf-beam-approx",
- type=int,
- help="the beam size for apporixmating the normalizing factor",
- )
- parser.add_argument(
- "--word-ins-loss-factor",
- type=float,
- help="weights on NAT loss used to co-training with CRF loss.",
- )
-
- def forward(
- self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
- ):
- # encoding
- encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
-
- # length prediction
- length_out = self.decoder.forward_length(
- normalize=False, encoder_out=encoder_out
- )
- length_tgt = self.decoder.forward_length_prediction(
- length_out, encoder_out, tgt_tokens
- )
-
- # decoding
- word_ins_out = self.decoder(
- normalize=False,
- prev_output_tokens=prev_output_tokens,
- encoder_out=encoder_out,
- )
- word_ins_tgt, word_ins_mask = tgt_tokens, tgt_tokens.ne(self.pad)
-
- # compute the log-likelihood of CRF
- crf_nll = -self.crf_layer(word_ins_out, word_ins_tgt, word_ins_mask)
- crf_nll = (crf_nll / word_ins_mask.type_as(crf_nll).sum(-1)).mean()
-
- return {
- "word_ins": {
- "out": word_ins_out,
- "tgt": word_ins_tgt,
- "mask": word_ins_mask,
- "ls": self.args.label_smoothing,
- "nll_loss": True,
- "factor": self.args.word_ins_loss_factor,
- },
- "word_crf": {"loss": crf_nll},
- "length": {
- "out": length_out,
- "tgt": length_tgt,
- "factor": self.decoder.length_loss_factor,
- },
- }
-
- def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs):
- output_tokens = decoder_out.output_tokens
- output_scores = decoder_out.output_scores
- history = decoder_out.history
-
- # execute the decoder and get emission scores
- output_masks = output_tokens.ne(self.pad)
- word_ins_out = self.decoder(
- normalize=False, prev_output_tokens=output_tokens, encoder_out=encoder_out
- )
-
- # run viterbi decoding through CRF
- _scores, _tokens = self.crf_layer.forward_decoder(word_ins_out, output_masks)
- output_tokens.masked_scatter_(output_masks, _tokens[output_masks])
- output_scores.masked_scatter_(output_masks, _scores[output_masks])
- if history is not None:
- history.append(output_tokens.clone())
-
- return decoder_out._replace(
- output_tokens=output_tokens,
- output_scores=output_scores,
- attn=None,
- history=history,
- )
-
-
-@register_model_architecture("nacrf_transformer", "nacrf_transformer")
-def nacrf_base_architecture(args):
- args.crf_lowrank_approx = getattr(args, "crf_lowrank_approx", 32)
- args.crf_beam_approx = getattr(args, "crf_beam_approx", 64)
- args.word_ins_loss_factor = getattr(args, "word_ins_loss_factor", 0.5)
- args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
- args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
- base_architecture(args)
diff --git a/spaces/Hexamind/GDOC/src/tools/list_tool.py b/spaces/Hexamind/GDOC/src/tools/list_tool.py
deleted file mode 100644
index 98b4fc59005ac97c1c628e97540ec54d13306a79..0000000000000000000000000000000000000000
--- a/spaces/Hexamind/GDOC/src/tools/list_tool.py
+++ /dev/null
@@ -1,13 +0,0 @@
-def keep_last_occurrences(lst):
- last_occurrences = {}
- result = []
-
- for index, string in lst:
- last_occurrences[index] = string
-
- for index, string in lst:
- if last_occurrences[index] == string:
- result.append((index, string))
- last_occurrences[index] = None
-
- return result
\ No newline at end of file
diff --git a/spaces/HuggingFaceH4/open_llm_leaderboard/src/get_model_info/hardocded_metadata/types.py b/spaces/HuggingFaceH4/open_llm_leaderboard/src/get_model_info/hardocded_metadata/types.py
deleted file mode 100644
index 75eeafe901ef44c68dbaa96207e52eed490d080d..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceH4/open_llm_leaderboard/src/get_model_info/hardocded_metadata/types.py
+++ /dev/null
@@ -1,555 +0,0 @@
-from dataclasses import dataclass
-from enum import Enum
-from typing import Dict
-
-
-@dataclass
-class ModelInfo:
- name: str
- symbol: str # emoji
-
-
-class ModelType(Enum):
- PT = ModelInfo(name="pretrained", symbol="🟢")
- FT = ModelInfo(name="fine-tuned", symbol="🔶")
- IFT = ModelInfo(name="instruction-tuned", symbol="⭕")
- RL = ModelInfo(name="RL-tuned", symbol="🟦")
- Unknown = ModelInfo(name="", symbol="?")
-
- def to_str(self, separator=" "):
- return f"{self.value.symbol}{separator}{self.value.name}"
-
-
-MODEL_TYPE_METADATA: Dict[str, ModelType] = {
- "tiiuae/falcon-180B": ModelType.PT,
- "tiiuae/falcon-180B-chat": ModelType.RL,
- "microsoft/phi-1_5": ModelType.PT,
- "Qwen/Qwen-7B": ModelType.PT,
- "Qwen/Qwen-7B-Chat": ModelType.RL,
- "notstoic/PygmalionCoT-7b": ModelType.IFT,
- "aisquared/dlite-v1-355m": ModelType.IFT,
- "aisquared/dlite-v1-1_5b": ModelType.IFT,
- "aisquared/dlite-v1-774m": ModelType.IFT,
- "aisquared/dlite-v1-124m": ModelType.IFT,
- "aisquared/chopt-2_7b": ModelType.IFT,
- "aisquared/dlite-v2-124m": ModelType.IFT,
- "aisquared/dlite-v2-774m": ModelType.IFT,
- "aisquared/dlite-v2-1_5b": ModelType.IFT,
- "aisquared/chopt-1_3b": ModelType.IFT,
- "aisquared/dlite-v2-355m": ModelType.IFT,
- "augtoma/qCammel-13": ModelType.IFT,
- "Aspik101/Llama-2-7b-hf-instruct-pl-lora_unload": ModelType.IFT,
- "Aspik101/vicuna-7b-v1.3-instruct-pl-lora_unload": ModelType.IFT,
- "TheBloke/alpaca-lora-65B-HF": ModelType.FT,
- "TheBloke/tulu-7B-fp16": ModelType.IFT,
- "TheBloke/guanaco-7B-HF": ModelType.FT,
- "TheBloke/koala-7B-HF": ModelType.FT,
- "TheBloke/wizardLM-7B-HF": ModelType.IFT,
- "TheBloke/airoboros-13B-HF": ModelType.IFT,
- "TheBloke/koala-13B-HF": ModelType.FT,
- "TheBloke/Wizard-Vicuna-7B-Uncensored-HF": ModelType.FT,
- "TheBloke/dromedary-65b-lora-HF": ModelType.IFT,
- "TheBloke/wizardLM-13B-1.0-fp16": ModelType.IFT,
- "TheBloke/WizardLM-13B-V1-1-SuperHOT-8K-fp16": ModelType.FT,
- "TheBloke/Wizard-Vicuna-30B-Uncensored-fp16": ModelType.FT,
- "TheBloke/wizard-vicuna-13B-HF": ModelType.IFT,
- "TheBloke/UltraLM-13B-fp16": ModelType.IFT,
- "TheBloke/OpenAssistant-FT-7-Llama-30B-HF": ModelType.FT,
- "TheBloke/vicuna-13B-1.1-HF": ModelType.IFT,
- "TheBloke/guanaco-13B-HF": ModelType.FT,
- "TheBloke/guanaco-65B-HF": ModelType.FT,
- "TheBloke/airoboros-7b-gpt4-fp16": ModelType.IFT,
- "TheBloke/llama-30b-supercot-SuperHOT-8K-fp16": ModelType.IFT,
- "TheBloke/Llama-2-13B-fp16": ModelType.PT,
- "TheBloke/llama-2-70b-Guanaco-QLoRA-fp16": ModelType.FT,
- "TheBloke/landmark-attention-llama7b-fp16": ModelType.IFT,
- "TheBloke/Planner-7B-fp16": ModelType.IFT,
- "TheBloke/Wizard-Vicuna-13B-Uncensored-HF": ModelType.FT,
- "TheBloke/gpt4-alpaca-lora-13B-HF": ModelType.IFT,
- "TheBloke/gpt4-x-vicuna-13B-HF": ModelType.IFT,
- "TheBloke/gpt4-alpaca-lora_mlp-65B-HF": ModelType.IFT,
- "TheBloke/tulu-13B-fp16": ModelType.IFT,
- "TheBloke/VicUnlocked-alpaca-65B-QLoRA-fp16": ModelType.IFT,
- "TheBloke/Llama-2-70B-fp16": ModelType.IFT,
- "TheBloke/WizardLM-30B-fp16": ModelType.IFT,
- "TheBloke/robin-13B-v2-fp16": ModelType.FT,
- "TheBloke/robin-33B-v2-fp16": ModelType.FT,
- "TheBloke/Vicuna-13B-CoT-fp16": ModelType.IFT,
- "TheBloke/Vicuna-33B-1-3-SuperHOT-8K-fp16": ModelType.IFT,
- "TheBloke/Wizard-Vicuna-30B-Superhot-8K-fp16": ModelType.FT,
- "TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16": ModelType.IFT,
- "TheBloke/GPlatty-30B-SuperHOT-8K-fp16": ModelType.FT,
- "TheBloke/CAMEL-33B-Combined-Data-SuperHOT-8K-fp16": ModelType.IFT,
- "TheBloke/Chinese-Alpaca-33B-SuperHOT-8K-fp16": ModelType.IFT,
- "jphme/orca_mini_v2_ger_7b": ModelType.IFT,
- "Ejafa/vicuna_7B_vanilla_1.1": ModelType.FT,
- "kevinpro/Vicuna-13B-CoT": ModelType.IFT,
- "AlekseyKorshuk/pygmalion-6b-vicuna-chatml": ModelType.FT,
- "AlekseyKorshuk/chatml-pyg-v1": ModelType.FT,
- "concedo/Vicuzard-30B-Uncensored": ModelType.FT,
- "concedo/OPT-19M-ChatSalad": ModelType.FT,
- "concedo/Pythia-70M-ChatSalad": ModelType.FT,
- "digitous/13B-HyperMantis": ModelType.IFT,
- "digitous/Adventien-GPTJ": ModelType.FT,
- "digitous/Alpacino13b": ModelType.IFT,
- "digitous/GPT-R": ModelType.IFT,
- "digitous/Javelin-R": ModelType.IFT,
- "digitous/Javalion-GPTJ": ModelType.IFT,
- "digitous/Javalion-R": ModelType.IFT,
- "digitous/Skegma-GPTJ": ModelType.FT,
- "digitous/Alpacino30b": ModelType.IFT,
- "digitous/Janin-GPTJ": ModelType.FT,
- "digitous/Janin-R": ModelType.FT,
- "digitous/Javelin-GPTJ": ModelType.FT,
- "SaylorTwift/gpt2_test": ModelType.PT,
- "anton-l/gpt-j-tiny-random": ModelType.FT,
- "Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca": ModelType.FT,
- "Lazycuber/pyg-instruct-wizardlm": ModelType.FT,
- "Lazycuber/Janemalion-6B": ModelType.FT,
- "IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1": ModelType.FT,
- "IDEA-CCNL/Ziya-LLaMA-13B-v1": ModelType.IFT,
- "dsvv-cair/alpaca-cleaned-llama-30b-bf16": ModelType.FT,
- "gpt2-medium": ModelType.PT,
- "camel-ai/CAMEL-13B-Combined-Data": ModelType.IFT,
- "camel-ai/CAMEL-13B-Role-Playing-Data": ModelType.FT,
- "camel-ai/CAMEL-33B-Combined-Data": ModelType.IFT,
- "PygmalionAI/pygmalion-6b": ModelType.FT,
- "PygmalionAI/metharme-1.3b": ModelType.IFT,
- "PygmalionAI/pygmalion-1.3b": ModelType.FT,
- "PygmalionAI/pygmalion-350m": ModelType.FT,
- "PygmalionAI/pygmalion-2.7b": ModelType.FT,
- "medalpaca/medalpaca-7b": ModelType.FT,
- "lilloukas/Platypus-30B": ModelType.IFT,
- "lilloukas/GPlatty-30B": ModelType.FT,
- "mncai/chatdoctor": ModelType.FT,
- "chaoyi-wu/MedLLaMA_13B": ModelType.FT,
- "LoupGarou/WizardCoder-Guanaco-15B-V1.0": ModelType.IFT,
- "LoupGarou/WizardCoder-Guanaco-15B-V1.1": ModelType.FT,
- "hakurei/instruct-12b": ModelType.IFT,
- "hakurei/lotus-12B": ModelType.FT,
- "shibing624/chinese-llama-plus-13b-hf": ModelType.IFT,
- "shibing624/chinese-alpaca-plus-7b-hf": ModelType.IFT,
- "shibing624/chinese-alpaca-plus-13b-hf": ModelType.IFT,
- "mosaicml/mpt-7b-instruct": ModelType.IFT,
- "mosaicml/mpt-30b-chat": ModelType.IFT,
- "mosaicml/mpt-7b-storywriter": ModelType.FT,
- "mosaicml/mpt-30b-instruct": ModelType.IFT,
- "mosaicml/mpt-7b-chat": ModelType.IFT,
- "mosaicml/mpt-30b": ModelType.PT,
- "Corianas/111m": ModelType.IFT,
- "Corianas/Quokka_1.3b": ModelType.IFT,
- "Corianas/256_5epoch": ModelType.FT,
- "Corianas/Quokka_256m": ModelType.IFT,
- "Corianas/Quokka_590m": ModelType.IFT,
- "Corianas/gpt-j-6B-Dolly": ModelType.FT,
- "Corianas/Quokka_2.7b": ModelType.IFT,
- "cyberagent/open-calm-7b": ModelType.FT,
- "Aspik101/Nous-Hermes-13b-pl-lora_unload": ModelType.IFT,
- "THUDM/chatglm2-6b": ModelType.IFT,
- "MetaIX/GPT4-X-Alpasta-30b": ModelType.IFT,
- "NYTK/PULI-GPTrio": ModelType.PT,
- "EleutherAI/pythia-1.3b": ModelType.PT,
- "EleutherAI/pythia-2.8b-deduped": ModelType.PT,
- "EleutherAI/gpt-neo-125m": ModelType.PT,
- "EleutherAI/pythia-160m": ModelType.PT,
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- "WizardLM/WizardLM-13B-V1.2": ModelType.IFT,
- "WizardLM/WizardLM-30B-V1.0": ModelType.IFT,
- "WizardLM/WizardCoder-15B-V1.0": ModelType.IFT,
- "gpt2": ModelType.PT,
- "keyfan/vicuna-chinese-replication-v1.1": ModelType.IFT,
- "nthngdy/pythia-owt2-70m-100k": ModelType.FT,
- "nthngdy/pythia-owt2-70m-50k": ModelType.FT,
- "quantumaikr/KoreanLM-hf": ModelType.FT,
- "quantumaikr/open_llama_7b_hf": ModelType.FT,
- "quantumaikr/QuantumLM-70B-hf": ModelType.IFT,
- "MayaPH/FinOPT-Lincoln": ModelType.FT,
- "MayaPH/FinOPT-Franklin": ModelType.FT,
- "MayaPH/GodziLLa-30B": ModelType.IFT,
- "MayaPH/GodziLLa-30B-plus": ModelType.IFT,
- "MayaPH/FinOPT-Washington": ModelType.FT,
- "ogimgio/gpt-neo-125m-neurallinguisticpioneers": ModelType.FT,
- "layoric/llama-2-13b-code-alpaca": ModelType.FT,
- "CobraMamba/mamba-gpt-3b": ModelType.FT,
- "CobraMamba/mamba-gpt-3b-v2": ModelType.FT,
- "CobraMamba/mamba-gpt-3b-v3": ModelType.FT,
- "timdettmers/guanaco-33b-merged": ModelType.FT,
- "elinas/chronos-33b": ModelType.IFT,
- "heegyu/RedTulu-Uncensored-3B-0719": ModelType.IFT,
- "heegyu/WizardVicuna-Uncensored-3B-0719": ModelType.IFT,
- "heegyu/WizardVicuna-3B-0719": ModelType.IFT,
- "meta-llama/Llama-2-7b-chat-hf": ModelType.RL,
- "meta-llama/Llama-2-7b-hf": ModelType.PT,
- "meta-llama/Llama-2-13b-chat-hf": ModelType.RL,
- "meta-llama/Llama-2-13b-hf": ModelType.PT,
- "meta-llama/Llama-2-70b-chat-hf": ModelType.RL,
- "meta-llama/Llama-2-70b-hf": ModelType.PT,
- "xhyi/PT_GPTNEO350_ATG": ModelType.FT,
- "h2oai/h2ogpt-gm-oasst1-en-1024-20b": ModelType.FT,
- "h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt": ModelType.FT,
- "h2oai/h2ogpt-oig-oasst1-512-6_9b": ModelType.IFT,
- "h2oai/h2ogpt-oasst1-512-12b": ModelType.IFT,
- "h2oai/h2ogpt-oig-oasst1-256-6_9b": ModelType.IFT,
- "h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt": ModelType.FT,
- "h2oai/h2ogpt-oasst1-512-20b": ModelType.IFT,
- "h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2": ModelType.FT,
- "h2oai/h2ogpt-gm-oasst1-en-1024-12b": ModelType.FT,
- "h2oai/h2ogpt-gm-oasst1-multilang-1024-20b": ModelType.FT,
- "bofenghuang/vigogne-13b-instruct": ModelType.IFT,
- "bofenghuang/vigogne-13b-chat": ModelType.FT,
- "bofenghuang/vigogne-2-7b-instruct": ModelType.IFT,
- "bofenghuang/vigogne-7b-instruct": ModelType.IFT,
- "bofenghuang/vigogne-7b-chat": ModelType.FT,
- "Vmware/open-llama-7b-v2-open-instruct": ModelType.IFT,
- "VMware/open-llama-0.7T-7B-open-instruct-v1.1": ModelType.IFT,
- "ewof/koishi-instruct-3b": ModelType.IFT,
- "gywy/llama2-13b-chinese-v1": ModelType.FT,
- "GOAT-AI/GOAT-7B-Community": ModelType.FT,
- "psyche/kollama2-7b": ModelType.FT,
- "TheTravellingEngineer/llama2-7b-hf-guanaco": ModelType.FT,
- "beaugogh/pythia-1.4b-deduped-sharegpt": ModelType.FT,
- "augtoma/qCammel-70-x": ModelType.IFT,
- "Lajonbot/Llama-2-7b-chat-hf-instruct-pl-lora_unload": ModelType.IFT,
- "anhnv125/pygmalion-6b-roleplay": ModelType.FT,
- "64bits/LexPodLM-13B": ModelType.FT,
-}
-
-
-def model_type_from_str(type):
- if "fine-tuned" in type or "🔶" in type:
- return ModelType.FT
- if "pretrained" in type or "🟢" in type:
- return ModelType.PT
- if "RL-tuned" in type or "🟦" in type:
- return ModelType.RL
- if "instruction-tuned" in type or "⭕" in type:
- return ModelType.IFT
- return ModelType.Unknown
diff --git a/spaces/HugoDzz/spaceship_drift/build/game/index.audio.worklet.js b/spaces/HugoDzz/spaceship_drift/build/game/index.audio.worklet.js
deleted file mode 100644
index d9330c735f3da52a20f5e54e0b463ac03b7dff70..0000000000000000000000000000000000000000
--- a/spaces/HugoDzz/spaceship_drift/build/game/index.audio.worklet.js
+++ /dev/null
@@ -1,211 +0,0 @@
-/**************************************************************************/
-/* audio.worklet.js */
-/**************************************************************************/
-/* This file is part of: */
-/* GODOT ENGINE */
-/* https://godotengine.org */
-/**************************************************************************/
-/* Copyright (c) 2014-present Godot Engine contributors (see AUTHORS.md). */
-/* Copyright (c) 2007-2014 Juan Linietsky, Ariel Manzur. */
-/* */
-/* Permission is hereby granted, free of charge, to any person obtaining */
-/* a copy of this software and associated documentation files (the */
-/* "Software"), to deal in the Software without restriction, including */
-/* without limitation the rights to use, copy, modify, merge, publish, */
-/* distribute, sublicense, and/or sell copies of the Software, and to */
-/* permit persons to whom the Software is furnished to do so, subject to */
-/* the following conditions: */
-/* */
-/* The above copyright notice and this permission notice shall be */
-/* included in all copies or substantial portions of the Software. */
-/* */
-/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
-/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
-/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. */
-/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
-/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
-/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
-/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
-/**************************************************************************/
-
-class RingBuffer {
- constructor(p_buffer, p_state, p_threads) {
- this.buffer = p_buffer;
- this.avail = p_state;
- this.threads = p_threads;
- this.rpos = 0;
- this.wpos = 0;
- }
-
- data_left() {
- return this.threads ? Atomics.load(this.avail, 0) : this.avail;
- }
-
- space_left() {
- return this.buffer.length - this.data_left();
- }
-
- read(output) {
- const size = this.buffer.length;
- let from = 0;
- let to_write = output.length;
- if (this.rpos + to_write > size) {
- const high = size - this.rpos;
- output.set(this.buffer.subarray(this.rpos, size));
- from = high;
- to_write -= high;
- this.rpos = 0;
- }
- if (to_write) {
- output.set(this.buffer.subarray(this.rpos, this.rpos + to_write), from);
- }
- this.rpos += to_write;
- if (this.threads) {
- Atomics.add(this.avail, 0, -output.length);
- Atomics.notify(this.avail, 0);
- } else {
- this.avail -= output.length;
- }
- }
-
- write(p_buffer) {
- const to_write = p_buffer.length;
- const mw = this.buffer.length - this.wpos;
- if (mw >= to_write) {
- this.buffer.set(p_buffer, this.wpos);
- this.wpos += to_write;
- if (mw === to_write) {
- this.wpos = 0;
- }
- } else {
- const high = p_buffer.subarray(0, mw);
- const low = p_buffer.subarray(mw);
- this.buffer.set(high, this.wpos);
- this.buffer.set(low);
- this.wpos = low.length;
- }
- if (this.threads) {
- Atomics.add(this.avail, 0, to_write);
- Atomics.notify(this.avail, 0);
- } else {
- this.avail += to_write;
- }
- }
-}
-
-class GodotProcessor extends AudioWorkletProcessor {
- constructor() {
- super();
- this.threads = false;
- this.running = true;
- this.lock = null;
- this.notifier = null;
- this.output = null;
- this.output_buffer = new Float32Array();
- this.input = null;
- this.input_buffer = new Float32Array();
- this.port.onmessage = (event) => {
- const cmd = event.data['cmd'];
- const data = event.data['data'];
- this.parse_message(cmd, data);
- };
- }
-
- process_notify() {
- if (this.notifier) {
- Atomics.add(this.notifier, 0, 1);
- Atomics.notify(this.notifier, 0);
- }
- }
-
- parse_message(p_cmd, p_data) {
- if (p_cmd === 'start' && p_data) {
- const state = p_data[0];
- let idx = 0;
- this.threads = true;
- this.lock = state.subarray(idx, ++idx);
- this.notifier = state.subarray(idx, ++idx);
- const avail_in = state.subarray(idx, ++idx);
- const avail_out = state.subarray(idx, ++idx);
- this.input = new RingBuffer(p_data[1], avail_in, true);
- this.output = new RingBuffer(p_data[2], avail_out, true);
- } else if (p_cmd === 'stop') {
- this.running = false;
- this.output = null;
- this.input = null;
- } else if (p_cmd === 'start_nothreads') {
- this.output = new RingBuffer(p_data[0], p_data[0].length, false);
- } else if (p_cmd === 'chunk') {
- this.output.write(p_data);
- }
- }
-
- static array_has_data(arr) {
- return arr.length && arr[0].length && arr[0][0].length;
- }
-
- process(inputs, outputs, parameters) {
- if (!this.running) {
- return false; // Stop processing.
- }
- if (this.output === null) {
- return true; // Not ready yet, keep processing.
- }
- const process_input = GodotProcessor.array_has_data(inputs);
- if (process_input) {
- const input = inputs[0];
- const chunk = input[0].length * input.length;
- if (this.input_buffer.length !== chunk) {
- this.input_buffer = new Float32Array(chunk);
- }
- if (!this.threads) {
- GodotProcessor.write_input(this.input_buffer, input);
- this.port.postMessage({ 'cmd': 'input', 'data': this.input_buffer });
- } else if (this.input.space_left() >= chunk) {
- GodotProcessor.write_input(this.input_buffer, input);
- this.input.write(this.input_buffer);
- } else {
- this.port.postMessage('Input buffer is full! Skipping input frame.');
- }
- }
- const process_output = GodotProcessor.array_has_data(outputs);
- if (process_output) {
- const output = outputs[0];
- const chunk = output[0].length * output.length;
- if (this.output_buffer.length !== chunk) {
- this.output_buffer = new Float32Array(chunk);
- }
- if (this.output.data_left() >= chunk) {
- this.output.read(this.output_buffer);
- GodotProcessor.write_output(output, this.output_buffer);
- if (!this.threads) {
- this.port.postMessage({ 'cmd': 'read', 'data': chunk });
- }
- } else {
- this.port.postMessage('Output buffer has not enough frames! Skipping output frame.');
- }
- }
- this.process_notify();
- return true;
- }
-
- static write_output(dest, source) {
- const channels = dest.length;
- for (let ch = 0; ch < channels; ch++) {
- for (let sample = 0; sample < dest[ch].length; sample++) {
- dest[ch][sample] = source[sample * channels + ch];
- }
- }
- }
-
- static write_input(dest, source) {
- const channels = source.length;
- for (let ch = 0; ch < channels; ch++) {
- for (let sample = 0; sample < source[ch].length; sample++) {
- dest[sample * channels + ch] = source[ch][sample];
- }
- }
- }
-}
-
-registerProcessor('godot-processor', GodotProcessor);
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/data/legacy/masked_lm_dictionary.py b/spaces/ICML2022/OFA/fairseq/fairseq/data/legacy/masked_lm_dictionary.py
deleted file mode 100644
index dee88f7a3ed72ea465ea4e8ffe7b1c01ff6f57f1..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/data/legacy/masked_lm_dictionary.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from fairseq.data import Dictionary
-
-
-class MaskedLMDictionary(Dictionary):
- """
- Dictionary for Masked Language Modelling tasks. This extends Dictionary by
- adding the mask symbol.
- """
-
- def __init__(
- self,
- pad="",
- eos="",
- unk="",
- mask="",
- ):
- super().__init__(pad=pad, eos=eos, unk=unk)
- self.mask_word = mask
- self.mask_index = self.add_symbol(mask)
- self.nspecial = len(self.symbols)
-
- def mask(self):
- """Helper to get index of mask symbol"""
- return self.mask_index
-
-
-class BertDictionary(MaskedLMDictionary):
- """
- Dictionary for BERT task. This extends MaskedLMDictionary by adding support
- for cls and sep symbols.
- """
-
- def __init__(
- self,
- pad="",
- eos="",
- unk="",
- mask="",
- cls="",
- sep="",
- ):
- super().__init__(pad=pad, eos=eos, unk=unk, mask=mask)
- self.cls_word = cls
- self.sep_word = sep
- self.cls_index = self.add_symbol(cls)
- self.sep_index = self.add_symbol(sep)
- self.nspecial = len(self.symbols)
-
- def cls(self):
- """Helper to get index of cls symbol"""
- return self.cls_index
-
- def sep(self):
- """Helper to get index of sep symbol"""
- return self.sep_index
diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/archs/degradat_arch.py b/spaces/Iceclear/StableSR/StableSR/basicsr/archs/degradat_arch.py
deleted file mode 100644
index ce09ad666a90f175fb6268435073b314df543813..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/basicsr/archs/degradat_arch.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from torch import nn as nn
-
-from basicsr.archs.arch_util import ResidualBlockNoBN, default_init_weights
-from basicsr.utils.registry import ARCH_REGISTRY
-
-@ARCH_REGISTRY.register()
-class DEResNet(nn.Module):
- """Degradation Estimator with ResNetNoBN arch. v2.1, no vector anymore
- As shown in paper 'Towards Flexible Blind JPEG Artifacts Removal',
- resnet arch works for image quality estimation.
- Args:
- num_in_ch (int): channel number of inputs. Default: 3.
- num_degradation (int): num of degradation the DE should estimate. Default: 2(blur+noise).
- degradation_embed_size (int): embedding size of each degradation vector.
- degradation_degree_actv (int): activation function for degradation degree scalar. Default: sigmoid.
- num_feats (list): channel number of each stage.
- num_blocks (list): residual block of each stage.
- downscales (list): downscales of each stage.
- """
-
- def __init__(self,
- num_in_ch=3,
- num_degradation=2,
- degradation_degree_actv='sigmoid',
- num_feats=(64, 128, 256, 512),
- num_blocks=(2, 2, 2, 2),
- downscales=(2, 2, 2, 1)):
- super(DEResNet, self).__init__()
-
- assert isinstance(num_feats, list)
- assert isinstance(num_blocks, list)
- assert isinstance(downscales, list)
- assert len(num_feats) == len(num_blocks) and len(num_feats) == len(downscales)
-
- num_stage = len(num_feats)
-
- self.conv_first = nn.ModuleList()
- for _ in range(num_degradation):
- self.conv_first.append(nn.Conv2d(num_in_ch, num_feats[0], 3, 1, 1))
- self.body = nn.ModuleList()
- for _ in range(num_degradation):
- body = list()
- for stage in range(num_stage):
- for _ in range(num_blocks[stage]):
- body.append(ResidualBlockNoBN(num_feats[stage]))
- if downscales[stage] == 1:
- if stage < num_stage - 1 and num_feats[stage] != num_feats[stage + 1]:
- body.append(nn.Conv2d(num_feats[stage], num_feats[stage + 1], 3, 1, 1))
- continue
- elif downscales[stage] == 2:
- body.append(nn.Conv2d(num_feats[stage], num_feats[min(stage + 1, num_stage - 1)], 3, 2, 1))
- else:
- raise NotImplementedError
- self.body.append(nn.Sequential(*body))
-
- # self.body = nn.Sequential(*body)
-
- self.num_degradation = num_degradation
- self.fc_degree = nn.ModuleList()
- if degradation_degree_actv == 'sigmoid':
- actv = nn.Sigmoid
- elif degradation_degree_actv == 'tanh':
- actv = nn.Tanh
- else:
- raise NotImplementedError(f'only sigmoid and tanh are supported for degradation_degree_actv, '
- f'{degradation_degree_actv} is not supported yet.')
- for _ in range(num_degradation):
- self.fc_degree.append(
- nn.Sequential(
- nn.Linear(num_feats[-1], 512),
- nn.ReLU(inplace=True),
- nn.Linear(512, 1),
- actv(),
- ))
-
- self.avg_pool = nn.AdaptiveAvgPool2d(1)
-
- default_init_weights([self.conv_first, self.body, self.fc_degree], 0.1)
-
- def forward(self, x):
- degrees = []
- for i in range(self.num_degradation):
- x_out = self.conv_first[i](x)
- feat = self.body[i](x_out)
- feat = self.avg_pool(feat)
- feat = feat.squeeze(-1).squeeze(-1)
- # for i in range(self.num_degradation):
- degrees.append(self.fc_degree[i](feat).squeeze(-1))
-
- return degrees
diff --git a/spaces/Illumotion/Koboldcpp/.devops/full-cuda.Dockerfile b/spaces/Illumotion/Koboldcpp/.devops/full-cuda.Dockerfile
deleted file mode 100644
index 360602d6567b8a835f23e7365cf222ca7299c1d7..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/.devops/full-cuda.Dockerfile
+++ /dev/null
@@ -1,33 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-# This needs to generally match the container host's environment.
-ARG CUDA_VERSION=11.7.1
-
-# Target the CUDA build image
-ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_CUDA_DEV_CONTAINER} as build
-
-# Unless otherwise specified, we make a fat build.
-ARG CUDA_DOCKER_ARCH=all
-
-RUN apt-get update && \
- apt-get install -y build-essential python3 python3-pip git
-
-COPY requirements.txt requirements.txt
-
-RUN pip install --upgrade pip setuptools wheel \
- && pip install -r requirements.txt
-
-WORKDIR /app
-
-COPY . .
-
-# Set nvcc architecture
-ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
-# Enable cuBLAS
-ENV LLAMA_CUBLAS=1
-
-RUN make
-
-ENTRYPOINT ["/app/.devops/tools.sh"]
diff --git a/spaces/Illumotion/Koboldcpp/expose.h b/spaces/Illumotion/Koboldcpp/expose.h
deleted file mode 100644
index c2fbb22670fe13fecc7a31d21e2dae12c6fe68ad..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/expose.h
+++ /dev/null
@@ -1,91 +0,0 @@
-#pragma once
-
-const int stop_token_max = 16;
-const int ban_token_max = 16;
-const int tensor_split_max = 16;
-// match kobold's sampler list and order
-enum samplers
-{
- KCPP_SAMPLER_TOP_K=0,
- KCPP_SAMPLER_TOP_A=1,
- KCPP_SAMPLER_TOP_P=2,
- KCPP_SAMPLER_TFS=3,
- KCPP_SAMPLER_TYP=4,
- KCPP_SAMPLER_TEMP=5,
- KCPP_SAMPLER_REP_PEN=6,
- KCPP_SAMPLER_MAX
-};
-enum stop_reason
-{
- INVALID=-1,
- OUT_OF_TOKENS=0,
- EOS_TOKEN=1,
- CUSTOM_STOPPER=2,
-};
-struct load_model_inputs
-{
- const int threads;
- const int blasthreads;
- const int max_context_length;
- const int batch_size;
- const bool f16_kv;
- const bool low_vram;
- const bool use_mmq;
- const char * executable_path;
- const char * model_filename;
- const char * lora_filename;
- const char * lora_base;
- const bool use_mmap;
- const bool use_mlock;
- const bool use_smartcontext;
- const int clblast_info = 0;
- const int cublas_info = 0;
- const int blasbatchsize = 512;
- const int debugmode = 0;
- const int forceversion = 0;
- const int gpulayers = 0;
- const float rope_freq_scale = 1.0f;
- const float rope_freq_base = 10000.0f;
- const char * banned_tokens[ban_token_max];
- const float tensor_split[tensor_split_max];
-};
-struct generation_inputs
-{
- const int seed;
- const char *prompt;
- const int max_context_length;
- const int max_length;
- const float temperature;
- const int top_k;
- const float top_a = 0.0f;
- const float top_p;
- const float typical_p;
- const float tfs;
- const float rep_pen;
- const int rep_pen_range;
- const int mirostat = 0;
- const float mirostat_eta;
- const float mirostat_tau;
- const samplers sampler_order[KCPP_SAMPLER_MAX];
- const int sampler_len;
- const bool unban_tokens_rt;
- const char * stop_sequence[stop_token_max];
- const bool stream_sse;
- const char * grammar;
- const bool grammar_retain_state;
-};
-struct generation_outputs
-{
- int status = -1;
- char text[24576]; //24kb should be enough for any response
-};
-
-extern std::string executable_path;
-extern std::string lora_filename;
-extern std::string lora_base;
-extern std::vector generated_tokens;
-extern bool generation_finished;
-extern float last_eval_time;
-extern float last_process_time;
-extern int last_token_count;
-extern stop_reason last_stop_reason;
diff --git a/spaces/Illumotion/Koboldcpp/include/CL/Utils/Detail.hpp b/spaces/Illumotion/Koboldcpp/include/CL/Utils/Detail.hpp
deleted file mode 100644
index 49cccd02cff0c4e4e0ea6493613cc92d638d962c..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/include/CL/Utils/Detail.hpp
+++ /dev/null
@@ -1,84 +0,0 @@
-#pragma once
-
-// STL includes
-#include
-#include // std::forward, std::integer_sequence
-#include // std::tuple, std::get
-#include // std::initializer_list
-
-namespace cl {
-namespace util {
- namespace detail {
- // Borrowed from:
- // https://www.fluentcpp.com/2019/03/05/for_each_arg-applying-a-function-to-each-argument-of-a-function-in-cpp/
- template F for_each_arg(F f, Args&&... args)
- {
- (void)std::initializer_list{ (
- (void)f(std::forward(args)), 0)... };
- return f;
- }
-
- namespace impl {
- // Borrowed from: https://stackoverflow.com/a/16387374/1476661
- template
- void for_each_in_tuple(T&& t, F&& f,
- std::integer_sequence)
- {
- auto l = {
- (std::forward(f)(std::get(std::forward(t))), 0)...
- };
- (void)l;
- }
- }
- template
- void for_each_in_tuple(std::tuple const& t, F&& f)
- {
- impl::for_each_in_tuple(
- t, std::forward(f),
- std::make_integer_sequence());
- }
-
- namespace impl {
- // Borrowed from
- // https://codereview.stackexchange.com/questions/193420/apply-a-function-to-each-element-of-a-tuple-map-a-tuple
- template
- auto transform_tuple(Tuple&& t, F&& f, std::index_sequence)
- {
- return std::make_tuple(std::forward(f)(std::get(t))...);
- }
- }
- template
- auto transform_tuple(const std::tuple& t, F&& f)
- {
- return impl::transform_tuple(
- t, std::forward(f),
- std::make_index_sequence{});
- }
-
- namespace impl {
- // Borrowed from
- // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2013/n3658.html
- // with modifications of Casey Carter at
- // https://stackoverflow.com/a/51365112/1476661
- template
- auto apply(F&& f, Tuple&& args, std::index_sequence)
- -> decltype(std::forward(f)(
- std::get(std::forward(args))...))
- {
- return std::forward(f)(
- std::get(std::forward(args))...);
- }
- }
- template >::value>>
- auto apply(F&& f, Tuple&& args)
- -> decltype(impl::apply(std::forward(f),
- std::forward(args), Indices()))
- {
- return impl::apply(std::forward(f), std::forward(args),
- Indices());
- }
- }
-}
-}
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/modules/base.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/modules/base.py
deleted file mode 100644
index a50c3fc7753a0bba64a5ab8c1ed64ff97e62313f..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/modules/base.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import abc
-from typing import Tuple, List
-
-import torch
-import torch.nn as nn
-
-from saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv
-from saicinpainting.training.modules.multidilated_conv import MultidilatedConv
-
-
-class BaseDiscriminator(nn.Module):
- @abc.abstractmethod
- def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
- """
- Predict scores and get intermediate activations. Useful for feature matching loss
- :return tuple (scores, list of intermediate activations)
- """
- raise NotImplemented()
-
-
-def get_conv_block_ctor(kind='default'):
- if not isinstance(kind, str):
- return kind
- if kind == 'default':
- return nn.Conv2d
- if kind == 'depthwise':
- return DepthWiseSeperableConv
- if kind == 'multidilated':
- return MultidilatedConv
- raise ValueError(f'Unknown convolutional block kind {kind}')
-
-
-def get_norm_layer(kind='bn'):
- if not isinstance(kind, str):
- return kind
- if kind == 'bn':
- return nn.BatchNorm2d
- if kind == 'in':
- return nn.InstanceNorm2d
- raise ValueError(f'Unknown norm block kind {kind}')
-
-
-def get_activation(kind='tanh'):
- if kind == 'tanh':
- return nn.Tanh()
- if kind == 'sigmoid':
- return nn.Sigmoid()
- if kind is False:
- return nn.Identity()
- raise ValueError(f'Unknown activation kind {kind}')
-
-
-class SimpleMultiStepGenerator(nn.Module):
- def __init__(self, steps: List[nn.Module]):
- super().__init__()
- self.steps = nn.ModuleList(steps)
-
- def forward(self, x):
- cur_in = x
- outs = []
- for step in self.steps:
- cur_out = step(cur_in)
- outs.append(cur_out)
- cur_in = torch.cat((cur_in, cur_out), dim=1)
- return torch.cat(outs[::-1], dim=1)
-
-def deconv_factory(kind, ngf, mult, norm_layer, activation, max_features):
- if kind == 'convtranspose':
- return [nn.ConvTranspose2d(min(max_features, ngf * mult),
- min(max_features, int(ngf * mult / 2)),
- kernel_size=3, stride=2, padding=1, output_padding=1),
- norm_layer(min(max_features, int(ngf * mult / 2))), activation]
- elif kind == 'bilinear':
- return [nn.Upsample(scale_factor=2, mode='bilinear'),
- DepthWiseSeperableConv(min(max_features, ngf * mult),
- min(max_features, int(ngf * mult / 2)),
- kernel_size=3, stride=1, padding=1),
- norm_layer(min(max_features, int(ngf * mult / 2))), activation]
- else:
- raise Exception(f"Invalid deconv kind: {kind}")
\ No newline at end of file
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/schedulers/scheduling_ddim.py b/spaces/Jackflack09/diffuse-custom/diffusers/schedulers/scheduling_ddim.py
deleted file mode 100644
index dd38bd63c27d9665d6e4d97ea5068eb40ec44c5f..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/schedulers/scheduling_ddim.py
+++ /dev/null
@@ -1,380 +0,0 @@
-# Copyright 2022 Stanford University Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
-# and https://github.com/hojonathanho/diffusion
-
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS, BaseOutput, deprecate
-from .scheduling_utils import SchedulerMixin
-
-
-@dataclass
-# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
-class DDIMSchedulerOutput(BaseOutput):
- """
- Output class for the scheduler's step function output.
-
- Args:
- prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
- Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
- denoising loop.
- pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
- The predicted denoised sample (x_{0}) based on the model output from the current timestep.
- `pred_original_sample` can be used to preview progress or for guidance.
- """
-
- prev_sample: torch.FloatTensor
- pred_original_sample: Optional[torch.FloatTensor] = None
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor:
- """
- Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
- (1-beta) over time from t = [0,1].
-
- Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
- to that part of the diffusion process.
-
-
- Args:
- num_diffusion_timesteps (`int`): the number of betas to produce.
- max_beta (`float`): the maximum beta to use; use values lower than 1 to
- prevent singularities.
-
- Returns:
- betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
- """
-
- def alpha_bar(time_step):
- return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
-
- betas = []
- for i in range(num_diffusion_timesteps):
- t1 = i / num_diffusion_timesteps
- t2 = (i + 1) / num_diffusion_timesteps
- betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
- return torch.tensor(betas)
-
-
-class DDIMScheduler(SchedulerMixin, ConfigMixin):
- """
- Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising
- diffusion probabilistic models (DDPMs) with non-Markovian guidance.
-
- [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
- function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
- [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
- [`~SchedulerMixin.from_pretrained`] functions.
-
- For more details, see the original paper: https://arxiv.org/abs/2010.02502
-
- Args:
- num_train_timesteps (`int`): number of diffusion steps used to train the model.
- beta_start (`float`): the starting `beta` value of inference.
- beta_end (`float`): the final `beta` value.
- beta_schedule (`str`):
- the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
- `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
- trained_betas (`np.ndarray`, optional):
- option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
- clip_sample (`bool`, default `True`):
- option to clip predicted sample between -1 and 1 for numerical stability.
- set_alpha_to_one (`bool`, default `True`):
- each diffusion step uses the value of alphas product at that step and at the previous one. For the final
- step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
- otherwise it uses the value of alpha at step 0.
- steps_offset (`int`, default `0`):
- an offset added to the inference steps. You can use a combination of `offset=1` and
- `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
- stable diffusion.
- prediction_type (`str`, default `epsilon`, optional):
- prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
- process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
- https://imagen.research.google/video/paper.pdf)
- """
-
- _compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()
- _deprecated_kwargs = ["predict_epsilon"]
- order = 1
-
- @register_to_config
- def __init__(
- self,
- num_train_timesteps: int = 1000,
- beta_start: float = 0.0001,
- beta_end: float = 0.02,
- beta_schedule: str = "linear",
- trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
- clip_sample: bool = True,
- set_alpha_to_one: bool = True,
- steps_offset: int = 0,
- prediction_type: str = "epsilon",
- **kwargs,
- ):
- message = (
- "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
- " DDIMScheduler.from_pretrained(, prediction_type='epsilon')`."
- )
- predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
- if predict_epsilon is not None:
- self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
-
- if trained_betas is not None:
- self.betas = torch.tensor(trained_betas, dtype=torch.float32)
- elif beta_schedule == "linear":
- self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
- elif beta_schedule == "scaled_linear":
- # this schedule is very specific to the latent diffusion model.
- self.betas = (
- torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
- )
- elif beta_schedule == "squaredcos_cap_v2":
- # Glide cosine schedule
- self.betas = betas_for_alpha_bar(num_train_timesteps)
- else:
- raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
- self.alphas = 1.0 - self.betas
- self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-
- # At every step in ddim, we are looking into the previous alphas_cumprod
- # For the final step, there is no previous alphas_cumprod because we are already at 0
- # `set_alpha_to_one` decides whether we set this parameter simply to one or
- # whether we use the final alpha of the "non-previous" one.
- self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
-
- # standard deviation of the initial noise distribution
- self.init_noise_sigma = 1.0
-
- # setable values
- self.num_inference_steps = None
- self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))
-
- def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
- """
- Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
- current timestep.
-
- Args:
- sample (`torch.FloatTensor`): input sample
- timestep (`int`, optional): current timestep
-
- Returns:
- `torch.FloatTensor`: scaled input sample
- """
- return sample
-
- def _get_variance(self, timestep, prev_timestep):
- alpha_prod_t = self.alphas_cumprod[timestep]
- alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
- beta_prod_t = 1 - alpha_prod_t
- beta_prod_t_prev = 1 - alpha_prod_t_prev
-
- variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
-
- return variance
-
- def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
- """
- Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
-
- Args:
- num_inference_steps (`int`):
- the number of diffusion steps used when generating samples with a pre-trained model.
- """
- self.num_inference_steps = num_inference_steps
- step_ratio = self.config.num_train_timesteps // self.num_inference_steps
- # creates integer timesteps by multiplying by ratio
- # casting to int to avoid issues when num_inference_step is power of 3
- timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)
- self.timesteps = torch.from_numpy(timesteps).to(device)
- self.timesteps += self.config.steps_offset
-
- def step(
- self,
- model_output: torch.FloatTensor,
- timestep: int,
- sample: torch.FloatTensor,
- eta: float = 0.0,
- use_clipped_model_output: bool = False,
- generator=None,
- variance_noise: Optional[torch.FloatTensor] = None,
- return_dict: bool = True,
- ) -> Union[DDIMSchedulerOutput, Tuple]:
- """
- Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
- process from the learned model outputs (most often the predicted noise).
-
- Args:
- model_output (`torch.FloatTensor`): direct output from learned diffusion model.
- timestep (`int`): current discrete timestep in the diffusion chain.
- sample (`torch.FloatTensor`):
- current instance of sample being created by diffusion process.
- eta (`float`): weight of noise for added noise in diffusion step.
- use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped
- predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when
- `self.config.clip_sample` is `True`. If no clipping has happened, "corrected" `model_output` would
- coincide with the one provided as input and `use_clipped_model_output` will have not effect.
- generator: random number generator.
- variance_noise (`torch.FloatTensor`): instead of generating noise for the variance using `generator`, we
- can directly provide the noise for the variance itself. This is useful for methods such as
- CycleDiffusion. (https://arxiv.org/abs/2210.05559)
- return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class
-
- Returns:
- [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:
- [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
- returning a tuple, the first element is the sample tensor.
-
- """
- if self.num_inference_steps is None:
- raise ValueError(
- "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
- )
-
- # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
- # Ideally, read DDIM paper in-detail understanding
-
- # Notation ( ->
- # - pred_noise_t -> e_theta(x_t, t)
- # - pred_original_sample -> f_theta(x_t, t) or x_0
- # - std_dev_t -> sigma_t
- # - eta -> η
- # - pred_sample_direction -> "direction pointing to x_t"
- # - pred_prev_sample -> "x_t-1"
-
- # 1. get previous step value (=t-1)
- prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
-
- # 2. compute alphas, betas
- alpha_prod_t = self.alphas_cumprod[timestep]
- alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
-
- beta_prod_t = 1 - alpha_prod_t
-
- # 3. compute predicted original sample from predicted noise also called
- # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
- if self.config.prediction_type == "epsilon":
- pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
- elif self.config.prediction_type == "sample":
- pred_original_sample = model_output
- elif self.config.prediction_type == "v_prediction":
- pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
- # predict V
- model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
- else:
- raise ValueError(
- f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
- " `v_prediction`"
- )
-
- # 4. Clip "predicted x_0"
- if self.config.clip_sample:
- pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
-
- # 5. compute variance: "sigma_t(η)" -> see formula (16)
- # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
- variance = self._get_variance(timestep, prev_timestep)
- std_dev_t = eta * variance ** (0.5)
-
- if use_clipped_model_output:
- # the model_output is always re-derived from the clipped x_0 in Glide
- model_output = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
-
- # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
- pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output
-
- # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
- prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
-
- if eta > 0:
- # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
- device = model_output.device
- if variance_noise is not None and generator is not None:
- raise ValueError(
- "Cannot pass both generator and variance_noise. Please make sure that either `generator` or"
- " `variance_noise` stays `None`."
- )
-
- if variance_noise is None:
- if device.type == "mps":
- # randn does not work reproducibly on mps
- variance_noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator)
- variance_noise = variance_noise.to(device)
- else:
- variance_noise = torch.randn(
- model_output.shape, generator=generator, device=device, dtype=model_output.dtype
- )
- variance = self._get_variance(timestep, prev_timestep) ** (0.5) * eta * variance_noise
-
- prev_sample = prev_sample + variance
-
- if not return_dict:
- return (prev_sample,)
-
- return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
-
- def add_noise(
- self,
- original_samples: torch.FloatTensor,
- noise: torch.FloatTensor,
- timesteps: torch.IntTensor,
- ) -> torch.FloatTensor:
- # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
- self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
- timesteps = timesteps.to(original_samples.device)
-
- sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
- sqrt_alpha_prod = sqrt_alpha_prod.flatten()
- while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
- sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
- sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
- sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
- while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
- sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
- noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
- return noisy_samples
-
- def get_velocity(
- self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
- ) -> torch.FloatTensor:
- # Make sure alphas_cumprod and timestep have same device and dtype as sample
- self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)
- timesteps = timesteps.to(sample.device)
-
- sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
- sqrt_alpha_prod = sqrt_alpha_prod.flatten()
- while len(sqrt_alpha_prod.shape) < len(sample.shape):
- sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
- sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
- sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
- while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
- sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
- velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
- return velocity
-
- def __len__(self):
- return self.config.num_train_timesteps
diff --git a/spaces/Jackie2235/QueryExpansionForEtsy/README.md b/spaces/Jackie2235/QueryExpansionForEtsy/README.md
deleted file mode 100644
index e79d19dbecc3d19c6031fee69f592039383beb9f..0000000000000000000000000000000000000000
--- a/spaces/Jackie2235/QueryExpansionForEtsy/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: QueryExpansion
-emoji: 👁
-colorFrom: pink
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.22.0
-app_file: app.py
-pinned: false
-duplicated_from: HarryLee/QueryExpansionForEtsy
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jasonyoyo/CodeFormer/CodeFormer/basicsr/ops/fused_act/fused_act.py b/spaces/Jasonyoyo/CodeFormer/CodeFormer/basicsr/ops/fused_act/fused_act.py
deleted file mode 100644
index 588f815e596ab0fc83ab0f9d21426c22ec5ed7c3..0000000000000000000000000000000000000000
--- a/spaces/Jasonyoyo/CodeFormer/CodeFormer/basicsr/ops/fused_act/fused_act.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501
-
-import torch
-from torch import nn
-from torch.autograd import Function
-
-try:
- from . import fused_act_ext
-except ImportError:
- import os
- BASICSR_JIT = os.getenv('BASICSR_JIT')
- if BASICSR_JIT == 'True':
- from torch.utils.cpp_extension import load
- module_path = os.path.dirname(__file__)
- fused_act_ext = load(
- 'fused',
- sources=[
- os.path.join(module_path, 'src', 'fused_bias_act.cpp'),
- os.path.join(module_path, 'src', 'fused_bias_act_kernel.cu'),
- ],
- )
-
-
-class FusedLeakyReLUFunctionBackward(Function):
-
- @staticmethod
- def forward(ctx, grad_output, out, negative_slope, scale):
- ctx.save_for_backward(out)
- ctx.negative_slope = negative_slope
- ctx.scale = scale
-
- empty = grad_output.new_empty(0)
-
- grad_input = fused_act_ext.fused_bias_act(grad_output, empty, out, 3, 1, negative_slope, scale)
-
- dim = [0]
-
- if grad_input.ndim > 2:
- dim += list(range(2, grad_input.ndim))
-
- grad_bias = grad_input.sum(dim).detach()
-
- return grad_input, grad_bias
-
- @staticmethod
- def backward(ctx, gradgrad_input, gradgrad_bias):
- out, = ctx.saved_tensors
- gradgrad_out = fused_act_ext.fused_bias_act(gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope,
- ctx.scale)
-
- return gradgrad_out, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
-
- @staticmethod
- def forward(ctx, input, bias, negative_slope, scale):
- empty = input.new_empty(0)
- out = fused_act_ext.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
- ctx.save_for_backward(out)
- ctx.negative_slope = negative_slope
- ctx.scale = scale
-
- return out
-
- @staticmethod
- def backward(ctx, grad_output):
- out, = ctx.saved_tensors
-
- grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(grad_output, out, ctx.negative_slope, ctx.scale)
-
- return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
-
- def __init__(self, channel, negative_slope=0.2, scale=2**0.5):
- super().__init__()
-
- self.bias = nn.Parameter(torch.zeros(channel))
- self.negative_slope = negative_slope
- self.scale = scale
-
- def forward(self, input):
- return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5):
- return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
diff --git a/spaces/JohanDL/GPT4Readability/README.md b/spaces/JohanDL/GPT4Readability/README.md
deleted file mode 100644
index dec9e4544533ed74dfedd5dbb89491f68b4d5078..0000000000000000000000000000000000000000
--- a/spaces/JohanDL/GPT4Readability/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: GPT4Readability
-emoji: 📊
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/lib/uvr5_pack/lib_v5/layers_123812KB .py b/spaces/Kangarroar/ApplioRVC-Inference/lib/uvr5_pack/lib_v5/layers_123812KB .py
deleted file mode 100644
index b82f06bb4993cd63f076e68d7e24185269b1bc42..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/lib/uvr5_pack/lib_v5/layers_123812KB .py
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(Conv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nout,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- bias=False,
- ),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(SeperableConv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nin,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- groups=nin,
- bias=False,
- ),
- nn.Conv2d(nin, nout, kernel_size=1, bias=False),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class Encoder(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
- super(Encoder, self).__init__()
- self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
- def __call__(self, x):
- skip = self.conv1(x)
- h = self.conv2(skip)
-
- return h, skip
-
-
-class Decoder(nn.Module):
- def __init__(
- self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
- ):
- super(Decoder, self).__init__()
- self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.dropout = nn.Dropout2d(0.1) if dropout else None
-
- def __call__(self, x, skip=None):
- x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
- if skip is not None:
- skip = spec_utils.crop_center(skip, x)
- x = torch.cat([x, skip], dim=1)
- h = self.conv(x)
-
- if self.dropout is not None:
- h = self.dropout(h)
-
- return h
-
-
-class ASPPModule(nn.Module):
- def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
- super(ASPPModule, self).__init__()
- self.conv1 = nn.Sequential(
- nn.AdaptiveAvgPool2d((1, None)),
- Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
- )
- self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
- self.conv3 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
- )
- self.conv4 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
- )
- self.conv5 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.bottleneck = nn.Sequential(
- Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
- )
-
- def forward(self, x):
- _, _, h, w = x.size()
- feat1 = F.interpolate(
- self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
- )
- feat2 = self.conv2(x)
- feat3 = self.conv3(x)
- feat4 = self.conv4(x)
- feat5 = self.conv5(x)
- out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
- bottle = self.bottleneck(out)
- return bottle
diff --git a/spaces/KevinQHLin/UniVTG/main/train_qfvs.py b/spaces/KevinQHLin/UniVTG/main/train_qfvs.py
deleted file mode 100644
index 65a3b155ed6d432da7eb0072f87e1bae18d8a994..0000000000000000000000000000000000000000
--- a/spaces/KevinQHLin/UniVTG/main/train_qfvs.py
+++ /dev/null
@@ -1,325 +0,0 @@
-import os
-import pdb
-import time
-import json
-import pprint
-import random
-import importlib
-import numpy as np
-from tqdm import tqdm, trange
-from collections import defaultdict
-
-import h5py
-import torch
-import torch.nn as nn
-import torch.backends.cudnn as cudnn
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-
-import sys
-sys.path.append('/Users/kevin/univtg')
-from main.config import BaseOptions, setup_model
-from main.dataset_qfvs import DatasetQFVS, prepare_batch_inputs_qfvs, start_end_collate_qfvs
-from utils.basic_utils import set_seed, AverageMeter, dict_to_markdown, save_json, save_jsonl, load_json, load_pickle, l2_normalize_np_array
-from utils.model_utils import count_parameters
-from eval.qfvs import calculate_semantic_matching, load_videos_tag
-
-import logging
-logger = logging.getLogger(__name__)
-logging.basicConfig(format="%(asctime)s.%(msecs)03d:%(levelname)s:%(name)s - %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- level=logging.INFO)
-
-def eval_epoch(model, config, opt):
- model.eval()
- f1_sum = 0; p_sum = 0; r_sum = 0
-
- assert len(config['test_videos']) == 1
- video_id = config['test_videos'][0]
- embedding = load_pickle(f"./data/qfvs/txt_clip/{config['txt_feature']}.pkl")
-
- feat_type = config['vid_feature']
- feat = h5py.File(f'./data/qfvs/processed/P0{video_id}_{feat_type}.h5', 'r')
- features = torch.from_numpy(feat['features'][()])
- seg_len = torch.from_numpy(feat['seg_len'][()])
- # seg_len = torch.tensor(feat['seg_len'][()]).unsqueeze(0).cuda()
-
- # dim = features.shape[-1]
- # ctx_l = seg_len.sum().cpu()
-
- # dim = features.shape[-1]
- # ctx_l = features.shape[1]
- # seg_len = torch.ones(ctx_l)
- # features = features.reshape(-1, dim)[:ctx_l]
-
- # tef_st = torch.arange(0, ctx_l, 1.0) / ctx_l
- # tef_ed = tef_st + 1.0 / ctx_l
- # tef = torch.stack([tef_st, tef_ed], dim=1).cuda() # (Lv, 2)
- # features = torch.cat([features, tef], dim=1) # (Lv, Dv+2)
-
- transfer = {"Cupglass": "Glass",
- "Musicalinstrument": "Instrument",
- "Petsanimal": "Animal"}
-
- for _,_,files in os.walk("./data/qfvs/metadata/origin_data/Query-Focused_Summaries/Oracle_Summaries/P0"+str(video_id)):
- evaluation_num=len(files)
-
- mask_GT = torch.zeros(config["max_segment_num"], config["max_frame_num"], dtype=torch.bool).cuda()
- for j in range(len(seg_len)):
- for k in range(seg_len[j]):
- mask_GT[j][k] = 1
-
- for file in files:
- summaries_GT=[]
- with open("./data/qfvs/metadata/origin_data/Query-Focused_Summaries/Oracle_Summaries/P0"+str(video_id)+"/"+file,"r") as f:
- for line in f.readlines():
- summaries_GT.append(int(line.strip()))
-
- concept1, concept2 = file.split('_')[0:2]
-
- ##############
- if concept1 in transfer:
- concept1 = transfer[concept1]
- if concept2 in transfer:
- concept2 = transfer[concept2]
- concept1 = embedding[concept1]
- concept2 = embedding[concept2]
-
- concept1 = l2_normalize_np_array(concept1)
- concept2 = l2_normalize_np_array(concept2)
-
- data = {
- 'features':features,
- 'seg_len': seg_len,
- 'tokens_pad1':torch.from_numpy(concept1),
- 'tokens_pad2':torch.from_numpy(concept2),
- 'mask_GT': mask_GT
- }
-
- input1, input2, input_oracle, mask = prepare_batch_inputs_qfvs(start_end_collate_qfvs([data]), config, eval=True)
-
- summaries_GT = [x - 1 for x in summaries_GT]
- video_shots_tag = load_videos_tag(mat_path="./eval/Tags.mat")
-
- if opt.f_loss_coef == 0:
- output_type = 'saliency_scores'
- elif opt.s_loss_intra_coef == 0:
- output_type = 'pred_logits'
- else:
- if config['qfvs_score_ensemble'] > 0:
- output_type = ['pred_logits', 'saliency_scores']
- else:
- output_type = 'pred_logits'
-
- with torch.no_grad():
- if not isinstance(output_type, list):
- score1 = model(**input1)[output_type].squeeze()
- score1 = score1.masked_select(mask_GT)
-
- score2 = model(**input2)[output_type].squeeze()
- score2 = score2.masked_select(mask_GT)
-
- score = model(**input_oracle)[output_type].squeeze()
- score = score.masked_select(mask_GT)
- else:
- score1, score2, score = torch.zeros((int(mask.sum().item()))).cuda(), torch.zeros((int(mask.sum().item()))).cuda(), torch.zeros((int(mask.sum().item()))).cuda()
- for output_t in output_type:
- score1 += model(**input1)[output_t].squeeze().masked_select(mask_GT)
- score2 += model(**input2)[output_t].squeeze().masked_select(mask_GT)
- score += model(**input_oracle)[output_t].squeeze().masked_select(mask_GT)
-
- if config['qfvs_score_gather'] > 0:
- score = score + score1 + score2
- else:
- score = score
-
- # since video4 features dim is greater than video_shots_tag.
- score = score[:min(score.shape[0], video_shots_tag[video_id-1].shape[0])]
- _, top_index = score.topk(int(score.shape[0] * config["top_percent"]))
-
- p, r, f1 = calculate_semantic_matching(list(top_index.cpu().numpy()), summaries_GT, video_shots_tag, video_id=video_id-1)
- f1_sum+=f1; r_sum+=r; p_sum+=p
-
- return {'F': round(100* f1_sum/evaluation_num,2) ,
- 'R': round(100* r_sum/evaluation_num,2) ,
- 'P': round(100* p_sum/evaluation_num,2) }
-
-def idx2time(idx):
- sec1, sec2 = idx*5, (idx+1)*5
-
- h1 = sec1 // 3600
- m1 = (sec1 - h1*3600) // 60
- s1 = sec1 % 60
-
- h2 = sec2 // 3600
- m2 = (sec2 - h2*3600) // 60
- s2 = sec2 % 60
- print(h1,m1,s1,'\t', h2,m2,s2)
-
-def train_epoch(model, criterion, train_loader, optimizer, opt, config, epoch_i, tb_writer):
- model.train()
- criterion.train()
-
- # init meters
- time_meters = defaultdict(AverageMeter)
- loss_meters = defaultdict(AverageMeter)
-
- timer_dataloading = time.time()
- loss_total = 0
-
- for batch_idx, batch in enumerate(tqdm(train_loader)):
- time_meters["dataloading_time"].update(time.time() - timer_dataloading)
- timer_start = time.time()
- model_input1, model_input2, model_input_oracle, \
- model_gt1, model_gt2, model_gt_oracle, \
- mask_GT = prepare_batch_inputs_qfvs(batch, config)
- time_meters["prepare_inputs_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
- output1 = model(**model_input1)
- output2 = model(**model_input2)
- output_oracle = model(**model_input_oracle)
-
- loss_dict = {}
- loss_dict1 = criterion(output1, model_gt1, mask_GT)
- loss_dict2 = criterion(output2, model_gt2, mask_GT)
- loss_dict3 = criterion(output_oracle, model_gt_oracle, mask_GT)
-
- weight_dict = criterion.weight_dict
- if config['qfvs_loss_gather'] > 0:
- for k in loss_dict1.keys():
- loss_dict[k] = loss_dict1[k] + loss_dict2[k] + loss_dict3[k]
- else:
- loss_dict = loss_dict3
-
- losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
- loss_total += losses.item()
-
- time_meters["model_forward_time"].update(time.time() - timer_start)
- timer_start = time.time()
- optimizer.zero_grad()
- losses.backward()
- if opt.grad_clip > 0:
- nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
- optimizer.step()
- time_meters["model_backward_time"].update(time.time() - timer_start)
-
- timer_dataloading = time.time()
- return round(loss_total / len(train_loader), 2)
-
-# train in single domain.
-def train(model, criterion, optimizer, lr_scheduler, train_loader, opt, config):
- # if opt.device.type == "cuda":
- # logger.info("CUDA enabled.")
- # model.to(opt.device)
-
- tb_writer = SummaryWriter(opt.tensorboard_log_dir)
- tb_writer.add_text("hyperparameters", dict_to_markdown(vars(opt), max_str_len=None))
- opt.train_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str}\n"
- opt.eval_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str} [Metrics] {eval_metrics_str}\n"
-
- prev_best_score = {'Fscore':0, 'Precision':0, 'Recall':0}
- if opt.start_epoch is None:
- start_epoch = -1 if opt.eval_init else 0
- else:
- start_epoch = opt.start_epoch
-
- val_score = eval_epoch(model, config, opt)
- tb_writer.add_scalar(f"Eval/QFVS-V{config['test_videos'][0]}-fscore", float(val_score['F']), 0)
- logger.info(f"[Epoch {0}] [Fscore: {val_score['F']} / {prev_best_score['Fscore']}]"
- f" [Precision: {val_score['P']} / {prev_best_score['Precision']}]"
- f" [Recall: {val_score['R']} / {prev_best_score['Recall']}]")
- for epoch_i in trange(start_epoch, opt.n_epoch, desc="Epoch"):
- if epoch_i > -1:
- loss_epoch = train_epoch(model, criterion, train_loader, optimizer, opt, config, epoch_i, tb_writer)
- lr_scheduler.step()
- eval_epoch_interval = opt.eval_epoch
- if opt.eval_path is not None and (epoch_i + 1) % eval_epoch_interval == 0:
- with torch.no_grad():
- val_score = eval_epoch(model, config, opt)
- tb_writer.add_scalar(f"Eval/QFVS-V{config['test_videos'][0]}-fscore", float(val_score['F']), epoch_i+1)
- logger.info(f"[Epoch {epoch_i + 1}, Loss {loss_epoch}] [Fscore: {val_score['F']} / {prev_best_score['Fscore']}]"
- f" [Precision: {val_score['P']} / {prev_best_score['Precision']}]"
- f" [Recall: {val_score['R']} / {prev_best_score['Recall']}]")
-
- if prev_best_score['Fscore'] < val_score['F']:
- prev_best_score['Fscore'] = val_score['F']
- prev_best_score['Precision'] = val_score['P']
- prev_best_score['Recall'] = val_score['R']
-
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", f"_V{config['test_videos'][0]}_best.ckpt"))
- tb_writer.close()
- return prev_best_score
-
-def update_config(opt, config):
- # for key in ["max_segment_num", "max_frame_num", "top_percent",
- # "qfvs_vid_feature", "qfvs_txt_feature", "qfvs_dense_shot",
- # "qfvs_score_ensemble", "qfvs_score_gather", "qfvs_loss_gather"]:
- config["max_segment_num"] = opt.max_segment_num
- config["max_frame_num"] = opt.max_frame_num
- config["top_percent"] = opt.top_percent
- config["vid_feature"] = opt.qfvs_vid_feature
- config["txt_feature"] = opt.qfvs_txt_feature
- config["qfvs_dense_shot"] = opt.qfvs_dense_shot
- config["qfvs_score_ensemble"] = opt.qfvs_score_ensemble
- config["qfvs_score_gather"] = opt.qfvs_score_gather
- config["qfvs_loss_gather"] = opt.qfvs_loss_gather
- return config
-
-def start_training():
- logger.info("Setup config, data and model...")
- opt = BaseOptions().parse()
- set_seed(opt.seed)
-
- # config = load_json("./main/config_qfvs.json")
- config = {}
- config = update_config(opt, config)
-
- tb_writer = SummaryWriter(opt.tensorboard_log_dir)
-
- # key -> test video; value -> training videos.
- qfvs_split = {
- 1: [2, 3, 4],
- 2: [1, 3, 4],
- 3: [1, 2, 4],
- 4: [1, 2, 3]
- }
-
- scores_videos = {}
- for test_id, splits in qfvs_split.items():
- logger.info(f"Start Training {opt.dset_name}: {test_id}")
- config['train_videos'] = qfvs_split[test_id]
- config['test_videos'] = [test_id]
- train_dataset = DatasetQFVS(config)
- train_loader = DataLoader(train_dataset, batch_size=opt.bsz, collate_fn=start_end_collate_qfvs, shuffle=True, num_workers=opt.num_workers)
-
- model, criterion, optimizer, lr_scheduler = setup_model(opt)
- count_parameters(model)
- best_score = train(model, criterion, optimizer, lr_scheduler, train_loader, opt, config)
- scores_videos['V'+str(test_id)] = best_score
-
- # save the final results.
- avg_fscore = sum([v['Fscore'] for k, v in scores_videos.items()]) / len(scores_videos)
- avg_precision = sum([v['Precision'] for k, v in scores_videos.items()]) / len(scores_videos)
- avg_recall = sum([v['Recall'] for k, v in scores_videos.items()]) / len(scores_videos)
- scores_videos['avg'] = {'Fscore':avg_fscore, 'Precision':avg_precision, 'Recall':avg_recall}
-
- save_metrics_path = os.path.join(opt.results_dir, f"best_{opt.dset_name}_{opt.eval_split_name}_preds_metrics.json")
- save_json( scores_videos, save_metrics_path, save_pretty=True, sort_keys=False)
-
- tb_writer.add_scalar(f"Eval/QFVS-avg-fscore", round(avg_fscore, 2), 1)
- tb_writer.add_text(f"Eval/QFVS-{opt.dset_name}", dict_to_markdown(scores_videos, max_str_len=None))
- tb_writer.close()
-
- print(scores_videos)
- return
-
-if __name__ == '__main__':
- start_training()
- results = logger.info("\n\n\nFINISHED TRAINING!!!")
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/coders/__init__.py b/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/coders/__init__.py
deleted file mode 100644
index e12fd64e12b5e76a014da9bd724f1b6f50b488c4..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/coders/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .base_bbox_coder import BaseBBoxCoder
-from .bucketing_bbox_coder import BucketingBBoxCoder
-from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder
-from .distance_point_bbox_coder import DistancePointBBoxCoder
-from .legacy_delta_xywh_bbox_coder import LegacyDeltaXYWHBBoxCoder
-from .pseudo_bbox_coder import PseudoBBoxCoder
-from .tblr_bbox_coder import TBLRBBoxCoder
-from .yolo_bbox_coder import YOLOBBoxCoder
-
-__all__ = [
- 'BaseBBoxCoder', 'PseudoBBoxCoder', 'DeltaXYWHBBoxCoder',
- 'LegacyDeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'YOLOBBoxCoder',
- 'BucketingBBoxCoder', 'DistancePointBBoxCoder'
-]
diff --git a/spaces/LanguageBind/LanguageBind/scripts/depth_language/train.sh b/spaces/LanguageBind/LanguageBind/scripts/depth_language/train.sh
deleted file mode 100644
index 152b34b0f3de12c7191c394618625eae97be7948..0000000000000000000000000000000000000000
--- a/spaces/LanguageBind/LanguageBind/scripts/depth_language/train.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-
-CACHE_DIR="path/to/pretrained/weight"
-TRAIN_DATA="path/to/data"
-# this script is for 1024 total batch_size (n(8) GPUs * batch_size(128) * accum_freq(1))
-cd /path/to/LanguageBind
-TORCH_DISTRIBUTED_DEBUG=DETAIL HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 torchrun --nnodes=$HOST_NUM --node_rank=$INDEX --nproc_per_node $HOST_GPU_NUM --master_addr $CHIEF_IP \
- -m main \
- --train-data ${TRAIN_DATA} \
- --train-num-samples 3020000 \
- --clip-type "dl" --max-depth 10 \
- --do_train \
- --lock-text --lock-image --text-type "polish_mplug" \
- --init-temp 0.07 --learn-temp \
- --model "ViT-L-14" --cache-dir ${CACHE_DIR} \
- --convert_to_lora --lora_r 2 \
- --lr 5e-4 --coef-lr 1e-3 \
- --beta1 0.9 --beta2 0.98 --wd 0.2 --eps 1e-6 \
- --num-frames 1 --force-patch-dropout 0.5 \
- --epochs 1 --batch-size 128 --accum-freq 1 --warmup 200 \
- --precision "amp" --workers 10 --video-decode-backend "imgs" \
- --save-frequency 1 --log-every-n-steps 20 --report-to "tensorboard" --resume "latest" \
- --do_eval \
- --val_d_cls_data "NYUV2"
-
-
diff --git a/spaces/Lightxr/sd-diffusers-webui/Dockerfile b/spaces/Lightxr/sd-diffusers-webui/Dockerfile
deleted file mode 100644
index 1c566db9b07c5fae065dad8970c497be9bc1b407..0000000000000000000000000000000000000000
--- a/spaces/Lightxr/sd-diffusers-webui/Dockerfile
+++ /dev/null
@@ -1,22 +0,0 @@
-# Dockerfile Public T4
-
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND noninteractive
-
-WORKDIR /content
-
-RUN apt-get update -y && apt-get upgrade -y && apt-get install -y libgl1 libglib2.0-0 wget git git-lfs python3-pip python-is-python3 && pip3 install --upgrade pip
-
-RUN pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchsde --extra-index-url https://download.pytorch.org/whl/cu113
-RUN pip install https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.16/xformers-0.0.16+814314d.d20230118-cp310-cp310-linux_x86_64.whl
-RUN pip install --pre triton
-RUN pip install numexpr einops transformers k_diffusion safetensors gradio diffusers==0.12.1
-
-ADD . .
-RUN adduser --disabled-password --gecos '' user
-RUN chown -R user:user /content
-RUN chmod -R 777 /content
-USER user
-
-EXPOSE 7860
-CMD python /content/app.py
diff --git a/spaces/Liviox24/LoanEligibilityPrediction/README.md b/spaces/Liviox24/LoanEligibilityPrediction/README.md
deleted file mode 100644
index 234b8065bba17206b179c8a65b1ce2ff2b3d6323..0000000000000000000000000000000000000000
--- a/spaces/Liviox24/LoanEligibilityPrediction/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: LoanEligibilityPrediction
-emoji: 📚
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.0.22
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mahiruoshi/lovelive-ShojoKageki-vits/utils.py b/spaces/Mahiruoshi/lovelive-ShojoKageki-vits/utils.py
deleted file mode 100644
index a870556e805c8cba7dc0540c686710a6a62819c4..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/lovelive-ShojoKageki-vits/utils.py
+++ /dev/null
@@ -1,307 +0,0 @@
-import argparse
-import glob
-import json
-import logging
-import os
-import subprocess
-import sys
-
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except Exception as e:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration,
- checkpoint_path):
- logger.info(
- "Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save(
- {
- 'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate
- }, checkpoint_path)
-
-
-def summarize(
- writer,
- global_step,
- scalars={}, # noqa
- histograms={}, # noqa
- images={}, # noqa
- audios={}, # noqa
- audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram,
- aspect="auto",
- origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3, ))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(),
- aspect='auto',
- origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3, ))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c',
- '--config',
- type=str,
- default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m',
- '--model',
- type=str,
- required=True,
- help='Model name')
- parser.add_argument('--train_data',
- type=str,
- required=True,
- help='train data')
- parser.add_argument('--val_data', type=str, required=True, help='val data')
- parser.add_argument('--phone_table',
- type=str,
- required=True,
- help='phone table')
- parser.add_argument('--speaker_table',
- type=str,
- default=None,
- help='speaker table, required for multiple speakers')
-
- args = parser.parse_args()
- model_dir = args.model
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r", encoding='utf8') as f:
- data = f.read()
- with open(config_save_path, "w", encoding='utf8') as f:
- f.write(data)
- else:
- with open(config_save_path, "r", encoding='utf8') as f:
- data = f.read()
- config = json.loads(data)
- config['data']['training_files'] = args.train_data
- config['data']['validation_files'] = args.val_data
- config['data']['phone_table'] = args.phone_table
- # 0 is kept for blank
- config['data']['num_phones'] = len(open(args.phone_table).readlines()) + 1
- if args.speaker_table is not None:
- config['data']['speaker_table'] = args.speaker_table
- # 0 is kept for unknown speaker
- config['data']['n_speakers'] = len(
- open(args.speaker_table).readlines()) + 1
- else:
- config['data']['n_speakers'] = 0
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn('''{} is not a git repository, therefore hash value
- comparison will be ignored.'''.format(source_dir))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn(
- "git hash values are different. {}(saved) != {}(current)".
- format(saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.INFO)
-
- formatter = logging.Formatter(
- "%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.INFO)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/Masutxrxd/Masutxrxd/Dockerfile b/spaces/Masutxrxd/Masutxrxd/Dockerfile
deleted file mode 100644
index eef259fa372a804549fb0af0913718a13344da34..0000000000000000000000000000000000000000
--- a/spaces/Masutxrxd/Masutxrxd/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM node:18-bullseye-slim
-RUN apt-get update && \
- apt-get install -y git
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-WORKDIR /app
-RUN npm install
-COPY Dockerfile greeting.md* .env* ./
-RUN npm run build
-EXPOSE 7860
-ENV NODE_ENV=production
-CMD [ "npm", "start" ]
diff --git a/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/apps/render_data.py b/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/apps/render_data.py
deleted file mode 100644
index 563c03fba6e304eced73ca283152a968a65c3b8e..0000000000000000000000000000000000000000
--- a/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/apps/render_data.py
+++ /dev/null
@@ -1,290 +0,0 @@
-#from data.config import raw_dataset, render_dataset, archive_dataset, model_list, zip_path
-
-from lib.renderer.camera import Camera
-import numpy as np
-from lib.renderer.mesh import load_obj_mesh, compute_tangent, compute_normal, load_obj_mesh_mtl
-from lib.renderer.camera import Camera
-import os
-import cv2
-import time
-import math
-import random
-import pyexr
-import argparse
-from tqdm import tqdm
-
-
-def make_rotate(rx, ry, rz):
- sinX = np.sin(rx)
- sinY = np.sin(ry)
- sinZ = np.sin(rz)
-
- cosX = np.cos(rx)
- cosY = np.cos(ry)
- cosZ = np.cos(rz)
-
- Rx = np.zeros((3,3))
- Rx[0, 0] = 1.0
- Rx[1, 1] = cosX
- Rx[1, 2] = -sinX
- Rx[2, 1] = sinX
- Rx[2, 2] = cosX
-
- Ry = np.zeros((3,3))
- Ry[0, 0] = cosY
- Ry[0, 2] = sinY
- Ry[1, 1] = 1.0
- Ry[2, 0] = -sinY
- Ry[2, 2] = cosY
-
- Rz = np.zeros((3,3))
- Rz[0, 0] = cosZ
- Rz[0, 1] = -sinZ
- Rz[1, 0] = sinZ
- Rz[1, 1] = cosZ
- Rz[2, 2] = 1.0
-
- R = np.matmul(np.matmul(Rz,Ry),Rx)
- return R
-
-def rotateSH(SH, R):
- SHn = SH
-
- # 1st order
- SHn[1] = R[1,1]*SH[1] - R[1,2]*SH[2] + R[1,0]*SH[3]
- SHn[2] = -R[2,1]*SH[1] + R[2,2]*SH[2] - R[2,0]*SH[3]
- SHn[3] = R[0,1]*SH[1] - R[0,2]*SH[2] + R[0,0]*SH[3]
-
- # 2nd order
- SHn[4:,0] = rotateBand2(SH[4:,0],R)
- SHn[4:,1] = rotateBand2(SH[4:,1],R)
- SHn[4:,2] = rotateBand2(SH[4:,2],R)
-
- return SHn
-
-def rotateBand2(x, R):
- s_c3 = 0.94617469575
- s_c4 = -0.31539156525
- s_c5 = 0.54627421529
-
- s_c_scale = 1.0/0.91529123286551084
- s_c_scale_inv = 0.91529123286551084
-
- s_rc2 = 1.5853309190550713*s_c_scale
- s_c4_div_c3 = s_c4/s_c3
- s_c4_div_c3_x2 = (s_c4/s_c3)*2.0
-
- s_scale_dst2 = s_c3 * s_c_scale_inv
- s_scale_dst4 = s_c5 * s_c_scale_inv
-
- sh0 = x[3] + x[4] + x[4] - x[1]
- sh1 = x[0] + s_rc2*x[2] + x[3] + x[4]
- sh2 = x[0]
- sh3 = -x[3]
- sh4 = -x[1]
-
- r2x = R[0][0] + R[0][1]
- r2y = R[1][0] + R[1][1]
- r2z = R[2][0] + R[2][1]
-
- r3x = R[0][0] + R[0][2]
- r3y = R[1][0] + R[1][2]
- r3z = R[2][0] + R[2][2]
-
- r4x = R[0][1] + R[0][2]
- r4y = R[1][1] + R[1][2]
- r4z = R[2][1] + R[2][2]
-
- sh0_x = sh0 * R[0][0]
- sh0_y = sh0 * R[1][0]
- d0 = sh0_x * R[1][0]
- d1 = sh0_y * R[2][0]
- d2 = sh0 * (R[2][0] * R[2][0] + s_c4_div_c3)
- d3 = sh0_x * R[2][0]
- d4 = sh0_x * R[0][0] - sh0_y * R[1][0]
-
- sh1_x = sh1 * R[0][2]
- sh1_y = sh1 * R[1][2]
- d0 += sh1_x * R[1][2]
- d1 += sh1_y * R[2][2]
- d2 += sh1 * (R[2][2] * R[2][2] + s_c4_div_c3)
- d3 += sh1_x * R[2][2]
- d4 += sh1_x * R[0][2] - sh1_y * R[1][2]
-
- sh2_x = sh2 * r2x
- sh2_y = sh2 * r2y
- d0 += sh2_x * r2y
- d1 += sh2_y * r2z
- d2 += sh2 * (r2z * r2z + s_c4_div_c3_x2)
- d3 += sh2_x * r2z
- d4 += sh2_x * r2x - sh2_y * r2y
-
- sh3_x = sh3 * r3x
- sh3_y = sh3 * r3y
- d0 += sh3_x * r3y
- d1 += sh3_y * r3z
- d2 += sh3 * (r3z * r3z + s_c4_div_c3_x2)
- d3 += sh3_x * r3z
- d4 += sh3_x * r3x - sh3_y * r3y
-
- sh4_x = sh4 * r4x
- sh4_y = sh4 * r4y
- d0 += sh4_x * r4y
- d1 += sh4_y * r4z
- d2 += sh4 * (r4z * r4z + s_c4_div_c3_x2)
- d3 += sh4_x * r4z
- d4 += sh4_x * r4x - sh4_y * r4y
-
- dst = x
- dst[0] = d0
- dst[1] = -d1
- dst[2] = d2 * s_scale_dst2
- dst[3] = -d3
- dst[4] = d4 * s_scale_dst4
-
- return dst
-
-def render_prt_ortho(out_path, folder_name, subject_name, shs, rndr, rndr_uv, im_size, angl_step=4, n_light=1, pitch=[0]):
- cam = Camera(width=im_size, height=im_size)
- cam.ortho_ratio = 0.4 * (512 / im_size)
- cam.near = -100
- cam.far = 100
- cam.sanity_check()
-
- # set path for obj, prt
- mesh_file = os.path.join(folder_name, subject_name + '_100k.obj')
- if not os.path.exists(mesh_file):
- print('ERROR: obj file does not exist!!', mesh_file)
- return
- prt_file = os.path.join(folder_name, 'bounce', 'bounce0.txt')
- if not os.path.exists(prt_file):
- print('ERROR: prt file does not exist!!!', prt_file)
- return
- face_prt_file = os.path.join(folder_name, 'bounce', 'face.npy')
- if not os.path.exists(face_prt_file):
- print('ERROR: face prt file does not exist!!!', prt_file)
- return
- text_file = os.path.join(folder_name, 'tex', subject_name + '_dif_2k.jpg')
- if not os.path.exists(text_file):
- print('ERROR: dif file does not exist!!', text_file)
- return
-
- texture_image = cv2.imread(text_file)
- texture_image = cv2.cvtColor(texture_image, cv2.COLOR_BGR2RGB)
-
- vertices, faces, normals, faces_normals, textures, face_textures = load_obj_mesh(mesh_file, with_normal=True, with_texture=True)
- vmin = vertices.min(0)
- vmax = vertices.max(0)
- up_axis = 1 if (vmax-vmin).argmax() == 1 else 2
-
- vmed = np.median(vertices, 0)
- vmed[up_axis] = 0.5*(vmax[up_axis]+vmin[up_axis])
- y_scale = 180/(vmax[up_axis] - vmin[up_axis])
-
- rndr.set_norm_mat(y_scale, vmed)
- rndr_uv.set_norm_mat(y_scale, vmed)
-
- tan, bitan = compute_tangent(vertices, faces, normals, textures, face_textures)
- prt = np.loadtxt(prt_file)
- face_prt = np.load(face_prt_file)
- rndr.set_mesh(vertices, faces, normals, faces_normals, textures, face_textures, prt, face_prt, tan, bitan)
- rndr.set_albedo(texture_image)
-
- rndr_uv.set_mesh(vertices, faces, normals, faces_normals, textures, face_textures, prt, face_prt, tan, bitan)
- rndr_uv.set_albedo(texture_image)
-
- os.makedirs(os.path.join(out_path, 'GEO', 'OBJ', subject_name),exist_ok=True)
- os.makedirs(os.path.join(out_path, 'PARAM', subject_name),exist_ok=True)
- os.makedirs(os.path.join(out_path, 'RENDER', subject_name),exist_ok=True)
- os.makedirs(os.path.join(out_path, 'MASK', subject_name),exist_ok=True)
- os.makedirs(os.path.join(out_path, 'UV_RENDER', subject_name),exist_ok=True)
- os.makedirs(os.path.join(out_path, 'UV_MASK', subject_name),exist_ok=True)
- os.makedirs(os.path.join(out_path, 'UV_POS', subject_name),exist_ok=True)
- os.makedirs(os.path.join(out_path, 'UV_NORMAL', subject_name),exist_ok=True)
-
- if not os.path.exists(os.path.join(out_path, 'val.txt')):
- f = open(os.path.join(out_path, 'val.txt'), 'w')
- f.close()
-
- # copy obj file
- cmd = 'cp %s %s' % (mesh_file, os.path.join(out_path, 'GEO', 'OBJ', subject_name))
- print(cmd)
- os.system(cmd)
-
- for p in pitch:
- for y in tqdm(range(0, 360, angl_step)):
- R = np.matmul(make_rotate(math.radians(p), 0, 0), make_rotate(0, math.radians(y), 0))
- if up_axis == 2:
- R = np.matmul(R, make_rotate(math.radians(90),0,0))
-
- rndr.rot_matrix = R
- rndr_uv.rot_matrix = R
- rndr.set_camera(cam)
- rndr_uv.set_camera(cam)
-
- for j in range(n_light):
- sh_id = random.randint(0,shs.shape[0]-1)
- sh = shs[sh_id]
- sh_angle = 0.2*np.pi*(random.random()-0.5)
- sh = rotateSH(sh, make_rotate(0, sh_angle, 0).T)
-
- dic = {'sh': sh, 'ortho_ratio': cam.ortho_ratio, 'scale': y_scale, 'center': vmed, 'R': R}
-
- rndr.set_sh(sh)
- rndr.analytic = False
- rndr.use_inverse_depth = False
- rndr.display()
-
- out_all_f = rndr.get_color(0)
- out_mask = out_all_f[:,:,3]
- out_all_f = cv2.cvtColor(out_all_f, cv2.COLOR_RGBA2BGR)
-
- np.save(os.path.join(out_path, 'PARAM', subject_name, '%d_%d_%02d.npy'%(y,p,j)),dic)
- cv2.imwrite(os.path.join(out_path, 'RENDER', subject_name, '%d_%d_%02d.jpg'%(y,p,j)),255.0*out_all_f)
- cv2.imwrite(os.path.join(out_path, 'MASK', subject_name, '%d_%d_%02d.png'%(y,p,j)),255.0*out_mask)
-
- rndr_uv.set_sh(sh)
- rndr_uv.analytic = False
- rndr_uv.use_inverse_depth = False
- rndr_uv.display()
-
- uv_color = rndr_uv.get_color(0)
- uv_color = cv2.cvtColor(uv_color, cv2.COLOR_RGBA2BGR)
- cv2.imwrite(os.path.join(out_path, 'UV_RENDER', subject_name, '%d_%d_%02d.jpg'%(y,p,j)),255.0*uv_color)
-
- if y == 0 and j == 0 and p == pitch[0]:
- uv_pos = rndr_uv.get_color(1)
- uv_mask = uv_pos[:,:,3]
- cv2.imwrite(os.path.join(out_path, 'UV_MASK', subject_name, '00.png'),255.0*uv_mask)
-
- data = {'default': uv_pos[:,:,:3]} # default is a reserved name
- pyexr.write(os.path.join(out_path, 'UV_POS', subject_name, '00.exr'), data)
-
- uv_nml = rndr_uv.get_color(2)
- uv_nml = cv2.cvtColor(uv_nml, cv2.COLOR_RGBA2BGR)
- cv2.imwrite(os.path.join(out_path, 'UV_NORMAL', subject_name, '00.png'),255.0*uv_nml)
-
-
-if __name__ == '__main__':
- shs = np.load('./env_sh.npy')
-
- parser = argparse.ArgumentParser()
- parser.add_argument('-i', '--input', type=str, default='/home/shunsuke/Downloads/rp_dennis_posed_004_OBJ')
- parser.add_argument('-o', '--out_dir', type=str, default='/home/shunsuke/Documents/hf_human')
- parser.add_argument('-m', '--ms_rate', type=int, default=1, help='higher ms rate results in less aliased output. MESA renderer only supports ms_rate=1.')
- parser.add_argument('-e', '--egl', action='store_true', help='egl rendering option. use this when rendering with headless server with NVIDIA GPU')
- parser.add_argument('-s', '--size', type=int, default=512, help='rendering image size')
- args = parser.parse_args()
-
- # NOTE: GL context has to be created before any other OpenGL function loads.
- from lib.renderer.gl.init_gl import initialize_GL_context
- initialize_GL_context(width=args.size, height=args.size, egl=args.egl)
-
- from lib.renderer.gl.prt_render import PRTRender
- rndr = PRTRender(width=args.size, height=args.size, ms_rate=args.ms_rate, egl=args.egl)
- rndr_uv = PRTRender(width=args.size, height=args.size, uv_mode=True, egl=args.egl)
-
- if args.input[-1] == '/':
- args.input = args.input[:-1]
- subject_name = args.input.split('/')[-1][:-4]
- render_prt_ortho(args.out_dir, args.input, subject_name, shs, rndr, rndr_uv, args.size, 1, 1, pitch=[0])
\ No newline at end of file
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/parsers/__init__.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/parsers/__init__.py
deleted file mode 100644
index fd37947107eba2d2cd54630f5d44360a046d7d32..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/parsers/__init__.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .base import BaseParser
-from .coco_parser import COCOTextDetAnnParser
-from .ctw1500_parser import CTW1500AnnParser
-from .funsd_parser import FUNSDTextDetAnnParser
-from .icdar_txt_parser import (ICDARTxtTextDetAnnParser,
- ICDARTxtTextRecogAnnParser)
-from .mjsynth_parser import MJSynthAnnParser
-from .naf_parser import NAFAnnParser
-from .sroie_parser import SROIETextDetAnnParser
-from .svt_parser import SVTTextDetAnnParser
-from .synthtext_parser import SynthTextAnnParser
-from .totaltext_parser import TotaltextTextDetAnnParser
-from .wildreceipt_parser import WildreceiptKIEAnnParser
-
-__all__ = [
- 'BaseParser', 'ICDARTxtTextDetAnnParser', 'ICDARTxtTextRecogAnnParser',
- 'TotaltextTextDetAnnParser', 'WildreceiptKIEAnnParser',
- 'COCOTextDetAnnParser', 'SVTTextDetAnnParser', 'FUNSDTextDetAnnParser',
- 'SROIETextDetAnnParser', 'NAFAnnParser', 'CTW1500AnnParser',
- 'SynthTextAnnParser', 'MJSynthAnnParser'
-]
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/visualization/textrecog_visualizer.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/visualization/textrecog_visualizer.py
deleted file mode 100644
index d2f529b47f40b97d46ffdd73ee467da46e2e92c4..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/visualization/textrecog_visualizer.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict, Optional, Tuple, Union
-
-import cv2
-import mmcv
-import numpy as np
-
-from mmocr.registry import VISUALIZERS
-from mmocr.structures import TextRecogDataSample
-from .base_visualizer import BaseLocalVisualizer
-
-
-@VISUALIZERS.register_module()
-class TextRecogLocalVisualizer(BaseLocalVisualizer):
- """MMOCR Text Detection Local Visualizer.
-
- Args:
- name (str): Name of the instance. Defaults to 'visualizer'.
- image (np.ndarray, optional): The origin image to draw. The format
- should be RGB. Defaults to None.
- vis_backends (list, optional): Visual backend config list.
- Defaults to None.
- save_dir (str, optional): Save file dir for all storage backends.
- If it is None, the backend storage will not save any data.
- gt_color (str or tuple[int, int, int]): Colors of GT text. The tuple of
- color should be in RGB order. Or using an abbreviation of color,
- such as `'g'` for `'green'`. Defaults to 'g'.
- pred_color (str or tuple[int, int, int]): Colors of Predicted text.
- The tuple of color should be in RGB order. Or using an abbreviation
- of color, such as `'r'` for `'red'`. Defaults to 'r'.
- """
-
- def __init__(self,
- name: str = 'visualizer',
- image: Optional[np.ndarray] = None,
- vis_backends: Optional[Dict] = None,
- save_dir: Optional[str] = None,
- gt_color: Optional[Union[str, Tuple[int, int, int]]] = 'g',
- pred_color: Optional[Union[str, Tuple[int, int, int]]] = 'r',
- **kwargs) -> None:
- super().__init__(
- name=name,
- image=image,
- vis_backends=vis_backends,
- save_dir=save_dir,
- **kwargs)
- self.gt_color = gt_color
- self.pred_color = pred_color
-
- def _draw_instances(self, image: np.ndarray, text: str) -> np.ndarray:
- """Draw text on image.
-
- Args:
- image (np.ndarray): The image to draw.
- text (str): The text to draw.
-
- Returns:
- np.ndarray: The image with text drawn.
- """
- height, width = image.shape[:2]
- empty_img = np.full_like(image, 255)
- self.set_image(empty_img)
- font_size = min(0.5 * width / (len(text) + 1), 0.5 * height)
- self.draw_texts(
- text,
- np.array([width / 2, height / 2]),
- colors=self.gt_color,
- font_sizes=font_size,
- vertical_alignments='center',
- horizontal_alignments='center',
- font_families=self.font_families,
- font_properties=self.font_properties)
- text_image = self.get_image()
- return text_image
-
- def add_datasample(self,
- name: str,
- image: np.ndarray,
- data_sample: Optional['TextRecogDataSample'] = None,
- draw_gt: bool = True,
- draw_pred: bool = True,
- show: bool = False,
- wait_time: int = 0,
- pred_score_thr: float = None,
- out_file: Optional[str] = None,
- step=0) -> None:
- """Visualize datasample and save to all backends.
-
- - If GT and prediction are plotted at the same time, they are
- displayed in a stitched image where the left image is the
- ground truth and the right image is the prediction.
- - If ``show`` is True, all storage backends are ignored, and
- the images will be displayed in a local window.
- - If ``out_file`` is specified, the drawn image will be
- saved to ``out_file``. This is usually used when the display
- is not available.
-
- Args:
- name (str): The image title. Defaults to 'image'.
- image (np.ndarray): The image to draw.
- data_sample (:obj:`TextRecogDataSample`, optional):
- TextRecogDataSample which contains gt and prediction.
- Defaults to None.
- draw_gt (bool): Whether to draw GT TextRecogDataSample.
- Defaults to True.
- draw_pred (bool): Whether to draw Predicted TextRecogDataSample.
- Defaults to True.
- show (bool): Whether to display the drawn image. Defaults to False.
- wait_time (float): The interval of show (s). Defaults to 0.
- out_file (str): Path to output file. Defaults to None.
- step (int): Global step value to record. Defaults to 0.
- pred_score_thr (float): Threshold of prediction score. It's not
- used in this function. Defaults to None.
- """
- height, width = image.shape[:2]
- resize_height = 64
- resize_width = int(1.0 * width / height * resize_height)
- image = cv2.resize(image, (resize_width, resize_height))
-
- if image.ndim == 2:
- image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
-
- cat_images = [image]
- if (draw_gt and data_sample is not None and 'gt_text' in data_sample
- and 'item' in data_sample.gt_text):
- gt_text = data_sample.gt_text.item
- cat_images.append(self._draw_instances(image, gt_text))
- if (draw_pred and data_sample is not None
- and 'pred_text' in data_sample
- and 'item' in data_sample.pred_text):
- pred_text = data_sample.pred_text.item
- cat_images.append(self._draw_instances(image, pred_text))
- cat_images = self._cat_image(cat_images, axis=0)
-
- if show:
- self.show(cat_images, win_name=name, wait_time=wait_time)
- else:
- self.add_image(name, cat_images, step)
-
- if out_file is not None:
- mmcv.imwrite(cat_images[..., ::-1], out_file)
-
- self.set_image(cat_images)
- return self.get_image()
diff --git a/spaces/NATSpeech/PortaSpeech/modules/commons/normalizing_flow/utils.py b/spaces/NATSpeech/PortaSpeech/modules/commons/normalizing_flow/utils.py
deleted file mode 100644
index 7eb56ec514bff822ba1a19a6474207ed82492410..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/modules/commons/normalizing_flow/utils.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import torch
-
-
-def squeeze(x, x_mask=None, n_sqz=2):
- b, c, t = x.size()
-
- t = (t // n_sqz) * n_sqz
- x = x[:, :, :t]
- x_sqz = x.view(b, c, t // n_sqz, n_sqz)
- x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz)
-
- if x_mask is not None:
- x_mask = x_mask[:, :, n_sqz - 1::n_sqz]
- else:
- x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype)
- return x_sqz * x_mask, x_mask
-
-
-def unsqueeze(x, x_mask=None, n_sqz=2):
- b, c, t = x.size()
-
- x_unsqz = x.view(b, n_sqz, c // n_sqz, t)
- x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz)
-
- if x_mask is not None:
- x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz)
- else:
- x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype)
- return x_unsqz * x_mask, x_mask
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/xlnet/xlnet_config.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/xlnet/xlnet_config.py
deleted file mode 100644
index 7852eadf469476b4772533dce563366cd3478317..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/xlnet/xlnet_config.py
+++ /dev/null
@@ -1,181 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Utility functions used in XLNet model."""
-
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
-import json
-import os
-
-import tensorflow as tf
-
-
-def create_run_config(is_training, is_finetune, flags):
- """Helper function for creating RunConfig."""
- kwargs = dict(
- is_training=is_training,
- use_tpu=flags.use_tpu,
- dropout=flags.dropout,
- dropout_att=flags.dropout_att,
- init_method=flags.init_method,
- init_range=flags.init_range,
- init_std=flags.init_std,
- clamp_len=flags.clamp_len)
-
- if not is_finetune:
- kwargs.update(dict(
- mem_len=flags.mem_len,
- reuse_len=flags.reuse_len,
- bi_data=flags.bi_data,
- clamp_len=flags.clamp_len,
- same_length=flags.same_length))
-
- return RunConfig(**kwargs)
-
-
-# TODO(hongkuny): refactor XLNetConfig and RunConfig.
-class XLNetConfig(object):
- """Configs for XLNet model.
-
- XLNetConfig contains hyperparameters that are specific to a model checkpoint;
- i.e., these hyperparameters should be the same between
- pretraining and finetuning.
-
- The following hyperparameters are defined:
- n_layer: int, the number of layers.
- d_model: int, the hidden size.
- n_head: int, the number of attention heads.
- d_head: int, the dimension size of each attention head.
- d_inner: int, the hidden size in feed-forward layers.
- ff_activation: str, "relu" or "gelu".
- untie_r: bool, whether to untie the biases in attention.
- n_token: int, the vocab size.
- """
-
- def __init__(self, FLAGS=None, json_path=None, args_dict=None):
- """Constructing an XLNetConfig.
-
- One of FLAGS or json_path should be provided.
-
- Args:
- FLAGS: An FLAGS instance.
- json_path: A path to a json config file.
- args_dict: A dict for args.
- """
-
- assert FLAGS is not None or json_path is not None or args_dict is not None
-
- self.keys = ['n_layer', 'd_model', 'n_head', 'd_head', 'd_inner',
- 'ff_activation', 'untie_r', 'n_token']
-
- if FLAGS is not None:
- self.init_from_flags(FLAGS)
-
- if json_path is not None:
- self.init_from_json(json_path)
-
- if args_dict is not None:
- self.init_from_dict(args_dict)
-
- def init_from_dict(self, args_dict):
- """Constructs a `BertConfig` from a Python dictionary of parameters."""
- for key in self.keys:
- setattr(self, key, args_dict[key])
-
- def init_from_flags(self, flags):
- for key in self.keys:
- setattr(self, key, getattr(flags, key))
-
- def init_from_json(self, json_path):
- with tf.io.gfile.GFile(json_path) as f:
- json_data = json.load(f)
- self.init_from_dict(json_data)
-
- def to_json(self, json_path):
- """Save XLNetConfig to a json file."""
- json_data = {}
- for key in self.keys:
- json_data[key] = getattr(self, key)
-
- json_dir = os.path.dirname(json_path)
- if not tf.io.gfile.exists(json_dir):
- tf.io.gfile.makedirs(json_dir)
- with tf.io.gfile.GFile(json_path, 'w') as f:
- json.dump(json_data, f, indent=4, sort_keys=True)
-
-
-class RunConfig(object):
- """Class of RunConfig.
-
- RunConfig contains hyperparameters that could be different
- between pretraining and finetuning.
- These hyperparameters can also be changed from run to run.
- We store them separately from XLNetConfig for flexibility.
- """
-
- def __init__(self,
- is_training,
- use_tpu,
- dropout,
- dropout_att,
- init_method='normal',
- init_range=0.1,
- init_std=0.02,
- mem_len=None,
- reuse_len=None,
- bi_data=False,
- clamp_len=-1,
- same_length=False,
- use_cls_mask=True):
- """Initializes RunConfig.
-
- Args:
- is_training: bool, whether in training mode.
- use_tpu: bool, whether TPUs are used.
- dropout: float, dropout rate.
- dropout_att: float, dropout rate on attention probabilities.
- init_method: str, the initialization scheme, either "normal" or "uniform".
- init_range: float, initialize the parameters with a uniform distribution
- in [-init_range, init_range]. Only effective when init="uniform".
- init_std: float, initialize the parameters with a normal distribution
- with mean 0 and stddev init_std. Only effective when init="normal".
- mem_len: int, the number of tokens to cache.
- reuse_len: int, the number of tokens in the currect batch to be cached
- and reused in the future.
- bi_data: bool, whether to use bidirectional input pipeline.
- Usually set to True during pretraining and False during finetuning.
- clamp_len: int, clamp all relative distances larger than clamp_len.
- -1 means no clamping.
- same_length: bool, whether to use the same attention length
- for each token.
- use_cls_mask: bool, whether to introduce cls mask.
- """
-
- self.init_method = init_method
- self.init_range = init_range
- self.init_std = init_std
- self.is_training = is_training
- self.dropout = dropout
- self.dropout_att = dropout_att
- self.use_tpu = use_tpu
- self.mem_len = mem_len
- self.reuse_len = reuse_len
- self.bi_data = bi_data
- self.clamp_len = clamp_len
- self.same_length = same_length
- self.use_cls_mask = use_cls_mask
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/optimizer_factory_test.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/optimizer_factory_test.py
deleted file mode 100644
index c952618c126b4ee18b4a7f0ee87a91cff873a109..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/optimizer_factory_test.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for optimizer_factory."""
-
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
-import tensorflow as tf
-
-from absl.testing import parameterized
-from official.vision.image_classification import optimizer_factory
-from official.vision.image_classification.configs import base_configs
-
-
-class OptimizerFactoryTest(tf.test.TestCase, parameterized.TestCase):
-
- @parameterized.named_parameters(
- ('sgd', 'sgd', 0., False),
- ('momentum', 'momentum', 0., False),
- ('rmsprop', 'rmsprop', 0., False),
- ('adam', 'adam', 0., False),
- ('adamw', 'adamw', 0., False),
- ('momentum_lookahead', 'momentum', 0., True),
- ('sgd_ema', 'sgd', 0.999, False),
- ('momentum_ema', 'momentum', 0.999, False),
- ('rmsprop_ema', 'rmsprop', 0.999, False))
- def test_optimizer(self, optimizer_name, moving_average_decay, lookahead):
- """Smoke test to be sure no syntax errors."""
- params = {
- 'learning_rate': 0.001,
- 'rho': 0.09,
- 'momentum': 0.,
- 'epsilon': 1e-07,
- 'moving_average_decay': moving_average_decay,
- 'lookahead': lookahead,
- }
- optimizer = optimizer_factory.build_optimizer(
- optimizer_name=optimizer_name,
- base_learning_rate=params['learning_rate'],
- params=params)
- self.assertTrue(issubclass(type(optimizer), tf.keras.optimizers.Optimizer))
-
- def test_unknown_optimizer(self):
- with self.assertRaises(ValueError):
- optimizer_factory.build_optimizer(
- optimizer_name='this_optimizer_does_not_exist',
- base_learning_rate=None,
- params=None)
-
- def test_learning_rate_without_decay_or_warmups(self):
- params = base_configs.LearningRateConfig(
- name='exponential',
- initial_lr=0.01,
- decay_rate=0.01,
- decay_epochs=None,
- warmup_epochs=None,
- scale_by_batch_size=0.01,
- examples_per_epoch=1,
- boundaries=[0],
- multipliers=[0, 1])
- batch_size = 1
- train_steps = 1
-
- lr = optimizer_factory.build_learning_rate(
- params=params,
- batch_size=batch_size,
- train_steps=train_steps)
- self.assertTrue(
- issubclass(
- type(lr), tf.keras.optimizers.schedules.LearningRateSchedule))
-
- @parameterized.named_parameters(
- ('exponential', 'exponential'),
- ('piecewise_constant_with_warmup', 'piecewise_constant_with_warmup'),
- ('cosine_with_warmup', 'cosine_with_warmup'))
- def test_learning_rate_with_decay_and_warmup(self, lr_decay_type):
- """Basic smoke test for syntax."""
- params = base_configs.LearningRateConfig(
- name=lr_decay_type,
- initial_lr=0.01,
- decay_rate=0.01,
- decay_epochs=1,
- warmup_epochs=1,
- scale_by_batch_size=0.01,
- examples_per_epoch=1,
- boundaries=[0],
- multipliers=[0, 1])
- batch_size = 1
- train_epochs = 1
- train_steps = 1
-
- lr = optimizer_factory.build_learning_rate(
- params=params,
- batch_size=batch_size,
- train_epochs=train_epochs,
- train_steps=train_steps)
- self.assertTrue(
- issubclass(
- type(lr), tf.keras.optimizers.schedules.LearningRateSchedule))
-
-
-if __name__ == '__main__':
- tf.test.main()
diff --git a/spaces/Nephele/bert-vits2-multi-voice/text/english_bert_mock.py b/spaces/Nephele/bert-vits2-multi-voice/text/english_bert_mock.py
deleted file mode 100644
index 3b894ced5b6d619a18d6bdd7d7606ba9e6532050..0000000000000000000000000000000000000000
--- a/spaces/Nephele/bert-vits2-multi-voice/text/english_bert_mock.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import torch
-
-
-def get_bert_feature(norm_text, word2ph):
- return torch.zeros(1024, sum(word2ph))
diff --git a/spaces/NickNYU/NickFriendsHouse/app.py b/spaces/NickNYU/NickFriendsHouse/app.py
deleted file mode 100644
index 4884fa89b788e3f312045f695bb5d035c04523f3..0000000000000000000000000000000000000000
--- a/spaces/NickNYU/NickFriendsHouse/app.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import streamlit as st
-import os
-import pickle
-from PyPDF2 import PdfReader
-from langchain import FAISS
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings.openai import OpenAIEmbeddings
-
-from langchain.vectorstores import Pinecone
-import pinecone
-from langchain.llms import OpenAI
-from langchain.chains.question_answering import load_qa_chain
-from langchain.callbacks import get_openai_callback
-
-# Sidebar contents
-with st.sidebar:
- st.title('🤗💬 LLM Chat App')
- st.markdown('''
- ## About
- This app is an LLM-powered chatbot built using:
- - [Streamlit](https://streamlit.io/)
- - [LangChain](https://python.langchain.com/)
- - [OpenAI](https://platform.openai.com/docs/models) LLM model
-
- ''')
- # add_vertical_space(5)
- st.write('Made by Nick')
-
-
-def main():
- st.header("Chat with PDF 💬")
-
- # upload a PDF file
- pdf = st.file_uploader("Upload your PDF", type='pdf')
-
- if pdf is not None:
- pdf_reader = PdfReader(pdf)
-
- text = ""
- for page in pdf_reader.pages:
- text += page.extract_text()
-
- text_splitter = RecursiveCharacterTextSplitter(
- chunk_size=512,
- chunk_overlap=128,
- length_function=len
- )
- chunks = text_splitter.split_text(text=text)
-
- # # embeddings
- store_name = pdf.name[:-4]
- st.write(f'{store_name}')
-
- if os.path.exists(f"{store_name}.pkl"):
- with open(f"{store_name}.pkl", "rb") as f:
- VectorStore = pickle.load(f)
- st.write('Embeddings Loaded from the Disk')
- else:
- st.write('Embeddings calculate to the Pinecone')
- embeddings = OpenAIEmbeddings()
- VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
- with open(f"{store_name}.pkl", "wb") as f:
- pickle.dump(VectorStore, f)
-
- # PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY', '894d5f1f-df46-4b01-8407-d9977eaee2eb')
- # PINECONE_API_ENV = os.environ.get('PINECONE_API_ENV',
- # 'asia-southeast1-gcp-free') # You may need to switch with your env
- # # initialize pinecone
- # pinecone.init(
- # api_key=PINECONE_API_KEY, # find at app.pinecone.io
- # environment=PINECONE_API_ENV # next to api key in console
- # )
- # index_name = "indexer" # put in the name of your pinecone index here
- # VectorStore = Pinecone.from_texts([t.page_content for t in chunks], embeddings, index_name=index_name)
-
- # Accept user questions/query
- query = st.text_input("Ask questions about your PDF file:")
- # st.write(query)
-
- if query:
- docs = VectorStore.similarity_search(query=query, k=3)
-
- llm = OpenAI()
- chain = load_qa_chain(llm=llm, chain_type="stuff")
- with get_openai_callback() as cb:
- response = chain.run(input_documents=docs, question=query)
- print(cb)
- st.write(response)
-
-
-if __name__ == '__main__':
- main()
\ No newline at end of file
diff --git a/spaces/Ntabukiraniro/Recipe/args.py b/spaces/Ntabukiraniro/Recipe/args.py
deleted file mode 100644
index d9aea99672a0afa5bd1a29e6ac38ba32f9d67ea9..0000000000000000000000000000000000000000
--- a/spaces/Ntabukiraniro/Recipe/args.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import argparse
-import os
-
-
-def get_parser():
-
- parser = argparse.ArgumentParser()
-
- parser.add_argument('--save_dir', type=str, default='path/to/save/models',
- help='path where checkpoints will be saved')
-
- parser.add_argument('--project_name', type=str, default='inversecooking',
- help='name of the directory where models will be saved within save_dir')
-
- parser.add_argument('--model_name', type=str, default='model',
- help='save_dir/project_name/model_name will be the path where logs and checkpoints are stored')
-
- parser.add_argument('--transfer_from', type=str, default='',
- help='specify model name to transfer from')
-
- parser.add_argument('--suff', type=str, default='',
- help='the id of the dictionary to load for training')
-
- parser.add_argument('--image_model', type=str, default='resnet50', choices=['resnet18', 'resnet50', 'resnet101',
- 'resnet152', 'inception_v3'])
-
- parser.add_argument('--recipe1m_dir', type=str, default='path/to/recipe1m',
- help='directory where recipe1m dataset is extracted')
-
- parser.add_argument('--aux_data_dir', type=str, default='../data',
- help='path to other necessary data files (eg. vocabularies)')
-
- parser.add_argument('--crop_size', type=int, default=224, help='size for randomly or center cropping images')
-
- parser.add_argument('--image_size', type=int, default=256, help='size to rescale images')
-
- parser.add_argument('--log_step', type=int , default=10, help='step size for printing log info')
-
- parser.add_argument('--learning_rate', type=float, default=0.001,
- help='base learning rate')
-
- parser.add_argument('--scale_learning_rate_cnn', type=float, default=0.01,
- help='lr multiplier for cnn weights')
-
- parser.add_argument('--lr_decay_rate', type=float, default=0.99,
- help='learning rate decay factor')
-
- parser.add_argument('--lr_decay_every', type=int, default=1,
- help='frequency of learning rate decay (default is every epoch)')
-
- parser.add_argument('--weight_decay', type=float, default=0.)
-
- parser.add_argument('--embed_size', type=int, default=512,
- help='hidden size for all projections')
-
- parser.add_argument('--n_att', type=int, default=8,
- help='number of attention heads in the instruction decoder')
-
- parser.add_argument('--n_att_ingrs', type=int, default=4,
- help='number of attention heads in the ingredient decoder')
-
- parser.add_argument('--transf_layers', type=int, default=16,
- help='number of transformer layers in the instruction decoder')
-
- parser.add_argument('--transf_layers_ingrs', type=int, default=4,
- help='number of transformer layers in the ingredient decoder')
-
- parser.add_argument('--num_epochs', type=int, default=400,
- help='maximum number of epochs')
-
- parser.add_argument('--batch_size', type=int, default=128)
-
- parser.add_argument('--num_workers', type=int, default=8)
-
- parser.add_argument('--dropout_encoder', type=float, default=0.3,
- help='dropout ratio for the image and ingredient encoders')
-
- parser.add_argument('--dropout_decoder_r', type=float, default=0.3,
- help='dropout ratio in the instruction decoder')
-
- parser.add_argument('--dropout_decoder_i', type=float, default=0.3,
- help='dropout ratio in the ingredient decoder')
-
- parser.add_argument('--finetune_after', type=int, default=-1,
- help='epoch to start training cnn. -1 is never, 0 is from the beginning')
-
- parser.add_argument('--loss_weight', nargs='+', type=float, default=[1.0, 0.0, 0.0, 0.0],
- help='training loss weights. 1) instruction, 2) ingredient, 3) eos 4) cardinality')
-
- parser.add_argument('--max_eval', type=int, default=4096,
- help='number of validation samples to evaluate during training')
-
- parser.add_argument('--label_smoothing_ingr', type=float, default=0.1,
- help='label smoothing for bce loss for ingredients')
-
- parser.add_argument('--patience', type=int, default=50,
- help='maximum number of epochs to allow before early stopping')
-
- parser.add_argument('--maxseqlen', type=int, default=15,
- help='maximum length of each instruction')
-
- parser.add_argument('--maxnuminstrs', type=int, default=10,
- help='maximum number of instructions')
-
- parser.add_argument('--maxnumims', type=int, default=5,
- help='maximum number of images per sample')
-
- parser.add_argument('--maxnumlabels', type=int, default=20,
- help='maximum number of ingredients per sample')
-
- parser.add_argument('--es_metric', type=str, default='loss', choices=['loss', 'iou_sample'],
- help='early stopping metric to track')
-
- parser.add_argument('--eval_split', type=str, default='val')
-
- parser.add_argument('--numgens', type=int, default=3)
-
- parser.add_argument('--greedy', dest='greedy', action='store_true',
- help='enables greedy sampling (inference only)')
- parser.set_defaults(greedy=False)
-
- parser.add_argument('--temperature', type=float, default=1.0,
- help='sampling temperature (when greedy is False)')
-
- parser.add_argument('--beam', type=int, default=-1,
- help='beam size. -1 means no beam search (either greedy or sampling)')
-
- parser.add_argument('--ingrs_only', dest='ingrs_only', action='store_true',
- help='train or evaluate the model only for ingredient prediction')
- parser.set_defaults(ingrs_only=False)
-
- parser.add_argument('--recipe_only', dest='recipe_only', action='store_true',
- help='train or evaluate the model only for instruction generation')
- parser.set_defaults(recipe_only=False)
-
- parser.add_argument('--log_term', dest='log_term', action='store_true',
- help='if used, shows training log in stdout instead of saving it to a file.')
- parser.set_defaults(log_term=False)
-
- parser.add_argument('--notensorboard', dest='tensorboard', action='store_false',
- help='if used, tensorboard logs will not be saved')
- parser.set_defaults(tensorboard=True)
-
- parser.add_argument('--resume', dest='resume', action='store_true',
- help='resume training from the checkpoint in model_name')
- parser.set_defaults(resume=False)
-
- parser.add_argument('--nodecay_lr', dest='decay_lr', action='store_false',
- help='disables learning rate decay')
- parser.set_defaults(decay_lr=True)
-
- parser.add_argument('--load_jpeg', dest='use_lmdb', action='store_false',
- help='if used, images are loaded from jpg files instead of lmdb')
- parser.set_defaults(use_lmdb=True)
-
- parser.add_argument('--get_perplexity', dest='get_perplexity', action='store_true',
- help='used to get perplexity in evaluation')
- parser.set_defaults(get_perplexity=False)
-
- parser.add_argument('--use_true_ingrs', dest='use_true_ingrs', action='store_true',
- help='if used, true ingredients will be used as input to obtain the recipe in evaluation')
- parser.set_defaults(use_true_ingrs=False)
-
- args = parser.parse_args()
-
- return args
diff --git a/spaces/Nyari/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/utils.py b/spaces/Nyari/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/utils.py
deleted file mode 100644
index 97258f1706cc76773011e24a11bf417ea76ae112..0000000000000000000000000000000000000000
--- a/spaces/Nyari/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/utils.py
+++ /dev/null
@@ -1,357 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os
-import queue
-import threading
-import torch
-from basicsr.utils.download_util import load_file_from_url
-from torch.nn import functional as F
-
-ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-
-class RealESRGANer:
- """A helper class for upsampling images with RealESRGAN.
-
- Args:
- scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
- model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
- model (nn.Module): The defined network. Default: None.
- tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
- input images into tiles, and then process each of them. Finally, they will be merged into one image.
- 0 denotes for do not use tile. Default: 0.
- tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
- pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
- half (float): Whether to use half precision during inference. Default: False.
- """
-
- def __init__(
- self,
- scale,
- model_path,
- dni_weight=None,
- model=None,
- tile=0,
- tile_pad=10,
- pre_pad=10,
- half=False,
- device=None,
- gpu_id=None,
- ):
- self.scale = scale
- self.tile_size = tile
- self.tile_pad = tile_pad
- self.pre_pad = pre_pad
- self.mod_scale = None
- self.half = half
-
- # initialize model
- if gpu_id:
- self.device = (
- torch.device(f"cuda:{gpu_id}" if torch.cuda.is_available() else "cpu")
- if device is None
- else device
- )
- else:
- self.device = (
- torch.device("cuda" if torch.cuda.is_available() else "cpu")
- if device is None
- else device
- )
-
- if isinstance(model_path, list):
- # dni
- assert len(model_path) == len(
- dni_weight
- ), "model_path and dni_weight should have the save length."
- loadnet = self.dni(model_path[0], model_path[1], dni_weight)
- else:
- # if the model_path starts with https, it will first download models to the folder: weights
- if model_path.startswith("https://"):
- model_path = load_file_from_url(
- url=model_path,
- model_dir=os.path.join(ROOT_DIR, "weights"),
- progress=True,
- file_name=None,
- )
- loadnet = torch.load(model_path, map_location=torch.device("cpu"))
-
- # prefer to use params_ema
- if "params_ema" in loadnet:
- keyname = "params_ema"
- else:
- keyname = "params"
- model.load_state_dict(loadnet[keyname], strict=True)
-
- model.eval()
- self.model = model.to(self.device)
- if self.half:
- self.model = self.model.half()
-
- def dni(self, net_a, net_b, dni_weight, key="params", loc="cpu"):
- """Deep network interpolation.
-
- ``Paper: Deep Network Interpolation for Continuous Imagery Effect Transition``
- """
- net_a = torch.load(net_a, map_location=torch.device(loc))
- net_b = torch.load(net_b, map_location=torch.device(loc))
- for k, v_a in net_a[key].items():
- net_a[key][k] = dni_weight[0] * v_a + dni_weight[1] * net_b[key][k]
- return net_a
-
- def pre_process(self, img):
- """Pre-process, such as pre-pad and mod pad, so that the images can be divisible"""
- img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
- self.img = img.unsqueeze(0).to(self.device)
- if self.half:
- self.img = self.img.half()
-
- # pre_pad
- if self.pre_pad != 0:
- self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), "reflect")
- # mod pad for divisible borders
- if self.scale == 2:
- self.mod_scale = 2
- elif self.scale == 1:
- self.mod_scale = 4
- if self.mod_scale is not None:
- self.mod_pad_h, self.mod_pad_w = 0, 0
- _, _, h, w = self.img.size()
- if h % self.mod_scale != 0:
- self.mod_pad_h = self.mod_scale - h % self.mod_scale
- if w % self.mod_scale != 0:
- self.mod_pad_w = self.mod_scale - w % self.mod_scale
- self.img = F.pad(
- self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), "reflect"
- )
-
- def process(self):
- # model inference
- self.output = self.model(self.img)
-
- def tile_process(self):
- """It will first crop input images to tiles, and then process each tile.
- Finally, all the processed tiles are merged into one images.
-
- Modified from: https://github.com/ata4/esrgan-launcher
- """
- batch, channel, height, width = self.img.shape
- output_height = height * self.scale
- output_width = width * self.scale
- output_shape = (batch, channel, output_height, output_width)
-
- # start with black image
- self.output = self.img.new_zeros(output_shape)
- tiles_x = math.ceil(width / self.tile_size)
- tiles_y = math.ceil(height / self.tile_size)
-
- # loop over all tiles
- for y in range(tiles_y):
- for x in range(tiles_x):
- # extract tile from input image
- ofs_x = x * self.tile_size
- ofs_y = y * self.tile_size
- # input tile area on total image
- input_start_x = ofs_x
- input_end_x = min(ofs_x + self.tile_size, width)
- input_start_y = ofs_y
- input_end_y = min(ofs_y + self.tile_size, height)
-
- # input tile area on total image with padding
- input_start_x_pad = max(input_start_x - self.tile_pad, 0)
- input_end_x_pad = min(input_end_x + self.tile_pad, width)
- input_start_y_pad = max(input_start_y - self.tile_pad, 0)
- input_end_y_pad = min(input_end_y + self.tile_pad, height)
-
- # input tile dimensions
- input_tile_width = input_end_x - input_start_x
- input_tile_height = input_end_y - input_start_y
- tile_idx = y * tiles_x + x + 1
- input_tile = self.img[
- :,
- :,
- input_start_y_pad:input_end_y_pad,
- input_start_x_pad:input_end_x_pad,
- ]
-
- # upscale tile
- try:
- with torch.no_grad():
- output_tile = self.model(input_tile)
- except RuntimeError as error:
- print("Error", error)
- print(f"\tTile {tile_idx}/{tiles_x * tiles_y}")
-
- # output tile area on total image
- output_start_x = input_start_x * self.scale
- output_end_x = input_end_x * self.scale
- output_start_y = input_start_y * self.scale
- output_end_y = input_end_y * self.scale
-
- # output tile area without padding
- output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
- output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
- output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
- output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
-
- # put tile into output image
- self.output[
- :, :, output_start_y:output_end_y, output_start_x:output_end_x
- ] = output_tile[
- :,
- :,
- output_start_y_tile:output_end_y_tile,
- output_start_x_tile:output_end_x_tile,
- ]
-
- def post_process(self):
- # remove extra pad
- if self.mod_scale is not None:
- _, _, h, w = self.output.size()
- self.output = self.output[
- :,
- :,
- 0 : h - self.mod_pad_h * self.scale,
- 0 : w - self.mod_pad_w * self.scale,
- ]
- # remove prepad
- if self.pre_pad != 0:
- _, _, h, w = self.output.size()
- self.output = self.output[
- :,
- :,
- 0 : h - self.pre_pad * self.scale,
- 0 : w - self.pre_pad * self.scale,
- ]
- return self.output
-
- @torch.no_grad()
- def enhance(self, img, outscale=None, alpha_upsampler="realesrgan"):
- h_input, w_input = img.shape[0:2]
- # img: numpy
- img = img.astype(np.float32)
- if np.max(img) > 256: # 16-bit image
- max_range = 65535
- print("\tInput is a 16-bit image")
- else:
- max_range = 255
- img = img / max_range
- if len(img.shape) == 2: # gray image
- img_mode = "L"
- img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
- elif img.shape[2] == 4: # RGBA image with alpha channel
- img_mode = "RGBA"
- alpha = img[:, :, 3]
- img = img[:, :, 0:3]
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
- if alpha_upsampler == "realesrgan":
- alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
- else:
- img_mode = "RGB"
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
- # ------------------- process image (without the alpha channel) ------------------- #
- self.pre_process(img)
- if self.tile_size > 0:
- self.tile_process()
- else:
- self.process()
- output_img = self.post_process()
- output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy()
- output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
- if img_mode == "L":
- output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
-
- # ------------------- process the alpha channel if necessary ------------------- #
- if img_mode == "RGBA":
- if alpha_upsampler == "realesrgan":
- self.pre_process(alpha)
- if self.tile_size > 0:
- self.tile_process()
- else:
- self.process()
- output_alpha = self.post_process()
- output_alpha = (
- output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
- )
- output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
- output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
- else: # use the cv2 resize for alpha channel
- h, w = alpha.shape[0:2]
- output_alpha = cv2.resize(
- alpha,
- (w * self.scale, h * self.scale),
- interpolation=cv2.INTER_LINEAR,
- )
-
- # merge the alpha channel
- output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
- output_img[:, :, 3] = output_alpha
-
- # ------------------------------ return ------------------------------ #
- if max_range == 65535: # 16-bit image
- output = (output_img * 65535.0).round().astype(np.uint16)
- else:
- output = (output_img * 255.0).round().astype(np.uint8)
-
- if outscale is not None and outscale != float(self.scale):
- output = cv2.resize(
- output,
- (
- int(w_input * outscale),
- int(h_input * outscale),
- ),
- interpolation=cv2.INTER_LANCZOS4,
- )
-
- return output, img_mode
-
-
-class PrefetchReader(threading.Thread):
- """Prefetch images.
-
- Args:
- img_list (list[str]): A image list of image paths to be read.
- num_prefetch_queue (int): Number of prefetch queue.
- """
-
- def __init__(self, img_list, num_prefetch_queue):
- super().__init__()
- self.que = queue.Queue(num_prefetch_queue)
- self.img_list = img_list
-
- def run(self):
- for img_path in self.img_list:
- img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
- self.que.put(img)
-
- self.que.put(None)
-
- def __next__(self):
- next_item = self.que.get()
- if next_item is None:
- raise StopIteration
- return next_item
-
- def __iter__(self):
- return self
-
-
-class IOConsumer(threading.Thread):
- def __init__(self, opt, que, qid):
- super().__init__()
- self._queue = que
- self.qid = qid
- self.opt = opt
-
- def run(self):
- while True:
- msg = self._queue.get()
- if isinstance(msg, str) and msg == "quit":
- break
-
- output = msg["output"]
- save_path = msg["save_path"]
- cv2.imwrite(save_path, output)
- print(f"IO worker {self.qid} is done.")
diff --git a/spaces/OAOA/DifFace/basicsr/ops/dcn/src/deform_conv_cuda.cpp b/spaces/OAOA/DifFace/basicsr/ops/dcn/src/deform_conv_cuda.cpp
deleted file mode 100644
index b465c493a3dd67d320b7a8997fbd501d2f89c807..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/basicsr/ops/dcn/src/deform_conv_cuda.cpp
+++ /dev/null
@@ -1,685 +0,0 @@
-// modify from
-// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c
-
-#include
-#include
-
-#include
-#include
-
-void deformable_im2col(const at::Tensor data_im, const at::Tensor data_offset,
- const int channels, const int height, const int width,
- const int ksize_h, const int ksize_w, const int pad_h,
- const int pad_w, const int stride_h, const int stride_w,
- const int dilation_h, const int dilation_w,
- const int parallel_imgs, const int deformable_group,
- at::Tensor data_col);
-
-void deformable_col2im(const at::Tensor data_col, const at::Tensor data_offset,
- const int channels, const int height, const int width,
- const int ksize_h, const int ksize_w, const int pad_h,
- const int pad_w, const int stride_h, const int stride_w,
- const int dilation_h, const int dilation_w,
- const int parallel_imgs, const int deformable_group,
- at::Tensor grad_im);
-
-void deformable_col2im_coord(
- const at::Tensor data_col, const at::Tensor data_im,
- const at::Tensor data_offset, const int channels, const int height,
- const int width, const int ksize_h, const int ksize_w, const int pad_h,
- const int pad_w, const int stride_h, const int stride_w,
- const int dilation_h, const int dilation_w, const int parallel_imgs,
- const int deformable_group, at::Tensor grad_offset);
-
-void modulated_deformable_im2col_cuda(
- const at::Tensor data_im, const at::Tensor data_offset,
- const at::Tensor data_mask, const int batch_size, const int channels,
- const int height_im, const int width_im, const int height_col,
- const int width_col, const int kernel_h, const int kenerl_w,
- const int pad_h, const int pad_w, const int stride_h, const int stride_w,
- const int dilation_h, const int dilation_w, const int deformable_group,
- at::Tensor data_col);
-
-void modulated_deformable_col2im_cuda(
- const at::Tensor data_col, const at::Tensor data_offset,
- const at::Tensor data_mask, const int batch_size, const int channels,
- const int height_im, const int width_im, const int height_col,
- const int width_col, const int kernel_h, const int kenerl_w,
- const int pad_h, const int pad_w, const int stride_h, const int stride_w,
- const int dilation_h, const int dilation_w, const int deformable_group,
- at::Tensor grad_im);
-
-void modulated_deformable_col2im_coord_cuda(
- const at::Tensor data_col, const at::Tensor data_im,
- const at::Tensor data_offset, const at::Tensor data_mask,
- const int batch_size, const int channels, const int height_im,
- const int width_im, const int height_col, const int width_col,
- const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w,
- const int stride_h, const int stride_w, const int dilation_h,
- const int dilation_w, const int deformable_group, at::Tensor grad_offset,
- at::Tensor grad_mask);
-
-void shape_check(at::Tensor input, at::Tensor offset, at::Tensor *gradOutput,
- at::Tensor weight, int kH, int kW, int dH, int dW, int padH,
- int padW, int dilationH, int dilationW, int group,
- int deformable_group) {
- TORCH_CHECK(weight.ndimension() == 4,
- "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, "
- "but got: %s",
- weight.ndimension());
-
- TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
-
- TORCH_CHECK(kW > 0 && kH > 0,
- "kernel size should be greater than zero, but got kH: %d kW: %d", kH,
- kW);
-
- TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW),
- "kernel size should be consistent with weight, ",
- "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", kH,
- kW, weight.size(2), weight.size(3));
-
- TORCH_CHECK(dW > 0 && dH > 0,
- "stride should be greater than zero, but got dH: %d dW: %d", dH, dW);
-
- TORCH_CHECK(
- dilationW > 0 && dilationH > 0,
- "dilation should be greater than 0, but got dilationH: %d dilationW: %d",
- dilationH, dilationW);
-
- int ndim = input.ndimension();
- int dimf = 0;
- int dimh = 1;
- int dimw = 2;
-
- if (ndim == 4) {
- dimf++;
- dimh++;
- dimw++;
- }
-
- TORCH_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s",
- ndim);
-
- long nInputPlane = weight.size(1) * group;
- long inputHeight = input.size(dimh);
- long inputWidth = input.size(dimw);
- long nOutputPlane = weight.size(0);
- long outputHeight =
- (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
- long outputWidth =
- (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
-
- TORCH_CHECK(nInputPlane % deformable_group == 0,
- "input channels must divide deformable group size");
-
- if (outputWidth < 1 || outputHeight < 1)
- AT_ERROR(
- "Given input size: (%ld x %ld x %ld). "
- "Calculated output size: (%ld x %ld x %ld). Output size is too small",
- nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight,
- outputWidth);
-
- TORCH_CHECK(input.size(1) == nInputPlane,
- "invalid number of input planes, expected: %d, but got: %d",
- nInputPlane, input.size(1));
-
- TORCH_CHECK((inputHeight >= kH && inputWidth >= kW),
- "input image is smaller than kernel");
-
- TORCH_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth),
- "invalid spatial size of offset, expected height: %d width: %d, but "
- "got height: %d width: %d",
- outputHeight, outputWidth, offset.size(2), offset.size(3));
-
- TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW),
- "invalid number of channels of offset");
-
- if (gradOutput != NULL) {
- TORCH_CHECK(gradOutput->size(dimf) == nOutputPlane,
- "invalid number of gradOutput planes, expected: %d, but got: %d",
- nOutputPlane, gradOutput->size(dimf));
-
- TORCH_CHECK((gradOutput->size(dimh) == outputHeight &&
- gradOutput->size(dimw) == outputWidth),
- "invalid size of gradOutput, expected height: %d width: %d , but "
- "got height: %d width: %d",
- outputHeight, outputWidth, gradOutput->size(dimh),
- gradOutput->size(dimw));
- }
-}
-
-int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight,
- at::Tensor offset, at::Tensor output,
- at::Tensor columns, at::Tensor ones, int kW,
- int kH, int dW, int dH, int padW, int padH,
- int dilationW, int dilationH, int group,
- int deformable_group, int im2col_step) {
- // todo: resize columns to include im2col: done
- // todo: add im2col_step as input
- // todo: add new output buffer and transpose it to output (or directly
- // transpose output) todo: possibly change data indexing because of
- // parallel_imgs
-
- shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, padW,
- dilationH, dilationW, group, deformable_group);
- at::DeviceGuard guard(input.device());
-
- input = input.contiguous();
- offset = offset.contiguous();
- weight = weight.contiguous();
-
- int batch = 1;
- if (input.ndimension() == 3) {
- // Force batch
- batch = 0;
- input.unsqueeze_(0);
- offset.unsqueeze_(0);
- }
-
- // todo: assert batchsize dividable by im2col_step
-
- long batchSize = input.size(0);
- long nInputPlane = input.size(1);
- long inputHeight = input.size(2);
- long inputWidth = input.size(3);
-
- long nOutputPlane = weight.size(0);
-
- long outputWidth =
- (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
- long outputHeight =
- (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
-
- TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
-
- output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane,
- outputHeight, outputWidth});
- columns = at::zeros(
- {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
- input.options());
-
- if (ones.ndimension() != 2 ||
- ones.size(0) * ones.size(1) < outputHeight * outputWidth) {
- ones = at::ones({outputHeight, outputWidth}, input.options());
- }
-
- input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
- inputHeight, inputWidth});
- offset =
- offset.view({batchSize / im2col_step, im2col_step,
- deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
- at::Tensor output_buffer =
- at::zeros({batchSize / im2col_step, nOutputPlane,
- im2col_step * outputHeight, outputWidth},
- output.options());
-
- output_buffer = output_buffer.view(
- {output_buffer.size(0), group, output_buffer.size(1) / group,
- output_buffer.size(2), output_buffer.size(3)});
-
- for (int elt = 0; elt < batchSize / im2col_step; elt++) {
- deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight,
- inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
- dilationW, im2col_step, deformable_group, columns);
-
- columns = columns.view({group, columns.size(0) / group, columns.size(1)});
- weight = weight.view({group, weight.size(0) / group, weight.size(1),
- weight.size(2), weight.size(3)});
-
- for (int g = 0; g < group; g++) {
- output_buffer[elt][g] = output_buffer[elt][g]
- .flatten(1)
- .addmm_(weight[g].flatten(1), columns[g])
- .view_as(output_buffer[elt][g]);
- }
- }
-
- output_buffer = output_buffer.view(
- {output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2),
- output_buffer.size(3), output_buffer.size(4)});
-
- output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane,
- im2col_step, outputHeight, outputWidth});
- output_buffer.transpose_(1, 2);
- output.copy_(output_buffer);
- output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth});
-
- input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
- offset = offset.view(
- {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
- if (batch == 0) {
- output = output.view({nOutputPlane, outputHeight, outputWidth});
- input = input.view({nInputPlane, inputHeight, inputWidth});
- offset = offset.view({offset.size(1), offset.size(2), offset.size(3)});
- }
-
- return 1;
-}
-
-int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset,
- at::Tensor gradOutput, at::Tensor gradInput,
- at::Tensor gradOffset, at::Tensor weight,
- at::Tensor columns, int kW, int kH, int dW,
- int dH, int padW, int padH, int dilationW,
- int dilationH, int group,
- int deformable_group, int im2col_step) {
- shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, padH, padW,
- dilationH, dilationW, group, deformable_group);
- at::DeviceGuard guard(input.device());
-
- input = input.contiguous();
- offset = offset.contiguous();
- gradOutput = gradOutput.contiguous();
- weight = weight.contiguous();
-
- int batch = 1;
-
- if (input.ndimension() == 3) {
- // Force batch
- batch = 0;
- input = input.view({1, input.size(0), input.size(1), input.size(2)});
- offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)});
- gradOutput = gradOutput.view(
- {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)});
- }
-
- long batchSize = input.size(0);
- long nInputPlane = input.size(1);
- long inputHeight = input.size(2);
- long inputWidth = input.size(3);
-
- long nOutputPlane = weight.size(0);
-
- long outputWidth =
- (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
- long outputHeight =
- (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
-
- TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset");
- gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth});
- columns = at::zeros(
- {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
- input.options());
-
- // change order of grad output
- gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step,
- nOutputPlane, outputHeight, outputWidth});
- gradOutput.transpose_(1, 2);
-
- gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane,
- inputHeight, inputWidth});
- input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
- inputHeight, inputWidth});
- gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step,
- deformable_group * 2 * kH * kW, outputHeight,
- outputWidth});
- offset =
- offset.view({batchSize / im2col_step, im2col_step,
- deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
- for (int elt = 0; elt < batchSize / im2col_step; elt++) {
- // divide into groups
- columns = columns.view({group, columns.size(0) / group, columns.size(1)});
- weight = weight.view({group, weight.size(0) / group, weight.size(1),
- weight.size(2), weight.size(3)});
- gradOutput = gradOutput.view(
- {gradOutput.size(0), group, gradOutput.size(1) / group,
- gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)});
-
- for (int g = 0; g < group; g++) {
- columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1),
- gradOutput[elt][g].flatten(1), 0.0f, 1.0f);
- }
-
- columns =
- columns.view({columns.size(0) * columns.size(1), columns.size(2)});
- gradOutput = gradOutput.view(
- {gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2),
- gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)});
-
- deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane,
- inputHeight, inputWidth, kH, kW, padH, padW, dH, dW,
- dilationH, dilationW, im2col_step, deformable_group,
- gradOffset[elt]);
-
- deformable_col2im(columns, offset[elt], nInputPlane, inputHeight,
- inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
- dilationW, im2col_step, deformable_group, gradInput[elt]);
- }
-
- gradOutput.transpose_(1, 2);
- gradOutput =
- gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth});
-
- gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth});
- input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
- gradOffset = gradOffset.view(
- {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
- offset = offset.view(
- {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
- if (batch == 0) {
- gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth});
- input = input.view({nInputPlane, inputHeight, inputWidth});
- gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth});
- offset = offset.view({offset.size(1), offset.size(2), offset.size(3)});
- gradOffset =
- gradOffset.view({offset.size(1), offset.size(2), offset.size(3)});
- }
-
- return 1;
-}
-
-int deform_conv_backward_parameters_cuda(
- at::Tensor input, at::Tensor offset, at::Tensor gradOutput,
- at::Tensor gradWeight, // at::Tensor gradBias,
- at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH,
- int padW, int padH, int dilationW, int dilationH, int group,
- int deformable_group, float scale, int im2col_step) {
- // todo: transpose and reshape outGrad
- // todo: reshape columns
- // todo: add im2col_step as input
-
- shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, dW, padH,
- padW, dilationH, dilationW, group, deformable_group);
- at::DeviceGuard guard(input.device());
-
- input = input.contiguous();
- offset = offset.contiguous();
- gradOutput = gradOutput.contiguous();
-
- int batch = 1;
-
- if (input.ndimension() == 3) {
- // Force batch
- batch = 0;
- input = input.view(
- at::IntList({1, input.size(0), input.size(1), input.size(2)}));
- gradOutput = gradOutput.view(
- {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)});
- }
-
- long batchSize = input.size(0);
- long nInputPlane = input.size(1);
- long inputHeight = input.size(2);
- long inputWidth = input.size(3);
-
- long nOutputPlane = gradWeight.size(0);
-
- long outputWidth =
- (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
- long outputHeight =
- (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
-
- TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
-
- columns = at::zeros(
- {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
- input.options());
-
- gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step,
- nOutputPlane, outputHeight, outputWidth});
- gradOutput.transpose_(1, 2);
-
- at::Tensor gradOutputBuffer = at::zeros_like(gradOutput);
- gradOutputBuffer =
- gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step,
- outputHeight, outputWidth});
- gradOutputBuffer.copy_(gradOutput);
- gradOutputBuffer =
- gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane,
- im2col_step * outputHeight, outputWidth});
-
- gradOutput.transpose_(1, 2);
- gradOutput =
- gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth});
-
- input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
- inputHeight, inputWidth});
- offset =
- offset.view({batchSize / im2col_step, im2col_step,
- deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
- for (int elt = 0; elt < batchSize / im2col_step; elt++) {
- deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight,
- inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
- dilationW, im2col_step, deformable_group, columns);
-
- // divide into group
- gradOutputBuffer = gradOutputBuffer.view(
- {gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group,
- gradOutputBuffer.size(2), gradOutputBuffer.size(3)});
- columns = columns.view({group, columns.size(0) / group, columns.size(1)});
- gradWeight =
- gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1),
- gradWeight.size(2), gradWeight.size(3)});
-
- for (int g = 0; g < group; g++) {
- gradWeight[g] = gradWeight[g]
- .flatten(1)
- .addmm_(gradOutputBuffer[elt][g].flatten(1),
- columns[g].transpose(1, 0), 1.0, scale)
- .view_as(gradWeight[g]);
- }
- gradOutputBuffer = gradOutputBuffer.view(
- {gradOutputBuffer.size(0),
- gradOutputBuffer.size(1) * gradOutputBuffer.size(2),
- gradOutputBuffer.size(3), gradOutputBuffer.size(4)});
- columns =
- columns.view({columns.size(0) * columns.size(1), columns.size(2)});
- gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1),
- gradWeight.size(2), gradWeight.size(3),
- gradWeight.size(4)});
- }
-
- input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
- offset = offset.view(
- {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
-
- if (batch == 0) {
- gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth});
- input = input.view({nInputPlane, inputHeight, inputWidth});
- }
-
- return 1;
-}
-
-void modulated_deform_conv_cuda_forward(
- at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
- at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns,
- int kernel_h, int kernel_w, const int stride_h, const int stride_w,
- const int pad_h, const int pad_w, const int dilation_h,
- const int dilation_w, const int group, const int deformable_group,
- const bool with_bias) {
- TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
- TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
- at::DeviceGuard guard(input.device());
-
- const int batch = input.size(0);
- const int channels = input.size(1);
- const int height = input.size(2);
- const int width = input.size(3);
-
- const int channels_out = weight.size(0);
- const int channels_kernel = weight.size(1);
- const int kernel_h_ = weight.size(2);
- const int kernel_w_ = weight.size(3);
-
- if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
- AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).",
- kernel_h_, kernel_w, kernel_h_, kernel_w_);
- if (channels != channels_kernel * group)
- AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).",
- channels, channels_kernel * group);
-
- const int height_out =
- (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
- const int width_out =
- (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
-
- if (ones.ndimension() != 2 ||
- ones.size(0) * ones.size(1) < height_out * width_out) {
- // Resize plane and fill with ones...
- ones = at::ones({height_out, width_out}, input.options());
- }
-
- // resize output
- output = output.view({batch, channels_out, height_out, width_out}).zero_();
- // resize temporary columns
- columns =
- at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out},
- input.options());
-
- output = output.view({output.size(0), group, output.size(1) / group,
- output.size(2), output.size(3)});
-
- for (int b = 0; b < batch; b++) {
- modulated_deformable_im2col_cuda(
- input[b], offset[b], mask[b], 1, channels, height, width, height_out,
- width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
- dilation_h, dilation_w, deformable_group, columns);
-
- // divide into group
- weight = weight.view({group, weight.size(0) / group, weight.size(1),
- weight.size(2), weight.size(3)});
- columns = columns.view({group, columns.size(0) / group, columns.size(1)});
-
- for (int g = 0; g < group; g++) {
- output[b][g] = output[b][g]
- .flatten(1)
- .addmm_(weight[g].flatten(1), columns[g])
- .view_as(output[b][g]);
- }
-
- weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
- weight.size(3), weight.size(4)});
- columns =
- columns.view({columns.size(0) * columns.size(1), columns.size(2)});
- }
-
- output = output.view({output.size(0), output.size(1) * output.size(2),
- output.size(3), output.size(4)});
-
- if (with_bias) {
- output += bias.view({1, bias.size(0), 1, 1});
- }
-}
-
-void modulated_deform_conv_cuda_backward(
- at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
- at::Tensor offset, at::Tensor mask, at::Tensor columns,
- at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,
- at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output,
- int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,
- int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,
- const bool with_bias) {
- TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
- TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
- at::DeviceGuard guard(input.device());
-
- const int batch = input.size(0);
- const int channels = input.size(1);
- const int height = input.size(2);
- const int width = input.size(3);
-
- const int channels_kernel = weight.size(1);
- const int kernel_h_ = weight.size(2);
- const int kernel_w_ = weight.size(3);
- if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
- AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).",
- kernel_h_, kernel_w, kernel_h_, kernel_w_);
- if (channels != channels_kernel * group)
- AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).",
- channels, channels_kernel * group);
-
- const int height_out =
- (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
- const int width_out =
- (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
-
- if (ones.ndimension() != 2 ||
- ones.size(0) * ones.size(1) < height_out * width_out) {
- // Resize plane and fill with ones...
- ones = at::ones({height_out, width_out}, input.options());
- }
-
- grad_input = grad_input.view({batch, channels, height, width});
- columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out},
- input.options());
-
- grad_output =
- grad_output.view({grad_output.size(0), group, grad_output.size(1) / group,
- grad_output.size(2), grad_output.size(3)});
-
- for (int b = 0; b < batch; b++) {
- // divide int group
- columns = columns.view({group, columns.size(0) / group, columns.size(1)});
- weight = weight.view({group, weight.size(0) / group, weight.size(1),
- weight.size(2), weight.size(3)});
-
- for (int g = 0; g < group; g++) {
- columns[g].addmm_(weight[g].flatten(1).transpose(0, 1),
- grad_output[b][g].flatten(1), 0.0f, 1.0f);
- }
-
- columns =
- columns.view({columns.size(0) * columns.size(1), columns.size(2)});
- weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
- weight.size(3), weight.size(4)});
-
- // gradient w.r.t. input coordinate data
- modulated_deformable_col2im_coord_cuda(
- columns, input[b], offset[b], mask[b], 1, channels, height, width,
- height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h,
- stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b],
- grad_mask[b]);
- // gradient w.r.t. input data
- modulated_deformable_col2im_cuda(
- columns, offset[b], mask[b], 1, channels, height, width, height_out,
- width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
- dilation_h, dilation_w, deformable_group, grad_input[b]);
-
- // gradient w.r.t. weight, dWeight should accumulate across the batch and
- // group
- modulated_deformable_im2col_cuda(
- input[b], offset[b], mask[b], 1, channels, height, width, height_out,
- width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
- dilation_h, dilation_w, deformable_group, columns);
-
- columns = columns.view({group, columns.size(0) / group, columns.size(1)});
- grad_weight = grad_weight.view({group, grad_weight.size(0) / group,
- grad_weight.size(1), grad_weight.size(2),
- grad_weight.size(3)});
- if (with_bias)
- grad_bias = grad_bias.view({group, grad_bias.size(0) / group});
-
- for (int g = 0; g < group; g++) {
- grad_weight[g] =
- grad_weight[g]
- .flatten(1)
- .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1))
- .view_as(grad_weight[g]);
- if (with_bias) {
- grad_bias[g] =
- grad_bias[g]
- .view({-1, 1})
- .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1}))
- .view(-1);
- }
- }
-
- columns =
- columns.view({columns.size(0) * columns.size(1), columns.size(2)});
- grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1),
- grad_weight.size(2), grad_weight.size(3),
- grad_weight.size(4)});
- if (with_bias)
- grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)});
- }
- grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1),
- grad_output.size(2), grad_output.size(3),
- grad_output.size(4)});
-}
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/new/decoders/decoder_config.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/new/decoders/decoder_config.py
deleted file mode 100644
index 659eb94a9b8187a7c126d7b439ac2742f9d72022..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/new/decoders/decoder_config.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass, field
-from typing import Optional
-
-from fairseq.dataclass.configs import FairseqDataclass
-from fairseq.dataclass.constants import ChoiceEnum
-from omegaconf import MISSING
-
-
-DECODER_CHOICES = ChoiceEnum(["viterbi", "kenlm", "fairseqlm"])
-
-
-@dataclass
-class DecoderConfig(FairseqDataclass):
- type: DECODER_CHOICES = field(
- default="viterbi",
- metadata={"help": "The type of decoder to use"},
- )
-
-
-@dataclass
-class FlashlightDecoderConfig(FairseqDataclass):
- nbest: int = field(
- default=1,
- metadata={"help": "Number of decodings to return"},
- )
- unitlm: bool = field(
- default=False,
- metadata={"help": "If set, use unit language model"},
- )
- lmpath: str = field(
- default=MISSING,
- metadata={"help": "Language model for KenLM decoder"},
- )
- lexicon: Optional[str] = field(
- default=None,
- metadata={"help": "Lexicon for Flashlight decoder"},
- )
- beam: int = field(
- default=50,
- metadata={"help": "Number of beams to use for decoding"},
- )
- beamthreshold: float = field(
- default=50.0,
- metadata={"help": "Threshold for beam search decoding"},
- )
- beamsizetoken: Optional[int] = field(
- default=None, metadata={"help": "Beam size to use"}
- )
- wordscore: float = field(
- default=-1,
- metadata={"help": "Word score for KenLM decoder"},
- )
- unkweight: float = field(
- default=-math.inf,
- metadata={"help": "Unknown weight for KenLM decoder"},
- )
- silweight: float = field(
- default=0,
- metadata={"help": "Silence weight for KenLM decoder"},
- )
- lmweight: float = field(
- default=2,
- metadata={"help": "Weight for LM while interpolating score"},
- )
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/decode.sh b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/decode.sh
deleted file mode 100644
index 811cb63c88bb7cdd03b0a250ef2db32b5eaa50df..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/decode.sh
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/bin/bash
-
-set -u
-
-val_sets="dev_other"
-graph_name=graph
-decode_suffix=""
-decode_script="steps/decode_fmllr.sh"
-decode_args=""
-nj=60
-
-. ./cmd.sh
-. ./path.sh
-. parse_options.sh
-
-set -x
-exp_dir=$1
-data_root=$2
-lang_test=$3
-
-graph=$exp_dir/$graph_name
-
-if [ ! -d $graph ]; then
- utils/mkgraph.sh $lang_test $exp_dir $graph
-fi
-
-for part in $val_sets; do
- dec_dir=$exp_dir/decode${decode_suffix}_${part}
- if [ ! -d $dec_dir ]; then
- echo "decoding $part for $exp_dir"
- $decode_script --nj $nj --cmd "$decode_cmd" $decode_args \
- $graph $data_root/$part $dec_dir &
- else
- echo "$dec_dir exists. skip"
- fi
-done
-
-wait
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp
deleted file mode 100644
index 707219105a17a691e43de1296a72bbaffa0c7fe9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
-Copyright (c) Microsoft Corporation.
-Licensed under the MIT License.
-*/
-
-#include
-#include
-
-/*
-CPP Binding for CUDA OP
-*/
-
-// CUDA forward declarations
-torch::Tensor ngram_repeat_block_cuda_forward(
- torch::Tensor tokens,
- torch::Tensor lprobs,
- int bsz,
- int step,
- int beam_size,
- int no_repeat_ngram_size);
-
-#define CHECK_CUDA(x) \
- TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) \
- TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) \
- CHECK_CUDA(x); \
- CHECK_CONTIGUOUS(x)
-
-// Input check and call to CUDA OP
-// Backward method not required
-torch::Tensor ngram_repeat_block_forward(
- torch::Tensor tokens,
- torch::Tensor lprobs,
- int bsz,
- int step,
- int beam_size,
- int no_repeat_ngram_size) {
- CHECK_INPUT(tokens);
- CHECK_INPUT(lprobs);
- assert(bsz > 0);
- assert(step >= 0);
- assert(beam_size > 0);
- assert(no_repeat_ngram_size > 0);
-
- return ngram_repeat_block_cuda_forward(
- tokens, lprobs, bsz, step, beam_size, no_repeat_ngram_size);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def(
- "forward",
- &ngram_repeat_block_forward,
- "No Repeat Ngram Block forward (CUDA)");
-}
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh
deleted file mode 100644
index ca3591b3db1715f136773d62e4b9b9ede97d436c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh
+++ /dev/null
@@ -1,225 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-#echo 'Cloning Moses github repository (for tokenization scripts)...'
-#git clone https://github.com/moses-smt/mosesdecoder.git
-
-if [ -z $WORKDIR_ROOT ] ;
-then
- echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..."
- exit
-fi
-
-
-
-data_root=${WORKDIR_ROOT}/iwsltv2
-DESTDIR=${WORKDIR_ROOT}/ML50/raw
-
-
-langs="ar_AR it_IT nl_XX ko_KR vi_VN"
-echo "data_root: $data_root"
-
-download_path=${data_root}/downloads
-raw=${DESTDIR}
-tmp=${data_root}/tmp
-orig=${data_root}/orig
-
-mkdir -p $download_path $orig $raw $tmp
-#######################
-download_iwslt(){
- iwslt_key=$1
- src=$2
- tgt=$3
- save_prefix=$4
- pushd ${download_path}
- if [[ ! -f ${save_prefix}$src-$tgt.tgz ]]; then
- wget https://wit3.fbk.eu/archive/${iwslt_key}/texts/$src/$tgt/$src-$tgt.tgz -O ${save_prefix}$src-$tgt.tgz
- [ $? -eq 0 ] && return 0
- fi
- popd
-}
-
-extract_iwslt(){
- src=$1
- tgt=$2
- prefix=$3
- pushd $orig
- tar zxvf ${download_path}/${prefix}$src-${tgt}.tgz
- popd
-}
-
-generate_train(){
- lsrc=$1
- ltgt=$2
- src=${lsrc:0:2}
- tgt=${ltgt:0:2}
- for ll in $lsrc $ltgt; do
- l=${ll:0:2}
- f="$orig/*/train.tags.$src-$tgt.$l"
- f_raw=$raw/train.$lsrc-$ltgt.$ll
- cat $f \
- | grep -v '' \
- | grep -v '' \
- | grep -v '' \
- | grep -v '' \
- | grep -v '' \
- | sed -e 's///g' \
- | sed -e 's/<\/title>//g' \
- | sed -e 's/<description>//g' \
- | sed -e 's/<\/description>//g' \
- | sed 's/^\s*//g' \
- | sed 's/\s*$//g' \
- > $f_raw
- [ $? -eq 0 ] && echo "extracted $f to $f_raw"
- done
- return 0
-}
-
-convert_valid_test(){
- src=$1
- tgt=$2
- for l in $src $tgt; do
- echo "lang: ${l}"
- for o in `ls $orig/*/IWSLT*.TED*.$src-$tgt.$l.xml`; do
- fname=${o##*/}
- f=$tmp/${fname%.*}
- echo "$o => $f"
- grep '<seg id' $o \
- | sed -e 's/<seg id="[0-9]*">\s*//g' \
- | sed -e 's/\s*<\/seg>\s*//g' \
- | sed -e "s/\’/\'/g" \
- > $f
- echo ""
- done
- done
-}
-
-generate_subset(){
- lsrc=$1
- ltgt=$2
- src=${lsrc:0:2}
- tgt=${ltgt:0:2}
- subset=$3
- prefix=$4
- for ll in $lsrc $ltgt; do
- l=${ll:0:2}
- f=$tmp/$prefix.${src}-${tgt}.$l
- if [[ -f $f ]]; then
- cp $f $raw/$subset.${lsrc}-$ltgt.${ll}
- fi
- done
-}
-#################
-
-echo "downloading iwslt training and dev data"
-# using multilingual for it, nl
-download_iwslt "2017-01-trnmted" DeEnItNlRo DeEnItNlRo
-download_iwslt "2017-01-trnted" ar en
-download_iwslt "2017-01-trnted" en ar
-download_iwslt "2017-01-trnted" ko en
-download_iwslt "2017-01-trnted" en ko
-download_iwslt "2015-01" vi en
-download_iwslt "2015-01" en vi
-
-echo "donwloading iwslt test data"
-download_iwslt "2017-01-mted-test" it en "test."
-download_iwslt "2017-01-mted-test" en it "test."
-download_iwslt "2017-01-mted-test" nl en "test."
-download_iwslt "2017-01-mted-test" en nl "test."
-
-download_iwslt "2017-01-ted-test" ar en "test."
-download_iwslt "2017-01-ted-test" en ar "test."
-download_iwslt "2017-01-ted-test" ko en "test."
-download_iwslt "2017-01-ted-test" en ko "test."
-download_iwslt "2015-01-test" vi en "test."
-download_iwslt "2015-01-test" en vi "test."
-
-echo "extract training data tar balls"
-extract_iwslt DeEnItNlRo DeEnItNlRo
-extract_iwslt ar en
-extract_iwslt en ar
-extract_iwslt ko en
-extract_iwslt en ko
-extract_iwslt vi en
-extract_iwslt en vi
-
-
-echo "extracting iwslt test data"
-for lang in $langs; do
- l=${lang:0:2}
- extract_iwslt $l en "test."
- extract_iwslt en $l "test."
-done
-
-echo "convert dev and test data"
-for lang in $langs; do
- s_lang=${lang:0:2}
- convert_valid_test $s_lang en
- convert_valid_test en $s_lang
-done
-
-
-
-echo "creating training data into $raw"
-for lang in $langs; do
- generate_train $lang en_XX
- generate_train en_XX $lang
-done
-
-echo "creating iwslt dev data into raw"
-generate_subset en_XX vi_VN valid "IWSLT15.TED.tst2013"
-generate_subset vi_VN en_XX valid "IWSLT15.TED.tst2013"
-
-generate_subset en_XX ar_AR valid "IWSLT17.TED.tst2016"
-generate_subset ar_AR en_XX valid "IWSLT17.TED.tst2016"
-generate_subset en_XX ko_KR valid "IWSLT17.TED.tst2016"
-generate_subset ko_KR en_XX valid "IWSLT17.TED.tst2016"
-
-
-generate_subset en_XX it_IT valid "IWSLT17.TED.tst2010"
-generate_subset it_IT en_XX valid "IWSLT17.TED.tst2010"
-generate_subset en_XX nl_XX valid "IWSLT17.TED.tst2010"
-generate_subset nl_XX en_XX valid "IWSLT17.TED.tst2010"
-
-echo "creating iswslt test data into raw"
-generate_subset en_XX vi_VN test "IWSLT15.TED.tst2015"
-generate_subset vi_VN en_XX test "IWSLT15.TED.tst2015"
-
-generate_subset en_XX ar_AR test "IWSLT17.TED.tst2017"
-generate_subset ar_AR en_XX test "IWSLT17.TED.tst2017"
-generate_subset en_XX ko_KR test "IWSLT17.TED.tst2017"
-generate_subset ko_KR en_XX test "IWSLT17.TED.tst2017"
-
-generate_subset en_XX it_IT test "IWSLT17.TED.tst2017.mltlng"
-generate_subset it_IT en_XX test "IWSLT17.TED.tst2017.mltlng"
-generate_subset en_XX nl_XX test "IWSLT17.TED.tst2017.mltlng"
-generate_subset nl_XX en_XX test "IWSLT17.TED.tst2017.mltlng"
-
-# normalze iwslt directions into x-en
-pushd $raw
-for lang in $langs; do
- for split in test valid; do
- x_en_f1=$split.$lang-en_XX.en_XX
- x_en_f2=$split.$lang-en_XX.${lang}
-
- en_x_f1=$split.en_XX-$lang.en_XX
- en_x_f2=$split.en_XX-$lang.${lang}
-
- if [ -f $en_x_f1 ] && [ ! -f $x_en_f1 ]; then
- echo "cp $en_x_f1 $x_en_f1"
- cp $en_x_f1 $x_en_f1
- fi
- if [ -f $x_en_f2 ] && [ ! -f $x_en_f2 ]; then
- echo "cp $en_x_f2 $x_en_f2"
- cp $en_x_f2 $x_en_f2
- fi
- done
-done
-popd
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/hubert/simple_kmeans/feature_utils.py b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/hubert/simple_kmeans/feature_utils.py
deleted file mode 100644
index f80bc4569768fac181133cdc8f76d1230e03bff6..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/hubert/simple_kmeans/feature_utils.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-import sys
-
-import tqdm
-from npy_append_array import NpyAppendArray
-
-
-logging.basicConfig(
- format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- level=os.environ.get("LOGLEVEL", "INFO").upper(),
- stream=sys.stdout,
-)
-logger = logging.getLogger("feature_utils")
-
-
-def get_shard_range(tot, nshard, rank):
- assert rank < nshard and rank >= 0, f"invaid rank/nshard {rank}/{nshard}"
- start = round(tot / nshard * rank)
- end = round(tot / nshard * (rank + 1))
- assert start < end, f"start={start}, end={end}"
- logger.info(
- f"rank {rank} of {nshard}, process {end-start} "
- f"({start}-{end}) out of {tot}"
- )
- return start, end
-
-
-def get_path_iterator(tsv, nshard, rank):
- with open(tsv, "r") as f:
- root = f.readline().rstrip()
- lines = [line.rstrip() for line in f]
- start, end = get_shard_range(len(lines), nshard, rank)
- lines = lines[start:end]
- def iterate():
- for line in lines:
- subpath, nsample = line.split("\t")
- yield f"{root}/{subpath}", int(nsample)
- return iterate, len(lines)
-
-
-def dump_feature(reader, generator, num, split, nshard, rank, feat_dir):
- iterator = generator()
-
- feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy"
- leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len"
-
- os.makedirs(feat_dir, exist_ok=True)
- if os.path.exists(feat_path):
- os.remove(feat_path)
-
- feat_f = NpyAppendArray(feat_path)
- with open(leng_path, "w") as leng_f:
- for path, nsample in tqdm.tqdm(iterator, total=num):
- feat = reader.get_feats(path, nsample)
- feat_f.append(feat.cpu().numpy())
- leng_f.write(f"{len(feat)}\n")
- logger.info("finished successfully")
-
-
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/bart/summarize.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/bart/summarize.py
deleted file mode 100644
index 04435f80e39c2d9d894696dae7cba5b381e13da9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/bart/summarize.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-from fairseq.models.bart import BARTModel
-import argparse
-
-XSUM_KWARGS = dict(beam=6, lenpen=1.0, max_len_b=60, min_len=10, no_repeat_ngram_size=3)
-CNN_KWARGS = dict(beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3)
-
-
-@torch.no_grad()
-def generate(bart, infile, outfile="bart_hypo.txt", bsz=32, n_obs=None, **eval_kwargs):
- count = 1
-
- # if n_obs is not None: bsz = min(bsz, n_obs)
-
- with open(infile) as source, open(outfile, "w") as fout:
- sline = source.readline().strip()
- slines = [sline]
- for sline in source:
- if n_obs is not None and count > n_obs:
- break
- if count % bsz == 0:
- hypotheses_batch = bart.sample(slines, **eval_kwargs)
- for hypothesis in hypotheses_batch:
- fout.write(hypothesis + "\n")
- fout.flush()
- slines = []
-
- slines.append(sline.strip())
- count += 1
-
- if slines != []:
- hypotheses_batch = bart.sample(slines, **eval_kwargs)
- for hypothesis in hypotheses_batch:
- fout.write(hypothesis + "\n")
- fout.flush()
-
-
-def main():
- """
- Usage::
-
- python examples/bart/summarize.py \
- --model-dir $HOME/bart.large.cnn \
- --model-file model.pt \
- --src $HOME/data-bin/cnn_dm/test.source
- """
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--model-dir",
- required=True,
- type=str,
- default="bart.large.cnn/",
- help="path containing model file and src_dict.txt",
- )
- parser.add_argument(
- "--model-file",
- default="checkpoint_best.pt",
- help="where in model_dir are weights saved",
- )
- parser.add_argument(
- "--src", default="test.source", help="text to summarize", type=str
- )
- parser.add_argument(
- "--out", default="test.hypo", help="where to save summaries", type=str
- )
- parser.add_argument("--bsz", default=32, help="where to save summaries", type=int)
- parser.add_argument(
- "--n", default=None, help="how many examples to summarize", type=int
- )
- parser.add_argument(
- "--xsum-kwargs",
- action="store_true",
- default=False,
- help="if true use XSUM_KWARGS else CNN_KWARGS",
- )
- args = parser.parse_args()
- eval_kwargs = XSUM_KWARGS if args.xsum_kwargs else CNN_KWARGS
- if args.model_dir == "pytorch/fairseq":
- bart = torch.hub.load("pytorch/fairseq", args.model_file)
- else:
- bart = BARTModel.from_pretrained(
- args.model_dir,
- checkpoint_file=args.model_file,
- data_name_or_path=args.model_dir,
- )
- bart = bart.eval()
- if torch.cuda.is_available():
- bart = bart.cuda().half()
- generate(
- bart, args.src, bsz=args.bsz, n_obs=args.n, outfile=args.out, **eval_kwargs
- )
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/translation/README.md b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/translation/README.md
deleted file mode 100644
index 2941f5eb8482dab61dca5eca27a71abd7ee5bf5c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/translation/README.md
+++ /dev/null
@@ -1,301 +0,0 @@
-# Neural Machine Translation
-
-This README contains instructions for [using pretrained translation models](#example-usage-torchhub)
-as well as [training new models](#training-a-new-model).
-
-## Pre-trained models
-
-Model | Description | Dataset | Download
----|---|---|---
-`conv.wmt14.en-fr` | Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2) <br> newstest2012/2013: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.ntst1213.tar.bz2)
-`conv.wmt14.en-de` | Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-de.newstest2014.tar.bz2)
-`conv.wmt17.en-de` | Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT17 English-German](http://statmt.org/wmt17/translation-task.html#Download) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.v2.en-de.newstest2014.tar.bz2)
-`transformer.wmt14.en-fr` | Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2)
-`transformer.wmt16.en-de` | Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2)
-`transformer.wmt18.en-de` | Transformer <br> ([Edunov et al., 2018](https://arxiv.org/abs/1808.09381)) <br> WMT'18 winner | [WMT'18 English-German](http://www.statmt.org/wmt18/translation-task.html) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz) <br> See NOTE in the archive
-`transformer.wmt19.en-de` | Transformer <br> ([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) <br> WMT'19 winner | [WMT'19 English-German](http://www.statmt.org/wmt19/translation-task.html) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz)
-`transformer.wmt19.de-en` | Transformer <br> ([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) <br> WMT'19 winner | [WMT'19 German-English](http://www.statmt.org/wmt19/translation-task.html) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz)
-`transformer.wmt19.en-ru` | Transformer <br> ([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) <br> WMT'19 winner | [WMT'19 English-Russian](http://www.statmt.org/wmt19/translation-task.html) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz)
-`transformer.wmt19.ru-en` | Transformer <br> ([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) <br> WMT'19 winner | [WMT'19 Russian-English](http://www.statmt.org/wmt19/translation-task.html) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz)
-
-## Example usage (torch.hub)
-
-We require a few additional Python dependencies for preprocessing:
-```bash
-pip install fastBPE sacremoses subword_nmt
-```
-
-Interactive translation via PyTorch Hub:
-```python
-import torch
-
-# List available models
-torch.hub.list('pytorch/fairseq') # [..., 'transformer.wmt16.en-de', ... ]
-
-# Load a transformer trained on WMT'16 En-De
-# Note: WMT'19 models use fastBPE instead of subword_nmt, see instructions below
-en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt16.en-de',
- tokenizer='moses', bpe='subword_nmt')
-en2de.eval() # disable dropout
-
-# The underlying model is available under the *models* attribute
-assert isinstance(en2de.models[0], fairseq.models.transformer.TransformerModel)
-
-# Move model to GPU for faster translation
-en2de.cuda()
-
-# Translate a sentence
-en2de.translate('Hello world!')
-# 'Hallo Welt!'
-
-# Batched translation
-en2de.translate(['Hello world!', 'The cat sat on the mat.'])
-# ['Hallo Welt!', 'Die Katze saß auf der Matte.']
-```
-
-Loading custom models:
-```python
-from fairseq.models.transformer import TransformerModel
-zh2en = TransformerModel.from_pretrained(
- '/path/to/checkpoints',
- checkpoint_file='checkpoint_best.pt',
- data_name_or_path='data-bin/wmt17_zh_en_full',
- bpe='subword_nmt',
- bpe_codes='data-bin/wmt17_zh_en_full/zh.code'
-)
-zh2en.translate('你好 世界')
-# 'Hello World'
-```
-
-If you are using a `transformer.wmt19` models, you will need to set the `bpe`
-argument to `'fastbpe'` and (optionally) load the 4-model ensemble:
-```python
-en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de',
- checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
- tokenizer='moses', bpe='fastbpe')
-en2de.eval() # disable dropout
-```
-
-## Example usage (CLI tools)
-
-Generation with the binarized test sets can be run in batch mode as follows, e.g. for WMT 2014 English-French on a GTX-1080ti:
-```bash
-mkdir -p data-bin
-curl https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2 | tar xvjf - -C data-bin
-curl https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2 | tar xvjf - -C data-bin
-fairseq-generate data-bin/wmt14.en-fr.newstest2014 \
- --path data-bin/wmt14.en-fr.fconv-py/model.pt \
- --beam 5 --batch-size 128 --remove-bpe | tee /tmp/gen.out
-# ...
-# | Translated 3003 sentences (96311 tokens) in 166.0s (580.04 tokens/s)
-# | Generate test with beam=5: BLEU4 = 40.83, 67.5/46.9/34.4/25.5 (BP=1.000, ratio=1.006, syslen=83262, reflen=82787)
-
-# Compute BLEU score
-grep ^H /tmp/gen.out | cut -f3- > /tmp/gen.out.sys
-grep ^T /tmp/gen.out | cut -f2- > /tmp/gen.out.ref
-fairseq-score --sys /tmp/gen.out.sys --ref /tmp/gen.out.ref
-# BLEU4 = 40.83, 67.5/46.9/34.4/25.5 (BP=1.000, ratio=1.006, syslen=83262, reflen=82787)
-```
-
-## Training a new model
-
-### IWSLT'14 German to English (Transformer)
-
-The following instructions can be used to train a Transformer model on the [IWSLT'14 German to English dataset](http://workshop2014.iwslt.org/downloads/proceeding.pdf).
-
-First download and preprocess the data:
-```bash
-# Download and prepare the data
-cd examples/translation/
-bash prepare-iwslt14.sh
-cd ../..
-
-# Preprocess/binarize the data
-TEXT=examples/translation/iwslt14.tokenized.de-en
-fairseq-preprocess --source-lang de --target-lang en \
- --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
- --destdir data-bin/iwslt14.tokenized.de-en \
- --workers 20
-```
-
-Next we'll train a Transformer translation model over this data:
-```bash
-CUDA_VISIBLE_DEVICES=0 fairseq-train \
- data-bin/iwslt14.tokenized.de-en \
- --arch transformer_iwslt_de_en --share-decoder-input-output-embed \
- --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
- --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
- --dropout 0.3 --weight-decay 0.0001 \
- --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
- --max-tokens 4096 \
- --eval-bleu \
- --eval-bleu-args '{"beam": 5, "max_len_a": 1.2, "max_len_b": 10}' \
- --eval-bleu-detok moses \
- --eval-bleu-remove-bpe \
- --eval-bleu-print-samples \
- --best-checkpoint-metric bleu --maximize-best-checkpoint-metric
-```
-
-Finally we can evaluate our trained model:
-```bash
-fairseq-generate data-bin/iwslt14.tokenized.de-en \
- --path checkpoints/checkpoint_best.pt \
- --batch-size 128 --beam 5 --remove-bpe
-```
-
-### WMT'14 English to German (Convolutional)
-
-The following instructions can be used to train a Convolutional translation model on the WMT English to German dataset.
-See the [Scaling NMT README](../scaling_nmt/README.md) for instructions to train a Transformer translation model on this data.
-
-The WMT English to German dataset can be preprocessed using the `prepare-wmt14en2de.sh` script.
-By default it will produce a dataset that was modeled after [Attention Is All You Need (Vaswani et al., 2017)](https://arxiv.org/abs/1706.03762), but with additional news-commentary-v12 data from WMT'17.
-
-To use only data available in WMT'14 or to replicate results obtained in the original [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](https://arxiv.org/abs/1705.03122) paper, please use the `--icml17` option.
-
-```bash
-# Download and prepare the data
-cd examples/translation/
-# WMT'17 data:
-bash prepare-wmt14en2de.sh
-# or to use WMT'14 data:
-# bash prepare-wmt14en2de.sh --icml17
-cd ../..
-
-# Binarize the dataset
-TEXT=examples/translation/wmt17_en_de
-fairseq-preprocess \
- --source-lang en --target-lang de \
- --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
- --destdir data-bin/wmt17_en_de --thresholdtgt 0 --thresholdsrc 0 \
- --workers 20
-
-# Train the model
-mkdir -p checkpoints/fconv_wmt_en_de
-fairseq-train \
- data-bin/wmt17_en_de \
- --arch fconv_wmt_en_de \
- --dropout 0.2 \
- --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
- --optimizer nag --clip-norm 0.1 \
- --lr 0.5 --lr-scheduler fixed --force-anneal 50 \
- --max-tokens 4000 \
- --save-dir checkpoints/fconv_wmt_en_de
-
-# Evaluate
-fairseq-generate data-bin/wmt17_en_de \
- --path checkpoints/fconv_wmt_en_de/checkpoint_best.pt \
- --beam 5 --remove-bpe
-```
-
-### WMT'14 English to French
-```bash
-# Download and prepare the data
-cd examples/translation/
-bash prepare-wmt14en2fr.sh
-cd ../..
-
-# Binarize the dataset
-TEXT=examples/translation/wmt14_en_fr
-fairseq-preprocess \
- --source-lang en --target-lang fr \
- --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
- --destdir data-bin/wmt14_en_fr --thresholdtgt 0 --thresholdsrc 0 \
- --workers 60
-
-# Train the model
-mkdir -p checkpoints/fconv_wmt_en_fr
-fairseq-train \
- data-bin/wmt14_en_fr \
- --arch fconv_wmt_en_fr \
- --dropout 0.1 \
- --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
- --optimizer nag --clip-norm 0.1 \
- --lr 0.5 --lr-scheduler fixed --force-anneal 50 \
- --max-tokens 3000 \
- --save-dir checkpoints/fconv_wmt_en_fr
-
-# Evaluate
-fairseq-generate \
- data-bin/fconv_wmt_en_fr \
- --path checkpoints/fconv_wmt_en_fr/checkpoint_best.pt \
- --beam 5 --remove-bpe
-```
-
-## Multilingual Translation
-
-We also support training multilingual translation models. In this example we'll
-train a multilingual `{de,fr}-en` translation model using the IWSLT'17 datasets.
-
-Note that we use slightly different preprocessing here than for the IWSLT'14
-En-De data above. In particular we learn a joint BPE code for all three
-languages and use fairseq-interactive and sacrebleu for scoring the test set.
-
-```bash
-# First install sacrebleu and sentencepiece
-pip install sacrebleu sentencepiece
-
-# Then download and preprocess the data
-cd examples/translation/
-bash prepare-iwslt17-multilingual.sh
-cd ../..
-
-# Binarize the de-en dataset
-TEXT=examples/translation/iwslt17.de_fr.en.bpe16k
-fairseq-preprocess --source-lang de --target-lang en \
- --trainpref $TEXT/train.bpe.de-en \
- --validpref $TEXT/valid0.bpe.de-en,$TEXT/valid1.bpe.de-en,$TEXT/valid2.bpe.de-en,$TEXT/valid3.bpe.de-en,$TEXT/valid4.bpe.de-en,$TEXT/valid5.bpe.de-en \
- --destdir data-bin/iwslt17.de_fr.en.bpe16k \
- --workers 10
-
-# Binarize the fr-en dataset
-# NOTE: it's important to reuse the en dictionary from the previous step
-fairseq-preprocess --source-lang fr --target-lang en \
- --trainpref $TEXT/train.bpe.fr-en \
- --validpref $TEXT/valid0.bpe.fr-en,$TEXT/valid1.bpe.fr-en,$TEXT/valid2.bpe.fr-en,$TEXT/valid3.bpe.fr-en,$TEXT/valid4.bpe.fr-en,$TEXT/valid5.bpe.fr-en \
- --tgtdict data-bin/iwslt17.de_fr.en.bpe16k/dict.en.txt \
- --destdir data-bin/iwslt17.de_fr.en.bpe16k \
- --workers 10
-
-# Train a multilingual transformer model
-# NOTE: the command below assumes 1 GPU, but accumulates gradients from
-# 8 fwd/bwd passes to simulate training on 8 GPUs
-mkdir -p checkpoints/multilingual_transformer
-CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt17.de_fr.en.bpe16k/ \
- --max-epoch 50 \
- --ddp-backend=legacy_ddp \
- --task multilingual_translation --lang-pairs de-en,fr-en \
- --arch multilingual_transformer_iwslt_de_en \
- --share-decoders --share-decoder-input-output-embed \
- --optimizer adam --adam-betas '(0.9, 0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --warmup-updates 4000 --warmup-init-lr '1e-07' \
- --label-smoothing 0.1 --criterion label_smoothed_cross_entropy \
- --dropout 0.3 --weight-decay 0.0001 \
- --save-dir checkpoints/multilingual_transformer \
- --max-tokens 4000 \
- --update-freq 8
-
-# Generate and score the test set with sacrebleu
-SRC=de
-sacrebleu --test-set iwslt17 --language-pair ${SRC}-en --echo src \
- | python scripts/spm_encode.py --model examples/translation/iwslt17.de_fr.en.bpe16k/sentencepiece.bpe.model \
- > iwslt17.test.${SRC}-en.${SRC}.bpe
-cat iwslt17.test.${SRC}-en.${SRC}.bpe \
- | fairseq-interactive data-bin/iwslt17.de_fr.en.bpe16k/ \
- --task multilingual_translation --lang-pairs de-en,fr-en \
- --source-lang ${SRC} --target-lang en \
- --path checkpoints/multilingual_transformer/checkpoint_best.pt \
- --buffer-size 2000 --batch-size 128 \
- --beam 5 --remove-bpe=sentencepiece \
- > iwslt17.test.${SRC}-en.en.sys
-grep ^H iwslt17.test.${SRC}-en.en.sys | cut -f3 \
- | sacrebleu --test-set iwslt17 --language-pair ${SRC}-en
-```
-
-##### Argument format during inference
-
-During inference it is required to specify a single `--source-lang` and
-`--target-lang`, which indicates the inference langauge direction.
-`--lang-pairs`, `--encoder-langtok`, `--decoder-langtok` have to be set to
-the same value as training.
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/decode.sh b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/decode.sh
deleted file mode 100644
index 811cb63c88bb7cdd03b0a250ef2db32b5eaa50df..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/decode.sh
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/bin/bash
-
-set -u
-
-val_sets="dev_other"
-graph_name=graph
-decode_suffix=""
-decode_script="steps/decode_fmllr.sh"
-decode_args=""
-nj=60
-
-. ./cmd.sh
-. ./path.sh
-. parse_options.sh
-
-set -x
-exp_dir=$1
-data_root=$2
-lang_test=$3
-
-graph=$exp_dir/$graph_name
-
-if [ ! -d $graph ]; then
- utils/mkgraph.sh $lang_test $exp_dir $graph
-fi
-
-for part in $val_sets; do
- dec_dir=$exp_dir/decode${decode_suffix}_${part}
- if [ ! -d $dec_dir ]; then
- echo "decoding $part for $exp_dir"
- $decode_script --nj $nj --cmd "$decode_cmd" $decode_args \
- $graph $data_root/$part $dec_dir &
- else
- echo "$dec_dir exists. skip"
- fi
-done
-
-wait
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_multi_corpus_sampled_dataset.py b/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_multi_corpus_sampled_dataset.py
deleted file mode 100644
index 05b20328c5605178767d138cc75e070824679842..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_multi_corpus_sampled_dataset.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import unittest
-from collections import OrderedDict
-
-import numpy as np
-import torch
-from fairseq.data import LanguagePairDataset, TokenBlockDataset
-from fairseq.data.multi_corpus_sampled_dataset import MultiCorpusSampledDataset
-from tests.test_train import mock_dict
-
-
-class TestMultiCorpusSampledDataset(unittest.TestCase):
- def setUp(self):
- d = mock_dict()
- tokens_1 = torch.LongTensor([1]).view(1, -1)
- tokens_ds1 = TokenBlockDataset(
- tokens_1,
- sizes=[tokens_1.size(-1)],
- block_size=1,
- pad=0,
- eos=1,
- include_targets=False,
- )
- self.dataset_1 = LanguagePairDataset(
- tokens_ds1, tokens_ds1.sizes, d, shuffle=False
- )
- tokens_2 = torch.LongTensor([2]).view(1, -1)
- tokens_ds2 = TokenBlockDataset(
- tokens_2,
- sizes=[tokens_2.size(-1)],
- block_size=1,
- pad=0,
- eos=1,
- include_targets=False,
- )
- self.dataset_2 = LanguagePairDataset(
- tokens_ds2, tokens_ds2.sizes, d, shuffle=False
- )
-
- def _test_sample_helper(
- self,
- expected_sample_from_first_ds_percentage,
- num_samples=1000,
- sampling_func=None,
- ):
- # To make sure test is not flaky
- np.random.seed(0)
- if sampling_func is None:
- m = MultiCorpusSampledDataset(
- OrderedDict({0: self.dataset_1, 1: self.dataset_2}),
- )
- else:
- m = MultiCorpusSampledDataset(
- OrderedDict({0: self.dataset_1, 1: self.dataset_2}),
- sampling_func=sampling_func,
- )
- m.ordered_indices()
- count_sample_from_first_dataset = 0
- for _ in range(num_samples):
- if m.collater([m[0], m[1]])["net_input"]["src_tokens"][0] == 1:
- count_sample_from_first_dataset += 1
- sample_from_first_ds_percentage = (
- 1.0 * count_sample_from_first_dataset / num_samples
- )
- self.assertLess(
- abs(
- sample_from_first_ds_percentage
- - expected_sample_from_first_ds_percentage
- ),
- 0.01,
- )
-
- def test_multi_corpus_sampled_dataset_uniform_sample(self):
- self._test_sample_helper(expected_sample_from_first_ds_percentage=0.5)
-
- def test_multi_corpus_sampled_dataset_weighted_sample(self):
- def naive_weighted_sample(weights):
- def f(l):
- v = np.random.random()
- agg = 0
- for i, weight in enumerate(weights):
- agg += weight
- if agg > v:
- return i
-
- return f
-
- self._test_sample_helper(
- expected_sample_from_first_ds_percentage=0.9,
- sampling_func=naive_weighted_sample(weights=[0.9, 0.1]),
- )
diff --git a/spaces/OIUGLK/bingo/src/lib/bots/bing/index.ts b/spaces/OIUGLK/bingo/src/lib/bots/bing/index.ts
deleted file mode 100644
index 2c4afae01a345b8415935228566cb30d695e768d..0000000000000000000000000000000000000000
--- a/spaces/OIUGLK/bingo/src/lib/bots/bing/index.ts
+++ /dev/null
@@ -1,421 +0,0 @@
-import { fetch, WebSocket, debug } from '@/lib/isomorphic'
-import WebSocketAsPromised from 'websocket-as-promised'
-import {
- SendMessageParams,
- BingConversationStyle,
- ConversationResponse,
- ChatResponseMessage,
- ConversationInfo,
- InvocationEventType,
- ChatError,
- ErrorCode,
- ChatUpdateCompleteResponse,
- ImageInfo,
- KBlobResponse
-} from './types'
-
-import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils'
-import { WatchDog, createChunkDecoder } from '@/lib/utils'
-
-type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }>
-
-const OPTIONS_SETS = [
- 'nlu_direct_response_filter',
- 'deepleo',
- 'disable_emoji_spoken_text',
- 'responsible_ai_policy_235',
- 'enablemm',
- 'iycapbing',
- 'iyxapbing',
- 'objopinion',
- 'rweasgv2',
- 'dagslnv1',
- 'dv3sugg',
- 'autosave',
- 'iyoloxap',
- 'iyoloneutral',
- 'clgalileo',
- 'gencontentv3',
-]
-
-export class BingWebBot {
- protected conversationContext?: ConversationInfo
- protected cookie: string
- protected ua: string
- protected endpoint = ''
- private lastText = ''
- private asyncTasks: Array<Promise<any>> = []
-
- constructor(opts: {
- cookie: string
- ua: string
- bingConversationStyle?: BingConversationStyle
- conversationContext?: ConversationInfo
- }) {
- const { cookie, ua, conversationContext } = opts
- this.cookie = cookie?.includes(';') ? cookie : `_EDGE_V=1; _U=${cookie}`
- this.ua = ua
- this.conversationContext = conversationContext
- }
-
- static buildChatRequest(conversation: ConversationInfo) {
- const optionsSets = OPTIONS_SETS
- if (conversation.conversationStyle === BingConversationStyle.Precise) {
- optionsSets.push('h3precise')
- } else if (conversation.conversationStyle === BingConversationStyle.Creative) {
- optionsSets.push('h3imaginative')
- }
- return {
- arguments: [
- {
- source: 'cib',
- optionsSets,
- allowedMessageTypes: [
- 'Chat',
- 'InternalSearchQuery',
- 'Disengaged',
- 'InternalLoaderMessage',
- 'SemanticSerp',
- 'GenerateContentQuery',
- 'SearchQuery',
- ],
- sliceIds: [
- 'winmuid1tf',
- 'anssupfor_c',
- 'imgchatgptv2',
- 'tts2cf',
- 'contansperf',
- 'mlchatpc8500w',
- 'mlchatpc2',
- 'ctrlworkpay',
- 'winshortmsgtf',
- 'cibctrl',
- 'sydtransctrl',
- 'sydconfigoptc',
- '0705trt4',
- '517opinion',
- '628ajcopus0',
- '330uaugs0',
- '529rwea',
- '0626snptrcs0',
- '424dagslnv1',
- ],
- isStartOfSession: conversation.invocationId === 0,
- message: {
- author: 'user',
- inputMethod: 'Keyboard',
- text: conversation.prompt,
- imageUrl: conversation.imageUrl,
- messageType: 'Chat',
- },
- conversationId: conversation.conversationId,
- conversationSignature: conversation.conversationSignature,
- participant: { id: conversation.clientId },
- },
- ],
- invocationId: conversation.invocationId.toString(),
- target: 'chat',
- type: InvocationEventType.StreamInvocation,
- }
- }
-
- async createConversation(): Promise<ConversationResponse> {
- const headers = {
- 'Accept-Encoding': 'gzip, deflate, br, zsdch',
- 'User-Agent': this.ua,
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- cookie: this.cookie,
- }
-
- let resp: ConversationResponse | undefined
- try {
- const response = await fetch(this.endpoint + '/api/create', { method: 'POST', headers, redirect: 'error', mode: 'cors', credentials: 'include' })
- if (response.status === 404) {
- throw new ChatError('Not Found', ErrorCode.NOTFOUND_ERROR)
- }
- resp = await response.json() as ConversationResponse
- } catch (err) {
- console.error('create conversation error', err)
- }
-
- if (!resp?.result) {
- throw new ChatError('Invalid response', ErrorCode.UNKOWN_ERROR)
- }
-
- const { value, message } = resp.result || {}
- if (value !== 'Success') {
- const errorMsg = `${value}: ${message}`
- if (value === 'UnauthorizedRequest') {
- throw new ChatError(errorMsg, ErrorCode.BING_UNAUTHORIZED)
- }
- if (value === 'Forbidden') {
- throw new ChatError(errorMsg, ErrorCode.BING_FORBIDDEN)
- }
- throw new ChatError(errorMsg, ErrorCode.UNKOWN_ERROR)
- }
- return resp
- }
-
- private async createContext(conversationStyle: BingConversationStyle) {
- if (!this.conversationContext) {
- const conversation = await this.createConversation()
- this.conversationContext = {
- conversationId: conversation.conversationId,
- conversationSignature: conversation.conversationSignature,
- clientId: conversation.clientId,
- invocationId: 0,
- conversationStyle,
- prompt: '',
- }
- }
- return this.conversationContext
- }
-
- async sendMessage(params: Params) {
- try {
- await this.createContext(params.options.bingConversationStyle)
- Object.assign(this.conversationContext!, { prompt: params.prompt, imageUrl: params.imageUrl })
- return this.sydneyProxy(params)
- } catch (error) {
- params.onEvent({
- type: 'ERROR',
- error: error instanceof ChatError ? error : new ChatError('Catch Error', ErrorCode.UNKOWN_ERROR),
- })
- }
- }
-
- private async sydneyProxy(params: Params) {
- const abortController = new AbortController()
- const response = await fetch(this.endpoint + '/api/sydney', {
- method: 'POST',
- headers: {
- 'Content-Type': 'application/json',
- },
- signal: abortController.signal,
- body: JSON.stringify(this.conversationContext!)
- })
- if (response.status !== 200) {
- params.onEvent({
- type: 'ERROR',
- error: new ChatError(
- 'Unknown error',
- ErrorCode.UNKOWN_ERROR,
- ),
- })
- }
- params.signal?.addEventListener('abort', () => {
- abortController.abort()
- })
-
- const textDecoder = createChunkDecoder()
- for await (const chunk of streamAsyncIterable(response.body!)) {
- this.parseEvents(params, websocketUtils.unpackMessage(textDecoder(chunk)))
- }
- }
-
- async sendWs() {
- const wsConfig: ConstructorParameters<typeof WebSocketAsPromised>[1] = {
- packMessage: websocketUtils.packMessage,
- unpackMessage: websocketUtils.unpackMessage,
- createWebSocket: (url) => new WebSocket(url, {
- headers: {
- 'accept-language': 'zh-CN,zh;q=0.9',
- 'cache-control': 'no-cache',
- 'User-Agent': this.ua,
- pragma: 'no-cache',
- cookie: this.cookie,
- }
- })
- }
- const wsp = new WebSocketAsPromised('wss://sydney.bing.com/sydney/ChatHub', wsConfig)
-
- wsp.open().then(() => {
- wsp.sendPacked({ protocol: 'json', version: 1 })
- wsp.sendPacked({ type: 6 })
- wsp.sendPacked(BingWebBot.buildChatRequest(this.conversationContext!))
- })
-
- return wsp
- }
-
- private async useWs(params: Params) {
- const wsp = await this.sendWs()
- const watchDog = new WatchDog()
- wsp.onUnpackedMessage.addListener((events) => {
- watchDog.watch(() => {
- wsp.sendPacked({ type: 6 })
- })
- this.parseEvents(params, events)
- })
-
- wsp.onClose.addListener(() => {
- watchDog.reset()
- params.onEvent({ type: 'DONE' })
- wsp.removeAllListeners()
- })
-
- params.signal?.addEventListener('abort', () => {
- wsp.removeAllListeners()
- wsp.close()
- })
- }
-
- private async createImage(prompt: string, id: string) {
- try {
- const headers = {
- 'Accept-Encoding': 'gzip, deflate, br, zsdch',
- 'User-Agent': this.ua,
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- cookie: this.cookie,
- }
- const query = new URLSearchParams({
- prompt,
- id
- })
- const response = await fetch(this.endpoint + '/api/image?' + query.toString(),
- {
- method: 'POST',
- headers,
- mode: 'cors',
- credentials: 'include'
- })
- .then(res => res.text())
- if (response) {
- this.lastText += '\n' + response
- }
- } catch (err) {
- console.error('Create Image Error', err)
- }
- }
-
- private buildKnowledgeApiPayload(imageUrl: string, conversationStyle: BingConversationStyle) {
- const imageInfo: ImageInfo = {}
- let imageBase64: string | undefined = undefined
- const knowledgeRequest = {
- imageInfo,
- knowledgeRequest: {
- invokedSkills: [
- 'ImageById'
- ],
- subscriptionId: 'Bing.Chat.Multimodal',
- invokedSkillsRequestData: {
- enableFaceBlur: true
- },
- convoData: {
- convoid: this.conversationContext?.conversationId,
- convotone: conversationStyle,
- }
- },
- }
-
- if (imageUrl.startsWith('data:image/')) {
- imageBase64 = imageUrl.replace('data:image/', '');
- const partIndex = imageBase64.indexOf(',')
- if (partIndex) {
- imageBase64 = imageBase64.substring(partIndex + 1)
- }
- } else {
- imageInfo.url = imageUrl
- }
- return { knowledgeRequest, imageBase64 }
- }
-
- async uploadImage(imageUrl: string, conversationStyle: BingConversationStyle = BingConversationStyle.Creative): Promise<KBlobResponse | undefined> {
- if (!imageUrl) {
- return
- }
- await this.createContext(conversationStyle)
- const payload = this.buildKnowledgeApiPayload(imageUrl, conversationStyle)
-
- const response = await fetch(this.endpoint + '/api/kblob',
- {
- headers: {
- 'Content-Type': 'application/json',
- },
- method: 'POST',
- mode: 'cors',
- credentials: 'include',
- body: JSON.stringify(payload),
- })
- .then(res => res.json())
- .catch(e => {
- console.log('Error', e)
- })
- return response
- }
-
- private async generateContent(message: ChatResponseMessage) {
- if (message.contentType === 'IMAGE') {
- this.asyncTasks.push(this.createImage(message.text, message.messageId))
- }
- }
-
- private async parseEvents(params: Params, events: any) {
- const conversation = this.conversationContext!
-
- events?.forEach(async (event: ChatUpdateCompleteResponse) => {
- debug('bing event', event)
- if (event.type === 3) {
- await Promise.all(this.asyncTasks)
- this.asyncTasks = []
- params.onEvent({ type: 'UPDATE_ANSWER', data: { text: this.lastText } })
- params.onEvent({ type: 'DONE' })
- conversation.invocationId = parseInt(event.invocationId, 10) + 1
- } else if (event.type === 1) {
- const messages = event.arguments[0].messages
- if (messages) {
- const text = convertMessageToMarkdown(messages[0])
- this.lastText = text
- params.onEvent({ type: 'UPDATE_ANSWER', data: { text, spokenText: messages[0].text, throttling: event.arguments[0].throttling } })
- }
- } else if (event.type === 2) {
- const messages = event.item.messages as ChatResponseMessage[] | undefined
- if (!messages) {
- params.onEvent({
- type: 'ERROR',
- error: new ChatError(
- event.item.result.error || 'Unknown error',
- event.item.result.value === 'Throttled' ? ErrorCode.THROTTLE_LIMIT
- : event.item.result.value === 'CaptchaChallenge' ? (this.conversationContext?.conversationId?.includes('BingProdUnAuthenticatedUsers') ? ErrorCode.BING_UNAUTHORIZED : ErrorCode.BING_CAPTCHA)
- : ErrorCode.UNKOWN_ERROR
- ),
- })
- return
- }
- const limited = messages.some((message) =>
- message.contentOrigin === 'TurnLimiter'
- || message.messageType === 'Disengaged'
- )
- if (limited) {
- params.onEvent({
- type: 'ERROR',
- error: new ChatError(
- 'Sorry, you have reached chat limit in this conversation.',
- ErrorCode.CONVERSATION_LIMIT,
- ),
- })
- return
- }
-
- const lastMessage = event.item.messages.at(-1) as ChatResponseMessage
- const specialMessage = event.item.messages.find(message => message.author === 'bot' && message.contentType === 'IMAGE')
- if (specialMessage) {
- this.generateContent(specialMessage)
- }
-
- if (lastMessage) {
- const text = convertMessageToMarkdown(lastMessage)
- this.lastText = text
- params.onEvent({
- type: 'UPDATE_ANSWER',
- data: { text, throttling: event.item.throttling, suggestedResponses: lastMessage.suggestedResponses, sourceAttributions: lastMessage.sourceAttributions },
- })
- }
- }
- })
- }
-
- resetConversation() {
- this.conversationContext = undefined
- }
-}
diff --git a/spaces/ORI-Muchim/MarinTTS/text/cleaners.py b/spaces/ORI-Muchim/MarinTTS/text/cleaners.py
deleted file mode 100644
index 57d924f38f3c58bc53ac23aab3f5c58da2bf26f6..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/MarinTTS/text/cleaners.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import re
-
-def japanese_cleaners(text):
- from text.japanese import japanese_to_romaji_with_accent
- text = japanese_to_romaji_with_accent(text)
- if len(text) == 0 or re.match('[A-Za-z]', text[-1]):
- text += '.'
- return text
-
-
-def japanese_cleaners2(text):
- text = text.replace('・・・', '…').replace('・', ' ')
- text = japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…') \
- .replace('(', '').replace(')', '') \
- .replace('[', '').replace(']', '') \
- .replace('*', ' ').replace('{', '').replace('}', '')
- return text
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/training/losses/constants.py b/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/training/losses/constants.py
deleted file mode 100644
index ae3e5e151342232be8e2c2a77fe6fd5798dc2a8c..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/saicinpainting/training/losses/constants.py
+++ /dev/null
@@ -1,152 +0,0 @@
-weights = {"ade20k":
- [6.34517766497462,
- 9.328358208955224,
- 11.389521640091116,
- 16.10305958132045,
- 20.833333333333332,
- 22.22222222222222,
- 25.125628140703515,
- 43.29004329004329,
- 50.5050505050505,
- 54.6448087431694,
- 55.24861878453038,
- 60.24096385542168,
- 62.5,
- 66.2251655629139,
- 84.74576271186442,
- 90.90909090909092,
- 91.74311926605505,
- 96.15384615384616,
- 96.15384615384616,
- 97.08737864077669,
- 102.04081632653062,
- 135.13513513513513,
- 149.2537313432836,
- 153.84615384615384,
- 163.93442622950818,
- 166.66666666666666,
- 188.67924528301887,
- 192.30769230769232,
- 217.3913043478261,
- 227.27272727272725,
- 227.27272727272725,
- 227.27272727272725,
- 303.03030303030306,
- 322.5806451612903,
- 333.3333333333333,
- 370.3703703703703,
- 384.61538461538464,
- 416.6666666666667,
- 416.6666666666667,
- 434.7826086956522,
- 434.7826086956522,
- 454.5454545454545,
- 454.5454545454545,
- 500.0,
- 526.3157894736842,
- 526.3157894736842,
- 555.5555555555555,
- 555.5555555555555,
- 555.5555555555555,
- 555.5555555555555,
- 555.5555555555555,
- 555.5555555555555,
- 555.5555555555555,
- 588.2352941176471,
- 588.2352941176471,
- 588.2352941176471,
- 588.2352941176471,
- 588.2352941176471,
- 666.6666666666666,
- 666.6666666666666,
- 666.6666666666666,
- 666.6666666666666,
- 714.2857142857143,
- 714.2857142857143,
- 714.2857142857143,
- 714.2857142857143,
- 714.2857142857143,
- 769.2307692307693,
- 769.2307692307693,
- 769.2307692307693,
- 833.3333333333334,
- 833.3333333333334,
- 833.3333333333334,
- 833.3333333333334,
- 909.090909090909,
- 1000.0,
- 1111.111111111111,
- 1111.111111111111,
- 1111.111111111111,
- 1111.111111111111,
- 1111.111111111111,
- 1250.0,
- 1250.0,
- 1250.0,
- 1250.0,
- 1250.0,
- 1428.5714285714287,
- 1428.5714285714287,
- 1428.5714285714287,
- 1428.5714285714287,
- 1428.5714285714287,
- 1428.5714285714287,
- 1428.5714285714287,
- 1666.6666666666667,
- 1666.6666666666667,
- 1666.6666666666667,
- 1666.6666666666667,
- 1666.6666666666667,
- 1666.6666666666667,
- 1666.6666666666667,
- 1666.6666666666667,
- 1666.6666666666667,
- 1666.6666666666667,
- 1666.6666666666667,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2000.0,
- 2500.0,
- 2500.0,
- 2500.0,
- 2500.0,
- 2500.0,
- 2500.0,
- 2500.0,
- 2500.0,
- 2500.0,
- 2500.0,
- 2500.0,
- 2500.0,
- 2500.0,
- 3333.3333333333335,
- 3333.3333333333335,
- 3333.3333333333335,
- 3333.3333333333335,
- 3333.3333333333335,
- 3333.3333333333335,
- 3333.3333333333335,
- 3333.3333333333335,
- 3333.3333333333335,
- 3333.3333333333335,
- 3333.3333333333335,
- 3333.3333333333335,
- 3333.3333333333335,
- 5000.0,
- 5000.0,
- 5000.0]
-}
\ No newline at end of file
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/models/ade20k/segm_lib/nn/modules/replicate.py b/spaces/OpenGVLab/InternGPT/third-party/lama/models/ade20k/segm_lib/nn/modules/replicate.py
deleted file mode 100644
index b71c7b8ed51a1d6c55b1f753bdd8d90bad79bd06..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/models/ade20k/segm_lib/nn/modules/replicate.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# -*- coding: utf-8 -*-
-# File : replicate.py
-# Author : Jiayuan Mao
-# Email : maojiayuan@gmail.com
-# Date : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import functools
-
-from torch.nn.parallel.data_parallel import DataParallel
-
-__all__ = [
- 'CallbackContext',
- 'execute_replication_callbacks',
- 'DataParallelWithCallback',
- 'patch_replication_callback'
-]
-
-
-class CallbackContext(object):
- pass
-
-
-def execute_replication_callbacks(modules):
- """
- Execute an replication callback `__data_parallel_replicate__` on each module created by original replication.
-
- The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
-
- Note that, as all modules are isomorphism, we assign each sub-module with a context
- (shared among multiple copies of this module on different devices).
- Through this context, different copies can share some information.
-
- We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback
- of any slave copies.
- """
- master_copy = modules[0]
- nr_modules = len(list(master_copy.modules()))
- ctxs = [CallbackContext() for _ in range(nr_modules)]
-
- for i, module in enumerate(modules):
- for j, m in enumerate(module.modules()):
- if hasattr(m, '__data_parallel_replicate__'):
- m.__data_parallel_replicate__(ctxs[j], i)
-
-
-class DataParallelWithCallback(DataParallel):
- """
- Data Parallel with a replication callback.
-
- An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by
- original `replicate` function.
- The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
-
- Examples:
- > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
- > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
- # sync_bn.__data_parallel_replicate__ will be invoked.
- """
-
- def replicate(self, module, device_ids):
- modules = super(DataParallelWithCallback, self).replicate(module, device_ids)
- execute_replication_callbacks(modules)
- return modules
-
-
-def patch_replication_callback(data_parallel):
- """
- Monkey-patch an existing `DataParallel` object. Add the replication callback.
- Useful when you have customized `DataParallel` implementation.
-
- Examples:
- > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
- > sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
- > patch_replication_callback(sync_bn)
- # this is equivalent to
- > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
- > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
- """
-
- assert isinstance(data_parallel, DataParallel)
-
- old_replicate = data_parallel.replicate
-
- @functools.wraps(old_replicate)
- def new_replicate(module, device_ids):
- modules = old_replicate(module, device_ids)
- execute_replication_callbacks(modules)
- return modules
-
- data_parallel.replicate = new_replicate
diff --git a/spaces/OptimalScale/Robin-33b/lmflow/models/regression_model.py b/spaces/OptimalScale/Robin-33b/lmflow/models/regression_model.py
deleted file mode 100644
index 43d0dfc7b06ec134cd81d19a2cf4e78c3af77ce9..0000000000000000000000000000000000000000
--- a/spaces/OptimalScale/Robin-33b/lmflow/models/regression_model.py
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-"""General regression model."""
-
-from lmflow.models.base_model import BaseModel
-
-
-class RegressionModel(BaseModel):
-
- def __init__(self, *args, **kwargs):
- pass
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/epoch_based_runner.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/epoch_based_runner.py
deleted file mode 100644
index 766a9ce6afdf09cd11b1b15005f5132583011348..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/epoch_based_runner.py
+++ /dev/null
@@ -1,187 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import platform
-import shutil
-import time
-import warnings
-
-import torch
-
-import annotator.uniformer.mmcv as mmcv
-from .base_runner import BaseRunner
-from .builder import RUNNERS
-from .checkpoint import save_checkpoint
-from .utils import get_host_info
-
-
-@RUNNERS.register_module()
-class EpochBasedRunner(BaseRunner):
- """Epoch-based Runner.
-
- This runner train models epoch by epoch.
- """
-
- def run_iter(self, data_batch, train_mode, **kwargs):
- if self.batch_processor is not None:
- outputs = self.batch_processor(
- self.model, data_batch, train_mode=train_mode, **kwargs)
- elif train_mode:
- outputs = self.model.train_step(data_batch, self.optimizer,
- **kwargs)
- else:
- outputs = self.model.val_step(data_batch, self.optimizer, **kwargs)
- if not isinstance(outputs, dict):
- raise TypeError('"batch_processor()" or "model.train_step()"'
- 'and "model.val_step()" must return a dict')
- if 'log_vars' in outputs:
- self.log_buffer.update(outputs['log_vars'], outputs['num_samples'])
- self.outputs = outputs
-
- def train(self, data_loader, **kwargs):
- self.model.train()
- self.mode = 'train'
- self.data_loader = data_loader
- self._max_iters = self._max_epochs * len(self.data_loader)
- self.call_hook('before_train_epoch')
- time.sleep(2) # Prevent possible deadlock during epoch transition
- for i, data_batch in enumerate(self.data_loader):
- self._inner_iter = i
- self.call_hook('before_train_iter')
- self.run_iter(data_batch, train_mode=True, **kwargs)
- self.call_hook('after_train_iter')
- self._iter += 1
-
- self.call_hook('after_train_epoch')
- self._epoch += 1
-
- @torch.no_grad()
- def val(self, data_loader, **kwargs):
- self.model.eval()
- self.mode = 'val'
- self.data_loader = data_loader
- self.call_hook('before_val_epoch')
- time.sleep(2) # Prevent possible deadlock during epoch transition
- for i, data_batch in enumerate(self.data_loader):
- self._inner_iter = i
- self.call_hook('before_val_iter')
- self.run_iter(data_batch, train_mode=False)
- self.call_hook('after_val_iter')
-
- self.call_hook('after_val_epoch')
-
- def run(self, data_loaders, workflow, max_epochs=None, **kwargs):
- """Start running.
-
- Args:
- data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
- and validation.
- workflow (list[tuple]): A list of (phase, epochs) to specify the
- running order and epochs. E.g, [('train', 2), ('val', 1)] means
- running 2 epochs for training and 1 epoch for validation,
- iteratively.
- """
- assert isinstance(data_loaders, list)
- assert mmcv.is_list_of(workflow, tuple)
- assert len(data_loaders) == len(workflow)
- if max_epochs is not None:
- warnings.warn(
- 'setting max_epochs in run is deprecated, '
- 'please set max_epochs in runner_config', DeprecationWarning)
- self._max_epochs = max_epochs
-
- assert self._max_epochs is not None, (
- 'max_epochs must be specified during instantiation')
-
- for i, flow in enumerate(workflow):
- mode, epochs = flow
- if mode == 'train':
- self._max_iters = self._max_epochs * len(data_loaders[i])
- break
-
- work_dir = self.work_dir if self.work_dir is not None else 'NONE'
- self.logger.info('Start running, host: %s, work_dir: %s',
- get_host_info(), work_dir)
- self.logger.info('Hooks will be executed in the following order:\n%s',
- self.get_hook_info())
- self.logger.info('workflow: %s, max: %d epochs', workflow,
- self._max_epochs)
- self.call_hook('before_run')
-
- while self.epoch < self._max_epochs:
- for i, flow in enumerate(workflow):
- mode, epochs = flow
- if isinstance(mode, str): # self.train()
- if not hasattr(self, mode):
- raise ValueError(
- f'runner has no method named "{mode}" to run an '
- 'epoch')
- epoch_runner = getattr(self, mode)
- else:
- raise TypeError(
- 'mode in workflow must be a str, but got {}'.format(
- type(mode)))
-
- for _ in range(epochs):
- if mode == 'train' and self.epoch >= self._max_epochs:
- break
- epoch_runner(data_loaders[i], **kwargs)
-
- time.sleep(1) # wait for some hooks like loggers to finish
- self.call_hook('after_run')
-
- def save_checkpoint(self,
- out_dir,
- filename_tmpl='epoch_{}.pth',
- save_optimizer=True,
- meta=None,
- create_symlink=True):
- """Save the checkpoint.
-
- Args:
- out_dir (str): The directory that checkpoints are saved.
- filename_tmpl (str, optional): The checkpoint filename template,
- which contains a placeholder for the epoch number.
- Defaults to 'epoch_{}.pth'.
- save_optimizer (bool, optional): Whether to save the optimizer to
- the checkpoint. Defaults to True.
- meta (dict, optional): The meta information to be saved in the
- checkpoint. Defaults to None.
- create_symlink (bool, optional): Whether to create a symlink
- "latest.pth" to point to the latest checkpoint.
- Defaults to True.
- """
- if meta is None:
- meta = {}
- elif not isinstance(meta, dict):
- raise TypeError(
- f'meta should be a dict or None, but got {type(meta)}')
- if self.meta is not None:
- meta.update(self.meta)
- # Note: meta.update(self.meta) should be done before
- # meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise
- # there will be problems with resumed checkpoints.
- # More details in https://github.com/open-mmlab/mmcv/pull/1108
- meta.update(epoch=self.epoch + 1, iter=self.iter)
-
- filename = filename_tmpl.format(self.epoch + 1)
- filepath = osp.join(out_dir, filename)
- optimizer = self.optimizer if save_optimizer else None
- save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta)
- # in some environments, `os.symlink` is not supported, you may need to
- # set `create_symlink` to False
- if create_symlink:
- dst_file = osp.join(out_dir, 'latest.pth')
- if platform.system() != 'Windows':
- mmcv.symlink(filename, dst_file)
- else:
- shutil.copy(filepath, dst_file)
-
-
-@RUNNERS.register_module()
-class Runner(EpochBasedRunner):
- """Deprecated name of EpochBasedRunner."""
-
- def __init__(self, *args, **kwargs):
- warnings.warn(
- 'Runner was deprecated, please use EpochBasedRunner instead')
- super().__init__(*args, **kwargs)
diff --git "a/spaces/ParagKesharDas360/MovieRecommadationApp/pages/4_\342\255\220_Rating Page.py" "b/spaces/ParagKesharDas360/MovieRecommadationApp/pages/4_\342\255\220_Rating Page.py"
deleted file mode 100644
index 06cfb44af70da77d07267e40bcb2bce73007ad2b..0000000000000000000000000000000000000000
--- "a/spaces/ParagKesharDas360/MovieRecommadationApp/pages/4_\342\255\220_Rating Page.py"
+++ /dev/null
@@ -1,192 +0,0 @@
-import streamlit as st
-import subprocess
-import csv
-from streamlit import cache
-import os
-
-import re
-from datetime import datetime
-# import streamlit as st
-import argparse
-os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
-
-import tensorflow as tf
-from ipaddress import summarize_address_range
-# import streamlit as st
-import pandas as pd
-import numpy as np
-import pickle
-import keras.optimizers
-import keras.regularizers
-from keras import layers
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-from sklearn.metrics.pairwise import linear_kernel
-import matplotlib.pyplot as plt
-import requests
-from typing import List
-# @st.cache(suppress_st_warning=True, allow_output_mutation=True)
-
-
-def load_movies():
- movies = pd.read_csv('movies.csv')
- return movies
-st.set_page_config(page_title="Login Page")
-
-if st.session_state["UserID"] == "" and st.session_state["UserName"] == "":
- st.error("Login First!!")
-else:
- username = str(st.session_state["UserName"])
- user_id = int(st.session_state["UserID"])
- # print("Hi else")
-
-
- # Open the login.csv file
- with open('login.csv', encoding="cp437", errors='ignore') as login_file:
- csv_reader = csv.reader(login_file)
- found_match = False
- movies = load_movies()
- ratings = pd.read_csv('ratings.csv')
-
- # Load login.csv file and create a dictionary to map usernames to new_user_ids
- login = pd.read_csv('login.csv')
-
- # Create a function to get the movieId based on the movie title
-
-
- def find_movie_id_by_title(title):
- with open('movies.csv', 'r',encoding='utf-8') as file:
- reader = csv.reader(file)
- next(reader) # skip header row
- for row in reader:
- if row[1] == title:
- return row[0]
- return None
-
- def find_movie_id_by_genres(title):
- with open('movies.csv', 'r',encoding='utf-8') as file:
- reader = csv.reader(file)
- next(reader) # skip header row
- for row in reader:
- if row[1] == title:
- return row[2]
- return None
-
- def find_user_rating(user_id, movie_id):
- user_ratings = ratings[(ratings['userId'] == user_id) & (ratings['movieId'] == movie_id)]
- if len(user_ratings) > 0:
- return user_ratings.index[0]
- else:
- return None
-##############################
- def getMovieTitle(title_with_year):
- # Extracting the title
- title = re.search(r"^(.*?)(?:,\s*The)?\s*\(", title_with_year).group(1).strip()
-
- # Extracting the year
- year = re.search(r"\((\d{4})\)$", title_with_year).group(1)
-
- # Printing the results
- print("Title:", title)
- print("Year:", year)
- og=title+'('+year+')'
- return title,og
- api_key = "3e67b4fa"
-
- def fetch_movie_poster(title):
- url = f"http://www.omdbapi.com/?apikey={api_key}&t={title}"
- response = requests.get(url)
- data = response.json()
- if "Poster" in data and data["Poster"] != "N/A":
- return data["Poster"]
- return "na1.png"
-
- def moviePoster(movie_title):
- poster_url = fetch_movie_poster(movie_title)
- if poster_url:
- st.image(poster_url, caption=movie_title, use_column_width=True, width=200)
- else:
- st.image("na1.png", caption=movie_title, use_column_width=True, width=200)
-
- # st.set_page_config(page_title="Rate a Movie", page_icon=":movie_camera:", layout="wide", initial_sidebar_state="expanded")
-
- # Retrieve the username and user ID arguments
-
- username = str(username)
- user_id = int(user_id)
-
- # Display the username and user ID
- st.subheader(f"Welcome, {username} !!")
-
-
- # Create the streamlit app
- st.title('Rate a Movie')
-
- # Get the movie title from the user
- # Get the new_user_id and movieId based on the inputs
-
- movie_title = st.selectbox("Movie Title", movies['title'].tolist())
- movie_id=find_movie_id_by_title(movie_title)
- movie_genres=find_movie_id_by_genres(movie_title)
- timestamp = str(int(datetime.now().timestamp()))
- # st.text(movie_title+"'s movie_id is "+movie_id+" of genres "+movie_genres+timestamp)
- row_index=find_user_rating(int(user_id),int(movie_id))
- if row_index:
- prevoius_rating=ratings.iloc[row_index].loc['rating']
- else:
- prevoius_rating=None
- st.subheader("Movie Details")
- st.info(f"Movie Title: {getMovieTitle(movie_title)[1] }")
- st.info(f"Movie ID: {movie_id}")
- st.info(f"Movie Genres: {movie_genres}")
- st.info(f"Movie Rating:{prevoius_rating}")
- MT=getMovieTitle(movie_title)[0]
- st.image(fetch_movie_poster(MT),caption=MT)
- # Center-align the element-container class
- centering_css = """
- <style>
- .etr89bj2 {
- display: flex;
- justify-content: center;
- align-items: center;
- hight:100vh
-
- }
- </style>
- """
- st.markdown(centering_css, unsafe_allow_html=True)
- # Get the rating from the user
- rating = st.slider('Rating', 1.0, 5.0, step=0.5)
- col11, col22 = st.columns(2)
-
- rateBtn = col11.button("Rate Now")
- unrateBtn = col22.button("Unrate Now")
-
- # st.text("Row Index is "+str(row_index))
- if rateBtn:
- if row_index is not None:
- # If the user has already rated this movie, update the rating in the ratings DataFrame
- ratings.at[row_index, 'rating'] = rating
- st.success(f"Rating has updated")
- else:
- # If the user has not yet rated this movie, add a new row to the ratings DataFrame
- new_row = {'userId': user_id, 'movieId': movie_id, 'rating': rating, 'timestamp': int(datetime.now().timestamp())}
- ratings = ratings.append(new_row, ignore_index=True)
- st.success(f"New rating has added ")
-
- # Write the updated ratings DataFrame to the ratings.csv file
- ratings.to_csv('ratings.csv', index=False)
-
-
- if unrateBtn:
- # If the "Unrate" button was clicked, delete the rating from the ratings DataFrame
- if row_index is not None:
- ratings.drop(row_index, inplace=True)
- st.success(f"Rating has deleted")
- else:
- st.warning("No rating found to delete.")
-
- # Write the updated ratings DataFrame to the ratings.csv file
- ratings.to_csv('ratings.csv', index=False)
-
\ No newline at end of file
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-2.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-2.go
deleted file mode 100644
index 07a913e79669820d80213865fdba7e0a18bfbc09..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-2.go and /dev/null differ
diff --git a/spaces/Pengyey/bingo-chuchu/tests/parse.ts b/spaces/Pengyey/bingo-chuchu/tests/parse.ts
deleted file mode 100644
index 92940fe6315f1d7cb2b267ba5e5a7e26460a1de3..0000000000000000000000000000000000000000
--- a/spaces/Pengyey/bingo-chuchu/tests/parse.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-import { promises as fs } from 'fs'
-import { join } from 'path'
-import { parseHeadersFromCurl } from '@/lib/utils'
-
-(async () => {
- const content = await fs.readFile(join(__dirname, './fixtures/curl.txt'), 'utf-8')
- const headers = parseHeadersFromCurl(content)
- console.log(headers)
-
- const cmdContent = await fs.readFile(join(__dirname, './fixtures/cmd.txt'), 'utf-8')
- const cmdHeaders = parseHeadersFromCurl(cmdContent)
- console.log(cmdHeaders)
-})()
diff --git a/spaces/Plurigrid/LifeSim/src/components/ui/dialog.tsx b/spaces/Plurigrid/LifeSim/src/components/ui/dialog.tsx
deleted file mode 100644
index c5621059f4149bbc1b008837dd68082c76a8a5c5..0000000000000000000000000000000000000000
--- a/spaces/Plurigrid/LifeSim/src/components/ui/dialog.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as DialogPrimitive from "@radix-ui/react-dialog"
-import { X } from "lucide-react"
-
-import { cn } from "@/lib/utils"
-
-const Dialog = DialogPrimitive.Root
-
-const DialogTrigger = DialogPrimitive.Trigger
-
-const DialogPortal = ({
- className,
- ...props
-}: DialogPrimitive.DialogPortalProps) => (
- <DialogPrimitive.Portal className={cn(className)} {...props} />
-)
-DialogPortal.displayName = DialogPrimitive.Portal.displayName
-
-const DialogOverlay = React.forwardRef<
- React.ElementRef<typeof DialogPrimitive.Overlay>,
- React.ComponentPropsWithoutRef<typeof DialogPrimitive.Overlay>
->(({ className, ...props }, ref) => (
- <DialogPrimitive.Overlay
- ref={ref}
- className={cn(
- "fixed inset-0 z-50 bg-white/80 backdrop-blur-sm data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 dark:bg-stone-950/80",
- className
- )}
- {...props}
- />
-))
-DialogOverlay.displayName = DialogPrimitive.Overlay.displayName
-
-const DialogContent = React.forwardRef<
- React.ElementRef<typeof DialogPrimitive.Content>,
- React.ComponentPropsWithoutRef<typeof DialogPrimitive.Content>
->(({ className, children, ...props }, ref) => (
- <DialogPortal>
- <DialogOverlay />
- <DialogPrimitive.Content
- ref={ref}
- className={cn(
- "fixed left-[50%] top-[50%] z-50 grid w-full max-w-lg translate-x-[-50%] translate-y-[-50%] gap-4 border border-stone-200 bg-white p-6 shadow-lg duration-200 data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[state=closed]:slide-out-to-left-1/2 data-[state=closed]:slide-out-to-top-[48%] data-[state=open]:slide-in-from-left-1/2 data-[state=open]:slide-in-from-top-[48%] sm:rounded-lg md:w-full dark:border-stone-800 dark:bg-stone-950",
- className
- )}
- {...props}
- >
- {children}
- <DialogPrimitive.Close className="absolute right-4 top-4 rounded-sm opacity-70 ring-offset-white transition-opacity hover:opacity-100 focus:outline-none focus:ring-2 focus:ring-stone-400 focus:ring-offset-2 disabled:pointer-events-none data-[state=open]:bg-stone-100 data-[state=open]:text-stone-500 dark:ring-offset-stone-950 dark:focus:ring-stone-800 dark:data-[state=open]:bg-stone-800 dark:data-[state=open]:text-stone-400">
- <X className="h-4 w-4" />
- <span className="sr-only">Close</span>
- </DialogPrimitive.Close>
- </DialogPrimitive.Content>
- </DialogPortal>
-))
-DialogContent.displayName = DialogPrimitive.Content.displayName
-
-const DialogHeader = ({
- className,
- ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
- <div
- className={cn(
- "flex flex-col space-y-1.5 text-center sm:text-left",
- className
- )}
- {...props}
- />
-)
-DialogHeader.displayName = "DialogHeader"
-
-const DialogFooter = ({
- className,
- ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
- <div
- className={cn(
- "flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2",
- className
- )}
- {...props}
- />
-)
-DialogFooter.displayName = "DialogFooter"
-
-const DialogTitle = React.forwardRef<
- React.ElementRef<typeof DialogPrimitive.Title>,
- React.ComponentPropsWithoutRef<typeof DialogPrimitive.Title>
->(({ className, ...props }, ref) => (
- <DialogPrimitive.Title
- ref={ref}
- className={cn(
- "text-lg font-semibold leading-none tracking-tight",
- className
- )}
- {...props}
- />
-))
-DialogTitle.displayName = DialogPrimitive.Title.displayName
-
-const DialogDescription = React.forwardRef<
- React.ElementRef<typeof DialogPrimitive.Description>,
- React.ComponentPropsWithoutRef<typeof DialogPrimitive.Description>
->(({ className, ...props }, ref) => (
- <DialogPrimitive.Description
- ref={ref}
- className={cn("text-sm text-stone-500 dark:text-stone-400", className)}
- {...props}
- />
-))
-DialogDescription.displayName = DialogPrimitive.Description.displayName
-
-export {
- Dialog,
- DialogTrigger,
- DialogContent,
- DialogHeader,
- DialogFooter,
- DialogTitle,
- DialogDescription,
-}
diff --git a/spaces/Podtekatel/Avatar2VSK/inference/onnx_model.py b/spaces/Podtekatel/Avatar2VSK/inference/onnx_model.py
deleted file mode 100644
index b5097703ec79dab6e91be0f7117e3dd5a829f7dd..0000000000000000000000000000000000000000
--- a/spaces/Podtekatel/Avatar2VSK/inference/onnx_model.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import numpy as np
-import onnxruntime
-
-
-class ONNXModel:
- def __init__(self, onnx_mode_path):
- self.path = onnx_mode_path
- self.ort_session = onnxruntime.InferenceSession(str(self.path))
- self.input_name = self.ort_session.get_inputs()[0].name
-
- def __call__(self, img):
- ort_inputs = {self.input_name: img.astype(dtype=np.float32)}
- ort_outs = self.ort_session.run(None, ort_inputs)[0]
- return ort_outs
\ No newline at end of file
diff --git a/spaces/Potanin/12345/lib/infer_pack/models.py b/spaces/Potanin/12345/lib/infer_pack/models.py
deleted file mode 100644
index 3665d03bc0514a6ed07d3372ea24717dae1e0a65..0000000000000000000000000000000000000000
--- a/spaces/Potanin/12345/lib/infer_pack/models.py
+++ /dev/null
@@ -1,1142 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(256, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(768, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(
- self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0,
- ):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(
- modules.ResidualCouplingLayer(
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- mean_only=True,
- )
- )
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
- def remove_weight_norm(self):
- for i in range(self.n_flows):
- self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=0,
- ):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(resblock_kernel_sizes, resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
- """Definition of sine generator
- SineGen(samp_rate, harmonic_num = 0,
- sine_amp = 0.1, noise_std = 0.003,
- voiced_threshold = 0,
- flag_for_pulse=False)
- samp_rate: sampling rate in Hz
- harmonic_num: number of harmonic overtones (default 0)
- sine_amp: amplitude of sine-wavefrom (default 0.1)
- noise_std: std of Gaussian noise (default 0.003)
- voiced_thoreshold: F0 threshold for U/V classification (default 0)
- flag_for_pulse: this SinGen is used inside PulseGen (default False)
- Note: when flag_for_pulse is True, the first time step of a voiced
- segment is always sin(np.pi) or cos(0)
- """
-
- def __init__(
- self,
- samp_rate,
- harmonic_num=0,
- sine_amp=0.1,
- noise_std=0.003,
- voiced_threshold=0,
- flag_for_pulse=False,
- ):
- super(SineGen, self).__init__()
- self.sine_amp = sine_amp
- self.noise_std = noise_std
- self.harmonic_num = harmonic_num
- self.dim = self.harmonic_num + 1
- self.sampling_rate = samp_rate
- self.voiced_threshold = voiced_threshold
-
- def _f02uv(self, f0):
- # generate uv signal
- uv = torch.ones_like(f0)
- uv = uv * (f0 > self.voiced_threshold)
- return uv
-
- def forward(self, f0, upp):
- """sine_tensor, uv = forward(f0)
- input F0: tensor(batchsize=1, length, dim=1)
- f0 for unvoiced steps should be 0
- output sine_tensor: tensor(batchsize=1, length, dim)
- output uv: tensor(batchsize=1, length, 1)
- """
- with torch.no_grad():
- f0 = f0[:, None].transpose(1, 2)
- f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
- # fundamental component
- f0_buf[:, :, 0] = f0[:, :, 0]
- for idx in np.arange(self.harmonic_num):
- f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
- idx + 2
- ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
- rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
- rand_ini = torch.rand(
- f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
- )
- rand_ini[:, 0] = 0
- rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
- tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
- tmp_over_one *= upp
- tmp_over_one = F.interpolate(
- tmp_over_one.transpose(2, 1),
- scale_factor=upp,
- mode="linear",
- align_corners=True,
- ).transpose(2, 1)
- rad_values = F.interpolate(
- rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(
- 2, 1
- ) #######
- tmp_over_one %= 1
- tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
- cumsum_shift = torch.zeros_like(rad_values)
- cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
- sine_waves = torch.sin(
- torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
- )
- sine_waves = sine_waves * self.sine_amp
- uv = self._f02uv(f0)
- uv = F.interpolate(
- uv.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(2, 1)
- noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
- noise = noise_amp * torch.randn_like(sine_waves)
- sine_waves = sine_waves * uv + noise
- return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
- """SourceModule for hn-nsf
- SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
- add_noise_std=0.003, voiced_threshod=0)
- sampling_rate: sampling_rate in Hz
- harmonic_num: number of harmonic above F0 (default: 0)
- sine_amp: amplitude of sine source signal (default: 0.1)
- add_noise_std: std of additive Gaussian noise (default: 0.003)
- note that amplitude of noise in unvoiced is decided
- by sine_amp
- voiced_threshold: threhold to set U/V given F0 (default: 0)
- Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
- F0_sampled (batchsize, length, 1)
- Sine_source (batchsize, length, 1)
- noise_source (batchsize, length 1)
- uv (batchsize, length, 1)
- """
-
- def __init__(
- self,
- sampling_rate,
- harmonic_num=0,
- sine_amp=0.1,
- add_noise_std=0.003,
- voiced_threshod=0,
- is_half=True,
- ):
- super(SourceModuleHnNSF, self).__init__()
-
- self.sine_amp = sine_amp
- self.noise_std = add_noise_std
- self.is_half = is_half
- # to produce sine waveforms
- self.l_sin_gen = SineGen(
- sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
- )
-
- # to merge source harmonics into a single excitation
- self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
- self.l_tanh = torch.nn.Tanh()
-
- def forward(self, x, upp=None):
- sine_wavs, uv, _ = self.l_sin_gen(x, upp)
- if self.is_half:
- sine_wavs = sine_wavs.half()
- sine_merge = self.l_tanh(self.l_linear(sine_wavs))
- return sine_merge, None, None # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- sr,
- is_half=False,
- ):
- super(GeneratorNSF, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
-
- self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
- self.m_source = SourceModuleHnNSF(
- sampling_rate=sr, harmonic_num=0, is_half=is_half
- )
- self.noise_convs = nn.ModuleList()
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- c_cur = upsample_initial_channel // (2 ** (i + 1))
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
- if i + 1 < len(upsample_rates):
- stride_f0 = np.prod(upsample_rates[i + 1 :])
- self.noise_convs.append(
- Conv1d(
- 1,
- c_cur,
- kernel_size=stride_f0 * 2,
- stride=stride_f0,
- padding=stride_f0 // 2,
- )
- )
- else:
- self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(resblock_kernel_sizes, resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- self.upp = np.prod(upsample_rates)
-
- def forward(self, x, f0, g=None):
- har_source, noi_source, uv = self.m_source(f0, self.upp)
- har_source = har_source.transpose(1, 2)
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- x_source = self.noise_convs[i](har_source)
- x = x + x_source
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-sr2sr = {
- "32k": 32000,
- "40k": 40000,
- "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr,
- **kwargs
- ):
- super().__init__()
- if type(sr) == type("strr"):
- sr = sr2sr[sr]
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- sr=sr,
- is_half=kwargs["is_half"],
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(
- self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
- ): # 这里ds是id，[bs,1]
- # print(1,pitch.shape)#[bs,t]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
- pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
- # print(-2,pitchf.shape,z_slice.shape)
- o = self.dec(z_slice, pitchf, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
- if rate:
- head = int(z_p.shape[2] * rate)
- z_p = z_p[:, :, -head:]
- x_mask = x_mask[:, :, -head:]
- nsff0 = nsff0[:, -head:]
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec(z * x_mask, nsff0, g=g)
- return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr,
- **kwargs
- ):
- super().__init__()
- if type(sr) == type("strr"):
- sr = sr2sr[sr]
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder768(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- sr=sr,
- is_half=kwargs["is_half"],
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(
- self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
- ): # 这里ds是id，[bs,1]
- # print(1,pitch.shape)#[bs,t]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
- pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
- # print(-2,pitchf.shape,z_slice.shape)
- o = self.dec(z_slice, pitchf, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
- if rate:
- head = int(z_p.shape[2] * rate)
- z_p = z_p[:, :, -head:]
- x_mask = x_mask[:, :, -head:]
- nsff0 = nsff0[:, -head:]
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec(z * x_mask, nsff0, g=g)
- return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr=None,
- **kwargs
- ):
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=False,
- )
- self.dec = Generator(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- o = self.dec(z_slice, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, sid, rate=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
- if rate:
- head = int(z_p.shape[2] * rate)
- z_p = z_p[:, :, -head:]
- x_mask = x_mask[:, :, -head:]
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec(z * x_mask, g=g)
- return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid_nono(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr=None,
- **kwargs
- ):
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder768(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=False,
- )
- self.dec = Generator(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- o = self.dec(z_slice, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, sid, rate=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
- if rate:
- head = int(z_p.shape[2] * rate)
- z_p = z_p[:, :, -head:]
- x_mask = x_mask[:, :, -head:]
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec(z * x_mask, g=g)
- return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2, 3, 5, 7, 11, 17]
- # periods = [3, 5, 7, 11, 17, 23, 37]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [
- DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
- ]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = [] #
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- # for j in range(len(fmap_r)):
- # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminatorV2, self).__init__()
- # periods = [2, 3, 5, 7, 11, 17]
- periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [
- DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
- ]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = [] #
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- # for j in range(len(fmap_r)):
- # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ]
- )
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(
- Conv2d(
- 1,
- 32,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 32,
- 128,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 128,
- 512,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 512,
- 1024,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 1024,
- 1024,
- (kernel_size, 1),
- 1,
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- ]
- )
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_pick.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_pick.py
deleted file mode 100644
index 4f6d8b2d79406012c5f8bae9c289ed5bf4d179cc..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_pick.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from typing import Optional
-
-
-def pick_bool(*values: Optional[bool]) -> bool:
- """Pick the first non-none bool or return the last value.
-
- Args:
- *values (bool): Any number of boolean or None values.
-
- Returns:
- bool: First non-none boolean.
- """
- assert values, "1 or more values required"
- for value in values:
- if value is not None:
- return value
- return bool(value)
diff --git a/spaces/Realcat/image-matching-webui/third_party/TopicFM/src/models/topic_fm.py b/spaces/Realcat/image-matching-webui/third_party/TopicFM/src/models/topic_fm.py
deleted file mode 100644
index 2556bdbb489574e13a5e5af60be87c546473d406..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/TopicFM/src/models/topic_fm.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import torch
-import torch.nn as nn
-from einops.einops import rearrange
-
-from .backbone import build_backbone
-from .modules import LocalFeatureTransformer, FinePreprocess, TopicFormer
-from .utils.coarse_matching import CoarseMatching
-from .utils.fine_matching import FineMatching
-
-
-class TopicFM(nn.Module):
- def __init__(self, config):
- super().__init__()
- # Misc
- self.config = config
-
- # Modules
- self.backbone = build_backbone(config)
-
- self.loftr_coarse = TopicFormer(config["coarse"])
- self.coarse_matching = CoarseMatching(config["match_coarse"])
- self.fine_preprocess = FinePreprocess(config)
- self.loftr_fine = LocalFeatureTransformer(config["fine"])
- self.fine_matching = FineMatching()
-
- def forward(self, data):
- """
- Update:
- data (dict): {
- 'image0': (torch.Tensor): (N, 1, H, W)
- 'image1': (torch.Tensor): (N, 1, H, W)
- 'mask0'(optional) : (torch.Tensor): (N, H, W) '0' indicates a padded position
- 'mask1'(optional) : (torch.Tensor): (N, H, W)
- }
- """
- # 1. Local Feature CNN
- data.update(
- {
- "bs": data["image0"].size(0),
- "hw0_i": data["image0"].shape[2:],
- "hw1_i": data["image1"].shape[2:],
- }
- )
-
- if data["hw0_i"] == data["hw1_i"]: # faster & better BN convergence
- feats_c, feats_f = self.backbone(
- torch.cat([data["image0"], data["image1"]], dim=0)
- )
- (feat_c0, feat_c1), (feat_f0, feat_f1) = feats_c.split(
- data["bs"]
- ), feats_f.split(data["bs"])
- else: # handle different input shapes
- (feat_c0, feat_f0), (feat_c1, feat_f1) = self.backbone(
- data["image0"]
- ), self.backbone(data["image1"])
-
- data.update(
- {
- "hw0_c": feat_c0.shape[2:],
- "hw1_c": feat_c1.shape[2:],
- "hw0_f": feat_f0.shape[2:],
- "hw1_f": feat_f1.shape[2:],
- }
- )
-
- # 2. coarse-level loftr module
- feat_c0 = rearrange(feat_c0, "n c h w -> n (h w) c")
- feat_c1 = rearrange(feat_c1, "n c h w -> n (h w) c")
-
- mask_c0 = mask_c1 = None # mask is useful in training
- if "mask0" in data:
- mask_c0, mask_c1 = data["mask0"].flatten(-2), data["mask1"].flatten(-2)
-
- feat_c0, feat_c1, conf_matrix, topic_matrix = self.loftr_coarse(
- feat_c0, feat_c1, mask_c0, mask_c1
- )
- data.update({"conf_matrix": conf_matrix, "topic_matrix": topic_matrix}) ######
-
- # 3. match coarse-level
- self.coarse_matching(data)
-
- # 4. fine-level refinement
- feat_f0_unfold, feat_f1_unfold = self.fine_preprocess(
- feat_f0, feat_f1, feat_c0.detach(), feat_c1.detach(), data
- )
- if feat_f0_unfold.size(0) != 0: # at least one coarse level predicted
- feat_f0_unfold, feat_f1_unfold = self.loftr_fine(
- feat_f0_unfold, feat_f1_unfold
- )
-
- # 5. match fine-level
- self.fine_matching(feat_f0_unfold, feat_f1_unfold, data)
-
- def load_state_dict(self, state_dict, *args, **kwargs):
- for k in list(state_dict.keys()):
- if k.startswith("matcher."):
- state_dict[k.replace("matcher.", "", 1)] = state_dict.pop(k)
- return super().load_state_dict(state_dict, *args, **kwargs)
diff --git a/spaces/Realcat/image-matching-webui/third_party/r2d2/download_training_data.sh b/spaces/Realcat/image-matching-webui/third_party/r2d2/download_training_data.sh
deleted file mode 100644
index 8257c83ef70eeab47b6b344d591ddef86ba848cd..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/r2d2/download_training_data.sh
+++ /dev/null
@@ -1,69 +0,0 @@
-# Copyright 2019-present NAVER Corp.
-# CC BY-NC-SA 3.0
-# Available only for non-commercial use
-
-CODE_ROOT=`pwd`
-if [ ! -e data ]; then
- echo "Error: missing data/ folder"
- echo "First, create a folder that can host (at least) 15 GB of data."
- echo "Then, create a soft-link named 'data' that points to it."
- exit -1
-fi
-
-# download web images from the revisitop1m dataset
-WEB_ROOT=data/revisitop1m
-mkdir -p $WEB_ROOT
-cd $WEB_ROOT
-if [ ! -e 0d3 ]; then
- for i in {1..5}; do
- echo "Installing the web images dataset ($i/5)..."
- if [ ! -f revisitop1m.$i.tar.gz ]; then
- wget http://ptak.felk.cvut.cz/revisitop/revisitop1m/jpg/revisitop1m.$i.tar.gz
- fi
- tar -xzvf revisitop1m.$i.tar.gz
- rm -f revisitop1m.$i.tar.gz
- done
-fi
-cd $CODE_ROOT
-
-# download aachen images
-AACHEN_ROOT=data/aachen
-mkdir -p $AACHEN_ROOT
-cd $AACHEN_ROOT
-if [ ! -e "images_upright" ]; then
- echo "Installing the Aachen dataset..."
- fname=database_and_query_images.zip
- if [ ! -f $fname ]; then
- echo "File not found: $fname"
- exit -1
- else
- unzip $fname
- rm -f $fname
- fi
-fi
-
-# download style transfer images
-if [ ! -e "style_transfer" ]; then
- echo "Installing the Aachen style-transfer dataset..."
- fname=aachen_style_transfer.zip
- if [ ! -f $fname ]; then
- wget http://download.europe.naverlabs.com/3DVision/aachen_style_transfer.zip $fname
- fi
- unzip $fname
- rm -f $fname
-fi
-
-# download optical flow pairs
-if [ ! -e "optical_flow" ]; then
- echo "Installing the Aachen optical flow dataset..."
- fname=aachen_optical_flow.zip
- if [ ! -f $fname ]; then
- wget http://download.europe.naverlabs.com/3DVision/aachen_optical_flow.zip $fname
- fi
- unzip $fname
- rm -f $fname
-fi
-cd $CODE_ROOT
-
-echo "Done!"
-
diff --git a/spaces/Reha2704/VToonify/vtoonify/model/stylegan/__init__.py b/spaces/Reha2704/VToonify/vtoonify/model/stylegan/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Ritori/play_with_baby_llama2/import subprocess.py b/spaces/Ritori/play_with_baby_llama2/import subprocess.py
deleted file mode 100644
index 9bb9fd7b16ec45eec255633c4f1f73831524c81f..0000000000000000000000000000000000000000
--- a/spaces/Ritori/play_with_baby_llama2/import subprocess.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import subprocess
-
-# Define the command as a list
-import gradio as gr
-
-def generate_text():
-
-
- cmd = ['./run', 'model.bin']
-
- # Use subprocess.run to execute the command
- result = subprocess.run(cmd, stdout=subprocess.PIPE, text=True)
- # 在这里运行你的模型，生成文本
- # 以下是一个示例
- text = "这是生成的文本。"
- title="运行来让小羊驼跑起来"
- return result
-
-iface = gr.Interface(fn=generate_text, inputs=[], outputs="text",submit_label="让小羊驼跑起来",title="和小羊驼一起玩")
-iface.launch()
-
-
-cmd = ['./run', 'model.bin']
-
-# Use subprocess.run to execute the command
-result = subprocess.run(cmd, stdout=subprocess.PIPE, text=True)
-
-# Print the output
-print(result.stdout)
diff --git a/spaces/Rmpmartinspro2/EimisAnimeDiffusion_1.0v/app.py b/spaces/Rmpmartinspro2/EimisAnimeDiffusion_1.0v/app.py
deleted file mode 100644
index 48cb4dc74dcf2841f40d4ee0fdfd720ef4834584..0000000000000000000000000000000000000000
--- a/spaces/Rmpmartinspro2/EimisAnimeDiffusion_1.0v/app.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-import gradio as gr
-
-API_KEY=os.environ.get('HUGGING_FACE_HUB_TOKEN', None)
-
-article = """---
-This space was created using [SD Space Creator](https://huggingface.co/spaces/anzorq/sd-space-creator)."""
-
-gr.Interface.load(
- name="models/eimiss/EimisAnimeDiffusion_1.0v",
- title="""Eimisanimediffusion 1.0v""",
- description="""Demo for <a href="https://huggingface.co/eimiss/EimisAnimeDiffusion_1.0v">Eimisanimediffusion 1.0v</a> Stable Diffusion model.""",
- article=article,
- api_key=API_KEY,
- ).queue(concurrency_count=20).launch()
diff --git a/spaces/RobLi/ControlNet-v1-1/app_openpose.py b/spaces/RobLi/ControlNet-v1-1/app_openpose.py
deleted file mode 100644
index a4dd2aa4a97d9526e239633e95fdd0d6162ffe9d..0000000000000000000000000000000000000000
--- a/spaces/RobLi/ControlNet-v1-1/app_openpose.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
- with gr.Blocks() as demo:
- with gr.Row():
- with gr.Column():
- image = gr.Image()
- prompt = gr.Textbox(label='Prompt')
- run_button = gr.Button(label='Run')
- with gr.Accordion('Advanced options', open=False):
- preprocessor_name = gr.Radio(label='Preprocessor',
- choices=['Openpose', 'None'],
- type='value',
- value='Openpose')
- num_samples = gr.Slider(label='Number of images',
- minimum=1,
- maximum=max_images,
- value=default_num_images,
- step=1)
- image_resolution = gr.Slider(label='Image resolution',
- minimum=256,
- maximum=512,
- value=512,
- step=256)
- preprocess_resolution = gr.Slider(
- label='Preprocess resolution',
- minimum=128,
- maximum=512,
- value=512,
- step=1)
- num_steps = gr.Slider(label='Number of steps',
- minimum=1,
- maximum=100,
- value=20,
- step=1)
- guidance_scale = gr.Slider(label='Guidance scale',
- minimum=0.1,
- maximum=30.0,
- value=9.0,
- step=0.1)
- seed = gr.Slider(label='Seed',
- minimum=0,
- maximum=1000000,
- step=1,
- value=0,
- randomize=True)
- randomize_seed = gr.Checkbox(label='Randomize seed',
- value=True)
- a_prompt = gr.Textbox(
- label='Additional prompt',
- value='best quality, extremely detailed')
- n_prompt = gr.Textbox(
- label='Negative prompt',
- value=
- 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
- )
- with gr.Column():
- result = gr.Gallery(label='Output', show_label=False).style(
- columns=2, object_fit='scale-down')
- inputs = [
- image,
- prompt,
- a_prompt,
- n_prompt,
- num_samples,
- image_resolution,
- preprocess_resolution,
- num_steps,
- guidance_scale,
- seed,
- preprocessor_name,
- ]
- prompt.submit(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- )
- run_button.click(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- api_name='openpose',
- )
- return demo
-
-
-if __name__ == '__main__':
- from model import Model
- model = Model(task_name='Openpose')
- demo = create_demo(model.process_openpose)
- demo.queue().launch()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/core/visualization/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/core/visualization/__init__.py
deleted file mode 100644
index 4ff995c0861490941f8cfc19ebbd41a2ee7e2d65..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/core/visualization/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .image import (color_val_matplotlib, imshow_det_bboxes,
- imshow_gt_det_bboxes)
-
-__all__ = ['imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib']
diff --git a/spaces/Salesforce/BLIP/models/blip_retrieval.py b/spaces/Salesforce/BLIP/models/blip_retrieval.py
deleted file mode 100644
index bc645f5ec3c2a17851bf6f54be6d97b1336b3c0a..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/BLIP/models/blip_retrieval.py
+++ /dev/null
@@ -1,322 +0,0 @@
-from models.med import BertConfig, BertModel
-from transformers import BertTokenizer
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from models.blip import create_vit, init_tokenizer, load_checkpoint
-
-class BLIP_Retrieval(nn.Module):
- def __init__(self,
- med_config = 'configs/med_config.json',
- image_size = 384,
- vit = 'base',
- vit_grad_ckpt = False,
- vit_ckpt_layer = 0,
- embed_dim = 256,
- queue_size = 57600,
- momentum = 0.995,
- negative_all_rank = False,
- ):
- """
- Args:
- med_config (str): path for the mixture of encoder-decoder model's configuration file
- image_size (int): input image size
- vit (str): model size of vision transformer
- """
- super().__init__()
-
- self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
- self.tokenizer = init_tokenizer()
- med_config = BertConfig.from_json_file(med_config)
- med_config.encoder_width = vision_width
- self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)
-
- text_width = self.text_encoder.config.hidden_size
-
- self.vision_proj = nn.Linear(vision_width, embed_dim)
- self.text_proj = nn.Linear(text_width, embed_dim)
-
- self.itm_head = nn.Linear(text_width, 2)
-
- # create momentum encoders
- self.visual_encoder_m, vision_width = create_vit(vit,image_size)
- self.vision_proj_m = nn.Linear(vision_width, embed_dim)
- self.text_encoder_m = BertModel(config=med_config, add_pooling_layer=False)
- self.text_proj_m = nn.Linear(text_width, embed_dim)
-
- self.model_pairs = [[self.visual_encoder,self.visual_encoder_m],
- [self.vision_proj,self.vision_proj_m],
- [self.text_encoder,self.text_encoder_m],
- [self.text_proj,self.text_proj_m],
- ]
- self.copy_params()
-
- # create the queue
- self.register_buffer("image_queue", torch.randn(embed_dim, queue_size))
- self.register_buffer("text_queue", torch.randn(embed_dim, queue_size))
- self.register_buffer("idx_queue", torch.full((1,queue_size),-100))
- self.register_buffer("ptr_queue", torch.zeros(1, dtype=torch.long))
-
- self.image_queue = nn.functional.normalize(self.image_queue, dim=0)
- self.text_queue = nn.functional.normalize(self.text_queue, dim=0)
-
- self.queue_size = queue_size
- self.momentum = momentum
- self.temp = nn.Parameter(0.07*torch.ones([]))
-
- self.negative_all_rank = negative_all_rank
-
-
- def forward(self, image, caption, alpha, idx):
- with torch.no_grad():
- self.temp.clamp_(0.001,0.5)
-
- image_embeds = self.visual_encoder(image)
- image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
- image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)
-
- text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35,
- return_tensors="pt").to(image.device)
-
- text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,
- return_dict = True, mode = 'text')
- text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)
-
- ###============== Image-text Contrastive Learning ===================###
- idx = idx.view(-1,1)
- idx_all = torch.cat([idx.t(), self.idx_queue.clone().detach()],dim=1)
- pos_idx = torch.eq(idx, idx_all).float()
- sim_targets = pos_idx / pos_idx.sum(1,keepdim=True)
-
- # get momentum features
- with torch.no_grad():
- self._momentum_update()
- image_embeds_m = self.visual_encoder_m(image)
- image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1)
- image_feat_m_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1)
-
- text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask,
- return_dict = True, mode = 'text')
- text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1)
- text_feat_m_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1)
-
- sim_i2t_m = image_feat_m @ text_feat_m_all / self.temp
- sim_t2i_m = text_feat_m @ image_feat_m_all / self.temp
-
- sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device)
- sim_targets.fill_diagonal_(1)
-
- sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets
- sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets
-
- sim_i2t = image_feat @ text_feat_m_all / self.temp
- sim_t2i = text_feat @ image_feat_m_all / self.temp
-
- loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean()
- loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean()
-
- loss_ita = (loss_i2t+loss_t2i)/2
-
- idxs = concat_all_gather(idx)
- self._dequeue_and_enqueue(image_feat_m, text_feat_m, idxs)
-
- ###============== Image-text Matching ===================###
- encoder_input_ids = text.input_ids.clone()
- encoder_input_ids[:,0] = self.tokenizer.enc_token_id
-
- # forward the positve image-text pair
- bs = image.size(0)
- output_pos = self.text_encoder(encoder_input_ids,
- attention_mask = text.attention_mask,
- encoder_hidden_states = image_embeds,
- encoder_attention_mask = image_atts,
- return_dict = True,
- )
-
-
- if self.negative_all_rank:
- # compute sample similarity
- with torch.no_grad():
- mask = torch.eq(idx, idxs.t())
-
- image_feat_world = concat_all_gather(image_feat)
- text_feat_world = concat_all_gather(text_feat)
-
- sim_i2t = image_feat @ text_feat_world.t() / self.temp
- sim_t2i = text_feat @ image_feat_world.t() / self.temp
-
- weights_i2t = F.softmax(sim_i2t,dim=1)
- weights_i2t.masked_fill_(mask, 0)
-
- weights_t2i = F.softmax(sim_t2i,dim=1)
- weights_t2i.masked_fill_(mask, 0)
-
- image_embeds_world = all_gather_with_grad(image_embeds)
-
- # select a negative image (from all ranks) for each text
- image_embeds_neg = []
- for b in range(bs):
- neg_idx = torch.multinomial(weights_t2i[b], 1).item()
- image_embeds_neg.append(image_embeds_world[neg_idx])
- image_embeds_neg = torch.stack(image_embeds_neg,dim=0)
-
- # select a negative text (from all ranks) for each image
- input_ids_world = concat_all_gather(encoder_input_ids)
- att_mask_world = concat_all_gather(text.attention_mask)
-
- text_ids_neg = []
- text_atts_neg = []
- for b in range(bs):
- neg_idx = torch.multinomial(weights_i2t[b], 1).item()
- text_ids_neg.append(input_ids_world[neg_idx])
- text_atts_neg.append(att_mask_world[neg_idx])
-
- else:
- with torch.no_grad():
- mask = torch.eq(idx, idx.t())
-
- sim_i2t = image_feat @ text_feat.t() / self.temp
- sim_t2i = text_feat @ image_feat.t() / self.temp
-
- weights_i2t = F.softmax(sim_i2t,dim=1)
- weights_i2t.masked_fill_(mask, 0)
-
- weights_t2i = F.softmax(sim_t2i,dim=1)
- weights_t2i.masked_fill_(mask, 0)
-
- # select a negative image (from same rank) for each text
- image_embeds_neg = []
- for b in range(bs):
- neg_idx = torch.multinomial(weights_t2i[b], 1).item()
- image_embeds_neg.append(image_embeds[neg_idx])
- image_embeds_neg = torch.stack(image_embeds_neg,dim=0)
-
- # select a negative text (from same rank) for each image
- text_ids_neg = []
- text_atts_neg = []
- for b in range(bs):
- neg_idx = torch.multinomial(weights_i2t[b], 1).item()
- text_ids_neg.append(encoder_input_ids[neg_idx])
- text_atts_neg.append(text.attention_mask[neg_idx])
-
- text_ids_neg = torch.stack(text_ids_neg,dim=0)
- text_atts_neg = torch.stack(text_atts_neg,dim=0)
-
- text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0)
- text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0)
-
- image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0)
- image_atts_all = torch.cat([image_atts,image_atts],dim=0)
-
- output_neg = self.text_encoder(text_ids_all,
- attention_mask = text_atts_all,
- encoder_hidden_states = image_embeds_all,
- encoder_attention_mask = image_atts_all,
- return_dict = True,
- )
-
-
- vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0)
- vl_output = self.itm_head(vl_embeddings)
-
- itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)],
- dim=0).to(image.device)
- loss_itm = F.cross_entropy(vl_output, itm_labels)
-
- return loss_ita, loss_itm
-
-
- @torch.no_grad()
- def copy_params(self):
- for model_pair in self.model_pairs:
- for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
- param_m.data.copy_(param.data) # initialize
- param_m.requires_grad = False # not update by gradient
-
-
- @torch.no_grad()
- def _momentum_update(self):
- for model_pair in self.model_pairs:
- for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
- param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum)
-
-
- @torch.no_grad()
- def _dequeue_and_enqueue(self, image_feat, text_feat, idxs):
- # gather keys before updating queue
- image_feats = concat_all_gather(image_feat)
- text_feats = concat_all_gather(text_feat)
-
-
- batch_size = image_feats.shape[0]
-
- ptr = int(self.ptr_queue)
- assert self.queue_size % batch_size == 0 # for simplicity
-
- # replace the keys at ptr (dequeue and enqueue)
- self.image_queue[:, ptr:ptr + batch_size] = image_feats.T
- self.text_queue[:, ptr:ptr + batch_size] = text_feats.T
- self.idx_queue[:, ptr:ptr + batch_size] = idxs.T
- ptr = (ptr + batch_size) % self.queue_size # move pointer
-
- self.ptr_queue[0] = ptr
-
-
-def blip_retrieval(pretrained='',**kwargs):
- model = BLIP_Retrieval(**kwargs)
- if pretrained:
- model,msg = load_checkpoint(model,pretrained)
- print("missing keys:")
- print(msg.missing_keys)
- return model
-
-
-@torch.no_grad()
-def concat_all_gather(tensor):
- """
- Performs all_gather operation on the provided tensors.
- *** Warning ***: torch.distributed.all_gather has no gradient.
- """
- tensors_gather = [torch.ones_like(tensor)
- for _ in range(torch.distributed.get_world_size())]
- torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
-
- output = torch.cat(tensors_gather, dim=0)
- return output
-
-
-class GatherLayer(torch.autograd.Function):
- """
- Gather tensors from all workers with support for backward propagation:
- This implementation does not cut the gradients as torch.distributed.all_gather does.
- """
-
- @staticmethod
- def forward(ctx, x):
- output = [torch.zeros_like(x) for _ in range(torch.distributed.get_world_size())]
- torch.distributed.all_gather(output, x)
- return tuple(output)
-
- @staticmethod
- def backward(ctx, *grads):
- all_gradients = torch.stack(grads)
- torch.distributed.all_reduce(all_gradients)
- return all_gradients[torch.distributed.get_rank()]
-
-
-def all_gather_with_grad(tensors):
- """
- Performs all_gather operation on the provided tensors.
- Graph remains connected for backward grad computation.
- """
- # Queue the gathered tensors
- world_size = torch.distributed.get_world_size()
- # There is no need for reduction in the single-proc case
- if world_size == 1:
- return tensors
-
- tensor_all = GatherLayer.apply(tensors)
-
- return torch.cat(tensor_all, dim=0)
diff --git a/spaces/Sambhavnoobcoder/StyleForge/Utils.py b/spaces/Sambhavnoobcoder/StyleForge/Utils.py
deleted file mode 100644
index 356faa8bb66430ee9640df40a5b53399dd02a9f1..0000000000000000000000000000000000000000
--- a/spaces/Sambhavnoobcoder/StyleForge/Utils.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel, LMSDiscreteScheduler
-from transformers import CLIPTextModel, CLIPTokenizer
-from tqdm.auto import tqdm
-from PIL import Image
-import torch
-
-class MingleModel:
-
- def __init__(self):
- # Set device
- self.torch_device = "cuda" if torch.cuda.is_available() else "cpu"
- # Load the autoencoder model which will be used to decode the latents into image space.
- use_auth_token = "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"
- self.vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae",
- use_auth_token=use_auth_token).to(self.torch_device)
-
- # Load the tokenizer and text encoder to tokenize and encode the text.
- self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", use_auth_token=use_auth_token)
- self.text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", use_auth_token=use_auth_token).to(self.torch_device)
-
- # # The UNet model for generating the latents.
- self.unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet",use_auth_token=use_auth_token).to(self.torch_device)
-
- # The noise scheduler
- self.scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
- num_train_timesteps=1000)
-
- def do_tokenizer(self, prompt):
- return self.tokenizer([prompt], padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True,
- return_tensors="pt")
-
- def get_text_encoder(self, text_input):
- return self.text_encoder(text_input.input_ids.to(self.torch_device))[0]
-
- def latents_to_pil(self, latents):
- # bath of latents -> list of images
- latents = (1 / 0.18215) * latents
- with torch.no_grad():
- image = self.vae.decode(latents).sample
- image = (image / 2 + 0.5).clamp(0, 1)
- image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
- images = (image * 255).round().astype("uint8")
- pil_images = [Image.fromarray(image) for image in images]
- return pil_images
-
- def generate_with_embs(self, text_embeddings, generator_int=32, num_inference_steps=30, guidance_scale=7.5):
- height = 512 # default height of Stable Diffusion
- width = 512 # default width of Stable Diffusion
- num_inference_steps = num_inference_steps # Number of denoising steps
- guidance_scale = guidance_scale # Scale for classifier-free guidance
- generator = torch.manual_seed(generator_int) # Seed generator to create the inital latent noise
- batch_size = 1
-
- max_length = 77
- uncond_input = self.tokenizer(
- [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
- )
- with torch.no_grad():
- uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.torch_device))[0]
- text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
- # Prep Scheduler
- self.scheduler.set_timesteps(num_inference_steps)
-
- # Prep latents
- latents = torch.randn((batch_size, self.unet.in_channels, height // 8, width // 8), generator=generator)
- latents = latents.to(self.torch_device)
- latents = latents * self.scheduler.init_noise_sigma
-
- # Loop
- for i, t in tqdm(enumerate(self.scheduler.timesteps)):
- # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
- latent_model_input = torch.cat([latents] * 2)
- sigma = self.scheduler.sigmas[i]
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # predict the noise residual
- with torch.no_grad():
- noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
-
- # perform guidance
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents).prev_sample
-
- return self.latents_to_pil(latents)[0]
\ No newline at end of file
diff --git a/spaces/Sanathkumar1603/hackathon/app/config.py b/spaces/Sanathkumar1603/hackathon/app/config.py
deleted file mode 100644
index 3260516aea6146a34d08bd8a3726e6731176c425..0000000000000000000000000000000000000000
--- a/spaces/Sanathkumar1603/hackathon/app/config.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import sys
-from typing import List
-
-from pydantic import AnyHttpUrl, BaseSettings
-
-class Settings(BaseSettings):
- API_V1_STR: str = "/api/v1"
-
- # Meta
-
- # BACKEND_CORS_ORIGINS is a comma-separated list of origins
- # e.g: http://localhost,http://localhost:4200,http://localhost:3000
- BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = [
- "http://localhost:3000", # type: ignore
- "http://localhost:8000", # type: ignore
- "https://localhost:3000", # type: ignore
- "https://localhost:8000", # type: ignore
- ]
-
- PROJECT_NAME: str = "Recognition API"
-
- class Config:
- case_sensitive = True
-
-settings = Settings()
diff --git a/spaces/Sandiago21/text-to-speech-italian/app.py b/spaces/Sandiago21/text-to-speech-italian/app.py
deleted file mode 100644
index 589aeb6f31a40a7ba159ed9102b4d1119db14f15..0000000000000000000000000000000000000000
--- a/spaces/Sandiago21/text-to-speech-italian/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import gradio as gr
-import torch
-from datasets import load_dataset
-from transformers import pipeline, SpeechT5Processor, SpeechT5HifiGan, SpeechT5ForTextToSpeech
-
-model_id = "Sandiago21/speecht5_finetuned_voxpopuli_it" # update with your model id
-# pipe = pipeline("automatic-speech-recognition", model=model_id)
-model = SpeechT5ForTextToSpeech.from_pretrained(model_id)
-vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
-embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
-speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0)
-
-checkpoint = "microsoft/speecht5_tts"
-processor = SpeechT5Processor.from_pretrained(checkpoint)
-
-replacements = [
- ("á", "a"),
- ("ç", "c"),
- ("è", "e"),
- ("ì", "i"),
- ("í", "i"),
- ("ò", "o"),
- ("ó", "o"),
- ("ù", "u"),
- ("ú", "u"),
- ("š", "s"),
- ("ï", "i"),
-]
-
-title = "Text-to-Speech"
-description = """
-Demo for text-to-speech translation in Italian. Demo uses [Sandiago21/speecht5_finetuned_voxpopuli_it](https://huggingface.co/Sandiago21/speecht5_finetuned_voxpopuli_it) checkpoint, which is based on Microsoft's
-[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model and is fine-tuned in Italian Audio dataset
-
-"""
-
-def cleanup_text(text):
- for src, dst in replacements:
- text = text.replace(src, dst)
- return text
-
-def synthesize_speech(text):
- text = cleanup_text(text)
- inputs = processor(text=text, return_tensors="pt")
-
- speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
-
- return gr.Audio.update(value=(16000, speech.cpu().numpy()))
-
-syntesize_speech_gradio = gr.Interface(
- synthesize_speech,
- inputs = gr.Textbox(label="Text", placeholder="Type something here..."),
- outputs=gr.Audio(),
- examples=["Grandi manifestazioni in polonia al momento della firma dell'accordo che hanno portato il governo polacco a decidere di non ratificare l'accordo per il momento"],
- title=title,
- description=description,
-).launch()
-
diff --git a/spaces/Sarath2002/YouTube_Video_Summarizer/app.py b/spaces/Sarath2002/YouTube_Video_Summarizer/app.py
deleted file mode 100644
index e98007a7cdb2bd541a51e0d59de04079f7546a07..0000000000000000000000000000000000000000
--- a/spaces/Sarath2002/YouTube_Video_Summarizer/app.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import gradio as gr
-from support import *
-import os
-import nltk
-nltk.download('punkt')
-os.system("pip install -r requirements.txt")
-
-description = """
-Repo on Github --> YouTube_Summarizer_using_BART --> https://github.com/Sarath1729-2002/YouTube_Summarizer_using_BART
-
-
-🎯 Input the link of the video to be summarized, and click ''submit''' to get the summary
-
-
-⌛️ It takes about 2 to 15 mins to generate detection results, depending on the video size. Best to use videos shorter than 10 mins for optimum efficiency
-
-
-🏠 Uses extractive summarisation by TextRank in step 1 to get the top-priority sentences, then using BART summarizer, this intermediate summary is then summarised within the given max_length
-
-
-Try these sample videos if in a hurry 😊
-
---> https://youtu.be/s4g1XFU8Gto
---> https://youtu.be/L5YHlH0f4Uw
-
-"""
-
-
-def initialize(link, max_length,min_length):
-
- path = "plaintext.txt"
- url = link
-
- video = get_vidid(url)
- vid_transcript(video)
-
- temp_summ = ext_summarizer(path)
- summ = abs_summarizer(temp_summ, max_length)
-
- return summ
-
-
-
-
-
-interface = gr.Interface(
- inputs=[
- gr.inputs.Textbox(lines=2,label="YouTube Link"),
- gr.Slider(minimum=200,maximum=500,default=350,label="Max Length")
- ],
- outputs=[
- gr.outputs.Textbox(label="Summarised YouTube Video "),
-
- ],
- fn=initialize,
- description=description)
-interface.launch()
\ No newline at end of file
diff --git a/spaces/Saturdays/spanish-quechua-detector/app.py b/spaces/Saturdays/spanish-quechua-detector/app.py
deleted file mode 100644
index 84ab50ee4284bcc9db2b405d91f537b41c0cf714..0000000000000000000000000000000000000000
--- a/spaces/Saturdays/spanish-quechua-detector/app.py
+++ /dev/null
@@ -1,32 +0,0 @@
-
-import pickle
-import gradio as gr
-from classifier import Classifier
-
-def classify(txt):
- with open('classifier.pickle', 'rb') as f:
- classifier = pickle.load(f)
- return classifier.predict(txt)
-
-
-title = 'Detector de Quechua y Español'
-description =( 'Bolivia lucha para que no desaparezcan los idiomas indígenas, sin embargo,' +
- 'es aún muy complicado acceder a recursos que ayuden a su asimilación y aprendizaje.' +
- 'Presentamos una herramienta de clasificación de idiomas, que si bien es una tarea sencilla, ' +
- 'resulta esencial para realizar tareas más complejas como la traducción automática.'
- )
-article = 'Demo del proyecto para Saturdays.\nAutores del modelo: Cota V. Andreina, Cusi L. Evelyn, Nina M. Juan Dilan'
-
-iface = gr.Interface(
- fn=classify,
- inputs= gr.inputs.Textbox(lines=3, label='TEXTO', placeholder='Introduzca un texto'),
- outputs= gr.outputs.Textbox(label='IDIOMA'),
- examples = ['¿Maytaq ashkallanchikega?', 'Entonces el Inka dijo ¡Mach\'a!', '¡Aragan kanki wamraqa!', 'Señora, ¿yanapariwayta atiwaqchu?', '¿A dónde vas?'],
- description = description,
- title = title,
- article = article,
- theme = 'peach'
- )
-
-iface.launch()
-
diff --git a/spaces/ServerX/PorcoDiaz/infer/lib/train/utils.py b/spaces/ServerX/PorcoDiaz/infer/lib/train/utils.py
deleted file mode 100644
index dd965fc4dd2af09e445a7f625f2681460874da7a..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/infer/lib/train/utils.py
+++ /dev/null
@@ -1,478 +0,0 @@
-import argparse
-import glob
-import json
-import logging
-import os
-import subprocess
-import sys
-import shutil
-
-import numpy as np
-import torch
-from scipy.io.wavfile import read
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-
- ##################
- def go(model, bkey):
- saved_state_dict = checkpoint_dict[bkey]
- if hasattr(model, "module"):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items(): # 模型需要的shape
- try:
- new_state_dict[k] = saved_state_dict[k]
- if saved_state_dict[k].shape != state_dict[k].shape:
- logger.warn(
- "shape-%s-mismatch. need: %s, get: %s",
- k,
- state_dict[k].shape,
- saved_state_dict[k].shape,
- ) #
- raise KeyError
- except:
- # logger.info(traceback.format_exc())
- logger.info("%s is not in the checkpoint", k) # pretrain缺失的
- new_state_dict[k] = v # 模型自带的随机值
- if hasattr(model, "module"):
- model.module.load_state_dict(new_state_dict, strict=False)
- else:
- model.load_state_dict(new_state_dict, strict=False)
- return model
-
- go(combd, "combd")
- model = go(sbd, "sbd")
- #############
- logger.info("Loaded model weights")
-
- iteration = checkpoint_dict["iteration"]
- learning_rate = checkpoint_dict["learning_rate"]
- if (
- optimizer is not None and load_opt == 1
- ): ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
- # try:
- optimizer.load_state_dict(checkpoint_dict["optimizer"])
- # except:
- # traceback.print_exc()
- logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-# def load_checkpoint(checkpoint_path, model, optimizer=None):
-# assert os.path.isfile(checkpoint_path)
-# checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-# iteration = checkpoint_dict['iteration']
-# learning_rate = checkpoint_dict['learning_rate']
-# if optimizer is not None:
-# optimizer.load_state_dict(checkpoint_dict['optimizer'])
-# # print(1111)
-# saved_state_dict = checkpoint_dict['model']
-# # print(1111)
-#
-# if hasattr(model, 'module'):
-# state_dict = model.module.state_dict()
-# else:
-# state_dict = model.state_dict()
-# new_state_dict= {}
-# for k, v in state_dict.items():
-# try:
-# new_state_dict[k] = saved_state_dict[k]
-# except:
-# logger.info("%s is not in the checkpoint" % k)
-# new_state_dict[k] = v
-# if hasattr(model, 'module'):
-# model.module.load_state_dict(new_state_dict)
-# else:
-# model.load_state_dict(new_state_dict)
-# logger.info("Loaded checkpoint '{}' (epoch {})" .format(
-# checkpoint_path, iteration))
-# return model, optimizer, learning_rate, iteration
-def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-
- saved_state_dict = checkpoint_dict["model"]
- if hasattr(model, "module"):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items(): # 模型需要的shape
- try:
- new_state_dict[k] = saved_state_dict[k]
- if saved_state_dict[k].shape != state_dict[k].shape:
- logger.warn(
- "shape-%s-mismatch|need-%s|get-%s",
- k,
- state_dict[k].shape,
- saved_state_dict[k].shape,
- ) #
- raise KeyError
- except:
- # logger.info(traceback.format_exc())
- logger.info("%s is not in the checkpoint", k) # pretrain缺失的
- new_state_dict[k] = v # 模型自带的随机值
- if hasattr(model, "module"):
- model.module.load_state_dict(new_state_dict, strict=False)
- else:
- model.load_state_dict(new_state_dict, strict=False)
- logger.info("Loaded model weights")
-
- iteration = checkpoint_dict["iteration"]
- learning_rate = checkpoint_dict["learning_rate"]
- if (
- optimizer is not None and load_opt == 1
- ): ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
- # try:
- optimizer.load_state_dict(checkpoint_dict["optimizer"])
- # except:
- # traceback.print_exc()
- logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info(
- "Saving model and optimizer state at epoch {} to {}".format(
- iteration, checkpoint_path
- )
- )
- if hasattr(model, "module"):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save(
- {
- "model": state_dict,
- "iteration": iteration,
- "optimizer": optimizer.state_dict(),
- "learning_rate": learning_rate,
- },
- checkpoint_path,
- )
-
-
-def save_checkpoint_d(combd, sbd, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info(
- "Saving model and optimizer state at epoch {} to {}".format(
- iteration, checkpoint_path
- )
- )
- if hasattr(combd, "module"):
- state_dict_combd = combd.module.state_dict()
- else:
- state_dict_combd = combd.state_dict()
- if hasattr(sbd, "module"):
- state_dict_sbd = sbd.module.state_dict()
- else:
- state_dict_sbd = sbd.state_dict()
- torch.save(
- {
- "combd": state_dict_combd,
- "sbd": state_dict_sbd,
- "iteration": iteration,
- "optimizer": optimizer.state_dict(),
- "learning_rate": learning_rate,
- },
- checkpoint_path,
- )
-
-
-def summarize(
- writer,
- global_step,
- scalars={},
- histograms={},
- images={},
- audios={},
- audio_sampling_rate=22050,
-):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats="HWC")
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- logger.debug(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
-
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger("matplotlib")
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
-
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger("matplotlib")
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(
- alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
- )
- fig.colorbar(im, ax=ax)
- xlabel = "Decoder timestep"
- if info is not None:
- xlabel += "\n\n" + info
- plt.xlabel(xlabel)
- plt.ylabel("Encoder timestep")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding="utf-8") as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- """
- todo:
- 结尾七人组：
- 保存频率、总epoch done
- bs done
- pretrainG、pretrainD done
- 卡号：os.en["CUDA_VISIBLE_DEVICES"] done
- if_latest done
- 模型：if_f0 done
- 采样率：自动选择config done
- 是否缓存数据集进GPU:if_cache_data_in_gpu done
-
- -m:
- 自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files done
- -c不要了
- """
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "-se",
- "--save_every_epoch",
- type=int,
- required=True,
- help="checkpoint save frequency (epoch)",
- )
- parser.add_argument(
- "-te", "--total_epoch", type=int, required=True, help="total_epoch"
- )
- parser.add_argument(
- "-pg", "--pretrainG", type=str, default="", help="Pretrained Discriminator path"
- )
- parser.add_argument(
- "-pd", "--pretrainD", type=str, default="", help="Pretrained Generator path"
- )
- parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -")
- parser.add_argument(
- "-bs", "--batch_size", type=int, required=True, help="batch size"
- )
- parser.add_argument(
- "-e", "--experiment_dir", type=str, required=True, help="experiment dir"
- ) # -m
- parser.add_argument(
- "-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
- )
- parser.add_argument(
- "-sw",
- "--save_every_weights",
- type=str,
- default="0",
- help="save the extracted model in weights directory when saving checkpoints",
- )
- parser.add_argument(
- "-v", "--version", type=str, required=True, help="model version"
- )
- parser.add_argument(
- "-f0",
- "--if_f0",
- type=int,
- required=True,
- help="use f0 as one of the inputs of the model, 1 or 0",
- )
- parser.add_argument(
- "-l",
- "--if_latest",
- type=int,
- required=True,
- help="if only save the latest G/D pth file, 1 or 0",
- )
- parser.add_argument(
- "-c",
- "--if_cache_data_in_gpu",
- type=int,
- required=True,
- help="if caching the dataset in GPU memory, 1 or 0",
- )
-
- args = parser.parse_args()
- name = args.experiment_dir
- experiment_dir = os.path.join("./logs", args.experiment_dir)
-
- config_save_path = os.path.join(experiment_dir, "config.json")
- with open(config_save_path, "r") as f:
- config = json.load(f)
-
- hparams = HParams(**config)
- hparams.model_dir = hparams.experiment_dir = experiment_dir
- hparams.save_every_epoch = args.save_every_epoch
- hparams.name = name
- hparams.total_epoch = args.total_epoch
- hparams.pretrainG = args.pretrainG
- hparams.pretrainD = args.pretrainD
- hparams.version = args.version
- hparams.gpus = args.gpus
- hparams.train.batch_size = args.batch_size
- hparams.sample_rate = args.sample_rate
- hparams.if_f0 = args.if_f0
- hparams.if_latest = args.if_latest
- hparams.save_every_weights = args.save_every_weights
- hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
- hparams.data.training_files = "%s/filelist.txt" % experiment_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn(
- "{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- )
- )
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn(
- "git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]
- )
- )
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams:
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/Sky5408er/vits-uma-genshin-honkai/commons.py b/spaces/Sky5408er/vits-uma-genshin-honkai/commons.py
deleted file mode 100644
index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000
--- a/spaces/Sky5408er/vits-uma-genshin-honkai/commons.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
- return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
- return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def intersperse(lst, item):
- result = [item] * (len(lst) * 2 + 1)
- result[1::2] = lst
- return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
- """KL(P||Q)"""
- kl = (logs_q - logs_p) - 0.5
- kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
- return kl
-
-
-def rand_gumbel(shape):
- """Sample from the Gumbel distribution, protect from overflows."""
- uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
- return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
- g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
- return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def get_timing_signal_1d(
- length, channels, min_timescale=1.0, max_timescale=1.0e4):
- position = torch.arange(length, dtype=torch.float)
- num_timescales = channels // 2
- log_timescale_increment = (
- math.log(float(max_timescale) / float(min_timescale)) /
- (num_timescales - 1))
- inv_timescales = min_timescale * torch.exp(
- torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
- scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
- signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
- signal = F.pad(signal, [0, 0, 0, channels % 2])
- signal = signal.view(1, channels, length)
- return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def shift_1d(x):
- x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
- return x
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2,3) * mask
- return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = list(filter(lambda p: p.grad is not None, parameters))
- norm_type = float(norm_type)
- if clip_value is not None:
- clip_value = float(clip_value)
-
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item() ** norm_type
- if clip_value is not None:
- p.grad.data.clamp_(min=-clip_value, max=clip_value)
- total_norm = total_norm ** (1. / norm_type)
- return total_norm
diff --git a/spaces/StarCore/PaddleOCR/README.md b/spaces/StarCore/PaddleOCR/README.md
deleted file mode 100644
index ee94dfe80795148e95618b395adc5b1f5d699d78..0000000000000000000000000000000000000000
--- a/spaces/StarCore/PaddleOCR/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: PaddleOCR
-emoji: 😻
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/StarFox7/Llama-2-ko-7B-chat-ggml/run-app.sh b/spaces/StarFox7/Llama-2-ko-7B-chat-ggml/run-app.sh
deleted file mode 100644
index cc559b4b3edfbefa8e7b5d8d0f233065da316cf8..0000000000000000000000000000000000000000
--- a/spaces/StarFox7/Llama-2-ko-7B-chat-ggml/run-app.sh
+++ /dev/null
@@ -1 +0,0 @@
-nodemon -w app.py -x python app.py
\ No newline at end of file
diff --git a/spaces/SuYuanS/AudioCraft_Plus/tests/data/test_audio_dataset.py b/spaces/SuYuanS/AudioCraft_Plus/tests/data/test_audio_dataset.py
deleted file mode 100644
index b591ea6137f48d0d97fcd1243c5f5d258670a474..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/tests/data/test_audio_dataset.py
+++ /dev/null
@@ -1,352 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from functools import partial
-from itertools import product
-import json
-import math
-import os
-import random
-import typing as tp
-
-import pytest
-import torch
-from torch.utils.data import DataLoader
-
-from audiocraft.data.audio_dataset import (
- AudioDataset,
- AudioMeta,
- _get_audio_meta,
- load_audio_meta,
- save_audio_meta
-)
-from audiocraft.data.zip import PathInZip
-
-from ..common_utils import TempDirMixin, get_white_noise, save_wav
-
-
-class TestAudioMeta(TempDirMixin):
-
- def test_get_audio_meta(self):
- sample_rates = [8000, 16_000]
- channels = [1, 2]
- duration = 1.
- for sample_rate, ch in product(sample_rates, channels):
- n_frames = int(duration * sample_rate)
- wav = get_white_noise(ch, n_frames)
- path = self.get_temp_path('sample.wav')
- save_wav(path, wav, sample_rate)
- m = _get_audio_meta(path, minimal=True)
- assert m.path == path, 'path does not match'
- assert m.sample_rate == sample_rate, 'sample rate does not match'
- assert m.duration == duration, 'duration does not match'
- assert m.amplitude is None
- assert m.info_path is None
-
- def test_save_audio_meta(self):
- audio_meta = [
- AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
- AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
- ]
- empty_audio_meta = []
- for idx, meta in enumerate([audio_meta, empty_audio_meta]):
- path = self.get_temp_path(f'data_{idx}_save.jsonl')
- save_audio_meta(path, meta)
- with open(path, 'r') as f:
- lines = f.readlines()
- read_meta = [AudioMeta.from_dict(json.loads(line)) for line in lines]
- assert len(read_meta) == len(meta)
- for m, read_m in zip(meta, read_meta):
- assert m == read_m
-
- def test_load_audio_meta(self):
- try:
- import dora
- except ImportError:
- dora = None # type: ignore
-
- audio_meta = [
- AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
- AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
- ]
- empty_meta = []
- for idx, meta in enumerate([audio_meta, empty_meta]):
- path = self.get_temp_path(f'data_{idx}_load.jsonl')
- with open(path, 'w') as f:
- for m in meta:
- json_str = json.dumps(m.to_dict()) + '\n'
- f.write(json_str)
- read_meta = load_audio_meta(path)
- assert len(read_meta) == len(meta)
- for m, read_m in zip(meta, read_meta):
- if dora:
- m.path = dora.git_save.to_absolute_path(m.path)
- assert m == read_m, f'original={m}, read={read_m}'
-
-
-class TestAudioDataset(TempDirMixin):
-
- def _create_audio_files(self,
- root_name: str,
- num_examples: int,
- durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
- sample_rate: int = 16_000,
- channels: int = 1):
- root_dir = self.get_temp_dir(root_name)
- for i in range(num_examples):
- if isinstance(durations, float):
- duration = durations
- elif isinstance(durations, tuple) and len(durations) == 1:
- duration = durations[0]
- elif isinstance(durations, tuple) and len(durations) == 2:
- duration = random.uniform(durations[0], durations[1])
- else:
- assert False
- n_frames = int(duration * sample_rate)
- wav = get_white_noise(channels, n_frames)
- path = os.path.join(root_dir, f'example_{i}.wav')
- save_wav(path, wav, sample_rate)
- return root_dir
-
- def _create_audio_dataset(self,
- root_name: str,
- total_num_examples: int,
- durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
- sample_rate: int = 16_000,
- channels: int = 1,
- segment_duration: tp.Optional[float] = None,
- num_examples: int = 10,
- shuffle: bool = True,
- return_info: bool = False):
- root_dir = self._create_audio_files(root_name, total_num_examples, durations, sample_rate, channels)
- dataset = AudioDataset.from_path(root_dir,
- minimal_meta=True,
- segment_duration=segment_duration,
- num_samples=num_examples,
- sample_rate=sample_rate,
- channels=channels,
- shuffle=shuffle,
- return_info=return_info)
- return dataset
-
- def test_dataset_full(self):
- total_examples = 10
- min_duration, max_duration = 1., 4.
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=(min_duration, max_duration),
- sample_rate=sample_rate, channels=channels, segment_duration=None)
- assert len(dataset) == total_examples
- assert dataset.sample_rate == sample_rate
- assert dataset.channels == channels
- for idx in range(len(dataset)):
- sample = dataset[idx]
- assert sample.shape[0] == channels
- assert sample.shape[1] <= int(max_duration * sample_rate)
- assert sample.shape[1] >= int(min_duration * sample_rate)
-
- def test_dataset_segment(self):
- total_examples = 10
- num_samples = 20
- min_duration, max_duration = 1., 4.
- segment_duration = 1.
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples)
- assert len(dataset) == num_samples
- assert dataset.sample_rate == sample_rate
- assert dataset.channels == channels
- for idx in range(len(dataset)):
- sample = dataset[idx]
- assert sample.shape[0] == channels
- assert sample.shape[1] == int(segment_duration * sample_rate)
-
- def test_dataset_equal_audio_and_segment_durations(self):
- total_examples = 1
- num_samples = 2
- audio_duration = 1.
- segment_duration = 1.
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples)
- assert len(dataset) == num_samples
- assert dataset.sample_rate == sample_rate
- assert dataset.channels == channels
- for idx in range(len(dataset)):
- sample = dataset[idx]
- assert sample.shape[0] == channels
- assert sample.shape[1] == int(segment_duration * sample_rate)
- # the random seek_time adds variability on audio read
- sample_1 = dataset[0]
- sample_2 = dataset[1]
- assert not torch.allclose(sample_1, sample_2)
-
- def test_dataset_samples(self):
- total_examples = 1
- num_samples = 2
- audio_duration = 1.
- segment_duration = 1.
- sample_rate = 16_000
- channels = 1
-
- create_dataset = partial(
- self._create_audio_dataset,
- 'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples,
- )
-
- dataset = create_dataset(shuffle=True)
- # when shuffle = True, we have different inputs for the same index across epoch
- sample_1 = dataset[0]
- sample_2 = dataset[0]
- assert not torch.allclose(sample_1, sample_2)
-
- dataset_noshuffle = create_dataset(shuffle=False)
- # when shuffle = False, we have same inputs for the same index across epoch
- sample_1 = dataset_noshuffle[0]
- sample_2 = dataset_noshuffle[0]
- assert torch.allclose(sample_1, sample_2)
-
- def test_dataset_return_info(self):
- total_examples = 10
- num_samples = 20
- min_duration, max_duration = 1., 4.
- segment_duration = 1.
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
- assert len(dataset) == num_samples
- assert dataset.sample_rate == sample_rate
- assert dataset.channels == channels
- for idx in range(len(dataset)):
- sample, segment_info = dataset[idx]
- assert sample.shape[0] == channels
- assert sample.shape[1] == int(segment_duration * sample_rate)
- assert segment_info.sample_rate == sample_rate
- assert segment_info.total_frames == int(segment_duration * sample_rate)
- assert segment_info.n_frames <= int(segment_duration * sample_rate)
- assert segment_info.seek_time >= 0
-
- def test_dataset_return_info_no_segment_duration(self):
- total_examples = 10
- num_samples = 20
- min_duration, max_duration = 1., 4.
- segment_duration = None
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
- assert len(dataset) == total_examples
- assert dataset.sample_rate == sample_rate
- assert dataset.channels == channels
- for idx in range(len(dataset)):
- sample, segment_info = dataset[idx]
- assert sample.shape[0] == channels
- assert sample.shape[1] == segment_info.total_frames
- assert segment_info.sample_rate == sample_rate
- assert segment_info.n_frames <= segment_info.total_frames
-
- def test_dataset_collate_fn(self):
- total_examples = 10
- num_samples = 20
- min_duration, max_duration = 1., 4.
- segment_duration = 1.
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=False)
- batch_size = 4
- dataloader = DataLoader(
- dataset,
- batch_size=batch_size,
- num_workers=0
- )
- for idx, batch in enumerate(dataloader):
- assert batch.shape[0] == batch_size
-
- @pytest.mark.parametrize("segment_duration", [1.0, None])
- def test_dataset_with_meta_collate_fn(self, segment_duration):
- total_examples = 10
- num_samples = 20
- min_duration, max_duration = 1., 4.
- segment_duration = 1.
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
- batch_size = 4
- dataloader = DataLoader(
- dataset,
- batch_size=batch_size,
- collate_fn=dataset.collater,
- num_workers=0
- )
- for idx, batch in enumerate(dataloader):
- wav, infos = batch
- assert wav.shape[0] == batch_size
- assert len(infos) == batch_size
-
- @pytest.mark.parametrize("segment_duration,sample_on_weight,sample_on_duration,a_hist,b_hist,c_hist", [
- [1, True, True, 0.5, 0.5, 0.0],
- [1, False, True, 0.25, 0.5, 0.25],
- [1, True, False, 0.666, 0.333, 0.0],
- [1, False, False, 0.333, 0.333, 0.333],
- [None, False, False, 0.333, 0.333, 0.333]])
- def test_sample_with_weight(self, segment_duration, sample_on_weight, sample_on_duration, a_hist, b_hist, c_hist):
- random.seed(1234)
- rng = torch.Generator()
- rng.manual_seed(1234)
-
- def _get_histogram(dataset, repetitions=20_000):
- counts = {file_meta.path: 0. for file_meta in meta}
- for _ in range(repetitions):
- file_meta = dataset.sample_file(0, rng)
- counts[file_meta.path] += 1
- return {name: count / repetitions for name, count in counts.items()}
-
- meta = [
- AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
- AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
- AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
- ]
- dataset = AudioDataset(
- meta, segment_duration=segment_duration, sample_on_weight=sample_on_weight,
- sample_on_duration=sample_on_duration)
- hist = _get_histogram(dataset)
- assert math.isclose(hist['a'], a_hist, abs_tol=0.01)
- assert math.isclose(hist['b'], b_hist, abs_tol=0.01)
- assert math.isclose(hist['c'], c_hist, abs_tol=0.01)
-
- def test_meta_duration_filter_all(self):
- meta = [
- AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
- AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
- AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
- ]
- try:
- AudioDataset(meta, segment_duration=11, min_segment_ratio=1)
- assert False
- except AssertionError:
- assert True
-
- def test_meta_duration_filter_long(self):
- meta = [
- AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
- AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
- AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
- ]
- dataset = AudioDataset(meta, segment_duration=None, min_segment_ratio=1, max_audio_duration=7)
- assert len(dataset) == 2
diff --git a/spaces/Sumit7864/Image-Enhancer/realesrgan/train.py b/spaces/Sumit7864/Image-Enhancer/realesrgan/train.py
deleted file mode 100644
index 8a9cec9ed80d9f362984779548dcec921a636a04..0000000000000000000000000000000000000000
--- a/spaces/Sumit7864/Image-Enhancer/realesrgan/train.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# flake8: noqa
-import os.path as osp
-from basicsr.train import train_pipeline
-
-import realesrgan.archs
-import realesrgan.data
-import realesrgan.models
-
-if __name__ == '__main__':
- root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
- train_pipeline(root_path)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/ImageSequence.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/ImageSequence.py
deleted file mode 100644
index c4bb6334acfde7d245c5bb1722b7c2381661e4ca..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/ImageSequence.py
+++ /dev/null
@@ -1,76 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# sequence support classes
-#
-# history:
-# 1997-02-20 fl Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1997 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-##
-
-
-class Iterator:
- """
- This class implements an iterator object that can be used to loop
- over an image sequence.
-
- You can use the ``[]`` operator to access elements by index. This operator
- will raise an :py:exc:`IndexError` if you try to access a nonexistent
- frame.
-
- :param im: An image object.
- """
-
- def __init__(self, im):
- if not hasattr(im, "seek"):
- msg = "im must have seek method"
- raise AttributeError(msg)
- self.im = im
- self.position = getattr(self.im, "_min_frame", 0)
-
- def __getitem__(self, ix):
- try:
- self.im.seek(ix)
- return self.im
- except EOFError as e:
- raise IndexError from e # end of sequence
-
- def __iter__(self):
- return self
-
- def __next__(self):
- try:
- self.im.seek(self.position)
- self.position += 1
- return self.im
- except EOFError as e:
- raise StopIteration from e
-
-
-def all_frames(im, func=None):
- """
- Applies a given function to all frames in an image or a list of images.
- The frames are returned as a list of separate images.
-
- :param im: An image, or a list of images.
- :param func: The function to apply to all of the image frames.
- :returns: A list of images.
- """
- if not isinstance(im, list):
- im = [im]
-
- ims = []
- for imSequence in im:
- current = imSequence.tell()
-
- ims += [im_frame.copy() for im_frame in Iterator(imSequence)]
-
- imSequence.seek(current)
- return [func(im) for im in ims] if func else ims
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/ImageStat.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/ImageStat.py
deleted file mode 100644
index b7ebddf066ab6eb115a79d6bc34e31ab0c1569bd..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/ImageStat.py
+++ /dev/null
@@ -1,148 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# global image statistics
-#
-# History:
-# 1996-04-05 fl Created
-# 1997-05-21 fl Added mask; added rms, var, stddev attributes
-# 1997-08-05 fl Added median
-# 1998-07-05 hk Fixed integer overflow error
-#
-# Notes:
-# This class shows how to implement delayed evaluation of attributes.
-# To get a certain value, simply access the corresponding attribute.
-# The __getattr__ dispatcher takes care of the rest.
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1996-97.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import functools
-import math
-import operator
-
-
-class Stat:
- def __init__(self, image_or_list, mask=None):
- try:
- if mask:
- self.h = image_or_list.histogram(mask)
- else:
- self.h = image_or_list.histogram()
- except AttributeError:
- self.h = image_or_list # assume it to be a histogram list
- if not isinstance(self.h, list):
- msg = "first argument must be image or list"
- raise TypeError(msg)
- self.bands = list(range(len(self.h) // 256))
-
- def __getattr__(self, id):
- """Calculate missing attribute"""
- if id[:4] == "_get":
- raise AttributeError(id)
- # calculate missing attribute
- v = getattr(self, "_get" + id)()
- setattr(self, id, v)
- return v
-
- def _getextrema(self):
- """Get min/max values for each band in the image"""
-
- def minmax(histogram):
- n = 255
- x = 0
- for i in range(256):
- if histogram[i]:
- n = min(n, i)
- x = max(x, i)
- return n, x # returns (255, 0) if there's no data in the histogram
-
- v = []
- for i in range(0, len(self.h), 256):
- v.append(minmax(self.h[i:]))
- return v
-
- def _getcount(self):
- """Get total number of pixels in each layer"""
-
- v = []
- for i in range(0, len(self.h), 256):
- v.append(functools.reduce(operator.add, self.h[i : i + 256]))
- return v
-
- def _getsum(self):
- """Get sum of all pixels in each layer"""
-
- v = []
- for i in range(0, len(self.h), 256):
- layer_sum = 0.0
- for j in range(256):
- layer_sum += j * self.h[i + j]
- v.append(layer_sum)
- return v
-
- def _getsum2(self):
- """Get squared sum of all pixels in each layer"""
-
- v = []
- for i in range(0, len(self.h), 256):
- sum2 = 0.0
- for j in range(256):
- sum2 += (j**2) * float(self.h[i + j])
- v.append(sum2)
- return v
-
- def _getmean(self):
- """Get average pixel level for each layer"""
-
- v = []
- for i in self.bands:
- v.append(self.sum[i] / self.count[i])
- return v
-
- def _getmedian(self):
- """Get median pixel level for each layer"""
-
- v = []
- for i in self.bands:
- s = 0
- half = self.count[i] // 2
- b = i * 256
- for j in range(256):
- s = s + self.h[b + j]
- if s > half:
- break
- v.append(j)
- return v
-
- def _getrms(self):
- """Get RMS for each layer"""
-
- v = []
- for i in self.bands:
- v.append(math.sqrt(self.sum2[i] / self.count[i]))
- return v
-
- def _getvar(self):
- """Get variance for each layer"""
-
- v = []
- for i in self.bands:
- n = self.count[i]
- v.append((self.sum2[i] - (self.sum[i] ** 2.0) / n) / n)
- return v
-
- def _getstddev(self):
- """Get standard deviation for each layer"""
-
- v = []
- for i in self.bands:
- v.append(math.sqrt(self.var[i]))
- return v
-
-
-Global = Stat # compatibility
diff --git a/spaces/Suniilkumaar/SwapMukham/upscaler/RealESRGAN/utils.py b/spaces/Suniilkumaar/SwapMukham/upscaler/RealESRGAN/utils.py
deleted file mode 100644
index 3e7cfd184c53e60cbb98234c24534076c6b94378..0000000000000000000000000000000000000000
--- a/spaces/Suniilkumaar/SwapMukham/upscaler/RealESRGAN/utils.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import numpy as np
-import torch
-from PIL import Image
-import os
-import io
-
-def pad_reflect(image, pad_size):
- imsize = image.shape
- height, width = imsize[:2]
- new_img = np.zeros([height+pad_size*2, width+pad_size*2, imsize[2]]).astype(np.uint8)
- new_img[pad_size:-pad_size, pad_size:-pad_size, :] = image
-
- new_img[0:pad_size, pad_size:-pad_size, :] = np.flip(image[0:pad_size, :, :], axis=0) #top
- new_img[-pad_size:, pad_size:-pad_size, :] = np.flip(image[-pad_size:, :, :], axis=0) #bottom
- new_img[:, 0:pad_size, :] = np.flip(new_img[:, pad_size:pad_size*2, :], axis=1) #left
- new_img[:, -pad_size:, :] = np.flip(new_img[:, -pad_size*2:-pad_size, :], axis=1) #right
-
- return new_img
-
-def unpad_image(image, pad_size):
- return image[pad_size:-pad_size, pad_size:-pad_size, :]
-
-
-def process_array(image_array, expand=True):
- """ Process a 3-dimensional array into a scaled, 4 dimensional batch of size 1. """
-
- image_batch = image_array / 255.0
- if expand:
- image_batch = np.expand_dims(image_batch, axis=0)
- return image_batch
-
-
-def process_output(output_tensor):
- """ Transforms the 4-dimensional output tensor into a suitable image format. """
-
- sr_img = output_tensor.clip(0, 1) * 255
- sr_img = np.uint8(sr_img)
- return sr_img
-
-
-def pad_patch(image_patch, padding_size, channel_last=True):
- """ Pads image_patch with with padding_size edge values. """
-
- if channel_last:
- return np.pad(
- image_patch,
- ((padding_size, padding_size), (padding_size, padding_size), (0, 0)),
- 'edge',
- )
- else:
- return np.pad(
- image_patch,
- ((0, 0), (padding_size, padding_size), (padding_size, padding_size)),
- 'edge',
- )
-
-
-def unpad_patches(image_patches, padding_size):
- return image_patches[:, padding_size:-padding_size, padding_size:-padding_size, :]
-
-
-def split_image_into_overlapping_patches(image_array, patch_size, padding_size=2):
- """ Splits the image into partially overlapping patches.
- The patches overlap by padding_size pixels.
- Pads the image twice:
- - first to have a size multiple of the patch size,
- - then to have equal padding at the borders.
- Args:
- image_array: numpy array of the input image.
- patch_size: size of the patches from the original image (without padding).
- padding_size: size of the overlapping area.
- """
-
- xmax, ymax, _ = image_array.shape
- x_remainder = xmax % patch_size
- y_remainder = ymax % patch_size
-
- # modulo here is to avoid extending of patch_size instead of 0
- x_extend = (patch_size - x_remainder) % patch_size
- y_extend = (patch_size - y_remainder) % patch_size
-
- # make sure the image is divisible into regular patches
- extended_image = np.pad(image_array, ((0, x_extend), (0, y_extend), (0, 0)), 'edge')
-
- # add padding around the image to simplify computations
- padded_image = pad_patch(extended_image, padding_size, channel_last=True)
-
- xmax, ymax, _ = padded_image.shape
- patches = []
-
- x_lefts = range(padding_size, xmax - padding_size, patch_size)
- y_tops = range(padding_size, ymax - padding_size, patch_size)
-
- for x in x_lefts:
- for y in y_tops:
- x_left = x - padding_size
- y_top = y - padding_size
- x_right = x + patch_size + padding_size
- y_bottom = y + patch_size + padding_size
- patch = padded_image[x_left:x_right, y_top:y_bottom, :]
- patches.append(patch)
-
- return np.array(patches), padded_image.shape
-
-
-def stich_together(patches, padded_image_shape, target_shape, padding_size=4):
- """ Reconstruct the image from overlapping patches.
- After scaling, shapes and padding should be scaled too.
- Args:
- patches: patches obtained with split_image_into_overlapping_patches
- padded_image_shape: shape of the padded image contructed in split_image_into_overlapping_patches
- target_shape: shape of the final image
- padding_size: size of the overlapping area.
- """
-
- xmax, ymax, _ = padded_image_shape
- patches = unpad_patches(patches, padding_size)
- patch_size = patches.shape[1]
- n_patches_per_row = ymax // patch_size
-
- complete_image = np.zeros((xmax, ymax, 3))
-
- row = -1
- col = 0
- for i in range(len(patches)):
- if i % n_patches_per_row == 0:
- row += 1
- col = 0
- complete_image[
- row * patch_size: (row + 1) * patch_size, col * patch_size: (col + 1) * patch_size,:
- ] = patches[i]
- col += 1
- return complete_image[0: target_shape[0], 0: target_shape[1], :]
\ No newline at end of file
diff --git a/spaces/Superlang/ImageProcessor/app.py b/spaces/Superlang/ImageProcessor/app.py
deleted file mode 100644
index 1a3ca063bb0660fd8baf26c9a36ade79b0c1b891..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/app.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import importlib
-import os
-
-import gradio as gr
-from omegaconf import OmegaConf
-
-from annotator.util import resize_image, HWC3
-
-config = OmegaConf.load("config/annotator.yaml")
-
-package_annotator = "annotator"
-
-
-def process_image(cls: str, img, res, *kwargs):
- img = resize_image(HWC3(img), res)
- # load_model()
- module_imp = importlib.import_module(package_annotator)
- model = getattr(module_imp, cls)
- image_processor = model()
- result = image_processor(img, *kwargs)
- if type(result) == tuple:
- return result
- return [result]
-
-
-def process(cls):
- def process_fc(img, res, *args):
- return process_image(cls, img, res, *args)
-
- return process_fc
-
-
-block = gr.Blocks().queue()
-examples = [os.path.join(os.path.dirname(__file__), "examples/demo.jpeg")]
-with block:
- for key in config.keys():
- cls, input_element = config[key]["process"], config[key].get("input")
- input_append = []
- with gr.Tab(key):
- with gr.Row():
- gr.Markdown("## " + key)
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="numpy")
- resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
-
- if input_element is not None:
- for item in input_element:
- input_append.append(getattr(gr, item["attr"])(**item["args"]))
-
- run_button = gr.Button(label="Run")
- gr.Examples(examples, input_image)
- with gr.Column():
- gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
-
- run_button.click(fn=process(cls),
- inputs=[input_image, resolution] + input_append,
- outputs=[gallery])
-
-block.launch()
diff --git a/spaces/Swying/text_generator/README.md b/spaces/Swying/text_generator/README.md
deleted file mode 100644
index a571d291205e1509e2b5fd4f848e970ba8b13549..0000000000000000000000000000000000000000
--- a/spaces/Swying/text_generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text Generator
-emoji: 🐨
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Sygil/INE-dataset-explorer/README.md b/spaces/Sygil/INE-dataset-explorer/README.md
deleted file mode 100644
index 901d834ec82e4b7f4ee080b05ebc76790fc6c3d1..0000000000000000000000000000000000000000
--- a/spaces/Sygil/INE-dataset-explorer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-license: openrail
-title: INE-dataset-explorer
-pinned: true
-sdk: docker
----
-
-Imaginary Network Expanded Dataset explorer.
-
-Data source: https://github.com/Sygil-Dev/INE-dataset
-
-
-Made by [Kevin Turner (keturn)](https://huggingface.co/keturn) and powered by [Datasette](https://datasette.io/).
\ No newline at end of file
diff --git a/spaces/TEnngal/bingo/src/components/external-link.tsx b/spaces/TEnngal/bingo/src/components/external-link.tsx
deleted file mode 100644
index 011265f364d5a64a770f4c7e9c65c5ade21d623a..0000000000000000000000000000000000000000
--- a/spaces/TEnngal/bingo/src/components/external-link.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-export function ExternalLink({
- href,
- children
-}: {
- href: string
- children: React.ReactNode
-}) {
- return (
- <a
- href={href}
- target="_blank"
- rel="noreferrer"
- className="inline-flex flex-1 justify-center gap-1 underline"
- >
- <span>{children}</span>
- <svg
- aria-hidden="true"
- height="7"
- viewBox="0 0 6 6"
- width="7"
- className="opacity-70"
- >
- <path
- d="M1.25215 5.54731L0.622742 4.9179L3.78169 1.75597H1.3834L1.38936 0.890915H5.27615V4.78069H4.40513L4.41109 2.38538L1.25215 5.54731Z"
- fill="currentColor"
- ></path>
- </svg>
- </a>
- )
-}
diff --git a/spaces/TEnngal/bingo/src/pages/api/kblob.ts b/spaces/TEnngal/bingo/src/pages/api/kblob.ts
deleted file mode 100644
index 0ce7e6063cdc06838e76f1cff1d5982d34ef52de..0000000000000000000000000000000000000000
--- a/spaces/TEnngal/bingo/src/pages/api/kblob.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import FormData from 'form-data'
-import { fetch } from '@/lib/isomorphic'
-import { KBlobRequest } from '@/lib/bots/bing/types'
-
-const API_DOMAIN = 'https://bing.vcanbb.top'
-
-export const config = {
- api: {
- bodyParser: {
- sizeLimit: '10mb' // Set desired value here
- }
- }
-}
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- try {
- const { knowledgeRequest, imageBase64 } = req.body as KBlobRequest
-
- const formData = new FormData()
- formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
- if (imageBase64) {
- formData.append('imageBase64', imageBase64)
- }
-
- const response = await fetch(`${API_DOMAIN}/images/kblob`,
- {
- method: 'POST',
- body: formData.getBuffer(),
- headers: {
- "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
- "sec-ch-ua-mobile": "?0",
- "sec-ch-ua-platform": "\"Windows\"",
- "Referer": `${API_DOMAIN}/web/index.html`,
- "Referrer-Policy": "origin-when-cross-origin",
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- ...formData.getHeaders()
- }
- }
- ).then(res => res.text())
-
- res.writeHead(200, {
- 'Content-Type': 'application/json',
- })
- res.end(response || JSON.stringify({ result: { value: 'UploadFailed', message: '请更换 IP 或代理后重试' } }))
- } catch (e) {
- return res.json({
- result: {
- value: 'UploadFailed',
- message: `${e}`
- }
- })
- }
-}
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py
deleted file mode 100644
index 17f2904ccad484f380b64efc668b9090d047d15e..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py
+++ /dev/null
@@ -1,469 +0,0 @@
-# This file is modified from https://github.com/aim-uofa/AdelaiDet/blob/master/adet/modeling/backbone/bifpn.py
-# The original file is under 2-clause BSD License for academic use, and *non-commercial use*.
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-
-from detectron2.modeling.backbone import Backbone, build_resnet_backbone
-from detectron2.modeling import BACKBONE_REGISTRY
-from .dlafpn import dla34
-
-__all__ = []
-
-
-def swish(x):
- return x * x.sigmoid()
-
-
-def split_name(name):
- for i, c in enumerate(name):
- if not c.isalpha():
- return name[:i], int(name[i:])
- raise ValueError()
-
-
-class FeatureMapResampler(nn.Module):
- def __init__(self, in_channels, out_channels, stride, norm=""):
- super(FeatureMapResampler, self).__init__()
- if in_channels != out_channels:
- self.reduction = Conv2d(
- in_channels, out_channels, kernel_size=1,
- bias=(norm == ""),
- norm=get_norm(norm, out_channels),
- activation=None
- )
- else:
- self.reduction = None
-
- assert stride <= 2
- self.stride = stride
-
- def forward(self, x):
- if self.reduction is not None:
- x = self.reduction(x)
-
- if self.stride == 2:
- x = F.max_pool2d(
- x, kernel_size=self.stride + 1,
- stride=self.stride, padding=1
- )
- elif self.stride == 1:
- pass
- else:
- raise NotImplementedError()
- return x
-
-
-class BackboneWithTopLevels(Backbone):
- def __init__(self, backbone, out_channels, num_top_levels, norm=""):
- super(BackboneWithTopLevels, self).__init__()
- self.backbone = backbone
- backbone_output_shape = backbone.output_shape()
-
- self._out_feature_channels = {name: shape.channels for name, shape in backbone_output_shape.items()}
- self._out_feature_strides = {name: shape.stride for name, shape in backbone_output_shape.items()}
- self._out_features = list(self._out_feature_strides.keys())
-
- last_feature_name = max(self._out_feature_strides.keys(), key=lambda x: split_name(x)[1])
- self.last_feature_name = last_feature_name
- self.num_top_levels = num_top_levels
-
- last_channels = self._out_feature_channels[last_feature_name]
- last_stride = self._out_feature_strides[last_feature_name]
-
- prefix, suffix = split_name(last_feature_name)
- prev_channels = last_channels
- for i in range(num_top_levels):
- name = prefix + str(suffix + i + 1)
- self.add_module(name, FeatureMapResampler(
- prev_channels, out_channels, 2, norm
- ))
- prev_channels = out_channels
-
- self._out_feature_channels[name] = out_channels
- self._out_feature_strides[name] = last_stride * 2 ** (i + 1)
- self._out_features.append(name)
-
- def forward(self, x):
- outputs = self.backbone(x)
- last_features = outputs[self.last_feature_name]
- prefix, suffix = split_name(self.last_feature_name)
-
- x = last_features
- for i in range(self.num_top_levels):
- name = prefix + str(suffix + i + 1)
- x = self.__getattr__(name)(x)
- outputs[name] = x
-
- return outputs
-
-
-class SingleBiFPN(Backbone):
- """
- This module implements Feature Pyramid Network.
- It creates pyramid features built on top of some input feature maps.
- """
-
- def __init__(
- self, in_channels_list, out_channels, norm=""
- ):
- """
- Args:
- bottom_up (Backbone): module representing the bottom up subnetwork.
- Must be a subclass of :class:`Backbone`. The multi-scale feature
- maps generated by the bottom up network, and listed in `in_features`,
- are used to generate FPN levels.
- in_features (list[str]): names of the input feature maps coming
- from the backbone to which FPN is attached. For example, if the
- backbone produces ["res2", "res3", "res4"], any *contiguous* sublist
- of these may be used; order must be from high to low resolution.
- out_channels (int): number of channels in the output feature maps.
- norm (str): the normalization to use.
- """
- super(SingleBiFPN, self).__init__()
-
- self.out_channels = out_channels
- # build 5-levels bifpn
- if len(in_channels_list) == 5:
- self.nodes = [
- {'feat_level': 3, 'inputs_offsets': [3, 4]},
- {'feat_level': 2, 'inputs_offsets': [2, 5]},
- {'feat_level': 1, 'inputs_offsets': [1, 6]},
- {'feat_level': 0, 'inputs_offsets': [0, 7]},
- {'feat_level': 1, 'inputs_offsets': [1, 7, 8]},
- {'feat_level': 2, 'inputs_offsets': [2, 6, 9]},
- {'feat_level': 3, 'inputs_offsets': [3, 5, 10]},
- {'feat_level': 4, 'inputs_offsets': [4, 11]},
- ]
- elif len(in_channels_list) == 3:
- self.nodes = [
- {'feat_level': 1, 'inputs_offsets': [1, 2]},
- {'feat_level': 0, 'inputs_offsets': [0, 3]},
- {'feat_level': 1, 'inputs_offsets': [1, 3, 4]},
- {'feat_level': 2, 'inputs_offsets': [2, 5]},
- ]
- else:
- raise NotImplementedError
-
- node_info = [_ for _ in in_channels_list]
-
- num_output_connections = [0 for _ in in_channels_list]
- for fnode in self.nodes:
- feat_level = fnode["feat_level"]
- inputs_offsets = fnode["inputs_offsets"]
- inputs_offsets_str = "_".join(map(str, inputs_offsets))
- for input_offset in inputs_offsets:
- num_output_connections[input_offset] += 1
-
- in_channels = node_info[input_offset]
- if in_channels != out_channels:
- lateral_conv = Conv2d(
- in_channels,
- out_channels,
- kernel_size=1,
- norm=get_norm(norm, out_channels)
- )
- self.add_module(
- "lateral_{}_f{}".format(input_offset, feat_level), lateral_conv
- )
- node_info.append(out_channels)
- num_output_connections.append(0)
-
- # generate attention weights
- name = "weights_f{}_{}".format(feat_level, inputs_offsets_str)
- self.__setattr__(name, nn.Parameter(
- torch.ones(len(inputs_offsets), dtype=torch.float32),
- requires_grad=True
- ))
-
- # generate convolutions after combination
- name = "outputs_f{}_{}".format(feat_level, inputs_offsets_str)
- self.add_module(name, Conv2d(
- out_channels,
- out_channels,
- kernel_size=3,
- padding=1,
- norm=get_norm(norm, out_channels),
- bias=(norm == "")
- ))
-
- def forward(self, feats):
- """
- Args:
- input (dict[str->Tensor]): mapping feature map name (e.g., "p5") to
- feature map tensor for each feature level in high to low resolution order.
- Returns:
- dict[str->Tensor]:
- mapping from feature map name to FPN feature map tensor
- in high to low resolution order. Returned feature names follow the FPN
- paper convention: "p<stage>", where stage has stride = 2 ** stage e.g.,
- ["n2", "n3", ..., "n6"].
- """
- feats = [_ for _ in feats]
- num_levels = len(feats)
- num_output_connections = [0 for _ in feats]
- for fnode in self.nodes:
- feat_level = fnode["feat_level"]
- inputs_offsets = fnode["inputs_offsets"]
- inputs_offsets_str = "_".join(map(str, inputs_offsets))
- input_nodes = []
- _, _, target_h, target_w = feats[feat_level].size()
- for input_offset in inputs_offsets:
- num_output_connections[input_offset] += 1
- input_node = feats[input_offset]
-
- # reduction
- if input_node.size(1) != self.out_channels:
- name = "lateral_{}_f{}".format(input_offset, feat_level)
- input_node = self.__getattr__(name)(input_node)
-
- # maybe downsample
- _, _, h, w = input_node.size()
- if h > target_h and w > target_w:
- height_stride_size = int((h - 1) // target_h + 1)
- width_stride_size = int((w - 1) // target_w + 1)
- assert height_stride_size == width_stride_size == 2
- input_node = F.max_pool2d(
- input_node, kernel_size=(height_stride_size + 1, width_stride_size + 1),
- stride=(height_stride_size, width_stride_size), padding=1
- )
- elif h <= target_h and w <= target_w:
- if h < target_h or w < target_w:
- input_node = F.interpolate(
- input_node,
- size=(target_h, target_w),
- mode="nearest"
- )
- else:
- raise NotImplementedError()
- input_nodes.append(input_node)
-
- # attention
- name = "weights_f{}_{}".format(feat_level, inputs_offsets_str)
- weights = F.relu(self.__getattr__(name))
- norm_weights = weights / (weights.sum() + 0.0001)
-
- new_node = torch.stack(input_nodes, dim=-1)
- new_node = (norm_weights * new_node).sum(dim=-1)
- new_node = swish(new_node)
-
- name = "outputs_f{}_{}".format(feat_level, inputs_offsets_str)
- feats.append(self.__getattr__(name)(new_node))
-
- num_output_connections.append(0)
-
- output_feats = []
- for idx in range(num_levels):
- for i, fnode in enumerate(reversed(self.nodes)):
- if fnode['feat_level'] == idx:
- output_feats.append(feats[-1 - i])
- break
- else:
- raise ValueError()
- return output_feats
-
-
-class BiFPN(Backbone):
- """
- This module implements Feature Pyramid Network.
- It creates pyramid features built on top of some input feature maps.
- """
-
- def __init__(
- self, bottom_up, in_features, out_channels, num_top_levels, num_repeats, norm=""
- ):
- """
- Args:
- bottom_up (Backbone): module representing the bottom up subnetwork.
- Must be a subclass of :class:`Backbone`. The multi-scale feature
- maps generated by the bottom up network, and listed in `in_features`,
- are used to generate FPN levels.
- in_features (list[str]): names of the input feature maps coming
- from the backbone to which FPN is attached. For example, if the
- backbone produces ["res2", "res3", "res4"], any *contiguous* sublist
- of these may be used; order must be from high to low resolution.
- out_channels (int): number of channels in the output feature maps.
- num_top_levels (int): the number of the top levels (p6 or p7).
- num_repeats (int): the number of repeats of BiFPN.
- norm (str): the normalization to use.
- """
- super(BiFPN, self).__init__()
- assert isinstance(bottom_up, Backbone)
-
- # add extra feature levels (i.e., 6 and 7)
- self.bottom_up = BackboneWithTopLevels(
- bottom_up, out_channels,
- num_top_levels, norm
- )
- bottom_up_output_shapes = self.bottom_up.output_shape()
-
- in_features = sorted(in_features, key=lambda x: split_name(x)[1])
- self._size_divisibility = 128 #bottom_up_output_shapes[in_features[-1]].stride
- self.out_channels = out_channels
- self.min_level = split_name(in_features[0])[1]
-
- # add the names for top blocks
- prefix, last_suffix = split_name(in_features[-1])
- for i in range(num_top_levels):
- in_features.append(prefix + str(last_suffix + i + 1))
- self.in_features = in_features
-
- # generate output features
- self._out_features = ["p{}".format(split_name(name)[1]) for name in in_features]
- self._out_feature_strides = {
- out_name: bottom_up_output_shapes[in_name].stride
- for out_name, in_name in zip(self._out_features, in_features)
- }
- self._out_feature_channels = {k: out_channels for k in self._out_features}
-
- # build bifpn
- self.repeated_bifpn = nn.ModuleList()
- for i in range(num_repeats):
- if i == 0:
- in_channels_list = [
- bottom_up_output_shapes[name].channels for name in in_features
- ]
- else:
- in_channels_list = [
- self._out_feature_channels[name] for name in self._out_features
- ]
- self.repeated_bifpn.append(SingleBiFPN(
- in_channels_list, out_channels, norm
- ))
-
- @property
- def size_divisibility(self):
- return self._size_divisibility
-
- def forward(self, x):
- """
- Args:
- input (dict[str->Tensor]): mapping feature map name (e.g., "p5") to
- feature map tensor for each feature level in high to low resolution order.
- Returns:
- dict[str->Tensor]:
- mapping from feature map name to FPN feature map tensor
- in high to low resolution order. Returned feature names follow the FPN
- paper convention: "p<stage>", where stage has stride = 2 ** stage e.g.,
- ["n2", "n3", ..., "n6"].
- """
- bottom_up_features = self.bottom_up(x)
- feats = [bottom_up_features[f] for f in self.in_features]
-
- for bifpn in self.repeated_bifpn:
- feats = bifpn(feats)
-
- return dict(zip(self._out_features, feats))
-
-
-def _assert_strides_are_log2_contiguous(strides):
- """
- Assert that each stride is 2x times its preceding stride, i.e. "contiguous in log2".
- """
- for i, stride in enumerate(strides[1:], 1):
- assert stride == 2 * strides[i - 1], "Strides {} {} are not log2 contiguous".format(
- stride, strides[i - 1]
- )
-
-
-@BACKBONE_REGISTRY.register()
-def build_fcos_resnet_bifpn_backbone(cfg, input_shape: ShapeSpec):
- """
- Args:
- cfg: a detectron2 CfgNode
- Returns:
- backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
- """
- bottom_up = build_resnet_backbone(cfg, input_shape)
- in_features = cfg.MODEL.FPN.IN_FEATURES
- out_channels = cfg.MODEL.BIFPN.OUT_CHANNELS
- num_repeats = cfg.MODEL.BIFPN.NUM_BIFPN
- top_levels = 2
-
- backbone = BiFPN(
- bottom_up=bottom_up,
- in_features=in_features,
- out_channels=out_channels,
- num_top_levels=top_levels,
- num_repeats=num_repeats,
- norm=cfg.MODEL.BIFPN.NORM
- )
- return backbone
-
-
-
-@BACKBONE_REGISTRY.register()
-def build_p35_fcos_resnet_bifpn_backbone(cfg, input_shape: ShapeSpec):
- """
- Args:
- cfg: a detectron2 CfgNode
- Returns:
- backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
- """
- bottom_up = build_resnet_backbone(cfg, input_shape)
- in_features = cfg.MODEL.FPN.IN_FEATURES
- out_channels = cfg.MODEL.BIFPN.OUT_CHANNELS
- num_repeats = cfg.MODEL.BIFPN.NUM_BIFPN
- top_levels = 0
-
- backbone = BiFPN(
- bottom_up=bottom_up,
- in_features=in_features,
- out_channels=out_channels,
- num_top_levels=top_levels,
- num_repeats=num_repeats,
- norm=cfg.MODEL.BIFPN.NORM
- )
- return backbone
-
-
-@BACKBONE_REGISTRY.register()
-def build_p35_fcos_dla_bifpn_backbone(cfg, input_shape: ShapeSpec):
- """
- Args:
- cfg: a detectron2 CfgNode
- Returns:
- backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
- """
- bottom_up = dla34(cfg)
- in_features = cfg.MODEL.FPN.IN_FEATURES
- out_channels = cfg.MODEL.BIFPN.OUT_CHANNELS
- num_repeats = cfg.MODEL.BIFPN.NUM_BIFPN
- top_levels = 0
-
- backbone = BiFPN(
- bottom_up=bottom_up,
- in_features=in_features,
- out_channels=out_channels,
- num_top_levels=top_levels,
- num_repeats=num_repeats,
- norm=cfg.MODEL.BIFPN.NORM
- )
- return backbone
-
-@BACKBONE_REGISTRY.register()
-def build_p37_fcos_dla_bifpn_backbone(cfg, input_shape: ShapeSpec):
- """
- Args:
- cfg: a detectron2 CfgNode
- Returns:
- backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
- """
- bottom_up = dla34(cfg)
- in_features = cfg.MODEL.FPN.IN_FEATURES
- out_channels = cfg.MODEL.BIFPN.OUT_CHANNELS
- num_repeats = cfg.MODEL.BIFPN.NUM_BIFPN
- assert cfg.MODEL.BIFPN.NUM_LEVELS == 5
- top_levels = 2
-
- backbone = BiFPN(
- bottom_up=bottom_up,
- in_features=in_features,
- out_channels=out_channels,
- num_top_levels=top_levels,
- num_repeats=num_repeats,
- norm=cfg.MODEL.BIFPN.NORM
- )
- return backbone
\ No newline at end of file
diff --git a/spaces/TheStinger/Ilaria_RVC/lib/infer_pack/attentions.py b/spaces/TheStinger/Ilaria_RVC/lib/infer_pack/attentions.py
deleted file mode 100644
index 05501be1871643f78dddbeaa529c96667031a8db..0000000000000000000000000000000000000000
--- a/spaces/TheStinger/Ilaria_RVC/lib/infer_pack/attentions.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from lib.infer_pack import commons
-from lib.infer_pack import modules
-from lib.infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- window_size=10,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
-
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- window_size=window_size,
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- proximal_bias=False,
- proximal_init=True,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.ModuleList()
- self.norm_layers_0 = nn.ModuleList()
- self.encdec_attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- proximal_bias=proximal_bias,
- proximal_init=proximal_init,
- )
- )
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(
- MultiHeadAttention(
- hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- causal=True,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
- device=x.device, dtype=x.dtype
- )
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class MultiHeadAttention(nn.Module):
- def __init__(
- self,
- channels,
- out_channels,
- n_heads,
- p_dropout=0.0,
- window_size=None,
- heads_share=True,
- block_length=None,
- proximal_bias=False,
- proximal_init=False,
- ):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
- self.conv_q = nn.Conv1d(channels, channels, 1)
- self.conv_k = nn.Conv1d(channels, channels, 1)
- self.conv_v = nn.Conv1d(channels, channels, 1)
- self.conv_o = nn.Conv1d(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
- self.emb_rel_k = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
- self.emb_rel_v = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
-
- nn.init.xavier_uniform_(self.conv_q.weight)
- nn.init.xavier_uniform_(self.conv_k.weight)
- nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with torch.no_grad():
- self.conv_k.weight.copy_(self.conv_q.weight)
- self.conv_k.bias.copy_(self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.size(), query.size(2))
- query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
- key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
- value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
- scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
- if self.window_size is not None:
- assert (
- t_s == t_t
- ), "Relative attention is only available for self-attention."
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(
- query / math.sqrt(self.k_channels), key_relative_embeddings
- )
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).to(
- device=scores.device, dtype=scores.dtype
- )
- if mask is not None:
- scores = scores.masked_fill(mask == 0, -1e4)
- if self.block_length is not None:
- assert (
- t_s == t_t
- ), "Local attention is only available for self-attention."
- block_mask = (
- torch.ones_like(scores)
- .triu(-self.block_length)
- .tril(self.block_length)
- )
- scores = scores.masked_fill(block_mask == 0, -1e4)
- p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = torch.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(
- self.emb_rel_v, t_s
- )
- output = output + self._matmul_with_relative_values(
- relative_weights, value_relative_embeddings
- )
- output = (
- output.transpose(2, 3).contiguous().view(b, d, t_t)
- ) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = torch.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- max_relative_position = 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padded_relative_embeddings = F.pad(
- relative_embeddings,
- commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
- )
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[
- :, slice_start_position:slice_end_position
- ]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.size()
- # Concat columns of pad to shift from relative to absolute indexing.
- x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.view([batch, heads, length * 2 * length])
- x_flat = F.pad(
- x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
- )
-
- # Reshape and slice out the padded elements.
- x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
- :, :, :length, length - 1 :
- ]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.size()
- # padd along column
- x = F.pad(
- x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
- )
- x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
- # add 0's in the beginning that will skew the elements after reshape
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
- x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = torch.arange(length, dtype=torch.float32)
- diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
- return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- filter_channels,
- kernel_size,
- p_dropout=0.0,
- activation=None,
- causal=False,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = self.conv_1(self.padding(x * x_mask))
- if self.activation == "gelu":
- x = x * torch.sigmoid(1.702 * x)
- else:
- x = torch.relu(x)
- x = self.drop(x)
- x = self.conv_2(self.padding(x * x_mask))
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
diff --git a/spaces/ThirdEyeData/Next_Failure_Prediction/app.py b/spaces/ThirdEyeData/Next_Failure_Prediction/app.py
deleted file mode 100644
index 7859340b890f3595c72a494ba9811837711e58d4..0000000000000000000000000000000000000000
--- a/spaces/ThirdEyeData/Next_Failure_Prediction/app.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-from datetime import datetime
-from datetime import timedelta
-from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, train_test_split
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.metrics import r2_score
-from sklearn.preprocessing import LabelEncoder
-from sklearn.preprocessing import StandardScaler
-import streamlit as st
-
-
-st.title("Next Failure Prediction")
-# Loading Dataset
-df1 = pd.read_csv(r'Final_Next_failure_Dataset.csv')
-
-
-# replace values in the Manufacturer column with company names
-
-replace_dict1 = {1: 'ABC Company', 2: 'DEF Company', 3: 'GHI Company', 4: 'JKL Company', 5: 'XYZ Company'}
-df1['Manufacturer'] = df1['Manufacturer'].replace(replace_dict1)
-
-
-# replace values in the Last_Maintenance_Type column again
-
-replace_dict2 = {1: 'Corrective', 2: 'Preventive'}
-df1['Last_Maintenance_Type'] = df1['Last_Maintenance_Type'].replace(replace_dict2)
-
-# replace values in the Prior_Maintenance column again
-
-replace_dict3 = {1: 'Irregular', 2: 'Regular'}
-df1['Prior_Maintenance'] = df1['Prior_Maintenance'].replace(replace_dict3)
-
-# replace values in the Repair_Type column again
-
-replace_dict4 = {1: 'Hardware', 2: 'Software'}
-df1['Repair_Type'] = df1['Repair_Type'].replace(replace_dict4)
-
-df = df1.copy()
-
-# For Manufacturer
-
-le_manu = LabelEncoder()
-df['Manufacturer'] = le_manu.fit_transform(df['Manufacturer'])
-
-
-# For Last_Maintenance_Type
-
-le_last = LabelEncoder()
-df['Last_Maintenance_Type'] = le_last.fit_transform(df['Last_Maintenance_Type'])
-
-# For Prior_Maintenance
-
-le_prior = LabelEncoder()
-df['Prior_Maintenance'] = le_prior.fit_transform(df['Prior_Maintenance'])
-
-# For Repair_Type
-
-le_repair = LabelEncoder()
-df['Repair_Type'] = le_repair.fit_transform(df['Repair_Type'])
-
-#Splitting the data train ans test data
-X = df.drop('Time_to_Failure_(hours)', axis = 1)
-y = df['Time_to_Failure_(hours)']
-
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state = 0)
-
-# Train Random Forest Regression model
-
-model = RandomForestRegressor(random_state = 0)
-model.fit(X_train, y_train)
-
-
-# Make predictions on train data
-
-y_pred_train = model.predict(X_train)
-
-# DATA from user
-def user_report():
- manufacturer = st.sidebar.selectbox("Manufacturer",
- ("JKL Company", "GHI Company","DEF Company","ABC Company","XYZ Company" ))
- if manufacturer=='JKL Company':
- manufacturer=3
- elif manufacturer=="GHI Company":
- manufacturer=2
- elif manufacturer=="DEF Company":
- manufacturer=1
- elif manufacturer=="ABC Company":
- manufacturer =0
- else:
- manufacturer=4
- total_operating_hours = st.sidebar.slider('Total Operating Hours)', 1000,2500, 1500 )
- Usage_Intensity = st.sidebar.slider("Usage_Intensity(hous/day)",1,10,4)
- Last_Maintenance_Type = st.sidebar.selectbox("Last Maintainece Type",("Corrective","Preventive"))
- if Last_Maintenance_Type =='Corrective':
- Last_Maintenance_Type=0
- else:
- Last_Maintenance_Type=1
- Prior_Maintenance = st.sidebar.selectbox("Prior Maintainece",("Regular","Irregular"))
- if Prior_Maintenance =='Regular':
- Prior_Maintenance=1
- else:
- Prior_Maintenance=0
-
- Average_Temperature= st.sidebar.slider('Average Temperature', 20,40, 35 )
- humidity = st.sidebar.slider('Humidity', 52,70, 55 )
- Vibration_Level = st.sidebar.slider('Vibration Level', 2,4, 2 )
- Pressure = st.sidebar.slider('Pressure', 28,32, 30 )
- Power_Input_Voltage= st.sidebar.slider('Power Input Voltage (V)',105,120,115)
- Repair_Type = st.sidebar.selectbox("Repair Type",("Hardware","Software"))
- if Repair_Type =='Software':
- Repair_Type=1
- else:
- Repair_Type=0
- load_factor = st.sidebar.number_input('Enter the Load Factor (any number between 0 to 1 )',min_value=0.0,max_value=1.0,step=0.1)
- engine_speed=st.sidebar.slider('Engine Speed',7000,8000,7800)
- Oil_Temperature=st.sidebar.slider('Oil Temperature',170,185,172)
-
-
- user_report_data = {
- 'Manufacturer': manufacturer,
- 'Total_Operating_Hours': total_operating_hours,
- 'Usage_Intensity_(hours/day)': Usage_Intensity ,
- 'Last_Maintenance_Type': Last_Maintenance_Type,
- "Prior_Maintenance":Prior_Maintenance,
- 'Average_Temperature':Average_Temperature,
- 'Humidity': humidity,
- 'Vibration_Level': Vibration_Level,
- 'Pressure': Pressure,
- 'Power_Input_Voltage': Power_Input_Voltage,
- 'Repair_Type': Repair_Type ,
- 'Load_Factor': load_factor,
- 'Engine_Speed': engine_speed,
- 'Oil_Temperature':Oil_Temperature
- }
- report_data = pd.DataFrame(user_report_data, index=[0])
-
- return report_data
-
-#Customer Data
-user_data = user_report()
-st.subheader("Component Details")
-st.write(user_data)
-
-
-# define the prediction function
-def prediction(user_data):
-
- predicted_max_number_of_repairs = model.predict(user_data)
-
- # return the predicted max number of repairs as output
- return np.round(predicted_max_number_of_repairs[0])
-# Function calling
-y_pred = prediction(user_data)
-st.write("Click here to see the Predictions")
-if st.button("Predict"):
- st.subheader(f"Next Failure is {y_pred} hours ")
\ No newline at end of file
diff --git a/spaces/TinkerFrank/AppleClassifier/README.md b/spaces/TinkerFrank/AppleClassifier/README.md
deleted file mode 100644
index 382f803e1c5ca640da528b695f8ae952e02a2fce..0000000000000000000000000000000000000000
--- a/spaces/TinkerFrank/AppleClassifier/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AppleClassifier
-emoji: 🚀
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Tinsae/CoWork/app.py b/spaces/Tinsae/CoWork/app.py
deleted file mode 100644
index fc299085e5b51999c348cc1274e6a4e06022a082..0000000000000000000000000000000000000000
--- a/spaces/Tinsae/CoWork/app.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from PIL import Image
-import numpy as np
-from rembg import remove
-import cv2
-import os
-from torchvision.transforms import GaussianBlur
-import gradio as gr
-import replicate
-import requests
-from io import BytesIO
-
-def create_mask(input):
- input_path = 'input.png'
- bg_removed_path = 'bg_removed.png'
- mask_name = 'blured_mask.png'
-
- input.save(input_path)
- bg_removed = remove(input)
-
- width, height = bg_removed.size
- max_dim = max(width, height)
- square_img = Image.new('RGB', (max_dim, max_dim), (255, 255, 255))
- paste_pos = ((max_dim - width) // 2, (max_dim - height) // 2)
- square_img.paste(bg_removed, paste_pos)
-
- square_img = square_img.resize((512, 512))
- square_img.save(bg_removed_path)
-
- img2_grayscale = square_img.convert('L')
- img2_a = np.array(img2_grayscale)
-
- mask = np.array(img2_grayscale)
- threshhold = 0
- mask[img2_a==threshhold] = 1
- mask[img2_a>threshhold] = 0
-
- strength = 1
- d = int(255 * (1-strength))
- mask *= 255-d
- mask += d
-
- mask = Image.fromarray(mask)
-
- blur = GaussianBlur(11,20)
- mask = blur(mask)
- mask = mask.resize((512, 512))
-
- mask.save(mask_name)
-
- return Image.open(mask_name)
-
-
-def generate_image(image, product_name, target_name):
- mask = create_mask(image)
- image = image.resize((512, 512))
- mask = mask.resize((512,512))
- guidance_scale=16
- num_samples = 1
-
- prompt = 'a product photography photo of' + product_name + ' on ' + target_name
-
- model = replicate.models.get("cjwbw/stable-diffusion-v2-inpainting")
- version = model.versions.get("f9bb0632bfdceb83196e85521b9b55895f8ff3d1d3b487fd1973210c0eb30bec")
- output = version.predict(prompt=prompt, image=open("bg_removed.png", "rb"), mask=open("blured_mask.png", "rb"))
- response = requests.get(output[0])
-
- return Image.open(BytesIO(response.content))
-
-with gr.Blocks() as demo:
- gr.Markdown("# Advertise better with AI")
- # with gr.Tab("Prompt Paint - Basic"):
- with gr.Row():
-
- with gr.Column():
- input_image = gr.Image(label = "Upload your product's photo", type = 'pil')
-
- product_name = gr.Textbox(label="Describe your product")
- target_name = gr.Textbox(label="Where do you want to put your product?")
- # result_prompt = product_name + ' in ' + target_name + 'product photograpy ultrarealist'
-
- image_button = gr.Button("Generate")
-
- with gr.Column():
- image_output = gr.Image()
-
- image_button.click(generate_image, inputs=[input_image, product_name, target_name ], outputs=image_output, api_name='test')
-
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/TusharNautiyal/BTC-Prediction/app.py b/spaces/TusharNautiyal/BTC-Prediction/app.py
deleted file mode 100644
index 0d860dd4fd53798ca3c0cadf08cee5c7a67e802a..0000000000000000000000000000000000000000
--- a/spaces/TusharNautiyal/BTC-Prediction/app.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import numpy as np
-import pandas as pd
-from Scheduler import isscheduled
-from build_model import model_builder
-from sklearn.preprocessing import MinMaxScaler
-import matplotlib.pyplot as plt
-from tensorflow import keras
-import yfinance
-import requests
-import streamlit as st
-from datetime import datetime
-import os
-# Getting Current Date
-
-def main():
-
- if 'Season' not in st.session_state:
- st.session_state['Season'] = True
- else:
- st.session_state['Season'] = False
- with open("prevdays.txt",'r') as file:
- previous_date = file.readline()
- file.close()
- with open("isdone.txt",'r') as file:
- isdone = file.readline()
- file.close()
- isExist = os.path.exists('LSTM_build.h5')
- data = requests.get("http://worldtimeapi.org/api/timezone/Asia/Kolkata")
- current_date = data.json()['datetime'].split('T')[0]
- if isExist == False and isdone == 'Done':
- with open("isdone.txt",'w') as file:
- file.write("Not Done")
- file.close()
-
- with st.spinner(f'Wait for Model Updation It will Retrain Every 10th day from the retraining day which was on {previous_date} It Will take only 15-20 mins Please Wait **Do not close The window**...'):
- builder = model_builder()
- # We will Schedule Our APP to re build model after 10 days
- with open('isdone.txt') as file:
- isDone = file.readline()
- file.close()
-
-
- isDone = isDone.strip()
- if isscheduled()==True or isExist==False and isDone != 'Done':
- # Getting The data
- with open('started.txt','w') as file:
- file.write("True")
- file.close()
- X = yfinance.download('BTC-USD',start = '2017-01-01')
- X = X['Close']
- data = X
- # Preprocessing data
-
- X = X[:len(X)-30]
- X_test = data[len(X):len(data)]
- # Scaling
- scaled = MinMaxScaler()
- values = scaled.fit_transform(np.array(X).reshape(-1,1))
- X_train = values
- X_test = scaled.fit_transform(np.array(X_test).reshape(-1,1))
-
- # Creating Time Series data to Feed To LSTM
- X_train,y_train = builder.preprocess_data(X_train,10)
- X_test,y_test = builder.preprocess_data(X_test,10)
- X_train = X_train[...,np.newaxis]
- X_test = X_test[...,np.newaxis]
-
- model = builder.fit(X_train,y_train)
- model.save('LSTM_build.h5',save_format = 'h5',overwrite = True)
- with open('isdone.txt','w') as file:
- file.write('Done')
- file.close()
- with open('prevdays.txt','w') as file:
- data = requests.get("http://worldtimeapi.org/api/timezone/Asia/Kolkata")
- date = data.json()['datetime'].split('T')[0]
- file.write(date)
- file.close()
-
- if previous_date != current_date and isscheduled()==False and isExist==True:
- with open('isdone.txt','w') as file:
- file.write('NotDone')
- file.close()
- with open("started.txt",'w') as file:
- file.write("False")
- file.close()
-
- model = keras.models.load_model('LSTM_build.h5')
- elif previous_date == current_date and isdone == 'Done' and isExist == True:
- model = keras.models.load_model('LSTM_build.h5')
- elif isExist == False and isdone =='Done':
- st.write("There Might Be some issues or the model is rebuilding in background please try and reload application after 5mins.")
- if st.session_state['Season'] == True:
- if int(current_date.split("-")[1])>=10 or int(current_date.split("-")[1])<=2:
- st.snow()
- st.session_state['season'] = 'Winter'
- else:
- st.balloons()
- st.session_state['season']= 'Summer'
-
- with open("prevdays.txt",'r') as file:
- previous_date = file.readline()
- file.close()
- st.success(f"Model Is updated On {previous_date} and will next Update after 10 days ")
-
- # Creating Future Outcomes
- def forecast_day():
- df = yfinance.download('BTC-USD',start = '2017-01-01')
- df = df.reset_index()
- X_future = df['Close'].shift(-2)
- X_dates = df['Date'].shift(-2)
- X_dates = X_dates[len(X_dates)-12:len(X_dates)]
- X_dates = X_dates[...,np.newaxis]
- X_dates = builder.preprocess_data(X_dates,10)
-
- X_future = X_future[len(X_future)-12:len(X_future)]
- scaled = MinMaxScaler()
- X_future = scaled.fit_transform(np.array(X_future).reshape(-1,1))
- X_future,y_future = builder.preprocess_data(X_future,10)
-
- X_future = X_future[...,np.newaxis]
- result = model.predict(X_future)
- ans = scaled.inverse_transform(result)
- if ans>df['Close'].iloc[-1]:
- return 'Positive 🟢',ans
- else:
- return 'Negative 🔴 ',ans
-
- def create_sample_data(df,days):
- store_index = []
- for day in range(days):
- # Creating Temporary DataFrame
- dt = df.index + pd.Timedelta(days = 1)
- next_data = pd.DataFrame({'Close':[1]},index =[dt[-1]])
- df = pd.concat([df,next_data])
- store_index.append(dt[-1])
- return df,store_index
-
- # This function Forecast Prices For 10 Days or less.
- def forecast_timeline(X,days):
- if days>10:
- return False
- final_values = []
- temp_data = X.iloc[-1]
- for day in range(days):
- X = X.shift(-2)
- X_future = X[len(X)-12:len(X)]
- X = X.dropna()
- scaled = MinMaxScaler()
- X_future = scaled.fit_transform(np.array(X_future).reshape(-1,1))
- X_future,y_future = builder.preprocess_data(X_future,10)
- result = model.predict(X_future)
- X = X.to_list()
- X.append(scaled.inverse_transform(result).reshape(1)[0])
- X = pd.Series(X)
- final_values.append(scaled.inverse_transform(result).reshape(1)[0])
- final_values.insert(0,temp_data)
- return final_values
-
- def predict_future(days):
- df = yfinance.download('BTC-USD',start = '2017-01-01')
- df.reset_index(inplace = True)
- X = df['Close']
- future_Values = forecast_timeline(X,days)
- df = df[['Close','Date']]
- df.set_index('Date',inplace = True)
- final_df,store_index = create_sample_data(df,days)
- for i,index in enumerate(store_index):
- final_df['Close'].loc[index] = future_Values[i]
-
- return final_df,future_Values
-
-
-
- st.title('Bitcoin Price Prediction ₿')
- st.write(f"HOWDY! Wonderfull ***{st.session_state['season']}*** Season. Welcome to Bitcoin ( ₿ ) Price Prediction APP It will Predict Closing Price For Bitcoin. These Predictions are based on **LSTM** Model Trained Over Historical Bitcoin Data From **2017 till {previous_date}** . the Model retratins every 10th day, The Prediction are totally based on previous Closing Values so do not invest money based on Such Predictions. Its only for Educational Purposes and should not be used for finacial purpose.")
- st.write("Why LSTM ? Because it Performed well on the data, I used LSTM,ArimaMax,SariMax,Temporal Fusion transformer,FbProphet, NeuralProphet many different time series model for predictions in which lstm performed the best so I Selected LSTM. If you want To check how we came to conclusion. Check out https://shorturl.at/cwHM4 For Code.")
- one_day = False
- days = int(st.number_input('Enter no of Days for Prediction'))
- st.write("or you can Select one day prediction from below ***IMPORTANT*** After **Checkbox is Clicked** Do Not Press Submit It Will automatically Run")
- if st.checkbox(label = 'One Day Prediction'):
- one_day = True
- if one_day == True:
- days = 1
- if st.button('Submit') and days<=10 and days>0:
- dataframe,values = predict_future(days)
- data = requests.get("http://worldtimeapi.org/api/timezone/Asia/Kolkata")
- date = data.json()['datetime'].split('T')[0]
- st.line_chart(data=dataframe)
- for i in range(len(values)-1):
- if values[i+1]>values[i]:
- st.write(f'Day{i+1}:Positive Growth 🟢')
- else:
- st.write(f'Day{i+1}:Negative Growth 🔴')
- if days == 1:
- result,ans = forecast_day()
- st.markdown(f"Tommorow **Bitcoin** Can Show {result} Movement.")
- st.markdown(f"And Price Can Be Around : $ {ans.reshape(1)[0]}.")
- elif days>10 or days<0:
- st.write("Please Renter Days As you have exceeded 10 days limit or the input is too small, If you think everything is correct still it's showing wront output please check if you are entering any spaces while input or send us feedback at info@tusharnautiyal.ml")
-if __name__ == '__main__':
- main()
\ No newline at end of file
diff --git a/spaces/Uday007/startup-profit-predictor/README.md b/spaces/Uday007/startup-profit-predictor/README.md
deleted file mode 100644
index db02a1192dd78f01fde5a98c6325bdf86349562c..0000000000000000000000000000000000000000
--- a/spaces/Uday007/startup-profit-predictor/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Startup Profit Predictor
-emoji: 🌍
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: cc-by-nc-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Vegecken/sovits4dzl/hubert/__init__.py b/spaces/Vegecken/sovits4dzl/hubert/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Vision-CAIR/minigpt4/minigpt4/models/modeling_llama.py b/spaces/Vision-CAIR/minigpt4/minigpt4/models/modeling_llama.py
deleted file mode 100644
index 2cebacf454dd9cf82823fc372d7673142b2082c7..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/minigpt4/minigpt4/models/modeling_llama.py
+++ /dev/null
@@ -1,772 +0,0 @@
-# coding=utf-8
-# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch LLaMA model."""
-import math
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
-from transformers.models.llama.configuration_llama import LlamaConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "LlamaConfig"
-
-
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
- input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
-):
- """
- Make causal mask used for bi-directional self-attention.
- """
- bsz, tgt_len = input_ids_shape
- mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
- mask_cond = torch.arange(mask.size(-1), device=device)
- mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
- mask = mask.to(dtype)
-
- if past_key_values_length > 0:
- mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
- return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
- """
- Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
- """
- bsz, src_len = mask.size()
- tgt_len = tgt_len if tgt_len is not None else src_len
-
- expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
- inverted_mask = 1.0 - expanded_mask
-
- return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-class LlamaRMSNorm(nn.Module):
- def __init__(self, hidden_size, eps=1e-6):
- """
- LlamaRMSNorm is equivalent to T5LayerNorm
- """
- super().__init__()
- self.weight = nn.Parameter(torch.ones(hidden_size))
- self.variance_epsilon = eps
-
- def forward(self, hidden_states):
- variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
- hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-
- # convert into half-precision if necessary
- if self.weight.dtype in [torch.float16, torch.bfloat16]:
- hidden_states = hidden_states.to(self.weight.dtype)
-
- return self.weight * hidden_states
-
-
-class LlamaRotaryEmbedding(torch.nn.Module):
- def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
- super().__init__()
- inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
- self.register_buffer("inv_freq", inv_freq)
-
- # Build here to make `torch.jit.trace` work.
- self.max_seq_len_cached = max_position_embeddings
- t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
- freqs = torch.einsum("i,j->ij", t, self.inv_freq)
- # Different from paper, but it uses a different permutation in order to obtain the same calculation
- emb = torch.cat((freqs, freqs), dim=-1)
- self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
- self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
-
- def forward(self, x, seq_len=None):
- # x: [bs, num_attention_heads, seq_len, head_size]
- # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
- if seq_len > self.max_seq_len_cached:
- self.max_seq_len_cached = seq_len
- t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
- freqs = torch.einsum("i,j->ij", t, self.inv_freq)
- # Different from paper, but it uses a different permutation in order to obtain the same calculation
- emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
- self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
- self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
- return (
- self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
- self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
- )
-
-
-def rotate_half(x):
- """Rotates half the hidden dims of the input."""
- x1 = x[..., : x.shape[-1] // 2]
- x2 = x[..., x.shape[-1] // 2 :]
- return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
- gather_indices = position_ids[:, None, :, None] # [bs, 1, seq_len, 1]
- gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
- cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
- sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
- q_embed = (q * cos) + (rotate_half(q) * sin)
- k_embed = (k * cos) + (rotate_half(k) * sin)
- return q_embed, k_embed
-
-
-class LlamaMLP(nn.Module):
- def __init__(
- self,
- hidden_size: int,
- intermediate_size: int,
- hidden_act: str,
- ):
- super().__init__()
- self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
- self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
- self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
- self.act_fn = ACT2FN[hidden_act]
-
- def forward(self, x):
- return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-
-
-class LlamaAttention(nn.Module):
- """Multi-headed attention from 'Attention Is All You Need' paper"""
-
- def __init__(self, config: LlamaConfig):
- super().__init__()
- self.config = config
- self.hidden_size = config.hidden_size
- self.num_heads = config.num_attention_heads
- self.head_dim = self.hidden_size // self.num_heads
- self.max_position_embeddings = config.max_position_embeddings
-
- if (self.head_dim * self.num_heads) != self.hidden_size:
- raise ValueError(
- f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
- f" and `num_heads`: {self.num_heads})."
- )
- self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
- self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
- self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
- self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
- self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
-
- def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
- return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- past_key_value: Optional[Tuple[torch.Tensor]] = None,
- output_attentions: bool = False,
- use_cache: bool = False,
- ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
- bsz, q_len, _ = hidden_states.size()
-
- query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
- key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
- value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-
- kv_seq_len = key_states.shape[-2]
- if past_key_value is not None:
- kv_seq_len += past_key_value[0].shape[-2]
- cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
- query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
- # [bsz, nh, t, hd]
-
- if past_key_value is not None:
- # reuse k, v, self_attention
- key_states = torch.cat([past_key_value[0], key_states], dim=2)
- value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
- past_key_value = (key_states, value_states) if use_cache else None
-
- attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
- if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
- raise ValueError(
- f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
- f" {attn_weights.size()}"
- )
-
- if attention_mask is not None:
- if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
- raise ValueError(
- f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
- )
- attn_weights = attn_weights + attention_mask
- attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
-
- # upcast attention to fp32
- attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
- attn_output = torch.matmul(attn_weights, value_states)
-
- if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
- raise ValueError(
- f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
- f" {attn_output.size()}"
- )
-
- attn_output = attn_output.transpose(1, 2)
- attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
- attn_output = self.o_proj(attn_output)
-
- if not output_attentions:
- attn_weights = None
-
- return attn_output, attn_weights, past_key_value
-
-
-class LlamaDecoderLayer(nn.Module):
- def __init__(self, config: LlamaConfig):
- super().__init__()
- self.hidden_size = config.hidden_size
- self.self_attn = LlamaAttention(config=config)
- self.mlp = LlamaMLP(
- hidden_size=self.hidden_size,
- intermediate_size=config.intermediate_size,
- hidden_act=config.hidden_act,
- )
- self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
- self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- past_key_value: Optional[Tuple[torch.Tensor]] = None,
- output_attentions: Optional[bool] = False,
- use_cache: Optional[bool] = False,
- ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
- """
- Args:
- hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
- attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
- `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
- output_attentions (`bool`, *optional*):
- Whether or not to return the attentions tensors of all attention layers. See `attentions` under
- returned tensors for more detail.
- use_cache (`bool`, *optional*):
- If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
- (see `past_key_values`).
- past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
- """
-
- residual = hidden_states
-
- hidden_states = self.input_layernorm(hidden_states)
-
- # Self Attention
- hidden_states, self_attn_weights, present_key_value = self.self_attn(
- hidden_states=hidden_states,
- attention_mask=attention_mask,
- position_ids=position_ids,
- past_key_value=past_key_value,
- output_attentions=output_attentions,
- use_cache=use_cache,
- )
- hidden_states = residual + hidden_states
-
- # Fully Connected
- residual = hidden_states
- hidden_states = self.post_attention_layernorm(hidden_states)
- hidden_states = self.mlp(hidden_states)
- hidden_states = residual + hidden_states
-
- outputs = (hidden_states,)
-
- if output_attentions:
- outputs += (self_attn_weights,)
-
- if use_cache:
- outputs += (present_key_value,)
-
- return outputs
-
-
-LLAMA_START_DOCSTRING = r"""
- This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
- library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
- etc.)
-
- This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
- Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
- and behavior.
-
- Parameters:
- config ([`LlamaConfig`]):
- Model configuration class with all the parameters of the model. Initializing with a config file does not
- load the weights associated with the model, only the configuration. Check out the
- [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-@add_start_docstrings(
- "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
- LLAMA_START_DOCSTRING,
-)
-class LlamaPreTrainedModel(PreTrainedModel):
- config_class = LlamaConfig
- base_model_prefix = "model"
- supports_gradient_checkpointing = True
- _no_split_modules = ["LlamaDecoderLayer"]
- _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
-
- def _init_weights(self, module):
- std = self.config.initializer_range
- if isinstance(module, nn.Linear):
- module.weight.data.normal_(mean=0.0, std=std)
- if module.bias is not None:
- module.bias.data.zero_()
- elif isinstance(module, nn.Embedding):
- module.weight.data.normal_(mean=0.0, std=std)
- if module.padding_idx is not None:
- module.weight.data[module.padding_idx].zero_()
-
- def _set_gradient_checkpointing(self, module, value=False):
- if isinstance(module, LlamaModel):
- module.gradient_checkpointing = value
-
-
-LLAMA_INPUTS_DOCSTRING = r"""
- Args:
- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
- it.
-
- Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
- [`PreTrainedTokenizer.__call__`] for details.
-
- [What are input IDs?](../glossary#input-ids)
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
- - 1 for tokens that are **not masked**,
- - 0 for tokens that are **masked**.
-
- [What are attention masks?](../glossary#attention-mask)
-
- Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
- [`PreTrainedTokenizer.__call__`] for details.
-
- If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
- `past_key_values`).
-
- If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
- and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
- information on the default strategy.
-
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
- config.n_positions - 1]`.
-
- [What are position IDs?](../glossary#position-ids)
- past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
- `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
- `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
-
- Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
- blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-
- If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
- don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
- `decoder_input_ids` of shape `(batch_size, sequence_length)`.
- inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
- Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
- is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
- model's internal embedding lookup matrix.
- use_cache (`bool`, *optional*):
- If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
- `past_key_values`).
- output_attentions (`bool`, *optional*):
- Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
- tensors for more detail.
- output_hidden_states (`bool`, *optional*):
- Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
- more detail.
- return_dict (`bool`, *optional*):
- Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
- "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
- LLAMA_START_DOCSTRING,
-)
-class LlamaModel(LlamaPreTrainedModel):
- """
- Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
-
- Args:
- config: LlamaConfig
- """
-
- def __init__(self, config: LlamaConfig):
- super().__init__(config)
- self.padding_idx = config.pad_token_id
- self.vocab_size = config.vocab_size
-
- self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
- self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
- self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
- self.gradient_checkpointing = False
- # Initialize weights and apply final processing
- self.post_init()
-
- def get_input_embeddings(self):
- return self.embed_tokens
-
- def set_input_embeddings(self, value):
- self.embed_tokens = value
-
- # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
- def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
- # create causal mask
- # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
- combined_attention_mask = None
- if input_shape[-1] > 1:
- combined_attention_mask = _make_causal_mask(
- input_shape,
- inputs_embeds.dtype,
- device=inputs_embeds.device,
- past_key_values_length=past_key_values_length,
- )
-
- if attention_mask is not None:
- # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
- expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
- inputs_embeds.device
- )
- combined_attention_mask = (
- expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
- )
-
- return combined_attention_mask
-
- @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.Tensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- past_key_values: Optional[List[torch.FloatTensor]] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- query_embeds: Optional[torch.FloatTensor] = None,
- use_cache: Optional[bool] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple, BaseModelOutputWithPast]:
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
- )
- use_cache = use_cache if use_cache is not None else self.config.use_cache
-
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- # retrieve input_ids and inputs_embeds
- if input_ids is not None and inputs_embeds is not None:
- raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
- elif input_ids is not None:
- batch_size, seq_length = input_ids.shape
- elif inputs_embeds is not None:
- batch_size, seq_length, _ = inputs_embeds.shape
- else:
- raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
-
- if inputs_embeds is None:
- inputs_embeds = self.embed_tokens(input_ids)
- if query_embeds is not None:
- inputs_embeds = torch.cat([query_embeds, inputs_embeds], dim=1)
- batch_size, seq_length, _ = inputs_embeds.shape
-
- seq_length_with_past = seq_length
- past_key_values_length = 0
-
- if past_key_values is not None:
- past_key_values_length = past_key_values[0][0].shape[2]
- seq_length_with_past = seq_length_with_past + past_key_values_length
-
- if position_ids is None:
- device = input_ids.device if input_ids is not None else inputs_embeds.device
- position_ids = torch.arange(
- past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
- )
- position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
- else:
- position_ids = position_ids.view(-1, seq_length).long()
-
- # embed positions
- if attention_mask is None:
- attention_mask = torch.ones(
- (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
- )
- attention_mask = self._prepare_decoder_attention_mask(
- attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
- )
-
- hidden_states = inputs_embeds
-
- if self.gradient_checkpointing and self.training:
- if use_cache:
- logger.warning_once(
- "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
- )
- use_cache = False
-
- # decoder layers
- all_hidden_states = () if output_hidden_states else None
- all_self_attns = () if output_attentions else None
- next_decoder_cache = () if use_cache else None
-
- for idx, decoder_layer in enumerate(self.layers):
- if output_hidden_states:
- all_hidden_states += (hidden_states,)
-
- past_key_value = past_key_values[idx] if past_key_values is not None else None
-
- if self.gradient_checkpointing and self.training:
-
- def create_custom_forward(module):
- def custom_forward(*inputs):
- # None for past_key_value
- return module(*inputs, output_attentions, None)
-
- return custom_forward
-
- layer_outputs = torch.utils.checkpoint.checkpoint(
- create_custom_forward(decoder_layer),
- hidden_states,
- attention_mask,
- position_ids,
- None,
- )
- else:
- layer_outputs = decoder_layer(
- hidden_states,
- attention_mask=attention_mask,
- position_ids=position_ids,
- past_key_value=past_key_value,
- output_attentions=output_attentions,
- use_cache=use_cache,
- )
-
- hidden_states = layer_outputs[0]
-
- if use_cache:
- next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
-
- if output_attentions:
- all_self_attns += (layer_outputs[1],)
-
- hidden_states = self.norm(hidden_states)
-
- # add hidden states from the last decoder layer
- if output_hidden_states:
- all_hidden_states += (hidden_states,)
-
- next_cache = next_decoder_cache if use_cache else None
- if not return_dict:
- return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
- return BaseModelOutputWithPast(
- last_hidden_state=hidden_states,
- past_key_values=next_cache,
- hidden_states=all_hidden_states,
- attentions=all_self_attns,
- )
-
-
-class LlamaForCausalLM(LlamaPreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
- self.model = LlamaModel(config)
-
- self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- def get_input_embeddings(self):
- return self.model.embed_tokens
-
- def set_input_embeddings(self, value):
- self.model.embed_tokens = value
-
- def get_output_embeddings(self):
- return self.lm_head
-
- def set_output_embeddings(self, new_embeddings):
- self.lm_head = new_embeddings
-
- def set_decoder(self, decoder):
- self.model = decoder
-
- def get_decoder(self):
- return self.model
-
- @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
- @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.Tensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- past_key_values: Optional[List[torch.FloatTensor]] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- query_embeds: Optional[torch.FloatTensor] = None,
- labels: Optional[torch.LongTensor] = None,
- use_cache: Optional[bool] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple, CausalLMOutputWithPast]:
- r"""
- Args:
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
- Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
- config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
- (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
- Returns:
-
- Example:
-
- ```python
- >>> from transformers import AutoTokenizer, LlamaForCausalLM
-
- >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
- >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
-
- >>> prompt = "Hey, are you consciours? Can you talk to me?"
- >>> inputs = tokenizer(prompt, return_tensors="pt")
-
- >>> # Generate
- >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
- >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
- "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
- ```"""
-
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
- )
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
- outputs = self.model(
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- past_key_values=past_key_values,
- inputs_embeds=inputs_embeds,
- query_embeds=query_embeds,
- use_cache=use_cache,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- hidden_states = outputs[0]
- logits = self.lm_head(hidden_states)
-
- loss = None
- if labels is not None:
- # Shift so that tokens < n predict n
- shift_logits = logits[..., :-1, :].contiguous()
- shift_labels = labels[..., 1:].contiguous()
- # Flatten the tokens
- loss_fct = CrossEntropyLoss()
- shift_logits = shift_logits.view(-1, self.config.vocab_size)
- shift_labels = shift_labels.view(-1)
- # Enable model parallelism
- shift_labels = shift_labels.to(shift_logits.device)
- loss = loss_fct(shift_logits, shift_labels)
-
- if not return_dict:
- output = (logits,) + outputs[1:]
- return (loss,) + output if loss is not None else output
-
- return CausalLMOutputWithPast(
- loss=loss,
- logits=logits,
- past_key_values=outputs.past_key_values,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
- def prepare_inputs_for_generation(
- self, input_ids, query_embeds=None, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
- ):
- if past_key_values:
- input_ids = input_ids[:, -1:]
-
- position_ids = kwargs.get("position_ids", None)
- if attention_mask is not None and position_ids is None:
- # create position_ids on the fly for batch generation
- position_ids = attention_mask.long().cumsum(-1) - 1
- position_ids.masked_fill_(attention_mask == 0, 1)
- if past_key_values:
- position_ids = position_ids[:, -1].unsqueeze(-1)
- query_embeds = None
-
- # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
- if inputs_embeds is not None and past_key_values is None:
- model_inputs = {"inputs_embeds": inputs_embeds}
- else:
- model_inputs = {"input_ids": input_ids}
-
- model_inputs.update(
- {
- "position_ids": position_ids,
- "query_embeds": query_embeds,
- "past_key_values": past_key_values,
- "use_cache": kwargs.get("use_cache"),
- "attention_mask": attention_mask,
- }
- )
- return model_inputs
-
- @staticmethod
- def _reorder_cache(past_key_values, beam_idx):
- reordered_past = ()
- for layer_past in past_key_values:
- reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
- return reordered_past
-
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/callbacks/tensorboard.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/callbacks/tensorboard.py
deleted file mode 100644
index a24d0799a74c3eb03313ad2ddbd2cb916803d6de..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/callbacks/tensorboard.py
+++ /dev/null
@@ -1,427 +0,0 @@
-"Provides convenient callbacks for Learners that write model images, metrics/losses, stats and histograms to Tensorboard"
-from ..basic_train import Learner
-from ..basic_data import DatasetType, DataBunch
-from ..vision import Image
-from ..vision.gan import GANLearner
-from ..callbacks import LearnerCallback
-from ..core import *
-from ..torch_core import *
-from threading import Thread, Event
-from time import sleep
-from queue import Queue
-import statistics
-import torchvision.utils as vutils
-from abc import ABC
-#This is an optional dependency in fastai. Must install separately.
-try: from tensorboardX import SummaryWriter
-except: print("To use this tracker, please run 'pip install tensorboardx'. Also you must have Tensorboard running to see results")
-
-__all__=['LearnerTensorboardWriter', 'GANTensorboardWriter', 'ImageGenTensorboardWriter']
-
-#---Example usage (applies to any of the callbacks)---
-# proj_id = 'Colorize'
-# tboard_path = Path('data/tensorboard/' + proj_id)
-# learn.callback_fns.append(partial(GANTensorboardWriter, base_dir=tboard_path, name='GanLearner'))
-
-class LearnerTensorboardWriter(LearnerCallback):
- "Broadly useful callback for Learners that writes to Tensorboard. Writes model histograms, losses/metrics, and gradient stats."
- def __init__(self, learn:Learner, base_dir:Path, name:str, loss_iters:int=25, hist_iters:int=500, stats_iters:int=100):
- super().__init__(learn=learn)
- self.base_dir,self.name,self.loss_iters,self.hist_iters,self.stats_iters = base_dir,name,loss_iters,hist_iters,stats_iters
- log_dir = base_dir/name
- self.tbwriter = SummaryWriter(str(log_dir))
- self.hist_writer = HistogramTBWriter()
- self.stats_writer = ModelStatsTBWriter()
- #self.graph_writer = GraphTBWriter()
- self.data = None
- self.metrics_root = '/metrics/'
- self._update_batches_if_needed()
-
- def _get_new_batch(self, ds_type:DatasetType)->Collection[Tensor]:
- "Retrieves new batch of DatasetType, and detaches it."
- return self.learn.data.one_batch(ds_type=ds_type, detach=True, denorm=False, cpu=False)
-
- def _update_batches_if_needed(self)->None:
- "one_batch function is extremely slow with large datasets. This is caching the result as an optimization."
- if self.learn.data.valid_dl is None: return # Running learning rate finder, so return
- update_batches = self.data is not self.learn.data
- if not update_batches: return
- self.data = self.learn.data
- self.trn_batch = self._get_new_batch(ds_type=DatasetType.Train)
- self.val_batch = self._get_new_batch(ds_type=DatasetType.Valid)
-
- def _write_model_stats(self, iteration:int)->None:
- "Writes gradient statistics to Tensorboard."
- self.stats_writer.write(model=self.learn.model, iteration=iteration, tbwriter=self.tbwriter)
-
- def _write_training_loss(self, iteration:int, last_loss:Tensor)->None:
- "Writes training loss to Tensorboard."
- scalar_value = to_np(last_loss)
- tag = self.metrics_root + 'train_loss'
- self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=iteration)
-
- def _write_weight_histograms(self, iteration:int)->None:
- "Writes model weight histograms to Tensorboard."
- self.hist_writer.write(model=self.learn.model, iteration=iteration, tbwriter=self.tbwriter)
-
- def _write_scalar(self, name:str, scalar_value, iteration:int)->None:
- "Writes single scalar value to Tensorboard."
- tag = self.metrics_root + name
- self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=iteration)
-
- #TODO: Relying on a specific hardcoded start_idx here isn't great. Is there a better solution?
- def _write_metrics(self, iteration:int, last_metrics:MetricsList, start_idx:int=2)->None:
- "Writes training metrics to Tensorboard."
- recorder = self.learn.recorder
- for i, name in enumerate(recorder.names[start_idx:]):
- if last_metrics is None or len(last_metrics) < i+1: return
- scalar_value = last_metrics[i]
- self._write_scalar(name=name, scalar_value=scalar_value, iteration=iteration)
-
- def on_train_begin(self, **kwargs: Any) -> None:
- #self.graph_writer.write(model=self.learn.model, tbwriter=self.tbwriter,
- #input_to_model=next(iter(self.learn.data.dl(DatasetType.Single)))[0])
- return
-
- def on_batch_end(self, last_loss:Tensor, iteration:int, **kwargs)->None:
- "Callback function that writes batch end appropriate data to Tensorboard."
- if iteration == 0: return
- self._update_batches_if_needed()
- if iteration % self.loss_iters == 0: self._write_training_loss(iteration=iteration, last_loss=last_loss)
- if iteration % self.hist_iters == 0: self._write_weight_histograms(iteration=iteration)
-
- # Doing stuff here that requires gradient info, because they get zeroed out afterwards in training loop
- def on_backward_end(self, iteration:int, **kwargs)->None:
- "Callback function that writes backward end appropriate data to Tensorboard."
- if iteration == 0: return
- self._update_batches_if_needed()
- if iteration % self.stats_iters == 0: self._write_model_stats(iteration=iteration)
-
- def on_epoch_end(self, last_metrics:MetricsList, iteration:int, **kwargs)->None:
- "Callback function that writes epoch end appropriate data to Tensorboard."
- self._write_metrics(iteration=iteration, last_metrics=last_metrics)
-
-# TODO: We're overriding almost everything here. Seems like a good idea to question that ("is a" vs "has a")
-class GANTensorboardWriter(LearnerTensorboardWriter):
- "Callback for GANLearners that writes to Tensorboard. Extends LearnerTensorboardWriter and adds output image writes."
- def __init__(self, learn:GANLearner, base_dir:Path, name:str, loss_iters:int=25, hist_iters:int=500,
- stats_iters:int=100, visual_iters:int=100):
- super().__init__(learn=learn, base_dir=base_dir, name=name, loss_iters=loss_iters, hist_iters=hist_iters, stats_iters=stats_iters)
- self.visual_iters = visual_iters
- self.img_gen_vis = ImageTBWriter()
- self.gen_stats_updated = True
- self.crit_stats_updated = True
-
- def _write_weight_histograms(self, iteration:int)->None:
- "Writes model weight histograms to Tensorboard."
- generator, critic = self.learn.gan_trainer.generator, self.learn.gan_trainer.critic
- self.hist_writer.write(model=generator, iteration=iteration, tbwriter=self.tbwriter, name='generator')
- self.hist_writer.write(model=critic, iteration=iteration, tbwriter=self.tbwriter, name='critic')
-
- def _write_gen_model_stats(self, iteration:int)->None:
- "Writes gradient statistics for generator to Tensorboard."
- generator = self.learn.gan_trainer.generator
- self.stats_writer.write(model=generator, iteration=iteration, tbwriter=self.tbwriter, name='gen_model_stats')
- self.gen_stats_updated = True
-
- def _write_critic_model_stats(self, iteration:int)->None:
- "Writes gradient statistics for critic to Tensorboard."
- critic = self.learn.gan_trainer.critic
- self.stats_writer.write(model=critic, iteration=iteration, tbwriter=self.tbwriter, name='crit_model_stats')
- self.crit_stats_updated = True
-
- def _write_model_stats(self, iteration:int)->None:
- "Writes gradient statistics to Tensorboard."
- # We don't want to write stats when model is not iterated on and hence has zeroed out gradients
- gen_mode = self.learn.gan_trainer.gen_mode
- if gen_mode and not self.gen_stats_updated: self._write_gen_model_stats(iteration=iteration)
- if not gen_mode and not self.crit_stats_updated: self._write_critic_model_stats(iteration=iteration)
-
- def _write_training_loss(self, iteration:int, last_loss:Tensor)->None:
- "Writes training loss to Tensorboard."
- recorder = self.learn.gan_trainer.recorder
- if len(recorder.losses) == 0: return
- scalar_value = to_np((recorder.losses[-1:])[0])
- tag = self.metrics_root + 'train_loss'
- self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=iteration)
-
- def _write_images(self, iteration:int)->None:
- "Writes model generated, original and real images to Tensorboard."
- trainer = self.learn.gan_trainer
- #TODO: Switching gen_mode temporarily seems a bit hacky here. Certainly not a good side-effect. Is there a better way?
- gen_mode = trainer.gen_mode
- try:
- trainer.switch(gen_mode=True)
- self.img_gen_vis.write(learn=self.learn, trn_batch=self.trn_batch, val_batch=self.val_batch,
- iteration=iteration, tbwriter=self.tbwriter)
- finally: trainer.switch(gen_mode=gen_mode)
-
- def on_batch_end(self, iteration:int, **kwargs)->None:
- "Callback function that writes batch end appropriate data to Tensorboard."
- super().on_batch_end(iteration=iteration, **kwargs)
- if iteration == 0: return
- if iteration % self.visual_iters == 0: self._write_images(iteration=iteration)
-
- def on_backward_end(self, iteration:int, **kwargs)->None:
- "Callback function that writes backward end appropriate data to Tensorboard."
- if iteration == 0: return
- self._update_batches_if_needed()
- #TODO: This could perhaps be implemented as queues of requests instead but that seemed like overkill.
- # But I'm not the biggest fan of maintaining these boolean flags either... Review pls.
- if iteration % self.stats_iters == 0: self.gen_stats_updated, self.crit_stats_updated = False, False
- if not (self.gen_stats_updated and self.crit_stats_updated): self._write_model_stats(iteration=iteration)
-
-class ImageGenTensorboardWriter(LearnerTensorboardWriter):
- "Callback for non-GAN image generating Learners that writes to Tensorboard. Extends LearnerTensorboardWriter and adds output image writes."
- def __init__(self, learn:Learner, base_dir:Path, name:str, loss_iters:int=25, hist_iters:int=500, stats_iters:int=100,
- visual_iters:int=100):
- super().__init__(learn=learn, base_dir=base_dir, name=name, loss_iters=loss_iters, hist_iters=hist_iters,
- stats_iters=stats_iters)
- self.visual_iters = visual_iters
- self.img_gen_vis = ImageTBWriter()
-
- def _write_images(self, iteration:int)->None:
- "Writes model generated, original and real images to Tensorboard"
- self.img_gen_vis.write(learn=self.learn, trn_batch=self.trn_batch, val_batch=self.val_batch, iteration=iteration,
- tbwriter=self.tbwriter)
-
- def on_batch_end(self, iteration:int, **kwargs)->None:
- "Callback function that writes batch end appropriate data to Tensorboard."
- super().on_batch_end(iteration=iteration, **kwargs)
- if iteration == 0: return
- if iteration % self.visual_iters == 0:
- self._write_images(iteration=iteration)
-
-class TBWriteRequest(ABC):
- "A request object for Tensorboard writes. Useful for queuing up and executing asynchronous writes."
- def __init__(self, tbwriter: SummaryWriter, iteration:int):
- super().__init__()
- self.tbwriter = tbwriter
- self.iteration = iteration
-
- @abstractmethod
- def write(self)->None: pass
-
-# SummaryWriter writes tend to block quite a bit. This gets around that and greatly boosts performance.
-# Not all tensorboard writes are using this- just the ones that take a long time. Note that the
-# SummaryWriter does actually use a threadsafe consumer/producer design ultimately to write to Tensorboard,
-# so writes done outside of this async loop should be fine.
-class AsyncTBWriter():
- "Callback for GANLearners that writes to Tensorboard. Extends LearnerTensorboardWriter and adds output image writes."
- def __init__(self):
- super().__init__()
- self.stop_request = Event()
- self.queue = Queue()
- self.thread = Thread(target=self._queue_processor, daemon=True)
- self.thread.start()
-
- def request_write(self, request: TBWriteRequest)->None:
- "Queues up an asynchronous write request to Tensorboard."
- if self.stop_request.isSet(): return
- self.queue.put(request)
-
- def _queue_processor(self)->None:
- "Processes queued up write requests asynchronously to Tensorboard."
- while not self.stop_request.isSet():
- while not self.queue.empty():
- if self.stop_request.isSet(): return
- request = self.queue.get()
- request.write()
- sleep(0.2)
-
- #Provided this to stop thread explicitly or by context management (with statement) but thread should end on its own
- # upon program exit, due to being a daemon. So using this is probably unecessary.
- def close(self)->None:
- "Stops asynchronous request queue processing thread."
- self.stop_request.set()
- self.thread.join()
-
- # Nothing to do, thread already started. Could start thread here to enforce use of context manager
- # (but that sounds like a pain and a bit unweildy and unecessary for actual usage)
- def __enter__(self): pass
-
- def __exit__(self, exc_type, exc_value, traceback): self.close()
-
-asyncTBWriter = AsyncTBWriter()
-
-class ModelImageSet():
- "Convenience object that holds the original, real(target) and generated versions of a single image fed to a model."
- @staticmethod
- def get_list_from_model(learn:Learner, ds_type:DatasetType, batch:Tuple)->[]:
- "Factory method to convert a batch of model images to a list of ModelImageSet."
- image_sets = []
- x,y = batch[0],batch[1]
- preds=[]
- preds = learn.pred_batch(ds_type=ds_type, batch=(x,y), reconstruct=True)
- for orig_px, real_px, gen in zip(x,y,preds):
- orig, real = Image(px=orig_px), Image(px=real_px)
- image_set = ModelImageSet(orig=orig, real=real, gen=gen)
- image_sets.append(image_set)
- return image_sets
-
- def __init__(self, orig:Image, real:Image, gen:Image): self.orig, self.real, self.gen = orig, real, gen
-
-class HistogramTBRequest(TBWriteRequest):
- "Request object for model histogram writes to Tensorboard."
- def __init__(self, model:nn.Module, iteration:int, tbwriter:SummaryWriter, name:str):
- super().__init__(tbwriter=tbwriter, iteration=iteration)
- self.params = [(name, values.clone().detach().cpu()) for (name, values) in model.named_parameters()]
- self.name = name
-
- def _write_histogram(self, param_name:str, values)->None:
- "Writes single model histogram to Tensorboard."
- tag = self.name + '/weights/' + param_name
- self.tbwriter.add_histogram(tag=tag, values=values, global_step=self.iteration)
-
- def write(self)->None:
- "Writes model histograms to Tensorboard."
- for param_name, values in self.params: self._write_histogram(param_name=param_name, values=values)
-
-#If this isn't done async then this is sloooooow
-class HistogramTBWriter():
- "Writes model histograms to Tensorboard."
- def __init__(self): super().__init__()
-
- def write(self, model:nn.Module, iteration:int, tbwriter:SummaryWriter, name:str='model')->None:
- "Writes model histograms to Tensorboard."
- request = HistogramTBRequest(model=model, iteration=iteration, tbwriter=tbwriter, name=name)
- asyncTBWriter.request_write(request)
-
-class ModelStatsTBRequest(TBWriteRequest):
- "Request object for model gradient statistics writes to Tensorboard."
- def __init__(self, model:nn.Module, iteration:int, tbwriter:SummaryWriter, name:str):
- super().__init__(tbwriter=tbwriter, iteration=iteration)
- self.gradients = [x.grad.clone().detach().cpu() for x in model.parameters() if x.grad is not None]
- self.name = name
-
- def _add_gradient_scalar(self, name:str, scalar_value)->None:
- "Writes a single scalar value for a gradient statistic to Tensorboard."
- tag = self.name + '/gradients/' + name
- self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=self.iteration)
-
- def _write_avg_norm(self, norms:[])->None:
- "Writes the average norm of the gradients to Tensorboard."
- avg_norm = sum(norms)/len(self.gradients)
- self._add_gradient_scalar('avg_norm', scalar_value=avg_norm)
-
- def _write_median_norm(self, norms:[])->None:
- "Writes the median norm of the gradients to Tensorboard."
- median_norm = statistics.median(norms)
- self._add_gradient_scalar('median_norm', scalar_value=median_norm)
-
- def _write_max_norm(self, norms:[])->None:
- "Writes the maximum norm of the gradients to Tensorboard."
- max_norm = max(norms)
- self._add_gradient_scalar('max_norm', scalar_value=max_norm)
-
- def _write_min_norm(self, norms:[])->None:
- "Writes the minimum norm of the gradients to Tensorboard."
- min_norm = min(norms)
- self._add_gradient_scalar('min_norm', scalar_value=min_norm)
-
- def _write_num_zeros(self)->None:
- "Writes the number of zeroes in the gradients to Tensorboard."
- gradient_nps = [to_np(x.data) for x in self.gradients]
- num_zeros = sum((np.asarray(x) == 0.0).sum() for x in gradient_nps)
- self._add_gradient_scalar('num_zeros', scalar_value=num_zeros)
-
- def _write_avg_gradient(self)->None:
- "Writes the average of the gradients to Tensorboard."
- avg_gradient = sum(x.data.mean() for x in self.gradients)/len(self.gradients)
- self._add_gradient_scalar('avg_gradient', scalar_value=avg_gradient)
-
- def _write_median_gradient(self)->None:
- "Writes the median of the gradients to Tensorboard."
- median_gradient = statistics.median(x.data.median() for x in self.gradients)
- self._add_gradient_scalar('median_gradient', scalar_value=median_gradient)
-
- def _write_max_gradient(self)->None:
- "Writes the maximum of the gradients to Tensorboard."
- max_gradient = max(x.data.max() for x in self.gradients)
- self._add_gradient_scalar('max_gradient', scalar_value=max_gradient)
-
- def _write_min_gradient(self)->None:
- "Writes the minimum of the gradients to Tensorboard."
- min_gradient = min(x.data.min() for x in self.gradients)
- self._add_gradient_scalar('min_gradient', scalar_value=min_gradient)
-
- def write(self)->None:
- "Writes model gradient statistics to Tensorboard."
- if len(self.gradients) == 0: return
- norms = [x.data.norm() for x in self.gradients]
- self._write_avg_norm(norms=norms)
- self._write_median_norm(norms=norms)
- self._write_max_norm(norms=norms)
- self._write_min_norm(norms=norms)
- self._write_num_zeros()
- self._write_avg_gradient()
- self._write_median_gradient()
- self._write_max_gradient()
- self._write_min_gradient()
-
-class ModelStatsTBWriter():
- "Writes model gradient statistics to Tensorboard."
- def write(self, model:nn.Module, iteration:int, tbwriter:SummaryWriter, name:str='model_stats')->None:
- "Writes model gradient statistics to Tensorboard."
- request = ModelStatsTBRequest(model=model, iteration=iteration, tbwriter=tbwriter, name=name)
- asyncTBWriter.request_write(request)
-
-class ImageTBRequest(TBWriteRequest):
- "Request object for model image output writes to Tensorboard."
- def __init__(self, learn:Learner, batch:Tuple, iteration:int, tbwriter:SummaryWriter, ds_type:DatasetType):
- super().__init__(tbwriter=tbwriter, iteration=iteration)
- self.image_sets = ModelImageSet.get_list_from_model(learn=learn, batch=batch, ds_type=ds_type)
- self.ds_type = ds_type
-
- def _write_images(self, name:str, images:[Tensor])->None:
- "Writes list of images as tensors to Tensorboard."
- tag = self.ds_type.name + ' ' + name
- self.tbwriter.add_image(tag=tag, img_tensor=vutils.make_grid(images, normalize=True), global_step=self.iteration)
-
- def _get_image_tensors(self)->([Tensor], [Tensor], [Tensor]):
- "Gets list of image tensors from lists of Image objects, as a tuple of original, generated and real(target) images."
- orig_images, gen_images, real_images = [], [], []
- for image_set in self.image_sets:
- orig_images.append(image_set.orig.px)
- gen_images.append(image_set.gen.px)
- real_images.append(image_set.real.px)
- return orig_images, gen_images, real_images
-
- def write(self)->None:
- "Writes original, generated and real(target) images to Tensorboard."
- orig_images, gen_images, real_images = self._get_image_tensors()
- self._write_images(name='orig images', images=orig_images)
- self._write_images(name='gen images', images=gen_images)
- self._write_images(name='real images', images=real_images)
-
-#If this isn't done async then this is noticeably slower
-class ImageTBWriter():
- "Writes model image output to Tensorboard."
- def __init__(self): super().__init__()
-
- def write(self, learn:Learner, trn_batch:Tuple, val_batch:Tuple, iteration:int, tbwriter:SummaryWriter)->None:
- "Writes training and validation batch images to Tensorboard."
- self._write_for_dstype(learn=learn, batch=val_batch, iteration=iteration, tbwriter=tbwriter, ds_type=DatasetType.Valid)
- self._write_for_dstype(learn=learn, batch=trn_batch, iteration=iteration, tbwriter=tbwriter, ds_type=DatasetType.Train)
-
- def _write_for_dstype(self, learn:Learner, batch:Tuple, iteration:int, tbwriter:SummaryWriter, ds_type:DatasetType)->None:
- "Writes batch images of specified DatasetType to Tensorboard."
- request = ImageTBRequest(learn=learn, batch=batch, iteration=iteration, tbwriter=tbwriter, ds_type=ds_type)
- asyncTBWriter.request_write(request)
-
-class GraphTBRequest(TBWriteRequest):
- "Request object for model histogram writes to Tensorboard."
- def __init__(self, model:nn.Module, tbwriter:SummaryWriter, input_to_model:torch.Tensor):
- super().__init__(tbwriter=tbwriter, iteration=0)
- self.model,self.input_to_model = model,input_to_model
-
- def write(self)->None:
- "Writes single model graph to Tensorboard."
- self.tbwriter.add_graph(model=self.model, input_to_model=self.input_to_model)
-
-class GraphTBWriter():
- "Writes model network graph to Tensorboard."
- def write(self, model:nn.Module, tbwriter:SummaryWriter, input_to_model:torch.Tensor)->None:
- "Writes model graph to Tensorboard."
- request = GraphTBRequest(model=model, tbwriter=tbwriter, input_to_model=input_to_model)
- asyncTBWriter.request_write(request)
diff --git a/spaces/Xenova/doodle-dash/assets/worker-2da0101e.js b/spaces/Xenova/doodle-dash/assets/worker-2da0101e.js
deleted file mode 100644
index 8d45185afc591e2fbda5485c1677950663fe49d4..0000000000000000000000000000000000000000
--- a/spaces/Xenova/doodle-dash/assets/worker-2da0101e.js
+++ /dev/null
@@ -1,1790 +0,0 @@
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x=this.high,I=this.low;return[x>>>24,x>>>16&255,x>>>8&255,255&x,I>>>24,I>>>16&255,I>>>8&255,255&I]},a.fromBytes=function(x,I,N){return N?a.fromBytesLE(x,I):a.fromBytesBE(x,I)},a.fromBytesLE=function(x,I){return new a(x[0]|x[1]<<8|x[2]<<16|x[3]<<24,x[4]|x[5]<<8|x[6]<<16|x[7]<<24,I)},a.fromBytesBE=function(x,I){return new a(x[4]<<24|x[5]<<16|x[6]<<8|x[7],x[0]<<24|x[1]<<16|x[2]<<8|x[3],I)}},1446:(y,n,a)=>{var u,c,p,s=a(2100),h=s.Reader,f=s.Writer,l=s.util,o=s.roots.default||(s.roots.default={});o.onnx=((p={}).Version=(u={},(c=Object.create(u))[u[0]="_START_VERSION"]=0,c[u[1]="IR_VERSION_2017_10_10"]=1,c[u[2]="IR_VERSION_2017_10_30"]=2,c[u[3]="IR_VERSION_2017_11_3"]=3,c[u[4]="IR_VERSION_2019_1_22"]=4,c[u[5]="IR_VERSION"]=5,c),p.AttributeProto=function(){function t(e){if(this.floats=[],this.ints=[],this.strings=[],this.tensors=[],this.graphs=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.name="",t.prototype.refAttrName="",t.prototype.docString="",t.prototype.type=0,t.prototype.f=0,t.prototype.i=l.Long?l.Long.fromBits(0,0,!1):0,t.prototype.s=l.newBuffer([]),t.prototype.t=null,t.prototype.g=null,t.prototype.floats=l.emptyArray,t.prototype.ints=l.emptyArray,t.prototype.strings=l.emptyArray,t.prototype.tensors=l.emptyArray,t.prototype.graphs=l.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.name!=null&&e.hasOwnProperty("name")&&r.uint32(10).string(e.name),e.f!=null&&e.hasOwnProperty("f")&&r.uint32(21).float(e.f),e.i!=null&&e.hasOwnProperty("i")&&r.uint32(24).int64(e.i),e.s!=null&&e.hasOwnProperty("s")&&r.uint32(34).bytes(e.s),e.t!=null&&e.hasOwnProperty("t")&&o.onnx.TensorProto.encode(e.t,r.uint32(42).fork()).ldelim(),e.g!=null&&e.hasOwnProperty("g")&&o.onnx.GraphProto.encode(e.g,r.uint32(50).fork()).ldelim(),e.floats!=null&&e.floats.length){r.uint32(58).fork();for(var 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g=e.uint32();switch(g>>>3){case 1:d.name=e.string();break;case 21:d.refAttrName=e.string();break;case 13:d.docString=e.string();break;case 20:d.type=e.int32();break;case 2:d.f=e.float();break;case 3:d.i=e.int64();break;case 4:d.s=e.bytes();break;case 5:d.t=o.onnx.TensorProto.decode(e,e.uint32());break;case 6:d.g=o.onnx.GraphProto.decode(e,e.uint32());break;case 7:if(d.floats&&d.floats.length||(d.floats=[]),(7&g)==2)for(var m=e.uint32()+e.pos;e.pos<m;)d.floats.push(e.float());else d.floats.push(e.float());break;case 8:if(d.ints&&d.ints.length||(d.ints=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)d.ints.push(e.int64());else d.ints.push(e.int64());break;case 9:d.strings&&d.strings.length||(d.strings=[]),d.strings.push(e.bytes());break;case 10:d.tensors&&d.tensors.length||(d.tensors=[]),d.tensors.push(o.onnx.TensorProto.decode(e,e.uint32()));break;case 11:d.graphs&&d.graphs.length||(d.graphs=[]),d.graphs.push(o.onnx.GraphProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.name!=null&&e.hasOwnProperty("name")&&!l.isString(e.name))return"name: string expected";if(e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&!l.isString(e.refAttrName))return"refAttrName: string expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!l.isString(e.docString))return"docString: string expected";if(e.type!=null&&e.hasOwnProperty("type"))switch(e.type){default:return"type: enum value expected";case 0:case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:}if(e.f!=null&&e.hasOwnProperty("f")&&typeof e.f!="number")return"f: number expected";if(e.i!=null&&e.hasOwnProperty("i")&&!(l.isInteger(e.i)||e.i&&l.isInteger(e.i.low)&&l.isInteger(e.i.high)))return"i: integer|Long expected";if(e.s!=null&&e.hasOwnProperty("s")&&!(e.s&&typeof e.s.length=="number"||l.isString(e.s)))return"s: buffer expected";if(e.t!=null&&e.hasOwnProperty("t")&&(i=o.onnx.TensorProto.verify(e.t)))return"t."+i;if(e.g!=null&&e.hasOwnProperty("g")&&(i=o.onnx.GraphProto.verify(e.g)))return"g."+i;if(e.floats!=null&&e.hasOwnProperty("floats")){if(!Array.isArray(e.floats))return"floats: array expected";for(var r=0;r<e.floats.length;++r)if(typeof e.floats[r]!="number")return"floats: number[] expected"}if(e.ints!=null&&e.hasOwnProperty("ints")){if(!Array.isArray(e.ints))return"ints: array expected";for(r=0;r<e.ints.length;++r)if(!(l.isInteger(e.ints[r])||e.ints[r]&&l.isInteger(e.ints[r].low)&&l.isInteger(e.ints[r].high)))return"ints: integer|Long[] expected"}if(e.strings!=null&&e.hasOwnProperty("strings")){if(!Array.isArray(e.strings))return"strings: array expected";for(r=0;r<e.strings.length;++r)if(!(e.strings[r]&&typeof e.strings[r].length=="number"||l.isString(e.strings[r])))return"strings: buffer[] expected"}if(e.tensors!=null&&e.hasOwnProperty("tensors")){if(!Array.isArray(e.tensors))return"tensors: array expected";for(r=0;r<e.tensors.length;++r)if(i=o.onnx.TensorProto.verify(e.tensors[r]))return"tensors."+i}if(e.graphs!=null&&e.hasOwnProperty("graphs")){if(!Array.isArray(e.graphs))return"graphs: array expected";for(r=0;r<e.graphs.length;++r){var i;if(i=o.onnx.GraphProto.verify(e.graphs[r]))return"graphs."+i}}return null},t.fromObject=function(e){if(e instanceof o.onnx.AttributeProto)return e;var r=new o.onnx.AttributeProto;switch(e.name!=null&&(r.name=String(e.name)),e.refAttrName!=null&&(r.refAttrName=String(e.refAttrName)),e.docString!=null&&(r.docString=String(e.docString)),e.type){case"UNDEFINED":case 0:r.type=0;break;case"FLOAT":case 1:r.type=1;break;case"INT":case 2:r.type=2;break;case"STRING":case 3:r.type=3;break;case"TENSOR":case 4:r.type=4;break;case"GRAPH":case 5:r.type=5;break;case"FLOATS":case 6:r.type=6;break;case"INTS":case 7:r.type=7;break;case"STRINGS":case 8:r.type=8;break;case"TENSORS":case 9:r.type=9;break;case"GRAPHS":case 10:r.type=10}if(e.f!=null&&(r.f=Number(e.f)),e.i!=null&&(l.Long?(r.i=l.Long.fromValue(e.i)).unsigned=!1:typeof e.i=="string"?r.i=parseInt(e.i,10):typeof e.i=="number"?r.i=e.i:typeof e.i=="object"&&(r.i=new l.LongBits(e.i.low>>>0,e.i.high>>>0).toNumber())),e.s!=null&&(typeof e.s=="string"?l.base64.decode(e.s,r.s=l.newBuffer(l.base64.length(e.s)),0):e.s.length&&(r.s=e.s)),e.t!=null){if(typeof e.t!="object")throw TypeError(".onnx.AttributeProto.t: object expected");r.t=o.onnx.TensorProto.fromObject(e.t)}if(e.g!=null){if(typeof e.g!="object")throw TypeError(".onnx.AttributeProto.g: object expected");r.g=o.onnx.GraphProto.fromObject(e.g)}if(e.floats){if(!Array.isArray(e.floats))throw TypeError(".onnx.AttributeProto.floats: array expected");r.floats=[];for(var i=0;i<e.floats.length;++i)r.floats[i]=Number(e.floats[i])}if(e.ints){if(!Array.isArray(e.ints))throw TypeError(".onnx.AttributeProto.ints: array 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TypeError(".onnx.AttributeProto.graphs: array expected");for(r.graphs=[],i=0;i<e.graphs.length;++i){if(typeof e.graphs[i]!="object")throw TypeError(".onnx.AttributeProto.graphs: object expected");r.graphs[i]=o.onnx.GraphProto.fromObject(e.graphs[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.floats=[],i.ints=[],i.strings=[],i.tensors=[],i.graphs=[]),r.defaults){if(i.name="",i.f=0,l.Long){var d=new l.Long(0,0,!1);i.i=r.longs===String?d.toString():r.longs===Number?d.toNumber():d}else i.i=r.longs===String?"0":0;r.bytes===String?i.s="":(i.s=[],r.bytes!==Array&&(i.s=l.newBuffer(i.s))),i.t=null,i.g=null,i.docString="",i.type=r.enums===String?"UNDEFINED":0,i.refAttrName=""}if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.f!=null&&e.hasOwnProperty("f")&&(i.f=r.json&&!isFinite(e.f)?String(e.f):e.f),e.i!=null&&e.hasOwnProperty("i")&&(typeof 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l.LongBits(e.ints[g].low>>>0,e.ints[g].high>>>0).toNumber():e.ints[g];if(e.strings&&e.strings.length)for(i.strings=[],g=0;g<e.strings.length;++g)i.strings[g]=r.bytes===String?l.base64.encode(e.strings[g],0,e.strings[g].length):r.bytes===Array?Array.prototype.slice.call(e.strings[g]):e.strings[g];if(e.tensors&&e.tensors.length)for(i.tensors=[],g=0;g<e.tensors.length;++g)i.tensors[g]=o.onnx.TensorProto.toObject(e.tensors[g],r);if(e.graphs&&e.graphs.length)for(i.graphs=[],g=0;g<e.graphs.length;++g)i.graphs[g]=o.onnx.GraphProto.toObject(e.graphs[g],r);return e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.type!=null&&e.hasOwnProperty("type")&&(i.type=r.enums===String?o.onnx.AttributeProto.AttributeType[e.type]:e.type),e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&(i.refAttrName=e.refAttrName),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t.AttributeType=function(){var e={},r=Object.create(e);return r[e[0]="UNDEFINED"]=0,r[e[1]="FLOAT"]=1,r[e[2]="INT"]=2,r[e[3]="STRING"]=3,r[e[4]="TENSOR"]=4,r[e[5]="GRAPH"]=5,r[e[6]="FLOATS"]=6,r[e[7]="INTS"]=7,r[e[8]="STRINGS"]=8,r[e[9]="TENSORS"]=9,r[e[10]="GRAPHS"]=10,r}(),t}(),p.ValueInfoProto=function(){function t(e){if(e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.name="",t.prototype.type=null,t.prototype.docString="",t.create=function(e){return new t(e)},t.encode=function(e,r){return r||(r=f.create()),e.name!=null&&e.hasOwnProperty("name")&&r.uint32(10).string(e.name),e.type!=null&&e.hasOwnProperty("type")&&o.onnx.TypeProto.encode(e.type,r.uint32(18).fork()).ldelim(),e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(26).string(e.docString),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.ValueInfoProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 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e.docString!=null&&(r.docString=String(e.docString)),r},t.toObject=function(e,r){r||(r={});var i={};return r.defaults&&(i.name="",i.type=null,i.docString=""),e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.type!=null&&e.hasOwnProperty("type")&&(i.type=o.onnx.TypeProto.toObject(e.type,r)),e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.NodeProto=function(){function t(e){if(this.input=[],this.output=[],this.attribute=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.input=l.emptyArray,t.prototype.output=l.emptyArray,t.prototype.name="",t.prototype.opType="",t.prototype.domain="",t.prototype.attribute=l.emptyArray,t.prototype.docString="",t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.input!=null&&e.input.length)for(var i=0;i<e.input.length;++i)r.uint32(10).string(e.input[i]);if(e.output!=null&&e.output.length)for(i=0;i<e.output.length;++i)r.uint32(18).string(e.output[i]);if(e.name!=null&&e.hasOwnProperty("name")&&r.uint32(26).string(e.name),e.opType!=null&&e.hasOwnProperty("opType")&&r.uint32(34).string(e.opType),e.attribute!=null&&e.attribute.length)for(i=0;i<e.attribute.length;++i)o.onnx.AttributeProto.encode(e.attribute[i],r.uint32(42).fork()).ldelim();return e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(50).string(e.docString),e.domain!=null&&e.hasOwnProperty("domain")&&r.uint32(58).string(e.domain),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.NodeProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:d.input&&d.input.length||(d.input=[]),d.input.push(e.string());break;case 2:d.output&&d.output.length||(d.output=[]),d.output.push(e.string());break;case 3:d.name=e.string();break;case 4:d.opType=e.string();break;case 7:d.domain=e.string();break;case 5:d.attribute&&d.attribute.length||(d.attribute=[]),d.attribute.push(o.onnx.AttributeProto.decode(e,e.uint32()));break;case 6:d.docString=e.string();break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.input!=null&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(var r=0;r<e.input.length;++r)if(!l.isString(e.input[r]))return"input: string[] expected"}if(e.output!=null&&e.hasOwnProperty("output")){if(!Array.isArray(e.output))return"output: array expected";for(r=0;r<e.output.length;++r)if(!l.isString(e.output[r]))return"output: string[] expected"}if(e.name!=null&&e.hasOwnProperty("name")&&!l.isString(e.name))return"name: string expected";if(e.opType!=null&&e.hasOwnProperty("opType")&&!l.isString(e.opType))return"opType: string expected";if(e.domain!=null&&e.hasOwnProperty("domain")&&!l.isString(e.domain))return"domain: string expected";if(e.attribute!=null&&e.hasOwnProperty("attribute")){if(!Array.isArray(e.attribute))return"attribute: array expected";for(r=0;r<e.attribute.length;++r){var i=o.onnx.AttributeProto.verify(e.attribute[r]);if(i)return"attribute."+i}}return e.docString!=null&&e.hasOwnProperty("docString")&&!l.isString(e.docString)?"docString: string expected":null},t.fromObject=function(e){if(e instanceof o.onnx.NodeProto)return e;var r=new o.onnx.NodeProto;if(e.input){if(!Array.isArray(e.input))throw TypeError(".onnx.NodeProto.input: array expected");r.input=[];for(var i=0;i<e.input.length;++i)r.input[i]=String(e.input[i])}if(e.output){if(!Array.isArray(e.output))throw TypeError(".onnx.NodeProto.output: array expected");for(r.output=[],i=0;i<e.output.length;++i)r.output[i]=String(e.output[i])}if(e.name!=null&&(r.name=String(e.name)),e.opType!=null&&(r.opType=String(e.opType)),e.domain!=null&&(r.domain=String(e.domain)),e.attribute){if(!Array.isArray(e.attribute))throw TypeError(".onnx.NodeProto.attribute: array expected");for(r.attribute=[],i=0;i<e.attribute.length;++i){if(typeof e.attribute[i]!="object")throw TypeError(".onnx.NodeProto.attribute: object expected");r.attribute[i]=o.onnx.AttributeProto.fromObject(e.attribute[i])}}return e.docString!=null&&(r.docString=String(e.docString)),r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.input=[],i.output=[],i.attribute=[]),r.defaults&&(i.name="",i.opType="",i.docString="",i.domain=""),e.input&&e.input.length){i.input=[];for(var d=0;d<e.input.length;++d)i.input[d]=e.input[d]}if(e.output&&e.output.length)for(i.output=[],d=0;d<e.output.length;++d)i.output[d]=e.output[d];if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.opType!=null&&e.hasOwnProperty("opType")&&(i.opType=e.opType),e.attribute&&e.attribute.length)for(i.attribute=[],d=0;d<e.attribute.length;++d)i.attribute[d]=o.onnx.AttributeProto.toObject(e.attribute[d],r);return e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.domain!=null&&e.hasOwnProperty("domain")&&(i.domain=e.domain),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.ModelProto=function(){function t(e){if(this.opsetImport=[],this.metadataProps=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.irVersion=l.Long?l.Long.fromBits(0,0,!1):0,t.prototype.opsetImport=l.emptyArray,t.prototype.producerName="",t.prototype.producerVersion="",t.prototype.domain="",t.prototype.modelVersion=l.Long?l.Long.fromBits(0,0,!1):0,t.prototype.docString="",t.prototype.graph=null,t.prototype.metadataProps=l.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.irVersion!=null&&e.hasOwnProperty("irVersion")&&r.uint32(8).int64(e.irVersion),e.producerName!=null&&e.hasOwnProperty("producerName")&&r.uint32(18).string(e.producerName),e.producerVersion!=null&&e.hasOwnProperty("producerVersion")&&r.uint32(26).string(e.producerVersion),e.domain!=null&&e.hasOwnProperty("domain")&&r.uint32(34).string(e.domain),e.modelVersion!=null&&e.hasOwnProperty("modelVersion")&&r.uint32(40).int64(e.modelVersion),e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(50).string(e.docString),e.graph!=null&&e.hasOwnProperty("graph")&&o.onnx.GraphProto.encode(e.graph,r.uint32(58).fork()).ldelim(),e.opsetImport!=null&&e.opsetImport.length)for(var i=0;i<e.opsetImport.length;++i)o.onnx.OperatorSetIdProto.encode(e.opsetImport[i],r.uint32(66).fork()).ldelim();if(e.metadataProps!=null&&e.metadataProps.length)for(i=0;i<e.metadataProps.length;++i)o.onnx.StringStringEntryProto.encode(e.metadataProps[i],r.uint32(114).fork()).ldelim();return r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.ModelProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:d.irVersion=e.int64();break;case 8:d.opsetImport&&d.opsetImport.length||(d.opsetImport=[]),d.opsetImport.push(o.onnx.OperatorSetIdProto.decode(e,e.uint32()));break;case 2:d.producerName=e.string();break;case 3:d.producerVersion=e.string();break;case 4:d.domain=e.string();break;case 5:d.modelVersion=e.int64();break;case 6:d.docString=e.string();break;case 7:d.graph=o.onnx.GraphProto.decode(e,e.uint32());break;case 14:d.metadataProps&&d.metadataProps.length||(d.metadataProps=[]),d.metadataProps.push(o.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.irVersion!=null&&e.hasOwnProperty("irVersion")&&!(l.isInteger(e.irVersion)||e.irVersion&&l.isInteger(e.irVersion.low)&&l.isInteger(e.irVersion.high)))return"irVersion: integer|Long expected";if(e.opsetImport!=null&&e.hasOwnProperty("opsetImport")){if(!Array.isArray(e.opsetImport))return"opsetImport: array expected";for(var r=0;r<e.opsetImport.length;++r)if(i=o.onnx.OperatorSetIdProto.verify(e.opsetImport[r]))return"opsetImport."+i}if(e.producerName!=null&&e.hasOwnProperty("producerName")&&!l.isString(e.producerName))return"producerName: string expected";if(e.producerVersion!=null&&e.hasOwnProperty("producerVersion")&&!l.isString(e.producerVersion))return"producerVersion: string expected";if(e.domain!=null&&e.hasOwnProperty("domain")&&!l.isString(e.domain))return"domain: string expected";if(e.modelVersion!=null&&e.hasOwnProperty("modelVersion")&&!(l.isInteger(e.modelVersion)||e.modelVersion&&l.isInteger(e.modelVersion.low)&&l.isInteger(e.modelVersion.high)))return"modelVersion: integer|Long expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!l.isString(e.docString))return"docString: string expected";if(e.graph!=null&&e.hasOwnProperty("graph")&&(i=o.onnx.GraphProto.verify(e.graph)))return"graph."+i;if(e.metadataProps!=null&&e.hasOwnProperty("metadataProps")){if(!Array.isArray(e.metadataProps))return"metadataProps: array expected";for(r=0;r<e.metadataProps.length;++r){var i;if(i=o.onnx.StringStringEntryProto.verify(e.metadataProps[r]))return"metadataProps."+i}}return null},t.fromObject=function(e){if(e instanceof o.onnx.ModelProto)return e;var r=new o.onnx.ModelProto;if(e.irVersion!=null&&(l.Long?(r.irVersion=l.Long.fromValue(e.irVersion)).unsigned=!1:typeof e.irVersion=="string"?r.irVersion=parseInt(e.irVersion,10):typeof e.irVersion=="number"?r.irVersion=e.irVersion:typeof e.irVersion=="object"&&(r.irVersion=new l.LongBits(e.irVersion.low>>>0,e.irVersion.high>>>0).toNumber())),e.opsetImport){if(!Array.isArray(e.opsetImport))throw TypeError(".onnx.ModelProto.opsetImport: array expected");r.opsetImport=[];for(var i=0;i<e.opsetImport.length;++i){if(typeof e.opsetImport[i]!="object")throw TypeError(".onnx.ModelProto.opsetImport: object expected");r.opsetImport[i]=o.onnx.OperatorSetIdProto.fromObject(e.opsetImport[i])}}if(e.producerName!=null&&(r.producerName=String(e.producerName)),e.producerVersion!=null&&(r.producerVersion=String(e.producerVersion)),e.domain!=null&&(r.domain=String(e.domain)),e.modelVersion!=null&&(l.Long?(r.modelVersion=l.Long.fromValue(e.modelVersion)).unsigned=!1:typeof e.modelVersion=="string"?r.modelVersion=parseInt(e.modelVersion,10):typeof e.modelVersion=="number"?r.modelVersion=e.modelVersion:typeof e.modelVersion=="object"&&(r.modelVersion=new l.LongBits(e.modelVersion.low>>>0,e.modelVersion.high>>>0).toNumber())),e.docString!=null&&(r.docString=String(e.docString)),e.graph!=null){if(typeof e.graph!="object")throw TypeError(".onnx.ModelProto.graph: object expected");r.graph=o.onnx.GraphProto.fromObject(e.graph)}if(e.metadataProps){if(!Array.isArray(e.metadataProps))throw TypeError(".onnx.ModelProto.metadataProps: array expected");for(r.metadataProps=[],i=0;i<e.metadataProps.length;++i){if(typeof e.metadataProps[i]!="object")throw TypeError(".onnx.ModelProto.metadataProps: object expected");r.metadataProps[i]=o.onnx.StringStringEntryProto.fromObject(e.metadataProps[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.opsetImport=[],i.metadataProps=[]),r.defaults){if(l.Long){var d=new l.Long(0,0,!1);i.irVersion=r.longs===String?d.toString():r.longs===Number?d.toNumber():d}else i.irVersion=r.longs===String?"0":0;i.producerName="",i.producerVersion="",i.domain="",l.Long?(d=new l.Long(0,0,!1),i.modelVersion=r.longs===String?d.toString():r.longs===Number?d.toNumber():d):i.modelVersion=r.longs===String?"0":0,i.docString="",i.graph=null}if(e.irVersion!=null&&e.hasOwnProperty("irVersion")&&(typeof e.irVersion=="number"?i.irVersion=r.longs===String?String(e.irVersion):e.irVersion:i.irVersion=r.longs===String?l.Long.prototype.toString.call(e.irVersion):r.longs===Number?new l.LongBits(e.irVersion.low>>>0,e.irVersion.high>>>0).toNumber():e.irVersion),e.producerName!=null&&e.hasOwnProperty("producerName")&&(i.producerName=e.producerName),e.producerVersion!=null&&e.hasOwnProperty("producerVersion")&&(i.producerVersion=e.producerVersion),e.domain!=null&&e.hasOwnProperty("domain")&&(i.domain=e.domain),e.modelVersion!=null&&e.hasOwnProperty("modelVersion")&&(typeof e.modelVersion=="number"?i.modelVersion=r.longs===String?String(e.modelVersion):e.modelVersion:i.modelVersion=r.longs===String?l.Long.prototype.toString.call(e.modelVersion):r.longs===Number?new l.LongBits(e.modelVersion.low>>>0,e.modelVersion.high>>>0).toNumber():e.modelVersion),e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.graph!=null&&e.hasOwnProperty("graph")&&(i.graph=o.onnx.GraphProto.toObject(e.graph,r)),e.opsetImport&&e.opsetImport.length){i.opsetImport=[];for(var g=0;g<e.opsetImport.length;++g)i.opsetImport[g]=o.onnx.OperatorSetIdProto.toObject(e.opsetImport[g],r)}if(e.metadataProps&&e.metadataProps.length)for(i.metadataProps=[],g=0;g<e.metadataProps.length;++g)i.metadataProps[g]=o.onnx.StringStringEntryProto.toObject(e.metadataProps[g],r);return i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.StringStringEntryProto=function(){function t(e){if(e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.key="",t.prototype.value="",t.create=function(e){return new t(e)},t.encode=function(e,r){return r||(r=f.create()),e.key!=null&&e.hasOwnProperty("key")&&r.uint32(10).string(e.key),e.value!=null&&e.hasOwnProperty("value")&&r.uint32(18).string(e.value),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.StringStringEntryProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:d.key=e.string();break;case 2:d.value=e.string();break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){return typeof e!="object"||e===null?"object expected":e.key!=null&&e.hasOwnProperty("key")&&!l.isString(e.key)?"key: string expected":e.value!=null&&e.hasOwnProperty("value")&&!l.isString(e.value)?"value: string expected":null},t.fromObject=function(e){if(e instanceof o.onnx.StringStringEntryProto)return e;var r=new o.onnx.StringStringEntryProto;return e.key!=null&&(r.key=String(e.key)),e.value!=null&&(r.value=String(e.value)),r},t.toObject=function(e,r){r||(r={});var i={};return r.defaults&&(i.key="",i.value=""),e.key!=null&&e.hasOwnProperty("key")&&(i.key=e.key),e.value!=null&&e.hasOwnProperty("value")&&(i.value=e.value),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.TensorAnnotation=function(){function t(e){if(this.quantParameterTensorNames=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.tensorName="",t.prototype.quantParameterTensorNames=l.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.tensorName!=null&&e.hasOwnProperty("tensorName")&&r.uint32(10).string(e.tensorName),e.quantParameterTensorNames!=null&&e.quantParameterTensorNames.length)for(var i=0;i<e.quantParameterTensorNames.length;++i)o.onnx.StringStringEntryProto.encode(e.quantParameterTensorNames[i],r.uint32(18).fork()).ldelim();return r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.TensorAnnotation;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:d.tensorName=e.string();break;case 2:d.quantParameterTensorNames&&d.quantParameterTensorNames.length||(d.quantParameterTensorNames=[]),d.quantParameterTensorNames.push(o.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.tensorName!=null&&e.hasOwnProperty("tensorName")&&!l.isString(e.tensorName))return"tensorName: string expected";if(e.quantParameterTensorNames!=null&&e.hasOwnProperty("quantParameterTensorNames")){if(!Array.isArray(e.quantParameterTensorNames))return"quantParameterTensorNames: array expected";for(var r=0;r<e.quantParameterTensorNames.length;++r){var i=o.onnx.StringStringEntryProto.verify(e.quantParameterTensorNames[r]);if(i)return"quantParameterTensorNames."+i}}return null},t.fromObject=function(e){if(e instanceof o.onnx.TensorAnnotation)return e;var r=new o.onnx.TensorAnnotation;if(e.tensorName!=null&&(r.tensorName=String(e.tensorName)),e.quantParameterTensorNames){if(!Array.isArray(e.quantParameterTensorNames))throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: array expected");r.quantParameterTensorNames=[];for(var i=0;i<e.quantParameterTensorNames.length;++i){if(typeof e.quantParameterTensorNames[i]!="object")throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: object expected");r.quantParameterTensorNames[i]=o.onnx.StringStringEntryProto.fromObject(e.quantParameterTensorNames[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.quantParameterTensorNames=[]),r.defaults&&(i.tensorName=""),e.tensorName!=null&&e.hasOwnProperty("tensorName")&&(i.tensorName=e.tensorName),e.quantParameterTensorNames&&e.quantParameterTensorNames.length){i.quantParameterTensorNames=[];for(var d=0;d<e.quantParameterTensorNames.length;++d)i.quantParameterTensorNames[d]=o.onnx.StringStringEntryProto.toObject(e.quantParameterTensorNames[d],r)}return i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.GraphProto=function(){function t(e){if(this.node=[],this.initializer=[],this.input=[],this.output=[],this.valueInfo=[],this.quantizationAnnotation=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.node=l.emptyArray,t.prototype.name="",t.prototype.initializer=l.emptyArray,t.prototype.docString="",t.prototype.input=l.emptyArray,t.prototype.output=l.emptyArray,t.prototype.valueInfo=l.emptyArray,t.prototype.quantizationAnnotation=l.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.node!=null&&e.node.length)for(var i=0;i<e.node.length;++i)o.onnx.NodeProto.encode(e.node[i],r.uint32(10).fork()).ldelim();if(e.name!=null&&e.hasOwnProperty("name")&&r.uint32(18).string(e.name),e.initializer!=null&&e.initializer.length)for(i=0;i<e.initializer.length;++i)o.onnx.TensorProto.encode(e.initializer[i],r.uint32(42).fork()).ldelim();if(e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(82).string(e.docString),e.input!=null&&e.input.length)for(i=0;i<e.input.length;++i)o.onnx.ValueInfoProto.encode(e.input[i],r.uint32(90).fork()).ldelim();if(e.output!=null&&e.output.length)for(i=0;i<e.output.length;++i)o.onnx.ValueInfoProto.encode(e.output[i],r.uint32(98).fork()).ldelim();if(e.valueInfo!=null&&e.valueInfo.length)for(i=0;i<e.valueInfo.length;++i)o.onnx.ValueInfoProto.encode(e.valueInfo[i],r.uint32(106).fork()).ldelim();if(e.quantizationAnnotation!=null&&e.quantizationAnnotation.length)for(i=0;i<e.quantizationAnnotation.length;++i)o.onnx.TensorAnnotation.encode(e.quantizationAnnotation[i],r.uint32(114).fork()).ldelim();return r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.GraphProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:d.node&&d.node.length||(d.node=[]),d.node.push(o.onnx.NodeProto.decode(e,e.uint32()));break;case 2:d.name=e.string();break;case 5:d.initializer&&d.initializer.length||(d.initializer=[]),d.initializer.push(o.onnx.TensorProto.decode(e,e.uint32()));break;case 10:d.docString=e.string();break;case 11:d.input&&d.input.length||(d.input=[]),d.input.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 12:d.output&&d.output.length||(d.output=[]),d.output.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 13:d.valueInfo&&d.valueInfo.length||(d.valueInfo=[]),d.valueInfo.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 14:d.quantizationAnnotation&&d.quantizationAnnotation.length||(d.quantizationAnnotation=[]),d.quantizationAnnotation.push(o.onnx.TensorAnnotation.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.node!=null&&e.hasOwnProperty("node")){if(!Array.isArray(e.node))return"node: array expected";for(var r=0;r<e.node.length;++r)if(i=o.onnx.NodeProto.verify(e.node[r]))return"node."+i}if(e.name!=null&&e.hasOwnProperty("name")&&!l.isString(e.name))return"name: string expected";if(e.initializer!=null&&e.hasOwnProperty("initializer")){if(!Array.isArray(e.initializer))return"initializer: array expected";for(r=0;r<e.initializer.length;++r)if(i=o.onnx.TensorProto.verify(e.initializer[r]))return"initializer."+i}if(e.docString!=null&&e.hasOwnProperty("docString")&&!l.isString(e.docString))return"docString: string expected";if(e.input!=null&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(r=0;r<e.input.length;++r)if(i=o.onnx.ValueInfoProto.verify(e.input[r]))return"input."+i}if(e.output!=null&&e.hasOwnProperty("output")){if(!Array.isArray(e.output))return"output: array expected";for(r=0;r<e.output.length;++r)if(i=o.onnx.ValueInfoProto.verify(e.output[r]))return"output."+i}if(e.valueInfo!=null&&e.hasOwnProperty("valueInfo")){if(!Array.isArray(e.valueInfo))return"valueInfo: array expected";for(r=0;r<e.valueInfo.length;++r)if(i=o.onnx.ValueInfoProto.verify(e.valueInfo[r]))return"valueInfo."+i}if(e.quantizationAnnotation!=null&&e.hasOwnProperty("quantizationAnnotation")){if(!Array.isArray(e.quantizationAnnotation))return"quantizationAnnotation: array expected";for(r=0;r<e.quantizationAnnotation.length;++r){var i;if(i=o.onnx.TensorAnnotation.verify(e.quantizationAnnotation[r]))return"quantizationAnnotation."+i}}return null},t.fromObject=function(e){if(e instanceof o.onnx.GraphProto)return e;var r=new o.onnx.GraphProto;if(e.node){if(!Array.isArray(e.node))throw TypeError(".onnx.GraphProto.node: array expected");r.node=[];for(var i=0;i<e.node.length;++i){if(typeof e.node[i]!="object")throw TypeError(".onnx.GraphProto.node: object expected");r.node[i]=o.onnx.NodeProto.fromObject(e.node[i])}}if(e.name!=null&&(r.name=String(e.name)),e.initializer){if(!Array.isArray(e.initializer))throw TypeError(".onnx.GraphProto.initializer: array expected");for(r.initializer=[],i=0;i<e.initializer.length;++i){if(typeof e.initializer[i]!="object")throw TypeError(".onnx.GraphProto.initializer: object expected");r.initializer[i]=o.onnx.TensorProto.fromObject(e.initializer[i])}}if(e.docString!=null&&(r.docString=String(e.docString)),e.input){if(!Array.isArray(e.input))throw TypeError(".onnx.GraphProto.input: array expected");for(r.input=[],i=0;i<e.input.length;++i){if(typeof e.input[i]!="object")throw TypeError(".onnx.GraphProto.input: object expected");r.input[i]=o.onnx.ValueInfoProto.fromObject(e.input[i])}}if(e.output){if(!Array.isArray(e.output))throw TypeError(".onnx.GraphProto.output: array expected");for(r.output=[],i=0;i<e.output.length;++i){if(typeof e.output[i]!="object")throw TypeError(".onnx.GraphProto.output: object expected");r.output[i]=o.onnx.ValueInfoProto.fromObject(e.output[i])}}if(e.valueInfo){if(!Array.isArray(e.valueInfo))throw TypeError(".onnx.GraphProto.valueInfo: array expected");for(r.valueInfo=[],i=0;i<e.valueInfo.length;++i){if(typeof e.valueInfo[i]!="object")throw TypeError(".onnx.GraphProto.valueInfo: object expected");r.valueInfo[i]=o.onnx.ValueInfoProto.fromObject(e.valueInfo[i])}}if(e.quantizationAnnotation){if(!Array.isArray(e.quantizationAnnotation))throw TypeError(".onnx.GraphProto.quantizationAnnotation: array expected");for(r.quantizationAnnotation=[],i=0;i<e.quantizationAnnotation.length;++i){if(typeof e.quantizationAnnotation[i]!="object")throw TypeError(".onnx.GraphProto.quantizationAnnotation: object expected");r.quantizationAnnotation[i]=o.onnx.TensorAnnotation.fromObject(e.quantizationAnnotation[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.node=[],i.initializer=[],i.input=[],i.output=[],i.valueInfo=[],i.quantizationAnnotation=[]),r.defaults&&(i.name="",i.docString=""),e.node&&e.node.length){i.node=[];for(var d=0;d<e.node.length;++d)i.node[d]=o.onnx.NodeProto.toObject(e.node[d],r)}if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.initializer&&e.initializer.length)for(i.initializer=[],d=0;d<e.initializer.length;++d)i.initializer[d]=o.onnx.TensorProto.toObject(e.initializer[d],r);if(e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.input&&e.input.length)for(i.input=[],d=0;d<e.input.length;++d)i.input[d]=o.onnx.ValueInfoProto.toObject(e.input[d],r);if(e.output&&e.output.length)for(i.output=[],d=0;d<e.output.length;++d)i.output[d]=o.onnx.ValueInfoProto.toObject(e.output[d],r);if(e.valueInfo&&e.valueInfo.length)for(i.valueInfo=[],d=0;d<e.valueInfo.length;++d)i.valueInfo[d]=o.onnx.ValueInfoProto.toObject(e.valueInfo[d],r);if(e.quantizationAnnotation&&e.quantizationAnnotation.length)for(i.quantizationAnnotation=[],d=0;d<e.quantizationAnnotation.length;++d)i.quantizationAnnotation[d]=o.onnx.TensorAnnotation.toObject(e.quantizationAnnotation[d],r);return i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.TensorProto=function(){function t(e){if(this.dims=[],this.floatData=[],this.int32Data=[],this.stringData=[],this.int64Data=[],this.externalData=[],this.doubleData=[],this.uint64Data=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.dims=l.emptyArray,t.prototype.dataType=0,t.prototype.segment=null,t.prototype.floatData=l.emptyArray,t.prototype.int32Data=l.emptyArray,t.prototype.stringData=l.emptyArray,t.prototype.int64Data=l.emptyArray,t.prototype.name="",t.prototype.docString="",t.prototype.rawData=l.newBuffer([]),t.prototype.externalData=l.emptyArray,t.prototype.dataLocation=0,t.prototype.doubleData=l.emptyArray,t.prototype.uint64Data=l.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.dims!=null&&e.dims.length){r.uint32(10).fork();for(var i=0;i<e.dims.length;++i)r.int64(e.dims[i]);r.ldelim()}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&r.uint32(16).int32(e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&o.onnx.TensorProto.Segment.encode(e.segment,r.uint32(26).fork()).ldelim(),e.floatData!=null&&e.floatData.length){for(r.uint32(34).fork(),i=0;i<e.floatData.length;++i)r.float(e.floatData[i]);r.ldelim()}if(e.int32Data!=null&&e.int32Data.length){for(r.uint32(42).fork(),i=0;i<e.int32Data.length;++i)r.int32(e.int32Data[i]);r.ldelim()}if(e.stringData!=null&&e.stringData.length)for(i=0;i<e.stringData.length;++i)r.uint32(50).bytes(e.stringData[i]);if(e.int64Data!=null&&e.int64Data.length){for(r.uint32(58).fork(),i=0;i<e.int64Data.length;++i)r.int64(e.int64Data[i]);r.ldelim()}if(e.name!=null&&e.hasOwnProperty("name")&&r.uint32(66).string(e.name),e.rawData!=null&&e.hasOwnProperty("rawData")&&r.uint32(74).bytes(e.rawData),e.doubleData!=null&&e.doubleData.length){for(r.uint32(82).fork(),i=0;i<e.doubleData.length;++i)r.double(e.doubleData[i]);r.ldelim()}if(e.uint64Data!=null&&e.uint64Data.length){for(r.uint32(90).fork(),i=0;i<e.uint64Data.length;++i)r.uint64(e.uint64Data[i]);r.ldelim()}if(e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(98).string(e.docString),e.externalData!=null&&e.externalData.length)for(i=0;i<e.externalData.length;++i)o.onnx.StringStringEntryProto.encode(e.externalData[i],r.uint32(106).fork()).ldelim();return e.dataLocation!=null&&e.hasOwnProperty("dataLocation")&&r.uint32(112).int32(e.dataLocation),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.TensorProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:if(d.dims&&d.dims.length||(d.dims=[]),(7&g)==2)for(var m=e.uint32()+e.pos;e.pos<m;)d.dims.push(e.int64());else d.dims.push(e.int64());break;case 2:d.dataType=e.int32();break;case 3:d.segment=o.onnx.TensorProto.Segment.decode(e,e.uint32());break;case 4:if(d.floatData&&d.floatData.length||(d.floatData=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)d.floatData.push(e.float());else d.floatData.push(e.float());break;case 5:if(d.int32Data&&d.int32Data.length||(d.int32Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)d.int32Data.push(e.int32());else d.int32Data.push(e.int32());break;case 6:d.stringData&&d.stringData.length||(d.stringData=[]),d.stringData.push(e.bytes());break;case 7:if(d.int64Data&&d.int64Data.length||(d.int64Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)d.int64Data.push(e.int64());else d.int64Data.push(e.int64());break;case 8:d.name=e.string();break;case 12:d.docString=e.string();break;case 9:d.rawData=e.bytes();break;case 13:d.externalData&&d.externalData.length||(d.externalData=[]),d.externalData.push(o.onnx.StringStringEntryProto.decode(e,e.uint32()));break;case 14:d.dataLocation=e.int32();break;case 10:if(d.doubleData&&d.doubleData.length||(d.doubleData=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)d.doubleData.push(e.double());else d.doubleData.push(e.double());break;case 11:if(d.uint64Data&&d.uint64Data.length||(d.uint64Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)d.uint64Data.push(e.uint64());else d.uint64Data.push(e.uint64());break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.dims!=null&&e.hasOwnProperty("dims")){if(!Array.isArray(e.dims))return"dims: array expected";for(var r=0;r<e.dims.length;++r)if(!(l.isInteger(e.dims[r])||e.dims[r]&&l.isInteger(e.dims[r].low)&&l.isInteger(e.dims[r].high)))return"dims: integer|Long[] expected"}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&!l.isInteger(e.dataType))return"dataType: integer expected";if(e.segment!=null&&e.hasOwnProperty("segment")&&(i=o.onnx.TensorProto.Segment.verify(e.segment)))return"segment."+i;if(e.floatData!=null&&e.hasOwnProperty("floatData")){if(!Array.isArray(e.floatData))return"floatData: array expected";for(r=0;r<e.floatData.length;++r)if(typeof e.floatData[r]!="number")return"floatData: number[] expected"}if(e.int32Data!=null&&e.hasOwnProperty("int32Data")){if(!Array.isArray(e.int32Data))return"int32Data: array expected";for(r=0;r<e.int32Data.length;++r)if(!l.isInteger(e.int32Data[r]))return"int32Data: integer[] expected"}if(e.stringData!=null&&e.hasOwnProperty("stringData")){if(!Array.isArray(e.stringData))return"stringData: array expected";for(r=0;r<e.stringData.length;++r)if(!(e.stringData[r]&&typeof e.stringData[r].length=="number"||l.isString(e.stringData[r])))return"stringData: buffer[] expected"}if(e.int64Data!=null&&e.hasOwnProperty("int64Data")){if(!Array.isArray(e.int64Data))return"int64Data: array expected";for(r=0;r<e.int64Data.length;++r)if(!(l.isInteger(e.int64Data[r])||e.int64Data[r]&&l.isInteger(e.int64Data[r].low)&&l.isInteger(e.int64Data[r].high)))return"int64Data: integer|Long[] expected"}if(e.name!=null&&e.hasOwnProperty("name")&&!l.isString(e.name))return"name: string expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!l.isString(e.docString))return"docString: string expected";if(e.rawData!=null&&e.hasOwnProperty("rawData")&&!(e.rawData&&typeof e.rawData.length=="number"||l.isString(e.rawData)))return"rawData: buffer expected";if(e.externalData!=null&&e.hasOwnProperty("externalData")){if(!Array.isArray(e.externalData))return"externalData: array expected";for(r=0;r<e.externalData.length;++r){var i;if(i=o.onnx.StringStringEntryProto.verify(e.externalData[r]))return"externalData."+i}}if(e.dataLocation!=null&&e.hasOwnProperty("dataLocation"))switch(e.dataLocation){default:return"dataLocation: enum value expected";case 0:case 1:}if(e.doubleData!=null&&e.hasOwnProperty("doubleData")){if(!Array.isArray(e.doubleData))return"doubleData: array expected";for(r=0;r<e.doubleData.length;++r)if(typeof e.doubleData[r]!="number")return"doubleData: number[] expected"}if(e.uint64Data!=null&&e.hasOwnProperty("uint64Data")){if(!Array.isArray(e.uint64Data))return"uint64Data: array expected";for(r=0;r<e.uint64Data.length;++r)if(!(l.isInteger(e.uint64Data[r])||e.uint64Data[r]&&l.isInteger(e.uint64Data[r].low)&&l.isInteger(e.uint64Data[r].high)))return"uint64Data: integer|Long[] expected"}return null},t.fromObject=function(e){if(e instanceof o.onnx.TensorProto)return e;var r=new o.onnx.TensorProto;if(e.dims){if(!Array.isArray(e.dims))throw TypeError(".onnx.TensorProto.dims: array expected");r.dims=[];for(var i=0;i<e.dims.length;++i)l.Long?(r.dims[i]=l.Long.fromValue(e.dims[i])).unsigned=!1:typeof e.dims[i]=="string"?r.dims[i]=parseInt(e.dims[i],10):typeof e.dims[i]=="number"?r.dims[i]=e.dims[i]:typeof e.dims[i]=="object"&&(r.dims[i]=new l.LongBits(e.dims[i].low>>>0,e.dims[i].high>>>0).toNumber())}if(e.dataType!=null&&(r.dataType=0|e.dataType),e.segment!=null){if(typeof e.segment!="object")throw TypeError(".onnx.TensorProto.segment: object expected");r.segment=o.onnx.TensorProto.Segment.fromObject(e.segment)}if(e.floatData){if(!Array.isArray(e.floatData))throw TypeError(".onnx.TensorProto.floatData: array expected");for(r.floatData=[],i=0;i<e.floatData.length;++i)r.floatData[i]=Number(e.floatData[i])}if(e.int32Data){if(!Array.isArray(e.int32Data))throw TypeError(".onnx.TensorProto.int32Data: array expected");for(r.int32Data=[],i=0;i<e.int32Data.length;++i)r.int32Data[i]=0|e.int32Data[i]}if(e.stringData){if(!Array.isArray(e.stringData))throw TypeError(".onnx.TensorProto.stringData: array expected");for(r.stringData=[],i=0;i<e.stringData.length;++i)typeof e.stringData[i]=="string"?l.base64.decode(e.stringData[i],r.stringData[i]=l.newBuffer(l.base64.length(e.stringData[i])),0):e.stringData[i].length&&(r.stringData[i]=e.stringData[i])}if(e.int64Data){if(!Array.isArray(e.int64Data))throw TypeError(".onnx.TensorProto.int64Data: array expected");for(r.int64Data=[],i=0;i<e.int64Data.length;++i)l.Long?(r.int64Data[i]=l.Long.fromValue(e.int64Data[i])).unsigned=!1:typeof e.int64Data[i]=="string"?r.int64Data[i]=parseInt(e.int64Data[i],10):typeof e.int64Data[i]=="number"?r.int64Data[i]=e.int64Data[i]:typeof e.int64Data[i]=="object"&&(r.int64Data[i]=new l.LongBits(e.int64Data[i].low>>>0,e.int64Data[i].high>>>0).toNumber())}if(e.name!=null&&(r.name=String(e.name)),e.docString!=null&&(r.docString=String(e.docString)),e.rawData!=null&&(typeof e.rawData=="string"?l.base64.decode(e.rawData,r.rawData=l.newBuffer(l.base64.length(e.rawData)),0):e.rawData.length&&(r.rawData=e.rawData)),e.externalData){if(!Array.isArray(e.externalData))throw TypeError(".onnx.TensorProto.externalData: array expected");for(r.externalData=[],i=0;i<e.externalData.length;++i){if(typeof e.externalData[i]!="object")throw TypeError(".onnx.TensorProto.externalData: object expected");r.externalData[i]=o.onnx.StringStringEntryProto.fromObject(e.externalData[i])}}switch(e.dataLocation){case"DEFAULT":case 0:r.dataLocation=0;break;case"EXTERNAL":case 1:r.dataLocation=1}if(e.doubleData){if(!Array.isArray(e.doubleData))throw TypeError(".onnx.TensorProto.doubleData: array expected");for(r.doubleData=[],i=0;i<e.doubleData.length;++i)r.doubleData[i]=Number(e.doubleData[i])}if(e.uint64Data){if(!Array.isArray(e.uint64Data))throw TypeError(".onnx.TensorProto.uint64Data: array expected");for(r.uint64Data=[],i=0;i<e.uint64Data.length;++i)l.Long?(r.uint64Data[i]=l.Long.fromValue(e.uint64Data[i])).unsigned=!0:typeof e.uint64Data[i]=="string"?r.uint64Data[i]=parseInt(e.uint64Data[i],10):typeof e.uint64Data[i]=="number"?r.uint64Data[i]=e.uint64Data[i]:typeof e.uint64Data[i]=="object"&&(r.uint64Data[i]=new l.LongBits(e.uint64Data[i].low>>>0,e.uint64Data[i].high>>>0).toNumber(!0))}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.dims=[],i.floatData=[],i.int32Data=[],i.stringData=[],i.int64Data=[],i.doubleData=[],i.uint64Data=[],i.externalData=[]),r.defaults&&(i.dataType=0,i.segment=null,i.name="",r.bytes===String?i.rawData="":(i.rawData=[],r.bytes!==Array&&(i.rawData=l.newBuffer(i.rawData))),i.docString="",i.dataLocation=r.enums===String?"DEFAULT":0),e.dims&&e.dims.length){i.dims=[];for(var d=0;d<e.dims.length;++d)typeof e.dims[d]=="number"?i.dims[d]=r.longs===String?String(e.dims[d]):e.dims[d]:i.dims[d]=r.longs===String?l.Long.prototype.toString.call(e.dims[d]):r.longs===Number?new l.LongBits(e.dims[d].low>>>0,e.dims[d].high>>>0).toNumber():e.dims[d]}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&(i.dataType=e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&(i.segment=o.onnx.TensorProto.Segment.toObject(e.segment,r)),e.floatData&&e.floatData.length)for(i.floatData=[],d=0;d<e.floatData.length;++d)i.floatData[d]=r.json&&!isFinite(e.floatData[d])?String(e.floatData[d]):e.floatData[d];if(e.int32Data&&e.int32Data.length)for(i.int32Data=[],d=0;d<e.int32Data.length;++d)i.int32Data[d]=e.int32Data[d];if(e.stringData&&e.stringData.length)for(i.stringData=[],d=0;d<e.stringData.length;++d)i.stringData[d]=r.bytes===String?l.base64.encode(e.stringData[d],0,e.stringData[d].length):r.bytes===Array?Array.prototype.slice.call(e.stringData[d]):e.stringData[d];if(e.int64Data&&e.int64Data.length)for(i.int64Data=[],d=0;d<e.int64Data.length;++d)typeof 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u.onnx.AttributeProto.AttributeType.GRAPH:return o.g;case u.onnx.AttributeProto.AttributeType.FLOATS:return o.floats;case u.onnx.AttributeProto.AttributeType.INTS:return o.ints;case u.onnx.AttributeProto.AttributeType.STRINGS:return o.strings;case u.onnx.AttributeProto.AttributeType.TENSORS:return o.tensors;case u.onnx.AttributeProto.AttributeType.GRAPHS:return o.graphs;default:throw new Error(`unsupported attribute type: ${u.onnx.AttributeProto.AttributeType[o.type]}`)}}static getValueNoCheckFromOrtFormat(o){switch(o.type()){case h.AttributeType.FLOAT:return o.f();case h.AttributeType.INT:return o.i();case h.AttributeType.STRING:return o.s();case h.AttributeType.TENSOR:return o.t();case h.AttributeType.GRAPH:return o.g();case h.AttributeType.FLOATS:return o.floatsArray();case h.AttributeType.INTS:{const t=[];for(let e=0;e<o.intsLength();e++)t.push(o.ints(e));return t}case h.AttributeType.STRINGS:{const t=[];for(let e=0;e<o.stringsLength();e++)t.push(o.strings(e));return t}case h.AttributeType.TENSORS:{const t=[];for(let e=0;e<o.tensorsLength();e++)t.push(o.tensors(e));return t}default:throw new Error(`unsupported attribute type: ${h.AttributeType[o.type()]}`)}}}n.Attribute=f},7091:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.resolveBackend=n.backend=void 0;const u=a(5038),c=new Map;async function p(s){const h=n.backend;if(h[s]!==void 0&&function(f){const l=f;return"initialize"in l&&typeof l.initialize=="function"&&"createSessionHandler"in l&&typeof l.createSessionHandler=="function"&&"dispose"in l&&typeof l.dispose=="function"}(h[s])){const f=h[s];let l=f.initialize();if(typeof l=="object"&&"then"in l&&(l=await l),l)return c.set(s,f),f}}n.backend={webgl:new u.WebGLBackend},n.resolveBackend=async function s(h){if(!h)return s(["webgl"]);{const f=typeof h=="string"?[h]:h;for(const l of f){const o=c.get(l);if(o)return o;const t=await p(l);if(t)return t}}throw new Error("no available backend to use")}},5038:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.WebGLBackend=void 0;const u=a(1670),c=a(6231),p=a(6416),s=a(7305);n.WebGLBackend=class{get contextId(){return u.env.webgl.contextId}set contextId(h){u.env.webgl.contextId=h}get matmulMaxBatchSize(){return u.env.webgl.matmulMaxBatchSize}set matmulMaxBatchSize(h){u.env.webgl.matmulMaxBatchSize=h}get textureCacheMode(){return u.env.webgl.textureCacheMode}set textureCacheMode(h){u.env.webgl.textureCacheMode=h}get pack(){return u.env.webgl.pack}set pack(h){u.env.webgl.pack=h}get async(){return u.env.webgl.async}set async(h){u.env.webgl.async=h}initialize(){try{return this.glContext=(0,s.createWebGLContext)(this.contextId),typeof this.matmulMaxBatchSize!="number"&&(this.matmulMaxBatchSize=16),typeof this.textureCacheMode!="string"&&(this.textureCacheMode="full"),typeof this.pack!="boolean"&&(this.pack=!1),typeof this.async!="boolean"&&(this.async=!1),c.Logger.setWithEnv(u.env),c.Logger.verbose("WebGLBackend",`Created WebGLContext: ${typeof this.glContext} with matmulMaxBatchSize: ${this.matmulMaxBatchSize}; textureCacheMode: ${this.textureCacheMode}; pack: ${this.pack}; async: ${this.async}.`),!0}catch(h){return c.Logger.warning("WebGLBackend",`Unable to initialize WebGLBackend. ${h}`),!1}}createSessionHandler(h){return new p.WebGLSessionHandler(this,h)}dispose(){this.glContext.dispose()}}},5107:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.CoordsGlslLib=void 0;const u=a(2517),c=a(8520),p=a(5060),s=a(7859),h=a(9390);class f extends c.GlslLib{constructor(o){super(o)}getFunctions(){return Object.assign(Object.assign(Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({},this.offsetToCoords()),this.coordsToOffset()),this.toVec()),this.valueFrom()),this.getCommonUtilFuncs()),this.getInputsSamplingSnippets()),this.getOutputSamplingSnippet())}getCustomTypes(){return{}}offsetToCoords(){return{offsetToCoords:new c.GlslLibRoutine(`
- vec2 offsetToCoords(int offset, int width, int height) {
- int t = offset / width;
- int s = offset - t*width;
- vec2 coords = (vec2(s,t) + vec2(0.5,0.5)) / vec2(width, height);
- return coords;
- }
- `)}}coordsToOffset(){return{coordsToOffset:new c.GlslLibRoutine(`
- int coordsToOffset(vec2 coords, int width, int height) {
- float s = coords.s * float(width);
- float t = coords.t * float(height);
- int offset = int(t) * width + int(s);
- return offset;
- }
- `)}}getOutputSamplingSnippet(){const o=this.context.outputTextureLayout;return o.isPacked?this.getPackedOutputSamplingSnippet(o):this.getUnpackedOutputSamplingSnippet(o)}getPackedOutputSamplingSnippet(o){const t=o.unpackedShape,e=[o.width,o.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputPacked1DCoords(t,e);break;case 2:r[i]=this.getOutputPacked2DCoords(t,e);break;case 3:r[i]=this.getOutputPacked3DCoords(t,e);break;default:r[i]=this.getOutputPackedNDCoords(t,e)}const d=`
- void setOutput(vec4 val) {
- ${(0,p.getGlsl)(this.context.glContext.version).output} = val;
- }
- `;return r.floatTextureSetRGBA=new c.GlslLibRoutine(d),r}getUnpackedOutputSamplingSnippet(o){const t=o.unpackedShape,e=[o.width,o.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputUnpacked1DCoords(t,e);break;case 2:r[i]=this.getOutputUnpacked2DCoords(t,e);break;case 3:r[i]=this.getOutputUnpacked3DCoords(t,e);break;case 4:r[i]=this.getOutputUnpacked4DCoords(t,e);break;case 5:r[i]=this.getOutputUnpacked5DCoords(t,e);break;case 6:r[i]=this.getOutputUnpacked6DCoords(t,e);break;default:throw new Error(`Unsupported output dimensionality: ${t.length}`)}const d=`
- void setOutput(float val) {
- ${(0,p.getGlsl)(this.context.glContext.version).output} = vec4(val, 0, 0, 0);
- }
- `;return r.floatTextureSetR=new c.GlslLibRoutine(d),r}getOutputScalarCoords(){return new c.GlslLibRoutine(`
- int getOutputCoords() {
- return 0;
- }
- `)}getOutputPacked1DCoords(o,t){const e=t;let r="";return e[0]===1?(r=`
- int getOutputCoords() {
- return 2 * int(TexCoords.y * ${e[1]}.0);
- }
- `,new c.GlslLibRoutine(r)):e[1]===1?(r=`
- int getOutputCoords() {
- return 2 * int(TexCoords.x * ${e[0]}.0);
- }
- `,new c.GlslLibRoutine(r)):(r=`
- int getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${e[0]}, ${e[1]}));
- return 2 * (resTexRC.y * ${e[0]} + resTexRC.x);
- }
- `,new c.GlslLibRoutine(r))}getOutputPacked2DCoords(o,t){let e="";if(u.ArrayUtil.arraysEqual(o,t))return e=`
- ivec2 getOutputCoords() {
- return 2 * ivec2(TexCoords.xy * vec2(${t[0]}, ${t[1]}));
- }
- `,new c.GlslLibRoutine(e);const r=t,i=Math.ceil(o[1]/2);return e=`
- ivec2 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${r[0]}, ${r[1]}));
-
- int index = resTexRC.y * ${r[0]} + resTexRC.x;
-
- // reverse r and c order for packed texture
- int r = imod(index, ${i}) * 2;
- int c = 2 * (index / ${i});
-
- return ivec2(r, c);
- }
- `,new c.GlslLibRoutine(e)}getOutputPacked3DCoords(o,t){const e=[t[0],t[1]],r=Math.ceil(o[2]/2),i=r*Math.ceil(o[1]/2),d=`
- ivec3 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${e[0]}, ${e[1]}));
- int index = resTexRC.y * ${e[0]} + resTexRC.x;
-
- int b = index / ${i};
- index -= b * ${i};
-
- // reverse r and c order for packed texture
- int r = imod(index, ${r}) * 2;
- int c = 2 * (index / ${r});
-
- return ivec3(b, r, c);
- }
- `;return new c.GlslLibRoutine(d)}getOutputPackedNDCoords(o,t){const e=[t[0],t[1]],r=Math.ceil(o[o.length-1]/2),i=r*Math.ceil(o[o.length-2]/2);let d=i,g="",m="b, r, c";for(let _=2;_<o.length-1;_++)d*=o[o.length-_-1],g=`
- int b${_} = index / ${d};
- index -= b${_} * ${d};
- `+g,m=`b${_}, `+m;const b=`
- ivec${o.length} getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${e[0]}, ${e[1]}));
- int index = resTexRC.y * ${e[0]} + resTexRC.x;
-
- ${g}
-
- int b = index / ${i};
- index -= b * ${i};
-
- // reverse r and c order for packed texture
- int r = imod(index, ${r}) * 2;
- int c = 2 * (index / ${r});
-
- return ivec${o.length}(${m});
- }
- `;return new c.GlslLibRoutine(b)}getOutputUnpacked1DCoords(o,t){const e=`
- int getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${t[0]}, ${t[1]}));
- return resTexRC.y * ${t[0]} + resTexRC.x;
- }
- `;return new c.GlslLibRoutine(e)}getOutputUnpacked2DCoords(o,t){const e=`
- ivec2 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${t[0]}, ${t[1]}));
- int index = resTexRC.y * ${t[0]} + resTexRC.x;
- int r = index / ${o[1]};
- int c = index - r * ${o[1]};
- return ivec2(r, c);
- }
- `;return new c.GlslLibRoutine(e)}getOutputUnpacked3DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const d=["r","c","d"],g=i.map((m,b)=>`int ${d[b]} = index / ${m}; ${b===i.length-1?`int ${d[b+1]} = index - ${d[b]} * ${m}`:`index -= ${d[b]} * ${m}`};`).join("");return e=`
- ivec3 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${t[0]}, ${t[1]}));
- int index = resTexRC.y * ${t[0]} + resTexRC.x;
- ${g}
- return ivec3(r, c, d);
- }
- `,new c.GlslLibRoutine(e)}getOutputUnpacked4DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const d=["r","c","d","d2"],g=i.map((m,b)=>`int ${d[b]} = index / ${m}; ${b===i.length-1?`int ${d[b+1]} = index - ${d[b]} * ${m}`:`index -= ${d[b]} * ${m}`};`).join("");return e=`
- ivec4 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${t[0]}, ${t[1]}));
- int index = resTexRC.y * ${t[0]} + resTexRC.x;
- ${g}
- return ivec4(r, c, d, d2);
- }
- `,new c.GlslLibRoutine(e)}getOutputUnpacked5DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const d=["r","c","d","d2","d3"],g=i.map((m,b)=>`int ${d[b]} = index / ${m}; ${b===i.length-1?`int ${d[b+1]} = index - ${d[b]} * ${m}`:`index -= ${d[b]} * ${m}`};`).join("");return e=`
- ivec5 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${t[0]}, ${t[1]}));
- int index = resTexRC.y * ${t[0]} + resTexRC.x;
- ${g}
- return ivec5(r, c, d, d2, d3);
- }
- `,new c.GlslLibRoutine(e)}getOutputUnpacked6DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const d=["r","c","d","d2","d3","d4"],g=i.map((m,b)=>`int ${d[b]} = index / ${m}; ${b===i.length-1?`int ${d[b+1]} = index - ${d[b]} * ${m}`:`index -= ${d[b]} * ${m}`};`).join("");return e=`
- ivec6 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${t[0]}, ${t[1]}));
- int index = resTexRC.y * ${t[0]} + resTexRC.x;
- ${g}
- return ivec6(r, c, d, d2, d3, d4);
- }
- `,new c.GlslLibRoutine(e)}getCommonUtilFuncs(){const o={};let t="uvFromFlat";o[t]=new c.GlslLibRoutine(`
- vec2 uvFromFlat(int texNumR, int texNumC, int index) {
- int texC = index / texNumR;
- int texR = index - texC * texNumR;
- // TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to
- // v.
- return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC);
- }
- `),t="packedUVfrom1D",o[t]=new c.GlslLibRoutine(`
- vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
- int texelIndex = index / 2;
- int texR = texelIndex / texNumC;
- int texC = texelIndex - texR * texNumC;
- return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
- }
- `),t="packedUVfrom2D",o[t]=new c.GlslLibRoutine(`
- vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) {
- int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
- int texR = texelIndex / texNumC;
- int texC = texelIndex - texR * texNumC;
- return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
- }
- `),t="packedUVfrom3D",o[t]=new c.GlslLibRoutine(`
- vec2 packedUVfrom3D(int texNumR, int texNumC,
- int texelsInBatch, int texelsInLogicalRow, int b,
- int row, int col) {
- int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
- int texR = index / texNumC;
- int texC = index - texR * texNumC;
- return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
- }
- `),t="sampleTexture";const e=(0,p.getGlsl)(this.context.glContext.version);return o[t]=new c.GlslLibRoutine(`
- float sampleTexture(sampler2D textureSampler, vec2 uv) {
- return ${e.texture2D}(textureSampler, uv).r;
- }`),o}getInputsSamplingSnippets(){const o={},t=this.context.outputTextureLayout;return this.context.programInfo.inputNames.forEach((e,r)=>{const i=this.context.inputTextureLayouts[r],d=(0,h.generateShaderFuncNameFromInputSamplerName)(e);i.isPacked?o[d]=this.getPackedSamplerFromInput(d,e,i):o[d]=this.getUnpackedSamplerFromInput(d,e,i);const g=(0,h.generateShaderFuncNameFromInputSamplerNameAtOutCoords)(e);i.unpackedShape.length<=t.unpackedShape.length&&(i.isPacked?o[g]=this.getPackedSamplerAtOutputCoords(g,i,t,e):o[g]=this.getUnpackedSamplerAtOutputCoords(g,i,t,e))}),o}getPackedSamplerAtOutputCoords(o,t,e,r){const i=t.unpackedShape,d=e.unpackedShape,g=r,m=(0,h.generateShaderFuncNameFromInputSamplerName)(g),b=i.length,_=d.length,v=u.BroadcastUtil.getBroadcastDims(i,d),w=(0,h.getCoordsDataType)(_),S=_-b;let A;const O=(0,h.getGlChannels)();A=b===0?"":_<2&&v.length>=1?"coords = 0;":v.map(F=>`coords.${O[F+S]} = 0;`).join(`
-`);let x="";x=_<2&&b>0?"coords":i.map((F,H)=>`coords.${O[H+S]}`).join(", ");let I="return outputValue;";const N=u.ShapeUtil.size(i)===1,B=u.ShapeUtil.size(d)===1;if(b!==1||N||B){if(N&&!B)I=_===1?`
- return vec4(outputValue.x, outputValue.x, 0., 0.);
- `:`
- return vec4(outputValue.x);
- `;else if(v.length){const F=b-2,H=b-1;v.indexOf(F)>-1&&v.indexOf(H)>-1?I="return vec4(outputValue.x);":v.indexOf(F)>-1?I="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":v.indexOf(H)>-1&&(I="return vec4(outputValue.xx, outputValue.zz);")}}else I=`
- return vec4(outputValue.xy, outputValue.xy);
- `;const L=`
- vec4 ${o}() {
- ${w} coords = getOutputCoords();
-
- int lastDim = coords.${O[_-1]};
- coords.${O[_-1]} = coords.${O[_-2]};
- coords.${O[_-2]} = lastDim;
-
- ${A}
- vec4 outputValue = ${m}(${x});
- ${I}
- }
- `;return new c.GlslLibRoutine(L,["coordinates.getOutputCoords"])}getUnpackedSamplerAtOutputCoords(o,t,e,r){const i=[e.width,e.height],d=[t.width,t.height],g=t.unpackedShape.length,m=e.unpackedShape.length,b=t.unpackedShape,_=e.unpackedShape,v=(0,h.generateShaderFuncNameFromInputSamplerName)(r);if(g===m&&u.ArrayUtil.arraysEqual(d,i)){const B=`
- float ${o}() {
- return sampleTexture(${r}, TexCoords);
- }
- `;return new c.GlslLibRoutine(B,["coordinates.sampleTexture"])}const w=(0,h.getCoordsDataType)(m),S=u.BroadcastUtil.getBroadcastDims(b,_),A=m-g;let O;const x=(0,h.getGlChannels)();O=g===0?"":m<2&&S.length>=1?"coords = 0;":S.map(B=>`coords.${x[B+A]} = 0;`).join(`
-`);let I="";I=m<2&&g>0?"coords":t.unpackedShape.map((B,L)=>`coords.${x[L+A]}`).join(", ");const N=`
- float ${o}() {
- ${w} coords = getOutputCoords();
- ${O}
- return ${v}(${I});
- }
- `;return new c.GlslLibRoutine(N,["coordinates.getOutputCoords"])}getPackedSamplerFromInput(o,t,e){switch(e.unpackedShape.length){case 0:return this.getPackedSamplerScalar(o,t);case 1:return this.getPackedSampler1D(o,t,e);case 2:return this.getPackedSampler2D(o,t,e);case 3:return this.getPackedSampler3D(o,t,e);default:return this.getPackedSamplerND(o,t,e)}}getUnpackedSamplerFromInput(o,t,e){const r=e.unpackedShape;switch(r.length){case 0:return this.getUnpackedSamplerScalar(o,t,e);case 1:return this.getUnpackedSampler1D(o,t,e);case 2:return this.getUnpackedSampler2D(o,t,e);case 3:return this.getUnpackedSampler3D(o,t,e);case 4:return this.getUnpackedSampler4D(o,t,e);case 5:return this.getUnpackedSampler5D(o,t,e);case 6:return this.getUnpackedSampler6D(o,t,e);default:throw new Error(`Unsupported dimension ${r.length}-D`)}}getPackedSamplerScalar(o,t){const e=`
- vec4 ${o}() {
- return ${(0,p.getGlsl)(this.context.glContext.version).texture2D}(${t}, halfCR);
- }
- `;return new c.GlslLibRoutine(e)}getPackedSampler1D(o,t,e){const r=[e.width,e.height],i=[r[1],r[0]],d=(0,p.getGlsl)(this.context.glContext.version),g=`vec4 ${o}(int index) {
- vec2 uv = packedUVfrom1D(
- ${i[0]}, ${i[1]}, index);
- return ${d.texture2D}(${t}, uv);
- }`;return new c.GlslLibRoutine(g,["coordinates.packedUVfrom1D"])}getPackedSampler2D(o,t,e){const r=e.unpackedShape,i=[e.width,e.height],d=(0,p.getGlsl)(this.context.glContext.version),g=i[0],m=i[1];if(i!=null&&u.ArrayUtil.arraysEqual(r,i)){const w=`vec4 ${o}(int row, int col) {
- vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${g}.0);
- return ${d.texture2D}(${t}, uv);
- }`;return new c.GlslLibRoutine(w)}const b=i,_=Math.ceil(r[1]/2),v=`vec4 ${o}(int row, int col) {
- vec2 uv = packedUVfrom2D(${b[1]}, ${b[0]}, ${_}, row, col);
- return ${d.texture2D}(${t}, uv);
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- int group_id = output_channel / ${L};
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-
- float value = ${I};
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- ivec2 wLoc = loc - wOff;
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- if (
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- wLocIn.y >= 0 && wLocIn.y < ${N[3]}
- ) {
- float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);
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- }
- }
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- ${Z}
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i=r.attributes,d=(0,s.parseInternalActivationAttributes)(i),g=i.getString("auto_pad","NOTSET"),m=i.getInts("dilations",[1,1]),b=i.getInt("group",1),_=i.getInts("kernel_shape",[]),v=i.getInts("output_padding",[0,0]),w=i.getInts("output_shape",[]),S=i.getInts("pads",[0,0,0,0]),A=i.getInts("strides",[1,1]);return(0,u.createAttributeWithCacheKey)(Object.assign({autoPad:g,dilations:m,group:b,kernelShape:_,outputPadding:v,outputShape:w,pads:S,strides:A},d))};const e=(r,i)=>{if(!r||r.length!==2&&r.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(r[0].dims.length!==4||r[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");if(r[0].dims[1]!==r[1].dims[0])throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");const d=r[1].dims[1]*i.group;if(r.length===3&&(r[2].dims.length!==1||r[2].dims[0]!==d))throw new Error("invalid bias");const g=r[0].dims.length-2;if(i.dilations.length!==g)throw new Error(`dilations should be ${g}D`);if(i.strides.length!==g)throw new Error(`strides should be ${g}D`);if(i.pads.length!==2*g)throw new Error(`pads should be ${2*g}D`);if(i.outputPadding.length!==g)throw new Error(`output_padding should be ${g}D`);if(i.kernelShape.length!==0&&i.kernelShape.length!==r[1].dims.length-2)throw new Error("invalid kernel shape");if(i.outputShape.length!==0&&i.outputShape.length!==r[0].dims.length-2)throw new Error("invalid output shape");if(r[0].type!=="float32"||r[1].type!=="float32")throw new Error("ConvTranspose input(X,W) should be float tensor");if(r.length===3&&r[2].type!=="float32")throw new Error("ConvTranspose input(bias) should be float tensor")}},8138:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseConvAttributes=n.conv=n.calculateOutputShape=void 0;const u=a(246),c=a(2517),p=a(4770),s=a(1386),h=a(9828),f=a(2823),l=a(3248),o=a(5623);n.calculateOutputShape=(g,m,b,_,v)=>{const w=g[0],S=g.slice(2),A=S.length,O=m[0],x=m.slice(2).map((N,B)=>N+(N-1)*(b[B]-1)),I=S.map((N,B)=>N+_[B]+_[B+A]).map((N,B)=>Math.floor((N-x[B]+v[B])/v[B]));return[w,O].concat(...I)},n.conv=(g,m,b)=>(d(m,b),t(g,m,b));const t=(g,m,b)=>{const _=i(b,m),v=g.session.pack,w=_.kernelShape[0]===1&&_.kernelShape[1]===1;return _.group>1?[g.run((0,p.createUnpackedGroupedConvProgramInfoLoader)(g,m,_),m)]:w&&v?[e(g,m,_)]:v&&m[0].dims.length===4&&m[0].dims[0]===1&&!w?[(0,s.conv2DPacked)(g,m,_)]:[r(g,m,_)]},e=(g,m,b)=>{const _=m[0].dims,v=m[1].dims,w=(0,n.calculateOutputShape)(_,v,b.dilations,b.pads,b.strides),S=g.reshapeUnpacked(m[0],[_[1],_[2]*_[3]]),A=g.reshapeUnpacked(m[1],[v[0],v[1]]),O=m.length>2?[A,S,m[2]]:[A,S],x=g.run((0,o.createMatmulProgramInfoLoader)(O,b),O);return g.reshapeUnpacked(x,w)},r=(g,m,b)=>{const _=m[0].dims,v=m[1].dims,w=(0,n.calculateOutputShape)(_,v,b.dilations,b.pads,b.strides),S=g.run((0,l.createIm2ColProgramInfoLoader)(g,m[0],m[1],w,b),[m[0]]),A=m.length===3?[S,m[1],m[2]]:[S,m[1]];return g.run((0,h.createDotProductProgramInfoLoader)(g,m,w,b),A)},i=(g,m)=>{const b=g.kernelShape.slice();if(g.kernelShape.length===0)for(let w=2;w<m[1].dims.length;++w)b.push(m[1].dims[w]);const _=g.pads.slice();c.PoolConvUtil.adjustPadsBasedOnAutoPad(m[0].dims,g.strides,g.dilations,b,_,g.autoPad);const v=Object.assign({},g);return Object.assign(v,{kernelShape:b,pads:_,cacheKey:g.cacheKey}),v};n.parseConvAttributes=g=>{const m=g.attributes,b=(0,f.parseInternalActivationAttributes)(m),_=m.getString("auto_pad","NOTSET"),v=m.getInts("dilations",[1,1]),w=m.getInt("group",1),S=m.getInts("kernel_shape",[]),A=m.getInts("pads",[0,0,0,0]),O=m.getInts("strides",[1,1]);return(0,u.createAttributeWithCacheKey)(Object.assign({autoPad:_,dilations:v,group:w,kernelShape:S,pads:A,strides:O},b))};const d=(g,m)=>{if(!g||g.length!==2&&g.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(g[0].dims.length!==4||g[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");if(g[0].dims[1]!==g[1].dims[1]*m.group)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(g.length===3&&(g[2].dims.length!==1||g[1].dims[0]!==g[2].dims[0]))throw new Error("invalid bias");const b=g[0].dims.length-2;if(m.dilations.length!==b)throw new Error(`dilations should be ${b}D`);if(m.strides.length!==b)throw new Error(`strides should be ${b}D`);if(m.pads.length!==2*b)throw new Error(`pads should be ${2*b}D`);if(m.kernelShape.length!==0&&m.kernelShape.length!==g[1].dims.length-2)throw new Error("invalid kernel shape");if(g[0].type!=="float32"||g[1].type!=="float32")throw new Error("Conv input(X,W) should be float tensor");if(g.length===3&&g[2].type!=="float32")throw new Error("Conv input(bias) should be float tensor")}},5193:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseDepthToSpaceAttributes=n.depthToSpace=void 0;const u=a(3738);n.depthToSpace=(p,s,h)=>{c(s);const f=h.blocksize,l=f*f,o=h.mode==="DCR"?[0,3,4,1,5,2]:[0,1,4,2,5,3],t=h.mode==="DCR"?[s[0].dims[0],f,f,s[0].dims[1]/l,s[0].dims[2],s[0].dims[3]]:[s[0].dims[0],s[0].dims[1]/l,f,f,s[0].dims[2],s[0].dims[3]],e=p.reshapeUnpacked(s[0],t),r={perm:o,cacheKey:`${o}`},[i]=(0,u.transpose)(p,[e],r),d=[s[0].dims[0],s[0].dims[1]/l,s[0].dims[2]*f,s[0].dims[3]*f];return[p.reshapeUnpacked(i,d)]},n.parseDepthToSpaceAttributes=p=>{const s=p.attributes.getInt("blocksize");if(s<1)throw new Error(`blocksize must be >= 1, but got : ${s} for DepthToSpace`);const h=p.attributes.getString("mode","DCR");if(h!=="DCR"&&h!=="CRD")throw new Error(`unrecognized mode: ${h} for DepthToSpace`);return{mode:h,blocksize:s}};const c=p=>{if(p.length!==1)throw new Error(`DepthToSpace expect 1 inputs, but got ${p.length}`);if(p[0].type==="string"||p[0].dims.length!==4)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}},9828:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createDotProductProgramInfoLoader=void 0;const u=a(2517),c=a(5060),p=a(2039),s=a(2823),h=a(3248);n.createDotProductProgramInfoLoader=(f,l,o,t)=>{const e=((r,i)=>({name:"ConvDotProduct",inputNames:r?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:r?[p.TextureType.unpacked,p.TextureType.packedLastDimension,p.TextureType.unpacked]:[p.TextureType.unpacked,p.TextureType.packedLastDimension],cacheKey:i.activationCacheKey}))(l.length>2,t);return Object.assign(Object.assign({},e),{get:()=>((r,i,d,g,m)=>{const b=d[0].dims,_=d[1].dims,v=[_[0],Math.ceil(b[1]*_[2]*_[3]/4)],w=(0,h.calculateIm2ColDims)(b,_,g),[S,A]=r.calculateTextureWidthAndHeight(v,p.TextureType.packedLastDimension),O=u.ShapeUtil.computeStrides(w),[x,I]=r.calculateTextureWidthAndHeight(w,p.TextureType.packedLastDimension),N=g.length,B=d.length<3?"0.0":"_B(b)",L=Math.ceil(b[1]*_[2]*_[3]/4),{activationFunction:F,applyActivation:H}=(0,s.getActivationSnippet)(m),D=(0,c.getGlsl)(r.session.backend.glContext.version),j=`
-${F}
-float process(int indices[${N}]) {
- int b[1];
- b[0] = indices[1];
- int im2col[4];
- im2col[0] = indices[0];
- im2col[1] = indices[2];
- im2col[2] = indices[3];
- int im2colOffset = im2col[0] * ${O[0]} + im2col[1] * ${O[1]} + im2col[2] * ${O[2]};
- int kernelOffset = indices[1] * ${v[1]};
- float value = ${B};
- for (int i = 0; i < ${L}; ++i) {
- vec2 im2colCoords = offsetToCoords(im2colOffset, ${x}, ${I});
- vec2 kernelCoords = offsetToCoords(kernelOffset, ${S}, ${A});
- value += dot(${D.texture2D}(Im2Col, im2colCoords), ${D.texture2D}(K, kernelCoords));
- ++im2colOffset;
- ++kernelOffset;
- }
- ${H}
- return value;
-}`;return Object.assign(Object.assign({},i),{output:{dims:g,type:d[0].type,textureType:p.TextureType.unpacked},shaderSource:j})})(f,e,l,o,t)})}},7992:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseFlattenAttributes=n.flatten=void 0;const u=a(2517);n.flatten=(p,s,h)=>{c(s,h);const f=u.ShapeUtil.flattenShape(s[0].dims,h);return[p.reshapeUnpacked(s[0],f)]},n.parseFlattenAttributes=p=>p.attributes.getInt("axis",1);const c=(p,s)=>{if(!p||p.length!==1)throw new Error("Flatten requires 1 input.");const h=p[0].dims.length;if(h===0)throw new Error("scalar tensor is not supported.");if(s<-h||s>h)throw new Error("Invalid axis");if(p[0].type==="string")throw new Error("string tensor is not supported.")}},2823:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInternalActivationAttributes=n.getActivationSnippet=void 0;const u=a(2517),c=a(4909);n.getActivationSnippet=function(p){let s;switch(p.activation){case"Relu":s=(0,c.glslRelu)();break;case"Sigmoid":s=(0,c.glslSigmoid)();break;case"Clip":s=(0,c.glslClip)(p.clipMin,p.clipMax);break;default:return{activationFunction:"",applyActivation:""}}const h=s.name;return{activationFunction:s.body,applyActivation:`value = ${h}_(value);`}},n.parseInternalActivationAttributes=p=>{const s=p.getString("activation","");if(s==="Clip"){const[h,f]=p.getFloats("activation_params",[u.MIN_CLIP,u.MAX_CLIP]);return{activation:s,clipMax:f,clipMin:h,activationCacheKey:`${s}:${h},${f}`}}return{activation:s,activationCacheKey:s}}},1253:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGatherAttributes=n.gather=void 0;const u=a(246),c=a(782),p=a(2517),s=a(2039);n.gather=(o,t,e)=>(l(t,e.axis),[o.run(f(o,t,e),t)]),n.parseGatherAttributes=o=>(0,u.createAttributeWithCacheKey)({axis:o.attributes.getInt("axis",0)});const h={name:"Gather",inputNames:["A","B"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},f=(o,t,e)=>{const r=Object.assign(Object.assign({},h),{cacheHint:e.cacheKey});return Object.assign(Object.assign({},r),{get:()=>((i,d,g,m)=>{const b=g[0].dims.slice(),_=g[1].dims.slice(),v=new Array(b.length+_.length-1);m=p.ShapeUtil.normalizeAxis(m,b.length);const w=[];for(let A=0;A<v.length;A++)A<m?(v[A]=b[A],w.push(`inputIdx[${A}] = outputIdx[${A}];`)):A<m+_.length?(v[A]=_[A-m],w.push(`indexDataIdx[${A-m}] = outputIdx[${A}];`)):(v[A]=b[A-_.length+1],w.push(`inputIdx[${A-_.length+1}] = outputIdx[${A}];`));const S=`
- float process(int outputIdx[${v.length||1}]) {
- int inputIdx[${b.length}];
- int indexDataIdx[${_.length||1}];
- indexDataIdx[0] = 0;
- ${w.join(`
- `)}
- int idx = int(_B(indexDataIdx));
- inputIdx[${m}] = idx < 0 ? idx + ${b[m]} : idx;
- return _A(inputIdx);
- }`;return Object.assign(Object.assign({},d),{output:{dims:v,type:g[0].type,textureType:s.TextureType.unpacked},shaderSource:S})})(0,r,t,e.axis)})},l=(o,t)=>{if(!o||o.length!==2)throw new Error("Gather requires 2 inputs.");const e=o[0].dims.length;if(e<1)throw new Error("Invalid input shape.");if(t<-e||t>e-1)throw new Error("Invalid axis.");if(c.NUMBER_TYPES.indexOf(o[0].type)===-1)throw new Error("Invaid input type.");if(o[1].type!=="int32"&&o[1].type!=="int16")throw new Error("Invaid input type.")}},4776:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGemmAttributesV11=n.parseGemmAttributesV7=n.gemm=void 0;const u=a(246),c=a(2517),p=a(2039);n.gemm=(o,t,e)=>(l(t,e),[o.run(h(t,e),t)]);const s=(o,t)=>{const e=o.attributes.getInt("transA",0)!==0,r=o.attributes.getInt("transB",0)!==0,i=o.attributes.getFloat("alpha",1),d=o.attributes.getFloat("beta",1);return(0,u.createAttributeWithCacheKey)({transA:e,transB:r,alpha:i,beta:d,isOptionalC:t})};n.parseGemmAttributesV7=o=>s(o,!1),n.parseGemmAttributesV11=o=>s(o,!0);const h=(o,t)=>{const e={name:"Gemm",inputNames:o.length===3?["A","B","C"]:["A","B"],inputTypes:o.length===3?[p.TextureType.unpacked,p.TextureType.unpacked,p.TextureType.unpacked]:[p.TextureType.unpacked,p.TextureType.unpacked],key:t.cacheKey};return Object.assign(Object.assign({},e),{get:()=>f(e,o,t)})},f=(o,t,e)=>{const r=t[0].dims.slice(),i=t[1].dims.slice(),[d,g]=c.GemmUtil.getShapeOfGemmResult(r,e.transA,i,e.transB,t.length===3?t[2].dims:void 0),m=[d,g];if(!m)throw new Error("Can't use gemm on the given tensors");let b=r[r.length-1],_="";e.transA&&(b=r[0]),e.transA&&e.transB?_="value += _A_T(a) * _B_T(b);":e.transA&&!e.transB?_="value += _A_T(a) * _B(b);":!e.transA&&e.transB?_="value += _A(a) * _B_T(b);":e.transA||e.transB||(_="value += _A(a) * _B(b);");const v=m.length,w=`
- float process(int indices[${v}]) {
- int a[${v}];
- int b[${v}];
- ${t.length===3?`int c[${t[2].dims.length}];`:""}
-
- copyVec(indices, a);
- copyVec(indices, b);
- ${t.length===3?"bcastIndices_C(indices, c);":""}
-
- float value = 0.0;
- for (int k=0; k<${b}; ++k) {
- a[${v-1}] = k;
- b[${v-2}] = k;
- ${_}
- }
-
- value = value * alpha;
- ${t.length===3?"value += beta * _C(c);":""}
- return value;
- }`;return Object.assign(Object.assign({},o),{output:{dims:m,type:t[0].type,textureType:p.TextureType.unpacked},variables:[{name:"alpha",type:"float",data:e.alpha},{name:"beta",type:"float",data:e.beta}],shaderSource:w})},l=(o,t)=>{if(!o)throw new Error("Input is missing");if(t.isOptionalC&&(o.length<2||o.length>3))throw new Error("Invaid input shape.");if(!t.isOptionalC&&o.length!==3)throw new Error("Gemm requires 3 inputs");if(o.length===3&&o[2].dims.length!==1&&o[2].dims.length!==2)throw new Error("Invalid input shape of C");if(o[0].type!=="float32"&&o[0].type!=="float64"||o[1].type!=="float32"&&o[1].type!=="float64"||o.length===3&&o[2].type!=="float32"&&o[2].type!=="float64")throw new Error("Invalid input type.");if(o[0].type!==o[1].type||o.length===3&&o[0].type!==o[2].type)throw new Error("Input types are mismatched")}},8555:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedIm2ColProgramInfoLoader=void 0;const u=a(5060),c=a(2039),p=a(2827);n.createPackedIm2ColProgramInfoLoader=(s,h,f,l,o)=>{const t=(e=o.cacheKey,{name:"Im2Col (packed)",inputNames:["A"],inputTypes:[c.TextureType.packed],cacheHint:e});var e;return Object.assign(Object.assign({},t),{get:()=>((r,i,d,g,m,b)=>{const _=d.dims,v=g.dims,w=m.length,S=[v[1]*v[2]*v[3],m[2]*m[3]],A=v[2]*v[3],O=(0,p.unpackFromChannel)(),x=(0,u.getGlsl)(r.session.backend.glContext.version);let I="";for(let B=0;B<=1;B++)for(let L=0;L<=1;L++)I+=`
- blockIndex = rc.x + ${L};
- pos = rc.y + ${B};
-
- if(blockIndex < ${S[1]} && pos < ${S[0]}) {
- offsetY = int(blockIndex / (${m[w-1]})) * ${b.strides[0]} -
- ${b.pads[0]};
- d0 = offsetY + ${b.dilations[0]} * (imod(pos, ${A}) / ${v[2]});
-
- if(d0 < ${_[2]} && d0 >= 0) {
- offsetX = imod(blockIndex, ${m[w-1]}) * ${b.strides[1]} -
- ${b.pads[1]};
- d1 = offsetX + ${b.dilations[1]} * imod(imod(pos, ${A}), ${v[2]});
-
- if(d1 < ${_[3]} && d1 >= 0) {
-
- ch = int(float(pos)/ ${A}.);
- innerDims = vec2(d0, d1);
- result[${2*B+L}] = getChannel(
- getA(0, ch, int(innerDims.x),
- int(innerDims.y)), innerDims);
- }
- }
- }
-
- `;const N=`
- ${O}
-
- void main() {
- ivec2 rc = getOutputCoords();
- vec4 result = vec4(0.0);
- int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
- vec2 innerDims;
- ${I}
- ${x.output} = result;
- }
- `;return Object.assign(Object.assign({},i),{output:{dims:S,type:d.type,textureType:c.TextureType.packed},shaderSource:N,hasMain:!0})})(s,t,h,f,l,o)})}},3248:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.calculateIm2ColDims=n.createIm2ColProgramInfoLoader=void 0;const u=a(2039);n.createIm2ColProgramInfoLoader=(c,p,s,h,f)=>{const l=(o=f.cacheKey,{name:"Im2Col",inputNames:["X"],inputTypes:[u.TextureType.unpacked],cacheHint:o});var o;return Object.assign(Object.assign({},l),{get:()=>((t,e,r,i,d,g)=>{const m=r.dims,b=i.dims,_=d.length,v=(0,n.calculateIm2ColDims)(m,b,d,4),w=`
- const int XC = ${m[1]};
- const int XH = ${m[2]};
- const int XW = ${m[3]};
- const int KH = ${g.kernelShape[0]};
- const int KW = ${g.kernelShape[1]};
- const int dilationH = ${g.dilations[0]};
- const int dilationW = ${g.dilations[1]};
- const int strideH = ${g.strides[0]};
- const int strideW = ${g.strides[1]};
- const int padH = ${g.pads[0]};
- const int padW = ${g.pads[1]};
- const int KHKW = KH*KW;
- const int XCKHKW = XC * KHKW;
- const int outputChannels = 4;
- vec4 process(int indices[${_}]) {
- int b = indices[0]; // batch size
- int oh = indices[1] * strideH - padH; //output height
- int ow = indices[2] * strideW - padW; //output width
- int p = indices[3] * outputChannels; //patch
- vec4 value = vec4(0.0);
- for(int i=0; i < outputChannels; ++i) {
- if(p < XCKHKW) {
- int patchC = p / KHKW;
- int patchH = (p - patchC*KHKW) / KW;
- int patchW = (p - patchC*KHKW) - patchH * KW;
- int xh2 = oh + patchH * dilationH;
- int xw2 = ow + patchW * dilationW;
- int x[${m.length}];
- x[0] = b;
- x[1] = patchC;
- x[2] = xh2;
- x[3] = xw2;
- if(xh2 >= 0 &&
- xh2 < XH &&
- xw2 >= 0 &&
- xw2 < XW) {
- value[i] = _X(x);
- }
- }
- ++p;
- }
- return value;
- }
- `;return Object.assign(Object.assign({},e),{output:{dims:v,type:r.type,textureType:u.TextureType.packedLastDimension},shaderSource:w})})(0,l,p,s,h,f)})},n.calculateIm2ColDims=(c,p,s,h=4)=>[s[0],s[2],s[3],Math.ceil(c[1]*p[2]*p[3]/h)]},6572:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseImageScalerAttributes=n.imageScaler=void 0;const u=a(246),c=a(2039);n.imageScaler=(l,o,t)=>(f(o),[l.run(s(l,o,t),o)]),n.parseImageScalerAttributes=l=>{const o=l.attributes.getFloat("scale"),t=l.attributes.getFloats("bias");return(0,u.createAttributeWithCacheKey)({scale:o,bias:t})};const p={name:"ImageScaler",inputNames:["X"],inputTypes:[c.TextureType.unpacked]},s=(l,o,t)=>{const e=Object.assign(Object.assign({},p),{cacheHint:t.cacheKey});return Object.assign(Object.assign({},e),{get:()=>((r,i,d,g)=>{const m=d[0].dims.slice(),b=m.length,_=`
- ${h(g.bias.length)}
- float process(int indices[${b}]) {
- return _X(indices) * scale + getBias(bias, indices[1]);
- }`;return Object.assign(Object.assign({},i),{output:{dims:m,type:d[0].type,textureType:c.TextureType.unpacked},variables:[{name:"bias",type:"float",arrayLength:g.bias.length,data:g.bias},{name:"scale",type:"float",data:g.scale}],shaderSource:_})})(0,e,o,t)})},h=l=>{const o=[`float getBias(float bias[${l}], int channel) {`];for(let t=0;t<l;++t)t===0?o.push(` if (channel == ${t}) { return bias[${t}]; }`):t===l-1?o.push(` else { return bias[${t}]; }`):o.push(` else if (channel == ${t}) { return bias[${t}]; }`);return o.push(" }"),o.join(`
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- int sb[1];
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- vec4 value = vec4(0);
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- float value;
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- ${x}
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-float getBiasForMatmul() {
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- ${v}
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- int r = ${v[_-2]};
- int c = ${v[_-1]};
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- rc + 1 >= ${N[0]} ? 0. : getA(rc + 1),
- 0, 0`;let F="";if(L>2)for(let H=0;H<L-2;++H)F+=`${B[H]},`;return`getA(${F}r, c),
- rEdge ? 0. : getA(${F}rp1, c),
- cEdge ? 0. : getA(${F}r, cp1),
- rEdge || cEdge ? 0. : getA(${F}rp1, cp1)`}(r,m),I=`
- void main() {
- ${g} rc = getOutputCoords();
-
- if(${O}) {
- ${e.output} = vec4(0);
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- ${b}
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- ${e.output} = vec4(${x});
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- }
- `;return Object.assign(Object.assign({},h),{hasMain:!0,output:{dims:t.dims,type:t.type,textureType:c.TextureType.packed},shaderSource:I})})(f,l)})},2827:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.unpackFromChannel=n.getChannels=n.getVecChannels=void 0;const u=a(9390);function c(p,s){return(0,u.getGlChannels)(s).map(h=>`${p}.${h}`)}n.getVecChannels=c,n.getChannels=function(p,s){return s===1?[p]:c(p,s)},n.unpackFromChannel=function(){return`
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-
- float getChannel(vec4 frag, vec2 innerDims) {
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- `}},2870:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parsePadAttributesV11=n.padV11=n.parsePadAttributesV2=n.padV2=void 0;const u=a(246),c=a(2517),p=a(5060),s=a(2039),h={name:"Pad",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.padV2=(g,m,b)=>(o(m),[g.run(Object.assign(Object.assign({},h),{cacheHint:b.cacheKey,get:()=>l(g,m[0],b)}),m)]),n.parsePadAttributesV2=g=>{const m=g.attributes.getString("mode","constant"),b=g.attributes.getFloat("value",0),_=g.attributes.getInts("pads");return(0,u.createAttributeWithCacheKey)({mode:m,value:b,pads:_})},n.padV11=(g,m,b)=>{t(m);const _=f(g,m,b);return(0,n.padV2)(g,[m[0]],_)},n.parsePadAttributesV11=g=>g.attributes.getString("mode","constant");const f=(g,m,b)=>{if(!g.session.isInitializer(m[1].dataId)||m.length>=3&&!g.session.isInitializer(m[2].dataId))throw new Error("dynamic pad attributes are not allowed");const _=Array.from(m[1].integerData),v=m.length>=3?m[2].floatData[0]:0;return(0,u.createAttributeWithCacheKey)({mode:b,pads:_,value:v})},l=(g,m,b)=>{const _=c.ShapeUtil.padShape(m.dims.slice(),b.pads),v=_.length,w=`
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- `},i=(g,m,b,_,v,w)=>{const S=m.length;let A="";for(let O=S-1;O>=0;--O)A+=`
- k = m[${O}] - ${w[O]};
- if (k < 0) { k = -k; }
- {
- const int _2n_1 = ${2*(m[O]-1)};
- k = int( mod( float(k), float(_2n_1) ) ) ;
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- }
- offset += k * ${b[O]};
- `;return`
- float padA(int m[${S}]) {
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- int k = 0;
- ${A}
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- float value = getColorAsFloat(${g.texture2D}(A, coords));
- return value;
- }
- `},d=(g,m,b,_,v,w)=>{const S=m.length;let A="";for(let O=S-1;O>=0;--O)A+=`
- k = m[${O}] - ${w[O]};
- if (k < 0) k = 0;
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- float padA(int m[${S}]) {
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- ${A}
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- float value = getColorAsFloat(${g.texture2D}(A, coords));
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- `}},2143:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.globalMaxPool=n.parseMaxPoolAttributes=n.maxPool=n.parseGlobalAveragePoolAttributes=n.globalAveragePool=n.parseAveragePoolAttributes=n.averagePool=void 0;const u=a(246),c=a(2517),p=a(2039);n.averagePool=(d,g,m)=>{t(g);const b={name:"AveragePool",inputNames:["X"],inputTypes:[p.TextureType.unpacked],cacheHint:m.cacheKey};return[d.run(Object.assign(Object.assign({},b),{get:()=>s(g,b,!1,m)}),g)]},n.parseAveragePoolAttributes=d=>{const g=d.attributes.getString("auto_pad","NOTSET"),m=d.attributes.getInt("ceil_mode",0),b=d.attributes.getInt("count_include_pad",0)!==0,_=d.attributes.getInts("kernel_shape"),v=d.attributes.getInts("strides",[]),w=d.attributes.getInts("pads",[]);if(m!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");return(0,u.createAttributeWithCacheKey)({autoPad:g,ceilMode:m,countIncludePad:b,kernelShape:_,strides:v,pads:w})};const s=(d,g,m,b)=>{const[_,v]=f(d,b,m),w=c.ShapeUtil.size(_.kernelShape);let S="";_.countIncludePad?S+=`value /= float(${w});`:S+=`value /= float(${w} - pad);`;const A=`
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- value = max(_X(x), value);
- `,"","-1e5")}
- `;return Object.assign(Object.assign({},g),{output:{dims:v,type:d[0].type,textureType:p.TextureType.unpacked},shaderSource:w})},f=(d,g,m)=>{const b=d[0].dims.slice(),_=Object.hasOwnProperty.call(g,"dilations"),v=g.kernelShape.slice(),w=g.strides.slice(),S=_?g.dilations.slice():[],A=g.pads.slice();c.PoolConvUtil.adjustPoolAttributes(m,b,v,w,S,A);const O=c.PoolConvUtil.computePoolOutputShape(m,b,w,S,v,A,g.autoPad),x=Object.assign({},g);return _?Object.assign(x,{kernelShape:v,strides:w,pads:A,dilations:S,cacheKey:g.cacheKey}):Object.assign(x,{kernelShape:v,strides:w,pads:A,cacheKey:g.cacheKey}),[x,O]},l={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},o={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[p.TextureType.unpacked]};n.globalMaxPool=(d,g)=>(t(g),[d.run(Object.assign(Object.assign({},o),{get:()=>h(g,o,!0,l)}),g)]);const t=d=>{if(!d||d.length!==1)throw new Error("Pool ops requires 1 input.");if(d[0].type!=="float32"&&d[0].type!=="float64")throw new Error("Invalid input type.")},e=(d,g,m,b,_)=>{const v=d.length;if(g.kernelShape.length<=2){const w=g.kernelShape[g.kernelShape.length-1],S=g.strides[g.strides.length-1],A=g.pads[g.pads.length/2-1],O=g.pads[g.pads.length-1],x=d[v-1];let I="",N="",B="";if(I=A+O!==0?`
- for (int i = 0; i < ${w}; i++) {
- x[${v} - 1] = indices[${v} - 1] * ${S} - ${A} + i;
- if (x[${v} - 1] < 0 || x[${v} - 1] >= ${x}) {
- pad++;
- continue;
- }
- ${m}
- }`:`
- for (int i = 0; i < ${w}; i++) {
- x[${v} - 1] = indices[${v} - 1] * ${S} - ${A} + i;
- ${m}
- }`,g.kernelShape.length===2){const L=g.kernelShape[g.kernelShape.length-2],F=g.strides[g.strides.length-2],H=g.pads[g.pads.length/2-2],D=g.pads[g.pads.length-2],j=d[v-2];N=H+D!==0?`
- for (int j = 0; j < ${L}; j++) {
- x[${v} - 2] = indices[${v} - 2] * ${F} - ${H} + j;
- if (x[${v} - 2] < 0 || x[${v} - 2] >= ${j}) {
- pad+= ${w};
- continue;
- }
- `:`
- for (int j = 0; j < ${L}; j++) {
- x[${v} - 2] = indices[${v} - 2] * ${F} - ${H} + j;
- `,B=`
- }
- `}return`
- float process(int indices[${v}]) {
- int x[${v}];
- copyVec(indices, x);
-
- float value = ${_};
- int pad = 0;
- ${N}
- ${I}
- ${B}
- ${b}
- return value;
- }
- `}{const w=c.ShapeUtil.size(g.kernelShape),S=c.ShapeUtil.computeStrides(g.kernelShape),A=S.length,O=g.pads.length,x=i(A),I=r(d,"inputDims"),N=r(g.pads,"pads"),B=r(S,"kernelStrides"),L=r(g.strides,"strides");let F="";return F=g.pads.reduce((H,D)=>H+D)?`
- if (x[j] >= inputDims[j] || x[j] < 0) {
- pad++;
- isPad = true;
- break;
- }
- }
- if (!isPad) {
- ${m}
- }`:`
- }
- ${m}
- `,`
- ${x}
- float process(int indices[${v}]) {
- int x[${v}];
- copyVec(indices, x);
- int offset[${A}];
- int pads[${O}];
- int inputDims[${v}];
- int kernelStrides[${A}];
- int strides[${A}];
- ${N}
- ${I}
- ${L}
- ${B}
-
- float value = ${_};
- int pad = 0;
- bool isPad = false;
- for (int i = 0; i < ${w}; i++) {
- offsetToIndices(i, kernelStrides, offset);
- isPad = false;
- for (int j = ${v} - ${A}; j < ${v}; j++) {
- x[j] = indices[j] * strides[j - ${v} + ${A}]
- + offset[j - ${v} + ${A}] - pads[j - 2];
- ${F}
- }
- ${b}
-
- return value;
- }
- `}},r=(d,g)=>{let m="";for(let b=0;b<d.length;b++)m+=`
- ${g}[${b}] = ${d[b]};
- `;return m},i=d=>`
- void offsetToIndices(int offset, int[${d}] strides, out int[${d}] indices) {
- if (${d} == 0) {
- return;
- }
- for (int i = 0; i < ${d} - 1; ++i) {
- indices[i] = offset / strides[i];
- offset -= indices[i] * strides[i];
- }
- indices[${d} - 1] = offset;
- }`},4939:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reduceLogSumSquare=n.reduceLogSum=n.reduceProd=n.reduceMin=n.reduceMax=n.reduceMean=n.reduceSum=n.parseReduceAttributes=void 0;const u=a(246),c=a(782),p=a(2517),s=a(2039),h=(o,t,e,r,i)=>{l(t);const d={name:r,inputNames:["A"],inputTypes:[s.TextureType.unpacked]};return[o.run(Object.assign(Object.assign({},d),{cacheHint:e.cacheKey,get:()=>f(o,t,e,r,i,d)}),t)]};n.parseReduceAttributes=o=>{const t=o.attributes.getInts("axes",[]),e=o.attributes.getInt("keepdims",1)===1;return(0,u.createAttributeWithCacheKey)({axes:t,keepDims:e})};const f=(o,t,e,r,i,d)=>{const g=[],m=t[0].dims.length||1,b=[],_=p.ShapeUtil.normalizeAxes(e.axes,t[0].dims.length),v=i(t,_);let w=v[1];for(let A=0;A<t[0].dims.length;A++)_.indexOf(A)>=0||_.length===0?(e.keepDims&&g.push(1),w=`
- for(int j${A} = 0; j${A} < ${t[0].dims[A]}; j${A}++) {
- inputIdx[${A}] = j${A};
- ${w}
- }`):(b.push(`inputIdx[${A}] = outputIdx[${g.length}];`),g.push(t[0].dims[A]));const S=`
- float process(int outputIdx[${g.length||1}]) {
- float value; // final result
- int inputIdx[${m}]; // addressing input data
- ${b.join(`
-`)}
- ${v[0]} // init ops for reduce max/min
- ${w}
- ${v[2]} // final computation for reduce mean
- return value;
- }`;return Object.assign(Object.assign({},d),{output:{dims:g,type:t[0].type,textureType:s.TextureType.unpacked},shaderSource:S})},l=o=>{if(!o||o.length!==1)throw new Error("Reduce op requires 1 input.");if(c.NUMBER_TYPES.indexOf(o[0].type)===-1)throw new Error("Invalid input type.")};n.reduceSum=(o,t,e)=>h(o,t,e,"ReduceSum",()=>["value = 0.0;","value += _A(inputIdx);",""]),n.reduceMean=(o,t,e)=>h(o,t,e,"ReduceMean",(r,i)=>{let d=1;for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&(d*=r[0].dims[g]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${d}.;`]}),n.reduceMax=(o,t,e)=>h(o,t,e,"ReduceMax",(r,i)=>{const d=[];for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&d.push(`inputIdx[${g}] = 0;`);return[`${d.join(`
-`)}
-value = _A(inputIdx);`,"value = max(value, _A(inputIdx));",""]}),n.reduceMin=(o,t,e)=>h(o,t,e,"ReduceMin",(r,i)=>{const d=[];for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&d.push(`inputIdx[${g}] = 0;`);return[`${d.join(`
-`)}
-value = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]}),n.reduceProd=(o,t,e)=>h(o,t,e,"ReduceProd",()=>["value = 1.0;","value *= _A(inputIdx);",""]),n.reduceLogSum=(o,t,e)=>h(o,t,e,"ReduceLogSum",()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"]),n.reduceLogSumSquare=(o,t,e)=>h(o,t,e,"ReduceLogSumSquare",()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""])},7019:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.isReshapeCheap=n.processDims3D=n.createPackedReshape3DProgramInfoLoader=void 0;const u=a(2517),c=a(5060),p=a(2039),s=a(2827);n.createPackedReshape3DProgramInfoLoader=(h,f,l)=>{const o=(t=>({name:"Reshape (packed)",inputTypes:[p.TextureType.packed],inputNames:["A"],cacheHint:`${t}`}))(l);return Object.assign(Object.assign({},o),{get:()=>((t,e,r,i)=>{const d=e.dims,g=i;let m="";for(let v=0;v<4;v++){let w="";switch(v){case 0:w="outputCoords = rc;";break;case 1:w="outputCoords = ivec3(rc.x, rc.y+1, rc.z);";break;case 2:w="outputCoords = ivec3(rc.x, rc.y, rc.z+1);";break;case 3:w="outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";break;default:throw new Error}m+=`
- ${w}
- ${v>0?"if(outputCoords.y < rows && outputCoords.z < cols){":""}
- int flattenedIndex = getFlattenedIndex(outputCoords);
-
- ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);
- vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));
-
- result[${v}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);
-
- ${v>0?"}":""}
- `}const b=(0,c.getGlsl)(t.session.backend.glContext.version),_=`
- ${function(v){const w=u.ShapeUtil.computeStrides(v),S=["b","r","c"],A="index";return`
- ivec3 inputCoordsFromReshapedOutCoords(int index) {
- ${w.map((O,x)=>`int ${S[x]} = ${A} / ${O}; ${x===w.length-1?`int ${S[x+1]} = ${A} - ${S[x]} * ${O}`:`index -= ${S[x]} * ${O}`};`).join("")}
- return ivec3(b, r, c);
- }
- `}(d)}
- ${function(v){const w=u.ShapeUtil.computeStrides(v);return`
- int getFlattenedIndex(ivec3 coords) {
- // reverse y, z order
- return coords.x * ${w[0]} + coords.z * ${w[1]} + coords.y;
- }
-`}(g)}
- ${(0,s.unpackFromChannel)()}
-
- void main() {
- ivec3 rc = getOutputCoords();
-
- vec4 result = vec4(0.0);
-
- ivec3 outputCoords;
- int rows = ${g[2]};
- int cols = ${g[1]};
-
- ${m}
- ${b.output} = result;
- }
- `;return Object.assign(Object.assign({},r),{output:{dims:g,type:e.type,textureType:p.TextureType.packed},shaderSource:_,hasMain:!0})})(h,f,o,l)})},n.processDims3D=function(h){if(h.length===0)return[1,1,1];let f=1;for(let l=0;l<h.length-2;++l)f*=h[l];return[f,h.length>1?h[h.length-2]:1,h[h.length-1]]},n.isReshapeCheap=function(h,f){let l=!1;return l=h.length===0||f.length===0||(h.length<2||f.length<2?h[h.length-1]===f[f.length-1]:h[h.length-1]===f[f.length-1]&&h[h.length-2]===f[f.length-2]),l}},718:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reshape=void 0;const u=a(2517);n.reshape=(c,p)=>{const s=u.ShapeUtil.calculateReshapedDims(p[0].dims,p[1].integerData);return c.session.pack?[c.reshapePacked(p[0],s)]:[c.reshapeUnpacked(p[0],s)]}},2268:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseResizeAttributesV11=n.parseResizeAttributesV10=n.resize=void 0;const u=a(5060),c=a(2039),p=a(9390),s=a(2827),h=a(9793),f={name:"Resize",inputNames:["A"],inputTypes:[c.TextureType.packed]};n.resize=(r,i,d)=>((0,h.validateInputs)(i,d),[r.run(Object.assign(Object.assign({},f),{cacheHint:d.cacheKey,get:()=>l(r,i,d)}),i)]),n.parseResizeAttributesV10=r=>(0,h.parseUpsampleAttributes)(r,10),n.parseResizeAttributesV11=r=>(0,h.parseUpsampleAttributes)(r,11);const l=(r,i,d)=>{const g=(0,u.getGlsl)(r.session.backend.glContext.version),[m,b]=o(i,d);if(m.every(F=>F===1)&&d.coordinateTransformMode!=="tf_crop_and_resize")return Object.assign(Object.assign({},f),{output:{dims:b,type:i[0].type,textureType:c.TextureType.packed},hasMain:!0,shaderSource:`void main() {
- vec4 v = ${g.texture2D}(X, TexCoords);
- ${g.output} = v;
- }`});const _=b.length;if(_<2)throw new Error(`output dimension should be at least 2, but got ${_}`);const v=b[_-2],w=b[_-1],S=i[0].dims;if(_!==S.length)throw new Error(`output dimension should match input ${S.length}, but got ${_}`);const A=S[_-2],O=S[_-1],x=m[_-2],I=m[_-1];let N="";if(d.mode!=="linear")throw new Error(`resize (packed) does not support mode: '${d.mode}'`);switch(d.coordinateTransformMode){case"asymmetric":N=`
- vec4 getSourceFracIndex(ivec4 coords) {
- return vec4(coords) / scaleWHWH;
- }
- `;break;case"half_pixel":N=`
- vec4 getSourceFracIndex(ivec4 coords) {
- return (vec4(coords) + 0.5) / scaleWHWH - 0.5;
- }
- `;break;case"pytorch_half_pixel":N=`
- vec4 getSourceFracIndex(ivec4 coords) {
- vec4 fcoords = vec4(coords);
- return vec4(
- ${w}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,
- ${v}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,
- ${w}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
- ${v}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
- );
- }
- `;break;case"align_corners":N=`
- vec4 getSourceFracIndex(ivec4 coords) {
- vec4 resized = vec4(${w}.0 - 1.0, ${v}.0 - 1.0, ${w}.0 - 1.0,
- ${v}.0 - 1.0);
- vec4 original = vec4(${O}.0 - 1.0, ${A}.0 - 1.0, ${O}.0 - 1.0,
- ${A}.0 - 1.0);
- vec4 new_scale = original / resized;
- return vec4(coords) * new_scale;
- }
- `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode: '${d.coordinateTransformMode}'`)}const B=(0,p.getCoordsDataType)(_),L=`
- const vec2 inputWH = vec2(${A}.0, ${O}.0);
- const vec4 scaleWHWH = vec4(float(${x}), float(${I}), float(${x}), float(${I}));
- ${(0,s.unpackFromChannel)()}
- ${N}
- float getAValue(int x10, int r, int c, int d) {
- return getChannel(getA(x10, r, c, d), vec2(c, d));
- }
- void main() {
- ${B} rc = getOutputCoords();
-
- int batch = rc[0];
- int depth = rc[1];
-
- // retrieve the 4 coordinates that is used in the 4 packed output values.
- ivec4 coords = ivec4(rc.wz, rc.w + 1, rc.z + 1);
-
- // calculate the source index in fraction
- vec4 sourceFrac = getSourceFracIndex(coords);
-
- // get the lower and upper bound of the 4 values that will be packed into one texel.
- ivec4 x00 = ivec4(max(sourceFrac.xy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xy)));
- ivec4 x01 = ivec4(max(sourceFrac.xw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xw)));
- ivec4 x10 = ivec4(max(sourceFrac.zy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zy)));
- ivec4 x11 = ivec4(max(sourceFrac.zw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zw)));
-
- bool hasNextRow = rc.w < ${v-1};
- bool hasNextCol = rc.z < ${w-1};
-
- // pack x00, x01, x10, x11's top-left corner into one vec4 structure
- vec4 topLeft = vec4(
- getAValue(batch, depth, x00.x, x00.y),
- hasNextCol ? getAValue(batch, depth, x01.x, x01.y) : 0.0,
- hasNextRow ? getAValue(batch, depth, x10.x, x10.y) : 0.0,
- (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.y) : 0.0);
-
- // pack x00, x01, x10, x11's top-right corner into one vec4 structure
- vec4 topRight = vec4(
- getAValue(batch, depth, x00.x, x00.w),
- hasNextCol ? getAValue(batch, depth, x01.x, x01.w) : 0.0,
- hasNextRow ? getAValue(batch, depth, x10.x, x10.w) : 0.0,
- (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.w) : 0.0);
-
- // pack x00, x01, x10, x11's bottom-left corner into one vec4 structure
- vec4 bottomLeft = vec4(
- getAValue(batch, depth, x00.z, x00.y),
- hasNextCol ? getAValue(batch, depth, x01.z, x01.y) : 0.0,
- hasNextRow ? getAValue(batch, depth, x10.z, x10.y) : 0.0,
- (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.y) : 0.0);
-
- // pack x00, x01, x10, x11's bottom-right corner into one vec4 structure
- vec4 bottomRight = vec4(
- getAValue(batch, depth, x00.z, x00.w),
- hasNextCol ? getAValue(batch, depth, x01.z, x01.w) : 0.0,
- hasNextRow ? getAValue(batch, depth, x10.z, x10.w) : 0.0,
- (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.w) : 0.0);
-
- // calculate the interpolation fraction on u and v direction
- vec4 frac = vec4(sourceFrac) - floor(sourceFrac);
- vec4 clampFrac = clamp(frac, vec4(0.0), vec4(1.0));
-
- vec4 top = mix(topLeft, topRight, clampFrac.ywyw);
- vec4 bottom = mix(bottomLeft, bottomRight, clampFrac.ywyw);
- vec4 newValue = mix(top, bottom, clampFrac.xxzz);
-
- ${g.output} = vec4(newValue);
- }
- `;return Object.assign(Object.assign({},f),{output:{dims:b,type:i[0].type,textureType:c.TextureType.packed},hasMain:!0,shaderSource:L})},o=(r,i)=>{const d=r[0].dims;let g,m=i.scales;if(m.length===0){const _=r[i.scalesInputIdx];if(_&&_.size!==0){if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");m=t(_,i.mode,i.isResize)}else{const v=r[i.sizesInputIdx];if(!v||v.size===0)throw new Error("Either scales or sizes MUST be provided as input.");g=Array.from(v.integerData),m=e(g,d,i.mode,i.isResize)}}else if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");const b=g||d.map((_,v)=>Math.floor(_*m[v]));return[m,b]},t=(r,i,d)=>{const g=Array.from(r.floatData);return(0,h.scalesValidation)(g,i,d),g},e=(r,i,d,g)=>{const m=i.length,b=new Array(m);for(let _=0,v=m;_<v;_++)if(i[_]===0){if(r[_]!==0)throw new Error("Input dim is zero but required output dim is non-zero.");b[_]=1}else b[_]=r[_]/i[_];return(0,h.scalesValidation)(b,d,g),b}},8117:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.shape=void 0;const u=a(9162);n.shape=(p,s)=>(c(s),[new u.Tensor([s[0].dims.length],"int32",void 0,void 0,new Int32Array(s[0].dims))]);const c=p=>{if(!p||p.length!==1)throw new Error("Shape requires 1 input.")}},2278:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sliceV10=n.parseSliceAttributes=n.slice=void 0;const u=a(246),c=a(782),p=a(2517),s=a(2039),h={name:"Slice",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.slice=(e,r,i)=>(l(r),[e.run(Object.assign(Object.assign({},h),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),r)]),n.parseSliceAttributes=e=>{const r=e.attributes.getInts("starts"),i=e.attributes.getInts("ends"),d=e.attributes.getInts("axes",[]);return(0,u.createAttributeWithCacheKey)({starts:r,ends:i,axes:d})};const f=(e,r,i)=>{const d=i.axes.length===0?r.dims.slice(0).map((S,A)=>A):i.axes,g=p.ShapeUtil.normalizeAxes(d,r.dims.length),m=i.starts.map((S,A)=>S>r.dims[g[A]]-1?r.dims[g[A]]:p.ShapeUtil.normalizeAxis(S,r.dims[g[A]])),b=i.ends.map((S,A)=>S>r.dims[g[A]]-1?r.dims[g[A]]:p.ShapeUtil.normalizeAxis(S,r.dims[g[A]])),_=r.dims.slice(),v=[];for(let S=0;S<g.length;S++)_[g[S]]=b[S]-m[S],m[S]>0&&v.push(`outputIdx[${g[S]}] += ${m[S]};`);const w=`
- float process(int outputIdx[${_.length}]) {
- ${v.join(`
- `)}
- return _A(outputIdx);
- }`;return Object.assign(Object.assign({},h),{output:{dims:_,type:r.type,textureType:s.TextureType.unpacked},shaderSource:w})},l=e=>{if(!e||e.length!==1)throw new Error("Slice requires 1 input.");if(c.NUMBER_TYPES.indexOf(e[0].type)===-1)throw new Error("Invalid input type.")};n.sliceV10=(e,r)=>{t(r);const i=o(e,r);return[e.run(Object.assign(Object.assign({},h),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),[r[0]])]};const o=(e,r)=>{if(!e.session.isInitializer(r[1].dataId)||!e.session.isInitializer(r[2].dataId)||r.length>=4&&!e.session.isInitializer(r[3].dataId)||r.length>=5&&!e.session.isInitializer(r[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(r.length>=5&&r[4].integerData.some(m=>m!==1))throw new Error("currently non-1 steps is not supported for Slice");const i=Array.from(r[1].integerData),d=Array.from(r[2].integerData),g=r.length>=4?Array.from(r[3].integerData):[];return{starts:i,ends:d,axes:g,cacheKey:`${g};${i};${d}`}},t=e=>{if(!e||e.length<3||e.length>5)throw new Error("Invalid input number.");if(e[1].type!=="int32"||e[1].dims.length!==1)throw new Error("Invalid input type.");if(e[2].type!=="int32"||e[2].dims.length!==1)throw new Error("Invalid input type.");if(e.length>=4&&(e[3].type!=="int32"||e[3].dims.length!==1))throw new Error("Invalid input type.");if(e.length>=5&&(e[4].type!=="int32"||e[4].dims.length!==1))throw new Error("Invalid input type.")}},5524:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.softmaxV13=n.parseSoftmaxAttributesV13=n.parseSoftmaxAttributes=n.softmax=void 0;const u=a(246),c=a(2517),p=a(5060),s=a(2039),h=a(3738),f={name:"SoftmaxComputeMax",inputNames:["A"],inputTypes:[s.TextureType.unpacked]},l={name:"SoftmaxComputeScale",inputNames:["A","Max"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},o={name:"SoftMax",inputNames:["A","Max","Norm"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked,s.TextureType.unpacked]};n.softmax=(g,m,b)=>{d(m);const _=m[0].dims.slice(),v=c.ShapeUtil.normalizeAxis(b.axis,_.length),w=c.ShapeUtil.sizeToDimension(_,v),S=c.ShapeUtil.sizeFromDimension(_,v);return t(g,m,b,w,S)},n.parseSoftmaxAttributes=g=>(0,u.createAttributeWithCacheKey)({axis:g.attributes.getInt("axis",1)}),n.parseSoftmaxAttributesV13=g=>(0,u.createAttributeWithCacheKey)({axis:g.attributes.getInt("axis",-1)}),n.softmaxV13=(g,m,b)=>{d(m);const _=m[0].dims.slice(),v=c.ShapeUtil.normalizeAxis(b.axis,_.length),w=_.length,S=v!==w-1,A=[];let O,x=[],I=[];S&&(x=Array.from({length:w}).map((F,H)=>H),x[v]=w-1,x[w-1]=v,x.map(F=>A.push(_[F])),O=(0,u.createAttributeWithCacheKey)({perm:x}),I=(0,h.transpose)(g,m,O));const N=S?c.ShapeUtil.sizeToDimension(A,w-1):c.ShapeUtil.sizeToDimension(_,w-1),B=S?c.ShapeUtil.sizeFromDimension(A,w-1):c.ShapeUtil.sizeFromDimension(_,w-1),L=t(g,S?I:m,b,N,B);return S?(0,h.transpose)(g,L,O):L};const t=(g,m,b,_,v)=>{const w=e(g,m[0],_,v,[_]),S=g.run(Object.assign(Object.assign({},f),{cacheHint:b.cacheKey,get:()=>w}),m),A=r(g,m[0],_,v,w.output.dims,[_]),O=g.run(Object.assign(Object.assign({},l),{cacheHint:b.cacheKey,get:()=>A}),[m[0],S]),x=i(g,m[0],_,v,w.output.dims,A.output.dims);return[g.run(Object.assign(Object.assign({},o),{cacheHint:b.cacheKey,get:()=>x}),[m[0],S,O])]},e=(g,m,b,_,v)=>{const[w,S]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),A=v.length;if(b<1||_<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(v.length!==1)throw new Error("Dimensionality of the output should be 1");if(v[0]!==b)throw new Error("Shape of the output should be equal to logical row count");const O=(0,p.getGlsl)(g.session.backend.glContext.version),x=`
- float process(int[${A}] indices) {
- int logical_row_start_offset = indices[0] * ${_};
-
- float max = getColorAsFloat(${O.texture2D}(A, offsetToCoords(logical_row_start_offset, ${w},
- ${S} )));
- for(int i=1; i<${_}; ++i)
- {
- float current = getColorAsFloat(${O.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
- ${w}, ${S})));
- if(current > max)
- max = current;
- }
-
- return max;
- }`;return Object.assign(Object.assign({},f),{output:{dims:v,type:m.type,textureType:s.TextureType.unpacked},shaderSource:x})},r=(g,m,b,_,v,w)=>{const[S,A]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),O=w.length;if(b<1||_<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(w.length!==1)throw new Error("Dimensionality of the output should be 1");if(w[0]!==b)throw new Error("Shape of the output should be equal to logical row count");if(v.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(v[0]!==b)throw new Error("Shape of the intermediate results should be equal to logical row count");const x=`
- float process(int[${O}] indices) {
- int logical_row_start_offset = indices[0] * ${_};
-
- float norm_factor = 0.0;
- float max = _Max(indices);
- for(int i=0; i<${_}; ++i)
- {
- norm_factor += exp(getColorAsFloat(${(0,p.getGlsl)(g.session.backend.glContext.version).texture2D}(A, offsetToCoords(logical_row_start_offset + i,
- ${S}, ${A}))) - max);
- }
-
- return norm_factor;
- }`;return Object.assign(Object.assign({},l),{output:{dims:w,type:m.type,textureType:s.TextureType.unpacked},shaderSource:x})},i=(g,m,b,_,v,w)=>{const[S,A]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),O=m.dims.length;if(b<1||_<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(v.length!==1||w.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(v[0]!==b||w[0]!==b)throw new Error("Shape of the intermediate results should be equal to logical row count");const x=`
- float process(int[${O}] indices) {
-
- // get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)
- int offset = coordsToOffset(TexCoords, ${S}, ${A});
-
- //determine the logical row for this index
- int logical_row_index[1];
- logical_row_index[0] = offset / ${_};
-
- float norm_factor = _Norm(logical_row_index);
-
- // avoid possible division by 0
- // if norm_facor is 0, all elements are zero
- // if so, return 0
- if(norm_factor == 0.0)
- return 0.0;
-
- return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
- }`;return Object.assign(Object.assign({},o),{output:{dims:m.dims,type:m.type,textureType:s.TextureType.unpacked},shaderSource:x})},d=g=>{if(!g||g.length!==1)throw new Error("Softmax requires 1 input.");if(g[0].type!=="float32"&&g[0].type!=="float64")throw new Error("Invalid input type")}},5975:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseSplitAttributes=n.split=void 0;const u=a(246),c=a(2517),p=a(2039),s={name:"Split",inputNames:["A"],inputTypes:[p.TextureType.unpacked]};n.split=(o,t,e)=>{l(t);const r=c.ShapeUtil.normalizeAxis(e.axis,t[0].dims.length),i=h(o,t,r,e),d=[];for(let g=0;g<i;++g)d.push(o.run(Object.assign(Object.assign({},s),{cacheHint:`${e.cacheKey};${g}`,get:()=>f(o,t[0],e,r,g)}),t));return d},n.parseSplitAttributes=o=>{const t=o.attributes.getInt("axis",0),e=o.attributes.getInts("split",[]),r=o.outputs.length;return(0,u.createAttributeWithCacheKey)({axis:t,split:e,numOutputs:r})};const h=(o,t,e,r)=>{const[,i]=c.SplitUtil.splitShape(t[0].dims,e,r.split,r.numOutputs);return i.length},f=(o,t,e,r,i)=>{const[d,g]=c.SplitUtil.splitShape(t.dims,r,e.split,e.numOutputs),m=g[i],b=d[i],_=`
- float process(int indices[${b.length}]) {
- indices[${r}] += ${m};
- return _A(indices);
- }
- `;return Object.assign(Object.assign({},s),{cacheHint:`${e.cacheKey}:${i}`,output:{dims:b,type:t.type,textureType:p.TextureType.unpacked},shaderSource:_})},l=o=>{if(!o||o.length!==1)throw new Error("Split requires one input.");if(o[0].type!=="int8"&&o[0].type!=="uint8"&&o[0].type!=="int16"&&o[0].type!=="uint16"&&o[0].type!=="int32"&&o[0].type!=="uint32"&&o[0].type!=="float32"&&o[0].type!=="float64"&&o[0].type!=="bool")throw new Error("Invalid input type.")}},3933:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseSqueezeAttributes=n.squeezeV13=n.squeeze=void 0;const u=a(2517);n.squeeze=(s,h,f)=>{c(h);const l=u.ShapeUtil.squeezeShape(h[0].dims,f);return[s.reshapeUnpacked(h[0],l)]},n.squeezeV13=(s,h)=>(p(h),(0,n.squeeze)(s,[h[0]],Array.from(h[1].integerData))),n.parseSqueezeAttributes=s=>s.attributes.getInts("axes");const c=s=>{if(!s||s.length!==1)throw new Error("Squeeze requires 1 input.");if(s[0].type==="string")throw new Error("invalid input tensor types.")},p=s=>{if(!s||s.length!==2)throw new Error("Squeeze requires 2 inputs.");if(s[1].type!=="int32")throw new Error("Invalid input type.")}},6558:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sum=void 0;const u=a(5060),c=a(2039);n.sum=(h,f)=>{s(f);const l={name:"Sum",inputNames:f.map((o,t)=>`X${t}`),inputTypes:new Array(f.length).fill(c.TextureType.unpacked)};return[h.run(Object.assign(Object.assign({},l),{get:()=>p(h,f,l)}),f)]};const p=(h,f,l)=>{const o=(0,u.getGlsl)(h.session.backend.glContext.version),t=f[0].dims.slice(),e=`
- void main() {
- vec4 result = ${f.map((r,i)=>`${o.texture2D}(X${i},TexCoords)`).join(" + ")};
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- }
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- float process(int outputIdx[${r}]) {
- int inputIdx[${r}];
- ${e.join(`
-`)}
- return _A(inputIdx);
- }
- `;return Object.assign(Object.assign({},l),{output:{dims:t,type:f[0].type,textureType:c.TextureType.unpacked},shaderSource:i})},s=h=>{if(!h||h.length!==2)throw new Error("Tile requires 2 input.");if(h[1].dims.length!==1)throw new Error("The second input shape must 1 dimension.");if(h[1].dims[0]!==h[0].dims.length)throw new Error("Invalid input shape.");if(u.NUMBER_TYPES.indexOf(h[0].type)===-1)throw new Error("Invalid input type.");if(h[1].type!=="int32"&&h[1].type!=="int16")throw new Error("Invalid repeat type.")}},3738:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseTransposeAttributes=n.transpose=void 0;const u=a(246),c=a(2517),p=a(2039),s={name:"Transpose",inputNames:["A"],inputTypes:[p.TextureType.unpacked]};n.transpose=(e,r,i)=>(t(r),[e.run(Object.assign(Object.assign({},s),{cacheHint:i.cacheKey,get:()=>h(e,r[0],i.perm)}),r)]),n.parseTransposeAttributes=e=>(0,u.createAttributeWithCacheKey)({perm:e.attributes.getInts("perm",[])});const h=(e,r,i)=>{const d=r.dims;i=f(d,i);const g=l(d,i),m=d.length,b=`
- ${o("perm",i,m)}
- float process(int indices[${m}]) {
- int a[${m}];
- perm(a, indices);
- return _A(a);
- }`;return Object.assign(Object.assign({},s),{output:{dims:g,type:r.type,textureType:p.TextureType.unpacked},shaderSource:b})},f=(e,r)=>(r&&r.length!==e.length&&(r=[...e.keys()].reverse()),r),l=(e,r)=>(r=f(e,r),c.ShapeUtil.sortBasedOnPerm(e,r)),o=(e,r,i)=>{const d=[];d.push(`void ${e}(out int a[${i}], int src[${i}]) {`);for(let g=0;g<i;++g)d.push(` a[${r[g]}]=src[${g}];`);return d.push(" }"),d.join(`
-`)},t=e=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(e[0].type!=="float32"&&e[0].type!=="float64")throw new Error("input should be float tensor")}},8710:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.encodeAsUint8=void 0;const u=a(5060),c=a(2039);n.encodeAsUint8=(p,s)=>{const h=s.shape,f=(0,u.getGlsl)(p.session.backend.glContext.version),l=`
- const float FLOAT_MAX = 1.70141184e38;
- const float FLOAT_MIN = 1.17549435e-38;
-
- bool isNaN(float val) {
- return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;
- }
-
- highp vec4 encodeAsUint8(highp float v) {
- if (isNaN(v)) {
- return vec4(255, 255, 255, 255);
- }
-
- highp float av = abs(v);
-
- if(av < FLOAT_MIN) {
- return vec4(0.0, 0.0, 0.0, 0.0);
- } else if(v > FLOAT_MAX) {
- return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
- } else if(v < -FLOAT_MAX) {
- return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
- }
-
- highp vec4 c = vec4(0,0,0,0);
-
- highp float e = floor(log2(av));
- highp float m = exp2(fract(log2(av))) - 1.0;
-
- c[2] = floor(128.0 * m);
- m -= c[2] / 128.0;
- c[1] = floor(32768.0 * m);
- m -= c[1] / 32768.0;
- c[0] = floor(8388608.0 * m);
-
- highp float ebias = e + 127.0;
- c[3] = floor(ebias / 2.0);
- ebias -= c[3] * 2.0;
- c[2] += floor(ebias) * 128.0;
-
- c[3] += 128.0 * step(0.0, -v);
-
- return c / 255.0;
- }
-
- void main() {
- float value = ${f.texture2D}(X,TexCoords).r;
- ${f.output} = encodeAsUint8(value);
- }`,o={name:"Uint8Encode",inputTypes:[c.TextureType.unpacked],inputNames:["X"],output:{dims:h,type:s.tensor.type,textureType:c.TextureType.downloadUint8AsFloat},shaderSource:l,hasMain:!0};return p.executeProgram(o,[s.tensor])}},4909:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.tanh=n.tan=n.sqrt=n.sin=n.sigmoid=n.relu=n.not=n.neg=n.log=n.parseLeakyReluAttributes=n.leakyRelu=n.identity=n.floor=n.exp=n.parseEluAttributes=n.elu=n.cos=n.ceil=n.clipV11=n.parseClipAttributes=n.clip=n.atan=n.asin=n.acos=n.abs=n.glslTanh=n.glslTan=n.glslSqrt=n.glslSigmoid=n.glslRelu=n.glslSin=n.glslNot=n.glslNeg=n.glslLog=n.glslLeakyRelu=n.glslIdentity=n.glslClip=n.glslFloor=n.glslExp=n.glslElu=n.glslCos=n.glslCeil=n.glslAtan=n.glslAsin=n.glslAcos=n.glslAbs=void 0;const u=a(246),c=a(2517),p=a(8520),s=a(5060),h=a(2039);function f(){return L("abs")}function l(){return L("acos")}function o(){return L("asin")}function t(){return L("atan")}function e(){return L("ceil")}function r(){return L("cos")}function i(D){const j="elu";return{body:`
- const float alpha = float(${D});
-
- float ${j}_(float a) {
- return a >= 0.0 ? a: (exp(a) - 1.0) * alpha;
- }
- vec4 ${j}_(vec4 v) {
- return vec4(${j}_(v.x), ${j}_(v.y), ${j}_(v.z), ${j}_(v.w));
- }
- `,name:j,type:p.FunctionType.ValueBased}}function d(){return L("exp")}function g(){return L("floor")}function m(D,j){const Z="clip";return{body:`
- const float min = float(${D});
- const float max = float(${j});
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- float ${Z}_(float a) {
- return clamp(a, min, max);
- }
- vec4 ${Z}_(vec4 v) {
- return clamp(v, min, max);
- }
- `,name:Z,type:p.FunctionType.ValueBased}}function b(){const D="indentity";return{body:`
- float ${D}_(float a) {
- return a;
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- vec4 ${D}_(vec4 v) {
- return v;
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- `,name:D,type:p.FunctionType.ValueBased}}function _(D){const j="leakyRelu";return{body:`
- const float alpha = float(${D});
-
- float ${j}_(float a) {
- return a < 0.0 ? a * alpha : a;
- }
- vec4 ${j}_(vec4 v) {
- return vec4(${j}_(v.x), ${j}_(v.y), ${j}_(v.z), ${j}_(v.w));
- }
- `,name:j,type:p.FunctionType.ValueBased}}function v(){return L("log")}function w(){const D="neg";return{body:`
- float ${D}_(float a) {
- return -a;
- }
- vec4 ${D}_(vec4 v) {
- return -v;
- }
- `,name:D,type:p.FunctionType.ValueBased}}function S(){const D="not";return{body:`
- float ${D}_(float a) {
- return float( ! bool(a) );
- }
- bool ${D}_(bool a) {
- return !a;
- }
- vec4 ${D}_(vec4 v) {
- return vec4(!bool(v.x), !bool(v.y), !bool(v.z), !bool(v.w));
- }
- bvec4 ${D}_(bvec4 v) {
- return bvec4(!v.x, !v.y, !v.z, !v.w);
- }
- `,name:D,type:p.FunctionType.ValueBased}}function A(){return L("sin")}function O(){const D="relu";return{body:`
- float ${D}_(float a) {
- return max( a, 0.0 );
- }
- vec4 ${D}_(vec4 v) {
- return max( v, 0.0 );
- }
- `,name:D,type:p.FunctionType.ValueBased}}function x(){const D="sigmoid";return{body:`
- float ${D}_(float a) {
- return 1.0 / (1.0 + exp(-a));
- }
- vec4 ${D}_(vec4 v) {
- return 1.0 / (1.0 + exp(-v));
- }
- `,name:D,type:p.FunctionType.ValueBased}}function I(){return L("sqrt")}function N(){return L("tan")}function B(){const D="tanh";return{body:`
- float ${D}_(float a) {
- a = clamp(a, -10., 10.);
- a = exp(2.*a);
- return (a - 1.) / (a + 1.);
- }
- vec4 ${D}_(vec4 v) {
- v = clamp(v, -10., 10.);
- v = exp(2.*v);
- return (v - 1.) / (v + 1.);
- }
- `,name:D,type:p.FunctionType.ValueBased}}function L(D){return{body:`
- float ${D}_(float a) {
- return ${D}(a);
- }
- vec4 ${D}_(vec4 v) {
- return ${D}(v);
- }
- `,name:D,type:p.FunctionType.ValueBased}}n.glslAbs=f,n.glslAcos=l,n.glslAsin=o,n.glslAtan=t,n.glslCeil=e,n.glslCos=r,n.glslElu=i,n.glslExp=d,n.glslFloor=g,n.glslClip=m,n.glslIdentity=b,n.glslLeakyRelu=_,n.glslLog=v,n.glslNeg=w,n.glslNot=S,n.glslSin=A,n.glslRelu=O,n.glslSigmoid=x,n.glslSqrt=I,n.glslTan=N,n.glslTanh=B;const F=(D,j,Z,X)=>{const J=D.session.pack?h.TextureType.packed:h.TextureType.unpacked,ee={name:Z.name,inputTypes:[J],inputNames:["A"],cacheHint:X};return Object.assign(Object.assign({},ee),{get:()=>((ue,Ae,ve,oe)=>{const _e=ue.session.pack?h.TextureType.packed:h.TextureType.unpacked,be=(0,s.getGlsl)(ue.session.backend.glContext.version);return Object.assign(Object.assign({},Ae),{output:{dims:ve.dims,type:ve.type,textureType:_e},shaderSource:`
- ${oe.body}
- void main() {
- vec4 v = ${be.texture2D}(A, TexCoords);
- v = ${oe.name}_(v);
- ${be.output} = v;
- }
- `,hasMain:!0})})(D,ee,j,Z)})};n.abs=(D,j)=>[D.run(F(D,j[0],f()),j)],n.acos=(D,j)=>[D.run(F(D,j[0],l()),j)],n.asin=(D,j)=>[D.run(F(D,j[0],o()),j)],n.atan=(D,j)=>[D.run(F(D,j[0],t()),j)],n.clip=(D,j,Z)=>[D.run(F(D,j[0],m(Z.min,Z.max),Z.cacheKey),j)],n.parseClipAttributes=D=>(0,u.createAttributeWithCacheKey)({min:D.attributes.getFloat("min",c.MIN_CLIP),max:D.attributes.getFloat("max",c.MAX_CLIP)}),n.clipV11=(D,j)=>{const Z=H(D,j);return(0,n.clip)(D,[j[0]],Z)};const H=(D,j)=>{if(j.length>=3&&(!D.session.isInitializer(j[1].dataId)||!D.session.isInitializer(j[2].dataId)))throw new Error("dynamic clip attributes are not allowed");const Z=j.length>=3?j[1].numberData[0]:c.MIN_CLIP,X=j.length>=3?j[2].numberData[0]:c.MAX_CLIP;return(0,u.createAttributeWithCacheKey)({min:Z,max:X})};n.ceil=(D,j)=>[D.run(F(D,j[0],e()),j)],n.cos=(D,j)=>[D.run(F(D,j[0],r()),j)],n.elu=(D,j,Z)=>[D.run(F(D,j[0],i(Z.alpha),Z.cacheKey),j)],n.parseEluAttributes=D=>(0,u.createAttributeWithCacheKey)({alpha:D.attributes.getFloat("alpha",1)}),n.exp=(D,j)=>[D.run(F(D,j[0],d()),j)],n.floor=(D,j)=>[D.run(F(D,j[0],g()),j)],n.identity=(D,j)=>[D.run(F(D,j[0],b()),j)],n.leakyRelu=(D,j,Z)=>[D.run(F(D,j[0],_(Z.alpha),Z.cacheKey),j)],n.parseLeakyReluAttributes=D=>(0,u.createAttributeWithCacheKey)({alpha:D.attributes.getFloat("alpha",.01)}),n.log=(D,j)=>[D.run(F(D,j[0],v()),j)],n.neg=(D,j)=>[D.run(F(D,j[0],w()),j)],n.not=(D,j)=>[D.run(F(D,j[0],S()),j)],n.relu=(D,j)=>[D.run(F(D,j[0],O()),j)],n.sigmoid=(D,j)=>[D.run(F(D,j[0],x()),j)],n.sin=(D,j)=>[D.run(F(D,j[0],A()),j)],n.sqrt=(D,j)=>[D.run(F(D,j[0],I()),j)],n.tan=(D,j)=>[D.run(F(D,j[0],N()),j)],n.tanh=(D,j)=>[D.run(F(D,j[0],B()),j)]},5611:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createUnpackProgramInfoLoader=n.createUnpackProgramInfo=void 0;const u=a(5060),c=a(2039),p=a(9390),s=a(2827),h={name:"unpack",inputNames:["A"],inputTypes:[c.TextureType.packed]};n.createUnpackProgramInfo=(f,l)=>{const o=l.dims.length,t=(0,s.getChannels)("rc",o),e=t.slice(-2),r=(0,p.getCoordsDataType)(o),i=(0,s.unpackFromChannel)(),d=l.dims.length===0?"":function(b,_){if(b===1)return"rc";let v="";for(let w=0;w<b;w++)v+=_[w],w<b-1&&(v+=",");return v}(o,t),g=o<=1?"rc":`vec2(${e.join(",")})`,m=`
- ${i}
- void main() {
- ${r} rc = getOutputCoords();
-
- // Sample the texture with the coords to get the rgba channel value.
- vec4 packedInput = getA(${d});
-
- ${(0,u.getGlsl)(f.session.backend.glContext.version).output} = vec4(getChannel(packedInput, ${g}), 0, 0, 0);
- }
- `;return Object.assign(Object.assign({},h),{hasMain:!0,output:{dims:l.dims,type:l.type,textureType:c.TextureType.unpacked},shaderSource:m})},n.createUnpackProgramInfoLoader=(f,l)=>Object.assign(Object.assign({},h),{get:()=>(0,n.createUnpackProgramInfo)(f,l)})},8428:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseUnsqueezeAttributes=n.unsqueezeV13=n.unsqueeze=void 0;const u=a(2517);n.unsqueeze=(s,h,f)=>{c(h);const l=u.ShapeUtil.unsqueezeShape(h[0].dims,f);return[s.reshapeUnpacked(h[0],l)]},n.unsqueezeV13=(s,h)=>(p(h),(0,n.unsqueeze)(s,[h[0]],Array.from(h[1].integerData))),n.parseUnsqueezeAttributes=s=>s.attributes.getInts("axes");const c=s=>{if(!s||s.length!==1)throw new Error("Unsqueeze requires 1 input.");if(s[0].type==="string")throw new Error("invalid input tensor types.")},p=s=>{if(!s||s.length!==2)throw new Error("Unsqueeze requires 2 inputs.");if(s[1].type!=="int32")throw new Error("Invalid input type.")}},9793:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.scalesValidation=n.validateInputs=n.parseUpsampleAttributes=n.parseUpsampleAttributesV9=n.parseUpsampleAttributesV7=n.upsample=void 0;const u=a(246),c=a(5060),p=a(2039),s={name:"Upsample",inputNames:["X"],inputTypes:[p.TextureType.unpacked]};n.upsample=(f,l,o)=>((0,n.validateInputs)(l,o),[f.run(Object.assign(Object.assign({},s),{cacheHint:o.cacheKey,get:()=>h(f,l,o)}),l)]),n.parseUpsampleAttributesV7=f=>(0,n.parseUpsampleAttributes)(f,7),n.parseUpsampleAttributesV9=f=>(0,n.parseUpsampleAttributes)(f,9),n.parseUpsampleAttributes=(f,l)=>{const o=l>=10,t=f.attributes.getString("mode","nearest");if(t!=="nearest"&&t!=="linear"&&(l<11||t!=="cubic"))throw new Error(`unrecognized mode: ${t}`);let e=[];l<9&&(e=f.attributes.getFloats("scales"),(0,n.scalesValidation)(e,t,o));const r=f.attributes.getFloat("extrapolation_value",0),i=l>10?f.attributes.getString("coordinate_transformation_mode","half_pixel"):"asymmetric";if(["asymmetric","pytorch_half_pixel","tf_half_pixel_for_nn","align_corners","tf_crop_and_resize","half_pixel"].indexOf(i)===-1)throw new Error(`coordinate_transform_mode '${i}' is not supported`);const d=i==="tf_crop_and_resize",g=d,m=t==="nearest"&&l>=11?f.attributes.getString("nearest_mode","round_prefer_floor"):"";if(["round_prefer_floor","round_prefer_ceil","floor","ceil",""].indexOf(m)===-1)throw new Error(`nearest_mode '${m}' is not supported`);const b=f.attributes.getFloat("cubic_coeff_a",-.75),_=f.attributes.getInt("exclude_outside",0)!==0;if(_&&t!=="cubic")throw new Error("exclude_outside can be set to 1 only when mode is CUBIC.");const v=l<11||t==="nearest"&&i==="asymmetric"&&m==="floor";let w=0,S=0,A=0;return l>10?f.inputs.length>2?(w=1,S=2,A=3):(S=1,A=2):l===9&&(S=1),(0,u.createAttributeWithCacheKey)({opset:l,isResize:o,mode:t,scales:e,extrapolationValue:r,coordinateTransformMode:i,useExtrapolation:g,needRoiInput:d,nearestMode:m,cubicCoefficientA:b,excludeOutside:_,useNearest2xOptimization:v,roiInputIdx:w,scalesInputIdx:S,sizesInputIdx:A})};const h=(f,l,o)=>{const t=(0,c.getGlsl)(f.session.backend.glContext.version),[e,r]=f.calculateTextureWidthAndHeight(l[0].dims,p.TextureType.unpacked),i=l[0].dims.map((A,O)=>Math.floor(A*o.scales[O])),[d,g]=f.calculateTextureWidthAndHeight(i,p.TextureType.unpacked),m=i.length,b=new Array(m),_=new Array(m);let v=`
- int output_pitches[${m}];
- int input_pitches[${m}];
- `;for(let A=m-1;A>=0;A--)b[A]=A===m-1?1:b[A+1]*i[A+1],_[A]=A===m-1?1:_[A+1]*l[0].dims[A+1],v+=`
- output_pitches[${A}] = ${b[A]};
- input_pitches[${A}] = ${_[A]};
- `;const w=`
- float getInputFloat(int index) {
- vec2 coords = offsetToCoords(index, ${e}, ${r});
- float value = getColorAsFloat(${t.texture2D}(X, coords));
- return value;
- }
- `,S=o.mode==="nearest"?`
- ${w}
- float process(int indices[${m}]) {
- int input_index = 0;
- int output_index = coordsToOffset(TexCoords, ${d}, ${g});
-
- ${v}
-
- int d, m;
- for (int dim = 0; dim < ${m}; ++dim) {
- d = output_index / output_pitches[dim];
- m = output_index - d * output_pitches[dim];
- output_index = m;
-
- if (scales[dim] != 1 && d > 0) {
- int d2 = d / scales[dim];
- m = d - d2 * scales[dim];
- d = d2;
- }
- input_index += input_pitches[dim] * d;
- }
-
- return getInputFloat(input_index);
- }`:m===4?`
- ${w}
- float process(int indices[4]) {
- int input_index = 0;
- int output_index = coordsToOffset(TexCoords, ${d}, ${g});
-
- ${v}
-
- int m;
- int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;
- index_of_dim0 = output_index / output_pitches[0];
- m = output_index - index_of_dim0 * output_pitches[0];
- index_of_dim1 = m / output_pitches[1];
- m = m - index_of_dim1 * output_pitches[1];
- index_of_dim2 = m / output_pitches[2];
- m = m - index_of_dim2 * output_pitches[2];
- index_of_dim3 = m;
-
- int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;
- index_of_input_dim2 = index_of_dim2 / scales[2];
- y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];
- index_of_input_dim3 = index_of_dim3 / scales[3];
- x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];
-
- input_index = index_of_dim0 * input_pitches[0] +
- index_of_dim1 * input_pitches[1] +
- index_of_input_dim2 * input_pitches[2] +
- index_of_input_dim3;
-
- float x00 = getInputFloat(input_index);
- float x10, x01, x11;
-
- bool end_of_dim2 = false;
- if (index_of_input_dim2 == (${l[0].dims[2]} - 1)) {
- // It's the end in dimension 2
- x01 = x00;
- end_of_dim2 = true;
- } else {
- x01 = getInputFloat(input_index + input_pitches[2]);
- }
-
- if (index_of_input_dim3 == (input_pitches[2] - 1)) {
- // It's the end in dimension 3
- x10 = x00;
- x11 = x01;
- }
- else {
- x10 = getInputFloat(input_index + 1);
- x11 = end_of_dim2 ? x10 : getInputFloat(input_index + input_pitches[2] + 1);
- }
-
- float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[2]);
- float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[2]);
- return y0 + float(x_offset) * (y1 - y0) / float(scales[3]);
- }`:`
- ${w}
- float process(int indices[2]) {
- int input_index = 0;
- int output_index = coordsToOffset(TexCoords, ${d}, ${g});
-
- ${v}
-
- int m;
- int index_of_dim0, index_of_dim1;
- index_of_dim0 = output_index / output_pitches[0];
- m = output_index - index_of_dim0 * output_pitches[0];
- index_of_dim1 = m;
-
- int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;
- index_of_input_dim0 = index_of_dim0 / scales[0];
- y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];
- index_of_input_dim1 = index_of_dim1 / scales[1];
- x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];
-
- input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;
-
- float x00 = getInputFloat(input_index);
- float x10, x01, x11;
-
- bool end_of_dim0 = false;
- if (index_of_input_dim0 == (${l[0].dims[0]} - 1)) {
- // It's the end in dimension 0
- x01 = x00;
- end_of_dim0 = true;
- } else {
- x01 = getInputFloat(input_index + input_pitches[0]);
- }
-
- if (index_of_input_dim1 == (input_pitches[0] - 1)) {
- // It's the end in dimension 1
- x10 = x00;
- x11 = x01;
- }
- else {
- x10 = getInputFloat(input_index + 1);
- x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1);
- }
-
- float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]);
- float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]);
- return y0 + float(x_offset) * (y1 - y0) / float(scales[1]);
- }`;return Object.assign(Object.assign({},s),{output:{dims:i,type:l[0].type,textureType:p.TextureType.unpacked},shaderSource:S,variables:[{name:"scales",type:"int",arrayLength:o.scales.length,data:o.scales.map(A=>Math.ceil(A))}]})};n.validateInputs=(f,l)=>{if(!f||l.opset<9&&f.length!==1||l.opset>=9&&l.opset<11&&f.length!==2||l.opset>=11&&f.length<2)throw new Error("invalid inputs.");if(l.scales.length>0&&f[0].dims.length!==l.scales.length)throw new Error("Invalid input shape.");if(f[0].type==="string")throw new Error("Invalid input tensor types.")},n.scalesValidation=(f,l,o)=>{if(o){for(const t of f)if(t<=0)throw new Error("Scale value should be greater than 0.")}else for(const t of f)if(t<1)throw new Error("Scale value should be greater than or equal to 1.");if(!(l!=="linear"&&l!=="cubic"||f.length===2||f.length===4&&f[0]===1&&f[1]===1))throw new Error(`'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the ${o?"Resize":"Upsample"} opeartor.`)}},1958:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ProgramManager=void 0;const u=a(1670),c=a(6231),p=a(8879),s=a(5060);n.ProgramManager=class{constructor(h,f,l){this.profiler=h,this.glContext=f,this.textureLayoutStrategy=l,this.repo=new Map,this.attributesBound=!1}getArtifact(h){return this.repo.get(h)}setArtifact(h,f){this.repo.set(h,f)}run(h,f,l){var o;this.profiler.event("op",`ProgramManager.run ${(o=h.programInfo.name)!==null&&o!==void 0?o:"unknown kernel"}`,()=>{var t;const e=this.glContext.gl,r=h.program;e.useProgram(r);try{this.bindOutput(l),this.attributesBound||this.bindAttributes(h.attribLocations),this.bindUniforms(h.uniformLocations,(t=h.programInfo.variables)!==null&&t!==void 0?t:[],f)}catch(i){throw c.Logger.error("ProgramManager",h.programInfo.shaderSource),i}this.profiler.event("backend","GlContext.draw()",()=>{this.glContext.draw()})},this.glContext)}dispose(){this.vertexShader&&this.glContext.deleteShader(this.vertexShader),this.repo.forEach(h=>this.glContext.deleteProgram(h.program))}build(h,f,l){return this.profiler.event("backend","ProgramManager.build",()=>{const o=new p.GlslPreprocessor(this.glContext,h,f,l),t=o.preprocess(),e=this.compile(t);return{programInfo:h,program:e,uniformLocations:this.getUniformLocations(e,o.context.programInfo.inputNames,o.context.programInfo.variables),attribLocations:this.getAttribLocations(e)}})}compile(h){if(!this.vertexShader){c.Logger.verbose("ProrgramManager","Compiling and caching Vertex shader for the first time");const o=(0,s.getVertexShaderSource)(this.glContext.version);this.vertexShader=this.glContext.compileShader(o,this.glContext.gl.VERTEX_SHADER)}u.env.debug&&c.Logger.verbose("ProrgramManager",`FragShader:
-${h}
-`);const f=this.glContext.compileShader(h,this.glContext.gl.FRAGMENT_SHADER),l=this.glContext.createProgram(this.vertexShader,f);return this.glContext.deleteShader(f),l}bindOutput(h){const f=h.width,l=h.height;c.Logger.verbose("ProrgramManager",`Binding output texture to Framebuffer: w/h=${f}/${l}, shape=${h.shape}, type=${h.tensor.type}`),this.glContext.attachFramebuffer(h.texture,f,l)}bindAttributes(h){const f=h.position,l=h.textureCoord;this.glContext.setVertexAttributes(f,l),this.attributesBound=!0}bindUniforms(h,f,l){var o;const t=this.glContext.gl;let e=0;for(const{name:r,type:i,location:d,arrayLength:g}of h){const m=(o=f.find(b=>b.name===r))===null||o===void 0?void 0:o.data;if(i!=="sampler2D"&&!m)throw new Error(`variable '${r}' does not have data defined in program info`);switch(i){case"sampler2D":this.bindTexture(l[e],d,e),e++;break;case"float":g?t.uniform1fv(d,m):t.uniform1f(d,m);break;case"int":g?t.uniform1iv(d,m):t.uniform1i(d,m);break;default:throw new Error(`Uniform not implemented: ${i}`)}}}bindTexture(h,f,l){this.glContext.bindTextureToUniform(h.texture,l,f)}getAttribLocations(h){return{position:this.getAttribLocation(h,"position"),textureCoord:this.getAttribLocation(h,"textureCoord")}}getUniformLocations(h,f,l){const o=[];if(f)for(const t of f)o.push({name:t,type:"sampler2D",location:this.getUniformLocation(h,t)});if(l)for(const t of l)o.push(Object.assign(Object.assign({},t),{location:this.getUniformLocation(h,t.name)}));return o}getUniformLocation(h,f){const l=this.glContext.gl.getUniformLocation(h,f);if(l===null)throw new Error(`Uniform ${f} not found.`);return l}getAttribLocation(h,f){return this.glContext.gl.getAttribLocation(h,f)}}},6416:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.WebGLSessionHandler=void 0;const u=a(6231),c=a(1047),p=a(8316),s=a(1640),h=a(1958),f=a(7859),l=a(5702);n.WebGLSessionHandler=class{constructor(o,t){this.backend=o,this.context=t,this.layoutStrategy=new f.PreferLogicalStrategy(o.glContext.maxTextureSize),this.programManager=new h.ProgramManager(this.context.profiler,o.glContext,this.layoutStrategy),this.textureManager=new l.TextureManager(o.glContext,this.layoutStrategy,this.context.profiler,{reuseTextures:o.textureCacheMode==="full"}),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache=new Map,this.pack=o.pack,this.pack2unpackMap=new Map,this.unpack2packMap=new Map}createInferenceHandler(){return new p.WebGLInferenceHandler(this)}onGraphInitialized(o){const t=o.getValues().filter(e=>e.from===-1&&e.tensor).map(e=>e.tensor.dataId);this.initializers=new Set(t)}isInitializer(o){return!!this.initializers&&this.initializers.has(o)}addInitializer(o){this.initializers.add(o)}getTextureData(o,t){return t?this.packedTextureDataCache.get(o):this.unpackedTextureDataCache.get(o)}setTextureData(o,t,e=!1){u.Logger.verbose("WebGLSessionHandler","Storing Texture data in cache"),e?this.packedTextureDataCache.set(o,t):this.unpackedTextureDataCache.set(o,t)}dispose(){this.programManager.dispose(),this.textureManager.clearActiveTextures(),this.packedTextureDataCache.forEach(o=>this.textureManager.releaseTexture(o,!0)),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache.forEach(o=>this.textureManager.releaseTexture(o,!0)),this.unpackedTextureDataCache=new Map}resolve(o,t,e){const r=(0,c.resolveOperator)(o,t,s.WEBGL_OP_RESOLVE_RULES);return{impl:r.opImpl,context:r.opInit?r.opInit(o,e):o}}}},7769:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Uint8DataEncoder=n.RGBAFloatDataEncoder=n.RedFloat32DataEncoder=void 0;const u=a(6231);n.RedFloat32DataEncoder=class{constructor(c,p=1){if(p===1)this.internalFormat=c.R32F,this.format=c.RED,this.textureType=c.FLOAT,this.channelSize=p;else{if(p!==4)throw new Error(`Invalid number of channels: ${p}`);this.internalFormat=c.RGBA32F,this.format=c.RGBA,this.textureType=c.FLOAT,this.channelSize=p}}encode(c,p){let s,h;return c.constructor!==Float32Array&&(u.Logger.warning("Encoder","data was not of type Float32; creating new Float32Array"),h=new Float32Array(c)),p*this.channelSize>c.length?(u.Logger.warning("Encoder","Source data too small. 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i=o.checkFramebufferStatus(o.FRAMEBUFFER)===o.FRAMEBUFFER_COMPLETE;return o.bindTexture(o.TEXTURE_2D,null),o.bindFramebuffer(o.FRAMEBUFFER,null),o.deleteTexture(t),o.deleteFramebuffer(r),i}checkRenderFloat32(){if(this.version===2){if(!this.colorBufferFloatExtension)return!1}else if(!this.textureFloatExtension)return!1;return this.isFloatTextureAttachableToFrameBuffer}checkFloat32Download(){if(this.version===2){if(!this.colorBufferFloatExtension)return!1}else if(!this.textureFloatExtension||!this.gl.getExtension("WEBGL_color_buffer_float"))return!1;return this.isFloatTextureAttachableToFrameBuffer}checkFloat32Blend(){const o=this.gl;let t,e,r,i,d;try{t=o.createTexture(),e=o.createFramebuffer(),o.bindTexture(o.TEXTURE_2D,t);const g=this.version===2?o.RGBA32F:o.RGBA;return o.texImage2D(o.TEXTURE_2D,0,g,1,1,0,o.RGBA,o.FLOAT,null),o.bindFramebuffer(o.FRAMEBUFFER,e),o.framebufferTexture2D(o.FRAMEBUFFER,o.COLOR_ATTACHMENT0,o.TEXTURE_2D,t,0),o.enable(o.BLEND),r=o.createShader(o.VERTEX_SHADER),!!r&&(o.shaderSource(r,"void main(){}"),o.compileShader(r),i=o.createShader(o.FRAGMENT_SHADER),!!i&&(o.shaderSource(i,"precision highp float;void main(){gl_FragColor=vec4(0.5);}"),o.compileShader(i),d=o.createProgram(),!!d&&(o.attachShader(d,r),o.attachShader(d,i),o.linkProgram(d),o.useProgram(d),o.drawArrays(o.POINTS,0,1),o.getError()===o.NO_ERROR)))}finally{o.disable(o.BLEND),d&&o.deleteProgram(d),r&&o.deleteShader(r),i&&o.deleteShader(i),e&&(o.bindFramebuffer(o.FRAMEBUFFER,null),o.deleteFramebuffer(e)),t&&(o.bindTexture(o.TEXTURE_2D,null),o.deleteTexture(t))}}beginTimer(){if(this.version===2&&this.disjointTimerQueryWebgl2Extension){const o=this.gl,t=this.disjointTimerQueryWebgl2Extension,e=o.createQuery();return o.beginQuery(t.TIME_ELAPSED_EXT,e),e}throw new Error("WebGL1 profiling currently not supported.")}endTimer(){if(this.version!==2||!this.disjointTimerQueryWebgl2Extension)throw new Error("WebGL1 profiling currently not supported");{const o=this.gl,t=this.disjointTimerQueryWebgl2Extension;o.endQuery(t.TIME_ELAPSED_EXT)}}isTimerResultAvailable(o){let t=!1,e=!1;if(this.version!==2||!this.disjointTimerQueryWebgl2Extension)throw new Error("WebGL1 profiling currently not supported");{const r=this.gl,i=this.disjointTimerQueryWebgl2Extension;t=r.getQueryParameter(o,r.QUERY_RESULT_AVAILABLE),e=r.getParameter(i.GPU_DISJOINT_EXT)}return t&&!e}getTimerResult(o){let t=0;if(this.version!==2)throw new Error("WebGL1 profiling currently not supported");{const e=this.gl;t=e.getQueryParameter(o,e.QUERY_RESULT),e.deleteQuery(o)}return t/1e6}async waitForQueryAndGetTime(o){return await(0,f.repeatedTry)(()=>this.isTimerResultAvailable(o)),this.getTimerResult(o)}async createAndWaitForFence(){const o=this.createFence(this.gl);return this.pollFence(o)}createFence(o){let t;const e=o,r=e.fenceSync(e.SYNC_GPU_COMMANDS_COMPLETE,0);return o.flush(),t=r===null?()=>!0:()=>{const i=e.clientWaitSync(r,0,0);return i===e.ALREADY_SIGNALED||i===e.CONDITION_SATISFIED},{query:r,isFencePassed:t}}async pollFence(o){return new Promise(t=>{this.addItemToPoll(()=>o.isFencePassed(),()=>t())})}pollItems(){const o=l(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=o;++t){const{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(o+1)}async addItemToPoll(o,t){this.itemsToPoll.push({isDoneFn:o,resolveFn:t}),this.itemsToPoll.length>1||await(0,f.repeatedTry)(()=>(this.pollItems(),this.itemsToPoll.length===0))}}},1036:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ExecutionPlan=void 0;const u=a(6231);class c{constructor(s,h){this.op=s,this.node=h}}n.ExecutionPlan=class{constructor(p,s,h){this.graph=p,this.profiler=h,this.initialize(s)}initialize(p){this.profiler.event("session","ExecutionPlan.initialize",()=>{const s=this.graph.getNodes();if(s.length!==p.length)throw new Error("The size of nodes and OPs do not match.");this._ops=p.map((h,f)=>new c(h,s[f])),this.reset(),this._starter=[],this._ops.forEach((h,f)=>{let l=!0;for(const o of h.node.inputs)if(!this._values[o]&&this.graph.getInputIndices().indexOf(o)===-1){l=!1;break}l&&this._starter.push(f)})})}reset(){this._values=this.graph.getValues().map(p=>p.tensor)}async execute(p,s){return this.profiler.event("session","ExecutionPlan.execute",async()=>{this.reset();const h=p.createInferenceHandler(),f=this.graph.getInputIndices();if(s.length!==f.length)throw new Error(`number of input tensors don't match the number of inputs to the model: actual: ${s.length} expected: ${f.length}`);s.forEach((i,d)=>{const g=f[d];this._values[g]=i});const l=this._starter.slice(0),o=this.graph.getValues(),t=this.graph.getNodes();let e=0;for(;e<l.length;){const i=l[e++],d=this._ops[i],g=d.node.inputs.map(v=>this._values[v]);if(g.indexOf(void 0)!==-1)throw new Error(`unresolved input detected: op: ${d.node}`);const m=g;u.Logger.verbose("ExecPlan",`Runing op:${d.node.name} (${m.map((v,w)=>`'${d.node.inputs[w]}': ${v.type}[${v.dims.join(",")}]`).join(", ")})`);const b=await this.profiler.event("node",d.node.name,async()=>d.op.impl(h,m,d.op.context));if(b.length!==d.node.outputs.length)throw new Error("the size of output does not match model definition.");b.forEach((v,w)=>{const S=d.node.outputs[w];if(this._values[S])throw new Error(`output [${S}] already has value: op:${d.node.name}`);this._values[S]=v});const _=new Set;b.forEach((v,w)=>{const S=d.node.outputs[w];for(const A of o[S].to){const O=t[A];let x=!0;for(const I of O.inputs)if(!this._values[I]){x=!1;break}x&&_.add(A)}}),l.push(..._)}const r=[];for(let i=0;i<this.graph.getOutputIndices().length;i++){const d=this.graph.getOutputIndices()[i],g=this._values[d];if(g===void 0)throw new Error(`required output [${d}] does not have value`);d===0?await g.getData():g.data,r.push(g)}return u.Logger.verbose("ExecPlan","disposing of inferenceHandler"),h.dispose(),r})}}},7070:(y,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Graph=void 0;const u=a(1446),c=a(7778),p=a(9395),s=a(9162),h=a(2517);var f=p.onnxruntime.experimental.fbs;n.Graph={from:(e,r)=>new t(e,r)};class l{constructor(r){this._from=void 0,this._to=[],this.tensor=void 0,this.type=void 0,r&&(this.type=h.ProtoUtil.tensorValueTypeFromProto(r.type.tensorType))}get from(){return this._from}get to(){return this._to}}class o{constructor(r,i){r instanceof u.onnx.NodeProto?(this.name=r.name,this.opType=r.opType,this.attributes=new c.Attribute(r.attribute)):r instanceof f.Node&&(this.name=i??r.name(),this.opType=r.opType(),this.attributes=new c.Attribute(h.ProtoUtil.tensorAttributesFromORTFormat(r))),this.inputs=[],this.outputs=[],this.executeNode=!0}}class t{constructor(r,i){if(!r)throw new TypeError("graph is empty");this.buildGraph(r),this.transformGraph(i),this.checkIsAcyclic()}getInputIndices(){return this._allInputIndices}getInputNames(){return this._allInputNames}getOutputIndices(){return this._allOutputIndices}getOutputNames(){return this._allOutputNames}getValues(){return this._allData}getNodes(){return this._nodes}buildGraph(r){if(r instanceof u.onnx.GraphProto)this.buildGraphFromOnnxFormat(r);else{if(!(r instanceof f.Graph))throw new TypeError("Graph type is not supported.");this.buildGraphFromOrtFormat(r)}}buildGraphFromOnnxFormat(r){const i=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];const d=new Map;if(!r.input)throw new Error("missing information in graph: input");const g=[];for(const m of r.input){if(i.has(m.name))throw new Error(`duplicated input name: ${m.name}`);const b=this._allData.push(new l(m))-1;i.set(m.name,b),g.push(m.name)}if(!r.initializer)throw new Error("missing information in graph: initializer");for(const m of r.initializer){let b=i.get(m.name);if(b===void 0){const _=new l;_.type={shape:{dims:h.ProtoUtil.tensorDimsFromProto(m.dims)},tensorType:h.ProtoUtil.tensorDataTypeFromProto(m.dataType)},b=this._allData.push(_)-1,i.set(m.name,b)}this._allData[b]._from=-1,this._allData[b].tensor=s.Tensor.fromProto(m)}for(let m=0;m<this._allData.length;m++)this._allData[m].tensor||(this._allInputIndices.push(m),this._allInputNames.push(g[m]));if(!r.output)throw new Error("missing information in graph: output");for(const m of r.output){if(i.has(m.name))throw new Error(`duplicated output name: ${m.name}`);const b=this._allData.push(new l(m))-1;i.set(m.name,b),this._allOutputIndices.push(b),this._allOutputNames.push(m.name)}if(!r.node)throw new Error("missing information in graph: node");for(const m of r.node){if(!m.name)for(let _=0;;_++){const v=`unnamed_${m.opType}_${_}`;if(!d.has(v)){m.name=v;break}}if(d.has(m.name))throw new Error(`duplicated node name: ${m.name}`);const b=this._nodes.push(new o(m))-1;d.set(m.name,b)}for(let m=0;m<this._nodes.length;m++){const b=this._nodes[m],_=r.node[m];if(!_.output)throw new Error(`missing output for node: ${_.name}`);for(const v of _.output){let w=i.get(v);if(w===void 0&&(w=this._allData.push(new l)-1,i.set(v,w)),b.outputs.push(w),this._allData[w]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${w}`);if(this._allData[w]._from=m,_.opType==="Constant"){if(!_.attribute||_.attribute.length!==1||!_.attribute[0].t)throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(!_.output||_.output.length!==1)throw new Error("missing output or incorrect number of outputs for this Constant operator");b.outputs.pop(),b.executeNode=!1,this._allData[w]._from=-1,this._allData[w].tensor=s.Tensor.fromProto(_.attribute[0].t)}}}for(let m=0;m<this._nodes.length;m++){const b=this._nodes[m],_=r.node[m];if(!_.input)throw new Error(`missing input for node: ${_.name}`);for(const v of _.input){const w=i.get(v);if(w===void 0){if(v===""&&_.input.length===3&&_.opType==="Resize")continue;throw new Error(`unrecognized input '${v}' for node: ${_.name}`)}b.inputs.push(w),this._allData[w]._to.push(m)}}return!0}buildGraphFromOrtFormat(r){var i,d,g;const m=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];const b=new Map,_=[];for(let v=0;v<r.inputsLength();v++){const w=r.inputs(v);if(m.has(w))throw new Error(`duplicated input name: ${w}`);for(let S=0;S<r.nodeArgsLength();S++)if(((i=r.nodeArgs(S))===null||i===void 0?void 0:i.name())===w){const A=new l;if(((g=(d=r.nodeArgs(S))===null||d===void 0?void 0:d.type())===null||g===void 0?void 0:g.valueType())!==f.TypeInfoValue.tensor_type)throw new Error("Unexpected value type for the nodeArg.");const O=r.nodeArgs(S).type().value(new f.TensorTypeAndShape),x=h.ProtoUtil.tensorDataTypeFromProto(O.elemType()),I=O.shape(),N=[];for(let L=0;L<I.dimLength();L++)N.push(h.LongUtil.longToNumber(I.dim(L).value().dimValue()));A.type={shape:{dims:N},tensorType:x};const B=this._allData.push(A)-1;m.set(w,B),_.push(w)}}for(let v=0;v<r.initializersLength();v++){const w=r.initializers(v);let S=m.get(w.name());if(S===void 0){const A=new l,O=h.ProtoUtil.tensorDimsFromORTFormat(w),x=h.ProtoUtil.tensorDataTypeFromProto(w.dataType());A.type={shape:{dims:O},tensorType:x},S=this._allData.push(A)-1,m.set(w.name(),S)}this._allData[S]._from=-1,this._allData[S].tensor=s.Tensor.fromOrtTensor(w)}for(let v=0;v<this._allData.length;v++)this._allData[v].tensor||(this._allInputIndices.push(v),this._allInputNames.push(_[v]));for(let v=0;v<r.outputsLength();v++){const w=r.outputs(v);if(m.has(w))throw new Error(`duplicated output name: ${w}`);const S=this._allData.push(new l)-1;m.set(w,S),this._allOutputIndices.push(S),this._allOutputNames.push(w)}if(!r.nodes)throw new Error("missing information in graph: node");for(let v=0;v<r.nodesLength();v++){const w=r.nodes(v);let S=w.name();if(!S)for(let O=0;S=`unnamed_${w.opType()}_${O}`,b.has(S);O++);if(b.has(S))throw new Error(`duplicated node name: ${S}`);const A=this._nodes.push(new o(w,S))-1;b.set(S,A)}for(let v=0;v<this._nodes.length;v++){const w=this._nodes[v],S=r.nodes(v);if(S==null)throw new Error(`No node exists at index ${v}`);if((S==null?void 0:S.outputsLength())===0)throw new Error(`missing output for node: ${S.name}`);for(let A=0;A<(S==null?void 0:S.outputsLength());A++){const O=S==null?void 0:S.outputs(A);let x=m.get(O);if(x===void 0&&(x=this._allData.push(new l)-1,m.set(O,x)),w.outputs.push(x),this._allData[x]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${x}`);if(this._allData[x]._from=v,S.opType()==="Constant"){if(S.attributesLength()!==1||!S.attributes(0).t())throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(S.outputsLength()!==1)throw new Error("missing output or incorrect number of outputs for this Constant operator");w.outputs.pop(),w.executeNode=!1,this._allData[x]._from=-1,this._allData[x].tensor=s.Tensor.fromOrtTensor(S.attributes(0).t())}}}for(let v=0;v<this._nodes.length;v++){const w=this._nodes[v],S=r.nodes(v);if(S.inputsLength()===0)throw new Error(`missing input for node: ${S.name}`);for(let A=0;A<S.inputsLength();A++){const O=S.inputs(A),x=m.get(O);if(x===void 0)throw new Error(`unrecognized input '${O}' for node: ${S.name()}`);w.inputs.push(x),this._allData[x]._to.push(v)}}}checkIsAcyclic(){const r=new Set;this._allInputIndices.forEach(g=>{this._allData[g]._to.forEach(m=>{r.add(m)})});const i=Array.from(r),d=new Array(this._nodes.length).fill("white");for(;i.length>0;){const g=i.pop();d[g]==="gray"?d[g]="black":(i.push(g),d[g]="gray",this._nodes[g].outputs.forEach(m=>{const b=this._allData[m];if(b.tensor!==void 0)throw new Error("node outputs should not be initialized");if(b._from!==g)throw new Error("from property of the Value object doesn't match index of Node being processed");b._to.forEach(_=>{if(d[_]==="gray")throw new Error("model graph is cyclic");d[_]==="white"&&i.push(_)})}))}}transformGraph(r){this.removeAllIdentityNodes(),this.removeAllDropoutNodes(),this.fuseConvActivationNodes(),r&&r.transformGraph(this),this.finalizeGraph()}finalizeGraph(){let r=0;for(let i=0;i<this._nodes.length;i++)this._nodes[i].executeNode?r>0&&(this._nodes[i].inputs.forEach(d=>{const g=this._allData[d]._to.indexOf(i+r);g!==-1&&(this._allData[d]._to[g]=i)}),this._nodes[i].outputs.forEach(d=>{this._allData[d]._from&&this._allData[d]._from===i+r&&(this._allData[d]._from=i)})):(r++,this._nodes[i].outputs.forEach(d=>{this._allData[d]._from=-2}),this._nodes.splice(i,1),i--);r=0;for(let i=0;i<this._allData.length;i++)if(this._allData[i].from!==-2||this._allOutputIndices.indexOf(i+r)!==-1){if(r>0){let d=-1;this._allData[i].from!==void 0&&this._allData[i].from!==-1?(d=this._nodes[this._allData[i].from].outputs.indexOf(i+r),d!==-1&&(this._nodes[this._allData[i].from].outputs[d]=i)):(d=this._allInputIndices.indexOf(i+r),d!==-1&&(this._allInputIndices[d]=i)),this._allData[i].to.forEach(g=>{d=this._nodes[g].inputs.indexOf(i+r),d!==-1&&(this._nodes[g].inputs[d]=i)}),this._allData[i].to.length===0&&(d=this._allOutputIndices.indexOf(i+r),d!==-1&&(this._allOutputIndices[d]=i))}}else r++,this._allData.splice(i,1),i--}deleteNode(r){const i=this._nodes[r];if(i.outputs.length>1){for(let v=1;v<i.outputs.length;v++)if(this._allData[i.outputs[v]].to.length>0)throw new Error("Node deletion with more than one output connected to other nodes is not supported. ")}i.executeNode=!1;const d=i.inputs[0],g=i.outputs[0],m=this._allData[g].to,b=this._allData[d].to.indexOf(r);if(b===-1)throw new Error("The Value object doesn't have the current Node in it's 'to' property ");this._allData[d].to.splice(b,1),this._allData[g]._to=[];const _=this._allOutputIndices.indexOf(g);if(_!==-1&&(this._allOutputIndices[_]=d),m&&m.length>0)for(const v of m){const w=this._nodes[v].inputs.indexOf(g);if(w===-1)throw new Error("The Node object doesn't have the output Value in it's 'inputs' property ");this._nodes[v].inputs[w]=d,this._allData[d].to.push(v)}}removeAllDropoutNodes(){let r=0;for(const i of this._nodes){if(i.opType==="Dropout"){if(i.inputs.length!==1)throw new Error("Dropout nodes should only contain one input. ");if(i.outputs.length!==1&&i.outputs.length!==2)throw new Error("Dropout nodes should contain either 1 or 2 output(s)");if(i.outputs.length===2&&this._allData[i.outputs[1]]._to.length!==0)throw new Error("Dropout nodes's second output should not be referenced by other nodes");this.deleteNode(r)}r++}}removeAllIdentityNodes(){let r=0;for(const i of this._nodes)i.opType==="Identity"&&this.deleteNode(r),r++}isActivation(r){switch(r.opType){case"Relu":case"Sigmoid":case"Clip":return!0;default:return!1}}fuseConvActivationNodes(){for(const r of this._nodes)if(r.opType==="Conv"){const i=this._allData[r.outputs[0]]._to;if(i.length===1&&this.isActivation(this._nodes[i[0]])){const 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e=this.bb.__offset(this.bb_pos,12);return e?this.bb.__string(this.bb_pos+e,t):null}modelVersion(){let t=this.bb.__offset(this.bb_pos,14);return t?this.bb.readInt64(this.bb_pos+t):this.bb.createLong(0,0)}docString(t){let e=this.bb.__offset(this.bb_pos,16);return e?this.bb.__string(this.bb_pos+e,t):null}graph(t){let e=this.bb.__offset(this.bb_pos,18);return e?(t||new s.experimental.fbs.Graph).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}graphDocString(t){let e=this.bb.__offset(this.bb_pos,20);return e?this.bb.__string(this.bb_pos+e,t):null}static startModel(t){t.startObject(9)}static addIrVersion(t,e){t.addFieldInt64(0,e,t.createLong(0,0))}static addOpsetImport(t,e){t.addFieldOffset(1,e,0)}static createOpsetImportVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startOpsetImportVector(t,e){t.startVector(4,e,4)}static addProducerName(t,e){t.addFieldOffset(2,e,0)}static 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getSizePrefixedRootAsKernelCreateInfos(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new l).__init(t.readInt32(t.position())+t.position(),t)}nodeIndices(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.readUint32(this.bb.__vector(this.bb_pos+e)+4*t):0}nodeIndicesLength(){let t=this.bb.__offset(this.bb_pos,4);return t?this.bb.__vector_len(this.bb_pos+t):0}nodeIndicesArray(){let t=this.bb.__offset(this.bb_pos,4);return t?new Uint32Array(this.bb.bytes().buffer,this.bb.bytes().byteOffset+this.bb.__vector(this.bb_pos+t),this.bb.__vector_len(this.bb_pos+t)):null}kernelDefHashes(t){let e=this.bb.__offset(this.bb_pos,6);return e?this.bb.readUint64(this.bb.__vector(this.bb_pos+e)+8*t):this.bb.createLong(0,0)}kernelDefHashesLength(){let t=this.bb.__offset(this.bb_pos,6);return t?this.bb.__vector_len(this.bb_pos+t):0}static startKernelCreateInfos(t){t.startObject(2)}static addNodeIndices(t,e){t.addFieldOffset(0,e,0)}static 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getSizePrefixedRootAsSubGraphSessionState(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new l).__init(t.readInt32(t.position())+t.position(),t)}graphId(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.__string(this.bb_pos+e,t):null}sessionState(t){let e=this.bb.__offset(this.bb_pos,6);return e?(t||new s.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}static startSubGraphSessionState(t){t.startObject(2)}static addGraphId(t,e){t.addFieldOffset(0,e,0)}static addSessionState(t,e){t.addFieldOffset(1,e,0)}static endSubGraphSessionState(t){let e=t.endObject();return t.requiredField(e,4),e}static createSubGraphSessionState(t,e,r){return l.startSubGraphSessionState(t),l.addGraphId(t,e),l.addSessionState(t,r),l.endSubGraphSessionState(t)}}f.SubGraphSessionState=l})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class 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e=this.bb.__offset(this.bb_pos,4);return e?this.bb.__string(this.bb_pos+e,t):null}model(t){let e=this.bb.__offset(this.bb_pos,6);return e?(t||new s.experimental.fbs.Model).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}sessionState(t){let e=this.bb.__offset(this.bb_pos,8);return e?(t||new s.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}static startInferenceSession(t){t.startObject(3)}static addOrtVersion(t,e){t.addFieldOffset(0,e,0)}static addModel(t,e){t.addFieldOffset(1,e,0)}static addSessionState(t,e){t.addFieldOffset(2,e,0)}static endInferenceSession(t){return t.endObject()}static finishInferenceSessionBuffer(t,e){t.finish(e,"ORTM")}static finishSizePrefixedInferenceSessionBuffer(t,e){t.finish(e,"ORTM",!0)}static createInferenceSession(t,e,r,i){return 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${l.length}`)}else{if(l.size!==o.length)throw new Error(`incorrect input map size: expected ${o.length} but got ${l.size}`);const t=new Array(l.size);let e=0;for(let r=0;r<o.length;++r){const i=l.get(o[r]);if(!i)throw new Error(`missing input tensor for: '${name}'`);t[e++]=i}l=t}if(this.context.graphInputTypes&&this.context.graphInputTypes.length!==0&&this.context.graphInputDims&&this.context.graphInputDims.length!==0)this.validateInputTensorDims(this.context.graphInputDims,l,!1);else{const t=this._model.graph.getInputIndices(),e=this._model.graph.getValues(),r=new Array(t.length);for(let i=0;i<t.length;++i){const d=e[t[i]];r[i]=d.type.shape.dims,this.context.graphInputTypes.push(d.type.tensorType),this.context.graphInputDims.push(l[i].dims)}this.validateInputTensorDims(r,l,!0)}return this.validateInputTensorTypes(this.context.graphInputTypes,l),l}validateInputTensorTypes(l,o){for(let t=0;t<o.length;t++){const e=l[t],r=o[t].type;if(e!==r)throw new Error(`input tensor[${t}] check 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c=a(3442),p=u(a(3720)),s=a(1446),h=a(9395),f=a(2517);var l=h.onnxruntime.experimental.fbs;class o{get data(){if(this.cache===void 0){const g=this.dataProvider(this.dataId);if(g.length!==this.size)throw new Error("Length of data provided by the Data Provider is inconsistent with the dims of this Tensor.");this.cache=g}return this.cache}get stringData(){if(this.type!=="string")throw new TypeError("data type is not string");return this.data}get integerData(){switch(this.type){case"uint8":case"int8":case"uint16":case"int16":case"int32":case"uint32":case"bool":return this.data;default:throw new TypeError("data type is not integer (uint8, int8, uint16, int16, int32, uint32, bool)")}}get floatData(){switch(this.type){case"float32":case"float64":return this.data;default:throw new TypeError("data type is not float (float32, float64)")}}get numberData(){if(this.type!=="string")return this.data;throw new TypeError("type cannot be non-number (string)")}get(g){return this.data[f.ShapeUtil.indicesToOffset(g,this.strides)]}set(g,m){this.data[f.ShapeUtil.indicesToOffset(g,this.strides)]=m}async getData(){return this.cache===void 0&&(this.cache=await this.asyncDataProvider(this.dataId)),this.cache}get strides(){return this._strides||(this._strides=f.ShapeUtil.computeStrides(this.dims)),this._strides}constructor(g,m,b,_,v,w=c.Guid.create()){this.dims=g,this.type=m,this.dataProvider=b,this.asyncDataProvider=_,this.cache=v,this.dataId=w,this.size=f.ShapeUtil.validateDimsAndCalcSize(g);const S=this.size,A=b===void 0&&_===void 0&&v===void 0;if(v!==void 0&&v.length!==S)throw new RangeError("Input dims doesn't match data length.");if(m==="string"){if(!(v===void 0||Array.isArray(v)&&v.every(O=>typeof O=="string")))throw new TypeError("cache should be a string array");A&&(this.cache=new Array(S))}else{if(v!==void 0){const O=e(m);if(!(v instanceof O))throw new TypeError(`cache should be type ${O.name}`)}if(A){const O=new ArrayBuffer(S*function(x){switch(x){case"bool":case"int8":case"uint8":return 1;case"int16":case"uint16":return 2;case"int32":case"uint32":case"float32":return 4;case"float64":return 8;default:throw new Error(`cannot calculate sizeof() on type ${x}`)}}(m));this.cache=function(x,I){return new(e(I))(x)}(O,m)}}}static fromProto(g){if(!g)throw new Error("cannot construct Value from an empty tensor");const m=f.ProtoUtil.tensorDataTypeFromProto(g.dataType),b=f.ProtoUtil.tensorDimsFromProto(g.dims),_=new o(b,m);if(m==="string")g.stringData.forEach((v,w)=>{_.data[w]=(0,f.decodeUtf8String)(v)});else if(g.rawData&&typeof g.rawData.byteLength=="number"&&g.rawData.byteLength>0){const v=_.data,w=new DataView(g.rawData.buffer,g.rawData.byteOffset,g.rawData.byteLength),S=t(g.dataType),A=g.rawData.byteLength/S;if(g.rawData.byteLength%S!=0)throw new Error("invalid buffer length");if(v.length!==A)throw new Error("buffer length mismatch");for(let O=0;O<A;O++){const x=i(w,g.dataType,O*S);v[O]=x}}else{let v;switch(g.dataType){case s.onnx.TensorProto.DataType.FLOAT:v=g.floatData;break;case s.onnx.TensorProto.DataType.INT32:case s.onnx.TensorProto.DataType.INT16:case s.onnx.TensorProto.DataType.UINT16:case s.onnx.TensorProto.DataType.INT8:case s.onnx.TensorProto.DataType.UINT8:case s.onnx.TensorProto.DataType.BOOL:v=g.int32Data;break;case s.onnx.TensorProto.DataType.INT64:v=g.int64Data;break;case s.onnx.TensorProto.DataType.DOUBLE:v=g.doubleData;break;case s.onnx.TensorProto.DataType.UINT32:case s.onnx.TensorProto.DataType.UINT64:v=g.uint64Data;break;default:throw new Error("unspecific error")}if(v==null)throw new Error("failed to populate data from a tensorproto value");const w=_.data;if(w.length!==v.length)throw new Error("array length mismatch");for(let S=0;S<v.length;S++){const A=v[S];p.default.isLong(A)?w[S]=r(A,g.dataType):w[S]=A}}return _}static fromData(g,m,b){return new o(m,b,void 0,void 0,g)}static fromOrtTensor(g){if(!g)throw new Error("cannot construct Value from an empty tensor");const m=f.ProtoUtil.tensorDimsFromORTFormat(g),b=f.ProtoUtil.tensorDataTypeFromProto(g.dataType()),_=new o(m,b);if(b==="string")for(let v=0;v<g.stringDataLength();v++)_.data[v]=g.stringData(v);else if(g.rawDataArray()&&typeof g.rawDataLength()=="number"&&g.rawDataLength()>0){const v=_.data,w=new DataView(g.rawDataArray().buffer,g.rawDataArray().byteOffset,g.rawDataLength()),S=t(g.dataType()),A=g.rawDataLength()/S;if(g.rawDataLength()%S!=0)throw new Error("invalid buffer length");if(v.length!==A)throw new Error("buffer length mismatch");for(let O=0;O<A;O++){const x=i(w,g.dataType(),O*S);v[O]=x}}return _}}function t(d){switch(d){case s.onnx.TensorProto.DataType.UINT8:case s.onnx.TensorProto.DataType.INT8:case s.onnx.TensorProto.DataType.BOOL:return 1;case s.onnx.TensorProto.DataType.UINT16:case s.onnx.TensorProto.DataType.INT16:return 2;case s.onnx.TensorProto.DataType.FLOAT:case s.onnx.TensorProto.DataType.INT32:case s.onnx.TensorProto.DataType.UINT32:return 4;case s.onnx.TensorProto.DataType.INT64:case s.onnx.TensorProto.DataType.DOUBLE:case s.onnx.TensorProto.DataType.UINT64:return 8;default:throw new Error(`cannot calculate sizeof() on type ${s.onnx.TensorProto.DataType[d]}`)}}function e(d){switch(d){case"bool":case"uint8":return Uint8Array;case"int8":return Int8Array;case"int16":return Int16Array;case"uint16":return Uint16Array;case"int32":return Int32Array;case"uint32":return Uint32Array;case"float32":return Float32Array;case"float64":return Float64Array;default:throw new Error("unspecified error")}}function r(d,g){if(g===s.onnx.TensorProto.DataType.INT64||g===l.TensorDataType.INT64){if(d.greaterThanOrEqual(2147483648)||d.lessThan(-2147483648))throw new TypeError("int64 is not supported")}else{if(g!==s.onnx.TensorProto.DataType.UINT32&&g!==l.TensorDataType.UINT32&&g!==s.onnx.TensorProto.DataType.UINT64&&g!==l.TensorDataType.UINT64)throw new TypeError(`not a LONG type: ${s.onnx.TensorProto.DataType[g]}`);if(d.greaterThanOrEqual(4294967296)||d.lessThan(0))throw new TypeError("uint64 is not supported")}return d.toNumber()}function i(d,g,m){switch(g){case s.onnx.TensorProto.DataType.BOOL:case s.onnx.TensorProto.DataType.UINT8:return d.getUint8(m);case s.onnx.TensorProto.DataType.INT8:return d.getInt8(m);case s.onnx.TensorProto.DataType.UINT16:return d.getUint16(m,!0);case s.onnx.TensorProto.DataType.INT16:return d.getInt16(m,!0);case s.onnx.TensorProto.DataType.FLOAT:return d.getFloat32(m,!0);case s.onnx.TensorProto.DataType.INT32:return d.getInt32(m,!0);case s.onnx.TensorProto.DataType.UINT32:return d.getUint32(m,!0);case s.onnx.TensorProto.DataType.INT64:return r(p.default.fromBits(d.getUint32(m,!0),d.getUint32(m+4,!0),!1),g);case s.onnx.TensorProto.DataType.DOUBLE:return d.getFloat64(m,!0);case s.onnx.TensorProto.DataType.UINT64:return r(p.default.fromBits(d.getUint32(m,!0),d.getUint32(m+4,!0),!0),g);default:throw new Error(`cannot read from DataView for type ${s.onnx.TensorProto.DataType[g]}`)}}n.Tensor=o},2517:function(y,n,a){var u=this&&this.__importDefault||function(g){return g&&g.__esModule?g:{default:g}};Object.defineProperty(n,"__esModule",{value:!0}),n.decodeUtf8String=n.MAX_CLIP=n.MIN_CLIP=n.PoolConvUtil=n.ReduceUtil=n.SplitUtil=n.MathUtil=n.ShapeUtil=n.LongUtil=n.ProtoUtil=n.GemmUtil=n.arrayCopyHelper=n.BroadcastUtil=n.MatMulUtil=n.ArrayUtil=n.assert=n.checkInputsShape=void 0;const c=a(5686),p=u(a(3720)),s=a(1446),h=a(9162);n.checkInputsShape=function(g,...m){if(!g||g.length!==m.length)return!1;for(let b=0;b<g.length;b++)if(!g[b].dims||g[b].dims.length!==m[b])return!1;return!0},n.assert=function(g,m){if(!g)throw new Error(typeof m=="string"?m:m())},n.ArrayUtil=class{static arraysEqual(g,m){if(g.length!==m.length)return!1;for(let b=0;b<g.length;b++)if(g[b]!==m[b])return!1;return!0}};class f{static preprocessInputShapes(m,b){return[m.length===1?[1,m[0]]:m,b.length===1?[b[0],1]:b]}static postprocessOutputShape(m,b,_){b===1&&m.splice(m.length-2,1),_===1&&m.pop()}static calcMatMulShape(m,b){return m[1]!==b[0]?void 0:[m[0],b[1]]}}n.MatMulUtil=f;class l{static calcShape(m,b,_=!1){const v=m.length,w=b.length;if(v===0)return b;if(w===0)return m;const S=Math.max(m.length,b.length),A=new Array(S);if(_){if(v<2||w<2)return;const O=f.calcMatMulShape([m[v-2],m[v-1]],[b[w-2],b[w-1]]);if(O===void 0)return;[A[S-2],A[S-1]]=O}for(let O=_?3:1;O<=S;O++){const x=v-O<0?1:m[v-O],I=w-O<0?1:b[w-O];if(x!==I&&x>1&&I>1)return;A[S-O]=Math.max(x,I)}return A}static index(m,b){const _=new Array(b.length);return l.fillIndex(m,b,_),_}static fillIndex(m,b,_){const v=m.length-b.length;for(let w=0;w<b.length;w++)_[w]=m[v+w]%b[w]}static calc(m,b,_,v,w){const S=l.calcShape(m.dims,b.dims);if(S){if(v&&!e.areEqual(S,m.dims))return;const A=e.size(S),O=v?m:new h.Tensor(S,w||m.type);if(S.length===0)O.set([],_(m.get([]),b.get([])));else{const x=new Array(S.length),I=new Array(m.dims.length),N=new Array(b.dims.length);let B,L=0,F=0,H=!1,D=!1;m.dims.length===0&&(L=m.get([]),H=!0),b.dims.length===0&&(F=b.get([]),D=!0);for(let j=0;j<A;j++){B=j;for(let Z=S.length-1;Z>=0;Z--)x[Z]=B%S[Z],B=Math.floor(B/S[Z]);H||(l.fillIndex(x,m.dims,I),L=m.get(I)),D||(l.fillIndex(x,b.dims,N),F=b.get(N)),O.set(x,_(L,F))}}return O}}static isValidBroadcast(m,b){const _=m.length,v=b.length;if(_>v)return!1;for(let w=1;w<=_;w++)if(m[_-w]!==1&&m[_-w]!==b[v-w])return!1;return!0}static getBroadcastDims(m,b){const _=m.length,v=[];for(let w=0;w<_;w++){const S=_-1-w,A=m[S]||1;(b[b.length-1-w]||1)>1&&A===1&&v.unshift(S)}return v}}n.BroadcastUtil=l,n.arrayCopyHelper=function(g,m,b,_,v){if(_<0||_>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(_+v>m.length)throw new Error("source indices to be copied are outside bounds");if(b+v>g.length)throw new Error("target array is too small to hold result");for(let w=0;w<v;w++)g[b+w]=m[_+w]},n.GemmUtil=class{static getShapeOfGemmResult(g,m,b,_,v){if(g.length!==2||b.length!==2)throw new Error("shape need to be of size 2");let w,S,A;m?(w=g[1],S=g[0]):(w=g[0],S=g[1]);let O=-1;if(_?(A=b[0],O=1):(A=b[1],O=0),b[O]!==S)throw new Error("dimension mismatch");if(w<=0||A<=0||S<=0)throw new Error("invalid shape specified");if(v&&!l.isValidBroadcast(v,[w,A]))throw new Error("gemm: invalid bias shape for broadcast");return[w,A,S]}};class o{static tensorDataTypeFromProto(m){switch(m){case s.onnx.TensorProto.DataType.INT8:return"int8";case s.onnx.TensorProto.DataType.UINT8:return"uint8";case s.onnx.TensorProto.DataType.BOOL:return"bool";case s.onnx.TensorProto.DataType.INT16:return"int16";case s.onnx.TensorProto.DataType.UINT16:return"uint16";case s.onnx.TensorProto.DataType.INT32:return"int32";case s.onnx.TensorProto.DataType.UINT32:return"uint32";case s.onnx.TensorProto.DataType.FLOAT:return"float32";case s.onnx.TensorProto.DataType.DOUBLE:return"float64";case s.onnx.TensorProto.DataType.STRING:return"string";case s.onnx.TensorProto.DataType.INT64:return"int32";case s.onnx.TensorProto.DataType.UINT64:return"uint32";default:throw new Error(`unsupported data type: ${s.onnx.TensorProto.DataType[m]}`)}}static tensorDataTypeStringToEnum(m){switch(m){case"int8":return s.onnx.TensorProto.DataType.INT8;case"uint8":return s.onnx.TensorProto.DataType.UINT8;case"bool":return s.onnx.TensorProto.DataType.BOOL;case"int16":return s.onnx.TensorProto.DataType.INT16;case"uint16":return s.onnx.TensorProto.DataType.UINT16;case"int32":return s.onnx.TensorProto.DataType.INT32;case"uint32":return s.onnx.TensorProto.DataType.UINT32;case"float32":return s.onnx.TensorProto.DataType.FLOAT;case"float64":return s.onnx.TensorProto.DataType.DOUBLE;case"string":return s.onnx.TensorProto.DataType.STRING;case"int64":return s.onnx.TensorProto.DataType.INT64;case"uint64":return s.onnx.TensorProto.DataType.UINT64;default:throw new Error(`unsupported data type: ${m}`)}}static tensorDimsFromProto(m){return m.map(b=>p.default.isLong(b)?b.toNumber():b)}static tensorValueTypeFromProto(m){return{tensorType:o.tensorDataTypeFromProto(m.elemType),shape:{dims:o.tensorDimsFromProto(m.shape.dim.map(b=>b.dimValue))}}}static tensorDimsFromORTFormat(m){const b=[];for(let _=0;_<m.dimsLength();_++)b.push(t.longToNumber(m.dims(_)));return b}static tensorAttributesFromORTFormat(m){const b=[];for(let _=0;_<m.attributesLength();_++)b.push(m.attributes(_));return b}}n.ProtoUtil=o;class t{static longToNumber(m,b){return p.default.isLong(m)?m.toNumber():m instanceof c.flatbuffers.Long?p.default.fromValue({low:m.low,high:m.high,unsigned:b!=null&&b}).toNumber():m}static isLong(m){return p.default.isLong(m)||m instanceof c.flatbuffers.Long}}n.LongUtil=t;class e{static size(m){return e.getSizeFromDimensionRange(m,0,m.length)}static sizeFromDimension(m,b){if(b<0||b>m.length)throw new Error(`invalid dimension of ${b} for sizeFromDimension as Tensor has ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,b,m.length)}static sizeToDimension(m,b){if(b<0||b>m.length)throw new Error(`invalid dimension of ${b} for sizeToDimension as Tensor has ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,0,b)}static getSizeFromDimensionRange(m,b,_){let v=1;for(let w=b;w<_;w++){if(m[w]<=0)throw new Error("cannot get valid size from specified dimension range. Most likely the range contains 0 or negative values in them.");v*=m[w]}return v}static computeStrides(m){const b=m.length;if(b===0)return[];if(b===1)return[1];const _=new Array(b);_[b-1]=1,_[b-2]=m[b-1];for(let v=b-3;v>=0;--v)_[v]=_[v+1]*m[v+1];return _}static transpose(m){return m.slice().reverse()}static indicesToOffset(m,b,_){_===void 0&&(_=m.length);let v=0;for(let w=0;w<_;++w)v+=b[w]*m[w];return v}static offsetToIndices(m,b){const _=b.length;if(_===0)return[];if(_===1)return[m*b[0]];const v=new Array(b.length);for(let w=0;w<v.length-1;++w)v[w]=Math.floor(m/b[w]),m-=v[w]*b[w];return v[v.length-1]=m,v}static normalizeAxis(m,b){if(m<-b&&m>=b)throw new Error("unsupported axis for this operation.");return m<0?m+b:m}static normalizeAxes(m,b){return m.map(_=>this.normalizeAxis(_,b))}static incrementIndex(m,b,_){if(b.length===0||m.length===0)throw new Error("Index incrementing unsupported for scalar Tensor");if(_===void 0)_=b.length;else if(_<=0||_>b.length)throw new Error("Incorrect axis to increment on");for(let v=_-1;v>=0&&(m[v]++,!(m[v]<b[v]));--v)m[v]=0}static calculateReshapedDims(m,b){if(b.length===0){if(m.length===0||e.size(m)===1)return[];throw new Error("cannot reshape to a scalar Tensor")}const _=b.length,v=new Array(_);let w=-1,S=1;for(let O=0;O<_;O++){if(b[O]<-1)throw new Error("a dimension in shape hints cannot be less than -1");if(b[O]===-1){if(w!==-1)throw new Error("at most one dimension in shape hints can be -1");w=O}else{if(b[O]===0){if(O>=m.length)throw new Error("the dimension with value zero exceeds the dimension size of the input tensor");v[O]=m[O]}else v[O]=b[O];S*=v[O]}}const A=e.size(m);if(w!==-1){if(A%S!=0)throw new Error(`the input tensor cannot be reshaped to the requested shape. Input shape: [${m}] Output shape: [${b}]`);v[w]=A/S}else if(S!==A)throw new Error("reshapedDims and originalDims don't have matching sizes");return v}static sortBasedOnPerm(m,b){return b?b.map(_=>m[_]):m.slice().reverse()}static padShape(m,b){const _=m.length;return m.map((v,w)=>v+b[w]+b[w+_])}static areEqual(m,b){return m.length===b.length&&m.every((_,v)=>_===b[v])}static validateDimsAndCalcSize(m){if(m.length>6)throw new TypeError("Only rank 0 to 6 is supported for tensor shape.");let b=1;for(const _ of m){if(!Number.isInteger(_))throw new TypeError(`Invalid shape: ${_} is not an integer`);if(_<0||_>2147483647)throw new TypeError(`Invalid shape: length ${_} is not allowed`);b*=_}return b}static flattenShape(m,b){b<0&&(b+=m.length);const _=m.reduce((w,S)=>w*S,1),v=m.slice(b).reduce((w,S)=>w*S,1);return[_/v,v]}static squeezeShape(m,b){const _=new Array;b=e.normalizeAxes(b,m.length);for(let v=0;v<m.length;v++){const w=b.indexOf(v)>=0;if(w&&m[v]!==1)throw new Error("squeeze an axis of size different than 1");(b.length===0&&m[v]>1||b.length>0&&!w)&&_.push(m[v])}return _}static unsqueezeShape(m,b){const _=new Array(m.length+b.length);_.fill(0);for(let w=0;w<b.length;w++){const S=e.normalizeAxis(b[w],_.length);if(S>=_.length)throw new Error("'axes' has an out of range axis");if(_[S]!==0)throw new Error("'axes' has a duplicate axis");_[S]=1}let v=0;for(let w=0;w<_.length;w++)_[w]===0&&(_[w]=m[v++]);if(v!==m.length)throw new Error("the unsqueezed dimension could not be established");return _}}n.ShapeUtil=e,n.MathUtil=class{static sqr(g,m,b,_,v){if(_<0||_>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(_+v>m.length)throw new Error("source indices to be copied are outside bounds");if(b+v>g.length)throw new Error("target array is too small to hold result");for(let w=0;w<v;w++)g[b+w]+=Math.pow(m[_+w],2)}static axpy(g,m,b,_,v,w){if(_<0||_>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(_+v>m.length)throw new Error("source indices to be copied are outside bounds");if(b+v>g.length)throw new Error("target array is too small to hold result");for(let S=0;S<v;S++)g[b+S]+=w*m[_+S]}static powx(g,m,b,_,v,w){if(_<0||_>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(_+v>m.length)throw new Error("source indices to be copied are outside bounds");if(b+v>g.length)throw new Error("target array is too small to hold result");for(let S=0;S<v;S++)g[b+S]=Math.pow(m[_+S],w)}static mul(g,m,b,_,v){if(_<0||_>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(_+v>m.length)throw new Error("source indices to be copied are outside bounds");if(b+v>g.length)throw new Error("target array is too small to hold result");for(let w=0;w<v;w++)g[b+w]=m[_+w]*g[b+w]}};class r{static splitShape(m,b,_,v){if(_.length===0){if(!v)throw new Error("need to know number of outputs when the 'split' attribute is not specified");r.determineSplit(m[b],v,_)}const w=[],S=[0];for(let A=0;A<_.length;++A){A!==0&&S.push(S[A-1]+_[A-1]);const O=m.slice();O[b]=_[A],w.push(O)}return[w,S]}static determineSplit(m,b,_){if(m%b!=0)throw new Error("cannot split tensor to equal sized parts");for(let v=0;v<b;++v)_.push(m/b)}}n.SplitUtil=r;class i{static calcReduce(m,b,_,v,w){const S=m.dims.slice(0);b.length===0&&S.forEach((L,F)=>b.push(F));const A=i.calcReduceShape(S,b,!0),O=e.size(A),x=new h.Tensor(A,m.type),I=e.computeStrides(A),N=e.computeStrides(S),B=new Array(S.length);for(let L=0;L<O;L++){const F=e.offsetToIndices(L,I);l.fillIndex(F,S,B),x.set(F,i.calcReduceByAxis(m.numberData,b,S,0,e.indicesToOffset(B,N),v,w))}return _?x:new h.Tensor(i.calcReduceShape(S,b,_),x.type,void 0,void 0,x.data,x.dataId)}static calcReduceByAxis(m,b,_,v,w,S,A){let O=0;if(v>=b.length)return S(m[w]);const x=b[v],I=x>=_.length?1:e.size(_.slice(x+1));for(let N=0;N<_[x];N++)O=N===0?i.calcReduceByAxis(m,b,_,v+1,w,S,A):A(O,i.calcReduceByAxis(m,b,_,v+1,w,S,A)),w+=I;return O}static calcReduceShape(m,b,_){const v=m.slice();for(let w=0;w<b.length;w++)v[b[w]]=_?1:0;return v.filter(w=>w!==0)}}n.ReduceUtil=i;class d{static adjustPoolAttributes(m,b,_,v,w,S){if(!m&&_.length!==b.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(m)for(let A=0;A<b.length-2;A++)A>=_.length?_.push(b[A+2]):_[A]=b[A+2];for(let A=0;A<_.length;A++)if(A<v.length){if(v[A]<0)throw new Error("strides should be greater than or equal to 1")}else v.push(1);for(let A=0;A<_.length;A++)if(A<w.length){if(w[A]<0)throw new Error("dilations should be greater than or equal to 1")}else w.push(1);for(let A=0;A<2*_.length;A++)if(A<S.length){if(S[A]<0)throw new Error("pad should be greater than or equal to 1")}else S.push(0);for(let A=0;A<_.length;A++){if(_[A]<=0)throw new 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t=o[l];this._singleRealTransform2(a,n,f,t>>>1,s>>>1)}else for(f=0,l=0;f<c;f+=h,++l){const t=o[l];this._singleRealTransform4(a,n,f,t>>>1,s>>>1,u)}for(s>>=2;s>=2;s>>=2){h=c/s<<1;const t=h>>>1,e=t>>>1,r=e>>>1;for(f=0;f<c;f+=h)for(let i=0,d=0;i<=r;i+=2,d+=s){const g=f+i,m=g+e,b=m+e,_=b+e,v=n[g],w=n[g+1],S=n[m],A=n[m+1],O=n[b],x=n[b+1],I=n[_],N=n[_+1],B=this.table[d],L=u*this.table[d+1],F=S*B-A*L,H=S*L+A*B,D=this.table[2*d],j=u*this.table[2*d+1],Z=O*D-x*j,X=O*j+x*D,J=this.table[3*d],ee=u*this.table[3*d+1],ue=I*J-N*ee,Ae=I*ee+N*J,ve=v+Z,oe=w+X,_e=v-Z,be=w-X,ke=F+ue,Fe=H+Ae,xe=u*(F-ue),Ne=u*(H-Ae);if(n[g]=ve+ke,n[g+1]=oe+Fe,n[m]=_e+Ne,n[m+1]=be-xe,i===0){n[b]=ve-ke,n[b+1]=oe-Fe;continue}if(i===r)continue;const Ce=f+e-i,Ee=f+t-i;n[Ce]=_e+-u*Ne,n[Ce+1]=-be-u*xe,n[Ee]=ve+-u*ke,n[Ee+1]=-oe+u*Fe}}}_singleRealTransform2(n,a,u,c,p){const s=n[c],h=n[c+p];a[u]=s+h,a[u+1]=0,a[u+2]=s-h,a[u+3]=0}_singleRealTransform4(n,a,u,c,p,s){const h=p*2,f=p*3,l=n[c],o=n[c+p],t=n[c+h],e=n[c+f],r=l+t,i=l-t,d=o+e,g=s*(o-e);a[u]=r+d,a[u+1]=0,a[u+2]=i,a[u+3]=-g,a[u+4]=r-d,a[u+5]=0,a[u+6]=i,a[u+7]=g}}const ONNXTensor$1=ONNX.Tensor;class Tensor extends ONNXTensor$1{constructor(...n){return n[0]instanceof ONNX.Tensor?super(n[0].type,n[0].data,n[0].dims):super(...n),new Proxy(this,{get:(a,u)=>{if(typeof u=="string"){let c=Number(u);if(Number.isInteger(c))return a._getitem(c)}return a[u]},set:(a,u,c)=>a[u]=c})}*[Symbol.iterator](){const[n,...a]=this.dims;if(a.length>0){const u=a.reduce((c,p)=>c*p);for(let c=0;c<n;++c)yield this._subarray(c,u,a)}else yield*this.data}_getitem(n){const[a,...u]=this.dims;if(n>=a||n<-a)throw new Error(`Index ${n} is out of bounds for dimension 0 with size ${a}`);if(n<0&&(n+=a),u.length>0){const c=u.reduce((p,s)=>p*s);return this._subarray(n,c,u)}else return new Tensor(this.type,[this.data[n]],u)}indexOf(n){for(let a=0;a<this.data.length;++a)if(this.data[a]==n)return a;return-1}_subarray(n,a,u){let c=this.data.subarray(n*a,(n+1)*a);return new Tensor(this.type,c,u)}item(){if(this.data.length!==1)throw new Error(`a Tensor with ${this.data.length} elements cannot be converted to Scalar`);return this.data[0]}tolist(){return reshape(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){for(let n=0;n<this.data.length;++n)this.data[n]=1/(1+Math.exp(-this.data[n]));return this}clone(){return new Tensor(this.type,this.data.slice(),this.dims.slice())}slice(...n){let a=[],u=[];for(let f=0;f<this.dims.length;++f){let l=n[f];if(l==null)u.push([0,this.dims[f]]),a.push(this.dims[f]);else if(typeof l=="number"){if(l<-this.dims[f]||l>=this.dims[f])throw new Error(`IndexError: index ${l} is out of bounds for dimension ${f} with size ${this.dims[f]}`);l<0&&(l+=this.dims[f]),u.push([l,l+1])}else if(Array.isArray(l)&&l.length===2){if(l[0]>l[1])throw new Error(`Invalid slice: ${l}`);let o=[Math.max(l[0],0),Math.min(l[1],this.dims[f])];u.push(o),a.push(o[1]-o[0])}else throw new Error(`Invalid slice: ${l}`)}let c=u.map(([f,l])=>l-f),p=c.reduce((f,l)=>f*l),s=new this.data.constructor(p);const h=new Array(this.dims.length);for(let f=c.length-1,l=1;f>=0;--f)h[f]=l,l*=this.dims[f];for(let f=0;f<p;++f){let l=0;for(let o=c.length-1,t=f;o>=0;--o){const e=c[o];l+=(t%e+u[o][0])*h[o],t=Math.floor(t/e)}s[f]=this.data[l]}return new Tensor(this.type,s,a)}transpose(...n){return transpose(this,n)}sum(n=null,a=!1){return this.norm(1,n,a)}norm(n="fro",a=null,u=!1){if(n==="fro")n=2;else if(typeof n=="string")throw Error(`Unsupported norm: ${n}`);if(a===null){let s=this.data.reduce((h,f)=>h+f**n,0)**(1/n);return new Tensor(this.type,[s],[1])}a<0&&(a+=this.dims.length);const c=this.dims.slice();c[a]=1;const p=new this.data.constructor(this.data.length/this.dims[a]);for(let s=0;s<this.data.length;++s){let h=0;for(let f=this.dims.length-1,l=s,o=1;f>=0;--f){const t=this.dims[f];if(f!==a){const e=l%t;h+=e*o,o*=c[f]}l=Math.floor(l/t)}p[h]+=this.data[s]**n}if(n!==1)for(let s=0;s<p.length;++s)p[s]=p[s]**(1/n);return u||c.splice(a,1),new Tensor(this.type,p,c)}normalize_(n=2,a=1){a<0&&(a+=this.dims.length);const u=this.norm(n,a,!0);for(let c=0;c<this.data.length;++c){let p=0;for(let s=this.dims.length-1,h=c,f=1;s>=0;--s){const l=this.dims[s];if(s!==a){const o=h%l;p+=o*f,f*=this.dims[s]}h=Math.floor(h/l)}this.data[c]/=u.data[p]}return this}normalize(n=2,a=1){return this.clone().normalize_(n,a)}stride(){const n=new Array(this.dims.length);for(let a=this.dims.length-1,u=1;a>=0;--a)n[a]=u,u*=this.dims[a];return n}squeeze(n=null){return new Tensor(this.type,this.data,calc_squeeze_dims(this.dims,n))}squeeze_(n=null){return this.dims=calc_squeeze_dims(this.dims,n),this}unsqueeze(n=null){return new Tensor(this.type,this.data,calc_unsqueeze_dims(this.dims,n))}unsqueeze_(n=null){return this.dims=calc_unsqueeze_dims(this.dims,n),this}flatten_(n=0,a=-1){a=(a+this.dims.length)%this.dims.length;let u=this.dims.slice(0,n),c=this.dims.slice(n,a+1),p=this.dims.slice(a+1);return this.dims=[...u,c.reduce((s,h)=>s*h,1),...p],this}flatten(n=0,a=-1){return this.clone().flatten_(n,a)}view(...n){let a=-1;for(let u=0;u<n.length;++u)if(n[u]===-1){if(a!==-1)throw new Error("Only one dimension can be inferred");a=u}if(a!==-1){const u=n.reduce((c,p,s)=>s!==a?c*p:c,1);n[a]=this.data.length/u}return new Tensor(this.type,this.data,n)}}function reshape(y,n){const a=y.length,u=n.reduce((p,s)=>p*s);if(a!==u)throw Error(`cannot reshape array of size ${a} into shape (${n})`);let c=y;for(let p=n.length-1;p>=0;p--)c=c.reduce((s,h)=>{let f=s[s.length-1];return f.length<n[p]?f.push(h):s.push([h]),s},[[]]);return c[0]}function transpose(y,n){const[a,u]=transpose_data(y.data,y.dims,n);return new Tensor(y.type,a,u)}function cat(y){if(y.length===0)return y[0];let n=y[0].type,a=[...y[0].dims];a[0]=y.length;let u=0;for(let s of y)u+=s.data.length;let c=new y[0].data.constructor(u),p=0;for(let s of y)c.set(s.data,p),p+=s.data.length;return new Tensor(n,c,a)}function interpolate(y,[n,a],u="bilinear",c=!1){const p=y.dims.at(-3)??1,s=y.dims.at(-2),h=y.dims.at(-1);let f=interpolate_data(y.data,[p,s,h],[n,a],u,c);return new Tensor(y.type,f,[p,n,a])}function mean_pooling(y,n){let a=[y.dims[0],y.dims[2]],u=new y.data.constructor(a[0]*a[1]),[c,p,s]=y.dims,h=0;for(let f=0;f<c;++f){let l=f*s*p;for(let o=0;o<s;++o){let t=0,e=0,r=f*p,i=l+o;for(let g=0;g<p;++g){let m=Number(n.data[r+g]);e+=m,t+=y.data[i+g*s]*m}let d=t/e;u[h++]=d}}return new Tensor(y.type,u,a)}function calc_squeeze_dims(y,n){return y=y.slice(),n===null?y=y.filter(a=>a!==1):typeof n=="number"?y[n]===1&&y.splice(n,1):Array.isArray(n)&&(y=y.filter((a,u)=>a!==1||!n.includes(u))),y}function calc_unsqueeze_dims(y,n){return y=y.slice(),n<0&&(n=(n%y.length+y.length)%y.length),y.splice(n,0,1),y}async function loadTokenizer(y,n){return await Promise.all([getModelJSON(y,"tokenizer.json",!0,n),getModelJSON(y,"tokenizer_config.json",!0,n)])}function createPattern(y,n=!0){return y.Regex?new RegExp(n?y.Regex:`(${y.Regex})`,"gu"):y.String?y.String:(console.warn("Unknown pattern type:",y),null)}function clean_up_tokenization(y){return y.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function fuse(y,n){let a=[],u=0;for(;u<y.length;){if(a.push(y[u]),y[u]!==n){++u;continue}for(;u<y.length&&y[u]===n;)++u}return a}function whitespace_split(y){return y.match(/\S+/g)||[]}const PUNCTUATION_REGEX="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E";class TokenizerModel extends Callable{constructor(n){super(),this.config=n,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=!1}static fromConfig(n,...a){switch(n.type){case"WordPiece":return new WordPieceTokenizer(n);case"Unigram":return new Unigram(n,...a);case"BPE":return new BPE(n,...a);default:throw new Error(`Unknown TokenizerModel type: ${n.type}`)}}_call(n){return this.encode(n)}encode(n){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(n){let a=n.map(u=>this.tokens_to_ids.get(u)??this.unk_token_id);return this.fuse_unk&&(a=fuse(a,this.unk_token_id)),a}convert_ids_to_tokens(n){return n.map(a=>this.vocab[a]??this.unk_token)}}class WordPieceTokenizer extends TokenizerModel{constructor(n){super(n),this.tokens_to_ids=n.vocab,this.unk_token_id=this.tokens_to_ids.get(n.unk_token),this.unk_token=n.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[a,u]of this.tokens_to_ids)this.vocab[u]=a}encode(n){let a=[];for(let u of n){let c=[...u],p=!1,s=0,h=[];for(;s<c.length;){let f=c.length,l=null;for(;s<f;){let o=c.slice(s,f).join("");if(s>0&&(o=this.config.continuing_subword_prefix+o),this.tokens_to_ids.has(o)){l=o;break}--f}if(l===null){p=!0;break}h.push(l),s=f}p?a.push(this.unk_token):a.push(...h)}return a}}class Unigram extends TokenizerModel{constructor(n,a){super(n),this.vocab=new Array(n.vocab.size),this.scores=new Array(n.vocab.size);let u=0;n.vocab.forEach((c,p)=>{this.vocab[u]=p,this.scores[u]=c,++u}),this.unk_token_id=n.unk_id,this.unk_token=this.vocab[n.unk_id],this.tokens_to_ids=new Map(this.vocab.map((c,p)=>[c,p])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=a.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=min(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(n){const a=n.sentence,u=a.length;let c=0;for(;c<u;){let s=!1;for(let h of this.trie.commonPrefixSearch(a.slice(c))){const f=this.tokens_to_ids.get(h),l=this.scores[f],o=h.length;n.insert(c,o,l,f),!s&&o===1&&(s=!0)}s||n.insert(c,1,this.unkScore,this.unk_token_id),c+=1}}tokenize(n){const a=new TokenLattice(n,this.bosTokenId,this.eosTokenId);return this.populateNodes(a),a.tokens()}encode(n){let a=[];for(let u of n){const c=this.tokenize(u);a.push(...c)}return a}}const BYTES_TO_UNICODE=(()=>{const y=[...Array.from({length:"~".charCodeAt(0)-"!".charCodeAt(0)+1},(c,p)=>p+"!".charCodeAt(0)),...Array.from({length:"¬".charCodeAt(0)-"¡".charCodeAt(0)+1},(c,p)=>p+"¡".charCodeAt(0)),...Array.from({length:"ÿ".charCodeAt(0)-"®".charCodeAt(0)+1},(c,p)=>p+"®".charCodeAt(0))];let n=y.slice(),a=0;for(let c=0;c<256;++c)y.includes(c)||(y.push(c),n.push(256+a),a+=1);let u=n.map(c=>String.fromCharCode(c));return Object.fromEntries(y.map((c,p)=>[c,u[p]]))})(),UNICODE_TO_BYTES=reverseDictionary(BYTES_TO_UNICODE);class BPE extends TokenizerModel{constructor(n){super(n),this.tokens_to_ids=n.vocab,this.unk_token_id=this.tokens_to_ids.get(n.unk_token),this.unk_token=n.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[a,u]of this.tokens_to_ids)this.vocab[u]=a;this.bpe_ranks=Object.fromEntries(n.merges.map((a,u)=>[a,u])),this.merges=n.merges.map(a=>a.split(/\s+/)),this.end_of_word_suffix=n.end_of_word_suffix,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=Object.create(null),this.fuse_unk??(this.fuse_unk=this.config.fuse_unk)}get_pairs(n){let a=new Set,u=n[0];for(let c=1;c<n.length;++c){let p=n[c];a.add(`${u} ${p}`),u=p}return Array.from(a)}bpe(n){if(n in this.cache)return this.cache[n];let a=Array.from(n);this.end_of_word_suffix&&(a[a.length-1]+=this.end_of_word_suffix);let u=this.get_pairs(a);if(!u.length)return this.end_of_word_suffix&&(n+=this.end_of_word_suffix),n;for(;;){let p=u.reduce((t,e)=>{let r=this.bpe_ranks[t]??1/0,i=this.bpe_ranks[e]??1/0;return r<=i?t:e});if(!(p in this.bpe_ranks))break;let[s,h]=p.split(/\s+/g),f=[],l=0,o=-1;for(;l<a.length;){try{if(o=a.indexOf(s,l),o===-1)throw"Error"}catch{f.push(...a.slice(l));break}f.push(...a.slice(l,o)),l=o,a[l]===s&&l<a.length-1&&a[l+1]===h?(f.push(s+h),l+=2):(f.push(a[l]),l+=1)}if(a=f,a.length===1)break;u=this.get_pairs(a)}let c=a.join(" ");return this.cache[n]=c,c}encode(n){let a=[];for(let u of n){let c=this.bpe(u).split(" ");for(let p of c)this.tokens_to_ids.has(p)?a.push(p):this.byte_fallback?a.push(...Array.from(this.text_encoder.encode(p)).map(s=>`<0x${s.toString(16).toUpperCase().padStart(2,"0")}>`)):a.push(this.unk_token)}return a}}class Normalizer extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){if(n===null)return null;switch(n.type){case"BertNormalizer":return new BertNormalizer(n);case"Precompiled":return new Precompiled(n);case"Sequence":return new NormalizerSequence(n);case"Replace":return new Replace(n);case"NFC":return new NFC(n);case"NFKD":return new NFKD(n);case"StripAccents":return new StripAccents(n);case"Lowercase":return new Lowercase(n);case"Prepend":return new Prepend(n);default:throw new Error(`Unknown Normalizer type: ${n.type}`)}}normalize(n){throw Error("normalize should be implemented in subclass.")}_call(n){return this.normalize(n)}}class Replace extends Normalizer{normalize(n){let a=createPattern(this.config.pattern);return a===null||(n=n.replaceAll(a,this.config.content)),n}}class NFC extends Normalizer{normalize(n){return n=n.normalize("NFC"),n}}class NFKD extends Normalizer{normalize(n){return n=n.normalize("NFKD"),n}}class StripAccents extends Normalizer{normalize(n){return n=n.replace(/[\u0300-\u036f]/g,""),n}}class Lowercase extends Normalizer{normalize(n){return n=n.toLowerCase(),n}}class Prepend extends Normalizer{normalize(n){return n=this.config.prepend+n,n}}class NormalizerSequence extends Normalizer{constructor(n){super(n),this.normalizers=n.normalizers.map(a=>Normalizer.fromConfig(a))}normalize(n){return this.normalizers.reduce((a,u)=>u.normalize(a),n)}}class BertNormalizer extends Normalizer{_tokenize_chinese_chars(n){let a=[];for(let u=0;u<n.length;++u){let c=n[u],p=c.charCodeAt(0);this._is_chinese_char(p)?(a.push(" "),a.push(c),a.push(" ")):a.push(c)}return a.join("")}_is_chinese_char(n){return n>=19968&&n<=40959||n>=13312&&n<=19903||n>=131072&&n<=173791||n>=173824&&n<=177983||n>=177984&&n<=178207||n>=178208&&n<=183983||n>=63744&&n<=64255||n>=194560&&n<=195103}stripAccents(n){return n.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}normalize(n){return this.config.handle_chinese_chars&&(n=this._tokenize_chinese_chars(n)),this.config.lowercase?(n=n.toLowerCase(),this.config.strip_accents!==!1&&(n=this.stripAccents(n))):this.config.strip_accents&&(n=this.stripAccents(n)),n}}class PreTokenizer extends Callable{static fromConfig(n){if(n===null)return null;switch(n.type){case"BertPreTokenizer":return new BertPreTokenizer(n);case"Sequence":return new PreTokenizerSequence(n);case"WhitespaceSplit":return new WhitespaceSplit(n);case"Metaspace":return new MetaspacePreTokenizer(n);case"ByteLevel":return new ByteLevelPreTokenizer(n);case"Split":return new SplitPreTokenizer(n);case"Punctuation":return new PunctuationPreTokenizer(n);case"Digits":return new DigitsPreTokenizer(n);default:throw new Error(`Unknown PreTokenizer type: ${n.type}`)}}pre_tokenize_text(n){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(n){let a=[];return Array.isArray(n)?a=n.map(u=>this.pre_tokenize_text(u)):a=this.pre_tokenize_text(n),a.flat()}_call(n){return this.pre_tokenize(n)}}class BertPreTokenizer extends PreTokenizer{constructor(n){super(),this.pattern=new RegExp(`[^\\s${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]`,"gu")}pre_tokenize_text(n){return n.trim().match(this.pattern)||[]}}class ByteLevelPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=BYTES_TO_UNICODE,this.text_encoder=new TextEncoder}pre_tokenize_text(n){return(this.use_regex?n.match(this.pattern)||[]:[n]).map(u=>(this.add_prefix_space&&!u.startsWith(" ")&&(u=" "+u),u=Array.from(this.text_encoder.encode(u),c=>this.byte_encoder[c]).join(""),u))}}class SplitPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=createPattern(this.config.pattern,this.config.invert)}pre_tokenize_text(n){return this.pattern===null?[]:this.config.invert?n.match(this.pattern)||[]:n.split(this.pattern).filter(a=>a)}}class PunctuationPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=new RegExp(`[^${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]+`,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class DigitsPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n;const a=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(a,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class PostProcessor extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){if(n===null)return null;switch(n.type){case"TemplateProcessing":return new TemplateProcessing(n);case"ByteLevel":return new ByteLevelPostProcessor(n);case"RobertaProcessing":return new RobertaProcessing(n);default:throw new Error(`Unknown PostProcessor type: ${n.type}`)}}post_process(n,...a){throw Error("post_process should be implemented in subclass.")}_call(n,...a){return this.post_process(n,...a)}}class RobertaProcessing extends PostProcessor{constructor(n){super(n),this.cls=n.cls[0],this.sep=n.sep[0]}post_process(n,a=null){return n=mergeArrays([this.cls],n,[this.sep]),a!==null&&(n=mergeArrays(n,[this.sep],a,[this.sep])),n}}class TemplateProcessing extends PostProcessor{constructor(n){super(n),this.single=n.single,this.pair=n.pair}post_process(n,a=null){let u=a===null?this.single:this.pair,c=[];for(let p of u)"SpecialToken"in p?c.push(p.SpecialToken.id):"Sequence"in p&&(p.Sequence.id==="A"?c=mergeArrays(c,n):p.Sequence.id==="B"&&(c=mergeArrays(c,a)));return c}}class ByteLevelPostProcessor extends PostProcessor{post_process(n){return n}}class Decoder extends Callable{constructor(n){super(),this.config=n,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=n.trim_offsets}static fromConfig(n){switch(n.type){case"WordPiece":return new WordPieceDecoder(n);case"Metaspace":return new MetaspaceDecoder(n);case"ByteLevel":return new ByteLevelDecoder(n);case"Replace":return new ReplaceDecoder(n);case"ByteFallback":return new ByteFallback(n);case"Fuse":return new FuseDecoder(n);case"Strip":return new StripDecoder(n);case"Sequence":return new DecoderSequence(n);default:throw new Error(`Unknown Decoder type: ${n.type}`)}}_call(n){return this.decode(n)}decode(n){return this.decode_chain(n).join("")}decode_chain(n){throw Error("`decode_chain` should be implemented in subclass.")}}class ReplaceDecoder extends Decoder{constructor(n){super(n)}decode_chain(n){let a=createPattern(this.config.pattern);return a===null?n:n.map(u=>u.replaceAll(a,this.config.content))}}class ByteFallback extends Decoder{constructor(n){super(n),this.text_decoder=new TextDecoder}decode_chain(n){let a=[],u=[];for(let c of n){let p=null;if(c.length===6&&c.startsWith("<0x")&&c.endsWith(">")){let s=parseInt(c.slice(3,5),16);isNaN(s)||(p=s)}if(p!==null)u.push(p);else{if(u.length>0){let s=this.text_decoder.decode(Uint8Array.from(u));a.push(s),u=[]}a.push(c)}}if(u.length>0){let c=this.text_decoder.decode(Uint8Array.from(u));a.push(c),u=[]}return a}}class FuseDecoder extends Decoder{constructor(n){super(n)}decode_chain(n){return[n.join("")]}}class StripDecoder extends Decoder{constructor(n){super(n),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(n){return n.map(a=>{let u=0;for(let p=0;p<this.start&&a[p]===this.content;++p){u=p+1;continue}let c=a.length;for(let p=0;p<this.stop;++p){const s=a.length-p-1;if(a[s]===this.content){c=s;continue}else break}return a.slice(u,c)})}}class WordPieceDecoder extends Decoder{constructor(n){super(n),this.cleanup=n.cleanup}decode_chain(n){return n.map((a,u)=>(u!==0&&(a.startsWith(this.config.prefix)?a=a.replace(this.config.prefix,""):a=" "+a),this.cleanup&&(a=clean_up_tokenization(a)),a))}}class ByteLevelDecoder extends Decoder{constructor(n){super(n),this.byte_decoder=UNICODE_TO_BYTES,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(n){let a=n.join(""),u=new Uint8Array([...a].map(p=>this.byte_decoder[p]));return this.text_decoder.decode(u)}decode_chain(n){let a=[],u=[];for(let c of n)this.added_tokens.includes(c)?(u.length>0&&(a.push(this.convert_tokens_to_string(u)),u=[]),a.push(c)):u.push(c);return u.length>0&&a.push(this.convert_tokens_to_string(u)),a}}class DecoderSequence extends Decoder{constructor(n){super(n),this.decoders=n.decoders.map(a=>Decoder.fromConfig(a))}decode_chain(n){return this.decoders.reduce((a,u)=>u.decode_chain(a),n)}}class MetaspacePreTokenizer extends PreTokenizer{constructor(n){super(),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement,this.strRep=n.str_rep||this.replacement}pre_tokenize(n){typeof n=="string"&&(n=n.trimStart().split(/\s+/));const a=[];for(let u of n){let c=u.replaceAll(" ",this.strRep);this.addPrefixSpace&&!c.startsWith(this.replacement)&&(c=this.strRep+c),a.push(c)}return a}}class MetaspaceDecoder extends Decoder{constructor(n){super(n),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement}decode_chain(n){let a=[];for(let u=0;u<n.length;++u){let c=n[u].replaceAll(this.replacement," ");this.addPrefixSpace&&u==0&&c.startsWith(" ")&&(c=c.substring(1)),a.push(c)}return a}}class Precompiled extends Normalizer{constructor(n){super(n),this.charsmap=n.precompiled_charsmap}normalize(n){return n}}class PreTokenizerSequence extends PreTokenizer{constructor(n){super(),this.tokenizers=n.pretokenizers.map(a=>PreTokenizer.fromConfig(a))}pre_tokenize_text(n){return typeof n=="string"&&(n=[n]),this.tokenizers.reduce((a,u)=>u.pre_tokenize(a),n)}}class WhitespaceSplit extends PreTokenizer{constructor(n){super()}pre_tokenize_text(n){return whitespace_split(n)}}class PreTrainedTokenizer extends Callable{constructor(n,a){super(),this.normalizer=Normalizer.fromConfig(n.normalizer),this.pre_tokenizer=PreTokenizer.fromConfig(n.pre_tokenizer),n.model.vocab&&(Array.isArray(n.model.vocab)||(n.model.vocab=Object.entries(n.model.vocab)),n.model.vocab=new Map(n.model.vocab)),this.model=TokenizerModel.fromConfig(n.model,a),this.post_processor=PostProcessor.fromConfig(n.post_processor),this.decoder=Decoder.fromConfig(n.decoder),this.decoder.end_of_word_suffix=this.model.end_of_word_suffix,this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(let u of n.added_tokens){let c=u.id,p=u.content;this.added_tokens.push(p),this.model.tokens_to_ids.set(p,c),this.model.vocab[c]=p,u.special&&(this.special_tokens.push(p),this.all_special_ids.push(c))}this.decoder.added_tokens=this.added_tokens,this.added_tokens_regex=new RegExp("("+this.added_tokens.map(escapeRegExp).join("|")+")"),this.mask_token=this.getToken(a,"mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken(a,"pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken(a,"sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.model_max_length=a.model_max_length,this.remove_space=a.remove_space,this.clean_up_tokenization_spaces=a.clean_up_tokenization_spaces??!0,this.padding_side="right"}getToken(n,...a){for(let u of a){let c=n[u];if(c)if(typeof c=="object"){if(c.__type==="AddedToken")return c.content;throw Error(`Unknown token: ${c}`)}else return c}return null}static async from_pretrained(n,{progress_callback:a=null,config:u=null,cache_dir:c=null,local_files_only:p=!1,revision:s="main"}={}){let h=await loadTokenizer(n,{progress_callback:a,config:u,cache_dir:c,local_files_only:p,revision:s});return new this(...h)}prepare_model_inputs(n){return n}_call(n,{text_pair:a=null,padding:u=!1,truncation:c=null,max_length:p=null,return_tensor:s=!0}={}){let h;if(Array.isArray(n)){if(n.length===0)throw Error("text array must be non-empty");if(a!==null){if(Array.isArray(a)){if(n.length!==a.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");h=n.map((t,e)=>this.encode(t,a[e]))}else h=n.map(t=>this.encode(t))}else{if(n===null)throw Error("text may not be null");if(Array.isArray(a))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");h=[this.encode(n,a)]}let f=max(h.map(t=>t.length))[0];p===null&&(p=f),p=Math.min(p,this.model_max_length);let l=[];if(u||c)for(let t=0;t<h.length;++t)if(h[t].length===p){l.push(new Array(h[t].length).fill(1));continue}else if(h[t].length>p)c&&(h[t]=h[t].slice(0,p)),l.push(new Array(h[t].length).fill(1));else if(u){let e=p-h[t].length;this.padding_side==="right"?(l.push(new Array(h[t].length).fill(1).concat(new Array(e).fill(0))),h[t].push(...new Array(e).fill(this.pad_token_id))):(l.push(new Array(e).fill(0).concat(new Array(h[t].length).fill(1))),h[t].unshift(...new Array(e).fill(this.pad_token_id)))}else l.push(new Array(h[t].length).fill(1));else l=h.map(t=>new Array(t.length).fill(1));if(s){if(!(u&&c)&&h.some(e=>e.length!==h[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");let t=[h.length,h[0].length];h=new Tensor("int64",BigInt64Array.from(h.flat().map(BigInt)),t),l=new Tensor("int64",BigInt64Array.from(l.flat().map(BigInt)),t)}else Array.isArray(n)||(h=h[0],l=l[0]);let o={input_ids:h,attention_mask:l};return o=this.prepare_model_inputs(o),o}_encode_text(n){return n===null?null:n.split(this.added_tokens_regex).filter(c=>c).map(c=>{if(this.added_tokens.includes(c))return c;{this.remove_space===!0&&(c=c.trim().split(/\s+/).join(" ")),this.normalizer!==null&&(c=this.normalizer(c));let p=this.pre_tokenizer!==null?this.pre_tokenizer(c):[c];return this.model(p)}}).flat()}encode(n,a=null){let u=this._encode_text(n),c=this._encode_text(a),p=this.post_processor!==null?this.post_processor(u,c):mergeArrays(u??[],c??[]);return this.model.convert_tokens_to_ids(p)}batch_decode(n,a={}){return n.map(u=>this.decode(u,a))}decode(n,a={}){if(!Array.isArray(n)||n.length===0||!isIntegralNumber(n[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(n,a)}decode_single(n,{skip_special_tokens:a=!1,clean_up_tokenization_spaces:u=null}){let c=this.model.convert_ids_to_tokens(n);a&&(c=c.filter(s=>!this.special_tokens.includes(s)));let p=this.decoder(c);return this.decoder.end_of_word_suffix&&(p=p.replaceAll(this.decoder.end_of_word_suffix," "),a&&(p=p.trim())),(u??this.clean_up_tokenization_spaces)&&(p=clean_up_tokenization(p)),p}}function add_token_types(y){if(y.input_ids instanceof Tensor)y.token_type_ids=new Tensor("int64",new BigInt64Array(y.input_ids.data.length),y.input_ids.dims);else if(Array.isArray(y.input_ids))Array.isArray(y.input_ids[0])?y.token_type_ids=y.input_ids.map(n=>new Array(n.length).fill(0)):y.token_type_ids=new Array(y.input_ids.length).fill(0);else throw new Error("Input ids must be a Tensor or an Array");return y}class BertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class AlbertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class MobileBertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class SqueezeBertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class DistilBertTokenizer extends PreTrainedTokenizer{}class T5Tokenizer extends PreTrainedTokenizer{}class GPT2Tokenizer extends PreTrainedTokenizer{}class BartTokenizer extends PreTrainedTokenizer{}class RobertaTokenizer extends PreTrainedTokenizer{}class BloomTokenizer extends PreTrainedTokenizer{}class LlamaTokenizer extends PreTrainedTokenizer{}class XLMRobertaTokenizer extends PreTrainedTokenizer{}class MPNetTokenizer extends PreTrainedTokenizer{}class FalconTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class GPTNeoXTokenizer extends PreTrainedTokenizer{}class NllbTokenizer extends PreTrainedTokenizer{constructor(n,a){super(n,a),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(u=>this.languageRegex.test(u))}_build_translation_inputs(n,a,u){if(!this.language_codes.includes(u.tgt_lang))throw new Error(`Target language code "${u.tgt_lang}" is not valid. Must be one of: {${this.language_codes.join(", ")}}`);if(u.src_lang!==void 0){if(!this.language_codes.includes(u.src_lang))throw new Error(`Source language code "${u.src_lang}" is not valid. Must be one of: {${this.language_codes.join(", ")}}`);for(let c of this.post_processor.config.single)if("SpecialToken"in c&&this.languageRegex.test(c.SpecialToken.id)){c.SpecialToken.id=u.src_lang;break}}return u.forced_bos_token_id=this.model.convert_tokens_to_ids([u.tgt_lang])[0],this._call(n,a)}}const WHISPER_LANGUAGES=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],WHISPER_LANGUAGE_MAPPING=new Map(WHISPER_LANGUAGES),WHISPER_TO_LANGUAGE_CODE_MAPPING=new Map([...WHISPER_LANGUAGES.map(([y,n])=>[n,y]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class WhisperTokenizer extends PreTrainedTokenizer{_decode_asr(n,{return_timestamps:a=!1,return_language:u=!1,time_precision:c=null,force_full_sequences:p=!0}={}){if(c===null)throw Error("Must specify time_precision");let s=null;function h(){return{language:s,timestamp:[null,null],text:""}}const f=[];let l=h(),o=0;const t=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let e=[],r=!1,i=null;const d=new Set(this.all_special_ids);for(let b of n){const _=b.tokens;let v=null,w=t;if("stride"in b){const[A,O,x]=b.stride;if(o-=O,i=A-x,O&&(w=O/c+t),x)for(let I=_.length-1;I>=0;--I){const N=_[I];if(N>=t){if(v!==null&&(N-t)*c<i)break;v=N}}}let S=[];for(const A of _)if(d.has(A)){const O=this.decode([A]);if(O[0]==="["&&O[O.length-1]==="]"){const x=WHISPER_LANGUAGE_MAPPING.get(O.slice(1,-1));if(x!==void 0){if(s!==null&&x!==s&&!a){e.push(S);const I=this.findLongestCommonSequence(e),N=this.decode(I);l.text=N,f.push(l),e=[],S=[],l=h()}s=l.language=x}}}else if(A>=t){const O=(A-t)*c+o,x=Math.round(O*100)/100;if(v!==null&&A>=v)r=!0;else if(r||e.length>0&&A<w)r=!1;else if(l.timestamp[0]===null)l.timestamp[0]=x;else if(x!==l.timestamp[0]){l.timestamp[1]=x,e.push(S);const I=this.findLongestCommonSequence(e),N=this.decode(I);l.text=N,f.push(l),e=[],S=[],l=h()}}else S.push(A);if("stride"in b){const[A,O,x]=b.stride;o+=A-x}S.length>0?e.push(S):e.every(A=>A.length===0)&&(l=h(),e=[],S=[])}if(e.length>0){if(p&&a)throw new Error("There was an error while processing timestamps, we haven't found a timestamp as last token.");const b=this.findLongestCommonSequence(e),_=this.decode(b);l.text=_,f.push(l)}let g=Object.create(null);const m=f.map(b=>b.text).join("");if(a||u){for(let b=0;b<f.length;++b){const _=f[b];a||delete _.timestamp,u||delete _.language}g={chunks:f}}return[m,g]}findLongestCommonSequence(n){let a=n[0],u=a.length,c=[];for(let p=1;p<n.length;++p){const s=n[p];let h=0,f=[u,u,0,0];const l=s.length;for(let g=1;g<u+l;++g){const m=g/1e4,b=Math.max(0,u-g),_=Math.min(u,u+l-g),v=a.slice(b,_),w=Math.max(0,g-u),S=Math.min(l,g),A=s.slice(w,S);if(v.length!==A.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");const O=v.filter((I,N)=>I===A[N]).length,x=O/g+m;O>1&&x>h&&(h=x,f=[b,_,w,S])}const[o,t,e,r]=f,i=Math.floor((t+o)/2),d=Math.floor((r+e)/2);c.push(...a.slice(0,i)),a=s.slice(d),u=a.length}return c.push(...a),c}get_decoder_prompt_ids({language:n=null,task:a=null,no_timestamps:u=!0}={}){let c=[];if(n){n=n.toLowerCase();let p=WHISPER_TO_LANGUAGE_CODE_MAPPING.get(n);if(p===void 0)if(WHISPER_LANGUAGE_MAPPING.has(n))p=n;else{const f=n.length===2?WHISPER_LANGUAGE_MAPPING.keys():WHISPER_LANGUAGE_MAPPING.values();throw new Error(`Language "${n}" is not supported. Must be one of: ${JSON.stringify(f)}`)}let s=this.model.tokens_to_ids.get(`<|${p}|>`);if(s===void 0)throw new Error(`Unable to find language "${p}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);c.push(s)}else c.push(null);if(a){if(a=a.toLowerCase(),a!=="transcribe"&&a!=="translate")throw new Error(`Task "${a}" is not supported. Must be one of: ["transcribe", "translate"]`);let p=this.model.tokens_to_ids.get(`<|${a}|>`);if(p===void 0)throw new Error(`Unable to find task "${a}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);c.push(p)}else c.push(null);if(u){let p=this.model.tokens_to_ids.get("<|notimestamps|>");if(p===void 0)throw new Error('Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.');c.push(p)}return c.map((p,s)=>[s+1,p]).filter(p=>p[1]!==null)}}class CodeGenTokenizer extends PreTrainedTokenizer{}class CLIPTokenizer extends PreTrainedTokenizer{}class MarianTokenizer extends PreTrainedTokenizer{constructor(n,a){super(n,a),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(u=>this.languageRegex.test(u)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(n){if(n===null)return null;let[a,...u]=n.trim().split(this.languageRegex);if(u.length===0)return super._encode_text(a);if(u.length===2){let[c,p]=u;return this.supported_language_codes.includes(c)||console.warn(`Unsupported language code "${c}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),mergeArrays([c],super._encode_text(p))}}}class CharTrie{constructor(){this.root=CharTrieNode.default()}extend(n){for(let a of n)this.push(a)}push(n){let a=this.root;for(let u of n){let c=a.children.get(u);c===void 0&&(c=CharTrieNode.default(),a.children.set(u,c)),a=c}a.isLeaf=!0}*commonPrefixSearch(n){let a=this.root,u="";for(let c=0;c<n.length&&a!==void 0;++c){const p=n[c];u+=p,a=a.children.get(p),a!==void 0&&a.isLeaf&&(yield u)}}}class CharTrieNode{constructor(n,a){this.isLeaf=n,this.children=a}static default(){return new CharTrieNode(!1,new Map)}}class TokenLattice{constructor(n,a,u){this.sentence=n,this.len=n.length,this.bosTokenId=a,this.eosTokenId=u,this.nodes=[],this.beginNodes=new Array(this.len+1),this.endNodes=new Array(this.len+1);for(let s=0;s<this.len+1;++s)this.beginNodes[s]=[],this.endNodes[s]=[];const c=new TokenLatticeNode(this.bosTokenId,0,0,0,0),p=new TokenLatticeNode(this.eosTokenId,1,this.len,0,0);this.nodes.push(c.clone()),this.nodes.push(p.clone()),this.beginNodes[this.len].push(p),this.endNodes[0].push(c)}insert(n,a,u,c){const p=this.nodes.length,s=new TokenLatticeNode(c,p,n,a,u);this.beginNodes[n].push(s),this.endNodes[n+a].push(s),this.nodes.push(s)}viterbi(){const n=this.len;let a=0;for(;a<=n;){if(this.beginNodes[a].length==0)return[];for(let h of this.beginNodes[a]){h.prev=null;let f=0,l=null;for(let o of this.endNodes[a]){const t=o.backtraceScore+h.score;(l===null||t>f)&&(l=o.clone(),f=t)}if(l!==null)h.prev=l,h.backtraceScore=f;else return[]}++a}const u=[],p=this.beginNodes[n][0].prev;if(p===null)return[];let s=p.clone();for(;s.prev!==null;)u.push(s.clone()),s=s.clone().prev.clone();return u.reverse(),u}piece(n){return this.sentence.slice(n.pos,n.pos+n.length)}tokens(){return this.viterbi().map(a=>this.piece(a))}tokenIds(){return this.viterbi().map(a=>a.tokenId)}}class TokenLatticeNode{constructor(n,a,u,c,p){this.tokenId=n,this.nodeId=a,this.pos=u,this.length=c,this.score=p,this.prev=null,this.backtraceScore=0}clone(){const n=new TokenLatticeNode(this.tokenId,this.nodeId,this.pos,this.length,this.score);return n.prev=this.prev,n.backtraceScore=this.backtraceScore,n}}class AutoTokenizer{static async from_pretrained(n,{quantized:a=!0,progress_callback:u=null,config:c=null,cache_dir:p=null,local_files_only:s=!1,revision:h="main"}={}){let[f,l]=await loadTokenizer(n,{quantized:a,progress_callback:u,config:c,cache_dir:p,local_files_only:s,revision:h}),o=l.tokenizer_class.replace(/Fast$/,""),t=this.TOKENIZER_CLASS_MAPPING[o];return t||(console.warn(`Unknown tokenizer class "${o}", attempting to construct from base class.`),t=PreTrainedTokenizer),new t(f,l)}}Se(AutoTokenizer,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer,DistilBertTokenizer,BertTokenizer,MobileBertTokenizer,SqueezeBertTokenizer,AlbertTokenizer,GPT2Tokenizer,BartTokenizer,RobertaTokenizer,WhisperTokenizer,CodeGenTokenizer,CLIPTokenizer,MarianTokenizer,BloomTokenizer,NllbTokenizer,LlamaTokenizer,XLMRobertaTokenizer,MPNetTokenizer,FalconTokenizer,GPTNeoXTokenizer,PreTrainedTokenizer});async function loadConfig(y,n){return await getModelJSON(y,"config.json",!0,n)}class PretrainedConfig{constructor(n){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,n)}static async from_pretrained(n,{progress_callback:a=null,config:u=null,cache_dir:c=null,local_files_only:p=!1,revision:s="main"}={}){let h=u??await loadConfig(n,{progress_callback:a,config:u,cache_dir:c,local_files_only:p,revision:s});return new this(h)}}class AutoConfig{static async from_pretrained(...n){return PretrainedConfig.from_pretrained(...n)}}class LogitsProcessorList extends Callable{constructor(){super(),this.processors=[]}push(n){this.processors.push(n)}extend(n){this.processors.push(...n)}_call(n,a){for(let u of a)this.processors.forEach(c=>c(n,u))}[Symbol.iterator](){return this.processors.values()}}class LogitsProcessor extends Callable{_call(n,a){throw Error("`_call` should be implemented in a subclass")}}class ForceTokensLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.force_token_map=Object.fromEntries(n??[])}_call(n,a){let u=this.force_token_map[n.length];return exists(u)&&(a.data.fill(-1/0),a.data[u]=0),a}}class ForcedBOSTokenLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.bos_token_id=n}_call(n,a){return n.length===1&&(a.data.fill(-1/0),a.data[this.bos_token_id]=0),a}}class ForcedEOSTokenLogitsProcessor extends LogitsProcessor{constructor(n,a){super(),this.max_length=n,this.forced_eos_token_id=a}_call(n,a){}}class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor{constructor(n,a){super(),this.begin_suppress_tokens=n,this.begin_index=a}_call(n,a){if(n.length===this.begin_index)for(let u of this.begin_suppress_tokens)a.data[u]=-1/0;return a}}class WhisperTimeStampLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.eos_token_id=n.eos_token_id,this.no_timestamps_token_id=n.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=(n.forced_decoder_ids||[]).length+2,n.forced_decoder_ids.slice(-1)[0][1]===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=n.max_initial_timestamp_index}_call(n,a){if(a.data[this.no_timestamps_token_id]=-1/0,n.length===this.begin_index-1)return a.data.fill(-1/0),a.data[this.timestamp_begin]=0,a;const u=n.slice(this.begin_index),c=u.length>=1&&u[u.length-1]>=this.timestamp_begin,p=u.length<2||u[u.length-2]>=this.timestamp_begin;if(c&&(p?a.data.subarray(this.timestamp_begin).fill(-1/0):a.data.subarray(0,this.eos_token_id).fill(-1/0)),n.length===this.begin_index&&this.max_initial_timestamp_index!==null){const l=this.timestamp_begin+this.max_initial_timestamp_index;a.data.subarray(l+1).fill(-1/0)}const s=log_softmax(a.data),h=Math.log(s.subarray(this.timestamp_begin).map(Math.exp).reduce((l,o)=>l+o)),f=max(s.subarray(0,this.timestamp_begin))[0];return h>f&&a.data.subarray(0,this.timestamp_begin).fill(-1/0),a}}class NoRepeatNGramLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.no_repeat_ngram_size=n}getNgrams(n){const a=n.length,u=[];for(let p=0;p<a+1-this.no_repeat_ngram_size;++p){const s=[];for(let h=0;h<this.no_repeat_ngram_size;++h)s.push(n[p+h]);u.push(s)}const c=new Map;for(const p of u){const s=p.slice(0,p.length-1),h=JSON.stringify(s),f=c.get(h)??[];f.push(p[p.length-1]),c.set(h,f)}return c}getGeneratedNgrams(n,a){const u=a.slice(a.length+1-this.no_repeat_ngram_size,a.length);return n.get(JSON.stringify(u))??[]}calcBannedNgramTokens(n){const a=[];if(n.length+1<this.no_repeat_ngram_size)return a;{const u=this.getNgrams(n);return this.getGeneratedNgrams(u,n)}}_call(n,a){const u=this.calcBannedNgramTokens(n);for(const c of u)a.data[c]=-1/0;return a}}class RepetitionPenaltyLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.penalty=n}_call(n,a){for(const u of n)a.data[u]<0?a.data[u]*=this.penalty:a.data[u]/=this.penalty;return a}}class GenerationConfig{constructor(n={}){this.max_length=n.max_length??20,this.max_new_tokens=n.max_new_tokens??null,this.min_length=n.min_length??0,this.min_new_tokens=n.min_new_tokens??null,this.early_stopping=n.early_stopping??!1,this.max_time=n.max_time??null,this.do_sample=n.do_sample??!1,this.num_beams=n.num_beams??1,this.num_beam_groups=n.num_beam_groups??1,this.penalty_alpha=n.penalty_alpha??null,this.use_cache=n.use_cache??!0,this.temperature=n.temperature??1,this.top_k=n.top_k??50,this.top_p=n.top_p??1,this.typical_p=n.typical_p??1,this.epsilon_cutoff=n.epsilon_cutoff??0,this.eta_cutoff=n.eta_cutoff??0,this.diversity_penalty=n.diversity_penalty??0,this.repetition_penalty=n.repetition_penalty??1,this.encoder_repetition_penalty=n.encoder_repetition_penalty??1,this.length_penalty=n.length_penalty??1,this.no_repeat_ngram_size=n.no_repeat_ngram_size??0,this.bad_words_ids=n.bad_words_ids??null,this.force_words_ids=n.force_words_ids??null,this.renormalize_logits=n.renormalize_logits??!1,this.constraints=n.constraints??null,this.forced_bos_token_id=n.forced_bos_token_id??null,this.forced_eos_token_id=n.forced_eos_token_id??null,this.remove_invalid_values=n.remove_invalid_values??!1,this.exponential_decay_length_penalty=n.exponential_decay_length_penalty??null,this.suppress_tokens=n.suppress_tokens??null,this.begin_suppress_tokens=n.begin_suppress_tokens??null,this.forced_decoder_ids=n.forced_decoder_ids??null,this.num_return_sequences=n.num_return_sequences??1,this.output_attentions=n.output_attentions??!1,this.output_hidden_states=n.output_hidden_states??!1,this.output_scores=n.output_scores??!1,this.return_dict_in_generate=n.return_dict_in_generate??!1,this.pad_token_id=n.pad_token_id??null,this.bos_token_id=n.bos_token_id??null,this.eos_token_id=n.eos_token_id??null,this.encoder_no_repeat_ngram_size=n.encoder_no_repeat_ngram_size??0,this.decoder_start_token_id=n.decoder_start_token_id??null,this.generation_kwargs=n.generation_kwargs??{}}}class Sampler extends Callable{constructor(n){super(),this.generation_config=n}_call(n,a=-1){return this.sample(n,a)}sample(n,a){throw Error("sample should be implemented in subclasses.")}getLogits(n,a){let u=n.dims.at(-1),c=n.data;if(a===-1)c=c.slice(-u);else{let p=a*u;c=c.slice(p,p+u)}return this.generation_config.temperature>0&&(c=c.map(p=>p/this.generation_config.temperature)),c}randomSelect(n){let a=n.reduce((c,p)=>c+p,0),u=Math.random()*a;for(let c=0;c<n.length;++c)if(u-=n[c],u<=0)return c;return 0}static getSampler(n){if(n.do_sample)return new MultinomialSampler(n);if(n.num_beams>1)return new BeamSearchSampler(n);if(n.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${n.num_return_sequences}.`);return new GreedySampler(n)}}class GreedySampler extends Sampler{sample(n,a=-1){let u=this.getLogits(n,a);return[[max(u)[1],0]]}}class MultinomialSampler extends Sampler{sample(n,a=-1){let u=n.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const c=this.getLogits(n,a),p=getTopItems(c,u),s=softmax(p.map(h=>h[1]));return Array.from({length:this.generation_config.num_beams},()=>{const h=this.randomSelect(s);return[p[h][0],Math.log(s[h])]})}}class BeamSearchSampler extends Sampler{sample(n,a=-1){let u=n.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const c=this.getLogits(n,a),p=getTopItems(c,u),s=softmax(p.map(h=>h[1]));return Array.from({length:this.generation_config.num_beams},(h,f)=>[p[f][0],Math.log(s[f])])}}const{InferenceSession,Tensor:ONNXTensor}=ONNX;class ModelType{}class EncoderOnlyModelType extends ModelType{}class EncoderDecoderModelType extends ModelType{}class Seq2SeqModelType extends EncoderDecoderModelType{}class DecoderOnlyModelType extends ModelType{}const MODEL_TYPE_MAPPING=new Map,MODEL_CLASS_MAPPING=new Map;async function forward(y,n){return MODEL_TYPE_MAPPING.get(y.constructor.name)===DecoderOnlyModelType?await decoderForward(y,n):await encoderForward(y,n)}async function constructSession(y,n,a){let u=`onnx/${n}${a.quantized?"_quantized":""}.onnx`,c=await getModelFile(y,u,!0,a);try{return await InferenceSession.create(c,{executionProviders})}catch(p){if(executionProviders.length===1&&executionProviders[0]==="wasm")throw p;return console.warn(p),console.warn("Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback. "),await InferenceSession.create(c,{executionProviders:["wasm"]})}}async function validateInputs(y,n){const a={},u=[];for(let s of y.inputNames)n[s]===void 0?u.push(s):a[s]=n[s];if(u.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${u.join(", ")}.`);const c=Object.keys(n).length,p=y.inputNames.length;if(c>p){let s=Object.keys(n).filter(h=>!y.inputNames.includes(h));console.warn(`WARNING: Too many inputs were provided (${c} > ${p}). The following inputs will be ignored: "${s.join(", ")}".`)}return a}async function sessionRun(y,n){const a=await validateInputs(y,n);try{let u=await y.run(a);return u=replaceTensors(u),u}catch(u){throw console.error(`An error occurred during model execution: "${u}".`),console.error("Inputs given to model:",a),u}}function replaceTensors(y){for(let n in y)y[n]instanceof ONNXTensor?y[n]=new Tensor(y[n]):typeof y[n]=="object"&&replaceTensors(y[n]);return y}function toI64Tensor(y){if(y instanceof Tensor)return y;if(y.length===0)throw Error("items must be non-empty");if(Array.isArray(y[0])){if(y.some(n=>n.length!==y[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new Tensor("int64",BigInt64Array.from(y.flat().map(n=>BigInt(n))),[y.length,y[0].length])}else return new Tensor("int64",BigInt64Array.from(y.map(n=>BigInt(n))),[1,y.length])}function prepareAttentionMask(y,n){let a=y.config.pad_token_id??null,u=y.config.eos_token_id??null;isIntegralNumber(u)&&(u=[u]);let c=n.indexOf(a)!==-1,p=u===null||!u.includes(a);if(c&&p){let s=BigInt64Array.from(n.data.map(h=>h!=a));return new Tensor("int64",s,n.dims)}else return new Tensor("int64",new BigInt64Array(n.data.length).fill(1n),n.dims)}function boolTensor(y){return new Tensor("bool",[y],[1])}async function seq2seqForward(y,n,{add_decoder_pkv:a=!0}={}){let{encoder_outputs:u,past_key_values:c}=n;u||(u=(await encoderForward(y,n)).last_hidden_state);let p={input_ids:n.decoder_input_ids,encoder_hidden_states:u,use_cache_branch:boolTensor(c!==null)};y.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(p.encoder_attention_mask=n.attention_mask),y.addPastKeyValues(p,c,a);const s=await sessionRun(y.decoder_merged_session,p);let h=s.logits;return c=y.getPastKeyValues(s,c),new Seq2SeqLMOutput({logits:h,past_key_values:c,encoder_outputs:u})}function seq2seqStartBeams(y,n,a,u=!0){let c=[],p=0,s=y.config.decoder_start_token_id;Array.isArray(s)||(s=[s]);for(let h of n){h.dims=[1,...h.dims];let f={inputs:h,encoder_outputs:null,past_key_values:null,output_token_ids:s,done:!1,score:0,id:p++};u&&(f.attention_mask=prepareAttentionMask(y,h)),c.push(f)}return c}async function seq2seqRunBeam(y,n,{input_name:a="input_ids"}={}){let u={[a]:n.inputs,decoder_input_ids:toI64Tensor(n.output_token_ids.slice(-1)),encoder_outputs:n.encoder_outputs,past_key_values:n.past_key_values};n.attention_mask&&(u.attention_mask=n.attention_mask);let c=await y.forward(u);return n.past_key_values=c.past_key_values,n.encoder_outputs=c.encoder_outputs,c}async function encoderForward(y,n){let a={};for(let u of y.session.inputNames)a[u]=n[u];return await sessionRun(y.session,a)}async function decoderForward(y,n){let a=n.past_key_values,u={input_ids:n.input_ids,attention_mask:n.attention_mask??prepareAttentionMask(y,n.input_ids),use_cache_branch:boolTensor(a!==null)};y.addPastKeyValues(u,a);let c=await sessionRun(y.session,u),p=c.logits;return a=y.getPastKeyValues(c,a),{logits:p,past_key_values:a}}function decoderStartBeams(y,n,a,u){let c=[],p=0;for(let s of n){let h=s.tolist().map(Number);s.dims=[1,...s.dims];let f;u?(f=u[p],f.dims=[1,...f.dims]):f=prepareAttentionMask(y,s);let l={input:s,model_input_ids:s,attention_mask:f,past_key_values:null,output_token_ids:h,num_output_tokens:a,done:!1,score:0,id:p++};c.push(l)}return c}async function decoderRunBeam(y,n){let a=new BigInt64Array(n.output_token_ids.length).fill(1n),u={input_ids:n.model_input_ids,attention_mask:new Tensor("int64",a,[1,a.length]),past_key_values:n.past_key_values},c=await y.forward(u);return n.past_key_values=c.past_key_values,c}function decoderUpdatebeam(y,n){y.output_token_ids=[...y.output_token_ids,n],y.model_input_ids=new Tensor("int64",[BigInt(n)],[1,1])}class PreTrainedModel extends Callable{constructor(n,a){super(),this.config=n,this.session=a}async dispose(){let n=[];for(let a of Object.keys(this)){let u=this[a];u instanceof InferenceSession&&n.push(u.handler.dispose())}return await Promise.all(n)}static async from_pretrained(n,{quantized:a=!0,progress_callback:u=null,config:c=null,cache_dir:p=null,local_files_only:s=!1,revision:h="main"}={}){let f={quantized:a,progress_callback:u,config:c,cache_dir:p,local_files_only:s,revision:h},l=MODEL_TYPE_MAPPING.get(this.name),o;if(l===DecoderOnlyModelType)o=await Promise.all([AutoConfig.from_pretrained(n,f),constructSession(n,"decoder_model_merged",f)]);else if(l===Seq2SeqModelType)o=await Promise.all([AutoConfig.from_pretrained(n,f),constructSession(n,"encoder_model",f),constructSession(n,"decoder_model_merged",f),getModelJSON(n,"generation_config.json",!1,f)]);else if(l===EncoderDecoderModelType)o=await Promise.all([AutoConfig.from_pretrained(n,f),constructSession(n,"encoder_model",f),constructSession(n,"decoder_model_merged",f)]);else if(l===EncoderOnlyModelType)o=await Promise.all([AutoConfig.from_pretrained(n,f),constructSession(n,"model",f)]);else throw console.warn("Malformed class definition.",this),Error(`Unable to load model: ${n}. Please report this bug at https://github.com/xenova/transformers.js/issues/new/choose.`);return new this(...o)}async _call(n){return await this.forward(n)}async forward(n){return await forward(this,n)}_get_logits_processor(n,a,u=null){const c=new LogitsProcessorList;if(n.repetition_penalty!==null&&n.repetition_penalty!==1&&c.push(new RepetitionPenaltyLogitsProcessor(n.repetition_penalty)),n.no_repeat_ngram_size!==null&&n.no_repeat_ngram_size>0&&c.push(new NoRepeatNGramLogitsProcessor(n.no_repeat_ngram_size)),n.forced_bos_token_id!==null&&c.push(new ForcedBOSTokenLogitsProcessor(n.forced_bos_token_id)),n.forced_eos_token_id!==null&&c.push(new ForcedEOSTokenLogitsProcessor(n.max_length,n.forced_eos_token_id)),n.begin_suppress_tokens!==null){let p=a>1||n.forced_bos_token_id===null?a:a+1;n.forced_decoder_ids!==null&&(p+=n.forced_decoder_ids[n.forced_decoder_ids.length-1][0]),c.push(new SuppressTokensAtBeginLogitsProcessor(n.begin_suppress_tokens,p))}return n.forced_decoder_ids!==null&&c.push(new ForceTokensLogitsProcessor(n.forced_decoder_ids)),u!==null&&c.extend(u),c}_get_generation_config(n){let a=new GenerationConfig;return"generation_config"in this&&Object.assign(a,this.generation_config),n!==null&&Object.assign(a,n),a}async generate(n,a=null,u=null,{inputs_attention_mask:c=null}={}){if(!(n instanceof Tensor)&&!isTypedArray(n)&&!Array.isArray(n))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${n.constructor.name}".`);let p;if(this.config.is_encoder_decoder)p=0;else if(p=n instanceof Tensor?n.dims[0]:n.length,p===0)throw Error("Must supply a non-empty array of input token ids.");a=this._get_generation_config(a),u=u??new LogitsProcessorList,u=this._get_logits_processor(a,p,u);let s=1;const h=s+(a.max_new_tokens??1/0),f=Number.isInteger(a.max_length)&&(a.max_new_tokens??null)===null;let l=Sampler.getSampler(a),o=this.getStartBeams(n,s,c);for(;o.some(t=>!t.done)&&s<h;){let t=[];for(let e of o){if(e.done){t.push(e);continue}if(f&&e.output_token_ids.length>=a.max_length){e.done=!0,t.push(e);continue}let i=(await this.runBeam(e)).logits.slice(null,-1,null);u(e.output_token_ids,i);let d=l(i);for(let[g,m]of d){let b={...e};this.updateBeam(b,g),b.score+=m,g===this.config.eos_token_id&&(b.done=!0),t.push(b)}}++s,t=this.groupBeams(t).map(e=>e.sort((r,i)=>i.score-r.score).slice(0,a.num_beams)),o=t.flat(),a.callback_function&&a.callback_function(o)}return this.groupBeams(o).map(t=>a.num_return_sequences>1?t.slice(0,a.num_return_sequences).map(e=>e.output_token_ids):[t[0].output_token_ids]).flat()}groupBeams(n){const a=Object.create(null);for(const u of n)a[u.id]===void 0?a[u.id]=[u]:a[u.id].push(u);return Object.values(a)}getPastKeyValues(n,a){const u=Object.create(null);for(const c in n)if(c.startsWith("present")){let p=c.replace("present","past_key_values");a!==null&&c.includes("encoder")?u[p]=a[p]:u[p]=n[c]}return u}addPastKeyValues(n,a,u=!1){if(a)Object.assign(n,a);else if(u){let c=[1,this.num_encoder_heads,0,this.encoder_dim_kv];for(let s=0;s<this.num_encoder_layers;++s)n[`past_key_values.${s}.encoder.key`]=new Tensor("float32",[],c),n[`past_key_values.${s}.encoder.value`]=new Tensor("float32",[],c);let p=[1,this.num_decoder_heads,0,this.decoder_dim_kv];for(let s=0;s<this.num_decoder_layers;++s)n[`past_key_values.${s}.decoder.key`]=new Tensor("float32",[],p),n[`past_key_values.${s}.decoder.value`]=new Tensor("float32",[],p)}else{let c=[1,this.num_heads,0,this.dim_kv];for(let p=0;p<this.num_layers;++p)n[`past_key_values.${p}.key`]=new Tensor("float32",[],c),n[`past_key_values.${p}.value`]=new Tensor("float32",[],c)}}}class ModelOutput{}class BertPreTrainedModel extends PreTrainedModel{}class BertModel extends BertPreTrainedModel{}class BertForMaskedLM extends BertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class BertForSequenceClassification extends BertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class BertForTokenClassification extends BertPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class BertForQuestionAnswering extends BertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class DistilBertPreTrainedModel extends PreTrainedModel{}class DistilBertModel extends DistilBertPreTrainedModel{}class DistilBertForSequenceClassification extends DistilBertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DistilBertForTokenClassification extends DistilBertPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class DistilBertForQuestionAnswering extends DistilBertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class DistilBertForMaskedLM extends DistilBertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class MobileBertPreTrainedModel extends PreTrainedModel{}class MobileBertModel extends MobileBertPreTrainedModel{}class MobileBertForMaskedLM extends MobileBertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class MobileBertForSequenceClassification extends MobileBertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MobileBertForQuestionAnswering extends MobileBertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class SqueezeBertPreTrainedModel extends PreTrainedModel{}class SqueezeBertModel extends SqueezeBertPreTrainedModel{}class SqueezeBertForMaskedLM extends SqueezeBertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class SqueezeBertForSequenceClassification extends SqueezeBertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class SqueezeBertForQuestionAnswering extends SqueezeBertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class AlbertPreTrainedModel extends PreTrainedModel{}class AlbertModel extends AlbertPreTrainedModel{}class AlbertForSequenceClassification extends AlbertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class AlbertForQuestionAnswering extends AlbertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class AlbertForMaskedLM extends AlbertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class T5PreTrainedModel extends PreTrainedModel{}class T5Model extends T5PreTrainedModel{async generate(...n){throw Error("The current model class (T5Model) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'T5ForConditionalGeneration'}")}}class T5ForConditionalGeneration extends T5PreTrainedModel{constructor(n,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}getStartBeams(n,a,...u){return seq2seqStartBeams(this,n)}async runBeam(n){return await seq2seqRunBeam(this,n)}updateBeam(n,a){n.output_token_ids=[...n.output_token_ids,a]}async forward(n){return await seq2seqForward(this,n)}}class MT5PreTrainedModel extends PreTrainedModel{}class MT5Model extends MT5PreTrainedModel{async generate(...n){throw Error("The current model class (MT5Model) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'MT5ForConditionalGeneration'}")}}class MT5ForConditionalGeneration extends MT5PreTrainedModel{constructor(n,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}getStartBeams(n,a,...u){return seq2seqStartBeams(this,n)}async runBeam(n){return await seq2seqRunBeam(this,n)}updateBeam(n,a){n.output_token_ids=[...n.output_token_ids,a]}async forward(n){return await seq2seqForward(this,n)}}class BartPretrainedModel extends PreTrainedModel{}class BartModel extends BartPretrainedModel{async generate(...n){throw Error("The current model class (BartModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'BartForConditionalGeneration'}")}}class BartForConditionalGeneration extends BartPretrainedModel{constructor(n,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}getStartBeams(n,a,...u){return seq2seqStartBeams(this,n)}async runBeam(n){return await seq2seqRunBeam(this,n)}updateBeam(n,a){n.output_token_ids=[...n.output_token_ids,a]}async forward(n){return await seq2seqForward(this,n)}}class BartForSequenceClassification extends BartPretrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class RobertaPreTrainedModel extends PreTrainedModel{}class RobertaModel extends RobertaPreTrainedModel{}class RobertaForMaskedLM extends RobertaPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class RobertaForSequenceClassification extends RobertaPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class RobertaForTokenClassification extends RobertaPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class RobertaForQuestionAnswering extends RobertaPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class XLMRobertaPreTrainedModel extends PreTrainedModel{}class XLMRobertaModel extends XLMRobertaPreTrainedModel{}class XLMRobertaForMaskedLM extends XLMRobertaPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class XLMRobertaForSequenceClassification extends XLMRobertaPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class XLMRobertaForTokenClassification extends XLMRobertaPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class XLMRobertaForQuestionAnswering extends XLMRobertaPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class WhisperPreTrainedModel extends PreTrainedModel{}class WhisperModel extends WhisperPreTrainedModel{async generate(...n){throw Error("The current model class (WhisperModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'WhisperForConditionalGeneration'}")}}class WhisperForConditionalGeneration extends WhisperPreTrainedModel{constructor(n,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}async generate(n,a=null,u=null){return a=this._get_generation_config(a),a.return_timestamps??(a.return_timestamps=!1),a.return_timestamps&&(u=[new WhisperTimeStampLogitsProcessor(a)]),super.generate(n,a,u)}getStartBeams(n,a,...u){return seq2seqStartBeams(this,n,a,!1)}async runBeam(n){return await seq2seqRunBeam(this,n,{input_name:"input_features"})}updateBeam(n,a){n.output_token_ids=[...n.output_token_ids,a]}async forward(n){return await seq2seqForward(this,n)}}class VisionEncoderDecoderModel extends PreTrainedModel{constructor(n,a,u){super(n,a),this.decoder_merged_session=u,this.num_layers=this.config.decoder.n_layer,this.num_heads=this.config.decoder.n_head,this.dim_kv=this.config.decoder.n_embd/this.num_heads}getStartBeams(n,a,...u){return seq2seqStartBeams(this,n)}async runBeam(n){return seq2seqRunBeam(this,n,{input_name:"pixel_values"})}updateBeam(n,a){n.output_token_ids=[...n.output_token_ids,a]}async forward(n){return await seq2seqForward(this,n,{add_decoder_pkv:!1})}}class CLIPPreTrainedModel extends PreTrainedModel{}class CLIPModel extends CLIPPreTrainedModel{}class GPT2PreTrainedModel extends PreTrainedModel{constructor(n,a){super(n,a),this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class GPT2Model extends GPT2PreTrainedModel{async generate(...n){throw Error("The current model class (GPT2Model) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'GPT2LMHeadModel'}")}}class GPT2LMHeadModel extends GPT2PreTrainedModel{getStartBeams(n,a,u){return decoderStartBeams(this,n,a,u)}async runBeam(n){return await decoderRunBeam(this,n)}updateBeam(n,a){return decoderUpdatebeam(n,a)}async forward(n){return await decoderForward(this,n)}}class GPTNeoPreTrainedModel extends PreTrainedModel{constructor(n,a){super(n,a),this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_heads,this.num_layers=this.config.num_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class GPTNeoModel extends GPTNeoPreTrainedModel{async generate(...n){throw Error("The current model class (GPTNeoModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'GPTNeoForCausalLM'}")}}class GPTNeoForCausalLM extends GPTNeoPreTrainedModel{getStartBeams(n,a,u){return decoderStartBeams(this,n,a,u)}async runBeam(n){return await decoderRunBeam(this,n)}updateBeam(n,a){return decoderUpdatebeam(n,a)}async forward(n){return await decoderForward(this,n)}}class CodeGenPreTrainedModel extends PreTrainedModel{constructor(n,a){super(n,a),this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class CodeGenModel extends CodeGenPreTrainedModel{async generate(...n){throw Error("The current model class (CodeGenModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'CodeGenForCausalLM'}")}}class CodeGenForCausalLM extends CodeGenPreTrainedModel{getStartBeams(n,a,u){return decoderStartBeams(this,n,a,u)}async runBeam(n){return await decoderRunBeam(this,n)}updateBeam(n,a){return decoderUpdatebeam(n,a)}async forward(n){return await decoderForward(this,n)}}class ViTPreTrainedModel extends PreTrainedModel{}class ViTForImageClassification extends ViTPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MobileViTPreTrainedModel extends PreTrainedModel{}class MobileViTForImageClassification extends MobileViTPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DetrPreTrainedModel extends PreTrainedModel{}class DetrForObjectDetection extends DetrPreTrainedModel{async _call(n){return new DetrObjectDetectionOutput(await super._call(n))}}class DetrForSegmentation extends DetrPreTrainedModel{async _call(n){return new DetrSegmentationOutput(await super._call(n))}}class DetrObjectDetectionOutput extends ModelOutput{constructor({logits:n,pred_boxes:a}){super(),this.logits=n,this.pred_boxes=a}}class DetrSegmentationOutput extends ModelOutput{constructor({logits:n,pred_boxes:a,pred_masks:u}){super(),this.logits=n,this.pred_boxes=a,this.pred_masks=u}}class SamPreTrainedModel extends PreTrainedModel{}class SamModel extends SamPreTrainedModel{async _call(n){return new SamImageSegmentationOutput(await super._call(n))}}class SamImageSegmentationOutput extends ModelOutput{constructor({iou_scores:n,pred_masks:a}){super(),this.iou_scores=n,this.pred_masks=a}}class MarianPreTrainedModel extends PreTrainedModel{}class MarianModel extends MarianPreTrainedModel{async generate(...n){throw Error("The current model class (MarianModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'MarianMTModel'}")}}class MarianMTModel extends MarianPreTrainedModel{constructor(n,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}getStartBeams(n,a,...u){return seq2seqStartBeams(this,n)}async runBeam(n){return await seq2seqRunBeam(this,n)}updateBeam(n,a){n.output_token_ids=[...n.output_token_ids,a]}async forward(n){return await seq2seqForward(this,n)}}class M2M100PreTrainedModel extends PreTrainedModel{}class M2M100Model extends M2M100PreTrainedModel{async generate(...n){throw Error("The current model class (M2M100Model) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'M2M100ForConditionalGeneration'}")}}class M2M100ForConditionalGeneration extends M2M100PreTrainedModel{constructor(n,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}getStartBeams(n,a,...u){return seq2seqStartBeams(this,n)}async runBeam(n){return await seq2seqRunBeam(this,n)}updateBeam(n,a){n.output_token_ids=[...n.output_token_ids,a]}async forward(n){return await seq2seqForward(this,n)}}class PretrainedMixin{static async from_pretrained(n,{quantized:a=!0,progress_callback:u=null,config:c=null,cache_dir:p=null,local_files_only:s=!1,revision:h="main"}={}){let f={quantized:a,progress_callback:u,config:c,cache_dir:p,local_files_only:s,revision:h};if(c=await AutoConfig.from_pretrained(n,f),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);let l;for(let o of this.MODEL_CLASS_MAPPINGS)if(l=o.get(c.model_type),!!l)return await l.from_pretrained(n,f);if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${c.model_type}", attempting to construct from base class.`),await PreTrainedModel.from_pretrained(n,f);throw Error(`Unsupported model type: ${c.model_type}`)}}Se(PretrainedMixin,"MODEL_CLASS_MAPPINGS",null),Se(PretrainedMixin,"BASE_IF_FAIL",!1);const MODEL_MAPPING_NAMES_ENCODER_ONLY=new Map([["bert",BertModel],["albert",AlbertModel],["distilbert",DistilBertModel],["roberta",RobertaModel],["xlm-roberta",XLMRobertaModel],["clip",CLIPModel],["mobilebert",MobileBertModel],["squeezebert",SqueezeBertModel],["sam",SamModel]]),MODEL_MAPPING_NAMES_ENCODER_DECODER=new Map([["t5",T5Model],["mt5",MT5Model],["bart",BartModel],["marian",MarianModel],["whisper",WhisperModel],["m2m_100",M2M100Model]]),MODEL_MAPPING_NAMES_DECODER_ONLY=new Map([["gpt2",GPT2Model],["gpt_neo",GPTNeoModel],["codegen",CodeGenModel]]),MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES=new Map([["bert",BertForSequenceClassification],["albert",AlbertForSequenceClassification],["distilbert",DistilBertForSequenceClassification],["roberta",RobertaForSequenceClassification],["xlm-roberta",XLMRobertaForSequenceClassification],["bart",BartForSequenceClassification],["mobilebert",MobileBertForSequenceClassification],["squeezebert",SqueezeBertForSequenceClassification]]),MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES=new Map([["bert",BertForTokenClassification],["distilbert",DistilBertForTokenClassification],["roberta",RobertaForTokenClassification],["xlm-roberta",XLMRobertaForTokenClassification]]),MODEL_FOR_SEQ_2_SEQ_MAPPING_NAMES=new Map([["t5",T5ForConditionalGeneration],["mt5",MT5ForConditionalGeneration],["bart",BartForConditionalGeneration],["whisper",WhisperForConditionalGeneration],["marian",MarianMTModel],["m2m_100",M2M100ForConditionalGeneration]]),MODEL_WITH_LM_HEAD_MAPPING_NAMES=new Map([["gpt2",GPT2LMHeadModel],["gpt_neo",GPTNeoForCausalLM],["codegen",CodeGenForCausalLM]]),MODEL_FOR_MASKED_LM_MAPPING_NAMES=new Map([["bert",BertForMaskedLM],["albert",AlbertForMaskedLM],["distilbert",DistilBertForMaskedLM],["roberta",RobertaForMaskedLM],["xlm-roberta",XLMRobertaForMaskedLM],["mobilebert",MobileBertForMaskedLM],["squeezebert",SqueezeBertForMaskedLM]]),MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES=new Map([["bert",BertForQuestionAnswering],["albert",AlbertForQuestionAnswering],["distilbert",DistilBertForQuestionAnswering],["roberta",RobertaForQuestionAnswering],["xlm-roberta",XLMRobertaForQuestionAnswering],["mobilebert",MobileBertForQuestionAnswering],["squeezebert",SqueezeBertForQuestionAnswering]]),MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES=new Map([["vision-encoder-decoder",VisionEncoderDecoderModel]]),MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES=new Map([["vit",ViTForImageClassification],["mobilevit",MobileViTForImageClassification]]),MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES=new Map([["detr",DetrForObjectDetection]]),MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES=new Map([["detr",DetrForSegmentation]]),MODEL_FOR_MASK_GENERATION_MAPPING_NAMES=new Map([["sam",SamModel]]),MODEL_CLASS_TYPE_MAPPING=[[MODEL_MAPPING_NAMES_ENCODER_ONLY,EncoderOnlyModelType],[MODEL_MAPPING_NAMES_ENCODER_DECODER,EncoderDecoderModelType],[MODEL_MAPPING_NAMES_DECODER_ONLY,DecoderOnlyModelType],[MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_SEQ_2_SEQ_MAPPING_NAMES,Seq2SeqModelType],[MODEL_WITH_LM_HEAD_MAPPING_NAMES,DecoderOnlyModelType],[MODEL_FOR_MASKED_LM_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES,EncoderDecoderModelType],[MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_MASK_GENERATION_MAPPING_NAMES,EncoderOnlyModelType]];for(let[y,n]of MODEL_CLASS_TYPE_MAPPING)for(let[a,u]of y.entries())MODEL_TYPE_MAPPING.set(u.name,n),MODEL_CLASS_MAPPING.set(u.name,a);class AutoModel extends PretrainedMixin{}Se(AutoModel,"MODEL_CLASS_MAPPINGS",[MODEL_MAPPING_NAMES_ENCODER_ONLY,MODEL_MAPPING_NAMES_ENCODER_DECODER,MODEL_MAPPING_NAMES_DECODER_ONLY]),Se(AutoModel,"BASE_IF_FAIL",!0);class AutoModelForSequenceClassification extends PretrainedMixin{}Se(AutoModelForSequenceClassification,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES]);class AutoModelForTokenClassification extends PretrainedMixin{}Se(AutoModelForTokenClassification,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES]);class AutoModelForSeq2SeqLM extends PretrainedMixin{}Se(AutoModelForSeq2SeqLM,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_SEQ_2_SEQ_MAPPING_NAMES]);class AutoModelForCausalLM extends PretrainedMixin{}Se(AutoModelForCausalLM,"MODEL_CLASS_MAPPINGS",[MODEL_WITH_LM_HEAD_MAPPING_NAMES]);class AutoModelForMaskedLM extends PretrainedMixin{}Se(AutoModelForMaskedLM,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_MASKED_LM_MAPPING_NAMES]);class AutoModelForQuestionAnswering extends PretrainedMixin{}Se(AutoModelForQuestionAnswering,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES]);class AutoModelForVision2Seq extends PretrainedMixin{}Se(AutoModelForVision2Seq,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES]);class AutoModelForImageClassification extends PretrainedMixin{}Se(AutoModelForImageClassification,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES]);class AutoModelForImageSegmentation extends PretrainedMixin{}Se(AutoModelForImageSegmentation,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES]);class AutoModelForObjectDetection extends PretrainedMixin{}Se(AutoModelForObjectDetection,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES]);class Seq2SeqLMOutput extends ModelOutput{constructor({logits:n,past_key_values:a,encoder_outputs:u}){super(),this.logits=n,this.past_key_values=a,this.encoder_outputs=u}}class SequenceClassifierOutput extends ModelOutput{constructor({logits:n}){super(),this.logits=n}}class TokenClassifierOutput extends ModelOutput{constructor({logits:n}){super(),this.logits=n}}class MaskedLMOutput extends ModelOutput{constructor({logits:n}){super(),this.logits=n}}class QuestionAnsweringModelOutput extends ModelOutput{constructor({start_logits:n,end_logits:a}){super(),this.start_logits=n,this.end_logits=a}}const BROWSER_ENV=typeof self<"u";let createCanvasFunction,ImageDataClass,loadImageFunction;if(BROWSER_ENV)createCanvasFunction=(y,n)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(y,n)},loadImageFunction=self.createImageBitmap,ImageDataClass=self.ImageData;else if(fs)loadImageFunction=async y=>{const a=(await y.metadata()).channels;let{data:u,info:c}=await y.raw().toBuffer({resolveWithObject:!0});const p=new RawImage(new Uint8ClampedArray(u),c.width,c.height,c.channels);return a!==void 0&&a!==c.channels&&p.convert(a),p};else throw new Error("Unable to load image processing library.");const RESAMPLING_MAPPING={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"};class RawImage{constructor(n,a,u,c){this._update(n,a,u,c)}static async read(n){if(n instanceof RawImage)return n;if(isString(n)||n instanceof URL)return await this.fromURL(n);throw new Error(`Unsupported input type: ${typeof n}`)}static async fromURL(n){let u=await(await getFile(n)).blob();return this.fromBlob(u)}static async fromBlob(n){if(BROWSER_ENV){let a=await loadImageFunction(n);const u=createCanvasFunction(a.width,a.height).getContext("2d");return u.drawImage(a,0,0),new this(u.getImageData(0,0,a.width,a.height).data,a.width,a.height,4)}else{let a=fs(await n.arrayBuffer());return await loadImageFunction(a)}}grayscale(){if(this.channels===1)return this;let n=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let a=0,u=0;a<this.data.length;a+=this.channels){const c=this.data[a],p=this.data[a+1],s=this.data[a+2];n[u++]=Math.round(.2989*c+.587*p+.114*s)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(n,this.width,this.height,1)}rgb(){if(this.channels===3)return this;let n=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let a=0,u=0;a<this.data.length;++a)n[u++]=this.data[a],n[u++]=this.data[a],n[u++]=this.data[a];break;case 4:for(let a=0,u=0;a<this.data.length;a+=4)n[u++]=this.data[a],n[u++]=this.data[a+1],n[u++]=this.data[a+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(n,this.width,this.height,3)}rgba(){if(this.channels===4)return this;let n=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let a=0,u=0;a<this.data.length;++a)n[u++]=this.data[a],n[u++]=this.data[a],n[u++]=this.data[a],n[u++]=255;break;case 3:for(let a=0,u=0;a<this.data.length;a+=3)n[u++]=this.data[a],n[u++]=this.data[a+1],n[u++]=this.data[a+2],n[u++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(n,this.width,this.height,4)}async resize(n,a,{resample:u=2}={}){let c=RESAMPLING_MAPPING[u]??u;if(BROWSER_ENV){let p=this.channels,s=this.toCanvas();const h=createCanvasFunction(n,a).getContext("2d");return h.drawImage(s,0,0,n,a),new RawImage(h.getImageData(0,0,n,a).data,n,a,4).convert(p)}else{let p=fs(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}});switch(c){case"box":case"hamming":(c==="box"||c==="hamming")&&(console.warn(`Resampling method ${c} is not yet supported. Using bilinear instead.`),c="bilinear");case"nearest":case"bilinear":case"bicubic":p=p.affine([n/this.width,0,0,a/this.height],{interpolator:c});break;case"lanczos":p=p.resize({width:n,height:a,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${c} is not supported.`)}return await loadImageFunction(p)}}async pad([n,a,u,c]){if(n=Math.max(n,0),a=Math.max(a,0),u=Math.max(u,0),c=Math.max(c,0),n===0&&a===0&&u===0&&c===0)return this;if(BROWSER_ENV){let p=this.channels,s=this.toCanvas(),h=this.width+n+a,f=this.height+u+c;const l=createCanvasFunction(h,f).getContext("2d");return l.drawImage(s,0,0,this.width,this.height,n,u,h,f),new RawImage(l.getImageData(0,0,h,f).data,h,f,4).convert(p)}else{let p=fs(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}}).extend({left:n,right:a,top:u,bottom:c});return await loadImageFunction(p)}}async center_crop(n,a){if(this.width===n&&this.height===a)return this;let u=(this.width-n)/2,c=(this.height-a)/2;if(BROWSER_ENV){let p=this.channels,s=this.toCanvas();const h=createCanvasFunction(n,a).getContext("2d");let f=0,l=0,o=0,t=0;return u>=0?f=u:o=-u,c>=0?l=c:t=-c,h.drawImage(s,f,l,n,a,o,t,n,a),new RawImage(h.getImageData(0,0,n,a).data,n,a,4).convert(p)}else{let p=fs(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}});if(u>=0&&c>=0)p=p.extract({left:Math.floor(u),top:Math.floor(c),width:n,height:a});else if(u<=0&&c<=0){let s=Math.floor(-c),h=Math.floor(-u);p=p.extend({top:s,left:h,right:n-this.width-h,bottom:a-this.height-s})}else{let s=[0,0],h=0;c<0?(s[0]=Math.floor(-c),s[1]=a-this.height-s[0]):h=Math.floor(c);let f=[0,0],l=0;u<0?(f[0]=Math.floor(-u),f[1]=n-this.width-f[0]):l=Math.floor(u),p=p.extend({top:s[0],bottom:s[1],left:f[0],right:f[1]}).extract({left:l,top:h,width:n,height:a})}return await loadImageFunction(p)}}toCanvas(){let n=this.clone().rgba(),a=createCanvasFunction(n.width,n.height),u=new ImageDataClass(n.data,n.width,n.height);return a.getContext("2d").putImageData(u,0,0),a}_update(n,a,u,c=null){return this.data=n,this.width=a,this.height=u,c!==null&&(this.channels=c),this}clone(){return new RawImage(this.data.slice(),this.width,this.height,this.channels)}convert(n){if(this.channels===n)return this;switch(n){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}save(n,a="image/png"){if(!env.useFS)throw new Error("Unable to save the image because filesystem is disabled in this environment.");const c=this.toCanvas().toBuffer(a);fs.writeFileSync(n,c)}}async function read_audio(y,n){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/tutorials/node-audio-processing.");const a=await(await getFile(y)).arrayBuffer(),u=new AudioContext({sampleRate:n});typeof n>"u"&&console.warn(`No sampling rate provided, using default of ${u.sampleRate}Hz.`);const c=await u.decodeAudioData(a);let p;if(c.numberOfChannels===2){const s=Math.sqrt(2);let h=c.getChannelData(0),f=c.getChannelData(1);p=new Float32Array(h.length);for(let l=0;l<c.length;++l)p[l]=s*(h[l]+f[l])/2}else p=c.getChannelData(0);return p}function getMelFilters(y,n,a=128){a=Math.floor(a);const u=Math.floor(1+n/2),c=new Array(a),p=rfftfreq(n,1/y),s=0,l=(45.245640471924965-s)/(a+1),o=0,t=200/3,e=new Array(a+2),r=1e3,i=(r-o)/t,d=Math.log(6.4)/27,g=new Array(e.length);for(let b=0;b<e.length;++b){const _=b*l+s;_>=i?e[b]=r*Math.exp(d*(_-i)):e[b]=o+t*_,g[b]=p.map(v=>e[b]-v)}const m=e.slice(1).map((b,_)=>1/(b-e[_]));for(let b=0;b<c.length;++b){c[b]=new Array(u);const _=m[b],v=m[b+1],w=g[b],S=g[b+2],A=2/(e[b+2]-e[b]);for(let O=0;O<c[b].length;++O){const x=-w[O]*_,I=S[O]*v;c[b][O]=Math.max(0,Math.min(x,I))*A}}return c}class FeatureExtractor extends Callable{constructor(n){super(),this.config=n}}class ImageFeatureExtractor extends FeatureExtractor{constructor(n){super(n),this.image_mean=this.config.image_mean,this.image_std=this.config.image_std,this.resample=this.config.resample??2,this.do_rescale=this.config.do_rescale??!0,this.rescale_factor=this.config.rescale_factor??1/255,this.do_normalize=this.config.do_normalize,this.do_resize=this.config.do_resize,this.size=this.config.size,this.do_center_crop=this.config.do_center_crop,this.crop_size=this.config.crop_size,this.do_convert_rgb=this.config.do_convert_rgb??!0,this.pad_size=this.config.pad_size,this.do_pad=(this.config.do_pad??!1)&&this.pad_size}async preprocess(n){this.do_convert_rgb&&(n=n.rgb());const a=n.width,u=n.height;if(this.do_resize){let l,o;if(Number.isInteger(this.size)?(l=this.size,o=this.config.max_size??l):(l=this.size.shortest_edge,o=this.size.longest_edge),l!==void 0||o!==void 0){const t=l===void 0?1:Math.max(l/a,l/u),e=a*t,r=u*t,i=o===void 0?1:Math.min(o/e,o/r);n=await n.resize(Math.floor(e*i),Math.floor(r*i),{resample:this.resample})}else if(this.size.width!==void 0&&this.size.height!==void 0)n=await n.resize(this.size.width,this.size.height,{resample:this.resample});else throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(this.size)}`)}if(this.do_center_crop){let l,o;Number.isInteger(this.crop_size)?(l=this.crop_size,o=this.crop_size):(l=this.crop_size.width,o=this.crop_size.height),n=await n.center_crop(l,o)}let c=[n.height,n.width];if(this.do_pad){let l=0,o=this.pad_size.width-n.width,t=0,e=this.pad_size.height-n.height;n=await n.pad([l,o,t,e])}const p=Float32Array.from(n.data);if(this.do_rescale)for(let l=0;l<p.length;++l)p[l]=this.rescale_factor*p[l];if(this.do_normalize){let l=this.image_mean;Array.isArray(this.image_mean)||(l=new Array(n.channels).fill(l));let o=this.image_std;if(Array.isArray(this.image_std)||(o=new Array(n.channels).fill(l)),l.length!==n.channels||o.length!==n.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${l.length}) and \`image_std\` (${o.length}) must match the number of channels in the image (${n.channels}).`);for(let t=0;t<p.length;t+=n.channels)for(let e=0;e<n.channels;++e)p[t+e]=(p[t+e]-this.image_mean[e])/this.image_std[e]}let s=[n.height,n.width,n.channels],h=new Tensor("float32",p,s),f=transpose(h,[2,0,1]);return{original_size:[u,a],reshaped_input_size:c,pixel_values:f}}async _call(n){Array.isArray(n)||(n=[n]);let a=await Promise.all(n.map(c=>this.preprocess(c)));return a.forEach(c=>c.pixel_values.dims=[1,...c.pixel_values.dims]),{pixel_values:cat(a.map(c=>c.pixel_values)),original_sizes:a.map(c=>c.original_size),reshaped_input_sizes:a.map(c=>c.reshaped_input_size)}}}class ViTFeatureExtractor extends ImageFeatureExtractor{}class MobileViTFeatureExtractor extends ImageFeatureExtractor{}class DetrFeatureExtractor extends ImageFeatureExtractor{async _call(n){let a=await super._call(n),u=[a.pixel_values.dims[0],64,64];return a.pixel_mask=new Tensor("int64",new BigInt64Array(u.reduce((c,p)=>c*p)).fill(1n),u),a}center_to_corners_format([n,a,u,c]){return[n-u/2,a-c/2,n+u/2,a+c/2]}post_process_object_detection(n,a=.5,u=null){const c=n.logits,p=n.pred_boxes,[s,h,f]=c.dims;if(u!==null&&u.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let l=[];for(let o=0;o<s;++o){let t=u!==null?u[o]:null,e={boxes:[],classes:[],scores:[]},r=c[o],i=p[o];for(let d=0;d<h;++d){let g=r[d],m=max(g.data)[1];if(m===f-1)continue;let _=softmax(g.data)[m];if(_>a){let v=i[d].data;v=this.center_to_corners_format(v),t!==null&&(v=v.map((w,S)=>w*t[(S+1)%2])),e.boxes.push(v),e.classes.push(m),e.scores.push(_)}}l.push(e)}return l}remove_low_and_no_objects(n,a,u,c){let p=[],s=[],h=[];for(let f=0;f<n.dims[0];++f){let l=n[f],o=a[f],t=max(l.data)[1];if(t===c)continue;let r=softmax(l.data)[t];r>u&&(p.push(o),s.push(r),h.push(t))}return[p,s,h]}check_segment_validity(n,a,u,c=.5,p=.8){let s=[],h=0,f=0;for(let o=0;o<n.length;++o)n[o]===u&&(s.push(o),++h),a[u].data[o]>=c&&++f;let l=h>0&&f>0;return l&&(l=h/f>p),[l,s]}compute_segments(n,a,u,c,p,s=null,h=null){let[f,l]=h??n[0].dims,o=new Tensor("int32",new Int32Array(f*l),[f,l]),t=[];if(h!==null)for(let d=0;d<n.length;++d)n[d]=interpolate(n[d],h,"bilinear",!1);let e=new Int32Array(n[0].data.length),r=new Float32Array(n[0].data.length);for(let d=0;d<n.length;++d){let g=a[d];for(let m=0;m<n[d].data.length;++m)n[d].data[m]*=g,n[d].data[m]>r[m]&&(e[m]=d,r[m]=n[d].data[m])}let i=0;for(let d=0;d<u.length;++d){let g=u[d],[m,b]=this.check_segment_validity(e,n,d,c,p);if(m){++i;for(let _ of b)o.data[_]=i;t.push({id:i,label_id:g,score:a[d]})}}return[o,t]}post_process_panoptic_segmentation(n,a=.5,u=.5,c=.8,p=null,s=null){p===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),p=new Set);const h=n.logits,l=n.pred_masks.sigmoid();let[o,t,e]=h.dims;if(e-=1,s!==null&&s.length!==o)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let r=[];for(let i=0;i<o;++i){let d=s!==null?s[i]:null,g=h[i],m=l[i],[b,_,v]=this.remove_low_and_no_objects(g,m,a,e);if(v.length===0){let[A,O]=d??m.dims.slice(-2),x=new Tensor("int32",new Int32Array(A*O).fill(-1),[A,O]);r.push({segmentation:x,segments_info:[]});continue}let[w,S]=this.compute_segments(b,_,v,u,c,p,d);r.push({segmentation:w,segments_info:S})}return r}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class SamImageProcessor extends ImageFeatureExtractor{async _call(n,a){let{pixel_values:u,original_sizes:c,reshaped_input_sizes:p}=await super._call(n),s=calculateDimensions(a);if(s.length===3)s=[1,...s],a=[a];else if(s.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let f=0;f<a.length;++f){let l=c[f],o=p[f],t=[o[0]/l[0],o[1]/l[1]];for(let e=0;e<a[f].length;++e)for(let r=0;r<a[f][e].length;++r)for(let i=0;i<a[f][e][r].length;++i)a[f][e][r][i]*=t[i]}let h=new Tensor("int64",BigInt64Array.from(a.flat(1/0).map(f=>BigInt(Math.round(f)))),s);return{pixel_values:u,original_sizes:c,reshaped_input_sizes:p,input_points:h}}post_process_masks(n,a,u,{mask_threshold:c=0,binarize:p=!0,pad_size:s=null}={}){let h=[];s=s??this.pad_size;let f=[s.height,s.width];for(let l=0;l<a.length;++l){let o=a[l],t=u[l],e=n[l],r=[];for(let d=0;d<e.dims[0];++d){let g=e[d],m=interpolate(g,f,"bilinear",!1);m=m.slice(null,[0,t[0]],[0,t[1]]),m=interpolate(e,o,"bilinear",!1),p&&(m=new Tensor("bool",Array.from(m.data).map(b=>b>c),m.dims)),m.dims=[1,...m.dims],r.push(m)}let i=cat(r);h.push(i)}return h}}class WhisperFeatureExtractor extends FeatureExtractor{constructor(n){var a;super(n),(a=this.config).mel_filters??(a.mel_filters=getMelFilters(this.config.sampling_rate,this.config.n_fft,this.config.feature_size))}calcOffset(n,a){return Math.abs((n+a)%(2*a)-a)}padReflect(n,a,u){const c=new Float32Array(n.length+a+u),p=n.length-1;for(let s=0;s<n.length;++s)c[a+s]=n[s];for(let s=1;s<=a;++s)c[a-s]=n[this.calcOffset(s,p)];for(let s=1;s<=u;++s)c[p+a+s]=n[this.calcOffset(p-s,p)];return c}stft(n,a){const u=this.config.n_fft,c=2*(u-1),p=2*(2*u-1),s=2**Math.ceil(Math.log2(p)),h=u+2,f=new Float32Array(h*n.length),l=new Float32Array(p),o=new Float32Array(s),t=new Float32Array(s),e=new Float32Array(s),r=new Float32Array(s),i=new Float32Array(s),d=new Float32Array(s),g=-2*Math.PI/u,m=Math.cos(g),b=Math.sin(g);for(let w=0;w<p>>1;++w){const S=(w+1-u)**2/2,A=Math.sqrt(m**2+b**2)**S,O=S*Math.atan2(b,m);let x=2*w;l[x]=A*Math.cos(O),l[x+1]=A*Math.sin(O),o[x]=l[x],o[x+1]=-l[x+1]}const _=l.subarray(c,p),v=new FFT(s>>1);v.transform(r,o);for(let w=0;w<n.length;++w){const S=n[w];for(let O=0;O<_.length;O+=2){const x=O+1,I=O>>1,N=S[I]*a[I];t[O]=N*_[O],t[x]=N*_[x]}v.transform(i,t);for(let O=0;O<r.length;O+=2){const x=O+1;e[O]=i[O]*r[O]-i[x]*r[x],e[x]=i[O]*r[x]+i[x]*r[O]}v.inverseTransform(d,e);const A=w*h;for(let O=0;O<h;O+=2){const x=d[O+c],I=d[O+c+1],N=_[O],B=_[O+1],L=A+O;f[L]=x*N-I*B,f[L+1]=x*B+I*N}}return{data:f,dims:[n.length,h]}}fram_wave(n,a=!0){const u=[],c=Math.floor((this.config.n_fft-1)/2)+1,p=n.length;for(let s=0;s<p+1;s+=this.config.hop_length){let h;if(a){let f=s>c?s-c:0,l=s<p-c?s+c:p;h=n.subarray(f,l),f===0?h=this.padReflect(h,-s+c,0):l===p&&(h=this.padReflect(h,0,s-p+c))}else{h=new Float32Array(this.config.n_fft);const f=n.subarray(s,s+this.config.n_fft);f.length<this.config.n_fft?(h.set(f),h.fill(0,f.length,this.config.n_fft)):h=f}u.push(h)}return u}hanning(n){if(n<1)return[];if(n===1)return[1];const a=n-1,u=new Float32Array(a);for(let c=0;c<a;++c){const p=2*c-n+1;u[c]=.5+.5*Math.cos(Math.PI*p/a)}return u}_extract_fbank_features(n){const a=new Float32Array(this.config.n_samples);a.set(n);const u=this.hanning(this.config.n_fft+1),c=this.fram_wave(a),p=this.stft(c,u),s=p.data,h=p.dims[0]-1,f=p.dims[1]>>1,l=new Float32Array(h*f);for(let m=0;m<h;++m)for(let b=0;b<f;++b){let _=m*f+b,v=_<<1,w=s[v]**2+s[v+1]**2;l[_]=w}const o=this.config.mel_filters,t=o.length,e=new Float32Array(t*h);let r=0;for(let m=0;m<t;++m){const b=o[m];for(let _=0;_<h;++_){let v=0;for(let w=0;w<f;++w)v+=b[w]*l[_*f+w];e[r++]=v}}const i=1e-10,d=new Float32Array(e.length);let g=0;for(let m=0;m<e.length;++m){const b=Math.max(i,e[m]),_=Math.log10(b);d[m]=_,g=Math.max(_,g)}for(let m=0;m<d.length;++m)d[m]=Math.max(d[m],g-8),d[m]=(d[m]+4)/4;return{data:d,dims:[t,h]}}async _call(n){n.length>this.config.n_samples&&console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`.");let a=n.slice(0,this.config.n_samples),u=this._extract_fbank_features(a);return{input_features:new Tensor("float32",u.data,[1,...u.dims])}}}class Processor extends Callable{constructor(n){super(),this.feature_extractor=n}async _call(n){return await this.feature_extractor(n)}}class SamProcessor extends Processor{async _call(n,a){return await this.feature_extractor(n,a)}post_process_masks(...n){return this.feature_extractor.post_process_masks(...n)}}class WhisperProcessor extends Processor{async _call(n){return await this.feature_extractor(n)}}class AutoProcessor{static async from_pretrained(n,{progress_callback:a=null,config:u=null,cache_dir:c=null,local_files_only:p=!1,revision:s="main"}={}){let h=u??await getModelJSON(n,"preprocessor_config.json",!0,{progress_callback:a,config:u,cache_dir:c,local_files_only:p,revision:s}),f=h.feature_extractor_type??h.image_processor_type,l=this.FEATURE_EXTRACTOR_CLASS_MAPPING[f];if(!l)if(h.size!==void 0)console.warn("Feature extractor type not specified, assuming ImageFeatureExtractor due to size parameter in config."),l=ImageFeatureExtractor;else throw new Error(`Unknown Feature Extractor type: ${h.feature_extractor_type}`);let o=this.PROCESSOR_CLASS_MAPPING[h.processor_class]??Processor,t=new l(h);return new o(t)}}Se(AutoProcessor,"FEATURE_EXTRACTOR_CLASS_MAPPING",{WhisperFeatureExtractor,ViTFeatureExtractor,MobileViTFeatureExtractor,DetrFeatureExtractor,SamImageProcessor}),Se(AutoProcessor,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor,SamProcessor});async function prepareImages(y){return Array.isArray(y)||(y=[y]),y=await Promise.all(y.map(n=>RawImage.read(n))),y}class Pipeline extends Callable{constructor(n,a,u){super(),this.task=n,this.tokenizer=a,this.model=u}async dispose(){await this.model.dispose()}async _call(n){let a=this.tokenizer(n,{padding:!0,truncation:!0}),u=await this.model(a);return[a,u]}}class TextClassificationPipeline extends Pipeline{async _call(n,{topk:a=1}={}){let[u,c]=await super._call(n),p=this.model.config.id2label,s=[];for(let h of c.logits){let l=getTopItems(softmax(h.data),a).map(function(o){return{label:p[o[0]],score:o[1]}});a===1?s.push(...l):s.push(l)}return Array.isArray(n)||a===1?s:s[0]}}class TokenClassificationPipeline extends Pipeline{async _call(n,{ignore_labels:a=["O"]}={}){let u=Array.isArray(n);u||(n=[n]);let c=this.tokenizer,[p,s]=await super._call(n),h=s.logits,f=this.model.config.id2label,l=[];for(let o=0;o<h.dims[0];++o){let t=p.input_ids[o],e=h[o],r=[];for(let i=0;i<e.dims[0];++i){let d=e[i],g=max(d.data)[1],m=f[g];if(a.includes(m))continue;let b=c.decode([t[i].item()],{skip_special_tokens:!0});if(b==="")continue;let _=softmax(d.data);r.push({entity:m,score:_[g],index:i,word:b,start:null,end:null})}l.push(r)}return u?l:l[0]}}class QuestionAnsweringPipeline extends Pipeline{async _call(n,a,{topk:u=1}={}){let c=this.tokenizer(n,{text_pair:a}),p=await this.model(c),s=[];for(let h=0;h<p.start_logits.dims[0];++h){let f=c.input_ids[h],l=f.indexOf(this.tokenizer.sep_token_id),o=Array.from(softmax(p.start_logits[h].data)).map((r,i)=>[r,i]).filter(r=>r[1]>l),t=Array.from(softmax(p.end_logits[h].data)).map((r,i)=>[r,i]).filter(r=>r[1]>l),e=product(o,t).filter(r=>r[0][1]<=r[1][1]).map(r=>[r[0][1],r[1][1],r[0][0]*r[1][0]]).sort((r,i)=>i[2]-r[2]);for(let r=0;r<Math.min(e.length,u);++r){let[i,d,g]=e[r],m=[...f].slice(i,d+1),b=this.tokenizer.decode(m,{skip_special_tokens:!0});s.push({answer:b,score:g})}}return u===1?s[0]:s}}class FillMaskPipeline extends Pipeline{async _call(n,{topk:a=5}={}){let[u,c]=await super._call(n),p=this.tokenizer,s=[];for(let h=0;h<u.input_ids.dims[0];++h){let f=u.input_ids[h],l=f.indexOf(this.tokenizer.mask_token_id);if(l===-1)throw Error(`Mask token (${p.mask_token}) not found in text.`);let t=c.logits[h][l],e=getTopItems(softmax(t.data),a);s.push(e.map(r=>{let i=[...f];return i[l]=r[0],{score:r[1],token:r[0],token_str:p.model.vocab[r[0]],sequence:p.decode(i,{skip_special_tokens:!0})}}))}return Array.isArray(n)?s:s[0]}}class Text2TextGenerationPipeline extends Pipeline{constructor(){super(...arguments);Se(this,"_key",null)}async _call(a,u={}){Array.isArray(a)||(a=[a]),this.model.config.prefix&&(a=a.map(l=>this.model.config.prefix+l));let c=this.model.config.task_specific_params;c&&c[this.task]&&c[this.task].prefix&&(a=a.map(l=>c[this.task].prefix+l));let p={padding:!0,truncation:!0},s;this instanceof TranslationPipeline&&"_build_translation_inputs"in this.tokenizer?s=this.tokenizer._build_translation_inputs(a,p,u).input_ids:s=this.tokenizer(a,p).input_ids;let h=await this.model.generate(s,u),f=this.tokenizer.batch_decode(h,{skip_special_tokens:!0});return this._key!==null&&(f=f.map(l=>this._key===null?l:{[this._key]:l})),f}}class SummarizationPipeline extends Text2TextGenerationPipeline{constructor(){super(...arguments);Se(this,"_key","summary_text")}}class TranslationPipeline extends Text2TextGenerationPipeline{constructor(){super(...arguments);Se(this,"_key","translation_text")}}class TextGenerationPipeline extends Pipeline{async _call(n,a={}){let u=typeof n=="string"||n instanceof String;u&&(n=[n]),this.tokenizer.padding_side="left";let c=this.tokenizer(n,{padding:!0,truncation:!0}),p=c.input_ids,s=c.attention_mask,h=await this.model.generate(p,a,null,{inputs_attention_mask:s});const f=this.tokenizer.batch_decode(h,{skip_special_tokens:!0}),l=Array.from({length:n.length},o=>[]);for(let o=0;o<f.length;++o){const t=Math.floor(o/h.length*n.length);l[t].push({generated_text:f[o]})}return u&&l.length===1?l[0]:l}}class ZeroShotClassificationPipeline extends Pipeline{constructor(n,a,u){super(n,a,u),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([c,p])=>[c.toLowerCase(),p])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(n,a,{hypothesis_template:u="This example is {}.",multi_label:c=!1}={}){let p=Array.isArray(n);p||(n=[n]),Array.isArray(a)||(a=[a]);let s=a.map(l=>u.replace("{}",l)),h=c||a.length===1,f=[];for(let l of n){let o=[];for(let r of s){let i=this.tokenizer(l,{text_pair:r}),d=await this.model(i);h?o.push([d.logits.data[this.contradiction_id],d.logits.data[this.entailment_id]]):o.push(d.logits.data[this.entailment_id])}let t;h?t=o.map(r=>softmax(r)[1]):t=softmax(o);let e=t.map((r,i)=>[r,i]).sort((r,i)=>i[0]-r[0]);f.push({sequence:l,labels:e.map(r=>a[r[1]]),scores:e.map(r=>r[0])})}return p?f:f[0]}}class FeatureExtractionPipeline extends Pipeline{async _call(n,{pooling:a="none",normalize:u=!1}={}){let[c,p]=await super._call(n),s=p.last_hidden_state??p.logits;if(a!=="none")if(a==="mean")s=mean_pooling(s,c.attention_mask);else throw Error(`Pooling method '${a}' not supported.`);return u&&(s=s.normalize(2,-1)),s}}class AutomaticSpeechRecognitionPipeline extends Pipeline{constructor(n,a,u,c){super(n,a,u),this.processor=c}async _preprocess(n,a){return isString(n)&&(n=await read_audio(n,a)),n}async _call(n,a={}){let u=a.return_timestamps??!1,c=a.chunk_length_s??0,p=a.stride_length_s??null,s=a.chunk_callback??null,h=a.force_full_sequences??!1,f=pop(a,"language",null),l=pop(a,"task",null);if(f||l||u){if(a.forced_decoder_ids)throw new Error("Cannot specify `language`/`task`/`return_timestamps` and `forced_decoder_ids` at the same time.");let i=this.tokenizer.get_decoder_prompt_ids({language:f,task:l,no_timestamps:!u});i.length>0&&(a.forced_decoder_ids=i)}let o=!Array.isArray(n);o&&(n=[n]);const t=this.processor.feature_extractor.config.sampling_rate,e=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions;let r=[];for(let i of n){i=await this._preprocess(i,t);let d=[];if(c>0){if(p===null)p=c/6;else if(c<=p)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const b=t*c,_=t*p,v=b-2*_;let w=0;for(;w<i.length;){let S=i.subarray(w,w+b),A=await this.processor(S),O=w===0,x=w+v>=i.length;d.push({stride:[S.length,O?0:_,x?0:_],input_features:A.input_features,is_last:x}),w+=v}}else d=[{stride:[i.length,0,0],input_features:(await this.processor(i)).input_features,is_last:!0}];for(let b of d){let _=await this.model.generate(b.input_features,a);b.tokens=_[0],b.stride=b.stride.map(v=>v/t),s!==null&&s(b)}let[g,m]=this.tokenizer._decode_asr(d,{time_precision:e,return_timestamps:u,force_full_sequences:h});r.push({text:g,...m})}return o?r[0]:r}}class ImageToTextPipeline extends Pipeline{constructor(n,a,u,c){super(n,a,u),this.processor=c}async _call(n,a={}){let u=Array.isArray(n);n=await prepareImages(n);let{pixel_values:c}=await this.processor(n),p=[];for(let s of c){s.dims=[1,...s.dims];let h=await this.model.generate(s,a),f=this.tokenizer.batch_decode(h,{skip_special_tokens:!0}).map(l=>({generated_text:l.trim()}));p.push(f)}return u?p:p[0]}}class ImageClassificationPipeline extends Pipeline{constructor(n,a,u){super(n,null,a),this.processor=u}async _call(n,{topk:a=1}={}){let u=Array.isArray(n);n=await prepareImages(n);let{pixel_values:c}=await this.processor(n),p=await this.model({pixel_values:c}),s=this.model.config.id2label,h=[];for(let f of p.logits){let o=getTopItems(softmax(f.data),a).map(function(t){return{label:s[t[0]],score:t[1]}});a===1?h.push(...o):h.push(o)}return u||a===1?h:h[0]}}class ImageSegmentationPipeline extends Pipeline{constructor(n,a,u){super(n,null,a),this.processor=u,this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(n,{threshold:a=.5,mask_threshold:u=.5,overlap_mask_area_threshold:c=.8,label_ids_to_fuse:p=null,target_sizes:s=null,subtask:h=null}={}){if(Array.isArray(n)&&n.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");n=await prepareImages(n);let l=n.map(d=>[d.height,d.width]),{pixel_values:o,pixel_mask:t}=await this.processor(n),e=await this.model({pixel_values:o,pixel_mask:t}),r=null;if(h!==null)r=this.subtasks_mapping[h];else for(let[d,g]of Object.entries(this.subtasks_mapping))if(g in this.processor.feature_extractor){r=this.processor.feature_extractor[g].bind(this.processor.feature_extractor),h=d;break}let i=[];if(h==="panoptic"||h==="instance"){let d=r(e,a,u,c,p,s??l)[0],g=d.segmentation,m=this.model.config.id2label;for(let b of d.segments_info){let _=new Uint8ClampedArray(g.data.length);for(let w=0;w<g.data.length;++w)g.data[w]===b.id&&(_[w]=255);let v=new RawImage(_,g.dims[1],g.dims[0],1);i.push({score:b.score,label:m[b.label_id],mask:v})}}else throw Error(h==="semantic"?"semantic segmentation not yet supported.":`Subtask ${h} not supported.`);return i}}class ZeroShotImageClassificationPipeline extends Pipeline{constructor(n,a,u,c){super(n,a,u),this.processor=c}async _call(n,a,{hypothesis_template:u="This is a photo of {}"}={}){let c=Array.isArray(n);n=await prepareImages(n);let p=a.map(o=>u.replace("{}",o)),s=this.tokenizer(p,{padding:!0,truncation:!0}),{pixel_values:h}=await this.processor(n),f=await this.model({...s,pixel_values:h}),l=[];for(let o of f.logits_per_image){let t=softmax(o.data);l.push([...t].map((e,r)=>({score:e,label:a[r]})))}return c?l:l[0]}}class ObjectDetectionPipeline extends Pipeline{constructor(n,a,u){super(n,null,a),this.processor=u}async _call(n,{threshold:a=.9,percentage:u=!1}={}){let c=Array.isArray(n);if(c&&n.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");n=await prepareImages(n);let p=u?null:n.map(t=>[t.height,t.width]),{pixel_values:s,pixel_mask:h}=await this.processor(n),f=await this.model({pixel_values:s,pixel_mask:h}),l=this.processor.feature_extractor.post_process_object_detection(f,a,p),o=this.model.config.id2label;return l.forEach(t=>t.labels=t.classes.map(e=>o[e])),c?l:l[0]}}const SUPPORTED_TASKS={"text-classification":{tokenizer:AutoTokenizer,pipeline:TextClassificationPipeline,model:AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:AutoTokenizer,pipeline:TokenClassificationPipeline,model:AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:AutoTokenizer,pipeline:QuestionAnsweringPipeline,model:AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:AutoTokenizer,pipeline:FillMaskPipeline,model:AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:AutoTokenizer,pipeline:SummarizationPipeline,model:AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:AutoTokenizer,pipeline:TranslationPipeline,model:AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:AutoTokenizer,pipeline:Text2TextGenerationPipeline,model:AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:AutoTokenizer,pipeline:TextGenerationPipeline,model:AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:AutoTokenizer,pipeline:ZeroShotClassificationPipeline,model:AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"automatic-speech-recognition":{tokenizer:AutoTokenizer,pipeline:AutomaticSpeechRecognitionPipeline,model:AutoModelForSeq2SeqLM,processor:AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"image-to-text":{tokenizer:AutoTokenizer,pipeline:ImageToTextPipeline,model:AutoModelForVision2Seq,processor:AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:ImageClassificationPipeline,model:AutoModelForImageClassification,processor:AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:ImageSegmentationPipeline,model:AutoModelForImageSegmentation,processor:AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:AutoTokenizer,pipeline:ZeroShotImageClassificationPipeline,model:AutoModel,processor:AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:ObjectDetectionPipeline,model:AutoModelForObjectDetection,processor:AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"feature-extraction":{tokenizer:AutoTokenizer,pipeline:FeatureExtractionPipeline,model:AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"}},TASK_ALIASES={"sentiment-analysis":"text-classification",ner:"token-classification",vqa:"visual-question-answering",asr:"automatic-speech-recognition",embeddings:"feature-extraction"};async function pipeline(y,n=null,{quantized:a=!0,progress_callback:u=null,config:c=null,cache_dir:p=null,local_files_only:s=!1,revision:h="main"}={}){y=TASK_ALIASES[y]??y;let f=SUPPORTED_TASKS[y.split("_",1)[0]];if(!f)throw Error(`Unsupported pipeline: ${y}. Must be one of [${Object.keys(SUPPORTED_TASKS)}]`);n||(n=f.default.model,console.log(`No model specified. Using default model: "${n}".`));let l=f.tokenizer,o=f.model,t=f.pipeline,e=f.processor,r=[],i={quantized:a,progress_callback:u,config:c,cache_dir:p,local_files_only:s,revision:h};l&&r.push(l.from_pretrained(n,i)),o&&r.push(o.from_pretrained(n,i)),e&&r.push(e.from_pretrained(n,i));let d=await Promise.all(r);return dispatchCallback(u,{status:"ready",task:y,model:n}),new t(y,...d)}function product(...y){return y.reduce((n,a)=>n.flatMap(u=>a.map(c=>[u,c])))}env.allowLocalModels=!1;class Singleton{constructor(n,a,u){this.tokenizer=n,this.model=a,this.quantized=u}static async getInstance(n=null){return this.instance===null&&(this.instance=pipeline(this.task,this.model,{quantized:this.quantized,progress_callback:n})),this.instance}}Se(Singleton,"task",null),Se(Singleton,"model",null),Se(Singleton,"quantized",null),Se(Singleton,"instance",null),self.addEventListener("message",async y=>{const n=y.data;if(n.action==="load"){await ImageClassificationPipelineSingleton.getInstance(),self.postMessage({status:"ready"});return}const a=new Uint8ClampedArray(n.image.data.length/4);for(let p=0;p<a.length;++p)a[p]=n.image.data[p*4+3];const u=new RawImage(a,n.image.width,n.image.height,1);let c=await classify(u);c!==null&&self.postMessage({status:"result",task:"image-classification",data:c})});class ImageClassificationPipelineSingleton extends Singleton{}Se(ImageClassificationPipelineSingleton,"task","image-classification"),Se(ImageClassificationPipelineSingleton,"model",`Xenova/${constants.DEFAULT_MODEL}`),Se(ImageClassificationPipelineSingleton,"quantized",constants.DEFAULT_QUANTIZED);const classify=async y=>await(await ImageClassificationPipelineSingleton.getInstance())(y,{topk:0}).catch(u=>(self.postMessage({status:"error",task:"image-classification",data:u}),null))})();
diff --git a/spaces/XzJosh/Carol-Bert-VITS2/text/symbols.py b/spaces/XzJosh/Carol-Bert-VITS2/text/symbols.py
deleted file mode 100644
index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Carol-Bert-VITS2/text/symbols.py
+++ /dev/null
@@ -1,51 +0,0 @@
-punctuation = ['!', '?', '…', ",", ".", "'", '-']
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = '_'
-
-# chinese
-zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
- 'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o',
- 'ong',
- 'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn',
- 'w', 'x', 'y', 'z', 'zh',
- "AA", "EE", "OO"]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
- 'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-num_ja_tones = 1
-
-# English
-en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
- 'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
- 'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {
- 'ZH': 0,
- "JA": 1,
- "EN": 2
-}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
- 'ZH': 0,
- "JA": num_zh_tones,
- "EN": num_zh_tones + num_ja_tones
-}
-
-if __name__ == '__main__':
- a = set(zh_symbols)
- b = set(en_symbols)
- print(sorted(a&b))
-
diff --git a/spaces/XzJosh/Echo-Bert-VITS2/app.py b/spaces/XzJosh/Echo-Bert-VITS2/app.py
deleted file mode 100644
index 2c9b40f3e4a5a2341eee5e6cf3c2cec349652389..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Echo-Bert-VITS2/app.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import sys, os
-
-if sys.platform == "darwin":
- os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-
-import logging
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s")
-
-logger = logging.getLogger(__name__)
-
-import torch
-import argparse
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-import gradio as gr
-import webbrowser
-
-
-net_g = None
-
-
-def get_text(text, language_str, hps):
- norm_text, phone, tone, word2ph = clean_text(text, language_str)
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
- if hps.data.add_blank:
- phone = commons.intersperse(phone, 0)
- tone = commons.intersperse(tone, 0)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert = get_bert(norm_text, word2ph, language_str)
- del word2ph
-
- assert bert.shape[-1] == len(phone)
-
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
-
- return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
- global net_g
- bert, phones, tones, lang_ids = get_text(text, "ZH", hps)
- with torch.no_grad():
- x_tst=phones.to(device).unsqueeze(0)
- tones=tones.to(device).unsqueeze(0)
- lang_ids=lang_ids.to(device).unsqueeze(0)
- bert = bert.to(device).unsqueeze(0)
- x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
- del phones
- speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
- audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio
- , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
- del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
- return audio
-
-def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
- with torch.no_grad():
- audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker)
- return "Success", (hps.data.sampling_rate, audio)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--model_dir", default="./logs/Echo/G_3100.pth", help="path of your model")
- parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file")
- parser.add_argument("--share", default=False, help="make link public")
- parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log")
-
- args = parser.parse_args()
- if args.debug:
- logger.info("Enable DEBUG-LEVEL log")
- logging.basicConfig(level=logging.DEBUG)
- hps = utils.get_hparams_from_file(args.config_dir)
- device = "cuda:0" if torch.cuda.is_available() else "cpu"
- '''
- device = (
- "cuda:0"
- if torch.cuda.is_available()
- else (
- "mps"
- if sys.platform == "darwin" and torch.backends.mps.is_available()
- else "cpu"
- )
- )
- '''
- net_g = SynthesizerTrn(
- len(symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model).to(device)
- _ = net_g.eval()
-
- _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True)
-
- speaker_ids = hps.data.spk2id
- speakers = list(speaker_ids.keys())
- with gr.Blocks() as app:
- with gr.Row():
- with gr.Column():
- gr.Markdown(value="""
- 【AI黑桃影①】在线语音合成（Bert-Vits2）\n
- 作者：Xz乔希 https://space.bilibili.com/5859321\n
- 声音归属：黑桃影 https://space.bilibili.com/456368455\n
- 【AI黑桃影②】https://huggingface.co/spaces/XzJosh/Spade-Bert-VITS2\n
- Bert-VITS2项目：https://github.com/Stardust-minus/Bert-VITS2\n
- 使用本模型请严格遵守法律法规！\n
- 发布二创作品请标注本项目作者及链接、作品使用Bert-VITS2 AI生成！\n
- """)
- text = gr.TextArea(label="Text", placeholder="Input Text Here",
- value="星空下的白色幻影，怪盗斯倍的埃叩参上！")
- speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker')
- sdp_ratio = gr.Slider(minimum=0.1, maximum=1, value=0.2, step=0.01, label='SDP/DP混合比')
- noise_scale = gr.Slider(minimum=0.1, maximum=1, value=0.5, step=0.01, label='感情调节')
- noise_scale_w = gr.Slider(minimum=0.1, maximum=1, value=0.9, step=0.01, label='音素长度')
- length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='生成长度')
- btn = gr.Button("点击生成", variant="primary")
- with gr.Column():
- text_output = gr.Textbox(label="Message")
- audio_output = gr.Audio(label="Output Audio")
- gr.Markdown(value="""
- 【AI塔菲】https://huggingface.co/spaces/XzJosh/Taffy-Bert-VITS2\n
- 【AI东雪莲】https://huggingface.co/spaces/XzJosh/Azuma-Bert-VITS2\n
- 【AI奶绿】https://huggingface.co/spaces/XzJosh/LAPLACE-Bert-VITS2\n
- 【AI七海】https://huggingface.co/spaces/XzJosh/Nana7mi-Bert-VITS2\n
- 【AI阿梓】https://huggingface.co/spaces/XzJosh/Azusa-Bert-VITS2\n
- 【AI嘉然】https://huggingface.co/spaces/XzJosh/Diana-Bert-VITS2\n
- 【AI向晚】https://huggingface.co/spaces/XzJosh/Ava-Bert-VITS2\n
- 【AI乃琳】https://huggingface.co/spaces/XzJosh/Eileen-Bert-VITS2\n
- 【AI贝拉】https://huggingface.co/spaces/XzJosh/Bella-Bert-VITS2\n
- 【AI珈乐】https://huggingface.co/spaces/XzJosh/Carol-Bert-VITS2\n
- 【AI电棍】https://huggingface.co/spaces/XzJosh/otto-Bert-VITS2\n
- 【AI星瞳】https://huggingface.co/spaces/XzJosh/XingTong-Bert-VITS2\n
- 【AI尼奈】https://huggingface.co/spaces/XzJosh/nine1-Bert-VITS2\n
- 【AI扇宝】https://huggingface.co/spaces/XzJosh/ShanBao-Bert-VITS2\n
- 【AI剑魔】https://huggingface.co/spaces/XzJosh/Aatrox-Bert-VITS2\n
- 【AI恬豆】https://huggingface.co/spaces/XzJosh/Bekki-Bert-VITS2\n
- """)
- btn.click(tts_fn,
- inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
- outputs=[text_output, audio_output])
-
-# webbrowser.open("http://127.0.0.1:6006")
-# app.launch(server_port=6006, show_error=True)
-
- app.launch(show_error=True)
diff --git a/spaces/XzJosh/ShanBao-Bert-VITS2/text/chinese_bert.py b/spaces/XzJosh/ShanBao-Bert-VITS2/text/chinese_bert.py
deleted file mode 100644
index cb84ce0b426cd0a1c7954ddcdf41322c10ed14fa..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/ShanBao-Bert-VITS2/text/chinese_bert.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large")
-model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(device)
-
-def get_bert_feature(text, word2ph):
- with torch.no_grad():
- inputs = tokenizer(text, return_tensors='pt')
- for i in inputs:
- inputs[i] = inputs[i].to(device)
- res = model(**inputs, output_hidden_states=True)
- res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu()
-
- assert len(word2ph) == len(text)+2
- word2phone = word2ph
- phone_level_feature = []
- for i in range(len(word2phone)):
- repeat_feature = res[i].repeat(word2phone[i], 1)
- phone_level_feature.append(repeat_feature)
-
- phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-
- return phone_level_feature.T
-
-if __name__ == '__main__':
- # feature = get_bert_feature('你好,我是说的道理。')
- import torch
-
- word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征
- word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1]
-
- # 计算总帧数
- total_frames = sum(word2phone)
- print(word_level_feature.shape)
- print(word2phone)
- phone_level_feature = []
- for i in range(len(word2phone)):
- print(word_level_feature[i].shape)
-
- # 对每个词重复word2phone[i]次
- repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
- phone_level_feature.append(repeat_feature)
-
- phone_level_feature = torch.cat(phone_level_feature, dim=0)
- print(phone_level_feature.shape) # torch.Size([36, 1024])
-
diff --git a/spaces/XzJosh/Wenjing-Bert-VITS2/text/english.py b/spaces/XzJosh/Wenjing-Bert-VITS2/text/english.py
deleted file mode 100644
index 781d0a56cef71f66fc67db51d76538be90d3ddd2..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Wenjing-Bert-VITS2/text/english.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import pickle
-import os
-import re
-from g2p_en import G2p
-from string import punctuation
-
-from text import symbols
-
-current_file_path = os.path.dirname(__file__)
-CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep')
-CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle')
-_g2p = G2p()
-
-arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'}
-
-
-def post_replace_ph(ph):
- rep_map = {
- '：': ',',
- '；': ',',
- '，': ',',
- '。': '.',
- '！': '!',
- '？': '?',
- '\n': '.',
- "·": ",",
- '、': ",",
- '...': '…',
- 'v': "V"
- }
- if ph in rep_map.keys():
- ph = rep_map[ph]
- if ph in symbols:
- return ph
- if ph not in symbols:
- ph = 'UNK'
- return ph
-
-def read_dict():
- g2p_dict = {}
- start_line = 49
- with open(CMU_DICT_PATH) as f:
- line = f.readline()
- line_index = 1
- while line:
- if line_index >= start_line:
- line = line.strip()
- word_split = line.split(' ')
- word = word_split[0]
-
- syllable_split = word_split[1].split(' - ')
- g2p_dict[word] = []
- for syllable in syllable_split:
- phone_split = syllable.split(' ')
- g2p_dict[word].append(phone_split)
-
- line_index = line_index + 1
- line = f.readline()
-
- return g2p_dict
-
-
-def cache_dict(g2p_dict, file_path):
- with open(file_path, 'wb') as pickle_file:
- pickle.dump(g2p_dict, pickle_file)
-
-
-def get_dict():
- if os.path.exists(CACHE_PATH):
- with open(CACHE_PATH, 'rb') as pickle_file:
- g2p_dict = pickle.load(pickle_file)
- else:
- g2p_dict = read_dict()
- cache_dict(g2p_dict, CACHE_PATH)
-
- return g2p_dict
-
-eng_dict = get_dict()
-
-def refine_ph(phn):
- tone = 0
- if re.search(r'\d$', phn):
- tone = int(phn[-1]) + 1
- phn = phn[:-1]
- return phn.lower(), tone
-
-def refine_syllables(syllables):
- tones = []
- phonemes = []
- for phn_list in syllables:
- for i in range(len(phn_list)):
- phn = phn_list[i]
- phn, tone = refine_ph(phn)
- phonemes.append(phn)
- tones.append(tone)
- return phonemes, tones
-
-
-def text_normalize(text):
- # todo: eng text normalize
- return text
-
-def g2p(text):
-
- phones = []
- tones = []
- words = re.split(r"([,;.\-\?\!\s+])", text)
- for w in words:
- if w.upper() in eng_dict:
- phns, tns = refine_syllables(eng_dict[w.upper()])
- phones += phns
- tones += tns
- else:
- phone_list = list(filter(lambda p: p != " ", _g2p(w)))
- for ph in phone_list:
- if ph in arpa:
- ph, tn = refine_ph(ph)
- phones.append(ph)
- tones.append(tn)
- else:
- phones.append(ph)
- tones.append(0)
- # todo: implement word2ph
- word2ph = [1 for i in phones]
-
- phones = [post_replace_ph(i) for i in phones]
- return phones, tones, word2ph
-
-if __name__ == "__main__":
- # print(get_dict())
- # print(eng_word_to_phoneme("hello"))
- print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
- # all_phones = set()
- # for k, syllables in eng_dict.items():
- # for group in syllables:
- # for ph in group:
- # all_phones.add(ph)
- # print(all_phones)
\ No newline at end of file
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/.github/workflows/levenshtein.js b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/.github/workflows/levenshtein.js
deleted file mode 100644
index 67a5e3613c0072d124035ee8933a23de2105cfe3..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/.github/workflows/levenshtein.js
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
-Copyright (c) 2011 Andrei Mackenzie
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-*/
-
-// Compute the edit distance between the two given strings
-exports.getEditDistance = function(a, b){
- if(a.length == 0) return b.length;
- if(b.length == 0) return a.length;
-
- var matrix = [];
-
- // increment along the first column of each row
- var i;
- for(i = 0; i <= b.length; i++){
- matrix[i] = [i];
- }
-
- // increment each column in the first row
- var j;
- for(j = 0; j <= a.length; j++){
- matrix[0][j] = j;
- }
-
- // Fill in the rest of the matrix
- for(i = 1; i <= b.length; i++){
- for(j = 1; j <= a.length; j++){
- if(b.charAt(i-1) == a.charAt(j-1)){
- matrix[i][j] = matrix[i-1][j-1];
- } else {
- matrix[i][j] = Math.min(matrix[i-1][j-1] + 1, // substitution
- Math.min(matrix[i][j-1] + 1, // insertion
- matrix[i-1][j] + 1)); // deletion
- }
- }
- }
-
- return matrix[b.length][a.length];
-};
diff --git a/spaces/Yudha515/Rvc-Models/audiocraft/utils/notebook.py b/spaces/Yudha515/Rvc-Models/audiocraft/utils/notebook.py
deleted file mode 100644
index 019b9d19e5bef976bedddf428fd25da42a8a9726..0000000000000000000000000000000000000000
--- a/spaces/Yudha515/Rvc-Models/audiocraft/utils/notebook.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-try:
- import IPython.display as ipd # type: ignore
-except ImportError:
- # Note in a notebook...
- pass
-
-
-import torch
-
-
-def display_audio(samples: torch.Tensor, sample_rate: int):
- """Renders an audio player for the given audio samples.
-
- Args:
- samples (torch.Tensor): a Tensor of decoded audio samples
- with shapes [B, C, T] or [C, T]
- sample_rate (int): sample rate audio should be displayed with.
- """
- assert samples.dim() == 2 or samples.dim() == 3
-
- samples = samples.detach().cpu()
- if samples.dim() == 2:
- samples = samples[None, ...]
-
- for audio in samples:
- ipd.display(ipd.Audio(audio, rate=sample_rate))
diff --git a/spaces/Yuliang/ICON/lib/net/BasePIFuNet.py b/spaces/Yuliang/ICON/lib/net/BasePIFuNet.py
deleted file mode 100644
index d1e5986298a38fc993b697169ec95b1007b58755..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ICON/lib/net/BasePIFuNet.py
+++ /dev/null
@@ -1,84 +0,0 @@
-
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import torch.nn as nn
-import pytorch_lightning as pl
-
-from .geometry import index, orthogonal, perspective
-
-
-class BasePIFuNet(pl.LightningModule):
- def __init__(
- self,
- projection_mode='orthogonal',
- error_term=nn.MSELoss(),
- ):
- """
- :param projection_mode:
- Either orthogonal or perspective.
- It will call the corresponding function for projection.
- :param error_term:
- nn Loss between the predicted [B, Res, N] and the label [B, Res, N]
- """
- super(BasePIFuNet, self).__init__()
- self.name = 'base'
-
- self.error_term = error_term
-
- self.index = index
- self.projection = orthogonal if projection_mode == 'orthogonal' else perspective
-
- def forward(self, points, images, calibs, transforms=None):
- '''
- :param points: [B, 3, N] world space coordinates of points
- :param images: [B, C, H, W] input images
- :param calibs: [B, 3, 4] calibration matrices for each image
- :param transforms: Optional [B, 2, 3] image space coordinate transforms
- :return: [B, Res, N] predictions for each point
- '''
- features = self.filter(images)
- preds = self.query(features, points, calibs, transforms)
- return preds
-
- def filter(self, images):
- '''
- Filter the input images
- store all intermediate features.
- :param images: [B, C, H, W] input images
- '''
- return None
-
- def query(self, features, points, calibs, transforms=None):
- '''
- Given 3D points, query the network predictions for each point.
- Image features should be pre-computed before this call.
- store all intermediate features.
- query() function may behave differently during training/testing.
- :param points: [B, 3, N] world space coordinates of points
- :param calibs: [B, 3, 4] calibration matrices for each image
- :param transforms: Optional [B, 2, 3] image space coordinate transforms
- :param labels: Optional [B, Res, N] gt labeling
- :return: [B, Res, N] predictions for each point
- '''
- return None
-
- def get_error(self, preds, labels):
- '''
- Get the network loss from the last query
- :return: loss term
- '''
- return self.error_term(preds, labels)
diff --git a/spaces/Yusin/Speech-ChatGPT-Speech/app.py b/spaces/Yusin/Speech-ChatGPT-Speech/app.py
deleted file mode 100644
index 5cbfe278ded49478a296a84d7c3e2dea51bfbaa4..0000000000000000000000000000000000000000
--- a/spaces/Yusin/Speech-ChatGPT-Speech/app.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import tempfile
-import gradio as gr
-from neon_tts_plugin_coqui import CoquiTTS
-LANGUAGES = list(CoquiTTS.langs.keys())
-LANGUAGES = LANGUAGES + ['cn', 'jp']
-default_lang = "en"
-#import whisper
-#whisper_model = whisper.load_model("small")
-#whisper = gr.Interface.load(name="spaces/abidlabs/whisper-large-v2")
-whisper = gr.Interface.load(name="spaces/sanchit-gandhi/whisper-large-v2")
-#cn_a_jp = gr.Blocks.load(name="spaces/Yusin/anime-tts_yusin")
-#chatgpt = gr.Blocks.load(name="spaces/fffiloni/whisper-to-chatGPT")
-#chatgpt = gr.Blocks.load(name="spaces/seawolf2357/chatgptclone")
-import os
-import json
-import openai
-#session_token = os.environ.get('SessionToken')
-api_key = os.environ.get('api_key')
-#if you have OpenAI API key as a string, enable the below
-openai.api_key = api_key
-
-title = "Speech to ChatGPT to Speech"
-#info = "more info at [Neon Coqui TTS Plugin](https://github.com/NeonGeckoCom/neon-tts-plugin-coqui), [Coqui TTS](https://github.com/coqui-ai/TTS)"
-#badge = "https://visitor-badge-reloaded.herokuapp.com/badge?page_id=neongeckocom.neon-tts-plugin-coqui"
-coquiTTS = CoquiTTS()
-
-
-# ChatGPT
-def chat_hf(audio, custom_token, language):
- try:
- whisper_text = translate(audio)
- if whisper_text == "ERROR: You have to either use the microphone or upload an audio file":
- gpt_response = "MISSING AUDIO: Record your voice by clicking the microphone button, do not forget to stop recording before sending your message ;)"
- else:
- #gpt_response = chatgpt(whisper_text, [], fn_index=0)
- #print(gpt_response)
- #gpt_response = gpt_response[0]
- gpt_response = openai_create(whisper_text)
-
- except:
- whisper_text = translate(audio)
- gpt_response = """Sorry, I'm quite busy right now, but please try again later :)"""
-
- # to voice
- with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
- coquiTTS.get_tts(gpt_response, fp, speaker = {"language" : language})
-
- return whisper_text, gpt_response, fp.name
-
-# whisper
-#def translate(audio):
-# print("""
-# —
-# Sending audio to Whisper ...
-# —
-# """)
-#
-# audio = whisper.load_audio(audio)
-# audio = whisper.pad_or_trim(audio)
-#
-# mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
-#
-# _, probs = whisper_model.detect_language(mel)
-#
-# transcript_options = whisper.DecodingOptions(task="transcribe", fp16 = False)
-#
-# transcription = whisper.decode(whisper_model, mel, transcript_options)
-#
-# print("language spoken: " + transcription.language)
-# print("transcript: " + transcription.text)
-# print("———————————————————————————————————————————")
-#
-# return transcription.text
-
-def translate(audio):
- print("""
- —
- Sending audio to Whisper ...
- —
- """)
- #_, text_result = whisper(audio, "", fn_index=0)
- text_result = whisper(audio, None, "transcribe", fn_index=0)
- print(text_result)
- return text_result
-
-
-def openai_create(prompt):
-
- response = openai.Completion.create(
- model="text-chat-davinci-002-20221122",
- prompt=prompt,
- temperature=0.9,
- max_tokens=150,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0.6,
- stop=[" Human:", " AI:"]
- )
- print(response.choices[0].text)
- return response.choices[0].text
-
-with gr.Blocks() as blocks:
- gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>"
- + title
- + "</h1>")
- #gr.Markdown(description)
- radio = gr.Radio(label="Language", choices=LANGUAGES, value=default_lang)
- with gr.Row(equal_height=True):# equal_height=False
- with gr.Column():# variant="panel"
- audio_file = gr.Audio(source="microphone", type="filepath")
- custom_token = gr.Textbox(label='If it fails, use your own session token', placeholder="your own session token")
- with gr.Row():# mobile_collapse=False
- submit = gr.Button("Submit", variant="primary")
- with gr.Column():
- text1 = gr.Textbox(label="Speech to Text")
- text2 = gr.Textbox(label="ChatGPT Response")
- audio = gr.Audio(label="Output", interactive=False)
- #gr.Markdown(info)
- #gr.Markdown("<center>"
- # +f'<img src={badge} alt="visitors badge"/>'
- # +"</center>")
-
- # actions
- submit.click(
- chat_hf,
- [audio_file, custom_token, radio],
- [text1, text2, audio],
- )
- #radio.change(lambda lang: CoquiTTS.langs[lang]["sentence"], radio, text2)
-
-
-blocks.launch(debug=True)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/mask_heads/mask_point_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/mask_heads/mask_point_head.py
deleted file mode 100644
index fb92903a9488a44b984a489a354d838cc88f8ad4..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/mask_heads/mask_point_head.py
+++ /dev/null
@@ -1,300 +0,0 @@
-# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_head/point_head.py # noqa
-
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, normal_init
-from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
-
-from mmdet.models.builder import HEADS, build_loss
-
-
-@HEADS.register_module()
-class MaskPointHead(nn.Module):
- """A mask point head use in PointRend.
-
- ``MaskPointHead`` use shared multi-layer perceptron (equivalent to
- nn.Conv1d) to predict the logit of input points. The fine-grained feature
- and coarse feature will be concatenate together for predication.
-
- Args:
- num_fcs (int): Number of fc layers in the head. Default: 3.
- in_channels (int): Number of input channels. Default: 256.
- fc_channels (int): Number of fc channels. Default: 256.
- num_classes (int): Number of classes for logits. Default: 80.
- class_agnostic (bool): Whether use class agnostic classification.
- If so, the output channels of logits will be 1. Default: False.
- coarse_pred_each_layer (bool): Whether concatenate coarse feature with
- the output of each fc layer. Default: True.
- conv_cfg (dict | None): Dictionary to construct and config conv layer.
- Default: dict(type='Conv1d'))
- norm_cfg (dict | None): Dictionary to construct and config norm layer.
- Default: None.
- loss_point (dict): Dictionary to construct and config loss layer of
- point head. Default: dict(type='CrossEntropyLoss', use_mask=True,
- loss_weight=1.0).
- """
-
- def __init__(self,
- num_classes,
- num_fcs=3,
- in_channels=256,
- fc_channels=256,
- class_agnostic=False,
- coarse_pred_each_layer=True,
- conv_cfg=dict(type='Conv1d'),
- norm_cfg=None,
- act_cfg=dict(type='ReLU'),
- loss_point=dict(
- type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)):
- super().__init__()
- self.num_fcs = num_fcs
- self.in_channels = in_channels
- self.fc_channels = fc_channels
- self.num_classes = num_classes
- self.class_agnostic = class_agnostic
- self.coarse_pred_each_layer = coarse_pred_each_layer
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- self.loss_point = build_loss(loss_point)
-
- fc_in_channels = in_channels + num_classes
- self.fcs = nn.ModuleList()
- for _ in range(num_fcs):
- fc = ConvModule(
- fc_in_channels,
- fc_channels,
- kernel_size=1,
- stride=1,
- padding=0,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg)
- self.fcs.append(fc)
- fc_in_channels = fc_channels
- fc_in_channels += num_classes if self.coarse_pred_each_layer else 0
-
- out_channels = 1 if self.class_agnostic else self.num_classes
- self.fc_logits = nn.Conv1d(
- fc_in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
- def init_weights(self):
- """Initialize last classification layer of MaskPointHead, conv layers
- are already initialized by ConvModule."""
- normal_init(self.fc_logits, std=0.001)
-
- def forward(self, fine_grained_feats, coarse_feats):
- """Classify each point base on fine grained and coarse feats.
-
- Args:
- fine_grained_feats (Tensor): Fine grained feature sampled from FPN,
- shape (num_rois, in_channels, num_points).
- coarse_feats (Tensor): Coarse feature sampled from CoarseMaskHead,
- shape (num_rois, num_classes, num_points).
-
- Returns:
- Tensor: Point classification results,
- shape (num_rois, num_class, num_points).
- """
-
- x = torch.cat([fine_grained_feats, coarse_feats], dim=1)
- for fc in self.fcs:
- x = fc(x)
- if self.coarse_pred_each_layer:
- x = torch.cat((x, coarse_feats), dim=1)
- return self.fc_logits(x)
-
- def get_targets(self, rois, rel_roi_points, sampling_results, gt_masks,
- cfg):
- """Get training targets of MaskPointHead for all images.
-
- Args:
- rois (Tensor): Region of Interest, shape (num_rois, 5).
- rel_roi_points: Points coordinates relative to RoI, shape
- (num_rois, num_points, 2).
- sampling_results (:obj:`SamplingResult`): Sampling result after
- sampling and assignment.
- gt_masks (Tensor) : Ground truth segmentation masks of
- corresponding boxes, shape (num_rois, height, width).
- cfg (dict): Training cfg.
-
- Returns:
- Tensor: Point target, shape (num_rois, num_points).
- """
-
- num_imgs = len(sampling_results)
- rois_list = []
- rel_roi_points_list = []
- for batch_ind in range(num_imgs):
- inds = (rois[:, 0] == batch_ind)
- rois_list.append(rois[inds])
- rel_roi_points_list.append(rel_roi_points[inds])
- pos_assigned_gt_inds_list = [
- res.pos_assigned_gt_inds for res in sampling_results
- ]
- cfg_list = [cfg for _ in range(num_imgs)]
-
- point_targets = map(self._get_target_single, rois_list,
- rel_roi_points_list, pos_assigned_gt_inds_list,
- gt_masks, cfg_list)
- point_targets = list(point_targets)
-
- if len(point_targets) > 0:
- point_targets = torch.cat(point_targets)
-
- return point_targets
-
- def _get_target_single(self, rois, rel_roi_points, pos_assigned_gt_inds,
- gt_masks, cfg):
- """Get training target of MaskPointHead for each image."""
- num_pos = rois.size(0)
- num_points = cfg.num_points
- if num_pos > 0:
- gt_masks_th = (
- gt_masks.to_tensor(rois.dtype, rois.device).index_select(
- 0, pos_assigned_gt_inds))
- gt_masks_th = gt_masks_th.unsqueeze(1)
- rel_img_points = rel_roi_point_to_rel_img_point(
- rois, rel_roi_points, gt_masks_th.shape[2:])
- point_targets = point_sample(gt_masks_th,
- rel_img_points).squeeze(1)
- else:
- point_targets = rois.new_zeros((0, num_points))
- return point_targets
-
- def loss(self, point_pred, point_targets, labels):
- """Calculate loss for MaskPointHead.
-
- Args:
- point_pred (Tensor): Point predication result, shape
- (num_rois, num_classes, num_points).
- point_targets (Tensor): Point targets, shape (num_roi, num_points).
- labels (Tensor): Class label of corresponding boxes,
- shape (num_rois, )
-
- Returns:
- dict[str, Tensor]: a dictionary of point loss components
- """
-
- loss = dict()
- if self.class_agnostic:
- loss_point = self.loss_point(point_pred, point_targets,
- torch.zeros_like(labels))
- else:
- loss_point = self.loss_point(point_pred, point_targets, labels)
- loss['loss_point'] = loss_point
- return loss
-
- def _get_uncertainty(self, mask_pred, labels):
- """Estimate uncertainty based on pred logits.
-
- We estimate uncertainty as L1 distance between 0.0 and the logits
- prediction in 'mask_pred' for the foreground class in `classes`.
-
- Args:
- mask_pred (Tensor): mask predication logits, shape (num_rois,
- num_classes, mask_height, mask_width).
-
- labels (list[Tensor]): Either predicted or ground truth label for
- each predicted mask, of length num_rois.
-
- Returns:
- scores (Tensor): Uncertainty scores with the most uncertain
- locations having the highest uncertainty score,
- shape (num_rois, 1, mask_height, mask_width)
- """
- if mask_pred.shape[1] == 1:
- gt_class_logits = mask_pred.clone()
- else:
- inds = torch.arange(mask_pred.shape[0], device=mask_pred.device)
- gt_class_logits = mask_pred[inds, labels].unsqueeze(1)
- return -torch.abs(gt_class_logits)
-
- def get_roi_rel_points_train(self, mask_pred, labels, cfg):
- """Get ``num_points`` most uncertain points with random points during
- train.
-
- Sample points in [0, 1] x [0, 1] coordinate space based on their
- uncertainty. The uncertainties are calculated for each point using
- '_get_uncertainty()' function that takes point's logit prediction as
- input.
-
- Args:
- mask_pred (Tensor): A tensor of shape (num_rois, num_classes,
- mask_height, mask_width) for class-specific or class-agnostic
- prediction.
- labels (list): The ground truth class for each instance.
- cfg (dict): Training config of point head.
-
- Returns:
- point_coords (Tensor): A tensor of shape (num_rois, num_points, 2)
- that contains the coordinates sampled points.
- """
- num_points = cfg.num_points
- oversample_ratio = cfg.oversample_ratio
- importance_sample_ratio = cfg.importance_sample_ratio
- assert oversample_ratio >= 1
- assert 0 <= importance_sample_ratio <= 1
- batch_size = mask_pred.shape[0]
- num_sampled = int(num_points * oversample_ratio)
- point_coords = torch.rand(
- batch_size, num_sampled, 2, device=mask_pred.device)
- point_logits = point_sample(mask_pred, point_coords)
- # It is crucial to calculate uncertainty based on the sampled
- # prediction value for the points. Calculating uncertainties of the
- # coarse predictions first and sampling them for points leads to
- # incorrect results. To illustrate this: assume uncertainty func(
- # logits)=-abs(logits), a sampled point between two coarse
- # predictions with -1 and 1 logits has 0 logits, and therefore 0
- # uncertainty value. However, if we calculate uncertainties for the
- # coarse predictions first, both will have -1 uncertainty,
- # and sampled point will get -1 uncertainty.
- point_uncertainties = self._get_uncertainty(point_logits, labels)
- num_uncertain_points = int(importance_sample_ratio * num_points)
- num_random_points = num_points - num_uncertain_points
- idx = torch.topk(
- point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
- shift = num_sampled * torch.arange(
- batch_size, dtype=torch.long, device=mask_pred.device)
- idx += shift[:, None]
- point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(
- batch_size, num_uncertain_points, 2)
- if num_random_points > 0:
- rand_roi_coords = torch.rand(
- batch_size, num_random_points, 2, device=mask_pred.device)
- point_coords = torch.cat((point_coords, rand_roi_coords), dim=1)
- return point_coords
-
- def get_roi_rel_points_test(self, mask_pred, pred_label, cfg):
- """Get ``num_points`` most uncertain points during test.
-
- Args:
- mask_pred (Tensor): A tensor of shape (num_rois, num_classes,
- mask_height, mask_width) for class-specific or class-agnostic
- prediction.
- pred_label (list): The predication class for each instance.
- cfg (dict): Testing config of point head.
-
- Returns:
- point_indices (Tensor): A tensor of shape (num_rois, num_points)
- that contains indices from [0, mask_height x mask_width) of the
- most uncertain points.
- point_coords (Tensor): A tensor of shape (num_rois, num_points, 2)
- that contains [0, 1] x [0, 1] normalized coordinates of the
- most uncertain points from the [mask_height, mask_width] grid .
- """
- num_points = cfg.subdivision_num_points
- uncertainty_map = self._get_uncertainty(mask_pred, pred_label)
- num_rois, _, mask_height, mask_width = uncertainty_map.shape
- h_step = 1.0 / mask_height
- w_step = 1.0 / mask_width
-
- uncertainty_map = uncertainty_map.view(num_rois,
- mask_height * mask_width)
- num_points = min(mask_height * mask_width, num_points)
- point_indices = uncertainty_map.topk(num_points, dim=1)[1]
- point_coords = uncertainty_map.new_zeros(num_rois, num_points, 2)
- point_coords[:, :, 0] = w_step / 2.0 + (point_indices %
- mask_width).float() * w_step
- point_coords[:, :, 1] = h_step / 2.0 + (point_indices //
- mask_width).float() * h_step
- return point_indices, point_coords
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/core/seg/builder.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/core/seg/builder.py
deleted file mode 100644
index db61f03d4abb2072f2532ce4429c0842495e015b..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/core/seg/builder.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from annotator.uniformer.mmcv.utils import Registry, build_from_cfg
-
-PIXEL_SAMPLERS = Registry('pixel sampler')
-
-
-def build_pixel_sampler(cfg, **default_args):
- """Build pixel sampler for segmentation map."""
- return build_from_cfg(cfg, PIXEL_SAMPLERS, default_args)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/fileio/handlers/yaml_handler.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/fileio/handlers/yaml_handler.py
deleted file mode 100644
index c5aa2eea1e8c76f8baf753d1c8c959dee665e543..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/fileio/handlers/yaml_handler.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import yaml
-
-try:
- from yaml import CLoader as Loader, CDumper as Dumper
-except ImportError:
- from yaml import Loader, Dumper
-
-from .base import BaseFileHandler # isort:skip
-
-
-class YamlHandler(BaseFileHandler):
-
- def load_from_fileobj(self, file, **kwargs):
- kwargs.setdefault('Loader', Loader)
- return yaml.load(file, **kwargs)
-
- def dump_to_fileobj(self, obj, file, **kwargs):
- kwargs.setdefault('Dumper', Dumper)
- yaml.dump(obj, file, **kwargs)
-
- def dump_to_str(self, obj, **kwargs):
- kwargs.setdefault('Dumper', Dumper)
- return yaml.dump(obj, **kwargs)
diff --git a/spaces/abyildirim/inst-inpaint/ldm/modules/distributions/distributions.py b/spaces/abyildirim/inst-inpaint/ldm/modules/distributions/distributions.py
deleted file mode 100644
index f2b8ef901130efc171aa69742ca0244d94d3f2e9..0000000000000000000000000000000000000000
--- a/spaces/abyildirim/inst-inpaint/ldm/modules/distributions/distributions.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import torch
-import numpy as np
-
-
-class AbstractDistribution:
- def sample(self):
- raise NotImplementedError()
-
- def mode(self):
- raise NotImplementedError()
-
-
-class DiracDistribution(AbstractDistribution):
- def __init__(self, value):
- self.value = value
-
- def sample(self):
- return self.value
-
- def mode(self):
- return self.value
-
-
-class DiagonalGaussianDistribution(object):
- def __init__(self, parameters, deterministic=False):
- self.parameters = parameters
- self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
- self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
- self.deterministic = deterministic
- self.std = torch.exp(0.5 * self.logvar)
- self.var = torch.exp(self.logvar)
- if self.deterministic:
- self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
-
- def sample(self):
- x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
- return x
-
- def kl(self, other=None):
- if self.deterministic:
- return torch.Tensor([0.])
- else:
- if other is None:
- return 0.5 * torch.sum(torch.pow(self.mean, 2)
- + self.var - 1.0 - self.logvar,
- dim=[1, 2, 3])
- else:
- return 0.5 * torch.sum(
- torch.pow(self.mean - other.mean, 2) / other.var
- + self.var / other.var - 1.0 - self.logvar + other.logvar,
- dim=[1, 2, 3])
-
- def nll(self, sample, dims=[1,2,3]):
- if self.deterministic:
- return torch.Tensor([0.])
- logtwopi = np.log(2.0 * np.pi)
- return 0.5 * torch.sum(
- logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
- dim=dims)
-
- def mode(self):
- return self.mean
-
-
-def normal_kl(mean1, logvar1, mean2, logvar2):
- """
- source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
- Compute the KL divergence between two gaussians.
- Shapes are automatically broadcasted, so batches can be compared to
- scalars, among other use cases.
- """
- tensor = None
- for obj in (mean1, logvar1, mean2, logvar2):
- if isinstance(obj, torch.Tensor):
- tensor = obj
- break
- assert tensor is not None, "at least one argument must be a Tensor"
-
- # Force variances to be Tensors. Broadcasting helps convert scalars to
- # Tensors, but it does not work for torch.exp().
- logvar1, logvar2 = [
- x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
- for x in (logvar1, logvar2)
- ]
-
- return 0.5 * (
- -1.0
- + logvar2
- - logvar1
- + torch.exp(logvar1 - logvar2)
- + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
- )
diff --git a/spaces/afmck/stable-diffusion-inpainting-segmentation/app.py b/spaces/afmck/stable-diffusion-inpainting-segmentation/app.py
deleted file mode 100644
index 4f7a194c77a3bf54ed8734df0b2f16b387dc1b21..0000000000000000000000000000000000000000
--- a/spaces/afmck/stable-diffusion-inpainting-segmentation/app.py
+++ /dev/null
@@ -1,239 +0,0 @@
-import io
-import requests
-import numpy as np
-import torch
-import os
-from PIL import Image
-from typing import List, Optional
-from functools import reduce
-from argparse import ArgumentParser
-
-import gradio as gr
-
-from transformers import DetrFeatureExtractor, DetrForSegmentation, DetrConfig
-from transformers.models.detr.feature_extraction_detr import rgb_to_id
-
-from diffusers import StableDiffusionInpaintPipeline, DPMSolverMultistepScheduler
-
-parser = ArgumentParser()
-parser.add_argument('--disable-cuda', action='store_true')
-parser.add_argument('--attention-slicing', action='store_true')
-args = parser.parse_args()
-
-auth_token = os.environ.get("READ_TOKEN")
-try_cuda = not args.disable_cuda
-
-torch.inference_mode()
-torch.no_grad()
-
-# Device helper
-def get_device(try_cuda=True):
- return torch.device('cuda' if try_cuda and torch.cuda.is_available() else 'cpu')
-
-device = get_device(try_cuda=try_cuda)
-
-# Load segmentation models
-def load_segmentation_models(model_name: str = 'facebook/detr-resnet-50-panoptic'):
- feature_extractor = DetrFeatureExtractor.from_pretrained(model_name)
- model = DetrForSegmentation.from_pretrained(model_name)
- cfg = DetrConfig.from_pretrained(model_name)
-
- return feature_extractor, model, cfg
-
-# Load diffusion pipeline
-def load_diffusion_pipeline(model_name: str = 'stabilityai/stable-diffusion-2-inpainting'):
- return StableDiffusionInpaintPipeline.from_pretrained(
- model_name,
- revision='fp16',
- torch_dtype=torch.float16 if try_cuda and torch.cuda.is_available() else torch.float32,
- use_auth_token=auth_token
- )
-
-def min_pool(x: torch.Tensor, kernel_size: int):
- pad_size = (kernel_size - 1) // 2
- return -torch.nn.functional.max_pool2d(-x, kernel_size, (1, 1), padding=pad_size)
-
-def max_pool(x: torch.Tensor, kernel_size: int):
- pad_size = (kernel_size - 1) // 2
- return torch.nn.functional.max_pool2d(x, kernel_size, (1, 1), padding=pad_size)
-
-# Apply min-max pooling to clean up mask
-def clean_mask(mask, max_kernel: int = 23, min_kernel: int = 5):
- mask = torch.Tensor(mask[None, None]).float().to(device)
- mask = min_pool(mask, min_kernel)
- mask = max_pool(mask, max_kernel)
- mask = mask.bool().squeeze().cpu().numpy()
- return mask
-
-
-feature_extractor, segmentation_model, segmentation_cfg = load_segmentation_models()
-pipe = load_diffusion_pipeline()
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-
-segmentation_model = segmentation_model.to(device)
-pipe = pipe.to(device)
-if args.attention_slicing:
- pipe.enable_attention_slicing()
-
-# Callback function that runs segmentation and updates CheckboxGroup
-def fn_segmentation(image, max_kernel, min_kernel):
- inputs = feature_extractor(images=image, return_tensors="pt").to(device)
- outputs = segmentation_model(**inputs)
-
- processed_sizes = torch.as_tensor(inputs["pixel_values"].shape[-2:]).unsqueeze(0)
- result = feature_extractor.post_process_panoptic(outputs, processed_sizes)[0]
-
- panoptic_seg = Image.open(io.BytesIO(result["png_string"])).resize((image.width, image.height))
- panoptic_seg = np.array(panoptic_seg, dtype=np.uint8)
-
- panoptic_seg_id = rgb_to_id(panoptic_seg)
-
- raw_masks = []
- for s in result['segments_info']:
- m = panoptic_seg_id == s['id']
- raw_masks.append(m.astype(np.uint8) * 255)
-
- checkbox_choices = [f"{s['id']}:{segmentation_cfg.id2label[s['category_id']]}" for s in result['segments_info']]
-
- checkbox_group = gr.CheckboxGroup.update(
- choices=checkbox_choices
- )
-
- return raw_masks, checkbox_group, gr.Image.update(value=np.zeros((image.height, image.width))), gr.Image.update(value=image)
-
-# Callback function that updates the displayed mask based on selected checkboxes
-def fn_update_mask(
- image: Image,
- masks: List[np.array],
- masks_enabled: List[int],
- max_kernel: int,
- min_kernel: int,
- invert_mask: bool
- ):
- masks_enabled = [int(m.split(':')[0]) for m in masks_enabled]
- combined_mask = reduce(lambda x, y: x | y, [masks[i] for i in masks_enabled], np.zeros_like(masks[0], dtype=bool))
-
- if invert_mask:
- combined_mask = ~combined_mask
-
- combined_mask = clean_mask(combined_mask, max_kernel, min_kernel)
-
- masked_image = np.array(image).copy()
- masked_image[combined_mask] = 0.0
-
- return combined_mask.astype(np.uint8) * 255, Image.fromarray(masked_image)
-
-# Callback function that runs diffusion given the current image, mask and prompt.
-def fn_diffusion(
- prompt: str,
- masked_image: Image,
- mask: Image,
- num_diffusion_steps: int,
- guidance_scale: float,
- negative_prompt: Optional[str] = None,
- ):
- if len(negative_prompt) == 0:
- negative_prompt = None
-
- # Resize image to a more stable diffusion friendly format.
- # TODO: remove magic number
- STABLE_DIFFUSION_SMALL_EDGE = 512
-
- w, h = masked_image.size
- is_width_larger = w > h
- resize_ratio = STABLE_DIFFUSION_SMALL_EDGE / (h if is_width_larger else w)
-
- new_width = int(w * resize_ratio) if is_width_larger else STABLE_DIFFUSION_SMALL_EDGE
- new_height = STABLE_DIFFUSION_SMALL_EDGE if is_width_larger else int(h * resize_ratio)
-
- new_width += 8 - (new_width % 8) if is_width_larger else 0
- new_height += 0 if is_width_larger else 8 - (new_height % 8)
-
- mask = Image.fromarray(mask).convert("RGB").resize((new_width, new_height))
- masked_image = masked_image.convert("RGB").resize((new_width, new_height))
-
- # Run diffusion
- inpainted_image = pipe(
- height=new_height,
- width=new_width,
- prompt=prompt,
- image=masked_image,
- mask_image=mask,
- num_inference_steps=num_diffusion_steps,
- guidance_scale=guidance_scale,
- negative_prompt=negative_prompt
- ).images[0]
-
- # Resize back to the original size
- inpainted_image = inpainted_image.resize((w, h))
-
- return inpainted_image
-
-demo = gr.Blocks(css=open('app.css').read())
-
-with demo:
- gr.HTML(open('app_header.html').read())
-
- if not try_cuda or not torch.cuda.is_available():
- gr.HTML('<div class="alert alert-warning" role="alert" style="color:red"><b>Warning: GPU not available! Diffusion will be slow.</b></div>')
-
- # Input image control
- input_image = gr.Image(value="example.png", type='pil', label="Input Image")
- # Combined mask controls
- bt_masks = gr.Button("Compute Masks")
- with gr.Row():
- mask_image = gr.Image(type='numpy', label="Diffusion Mask")
- masked_image = gr.Image(type='pil', label="Masked Image")
- mask_storage = gr.State()
-
- # Mask editing controls
- with gr.Row():
- max_slider = gr.Slider(minimum=1, maximum=99, value=23, step=2, label="Mask Overflow")
- min_slider = gr.Slider(minimum=1, maximum=99, value=5, step=2, label="Mask Denoising")
-
- with gr.Row():
- invert_mask = gr.Checkbox(label="Invert Mask")
- mask_checkboxes = gr.CheckboxGroup(interactive=True, label="Mask Selection")
-
- # Diffusion controls and output
- with gr.Row():
- with gr.Column():
- prompt = gr.Textbox("An angry dog floating in outer deep space. Twinkling stars in the background. High definition.", label="Prompt")
- negative_prompt = gr.Textbox(label="Negative Prompt")
- with gr.Column():
- steps_slider = gr.Slider(minimum=1, maximum=100, value=50, label="Inference Steps")
- guidance_slider = gr.Slider(minimum=0.0, maximum=50.0, value=7.5, step=0.1, label="Guidance Scale")
- bt_diffusion = gr.Button("Run Diffusion")
-
- inpainted_image = gr.Image(type='pil', label="Inpainted Image")
-
- # TODO: saw a better way of handling many inputs online..
- # forgot where though
- update_mask_inputs = [input_image, mask_storage, mask_checkboxes, max_slider, min_slider, invert_mask]
- update_mask_outputs = [mask_image, masked_image]
-
- # Clear checkbox group on input image change
- input_image.change(lambda: gr.CheckboxGroup.update(choices=[], value=[]), outputs=mask_checkboxes)
- input_image.change(lambda: gr.Checkbox.update(value=False), outputs=invert_mask)
-
- # Segmentation button callback
- bt_masks.click(fn_segmentation, inputs=[input_image, max_slider, min_slider], outputs=[mask_storage, mask_checkboxes, mask_image, masked_image])
-
- # Update mask callbacks
- max_slider.change(fn_update_mask, inputs=update_mask_inputs, outputs=update_mask_outputs, show_progress=False)
- min_slider.change(fn_update_mask, inputs=update_mask_inputs, outputs=update_mask_outputs, show_progress=False)
- mask_checkboxes.change(fn_update_mask, inputs=update_mask_inputs, outputs=update_mask_outputs, show_progress=False)
- invert_mask.change(fn_update_mask, inputs=update_mask_inputs, outputs=update_mask_outputs, show_progress=False)
-
- # Diffusion button callback
- bt_diffusion.click(fn_diffusion, inputs=[
- prompt,
- masked_image,
- mask_image,
- steps_slider,
- guidance_slider,
- negative_prompt
- ], outputs=inpainted_image)
- gr.HTML(open('app_license.html').read())
-
-demo.launch()
diff --git a/spaces/agueroooooooooo/Transport_Mode_Detector/README.md b/spaces/agueroooooooooo/Transport_Mode_Detector/README.md
deleted file mode 100644
index 4f52d56cc49de29a02f653519ab264b8affabf17..0000000000000000000000000000000000000000
--- a/spaces/agueroooooooooo/Transport_Mode_Detector/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Transport_Mode_Detector
-emoji: 🚀
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/akhaliq/GFPGAN/app.py b/spaces/akhaliq/GFPGAN/app.py
deleted file mode 100644
index d9d96de955246af5338eb7187dc55c46732d45af..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/GFPGAN/app.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import os
-
-import cv2
-import gradio as gr
-import torch
-from basicsr.archs.srvgg_arch import SRVGGNetCompact
-from gfpgan.utils import GFPGANer
-from realesrgan.utils import RealESRGANer
-
-os.system("pip freeze")
-os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
-os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
-os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
-
-torch.hub.download_url_to_file(
- 'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
- 'lincoln.jpg')
-torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
- 'AI-generate.jpg')
-torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
- 'Blake_Lively.jpg')
-torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
- '10045.png')
-
-# background enhancer with RealESRGAN
-model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-model_path = 'realesr-general-x4v3.pth'
-half = True if torch.cuda.is_available() else False
-upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
-
-# Use GFPGAN for face enhancement
-face_enhancer_v3 = GFPGANer(
- model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-face_enhancer_v2 = GFPGANer(
- model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-os.makedirs('output', exist_ok=True)
-
-
-def inference(img, version, scale):
- print(img, version, scale)
- try:
- img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
- if len(img.shape) == 3 and img.shape[2] == 4:
- img_mode = 'RGBA'
- else:
- img_mode = None
-
- h, w = img.shape[0:2]
- if h < 300:
- img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
-
- if version == 'v1.2':
- face_enhancer = face_enhancer_v2
- else:
- face_enhancer = face_enhancer_v3
- try:
- _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
- except RuntimeError as error:
- print('Error', error)
- else:
- extension = 'png'
-
- try:
- if scale != 2:
- interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
- h, w = img.shape[0:2]
- output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
- except Exception as error:
- print('wrong scale input.', error)
- if img_mode == 'RGBA': # RGBA images should be saved in png format
- extension = 'png'
- else:
- extension = 'jpg'
- save_path = f'output/out.{extension}'
- cv2.imwrite(save_path, output)
-
- output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
- return output, save_path
- except Exception as error:
- print('global exception', error)
- return None, None
-
-
-title = "GFPGAN: Practical Face Restoration Algorithm UPDATE: Space for GFPGAN is now at <a href='https://huggingface.co/spaces/Xintao/GFPGAN' target='_blank'>https://huggingface.co/spaces/Xintao/GFPGAN</a>"
-description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.<br>
-It can be used to restore your **old photos** or improve **AI-generated faces**.<br>
-To use it, simply upload your image.<br>
-If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a> and recommend it to your friends 😊
-"""
-article = r"""
-
-[](https://github.com/TencentARC/GFPGAN/releases)
-[](https://github.com/TencentARC/GFPGAN)
-[](https://arxiv.org/abs/2101.04061)
-
-If you have any question, please email 📧 `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
-
-<center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>
-"""
-gr.Interface(
- inference, [
- gr.inputs.Image(type="filepath", label="Input"),
- gr.inputs.Radio(['v1.2', 'v1.3'], type="value", default='v1.3', label='GFPGAN version'),
- gr.inputs.Number(label="Rescaling factor", default=2)
- ], [
- gr.outputs.Image(type="numpy", label="Output (The whole image)"),
- gr.outputs.File(label="Download the output image")
- ],
- title=title,
- description=description,
- article=article,
- examples=[['AI-generate.jpg', 'v1.3', 2], ['lincoln.jpg', 'v1.3', 2], ['Blake_Lively.jpg', 'v1.3', 2],
- ['10045.png', 'v1.3', 2]]).launch()
diff --git a/spaces/akhaliq/Kapao/utils/loggers/wandb/__init__.py b/spaces/akhaliq/Kapao/utils/loggers/wandb/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/akhaliq/pedalboard/README.md b/spaces/akhaliq/pedalboard/README.md
deleted file mode 100644
index 60b43a984055a5c28a49ef2aac10d9374583e8ea..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/pedalboard/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: Pedalboard
-emoji: 🐠
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/alaka/tinder-data-explorer/tinder_plots.py b/spaces/alaka/tinder-data-explorer/tinder_plots.py
deleted file mode 100644
index 710ed7fe194b4954f6f86239ea5c55d41668e8a4..0000000000000000000000000000000000000000
--- a/spaces/alaka/tinder-data-explorer/tinder_plots.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import json
-from lib2to3.pgen2.pgen import DFAState
-import plotly.express as px
-import pandas as pd
-from tqdm.auto import tqdm
-import plotly.graph_objects as go
-import tempfile
-import copy
-import zipfile
-import os
-
-TEMPLATE_PLOT = None # "ggplot2"
-
-
-def get_usage_df(file):
- if isinstance(file, tempfile._TemporaryFileWrapper):
- file = file.name
-
- if file.endswith(".zip"):
- with tempfile.TemporaryDirectory() as tmp_dir:
- with zipfile.ZipFile(file, "r") as zip_ref:
- zip_ref.extractall(tmp_dir)
- file = os.path.join(tmp_dir, "data.json")
- with open(file) as f:
- data = json.load(f)
- else:
- with open(file) as f:
- data = json.load(f)
-
- new_data = {}
- new_data["date"] = []
- new_data["value"] = []
- new_data["raw_value"] = []
- new_data["kind"] = []
- for k in data["Usage"]:
- if k in ["idfa", "advertising_id"]:
- continue
- new_value = True
- for date, v in data["Usage"][k].items():
- new_data["date"].append(date)
- new_data["kind"].append(k)
- new_data["raw_value"].append(v)
- if len(new_data["value"]) > 0 and not new_value:
- v += new_data["value"][-1]
- new_data["value"].append(v)
- new_value = False
- df = pd.DataFrame(new_data)
- return df
-
-
-def tinder_usage_overview(df):
- fig = px.scatter(
- df,
- "date",
- "value",
- color="kind",
- symbol="id",
- opacity=0.8,
- template=TEMPLATE_PLOT,
- labels={"x": "date", "y": "Cumulative quantity"},
- )
- return fig
-
-
-def tinder_seasonality(df):
- df = _separate_df_columns(df)
- tmp = pd.to_datetime(df["date"])
-
- # create season from date
- season = (tmp.dt.month - 1) // 3
- df["season"] = season
- df["season"][df["season"] == 0] = "winter"
- df["season"][df["season"] == 1] = "spring"
- df["season"][df["season"] == 2] = "summer"
- df["season"][df["season"] == 3] = "fall"
-
- fig = px.histogram(
- df,
- x="date",
- y="matches",
- color="season",
- pattern_shape="id",
- template=TEMPLATE_PLOT,
- labels={"x": "date", "y": "Matches"},
- )
- return fig
-
-
-def _separate_df_columns(df):
- df_orig = copy.deepcopy(df)
- df = copy.deepcopy(df)
- df = df[df["kind"] == "matches"]
- df["matches"] = df["raw_value"].values
- df["swipes_likes"] = df_orig["raw_value"][df_orig["kind"] == "swipes_likes"].values
- return df
-
-
-def tinder_colored_matches(df):
- df = _separate_df_columns(df)
- fig = px.scatter(
- df,
- x="date",
- y="matches",
- symbol="id",
- hover_data=["swipes_likes"],
- color="swipes_likes",
- template=TEMPLATE_PLOT,
- labels={"x": "date", "y": "Matches"},
- )
- return fig
diff --git a/spaces/albarji/mixture-of-diffusers/app.py b/spaces/albarji/mixture-of-diffusers/app.py
deleted file mode 100644
index 241369ee977f2339e8947556ae6c74d1dfa19214..0000000000000000000000000000000000000000
--- a/spaces/albarji/mixture-of-diffusers/app.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import gradio as gr
-import torch
-
-from diffusers import LMSDiscreteScheduler
-from mixdiff import StableDiffusionCanvasPipeline, Text2ImageRegion
-
-article = """
-## Usage
-
-In this demo you can use Mixture of Diffusers to configure a canvas made up of 3 diffusion regions. Play around with the prompts and guidance values in each region! You can also change increment the overlap between regions if seams appear in the image.
-
-In the full version of Mixture of Diffusers you will find further freedom to configure the regions in the canvas. Check the [github repo](https://github.com/albarji/mixture-of-diffusers)!
-
-## Motivation
-
-Current image generation methods, such as Stable Diffusion, struggle to position objects at specific locations. While the content of the generated image (somewhat) reflects the objects present in the prompt, it is difficult to frame the prompt in a way that creates an specific composition. For instance, take a prompt expressing a complex composition such as
-
-> A charming house in the countryside on the left,
-> in the center a dirt road in the countryside crossing pastures,
-> on the right an old and rusty giant robot lying on a dirt road,
-> by jakub rozalski,
-> sunset lighting on the left and center, dark sunset lighting on the right
-> elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece
-
-Out of a sample of 20 Stable Diffusion generations with different seeds, the generated images that align best with the prompt are the following:
-
-<table>
- <tr>
- <td><img src="https://user-images.githubusercontent.com/9654655/195373001-ad23b7c4-f5b1-4e5b-9aa1-294441ed19ed.png" width="300"></td>
- <td><img src="https://user-images.githubusercontent.com/9654655/195373174-8d85dd96-310e-48fa-b112-d9902685f22e.png" width="300"></td>
- <td><img src="https://user-images.githubusercontent.com/9654655/195373200-59eeec1e-e1b8-464d-b72e-e28a9004d269.png" width="300"></td>
- </tr>
-</table>
-
-The method proposed here strives to provide a better tool for image composition by using several diffusion processes in parallel, each configured with a specific prompt and settings, and focused on a particular region of the image. You can try it out in the example above! The mixture of diffusion processes is done in a way that harmonizes the generation process, preventing "seam" effects in the generated image.
-
-Using several diffusion processes in parallel has also practical advantages when generating very large images, as the GPU memory requirements are similar to that of generating an image of the size of a single tile.
-
-## Responsible use
-
-The same recommendations as in Stable Diffusion apply, so please check the corresponding [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4).
-
-More broadly speaking, always bear this in mind: YOU are responsible for the content you create using this tool. Do not fully blame, credit, or place the responsibility on the software.
-
-## Gallery
-
-Here are some relevant illustrations created using this software (and putting quite a few hours into them!).
-
-### Darkness Dawning
-
-
-
-### Yog-Sothoth
-
-
-
-### Looking through the eyes of giants
-
-
-
-[Follow me on DeviantArt for more!](https://www.deviantart.com/albarji)
-
-## Acknowledgements
-
-First and foremost, my most sincere appreciation for the [Stable Diffusion team](https://stability.ai/blog/stable-diffusion-public-release) for releasing such an awesome model, and for letting me take part of the closed beta. Kudos also to the Hugging Face community and developers for implementing the [Diffusers library](https://github.com/huggingface/diffusers).
-
-Thanks to Hugging Face for providing support and a GPU spaces for running this demo. Thanks also to Instituto de Ingeniería del Conocimiento and Grupo de Aprendizaje Automático (Universidad Autónoma de Madrid) for providing GPU resources for testing and experimenting this library.
-
-Thanks also to the vibrant communities of the Stable Diffusion discord channel and [Lexica](https://lexica.art/), where I have learned about many amazing artists and styles. And to my friend Abril for sharing many tips on cool artists!
-"""
-
-
-# Creater scheduler and model (similar to StableDiffusionPipeline)
-scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
-pipeline = StableDiffusionCanvasPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler).to("cuda" if torch.cuda.is_available() else "cpu")
-
-def generate(prompt1, prompt2, prompt3, gc1, gc2, gc3, overlap, steps, seed):
- """Mixture of Diffusers generation"""
- tile_width = 640
- tile_height = 640
- return pipeline(
- canvas_height=tile_height,
- canvas_width=tile_width + (tile_width - overlap) * 2,
- regions=[
- Text2ImageRegion(0, tile_height, 0, tile_width, guidance_scale=gc1,
- prompt=prompt1),
- Text2ImageRegion(0, tile_height, tile_width - overlap, tile_width - overlap + tile_width, guidance_scale=gc2,
- prompt=prompt2),
- Text2ImageRegion(0, tile_height, (tile_width - overlap) * 2, (tile_width - overlap) * 2 + tile_width, guidance_scale=gc3,
- prompt=prompt3),
- ],
- num_inference_steps=steps,
- seed=seed,
- )["sample"][0]
-
-with gr.Blocks(title="Mixture of Diffusers") as demo:
- gr.HTML(
- """
- <div style="text-align: center; max-width: 700px; margin: 0 auto;">
- <div
- style="
- display: inline-flex;
- align-items: center;
- gap: 0.8rem;
- font-size: 1.75rem;
- "
- >
- <h1 style="font-weight: 1000; margin-bottom: 7px; line-height: normal;">
- Mixture of Diffusers
- </h1>
- </div>
- <p style="margin-bottom: 10px; font-size: 94%">
- <a href="https://arxiv.org/abs/2302.02412">[Paper]</a> <a href="https://github.com/albarji/mixture-of-diffusers">[Code in Github]</a> <a href="https://huggingface.co/spaces/albarji/mixture-of-diffusers?duplicate=true">
- </p>
- </div>
- """
- )
- gr.HTML("""
- <p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
- <br/>
- <a href="https://huggingface.co/spaces/albarji/mixture-of-diffusers?duplicate=true">
- <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
- <p/>
- """)
- with gr.Row():
- with gr.Column(scale=1):
- gr.Markdown("### Left region")
- left_prompt = gr.Textbox(lines=2, label="Prompt (what do you want to see in the left side of the image?)")
- left_gs = gr.Slider(minimum=0, maximum=15, value=8, step=1, label="Guidance scale")
- with gr.Column(scale=1):
- gr.Markdown("### Center region")
- center_prompt = gr.Textbox(lines=2, label="Prompt (what do you want to see in the center of the image?)")
- center_gs = gr.Slider(minimum=0, maximum=15, value=8, step=1, label="Guidance scale")
- with gr.Column(scale=1):
- gr.Markdown("### Right region")
- right_prompt = gr.Textbox(lines=2, label="Prompt (what do you want to see in the right side of the image?)")
- right_gs = gr.Slider(minimum=0, maximum=15, value=8, step=1, label="Guidance scale")
- gr.Markdown("### General parameters")
- with gr.Row():
- with gr.Column(scale=1):
- overlap = gr.Slider(minimum=128, maximum=320, value=256, step=8, label="Overlap between diffusion regions")
- with gr.Column(scale=1):
- steps = gr.Slider(minimum=1, maximum=50, value=15, step=1, label="Number of diffusion steps")
- with gr.Column(scale=1):
- seed = gr.Number(value=12345, precision=0, label="Random seed")
- with gr.Row():
- button = gr.Button(value="Generate")
- with gr.Row():
- output = gr.Image(label="Generated image")
- with gr.Row():
- gr.Examples(
- examples=[
- [
- "A charming house in the countryside, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
- "A dirt road in the countryside crossing pastures, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
- "An old and rusty giant robot lying on a dirt road, by jakub rozalski, dark sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
- 8, 8, 8,
- 256,
- 50,
- 7178915308
- ],
- [
- "Abstract decorative illustration, by joan miro and gustav klimt and marlina vera and loish, elegant, intricate, highly detailed, smooth, sharp focus, vibrant colors, artstation, stunning masterpiece",
- "Abstract decorative illustration, by joan miro and gustav klimt and marlina vera and loish, elegant, intricate, highly detailed, smooth, sharp focus, vibrant colors, artstation, stunning masterpiece",
- "Abstract decorative illustration, by joan miro and gustav klimt and marlina vera and loish, elegant, intricate, highly detailed, smooth, sharp focus, vibrant colors, artstation, stunning masterpiece",
- 8, 8, 8,
- 256,
- 35,
- 21156517
- ],
- [
- "Magical diagrams and runes written with chalk on a blackboard, elegant, intricate, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
- "Magical diagrams and runes written with chalk on a blackboard, elegant, intricate, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
- "Magical diagrams and runes written with chalk on a blackboard, elegant, intricate, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
- 12, 12, 12,
- 256,
- 35,
- 12591765619
- ]
- ],
- inputs=[left_prompt, center_prompt, right_prompt, left_gs, center_gs, right_gs, overlap, steps, seed],
- outputs=output,
- fn=generate,
- cache_examples=True
- )
-
- button.click(
- generate,
- inputs=[left_prompt, center_prompt, right_prompt, left_gs, center_gs, right_gs, overlap, steps, seed],
- outputs=output
- )
-
- with gr.Row():
- gr.Markdown(article)
-
-demo.launch(server_name="0.0.0.0")
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/metadata/pkg_resources.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/metadata/pkg_resources.py
deleted file mode 100644
index d39f0ba31da5342b7d067ba2e1e92f2d6e79afdb..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/metadata/pkg_resources.py
+++ /dev/null
@@ -1,256 +0,0 @@
-import email.message
-import email.parser
-import logging
-import os
-import pathlib
-import zipfile
-from typing import Collection, Iterable, Iterator, List, Mapping, NamedTuple, Optional
-
-from pip._vendor import pkg_resources
-from pip._vendor.packaging.requirements import Requirement
-from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
-from pip._vendor.packaging.version import parse as parse_version
-
-from pip._internal.exceptions import InvalidWheel, NoneMetadataError, UnsupportedWheel
-from pip._internal.utils.misc import display_path
-from pip._internal.utils.wheel import parse_wheel, read_wheel_metadata_file
-
-from .base import (
- BaseDistribution,
- BaseEntryPoint,
- BaseEnvironment,
- DistributionVersion,
- InfoPath,
- Wheel,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class EntryPoint(NamedTuple):
- name: str
- value: str
- group: str
-
-
-class WheelMetadata:
- """IMetadataProvider that reads metadata files from a dictionary.
-
- This also maps metadata decoding exceptions to our internal exception type.
- """
-
- def __init__(self, metadata: Mapping[str, bytes], wheel_name: str) -> None:
- self._metadata = metadata
- self._wheel_name = wheel_name
-
- def has_metadata(self, name: str) -> bool:
- return name in self._metadata
-
- def get_metadata(self, name: str) -> str:
- try:
- return self._metadata[name].decode()
- except UnicodeDecodeError as e:
- # Augment the default error with the origin of the file.
- raise UnsupportedWheel(
- f"Error decoding metadata for {self._wheel_name}: {e} in {name} file"
- )
-
- def get_metadata_lines(self, name: str) -> Iterable[str]:
- return pkg_resources.yield_lines(self.get_metadata(name))
-
- def metadata_isdir(self, name: str) -> bool:
- return False
-
- def metadata_listdir(self, name: str) -> List[str]:
- return []
-
- def run_script(self, script_name: str, namespace: str) -> None:
- pass
-
-
-class Distribution(BaseDistribution):
- def __init__(self, dist: pkg_resources.Distribution) -> None:
- self._dist = dist
-
- @classmethod
- def from_directory(cls, directory: str) -> "Distribution":
- dist_dir = directory.rstrip(os.sep)
-
- # Build a PathMetadata object, from path to metadata. :wink:
- base_dir, dist_dir_name = os.path.split(dist_dir)
- metadata = pkg_resources.PathMetadata(base_dir, dist_dir)
-
- # Determine the correct Distribution object type.
- if dist_dir.endswith(".egg-info"):
- dist_cls = pkg_resources.Distribution
- dist_name = os.path.splitext(dist_dir_name)[0]
- else:
- assert dist_dir.endswith(".dist-info")
- dist_cls = pkg_resources.DistInfoDistribution
- dist_name = os.path.splitext(dist_dir_name)[0].split("-")[0]
-
- dist = dist_cls(base_dir, project_name=dist_name, metadata=metadata)
- return cls(dist)
-
- @classmethod
- def from_wheel(cls, wheel: Wheel, name: str) -> "Distribution":
- """Load the distribution from a given wheel.
-
- :raises InvalidWheel: Whenever loading of the wheel causes a
- :py:exc:`zipfile.BadZipFile` exception to be thrown.
- :raises UnsupportedWheel: If the wheel is a valid zip, but malformed
- internally.
- """
- try:
- with wheel.as_zipfile() as zf:
- info_dir, _ = parse_wheel(zf, name)
- metadata_text = {
- path.split("/", 1)[-1]: read_wheel_metadata_file(zf, path)
- for path in zf.namelist()
- if path.startswith(f"{info_dir}/")
- }
- except zipfile.BadZipFile as e:
- raise InvalidWheel(wheel.location, name) from e
- except UnsupportedWheel as e:
- raise UnsupportedWheel(f"{name} has an invalid wheel, {e}")
- dist = pkg_resources.DistInfoDistribution(
- location=wheel.location,
- metadata=WheelMetadata(metadata_text, wheel.location),
- project_name=name,
- )
- return cls(dist)
-
- @property
- def location(self) -> Optional[str]:
- return self._dist.location
-
- @property
- def info_location(self) -> Optional[str]:
- return self._dist.egg_info
-
- @property
- def installed_by_distutils(self) -> bool:
- # A distutils-installed distribution is provided by FileMetadata. This
- # provider has a "path" attribute not present anywhere else. Not the
- # best introspection logic, but pip has been doing this for a long time.
- try:
- return bool(self._dist._provider.path)
- except AttributeError:
- return False
-
- @property
- def canonical_name(self) -> NormalizedName:
- return canonicalize_name(self._dist.project_name)
-
- @property
- def version(self) -> DistributionVersion:
- return parse_version(self._dist.version)
-
- def is_file(self, path: InfoPath) -> bool:
- return self._dist.has_metadata(str(path))
-
- def iterdir(self, path: InfoPath) -> Iterator[pathlib.PurePosixPath]:
- name = str(path)
- if not self._dist.has_metadata(name):
- raise FileNotFoundError(name)
- if not self._dist.isdir(name):
- raise NotADirectoryError(name)
- for child in self._dist.metadata_listdir(name):
- yield pathlib.PurePosixPath(path, child)
-
- def read_text(self, path: InfoPath) -> str:
- name = str(path)
- if not self._dist.has_metadata(name):
- raise FileNotFoundError(name)
- content = self._dist.get_metadata(name)
- if content is None:
- raise NoneMetadataError(self, name)
- return content
-
- def iter_entry_points(self) -> Iterable[BaseEntryPoint]:
- for group, entries in self._dist.get_entry_map().items():
- for name, entry_point in entries.items():
- name, _, value = str(entry_point).partition("=")
- yield EntryPoint(name=name.strip(), value=value.strip(), group=group)
-
- @property
- def metadata(self) -> email.message.Message:
- """
- :raises NoneMetadataError: if the distribution reports `has_metadata()`
- True but `get_metadata()` returns None.
- """
- if isinstance(self._dist, pkg_resources.DistInfoDistribution):
- metadata_name = "METADATA"
- else:
- metadata_name = "PKG-INFO"
- try:
- metadata = self.read_text(metadata_name)
- except FileNotFoundError:
- if self.location:
- displaying_path = display_path(self.location)
- else:
- displaying_path = repr(self.location)
- logger.warning("No metadata found in %s", displaying_path)
- metadata = ""
- feed_parser = email.parser.FeedParser()
- feed_parser.feed(metadata)
- return feed_parser.close()
-
- def iter_dependencies(self, extras: Collection[str] = ()) -> Iterable[Requirement]:
- if extras: # pkg_resources raises on invalid extras, so we sanitize.
- extras = frozenset(extras).intersection(self._dist.extras)
- return self._dist.requires(extras)
-
- def iter_provided_extras(self) -> Iterable[str]:
- return self._dist.extras
-
-
-class Environment(BaseEnvironment):
- def __init__(self, ws: pkg_resources.WorkingSet) -> None:
- self._ws = ws
-
- @classmethod
- def default(cls) -> BaseEnvironment:
- return cls(pkg_resources.working_set)
-
- @classmethod
- def from_paths(cls, paths: Optional[List[str]]) -> BaseEnvironment:
- return cls(pkg_resources.WorkingSet(paths))
-
- def _search_distribution(self, name: str) -> Optional[BaseDistribution]:
- """Find a distribution matching the ``name`` in the environment.
-
- This searches from *all* distributions available in the environment, to
- match the behavior of ``pkg_resources.get_distribution()``.
- """
- canonical_name = canonicalize_name(name)
- for dist in self.iter_distributions():
- if dist.canonical_name == canonical_name:
- return dist
- return None
-
- def get_distribution(self, name: str) -> Optional[BaseDistribution]:
- # Search the distribution by looking through the working set.
- dist = self._search_distribution(name)
- if dist:
- return dist
-
- # If distribution could not be found, call working_set.require to
- # update the working set, and try to find the distribution again.
- # This might happen for e.g. when you install a package twice, once
- # using setup.py develop and again using setup.py install. Now when
- # running pip uninstall twice, the package gets removed from the
- # working set in the first uninstall, so we have to populate the
- # working set again so that pip knows about it and the packages gets
- # picked up and is successfully uninstalled the second time too.
- try:
- # We didn't pass in any version specifiers, so this can never
- # raise pkg_resources.VersionConflict.
- self._ws.require(name)
- except pkg_resources.DistributionNotFound:
- return None
- return self._search_distribution(name)
-
- def _iter_distributions(self) -> Iterator[BaseDistribution]:
- for dist in self._ws:
- yield Distribution(dist)
diff --git a/spaces/allknowingroger/Image-Models-Test165/README.md b/spaces/allknowingroger/Image-Models-Test165/README.md
deleted file mode 100644
index f91e4b31ab345f987b425de029c057bfb69d9e1b..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test165/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/allknowingroger/huggingface/assets/index-d15ae4ce.js b/spaces/allknowingroger/huggingface/assets/index-d15ae4ce.js
deleted file mode 100644
index 20ce5614269312627323032880fbe8d62916a8d1..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/huggingface/assets/index-d15ae4ce.js
+++ /dev/null
@@ -1,41 +0,0 @@
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- * @license React
- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
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diff --git a/spaces/amin2809/rvc-models2023/app.py b/spaces/amin2809/rvc-models2023/app.py
deleted file mode 100644
index 3eea1979c8f7338d48722ad8ef74271a77fd86b2..0000000000000000000000000000000000000000
--- a/spaces/amin2809/rvc-models2023/app.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import os
-import json
-import argparse
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-from datetime import datetime
-from fairseq import checkpoint_utils
-from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
-from vc_infer_pipeline import VC
-from config import (
- is_half,
- device
-)
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces
-
-def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
- def vc_fn(
- input_audio,
- f0_up_key,
- f0_method,
- index_rate,
- tts_mode,
- tts_text,
- tts_voice
- ):
- try:
- if tts_mode:
- if len(tts_text) > 100 and limitation:
- return "Text is too long", None
- if tts_text is None or tts_voice is None:
- return "You need to enter text and select a voice", None
- asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
- audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
- else:
- if args.files:
- audio, sr = librosa.load(input_audio, sr=16000, mono=True)
- else:
- if input_audio is None:
- return "You need to upload an audio", None
- sampling_rate, audio = input_audio
- duration = audio.shape[0] / sampling_rate
- if duration > 60 and limitation:
- return "Please upload an audio file that is less than 2000 seconds. If you need to generate a longer audio file, please use Colab.", None
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- times = [0, 0, 0]
- f0_up_key = int(f0_up_key)
- audio_opt = vc.pipeline(
- hubert_model,
- net_g,
- 0,
- audio,
- times,
- f0_up_key,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
- if_f0,
- )
- print(
- f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
- )
- return "Success", (tgt_sr, audio_opt)
- except:
- info = traceback.format_exc()
- print(info)
- return info, (None, None)
- return vc_fn
-
-def load_hubert():
- global hubert_model
- models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
- ["hubert_base.pt"],
- suffix="",
- )
- hubert_model = models[0]
- hubert_model = hubert_model.to(device)
- if is_half:
- hubert_model = hubert_model.half()
- else:
- hubert_model = hubert_model.float()
- hubert_model.eval()
-
-def change_to_tts_mode(tts_mode):
- if tts_mode:
- return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
- else:
- return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--api', action="store_true", default=False)
- parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
- parser.add_argument("--files", action="store_true", default=False, help="load audio from path")
- args, unknown = parser.parse_known_args()
- load_hubert()
- models = []
- tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
- voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
- with open("weights/model_info.json", "r", encoding="utf-8") as f:
- models_info = json.load(f)
- for name, info in models_info.items():
- if not info['enable']:
- continue
- title = info['title']
- author = info.get("author", None)
- cover = f"weights/{name}/{info['cover']}"
- index = f"weights/{name}/{info['feature_retrieval_library']}"
- npy = f"weights/{name}/{info['feature_file']}"
- cpt = torch.load(f"weights/{name}/{name}.pth", map_location="cpu")
- tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- if_f0 = cpt.get("f0", 1)
- if if_f0 == 1:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
- else:
- net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- del net_g.enc_q
- print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净, 真奇葩
- net_g.eval().to(device)
- if is_half:
- net_g = net_g.half()
- else:
- net_g = net_g.float()
- vc = VC(tgt_sr, device, is_half)
- models.append((name, title, author, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index, npy)))
- with gr.Blocks() as app:
- gr.Markdown(
- "# <center> RVC Models\n"
- "## <center> The input audio should be clean and pure voice without background music.\n"
- "\n\n"
- "[](https://colab.research.google.com/drive/12rbZk9CoXD1m84dqBW5IKMBjiVY6tcoj?usp=share_link)\n\n"
- "[](https://huggingface.co/spaces/ardha27pi/rvc-models?duplicate=true)\n\n"
- "[](https://github.com/ardha27/AI-Song-Cover-RVC)\n\n"
- "[](https://ko-fi.com/R6R7AH1FA)\n\n"
- )
- with gr.Tabs():
- for (name, title, author, cover, vc_fn) in models:
- with gr.TabItem(name):
- with gr.Row():
- gr.Markdown(
- '<div align="center">'
- f'<div>{title}</div>\n'+
- (f'<div>Model author: {author}</div>' if author else "")+
- (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
- '</div>'
- )
- with gr.Row():
- with gr.Column():
- if args.files:
- vc_input = gr.Textbox(label="Input audio path")
- else:
- vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
- vc_transpose = gr.Number(label="Transpose", value=0)
- vc_f0method = gr.Radio(
- label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies",
- choices=["pm", "harvest"],
- value="pm",
- interactive=True,
- )
- vc_index_ratio = gr.Slider(
- minimum=0,
- maximum=1,
- label="Retrieval feature ratio",
- value=0.6,
- interactive=True,
- )
- tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
- tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
- tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
- vc_submit = gr.Button("Generate", variant="primary")
- with gr.Column():
- vc_output1 = gr.Textbox(label="Output Message")
- vc_output2 = gr.Audio(label="Output Audio")
- vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2])
- tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
- app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.share)
\ No newline at end of file
diff --git a/spaces/amin2809/rvc-models2023/infer_pack/models_onnx.py b/spaces/amin2809/rvc-models2023/infer_pack/models_onnx.py
deleted file mode 100644
index 3cdae2f7f8591a1e43b1d8520baa37b7e9744d72..0000000000000000000000000000000000000000
--- a/spaces/amin2809/rvc-models2023/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,849 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(256, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(256, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- x = self.proj(x) * x_mask
- return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(
- self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0,
- ):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(
- modules.ResidualCouplingLayer(
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- mean_only=True,
- )
- )
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
- def remove_weight_norm(self):
- for i in range(self.n_flows):
- self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=0,
- ):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(resblock_kernel_sizes, resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
- """Definition of sine generator
- SineGen(samp_rate, harmonic_num = 0,
- sine_amp = 0.1, noise_std = 0.003,
- voiced_threshold = 0,
- flag_for_pulse=False)
- samp_rate: sampling rate in Hz
- harmonic_num: number of harmonic overtones (default 0)
- sine_amp: amplitude of sine-wavefrom (default 0.1)
- noise_std: std of Gaussian noise (default 0.003)
- voiced_thoreshold: F0 threshold for U/V classification (default 0)
- flag_for_pulse: this SinGen is used inside PulseGen (default False)
- Note: when flag_for_pulse is True, the first time step of a voiced
- segment is always sin(np.pi) or cos(0)
- """
-
- def __init__(
- self,
- samp_rate,
- harmonic_num=0,
- sine_amp=0.1,
- noise_std=0.003,
- voiced_threshold=0,
- flag_for_pulse=False,
- ):
- super(SineGen, self).__init__()
- self.sine_amp = sine_amp
- self.noise_std = noise_std
- self.harmonic_num = harmonic_num
- self.dim = self.harmonic_num + 1
- self.sampling_rate = samp_rate
- self.voiced_threshold = voiced_threshold
-
- def _f02uv(self, f0):
- # generate uv signal
- uv = torch.ones_like(f0)
- uv = uv * (f0 > self.voiced_threshold)
- return uv
-
- def forward(self, f0, upp):
- """sine_tensor, uv = forward(f0)
- input F0: tensor(batchsize=1, length, dim=1)
- f0 for unvoiced steps should be 0
- output sine_tensor: tensor(batchsize=1, length, dim)
- output uv: tensor(batchsize=1, length, 1)
- """
- with torch.no_grad():
- f0 = f0[:, None].transpose(1, 2)
- f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
- # fundamental component
- f0_buf[:, :, 0] = f0[:, :, 0]
- for idx in np.arange(self.harmonic_num):
- f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
- idx + 2
- ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
- rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
- rand_ini = torch.rand(
- f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
- )
- rand_ini[:, 0] = 0
- rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
- tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
- tmp_over_one *= upp
- tmp_over_one = F.interpolate(
- tmp_over_one.transpose(2, 1),
- scale_factor=upp,
- mode="linear",
- align_corners=True,
- ).transpose(2, 1)
- rad_values = F.interpolate(
- rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(
- 2, 1
- ) #######
- tmp_over_one %= 1
- tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
- cumsum_shift = torch.zeros_like(rad_values)
- cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
- sine_waves = torch.sin(
- torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
- )
- sine_waves = sine_waves * self.sine_amp
- uv = self._f02uv(f0)
- uv = F.interpolate(
- uv.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(2, 1)
- noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
- noise = noise_amp * torch.randn_like(sine_waves)
- sine_waves = sine_waves * uv + noise
- return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
- """SourceModule for hn-nsf
- SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
- add_noise_std=0.003, voiced_threshod=0)
- sampling_rate: sampling_rate in Hz
- harmonic_num: number of harmonic above F0 (default: 0)
- sine_amp: amplitude of sine source signal (default: 0.1)
- add_noise_std: std of additive Gaussian noise (default: 0.003)
- note that amplitude of noise in unvoiced is decided
- by sine_amp
- voiced_threshold: threhold to set U/V given F0 (default: 0)
- Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
- F0_sampled (batchsize, length, 1)
- Sine_source (batchsize, length, 1)
- noise_source (batchsize, length 1)
- uv (batchsize, length, 1)
- """
-
- def __init__(
- self,
- sampling_rate,
- harmonic_num=0,
- sine_amp=0.1,
- add_noise_std=0.003,
- voiced_threshod=0,
- is_half=True,
- ):
- super(SourceModuleHnNSF, self).__init__()
-
- self.sine_amp = sine_amp
- self.noise_std = add_noise_std
- self.is_half = is_half
- # to produce sine waveforms
- self.l_sin_gen = SineGen(
- sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
- )
-
- # to merge source harmonics into a single excitation
- self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
- self.l_tanh = torch.nn.Tanh()
-
- def forward(self, x, upp=None):
- sine_wavs, uv, _ = self.l_sin_gen(x, upp)
- if self.is_half:
- sine_wavs = sine_wavs.half()
- sine_merge = self.l_tanh(self.l_linear(sine_wavs))
- return sine_merge, None, None # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- sr,
- is_half=False,
- ):
- super(GeneratorNSF, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
-
- self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
- self.m_source = SourceModuleHnNSF(
- sampling_rate=sr, harmonic_num=0, is_half=is_half
- )
- self.noise_convs = nn.ModuleList()
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- c_cur = upsample_initial_channel // (2 ** (i + 1))
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
- if i + 1 < len(upsample_rates):
- stride_f0 = np.prod(upsample_rates[i + 1 :])
- self.noise_convs.append(
- Conv1d(
- 1,
- c_cur,
- kernel_size=stride_f0 * 2,
- stride=stride_f0,
- padding=stride_f0 // 2,
- )
- )
- else:
- self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(resblock_kernel_sizes, resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- self.upp = np.prod(upsample_rates)
-
- def forward(self, x, f0, g=None):
- har_source, noi_source, uv = self.m_source(f0, self.upp)
- har_source = har_source.transpose(1, 2)
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- x_source = self.noise_convs[i](har_source)
- x = x + x_source
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-sr2sr = {
- "32k": 32000,
- "40k": 40000,
- "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr,
- **kwargs
- ):
- super().__init__()
- if type(sr) == type("strr"):
- sr = sr2sr[sr]
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- sr=sr,
- is_half=kwargs["is_half"],
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
- return o
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- # hop_length,
- gin_channels=0,
- use_sdp=True,
- **kwargs
- ):
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256Sim(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- is_half=kwargs["is_half"],
- )
-
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(
- self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
- ): # y是spec不需要了现在
- g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- x, x_mask = self.enc_p(phone, pitch, phone_lengths)
- x = self.flow(x, x_mask, g=g, reverse=True)
- o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
- return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2, 3, 5, 7, 11, 17]
- # periods = [3, 5, 7, 11, 17, 23, 37]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [
- DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
- ]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = [] #
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- # for j in range(len(fmap_r)):
- # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ]
- )
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(
- Conv2d(
- 1,
- 32,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 32,
- 128,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 128,
- 512,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 512,
- 1024,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 1024,
- 1024,
- (kernel_size, 1),
- 1,
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- ]
- )
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
diff --git a/spaces/aodianyun/panoptic-segment-anything/segment_anything/segment_anything/utils/onnx.py b/spaces/aodianyun/panoptic-segment-anything/segment_anything/segment_anything/utils/onnx.py
deleted file mode 100644
index 4297b31291e036700d6ad0b818afb7dd72da3054..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/panoptic-segment-anything/segment_anything/segment_anything/utils/onnx.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-
-from typing import Tuple
-
-from ..modeling import Sam
-from .amg import calculate_stability_score
-
-
-class SamOnnxModel(nn.Module):
- """
- This model should not be called directly, but is used in ONNX export.
- It combines the prompt encoder, mask decoder, and mask postprocessing of Sam,
- with some functions modified to enable model tracing. Also supports extra
- options controlling what information. See the ONNX export script for details.
- """
-
- def __init__(
- self,
- model: Sam,
- return_single_mask: bool,
- use_stability_score: bool = False,
- return_extra_metrics: bool = False,
- ) -> None:
- super().__init__()
- self.mask_decoder = model.mask_decoder
- self.model = model
- self.img_size = model.image_encoder.img_size
- self.return_single_mask = return_single_mask
- self.use_stability_score = use_stability_score
- self.stability_score_offset = 1.0
- self.return_extra_metrics = return_extra_metrics
-
- @staticmethod
- def resize_longest_image_size(
- input_image_size: torch.Tensor, longest_side: int
- ) -> torch.Tensor:
- input_image_size = input_image_size.to(torch.float32)
- scale = longest_side / torch.max(input_image_size)
- transformed_size = scale * input_image_size
- transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)
- return transformed_size
-
- def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:
- point_coords = point_coords + 0.5
- point_coords = point_coords / self.img_size
- point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)
- point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)
-
- point_embedding = point_embedding * (point_labels != -1)
- point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (
- point_labels == -1
- )
-
- for i in range(self.model.prompt_encoder.num_point_embeddings):
- point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[
- i
- ].weight * (point_labels == i)
-
- return point_embedding
-
- def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor:
- mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask)
- mask_embedding = mask_embedding + (
- 1 - has_mask_input
- ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)
- return mask_embedding
-
- def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor:
- masks = F.interpolate(
- masks,
- size=(self.img_size, self.img_size),
- mode="bilinear",
- align_corners=False,
- )
-
- prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size)
- masks = masks[..., : int(prepadded_size[0]), : int(prepadded_size[1])]
-
- orig_im_size = orig_im_size.to(torch.int64)
- h, w = orig_im_size[0], orig_im_size[1]
- masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False)
- return masks
-
- def select_masks(
- self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- # Determine if we should return the multiclick mask or not from the number of points.
- # The reweighting is used to avoid control flow.
- score_reweight = torch.tensor(
- [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]
- ).to(iou_preds.device)
- score = iou_preds + (num_points - 2.5) * score_reweight
- best_idx = torch.argmax(score, dim=1)
- masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)
- iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)
-
- return masks, iou_preds
-
- @torch.no_grad()
- def forward(
- self,
- image_embeddings: torch.Tensor,
- point_coords: torch.Tensor,
- point_labels: torch.Tensor,
- mask_input: torch.Tensor,
- has_mask_input: torch.Tensor,
- orig_im_size: torch.Tensor,
- ):
- sparse_embedding = self._embed_points(point_coords, point_labels)
- dense_embedding = self._embed_masks(mask_input, has_mask_input)
-
- masks, scores = self.model.mask_decoder.predict_masks(
- image_embeddings=image_embeddings,
- image_pe=self.model.prompt_encoder.get_dense_pe(),
- sparse_prompt_embeddings=sparse_embedding,
- dense_prompt_embeddings=dense_embedding,
- )
-
- if self.use_stability_score:
- scores = calculate_stability_score(
- masks, self.model.mask_threshold, self.stability_score_offset
- )
-
- if self.return_single_mask:
- masks, scores = self.select_masks(masks, scores, point_coords.shape[1])
-
- upscaled_masks = self.mask_postprocessing(masks, orig_im_size)
-
- if self.return_extra_metrics:
- stability_scores = calculate_stability_score(
- upscaled_masks, self.model.mask_threshold, self.stability_score_offset
- )
- areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)
- return upscaled_masks, scores, stability_scores, areas, masks
-
- return upscaled_masks, scores, masks
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/dateutil/tz/win.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/dateutil/tz/win.py
deleted file mode 100644
index cde07ba792c40903f0c334839140173b39fd8124..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/dateutil/tz/win.py
+++ /dev/null
@@ -1,370 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module provides an interface to the native time zone data on Windows,
-including :py:class:`datetime.tzinfo` implementations.
-
-Attempting to import this module on a non-Windows platform will raise an
-:py:obj:`ImportError`.
-"""
-# This code was originally contributed by Jeffrey Harris.
-import datetime
-import struct
-
-from six.moves import winreg
-from six import text_type
-
-try:
- import ctypes
- from ctypes import wintypes
-except ValueError:
- # ValueError is raised on non-Windows systems for some horrible reason.
- raise ImportError("Running tzwin on non-Windows system")
-
-from ._common import tzrangebase
-
-__all__ = ["tzwin", "tzwinlocal", "tzres"]
-
-ONEWEEK = datetime.timedelta(7)
-
-TZKEYNAMENT = r"SOFTWARE\Microsoft\Windows NT\CurrentVersion\Time Zones"
-TZKEYNAME9X = r"SOFTWARE\Microsoft\Windows\CurrentVersion\Time Zones"
-TZLOCALKEYNAME = r"SYSTEM\CurrentControlSet\Control\TimeZoneInformation"
-
-
-def _settzkeyname():
- handle = winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE)
- try:
- winreg.OpenKey(handle, TZKEYNAMENT).Close()
- TZKEYNAME = TZKEYNAMENT
- except WindowsError:
- TZKEYNAME = TZKEYNAME9X
- handle.Close()
- return TZKEYNAME
-
-
-TZKEYNAME = _settzkeyname()
-
-
-class tzres(object):
- """
- Class for accessing ``tzres.dll``, which contains timezone name related
- resources.
-
- .. versionadded:: 2.5.0
- """
- p_wchar = ctypes.POINTER(wintypes.WCHAR) # Pointer to a wide char
-
- def __init__(self, tzres_loc='tzres.dll'):
- # Load the user32 DLL so we can load strings from tzres
- user32 = ctypes.WinDLL('user32')
-
- # Specify the LoadStringW function
- user32.LoadStringW.argtypes = (wintypes.HINSTANCE,
- wintypes.UINT,
- wintypes.LPWSTR,
- ctypes.c_int)
-
- self.LoadStringW = user32.LoadStringW
- self._tzres = ctypes.WinDLL(tzres_loc)
- self.tzres_loc = tzres_loc
-
- def load_name(self, offset):
- """
- Load a timezone name from a DLL offset (integer).
-
- >>> from dateutil.tzwin import tzres
- >>> tzr = tzres()
- >>> print(tzr.load_name(112))
- 'Eastern Standard Time'
-
- :param offset:
- A positive integer value referring to a string from the tzres dll.
-
- .. note::
-
- Offsets found in the registry are generally of the form
- ``@tzres.dll,-114``. The offset in this case is 114, not -114.
-
- """
- resource = self.p_wchar()
- lpBuffer = ctypes.cast(ctypes.byref(resource), wintypes.LPWSTR)
- nchar = self.LoadStringW(self._tzres._handle, offset, lpBuffer, 0)
- return resource[:nchar]
-
- def name_from_string(self, tzname_str):
- """
- Parse strings as returned from the Windows registry into the time zone
- name as defined in the registry.
-
- >>> from dateutil.tzwin import tzres
- >>> tzr = tzres()
- >>> print(tzr.name_from_string('@tzres.dll,-251'))
- 'Dateline Daylight Time'
- >>> print(tzr.name_from_string('Eastern Standard Time'))
- 'Eastern Standard Time'
-
- :param tzname_str:
- A timezone name string as returned from a Windows registry key.
-
- :return:
- Returns the localized timezone string from tzres.dll if the string
- is of the form `@tzres.dll,-offset`, else returns the input string.
- """
- if not tzname_str.startswith('@'):
- return tzname_str
-
- name_splt = tzname_str.split(',-')
- try:
- offset = int(name_splt[1])
- except:
- raise ValueError("Malformed timezone string.")
-
- return self.load_name(offset)
-
-
-class tzwinbase(tzrangebase):
- """tzinfo class based on win32's timezones available in the registry."""
- def __init__(self):
- raise NotImplementedError('tzwinbase is an abstract base class')
-
- def __eq__(self, other):
- # Compare on all relevant dimensions, including name.
- if not isinstance(other, tzwinbase):
- return NotImplemented
-
- return (self._std_offset == other._std_offset and
- self._dst_offset == other._dst_offset and
- self._stddayofweek == other._stddayofweek and
- self._dstdayofweek == other._dstdayofweek and
- self._stdweeknumber == other._stdweeknumber and
- self._dstweeknumber == other._dstweeknumber and
- self._stdhour == other._stdhour and
- self._dsthour == other._dsthour and
- self._stdminute == other._stdminute and
- self._dstminute == other._dstminute and
- self._std_abbr == other._std_abbr and
- self._dst_abbr == other._dst_abbr)
-
- @staticmethod
- def list():
- """Return a list of all time zones known to the system."""
- with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
- with winreg.OpenKey(handle, TZKEYNAME) as tzkey:
- result = [winreg.EnumKey(tzkey, i)
- for i in range(winreg.QueryInfoKey(tzkey)[0])]
- return result
-
- def display(self):
- """
- Return the display name of the time zone.
- """
- return self._display
-
- def transitions(self, year):
- """
- For a given year, get the DST on and off transition times, expressed
- always on the standard time side. For zones with no transitions, this
- function returns ``None``.
-
- :param year:
- The year whose transitions you would like to query.
-
- :return:
- Returns a :class:`tuple` of :class:`datetime.datetime` objects,
- ``(dston, dstoff)`` for zones with an annual DST transition, or
- ``None`` for fixed offset zones.
- """
-
- if not self.hasdst:
- return None
-
- dston = picknthweekday(year, self._dstmonth, self._dstdayofweek,
- self._dsthour, self._dstminute,
- self._dstweeknumber)
-
- dstoff = picknthweekday(year, self._stdmonth, self._stddayofweek,
- self._stdhour, self._stdminute,
- self._stdweeknumber)
-
- # Ambiguous dates default to the STD side
- dstoff -= self._dst_base_offset
-
- return dston, dstoff
-
- def _get_hasdst(self):
- return self._dstmonth != 0
-
- @property
- def _dst_base_offset(self):
- return self._dst_base_offset_
-
-
-class tzwin(tzwinbase):
- """
- Time zone object created from the zone info in the Windows registry
-
- These are similar to :py:class:`dateutil.tz.tzrange` objects in that
- the time zone data is provided in the format of a single offset rule
- for either 0 or 2 time zone transitions per year.
-
- :param: name
- The name of a Windows time zone key, e.g. "Eastern Standard Time".
- The full list of keys can be retrieved with :func:`tzwin.list`.
- """
-
- def __init__(self, name):
- self._name = name
-
- with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
- tzkeyname = text_type("{kn}\\{name}").format(kn=TZKEYNAME, name=name)
- with winreg.OpenKey(handle, tzkeyname) as tzkey:
- keydict = valuestodict(tzkey)
-
- self._std_abbr = keydict["Std"]
- self._dst_abbr = keydict["Dlt"]
-
- self._display = keydict["Display"]
-
- # See http://ww_winreg.jsiinc.com/SUBA/tip0300/rh0398.htm
- tup = struct.unpack("=3l16h", keydict["TZI"])
- stdoffset = -tup[0]-tup[1] # Bias + StandardBias * -1
- dstoffset = stdoffset-tup[2] # + DaylightBias * -1
- self._std_offset = datetime.timedelta(minutes=stdoffset)
- self._dst_offset = datetime.timedelta(minutes=dstoffset)
-
- # for the meaning see the win32 TIME_ZONE_INFORMATION structure docs
- # http://msdn.microsoft.com/en-us/library/windows/desktop/ms725481(v=vs.85).aspx
- (self._stdmonth,
- self._stddayofweek, # Sunday = 0
- self._stdweeknumber, # Last = 5
- self._stdhour,
- self._stdminute) = tup[4:9]
-
- (self._dstmonth,
- self._dstdayofweek, # Sunday = 0
- self._dstweeknumber, # Last = 5
- self._dsthour,
- self._dstminute) = tup[12:17]
-
- self._dst_base_offset_ = self._dst_offset - self._std_offset
- self.hasdst = self._get_hasdst()
-
- def __repr__(self):
- return "tzwin(%s)" % repr(self._name)
-
- def __reduce__(self):
- return (self.__class__, (self._name,))
-
-
-class tzwinlocal(tzwinbase):
- """
- Class representing the local time zone information in the Windows registry
-
- While :class:`dateutil.tz.tzlocal` makes system calls (via the :mod:`time`
- module) to retrieve time zone information, ``tzwinlocal`` retrieves the
- rules directly from the Windows registry and creates an object like
- :class:`dateutil.tz.tzwin`.
-
- Because Windows does not have an equivalent of :func:`time.tzset`, on
- Windows, :class:`dateutil.tz.tzlocal` instances will always reflect the
- time zone settings *at the time that the process was started*, meaning
- changes to the machine's time zone settings during the run of a program
- on Windows will **not** be reflected by :class:`dateutil.tz.tzlocal`.
- Because ``tzwinlocal`` reads the registry directly, it is unaffected by
- this issue.
- """
- def __init__(self):
- with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
- with winreg.OpenKey(handle, TZLOCALKEYNAME) as tzlocalkey:
- keydict = valuestodict(tzlocalkey)
-
- self._std_abbr = keydict["StandardName"]
- self._dst_abbr = keydict["DaylightName"]
-
- try:
- tzkeyname = text_type('{kn}\\{sn}').format(kn=TZKEYNAME,
- sn=self._std_abbr)
- with winreg.OpenKey(handle, tzkeyname) as tzkey:
- _keydict = valuestodict(tzkey)
- self._display = _keydict["Display"]
- except OSError:
- self._display = None
-
- stdoffset = -keydict["Bias"]-keydict["StandardBias"]
- dstoffset = stdoffset-keydict["DaylightBias"]
-
- self._std_offset = datetime.timedelta(minutes=stdoffset)
- self._dst_offset = datetime.timedelta(minutes=dstoffset)
-
- # For reasons unclear, in this particular key, the day of week has been
- # moved to the END of the SYSTEMTIME structure.
- tup = struct.unpack("=8h", keydict["StandardStart"])
-
- (self._stdmonth,
- self._stdweeknumber, # Last = 5
- self._stdhour,
- self._stdminute) = tup[1:5]
-
- self._stddayofweek = tup[7]
-
- tup = struct.unpack("=8h", keydict["DaylightStart"])
-
- (self._dstmonth,
- self._dstweeknumber, # Last = 5
- self._dsthour,
- self._dstminute) = tup[1:5]
-
- self._dstdayofweek = tup[7]
-
- self._dst_base_offset_ = self._dst_offset - self._std_offset
- self.hasdst = self._get_hasdst()
-
- def __repr__(self):
- return "tzwinlocal()"
-
- def __str__(self):
- # str will return the standard name, not the daylight name.
- return "tzwinlocal(%s)" % repr(self._std_abbr)
-
- def __reduce__(self):
- return (self.__class__, ())
-
-
-def picknthweekday(year, month, dayofweek, hour, minute, whichweek):
- """ dayofweek == 0 means Sunday, whichweek 5 means last instance """
- first = datetime.datetime(year, month, 1, hour, minute)
-
- # This will work if dayofweek is ISO weekday (1-7) or Microsoft-style (0-6),
- # Because 7 % 7 = 0
- weekdayone = first.replace(day=((dayofweek - first.isoweekday()) % 7) + 1)
- wd = weekdayone + ((whichweek - 1) * ONEWEEK)
- if (wd.month != month):
- wd -= ONEWEEK
-
- return wd
-
-
-def valuestodict(key):
- """Convert a registry key's values to a dictionary."""
- dout = {}
- size = winreg.QueryInfoKey(key)[1]
- tz_res = None
-
- for i in range(size):
- key_name, value, dtype = winreg.EnumValue(key, i)
- if dtype == winreg.REG_DWORD or dtype == winreg.REG_DWORD_LITTLE_ENDIAN:
- # If it's a DWORD (32-bit integer), it's stored as unsigned - convert
- # that to a proper signed integer
- if value & (1 << 31):
- value = value - (1 << 32)
- elif dtype == winreg.REG_SZ:
- # If it's a reference to the tzres DLL, load the actual string
- if value.startswith('@tzres'):
- tz_res = tz_res or tzres()
- value = tz_res.name_from_string(value)
-
- value = value.rstrip('\x00') # Remove trailing nulls
-
- dout[key_name] = value
-
- return dout
diff --git a/spaces/ashutosh1919/quantum-perceptron/gradio_app.py b/spaces/ashutosh1919/quantum-perceptron/gradio_app.py
deleted file mode 100644
index f82dcf8a2db9d44018d6cd41552faeae3a8f3d61..0000000000000000000000000000000000000000
--- a/spaces/ashutosh1919/quantum-perceptron/gradio_app.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import gradio as gr
-import matplotlib
-import matplotlib.pyplot as plt
-from quantum_perceptron import Perceptron
-
-matplotlib.pyplot.switch_backend('Agg')
-
-
-def run_perceptron(
- num_qubits: int,
- input_value: int,
- weight_value: int,
- num_iters: int
-):
- p = Perceptron(num_qubits, weight_value, input_value)
- counts = p.measure_circuit(num_iters)
- counts.setdefault('0', 0)
- counts.setdefault('1', 0)
- prob_1 = counts['1'] / num_iters
- freq_hist = plt.figure()
- plt.bar(counts.keys(), counts.values(), width=0.5)
- for i, v in enumerate(list(counts.values())):
- plt.text(i, v+10, v)
- plt.xlabel('Measured State')
- plt.ylabel('Frequency of Measured State')
- freq_hist.subplots_adjust(top=0.2)
- freq_hist.tight_layout()
- return prob_1, freq_hist
-
-
-app_inputs = [
- gr.Slider(1, 9, value=2, step=1, label="Number of Qubits"),
- gr.Number(value=12, label="Input Value", precision=0),
- gr.Number(value=13, label="Weight Value", precision=0),
- gr.Number(value=1000,
- label="Number of Measurement Iterations",
- precision=0),
-]
-
-app_outputs = [
- gr.Number(precision=2, label="Probability of Firing Perceptron"),
- gr.Plot(label="Distribution of Measurement Frequencies")
-]
-
-demo = gr.Interface(
- fn=run_perceptron,
- inputs=app_inputs,
- outputs=app_outputs,
- title="Simulate Quantum Perceptron",
-)
-demo.launch()
diff --git a/spaces/asigalov61/Allegro-Music-Transformer/TMIDIX.py b/spaces/asigalov61/Allegro-Music-Transformer/TMIDIX.py
deleted file mode 100644
index f023e673586b78b1fb8e337b11d48978343b2a9f..0000000000000000000000000000000000000000
--- a/spaces/asigalov61/Allegro-Music-Transformer/TMIDIX.py
+++ /dev/null
@@ -1,3202 +0,0 @@
-#! /usr/bin/python3
-
-
-r'''###############################################################################
-###################################################################################
-#
-#
-# Tegridy MIDI X Module (TMIDI X / tee-midi eks)
-# Version 1.0
-#
-# NOTE: TMIDI X Module starts after the partial MIDI.py module @ line 1342
-#
-# Based upon MIDI.py module v.6.7. by Peter Billam / pjb.com.au
-#
-# Project Los Angeles
-#
-# Tegridy Code 2021
-#
-# https://github.com/Tegridy-Code/Project-Los-Angeles
-#
-#
-###################################################################################
-###################################################################################
-# Copyright 2021 Project Los Angeles / Tegridy Code
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-###################################################################################
-###################################################################################
-#
-# PARTIAL MIDI.py Module v.6.7. by Peter Billam
-# Please see TMIDI 2.3/tegridy-tools repo for full MIDI.py module code
-#
-# Or you can always download the latest full version from:
-# https://pjb.com.au/
-#
-# Copyright 2020 Peter Billam
-#
-###################################################################################
-###################################################################################'''
-
-import sys, struct, copy
-Version = '6.7'
-VersionDate = '20201120'
-
-_previous_warning = '' # 5.4
-_previous_times = 0 # 5.4
-#------------------------------- Encoding stuff --------------------------
-
-def opus2midi(opus=[], text_encoding='ISO-8859-1'):
- r'''The argument is a list: the first item in the list is the "ticks"
-parameter, the others are the tracks. Each track is a list
-of midi-events, and each event is itself a list; see above.
-opus2midi() returns a bytestring of the MIDI, which can then be
-written either to a file opened in binary mode (mode='wb'),
-or to stdout by means of: sys.stdout.buffer.write()
-
-my_opus = [
- 96,
- [ # track 0:
- ['patch_change', 0, 1, 8], # and these are the events...
- ['note_on', 5, 1, 25, 96],
- ['note_off', 96, 1, 25, 0],
- ['note_on', 0, 1, 29, 96],
- ['note_off', 96, 1, 29, 0],
- ], # end of track 0
-]
-my_midi = opus2midi(my_opus)
-sys.stdout.buffer.write(my_midi)
-'''
- if len(opus) < 2:
- opus=[1000, [],]
- tracks = copy.deepcopy(opus)
- ticks = int(tracks.pop(0))
- ntracks = len(tracks)
- if ntracks == 1:
- format = 0
- else:
- format = 1
-
- my_midi = b"MThd\x00\x00\x00\x06"+struct.pack('>HHH',format,ntracks,ticks)
- for track in tracks:
- events = _encode(track, text_encoding=text_encoding)
- my_midi += b'MTrk' + struct.pack('>I',len(events)) + events
- _clean_up_warnings()
- return my_midi
-
-
-def score2opus(score=None, text_encoding='ISO-8859-1'):
- r'''
-The argument is a list: the first item in the list is the "ticks"
-parameter, the others are the tracks. Each track is a list
-of score-events, and each event is itself a list. A score-event
-is similar to an opus-event (see above), except that in a score:
- 1) the times are expressed as an absolute number of ticks
- from the track's start time
- 2) the pairs of 'note_on' and 'note_off' events in an "opus"
- are abstracted into a single 'note' event in a "score":
- ['note', start_time, duration, channel, pitch, velocity]
-score2opus() returns a list specifying the equivalent "opus".
-
-my_score = [
- 96,
- [ # track 0:
- ['patch_change', 0, 1, 8],
- ['note', 5, 96, 1, 25, 96],
- ['note', 101, 96, 1, 29, 96]
- ], # end of track 0
-]
-my_opus = score2opus(my_score)
-'''
- if len(score) < 2:
- score=[1000, [],]
- tracks = copy.deepcopy(score)
- ticks = int(tracks.pop(0))
- opus_tracks = []
- for scoretrack in tracks:
- time2events = dict([])
- for scoreevent in scoretrack:
- if scoreevent[0] == 'note':
- note_on_event = ['note_on',scoreevent[1],
- scoreevent[3],scoreevent[4],scoreevent[5]]
- note_off_event = ['note_off',scoreevent[1]+scoreevent[2],
- scoreevent[3],scoreevent[4],scoreevent[5]]
- if time2events.get(note_on_event[1]):
- time2events[note_on_event[1]].append(note_on_event)
- else:
- time2events[note_on_event[1]] = [note_on_event,]
- if time2events.get(note_off_event[1]):
- time2events[note_off_event[1]].append(note_off_event)
- else:
- time2events[note_off_event[1]] = [note_off_event,]
- continue
- if time2events.get(scoreevent[1]):
- time2events[scoreevent[1]].append(scoreevent)
- else:
- time2events[scoreevent[1]] = [scoreevent,]
-
- sorted_times = [] # list of keys
- for k in time2events.keys():
- sorted_times.append(k)
- sorted_times.sort()
-
- sorted_events = [] # once-flattened list of values sorted by key
- for time in sorted_times:
- sorted_events.extend(time2events[time])
-
- abs_time = 0
- for event in sorted_events: # convert abs times => delta times
- delta_time = event[1] - abs_time
- abs_time = event[1]
- event[1] = delta_time
- opus_tracks.append(sorted_events)
- opus_tracks.insert(0,ticks)
- _clean_up_warnings()
- return opus_tracks
-
-def score2midi(score=None, text_encoding='ISO-8859-1'):
- r'''
-Translates a "score" into MIDI, using score2opus() then opus2midi()
-'''
- return opus2midi(score2opus(score, text_encoding), text_encoding)
-
-#--------------------------- Decoding stuff ------------------------
-
-def midi2opus(midi=b''):
- r'''Translates MIDI into a "opus". For a description of the
-"opus" format, see opus2midi()
-'''
- my_midi=bytearray(midi)
- if len(my_midi) < 4:
- _clean_up_warnings()
- return [1000,[],]
- id = bytes(my_midi[0:4])
- if id != b'MThd':
- _warn("midi2opus: midi starts with "+str(id)+" instead of 'MThd'")
- _clean_up_warnings()
- return [1000,[],]
- [length, format, tracks_expected, ticks] = struct.unpack(
- '>IHHH', bytes(my_midi[4:14]))
- if length != 6:
- _warn("midi2opus: midi header length was "+str(length)+" instead of 6")
- _clean_up_warnings()
- return [1000,[],]
- my_opus = [ticks,]
- my_midi = my_midi[14:]
- track_num = 1 # 5.1
- while len(my_midi) >= 8:
- track_type = bytes(my_midi[0:4])
- if track_type != b'MTrk':
- #_warn('midi2opus: Warning: track #'+str(track_num)+' type is '+str(track_type)+" instead of b'MTrk'")
- pass
- [track_length] = struct.unpack('>I', my_midi[4:8])
- my_midi = my_midi[8:]
- if track_length > len(my_midi):
- _warn('midi2opus: track #'+str(track_num)+' length '+str(track_length)+' is too large')
- _clean_up_warnings()
- return my_opus # 5.0
- my_midi_track = my_midi[0:track_length]
- my_track = _decode(my_midi_track)
- my_opus.append(my_track)
- my_midi = my_midi[track_length:]
- track_num += 1 # 5.1
- _clean_up_warnings()
- return my_opus
-
-def opus2score(opus=[]):
- r'''For a description of the "opus" and "score" formats,
-see opus2midi() and score2opus().
-'''
- if len(opus) < 2:
- _clean_up_warnings()
- return [1000,[],]
- tracks = copy.deepcopy(opus) # couple of slices probably quicker...
- ticks = int(tracks.pop(0))
- score = [ticks,]
- for opus_track in tracks:
- ticks_so_far = 0
- score_track = []
- chapitch2note_on_events = dict([]) # 4.0
- for opus_event in opus_track:
- ticks_so_far += opus_event[1]
- if opus_event[0] == 'note_off' or (opus_event[0] == 'note_on' and opus_event[4] == 0): # 4.8
- cha = opus_event[2]
- pitch = opus_event[3]
- key = cha*128 + pitch
- if chapitch2note_on_events.get(key):
- new_event = chapitch2note_on_events[key].pop(0)
- new_event[2] = ticks_so_far - new_event[1]
- score_track.append(new_event)
- elif pitch > 127:
- pass #_warn('opus2score: note_off with no note_on, bad pitch='+str(pitch))
- else:
- pass #_warn('opus2score: note_off with no note_on cha='+str(cha)+' pitch='+str(pitch))
- elif opus_event[0] == 'note_on':
- cha = opus_event[2]
- pitch = opus_event[3]
- key = cha*128 + pitch
- new_event = ['note',ticks_so_far,0,cha,pitch, opus_event[4]]
- if chapitch2note_on_events.get(key):
- chapitch2note_on_events[key].append(new_event)
- else:
- chapitch2note_on_events[key] = [new_event,]
- else:
- opus_event[1] = ticks_so_far
- score_track.append(opus_event)
- # check for unterminated notes (Oisín) -- 5.2
- for chapitch in chapitch2note_on_events:
- note_on_events = chapitch2note_on_events[chapitch]
- for new_e in note_on_events:
- new_e[2] = ticks_so_far - new_e[1]
- score_track.append(new_e)
- pass #_warn("opus2score: note_on with no note_off cha="+str(new_e[3])+' pitch='+str(new_e[4])+'; adding note_off at end')
- score.append(score_track)
- _clean_up_warnings()
- return score
-
-def midi2score(midi=b''):
- r'''
-Translates MIDI into a "score", using midi2opus() then opus2score()
-'''
- return opus2score(midi2opus(midi))
-
-def midi2ms_score(midi=b''):
- r'''
-Translates MIDI into a "score" with one beat per second and one
-tick per millisecond, using midi2opus() then to_millisecs()
-then opus2score()
-'''
- return opus2score(to_millisecs(midi2opus(midi)))
-
-#------------------------ Other Transformations ---------------------
-
-def to_millisecs(old_opus=None, desired_time_in_ms=1):
- r'''Recallibrates all the times in an "opus" to use one beat
-per second and one tick per millisecond. This makes it
-hard to retrieve any information about beats or barlines,
-but it does make it easy to mix different scores together.
-'''
- if old_opus == None:
- return [1000 * desired_time_in_ms,[],]
- try:
- old_tpq = int(old_opus[0])
- except IndexError: # 5.0
- _warn('to_millisecs: the opus '+str(type(old_opus))+' has no elements')
- return [1000 * desired_time_in_ms,[],]
- new_opus = [1000 * desired_time_in_ms,]
- # 6.7 first go through building a table of set_tempos by absolute-tick
- ticks2tempo = {}
- itrack = 1
- while itrack < len(old_opus):
- ticks_so_far = 0
- for old_event in old_opus[itrack]:
- if old_event[0] == 'note':
- raise TypeError('to_millisecs needs an opus, not a score')
- ticks_so_far += old_event[1]
- if old_event[0] == 'set_tempo':
- ticks2tempo[ticks_so_far] = old_event[2]
- itrack += 1
- # then get the sorted-array of their keys
- tempo_ticks = [] # list of keys
- for k in ticks2tempo.keys():
- tempo_ticks.append(k)
- tempo_ticks.sort()
- # then go through converting to millisec, testing if the next
- # set_tempo lies before the next track-event, and using it if so.
- itrack = 1
- while itrack < len(old_opus):
- ms_per_old_tick = 400 / old_tpq # float: will round later 6.3
- i_tempo_ticks = 0
- ticks_so_far = 0
- ms_so_far = 0.0
- previous_ms_so_far = 0.0
- new_track = [['set_tempo',0,1000000 * desired_time_in_ms],] # new "crochet" is 1 sec
- for old_event in old_opus[itrack]:
- # detect if ticks2tempo has something before this event
- # 20160702 if ticks2tempo is at the same time, leave it
- event_delta_ticks = old_event[1] * desired_time_in_ms
- if (i_tempo_ticks < len(tempo_ticks) and
- tempo_ticks[i_tempo_ticks] < (ticks_so_far + old_event[1]) * desired_time_in_ms):
- delta_ticks = tempo_ticks[i_tempo_ticks] - ticks_so_far
- ms_so_far += (ms_per_old_tick * delta_ticks * desired_time_in_ms)
- ticks_so_far = tempo_ticks[i_tempo_ticks]
- ms_per_old_tick = ticks2tempo[ticks_so_far] / (1000.0*old_tpq * desired_time_in_ms)
- i_tempo_ticks += 1
- event_delta_ticks -= delta_ticks
- new_event = copy.deepcopy(old_event) # now handle the new event
- ms_so_far += (ms_per_old_tick * old_event[1] * desired_time_in_ms)
- new_event[1] = round(ms_so_far - previous_ms_so_far)
- if old_event[0] != 'set_tempo':
- previous_ms_so_far = ms_so_far
- new_track.append(new_event)
- ticks_so_far += event_delta_ticks
- new_opus.append(new_track)
- itrack += 1
- _clean_up_warnings()
- return new_opus
-
-def event2alsaseq(event=None): # 5.5
- r'''Converts an event into the format needed by the alsaseq module,
-http://pp.com.mx/python/alsaseq
-The type of track (opus or score) is autodetected.
-'''
- pass
-
-def grep(score=None, channels=None):
- r'''Returns a "score" containing only the channels specified
-'''
- if score == None:
- return [1000,[],]
- ticks = score[0]
- new_score = [ticks,]
- if channels == None:
- return new_score
- channels = set(channels)
- global Event2channelindex
- itrack = 1
- while itrack < len(score):
- new_score.append([])
- for event in score[itrack]:
- channel_index = Event2channelindex.get(event[0], False)
- if channel_index:
- if event[channel_index] in channels:
- new_score[itrack].append(event)
- else:
- new_score[itrack].append(event)
- itrack += 1
- return new_score
-
-def play_score(score=None):
- r'''Converts the "score" to midi, and feeds it into 'aplaymidi -'
-'''
- if score == None:
- return
- import subprocess
- pipe = subprocess.Popen(['aplaymidi','-'], stdin=subprocess.PIPE)
- if score_type(score) == 'opus':
- pipe.stdin.write(opus2midi(score))
- else:
- pipe.stdin.write(score2midi(score))
- pipe.stdin.close()
-
-def score2stats(opus_or_score=None):
- r'''Returns a dict of some basic stats about the score, like
-bank_select (list of tuples (msb,lsb)),
-channels_by_track (list of lists), channels_total (set),
-general_midi_mode (list),
-ntracks, nticks, patch_changes_by_track (list of dicts),
-num_notes_by_channel (list of numbers),
-patch_changes_total (set),
-percussion (dict histogram of channel 9 events),
-pitches (dict histogram of pitches on channels other than 9),
-pitch_range_by_track (list, by track, of two-member-tuples),
-pitch_range_sum (sum over tracks of the pitch_ranges),
-'''
- bank_select_msb = -1
- bank_select_lsb = -1
- bank_select = []
- channels_by_track = []
- channels_total = set([])
- general_midi_mode = []
- num_notes_by_channel = dict([])
- patches_used_by_track = []
- patches_used_total = set([])
- patch_changes_by_track = []
- patch_changes_total = set([])
- percussion = dict([]) # histogram of channel 9 "pitches"
- pitches = dict([]) # histogram of pitch-occurrences channels 0-8,10-15
- pitch_range_sum = 0 # u pitch-ranges of each track
- pitch_range_by_track = []
- is_a_score = True
- if opus_or_score == None:
- return {'bank_select':[], 'channels_by_track':[], 'channels_total':[],
- 'general_midi_mode':[], 'ntracks':0, 'nticks':0,
- 'num_notes_by_channel':dict([]),
- 'patch_changes_by_track':[], 'patch_changes_total':[],
- 'percussion':{}, 'pitches':{}, 'pitch_range_by_track':[],
- 'ticks_per_quarter':0, 'pitch_range_sum':0}
- ticks_per_quarter = opus_or_score[0]
- i = 1 # ignore first element, which is ticks
- nticks = 0
- while i < len(opus_or_score):
- highest_pitch = 0
- lowest_pitch = 128
- channels_this_track = set([])
- patch_changes_this_track = dict({})
- for event in opus_or_score[i]:
- if event[0] == 'note':
- num_notes_by_channel[event[3]] = num_notes_by_channel.get(event[3],0) + 1
- if event[3] == 9:
- percussion[event[4]] = percussion.get(event[4],0) + 1
- else:
- pitches[event[4]] = pitches.get(event[4],0) + 1
- if event[4] > highest_pitch:
- highest_pitch = event[4]
- if event[4] < lowest_pitch:
- lowest_pitch = event[4]
- channels_this_track.add(event[3])
- channels_total.add(event[3])
- finish_time = event[1] + event[2]
- if finish_time > nticks:
- nticks = finish_time
- elif event[0] == 'note_off' or (event[0] == 'note_on' and event[4] == 0): # 4.8
- finish_time = event[1]
- if finish_time > nticks:
- nticks = finish_time
- elif event[0] == 'note_on':
- is_a_score = False
- num_notes_by_channel[event[2]] = num_notes_by_channel.get(event[2],0) + 1
- if event[2] == 9:
- percussion[event[3]] = percussion.get(event[3],0) + 1
- else:
- pitches[event[3]] = pitches.get(event[3],0) + 1
- if event[3] > highest_pitch:
- highest_pitch = event[3]
- if event[3] < lowest_pitch:
- lowest_pitch = event[3]
- channels_this_track.add(event[2])
- channels_total.add(event[2])
- elif event[0] == 'patch_change':
- patch_changes_this_track[event[2]] = event[3]
- patch_changes_total.add(event[3])
- elif event[0] == 'control_change':
- if event[3] == 0: # bank select MSB
- bank_select_msb = event[4]
- elif event[3] == 32: # bank select LSB
- bank_select_lsb = event[4]
- if bank_select_msb >= 0 and bank_select_lsb >= 0:
- bank_select.append((bank_select_msb,bank_select_lsb))
- bank_select_msb = -1
- bank_select_lsb = -1
- elif event[0] == 'sysex_f0':
- if _sysex2midimode.get(event[2], -1) >= 0:
- general_midi_mode.append(_sysex2midimode.get(event[2]))
- if is_a_score:
- if event[1] > nticks:
- nticks = event[1]
- else:
- nticks += event[1]
- if lowest_pitch == 128:
- lowest_pitch = 0
- channels_by_track.append(channels_this_track)
- patch_changes_by_track.append(patch_changes_this_track)
- pitch_range_by_track.append((lowest_pitch,highest_pitch))
- pitch_range_sum += (highest_pitch-lowest_pitch)
- i += 1
-
- return {'bank_select':bank_select,
- 'channels_by_track':channels_by_track,
- 'channels_total':channels_total,
- 'general_midi_mode':general_midi_mode,
- 'ntracks':len(opus_or_score)-1,
- 'nticks':nticks,
- 'num_notes_by_channel':num_notes_by_channel,
- 'patch_changes_by_track':patch_changes_by_track,
- 'patch_changes_total':patch_changes_total,
- 'percussion':percussion,
- 'pitches':pitches,
- 'pitch_range_by_track':pitch_range_by_track,
- 'pitch_range_sum':pitch_range_sum,
- 'ticks_per_quarter':ticks_per_quarter}
-
-#----------------------------- Event stuff --------------------------
-
-_sysex2midimode = {
- "\x7E\x7F\x09\x01\xF7": 1,
- "\x7E\x7F\x09\x02\xF7": 0,
- "\x7E\x7F\x09\x03\xF7": 2,
-}
-
-# Some public-access tuples:
-MIDI_events = tuple('''note_off note_on key_after_touch
-control_change patch_change channel_after_touch
-pitch_wheel_change'''.split())
-
-Text_events = tuple('''text_event copyright_text_event
-track_name instrument_name lyric marker cue_point text_event_08
-text_event_09 text_event_0a text_event_0b text_event_0c
-text_event_0d text_event_0e text_event_0f'''.split())
-
-Nontext_meta_events = tuple('''end_track set_tempo
-smpte_offset time_signature key_signature sequencer_specific
-raw_meta_event sysex_f0 sysex_f7 song_position song_select
-tune_request'''.split())
-# unsupported: raw_data
-
-# Actually, 'tune_request' is is F-series event, not strictly a meta-event...
-Meta_events = Text_events + Nontext_meta_events
-All_events = MIDI_events + Meta_events
-
-# And three dictionaries:
-Number2patch = { # General MIDI patch numbers:
-0:'Acoustic Grand',
-1:'Bright Acoustic',
-2:'Electric Grand',
-3:'Honky-Tonk',
-4:'Electric Piano 1',
-5:'Electric Piano 2',
-6:'Harpsichord',
-7:'Clav',
-8:'Celesta',
-9:'Glockenspiel',
-10:'Music Box',
-11:'Vibraphone',
-12:'Marimba',
-13:'Xylophone',
-14:'Tubular Bells',
-15:'Dulcimer',
-16:'Drawbar Organ',
-17:'Percussive Organ',
-18:'Rock Organ',
-19:'Church Organ',
-20:'Reed Organ',
-21:'Accordion',
-22:'Harmonica',
-23:'Tango Accordion',
-24:'Acoustic Guitar(nylon)',
-25:'Acoustic Guitar(steel)',
-26:'Electric Guitar(jazz)',
-27:'Electric Guitar(clean)',
-28:'Electric Guitar(muted)',
-29:'Overdriven Guitar',
-30:'Distortion Guitar',
-31:'Guitar Harmonics',
-32:'Acoustic Bass',
-33:'Electric Bass(finger)',
-34:'Electric Bass(pick)',
-35:'Fretless Bass',
-36:'Slap Bass 1',
-37:'Slap Bass 2',
-38:'Synth Bass 1',
-39:'Synth Bass 2',
-40:'Violin',
-41:'Viola',
-42:'Cello',
-43:'Contrabass',
-44:'Tremolo Strings',
-45:'Pizzicato Strings',
-46:'Orchestral Harp',
-47:'Timpani',
-48:'String Ensemble 1',
-49:'String Ensemble 2',
-50:'SynthStrings 1',
-51:'SynthStrings 2',
-52:'Choir Aahs',
-53:'Voice Oohs',
-54:'Synth Voice',
-55:'Orchestra Hit',
-56:'Trumpet',
-57:'Trombone',
-58:'Tuba',
-59:'Muted Trumpet',
-60:'French Horn',
-61:'Brass Section',
-62:'SynthBrass 1',
-63:'SynthBrass 2',
-64:'Soprano Sax',
-65:'Alto Sax',
-66:'Tenor Sax',
-67:'Baritone Sax',
-68:'Oboe',
-69:'English Horn',
-70:'Bassoon',
-71:'Clarinet',
-72:'Piccolo',
-73:'Flute',
-74:'Recorder',
-75:'Pan Flute',
-76:'Blown Bottle',
-77:'Skakuhachi',
-78:'Whistle',
-79:'Ocarina',
-80:'Lead 1 (square)',
-81:'Lead 2 (sawtooth)',
-82:'Lead 3 (calliope)',
-83:'Lead 4 (chiff)',
-84:'Lead 5 (charang)',
-85:'Lead 6 (voice)',
-86:'Lead 7 (fifths)',
-87:'Lead 8 (bass+lead)',
-88:'Pad 1 (new age)',
-89:'Pad 2 (warm)',
-90:'Pad 3 (polysynth)',
-91:'Pad 4 (choir)',
-92:'Pad 5 (bowed)',
-93:'Pad 6 (metallic)',
-94:'Pad 7 (halo)',
-95:'Pad 8 (sweep)',
-96:'FX 1 (rain)',
-97:'FX 2 (soundtrack)',
-98:'FX 3 (crystal)',
-99:'FX 4 (atmosphere)',
-100:'FX 5 (brightness)',
-101:'FX 6 (goblins)',
-102:'FX 7 (echoes)',
-103:'FX 8 (sci-fi)',
-104:'Sitar',
-105:'Banjo',
-106:'Shamisen',
-107:'Koto',
-108:'Kalimba',
-109:'Bagpipe',
-110:'Fiddle',
-111:'Shanai',
-112:'Tinkle Bell',
-113:'Agogo',
-114:'Steel Drums',
-115:'Woodblock',
-116:'Taiko Drum',
-117:'Melodic Tom',
-118:'Synth Drum',
-119:'Reverse Cymbal',
-120:'Guitar Fret Noise',
-121:'Breath Noise',
-122:'Seashore',
-123:'Bird Tweet',
-124:'Telephone Ring',
-125:'Helicopter',
-126:'Applause',
-127:'Gunshot',
-}
-Notenum2percussion = { # General MIDI Percussion (on Channel 9):
-35:'Acoustic Bass Drum',
-36:'Bass Drum 1',
-37:'Side Stick',
-38:'Acoustic Snare',
-39:'Hand Clap',
-40:'Electric Snare',
-41:'Low Floor Tom',
-42:'Closed Hi-Hat',
-43:'High Floor Tom',
-44:'Pedal Hi-Hat',
-45:'Low Tom',
-46:'Open Hi-Hat',
-47:'Low-Mid Tom',
-48:'Hi-Mid Tom',
-49:'Crash Cymbal 1',
-50:'High Tom',
-51:'Ride Cymbal 1',
-52:'Chinese Cymbal',
-53:'Ride Bell',
-54:'Tambourine',
-55:'Splash Cymbal',
-56:'Cowbell',
-57:'Crash Cymbal 2',
-58:'Vibraslap',
-59:'Ride Cymbal 2',
-60:'Hi Bongo',
-61:'Low Bongo',
-62:'Mute Hi Conga',
-63:'Open Hi Conga',
-64:'Low Conga',
-65:'High Timbale',
-66:'Low Timbale',
-67:'High Agogo',
-68:'Low Agogo',
-69:'Cabasa',
-70:'Maracas',
-71:'Short Whistle',
-72:'Long Whistle',
-73:'Short Guiro',
-74:'Long Guiro',
-75:'Claves',
-76:'Hi Wood Block',
-77:'Low Wood Block',
-78:'Mute Cuica',
-79:'Open Cuica',
-80:'Mute Triangle',
-81:'Open Triangle',
-}
-
-Event2channelindex = { 'note':3, 'note_off':2, 'note_on':2,
- 'key_after_touch':2, 'control_change':2, 'patch_change':2,
- 'channel_after_touch':2, 'pitch_wheel_change':2
-}
-
-################################################################
-# The code below this line is full of frightening things, all to
-# do with the actual encoding and decoding of binary MIDI data.
-
-def _twobytes2int(byte_a):
- r'''decode a 16 bit quantity from two bytes,'''
- return (byte_a[1] | (byte_a[0] << 8))
-
-def _int2twobytes(int_16bit):
- r'''encode a 16 bit quantity into two bytes,'''
- return bytes([(int_16bit>>8) & 0xFF, int_16bit & 0xFF])
-
-def _read_14_bit(byte_a):
- r'''decode a 14 bit quantity from two bytes,'''
- return (byte_a[0] | (byte_a[1] << 7))
-
-def _write_14_bit(int_14bit):
- r'''encode a 14 bit quantity into two bytes,'''
- return bytes([int_14bit & 0x7F, (int_14bit>>7) & 0x7F])
-
-def _ber_compressed_int(integer):
- r'''BER compressed integer (not an ASN.1 BER, see perlpacktut for
-details). Its bytes represent an unsigned integer in base 128,
-most significant digit first, with as few digits as possible.
-Bit eight (the high bit) is set on each byte except the last.
-'''
- ber = bytearray(b'')
- seven_bits = 0x7F & integer
- ber.insert(0, seven_bits) # XXX surely should convert to a char ?
- integer >>= 7
- while integer > 0:
- seven_bits = 0x7F & integer
- ber.insert(0, 0x80|seven_bits) # XXX surely should convert to a char ?
- integer >>= 7
- return ber
-
-def _unshift_ber_int(ba):
- r'''Given a bytearray, returns a tuple of (the ber-integer at the
-start, and the remainder of the bytearray).
-'''
- if not len(ba): # 6.7
- _warn('_unshift_ber_int: no integer found')
- return ((0, b""))
- byte = ba.pop(0)
- integer = 0
- while True:
- integer += (byte & 0x7F)
- if not (byte & 0x80):
- return ((integer, ba))
- if not len(ba):
- _warn('_unshift_ber_int: no end-of-integer found')
- return ((0, ba))
- byte = ba.pop(0)
- integer <<= 7
-
-def _clean_up_warnings(): # 5.4
- # Call this before returning from any publicly callable function
- # whenever there's a possibility that a warning might have been printed
- # by the function, or by any private functions it might have called.
- global _previous_times
- global _previous_warning
- if _previous_times > 1:
- # E:1176, 0: invalid syntax (<string>, line 1176) (syntax-error) ???
- # print(' previous message repeated '+str(_previous_times)+' times', file=sys.stderr)
- # 6.7
- sys.stderr.write(' previous message repeated {0} times\n'.format(_previous_times))
- elif _previous_times > 0:
- sys.stderr.write(' previous message repeated\n')
- _previous_times = 0
- _previous_warning = ''
-
-def _warn(s=''):
- global _previous_times
- global _previous_warning
- if s == _previous_warning: # 5.4
- _previous_times = _previous_times + 1
- else:
- _clean_up_warnings()
- sys.stderr.write(str(s)+"\n")
- _previous_warning = s
-
-def _some_text_event(which_kind=0x01, text=b'some_text', text_encoding='ISO-8859-1'):
- if str(type(text)).find("'str'") >= 0: # 6.4 test for back-compatibility
- data = bytes(text, encoding=text_encoding)
- else:
- data = bytes(text)
- return b'\xFF'+bytes((which_kind,))+_ber_compressed_int(len(data))+data
-
-def _consistentise_ticks(scores): # 3.6
- # used by mix_scores, merge_scores, concatenate_scores
- if len(scores) == 1:
- return copy.deepcopy(scores)
- are_consistent = True
- ticks = scores[0][0]
- iscore = 1
- while iscore < len(scores):
- if scores[iscore][0] != ticks:
- are_consistent = False
- break
- iscore += 1
- if are_consistent:
- return copy.deepcopy(scores)
- new_scores = []
- iscore = 0
- while iscore < len(scores):
- score = scores[iscore]
- new_scores.append(opus2score(to_millisecs(score2opus(score))))
- iscore += 1
- return new_scores
-
-
-###########################################################################
-
-def _decode(trackdata=b'', exclude=None, include=None,
- event_callback=None, exclusive_event_callback=None, no_eot_magic=False):
- r'''Decodes MIDI track data into an opus-style list of events.
-The options:
- 'exclude' is a list of event types which will be ignored SHOULD BE A SET
- 'include' (and no exclude), makes exclude a list
- of all possible events, /minus/ what include specifies
- 'event_callback' is a coderef
- 'exclusive_event_callback' is a coderef
-'''
- trackdata = bytearray(trackdata)
- if exclude == None:
- exclude = []
- if include == None:
- include = []
- if include and not exclude:
- exclude = All_events
- include = set(include)
- exclude = set(exclude)
-
- # Pointer = 0; not used here; we eat through the bytearray instead.
- event_code = -1; # used for running status
- event_count = 0;
- events = []
-
- while(len(trackdata)):
- # loop while there's anything to analyze ...
- eot = False # When True, the event registrar aborts this loop
- event_count += 1
-
- E = []
- # E for events - we'll feed it to the event registrar at the end.
-
- # Slice off the delta time code, and analyze it
- [time, remainder] = _unshift_ber_int(trackdata)
-
- # Now let's see what we can make of the command
- first_byte = trackdata.pop(0) & 0xFF
-
- if (first_byte < 0xF0): # It's a MIDI event
- if (first_byte & 0x80):
- event_code = first_byte
- else:
- # It wants running status; use last event_code value
- trackdata.insert(0, first_byte)
- if (event_code == -1):
- _warn("Running status not set; Aborting track.")
- return []
-
- command = event_code & 0xF0
- channel = event_code & 0x0F
-
- if (command == 0xF6): # 0-byte argument
- pass
- elif (command == 0xC0 or command == 0xD0): # 1-byte argument
- parameter = trackdata.pop(0) # could be B
- else: # 2-byte argument could be BB or 14-bit
- parameter = (trackdata.pop(0), trackdata.pop(0))
-
- #################################################################
- # MIDI events
-
- if (command == 0x80):
- if 'note_off' in exclude:
- continue
- E = ['note_off', time, channel, parameter[0], parameter[1]]
- elif (command == 0x90):
- if 'note_on' in exclude:
- continue
- E = ['note_on', time, channel, parameter[0], parameter[1]]
- elif (command == 0xA0):
- if 'key_after_touch' in exclude:
- continue
- E = ['key_after_touch',time,channel,parameter[0],parameter[1]]
- elif (command == 0xB0):
- if 'control_change' in exclude:
- continue
- E = ['control_change',time,channel,parameter[0],parameter[1]]
- elif (command == 0xC0):
- if 'patch_change' in exclude:
- continue
- E = ['patch_change', time, channel, parameter]
- elif (command == 0xD0):
- if 'channel_after_touch' in exclude:
- continue
- E = ['channel_after_touch', time, channel, parameter]
- elif (command == 0xE0):
- if 'pitch_wheel_change' in exclude:
- continue
- E = ['pitch_wheel_change', time, channel,
- _read_14_bit(parameter)-0x2000]
- else:
- _warn("Shouldn't get here; command="+hex(command))
-
- elif (first_byte == 0xFF): # It's a Meta-Event! ##################
- #[command, length, remainder] =
- # unpack("xCwa*", substr(trackdata, $Pointer, 6));
- #Pointer += 6 - len(remainder);
- # # Move past JUST the length-encoded.
- command = trackdata.pop(0) & 0xFF
- [length, trackdata] = _unshift_ber_int(trackdata)
- if (command == 0x00):
- if (length == 2):
- E = ['set_sequence_number',time,_twobytes2int(trackdata)]
- else:
- _warn('set_sequence_number: length must be 2, not '+str(length))
- E = ['set_sequence_number', time, 0]
-
- elif command >= 0x01 and command <= 0x0f: # Text events
- # 6.2 take it in bytes; let the user get the right encoding.
- # text_str = trackdata[0:length].decode('ascii','ignore')
- # text_str = trackdata[0:length].decode('ISO-8859-1')
- # 6.4 take it in bytes; let the user get the right encoding.
- text_data = bytes(trackdata[0:length]) # 6.4
- # Defined text events
- if (command == 0x01):
- E = ['text_event', time, text_data]
- elif (command == 0x02):
- E = ['copyright_text_event', time, text_data]
- elif (command == 0x03):
- E = ['track_name', time, text_data]
- elif (command == 0x04):
- E = ['instrument_name', time, text_data]
- elif (command == 0x05):
- E = ['lyric', time, text_data]
- elif (command == 0x06):
- E = ['marker', time, text_data]
- elif (command == 0x07):
- E = ['cue_point', time, text_data]
- # Reserved but apparently unassigned text events
- elif (command == 0x08):
- E = ['text_event_08', time, text_data]
- elif (command == 0x09):
- E = ['text_event_09', time, text_data]
- elif (command == 0x0a):
- E = ['text_event_0a', time, text_data]
- elif (command == 0x0b):
- E = ['text_event_0b', time, text_data]
- elif (command == 0x0c):
- E = ['text_event_0c', time, text_data]
- elif (command == 0x0d):
- E = ['text_event_0d', time, text_data]
- elif (command == 0x0e):
- E = ['text_event_0e', time, text_data]
- elif (command == 0x0f):
- E = ['text_event_0f', time, text_data]
-
- # Now the sticky events -------------------------------------
- elif (command == 0x2F):
- E = ['end_track', time]
- # The code for handling this, oddly, comes LATER,
- # in the event registrar.
- elif (command == 0x51): # DTime, Microseconds/Crochet
- if length != 3:
- _warn('set_tempo event, but length='+str(length))
- E = ['set_tempo', time,
- struct.unpack(">I", b'\x00'+trackdata[0:3])[0]]
- elif (command == 0x54):
- if length != 5: # DTime, HR, MN, SE, FR, FF
- _warn('smpte_offset event, but length='+str(length))
- E = ['smpte_offset',time] + list(struct.unpack(">BBBBB",trackdata[0:5]))
- elif (command == 0x58):
- if length != 4: # DTime, NN, DD, CC, BB
- _warn('time_signature event, but length='+str(length))
- E = ['time_signature', time]+list(trackdata[0:4])
- elif (command == 0x59):
- if length != 2: # DTime, SF(signed), MI
- _warn('key_signature event, but length='+str(length))
- E = ['key_signature',time] + list(struct.unpack(">bB",trackdata[0:2]))
- elif (command == 0x7F): # 6.4
- E = ['sequencer_specific',time, bytes(trackdata[0:length])]
- else:
- E = ['raw_meta_event', time, command,
- bytes(trackdata[0:length])] # 6.0
- #"[uninterpretable meta-event command of length length]"
- # DTime, Command, Binary Data
- # It's uninterpretable; record it as raw_data.
-
- # Pointer += length; # Now move Pointer
- trackdata = trackdata[length:]
-
- ######################################################################
- elif (first_byte == 0xF0 or first_byte == 0xF7):
- # Note that sysexes in MIDI /files/ are different than sysexes
- # in MIDI transmissions!! The vast majority of system exclusive
- # messages will just use the F0 format. For instance, the
- # transmitted message F0 43 12 00 07 F7 would be stored in a
- # MIDI file as F0 05 43 12 00 07 F7. As mentioned above, it is
- # required to include the F7 at the end so that the reader of the
- # MIDI file knows that it has read the entire message. (But the F7
- # is omitted if this is a non-final block in a multiblock sysex;
- # but the F7 (if there) is counted in the message's declared
- # length, so we don't have to think about it anyway.)
- #command = trackdata.pop(0)
- [length, trackdata] = _unshift_ber_int(trackdata)
- if first_byte == 0xF0:
- # 20091008 added ISO-8859-1 to get an 8-bit str
- # 6.4 return bytes instead
- E = ['sysex_f0', time, bytes(trackdata[0:length])]
- else:
- E = ['sysex_f7', time, bytes(trackdata[0:length])]
- trackdata = trackdata[length:]
-
- ######################################################################
- # Now, the MIDI file spec says:
- # <track data> = <MTrk event>+
- # <MTrk event> = <delta-time> <event>
- # <event> = <MIDI event> | <sysex event> | <meta-event>
- # I know that, on the wire, <MIDI event> can include note_on,
- # note_off, and all the other 8x to Ex events, AND Fx events
- # other than F0, F7, and FF -- namely, <song position msg>,
- # <song select msg>, and <tune request>.
- #
- # Whether these can occur in MIDI files is not clear specified
- # from the MIDI file spec. So, I'm going to assume that
- # they CAN, in practice, occur. I don't know whether it's
- # proper for you to actually emit these into a MIDI file.
-
- elif (first_byte == 0xF2): # DTime, Beats
- # <song position msg> ::= F2 <data pair>
- E = ['song_position', time, _read_14_bit(trackdata[:2])]
- trackdata = trackdata[2:]
-
- elif (first_byte == 0xF3): # <song select msg> ::= F3 <data singlet>
- # E = ['song_select', time, struct.unpack('>B',trackdata.pop(0))[0]]
- E = ['song_select', time, trackdata[0]]
- trackdata = trackdata[1:]
- # DTime, Thing (what?! song number? whatever ...)
-
- elif (first_byte == 0xF6): # DTime
- E = ['tune_request', time]
- # What would a tune request be doing in a MIDI /file/?
-
- #########################################################
- # ADD MORE META-EVENTS HERE. TODO:
- # f1 -- MTC Quarter Frame Message. One data byte follows
- # the Status; it's the time code value, from 0 to 127.
- # f8 -- MIDI clock. no data.
- # fa -- MIDI start. no data.
- # fb -- MIDI continue. no data.
- # fc -- MIDI stop. no data.
- # fe -- Active sense. no data.
- # f4 f5 f9 fd -- unallocated
-
- r'''
- elif (first_byte > 0xF0) { # Some unknown kinda F-series event ####
- # Here we only produce a one-byte piece of raw data.
- # But the encoder for 'raw_data' accepts any length of it.
- E = [ 'raw_data',
- time, substr(trackdata,Pointer,1) ]
- # DTime and the Data (in this case, the one Event-byte)
- ++Pointer; # itself
-
-'''
- elif first_byte > 0xF0: # Some unknown F-series event
- # Here we only produce a one-byte piece of raw data.
- # E = ['raw_data', time, bytest(trackdata[0])] # 6.4
- E = ['raw_data', time, trackdata[0]] # 6.4 6.7
- trackdata = trackdata[1:]
- else: # Fallthru.
- _warn("Aborting track. Command-byte first_byte="+hex(first_byte))
- break
- # End of the big if-group
-
-
- ######################################################################
- # THE EVENT REGISTRAR...
- if E and (E[0] == 'end_track'):
- # This is the code for exceptional handling of the EOT event.
- eot = True
- if not no_eot_magic:
- if E[1] > 0: # a null text-event to carry the delta-time
- E = ['text_event', E[1], '']
- else:
- E = [] # EOT with a delta-time of 0; ignore it.
-
- if E and not (E[0] in exclude):
- #if ( $exclusive_event_callback ):
- # &{ $exclusive_event_callback }( @E );
- #else:
- # &{ $event_callback }( @E ) if $event_callback;
- events.append(E)
- if eot:
- break
-
- # End of the big "Event" while-block
-
- return events
-
-
-###########################################################################
-def _encode(events_lol, unknown_callback=None, never_add_eot=False,
- no_eot_magic=False, no_running_status=False, text_encoding='ISO-8859-1'):
- # encode an event structure, presumably for writing to a file
- # Calling format:
- # $data_r = MIDI::Event::encode( \@event_lol, { options } );
- # Takes a REFERENCE to an event structure (a LoL)
- # Returns an (unblessed) REFERENCE to track data.
-
- # If you want to use this to encode a /single/ event,
- # you still have to do it as a reference to an event structure (a LoL)
- # that just happens to have just one event. I.e.,
- # encode( [ $event ] ) or encode( [ [ 'note_on', 100, 5, 42, 64] ] )
- # If you're doing this, consider the never_add_eot track option, as in
- # print MIDI ${ encode( [ $event], { 'never_add_eot' => 1} ) };
-
- data = [] # what I'll store the chunks of byte-data in
-
- # This is so my end_track magic won't corrupt the original
- events = copy.deepcopy(events_lol)
-
- if not never_add_eot:
- # One way or another, tack on an 'end_track'
- if events:
- last = events[-1]
- if not (last[0] == 'end_track'): # no end_track already
- if (last[0] == 'text_event' and len(last[2]) == 0):
- # 0-length text event at track-end.
- if no_eot_magic:
- # Exceptional case: don't mess with track-final
- # 0-length text_events; just peg on an end_track
- events.append(['end_track', 0])
- else:
- # NORMAL CASE: replace with an end_track, leaving DTime
- last[0] = 'end_track'
- else:
- # last event was neither 0-length text_event nor end_track
- events.append(['end_track', 0])
- else: # an eventless track!
- events = [['end_track', 0],]
-
- # maybe_running_status = not no_running_status # unused? 4.7
- last_status = -1
-
- for event_r in (events):
- E = copy.deepcopy(event_r)
- # otherwise the shifting'd corrupt the original
- if not E:
- continue
-
- event = E.pop(0)
- if not len(event):
- continue
-
- dtime = int(E.pop(0))
- # print('event='+str(event)+' dtime='+str(dtime))
-
- event_data = ''
-
- if ( # MIDI events -- eligible for running status
- event == 'note_on'
- or event == 'note_off'
- or event == 'control_change'
- or event == 'key_after_touch'
- or event == 'patch_change'
- or event == 'channel_after_touch'
- or event == 'pitch_wheel_change' ):
-
- # This block is where we spend most of the time. Gotta be tight.
- if (event == 'note_off'):
- status = 0x80 | (int(E[0]) & 0x0F)
- parameters = struct.pack('>BB', int(E[1])&0x7F, int(E[2])&0x7F)
- elif (event == 'note_on'):
- status = 0x90 | (int(E[0]) & 0x0F)
- parameters = struct.pack('>BB', int(E[1])&0x7F, int(E[2])&0x7F)
- elif (event == 'key_after_touch'):
- status = 0xA0 | (int(E[0]) & 0x0F)
- parameters = struct.pack('>BB', int(E[1])&0x7F, int(E[2])&0x7F)
- elif (event == 'control_change'):
- status = 0xB0 | (int(E[0]) & 0x0F)
- parameters = struct.pack('>BB', int(E[1])&0xFF, int(E[2])&0xFF)
- elif (event == 'patch_change'):
- status = 0xC0 | (int(E[0]) & 0x0F)
- parameters = struct.pack('>B', int(E[1]) & 0xFF)
- elif (event == 'channel_after_touch'):
- status = 0xD0 | (int(E[0]) & 0x0F)
- parameters = struct.pack('>B', int(E[1]) & 0xFF)
- elif (event == 'pitch_wheel_change'):
- status = 0xE0 | (int(E[0]) & 0x0F)
- parameters = _write_14_bit(int(E[1]) + 0x2000)
- else:
- _warn("BADASS FREAKOUT ERROR 31415!")
-
- # And now the encoding
- # w = BER compressed integer (not ASN.1 BER, see perlpacktut for
- # details). Its bytes represent an unsigned integer in base 128,
- # most significant digit first, with as few digits as possible.
- # Bit eight (the high bit) is set on each byte except the last.
-
- data.append(_ber_compressed_int(dtime))
- if (status != last_status) or no_running_status:
- data.append(struct.pack('>B', status))
- data.append(parameters)
-
- last_status = status
- continue
- else:
- # Not a MIDI event.
- # All the code in this block could be more efficient,
- # but this is not where the code needs to be tight.
- # print "zaz $event\n";
- last_status = -1
-
- if event == 'raw_meta_event':
- event_data = _some_text_event(int(E[0]), E[1], text_encoding)
- elif (event == 'set_sequence_number'): # 3.9
- event_data = b'\xFF\x00\x02'+_int2twobytes(E[0])
-
- # Text meta-events...
- # a case for a dict, I think (pjb) ...
- elif (event == 'text_event'):
- event_data = _some_text_event(0x01, E[0], text_encoding)
- elif (event == 'copyright_text_event'):
- event_data = _some_text_event(0x02, E[0], text_encoding)
- elif (event == 'track_name'):
- event_data = _some_text_event(0x03, E[0], text_encoding)
- elif (event == 'instrument_name'):
- event_data = _some_text_event(0x04, E[0], text_encoding)
- elif (event == 'lyric'):
- event_data = _some_text_event(0x05, E[0], text_encoding)
- elif (event == 'marker'):
- event_data = _some_text_event(0x06, E[0], text_encoding)
- elif (event == 'cue_point'):
- event_data = _some_text_event(0x07, E[0], text_encoding)
- elif (event == 'text_event_08'):
- event_data = _some_text_event(0x08, E[0], text_encoding)
- elif (event == 'text_event_09'):
- event_data = _some_text_event(0x09, E[0], text_encoding)
- elif (event == 'text_event_0a'):
- event_data = _some_text_event(0x0A, E[0], text_encoding)
- elif (event == 'text_event_0b'):
- event_data = _some_text_event(0x0B, E[0], text_encoding)
- elif (event == 'text_event_0c'):
- event_data = _some_text_event(0x0C, E[0], text_encoding)
- elif (event == 'text_event_0d'):
- event_data = _some_text_event(0x0D, E[0], text_encoding)
- elif (event == 'text_event_0e'):
- event_data = _some_text_event(0x0E, E[0], text_encoding)
- elif (event == 'text_event_0f'):
- event_data = _some_text_event(0x0F, E[0], text_encoding)
- # End of text meta-events
-
- elif (event == 'end_track'):
- event_data = b"\xFF\x2F\x00"
-
- elif (event == 'set_tempo'):
- #event_data = struct.pack(">BBwa*", 0xFF, 0x51, 3,
- # substr( struct.pack('>I', E[0]), 1, 3))
- event_data = b'\xFF\x51\x03'+struct.pack('>I',E[0])[1:]
- elif (event == 'smpte_offset'):
- # event_data = struct.pack(">BBwBBBBB", 0xFF, 0x54, 5, E[0:5] )
- event_data = struct.pack(">BBBbBBBB", 0xFF,0x54,0x05,E[0],E[1],E[2],E[3],E[4])
- elif (event == 'time_signature'):
- # event_data = struct.pack(">BBwBBBB", 0xFF, 0x58, 4, E[0:4] )
- event_data = struct.pack(">BBBbBBB", 0xFF, 0x58, 0x04, E[0],E[1],E[2],E[3])
- elif (event == 'key_signature'):
- event_data = struct.pack(">BBBbB", 0xFF, 0x59, 0x02, E[0],E[1])
- elif (event == 'sequencer_specific'):
- # event_data = struct.pack(">BBwa*", 0xFF,0x7F, len(E[0]), E[0])
- event_data = _some_text_event(0x7F, E[0], text_encoding)
- # End of Meta-events
-
- # Other Things...
- elif (event == 'sysex_f0'):
- #event_data = struct.pack(">Bwa*", 0xF0, len(E[0]), E[0])
- #B=bitstring w=BER-compressed-integer a=null-padded-ascii-str
- event_data = bytearray(b'\xF0')+_ber_compressed_int(len(E[0]))+bytearray(E[0])
- elif (event == 'sysex_f7'):
- #event_data = struct.pack(">Bwa*", 0xF7, len(E[0]), E[0])
- event_data = bytearray(b'\xF7')+_ber_compressed_int(len(E[0]))+bytearray(E[0])
-
- elif (event == 'song_position'):
- event_data = b"\xF2" + _write_14_bit( E[0] )
- elif (event == 'song_select'):
- event_data = struct.pack('>BB', 0xF3, E[0] )
- elif (event == 'tune_request'):
- event_data = b"\xF6"
- elif (event == 'raw_data'):
- _warn("_encode: raw_data event not supported")
- # event_data = E[0]
- continue
- # End of Other Stuff
-
- else:
- # The Big Fallthru
- if unknown_callback:
- # push(@data, &{ $unknown_callback }( @$event_r ))
- pass
- else:
- _warn("Unknown event: "+str(event))
- # To surpress complaint here, just set
- # 'unknown_callback' => sub { return () }
- continue
-
- #print "Event $event encoded part 2\n"
- if str(type(event_data)).find("'str'") >= 0:
- event_data = bytearray(event_data.encode('Latin1', 'ignore'))
- if len(event_data): # how could $event_data be empty
- # data.append(struct.pack('>wa*', dtime, event_data))
- # print(' event_data='+str(event_data))
- data.append(_ber_compressed_int(dtime)+event_data)
-
- return b''.join(data)
-
-###################################################################################
-###################################################################################
-###################################################################################
-#
-# Tegridy MIDI X Module (TMIDI X / tee-midi eks)
-# Version 1.0
-#
-# Based upon and includes the amazing MIDI.py module v.6.7. by Peter Billam
-# pjb.com.au
-#
-# Project Los Angeles
-# Tegridy Code 2021
-# https://github.com/Tegridy-Code/Project-Los-Angeles
-#
-###################################################################################
-###################################################################################
-###################################################################################
-
-import os
-
-import datetime
-
-import copy
-
-from datetime import datetime
-
-import secrets
-
-import random
-
-import pickle
-
-import csv
-
-import tqdm
-
-from itertools import zip_longest
-from itertools import groupby
-
-from operator import itemgetter
-
-import sys
-
-from abc import ABC, abstractmethod
-
-from difflib import SequenceMatcher as SM
-
-import statistics
-
-###################################################################################
-#
-# Original TMIDI Tegridy helper functions
-#
-###################################################################################
-
-def Tegridy_TXT_to_INT_Converter(input_TXT_string, line_by_line_INT_string=True, max_INT = 0):
-
- '''Tegridy TXT to Intergers Converter
-
- Input: Input TXT string in the TMIDI-TXT format
-
- Type of output TXT INT string: line-by-line or one long string
-
- Maximum absolute integer to process. Maximum is inclusive
- Default = process all integers. This helps to remove outliers/unwanted ints
-
- Output: List of pure intergers
- String of intergers in the specified format: line-by-line or one long string
- Number of processed integers
- Number of skipped integers
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- print('Tegridy TXT to Intergers Converter')
-
- output_INT_list = []
-
- npi = 0
- nsi = 0
-
- TXT_List = list(input_TXT_string)
- for char in TXT_List:
- if max_INT != 0:
- if abs(ord(char)) <= max_INT:
- output_INT_list.append(ord(char))
- npi += 1
- else:
- nsi += 1
- else:
- output_INT_list.append(ord(char))
- npi += 1
-
- if line_by_line_INT_string:
- output_INT_string = '\n'.join([str(elem) for elem in output_INT_list])
- else:
- output_INT_string = ' '.join([str(elem) for elem in output_INT_list])
-
- print('Converted TXT to INTs:', npi, ' / ', nsi)
-
- return output_INT_list, output_INT_string, npi, nsi
-
-###################################################################################
-
-def Tegridy_INT_to_TXT_Converter(input_INT_list):
-
- '''Tegridy Intergers to TXT Converter
-
- Input: List of intergers in TMIDI-TXT-INT format
- Output: Decoded TXT string in TMIDI-TXT format
- Project Los Angeles
- Tegridy Code 2020'''
-
- output_TXT_string = ''
-
- for i in input_INT_list:
- output_TXT_string += chr(int(i))
-
- return output_TXT_string
-
-###################################################################################
-
-def Tegridy_INT_String_to_TXT_Converter(input_INT_String, line_by_line_input=True):
-
- '''Tegridy Intergers String to TXT Converter
-
- Input: List of intergers in TMIDI-TXT-INT-String format
- Output: Decoded TXT string in TMIDI-TXT format
- Project Los Angeles
- Tegridy Code 2020'''
-
- print('Tegridy Intergers String to TXT Converter')
-
- if line_by_line_input:
- input_string = input_INT_String.split('\n')
- else:
- input_string = input_INT_String.split(' ')
-
- output_TXT_string = ''
-
- for i in input_string:
- try:
- output_TXT_string += chr(abs(int(i)))
- except:
- print('Bad note:', i)
- continue
-
- print('Done!')
-
- return output_TXT_string
-
-###################################################################################
-
-def Tegridy_SONG_to_MIDI_Converter(SONG,
- output_signature = 'Tegridy TMIDI Module',
- track_name = 'Composition Track',
- number_of_ticks_per_quarter = 425,
- list_of_MIDI_patches = [0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 0, 0, 0, 0, 0, 0],
- output_file_name = 'TMIDI-Composition',
- text_encoding='ISO-8859-1'):
-
- '''Tegridy SONG to MIDI Converter
-
- Input: Input SONG in TMIDI SONG/MIDI.py Score format
- Output MIDI Track 0 name / MIDI Signature
- Output MIDI Track 1 name / Composition track name
- Number of ticks per quarter for the output MIDI
- List of 16 MIDI patch numbers for output MIDI. Def. is MuseNet compatible patches.
- Output file name w/o .mid extension.
- Optional text encoding if you are working with text_events/lyrics. This is especially useful for Karaoke. Please note that anything but ISO-8859-1 is a non-standard way of encoding text_events according to MIDI specs.
-
- Output: MIDI File
- Detailed MIDI stats
-
- Project Los Angeles
- Tegridy Code 2020'''
-
- print('Converting to MIDI. Please stand-by...')
-
- output_header = [number_of_ticks_per_quarter,
- [['track_name', 0, bytes(output_signature, text_encoding)]]]
-
- patch_list = [['patch_change', 0, 0, list_of_MIDI_patches[0]],
- ['patch_change', 0, 1, list_of_MIDI_patches[1]],
- ['patch_change', 0, 2, list_of_MIDI_patches[2]],
- ['patch_change', 0, 3, list_of_MIDI_patches[3]],
- ['patch_change', 0, 4, list_of_MIDI_patches[4]],
- ['patch_change', 0, 5, list_of_MIDI_patches[5]],
- ['patch_change', 0, 6, list_of_MIDI_patches[6]],
- ['patch_change', 0, 7, list_of_MIDI_patches[7]],
- ['patch_change', 0, 8, list_of_MIDI_patches[8]],
- ['patch_change', 0, 9, list_of_MIDI_patches[9]],
- ['patch_change', 0, 10, list_of_MIDI_patches[10]],
- ['patch_change', 0, 11, list_of_MIDI_patches[11]],
- ['patch_change', 0, 12, list_of_MIDI_patches[12]],
- ['patch_change', 0, 13, list_of_MIDI_patches[13]],
- ['patch_change', 0, 14, list_of_MIDI_patches[14]],
- ['patch_change', 0, 15, list_of_MIDI_patches[15]],
- ['track_name', 0, bytes(track_name, text_encoding)]]
-
- output = output_header + [patch_list + SONG]
-
- midi_data = score2midi(output, text_encoding)
- detailed_MIDI_stats = score2stats(output)
-
- with open(output_file_name + '.mid', 'wb') as midi_file:
- midi_file.write(midi_data)
- midi_file.close()
-
- print('Done! Enjoy! :)')
-
- return detailed_MIDI_stats
-
-###################################################################################
-
-def Tegridy_File_Time_Stamp(input_file_name='File_Created_on_', ext = ''):
-
- '''Tegridy File Time Stamp
-
- Input: Full path and file name without extention
- File extension
-
- Output: File name string with time-stamp and extension (time-stamped file name)
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- print('Time-stamping output file...')
-
- now = ''
- now_n = str(datetime.now())
- now_n = now_n.replace(' ', '_')
- now_n = now_n.replace(':', '_')
- now = now_n.replace('.', '_')
-
- fname = input_file_name + str(now) + ext
-
- return(fname)
-
-###################################################################################
-
-def Tegridy_Any_Pickle_File_Writer(Data, input_file_name='TMIDI_Pickle_File'):
-
- '''Tegridy Pickle File Writer
-
- Input: Data to write (I.e. a list)
- Full path and file name without extention
-
- Output: Named Pickle file
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- print('Tegridy Pickle File Writer')
-
- full_path_to_output_dataset_to = input_file_name + '.pickle'
-
- if os.path.exists(full_path_to_output_dataset_to):
- os.remove(full_path_to_output_dataset_to)
- print('Removing old Dataset...')
- else:
- print("Creating new Dataset file...")
-
- with open(full_path_to_output_dataset_to, 'wb') as filehandle:
- # store the data as binary data stream
- pickle.dump(Data, filehandle, protocol=pickle.HIGHEST_PROTOCOL)
-
- print('Dataset was saved as:', full_path_to_output_dataset_to)
- print('Task complete. Enjoy! :)')
-
-###################################################################################
-
-def Tegridy_Any_Pickle_File_Reader(input_file_name='TMIDI_Pickle_File', ext='.pickle'):
-
- '''Tegridy Pickle File Loader
-
- Input: Full path and file name without extention
- File extension if different from default .pickle
-
- Output: Standard Python 3 unpickled data object
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- print('Tegridy Pickle File Loader')
- print('Loading the pickle file. Please wait...')
-
- with open(input_file_name + ext, 'rb') as pickle_file:
- content = pickle.load(pickle_file)
-
- return content
-
-###################################################################################
-
-# TMIDI X Code is below
-
-###################################################################################
-
-def Optimus_MIDI_TXT_Processor(MIDI_file,
- line_by_line_output=True,
- chordify_TXT=False,
- dataset_MIDI_events_time_denominator=1,
- output_velocity=True,
- output_MIDI_channels = False,
- MIDI_channel=0,
- MIDI_patch=[0, 1],
- char_offset = 30000,
- transpose_by = 0,
- flip=False,
- melody_conditioned_encoding=False,
- melody_pitch_baseline = 0,
- number_of_notes_to_sample = -1,
- sampling_offset_from_start = 0,
- karaoke=False,
- karaoke_language_encoding='utf-8',
- song_name='Song',
- perfect_timings=False,
- musenet_encoding=False,
- transform=0,
- zero_token=False,
- reset_timings=False):
-
- '''Project Los Angeles
- Tegridy Code 2021'''
-
-###########
-
- debug = False
-
- ev = 0
-
- chords_list_final = []
- chords_list = []
- events_matrix = []
- melody = []
- melody1 = []
-
- itrack = 1
-
- min_note = 0
- max_note = 0
- ev = 0
- patch = 0
-
- score = []
- rec_event = []
-
- txt = ''
- txtc = ''
- chords = []
- melody_chords = []
-
- karaoke_events_matrix = []
- karaokez = []
-
- sample = 0
- start_sample = 0
-
- bass_melody = []
-
- INTS = []
- bints = 0
-
-###########
-
- def list_average(num):
- sum_num = 0
- for t in num:
- sum_num = sum_num + t
-
- avg = sum_num / len(num)
- return avg
-
-###########
-
- #print('Loading MIDI file...')
- midi_file = open(MIDI_file, 'rb')
- if debug: print('Processing File:', file_address)
-
- try:
- opus = midi2opus(midi_file.read())
-
- except:
- print('Problematic MIDI. Skipping...')
- print('File name:', MIDI_file)
- midi_file.close()
- return txt, melody, chords
-
- midi_file.close()
-
- score1 = to_millisecs(opus)
- score2 = opus2score(score1)
-
- # score2 = opus2score(opus) # TODO Improve score timings when it will be possible.
-
- if MIDI_channel == 16: # Process all MIDI channels
- score = score2
-
- if MIDI_channel >= 0 and MIDI_channel <= 15: # Process only a selected single MIDI channel
- score = grep(score2, [MIDI_channel])
-
- if MIDI_channel == -1: # Process all channels except drums (except channel 9)
- score = grep(score2, [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15])
-
- #print('Reading all MIDI events from the MIDI file...')
- while itrack < len(score):
- for event in score[itrack]:
-
- if perfect_timings:
- if event[0] == 'note':
- event[1] = round(event[1], -1)
- event[2] = round(event[2], -1)
-
- if event[0] == 'text_event' or event[0] == 'lyric' or event[0] == 'note':
- if perfect_timings:
- event[1] = round(event[1], -1)
- karaokez.append(event)
-
- if event[0] == 'text_event' or event[0] == 'lyric':
- if perfect_timings:
- event[1] = round(event[1], -1)
- try:
- event[2] = str(event[2].decode(karaoke_language_encoding, 'replace')).replace('/', '').replace(' ', '').replace('\\', '')
- except:
- event[2] = str(event[2]).replace('/', '').replace(' ', '').replace('\\', '')
- continue
- karaoke_events_matrix.append(event)
-
- if event[0] == 'patch_change':
- patch = event[3]
-
- if event[0] == 'note' and patch in MIDI_patch:
- if len(event) == 6: # Checking for bad notes...
- eve = copy.deepcopy(event)
-
- eve[1] = int(event[1] / dataset_MIDI_events_time_denominator)
- eve[2] = int(event[2] / dataset_MIDI_events_time_denominator)
-
- eve[4] = int(event[4] + transpose_by)
-
- if flip == True:
- eve[4] = int(127 - (event[4] + transpose_by))
-
- if number_of_notes_to_sample > -1:
- if sample <= number_of_notes_to_sample:
- if start_sample >= sampling_offset_from_start:
- events_matrix.append(eve)
- sample += 1
- ev += 1
- else:
- start_sample += 1
-
- else:
- events_matrix.append(eve)
- ev += 1
- start_sample += 1
-
- itrack +=1 # Going to next track...
-
- #print('Doing some heavy pythonic sorting...Please stand by...')
-
- fn = os.path.basename(MIDI_file)
- song_name = song_name.replace(' ', '_').replace('=', '_').replace('\'', '-')
- if song_name == 'Song':
- sng_name = fn.split('.')[0].replace(' ', '_').replace('=', '_').replace('\'', '-')
- song_name = sng_name
-
- # Zero token
- if zero_token:
- txt += chr(char_offset) + chr(char_offset)
- if output_MIDI_channels:
- txt += chr(char_offset)
- if output_velocity:
- txt += chr(char_offset) + chr(char_offset)
- else:
- txt += chr(char_offset)
-
- txtc += chr(char_offset) + chr(char_offset)
- if output_MIDI_channels:
- txtc += chr(char_offset)
- if output_velocity:
- txtc += chr(char_offset) + chr(char_offset)
- else:
- txtc += chr(char_offset)
-
- txt += '=' + song_name + '_with_' + str(len(events_matrix)-1) + '_notes'
- txtc += '=' + song_name + '_with_' + str(len(events_matrix)-1) + '_notes'
-
- else:
- # Song stamp
- txt += 'SONG=' + song_name + '_with_' + str(len(events_matrix)-1) + '_notes'
- txtc += 'SONG=' + song_name + '_with_' + str(len(events_matrix)-1) + '_notes'
-
- if line_by_line_output:
- txt += chr(10)
- txtc += chr(10)
- else:
- txt += chr(32)
- txtc += chr(32)
-
- #print('Sorting input by start time...')
- events_matrix.sort(key=lambda x: x[1]) # Sorting input by start time
-
- #print('Timings converter')
- if reset_timings:
- ev_matrix = Tegridy_Timings_Converter(events_matrix)[0]
- else:
- ev_matrix = events_matrix
-
- chords.extend(ev_matrix)
- #print(chords)
-
- #print('Extracting melody...')
- melody_list = []
-
- #print('Grouping by start time. This will take a while...')
- values = set(map(lambda x:x[1], ev_matrix)) # Non-multithreaded function version just in case
-
- groups = [[y for y in ev_matrix if y[1]==x and len(y) == 6] for x in values] # Grouping notes into chords while discarting bad notes...
-
- #print('Sorting events...')
- for items in groups:
-
- items.sort(reverse=True, key=lambda x: x[4]) # Sorting events by pitch
-
- if melody_conditioned_encoding: items[0][3] = 0 # Melody should always bear MIDI Channel 0 for code to work
-
- melody_list.append(items[0]) # Creating final melody list
- melody_chords.append(items) # Creating final chords list
- bass_melody.append(items[-1]) # Creating final bass melody list
-
- # [WIP] Melody-conditioned chords list
- if melody_conditioned_encoding == True:
- if not karaoke:
-
- previous_event = copy.deepcopy(melody_chords[0][0])
-
- for ev in melody_chords:
- hp = True
- ev.sort(reverse=False, key=lambda x: x[4]) # Sorting chord events by pitch
- for event in ev:
-
- # Computing events details
- start_time = int(abs(event[1] - previous_event[1]))
-
- duration = int(previous_event[2])
-
- if hp == True:
- if int(previous_event[4]) >= melody_pitch_baseline:
- channel = int(0)
- hp = False
- else:
- channel = int(previous_event[3]+1)
- hp = False
- else:
- channel = int(previous_event[3]+1)
- hp = False
-
- pitch = int(previous_event[4])
-
- velocity = int(previous_event[5])
-
- # Writing INTergerS...
- try:
- INTS.append([(start_time)+char_offset, (duration)+char_offset, channel+char_offset, pitch+char_offset, velocity+char_offset])
- except:
- bints += 1
-
- # Converting to TXT if possible...
- try:
- txtc += str(chr(start_time + char_offset))
- txtc += str(chr(duration + char_offset))
- txtc += str(chr(pitch + char_offset))
- if output_velocity:
- txtc += str(chr(velocity + char_offset))
- if output_MIDI_channels:
- txtc += str(chr(channel + char_offset))
-
- if line_by_line_output:
-
-
- txtc += chr(10)
- else:
-
- txtc += chr(32)
-
- previous_event = copy.deepcopy(event)
-
- except:
- # print('Problematic MIDI event! Skipping...')
- continue
-
- if not line_by_line_output:
- txtc += chr(10)
-
- txt = txtc
- chords = melody_chords
-
- # Default stuff (not melody-conditioned/not-karaoke)
- else:
- if not karaoke:
- melody_chords.sort(reverse=False, key=lambda x: x[0][1])
- mel_chords = []
- for mc in melody_chords:
- mel_chords.extend(mc)
-
- if transform != 0:
- chords = Tegridy_Transform(mel_chords, transform)
- else:
- chords = mel_chords
-
- # TXT Stuff
- previous_event = copy.deepcopy(chords[0])
- for event in chords:
-
- # Computing events details
- start_time = int(abs(event[1] - previous_event[1]))
-
- duration = int(previous_event[2])
-
- channel = int(previous_event[3])
-
- pitch = int(previous_event[4] + transpose_by)
- if flip == True:
- pitch = 127 - int(previous_event[4] + transpose_by)
-
- velocity = int(previous_event[5])
-
- # Writing INTergerS...
- try:
- INTS.append([(start_time)+char_offset, (duration)+char_offset, channel+char_offset, pitch+char_offset, velocity+char_offset])
- except:
- bints += 1
-
- # Converting to TXT if possible...
- try:
- txt += str(chr(start_time + char_offset))
- txt += str(chr(duration + char_offset))
- txt += str(chr(pitch + char_offset))
- if output_velocity:
- txt += str(chr(velocity + char_offset))
- if output_MIDI_channels:
- txt += str(chr(channel + char_offset))
-
-
- if chordify_TXT == True and int(event[1] - previous_event[1]) == 0:
- txt += ''
- else:
- if line_by_line_output:
- txt += chr(10)
- else:
- txt += chr(32)
-
- previous_event = copy.deepcopy(event)
-
- except:
- # print('Problematic MIDI event. Skipping...')
- continue
-
- if not line_by_line_output:
- txt += chr(10)
-
- # Karaoke stuff
- if karaoke:
-
- melody_chords.sort(reverse=False, key=lambda x: x[0][1])
- mel_chords = []
- for mc in melody_chords:
- mel_chords.extend(mc)
-
- if transform != 0:
- chords = Tegridy_Transform(mel_chords, transform)
- else:
- chords = mel_chords
-
- previous_event = copy.deepcopy(chords[0])
- for event in chords:
-
- # Computing events details
- start_time = int(abs(event[1] - previous_event[1]))
-
- duration = int(previous_event[2])
-
- channel = int(previous_event[3])
-
- pitch = int(previous_event[4] + transpose_by)
-
- velocity = int(previous_event[5])
-
- # Converting to TXT
- txt += str(chr(start_time + char_offset))
- txt += str(chr(duration + char_offset))
- txt += str(chr(pitch + char_offset))
-
- txt += str(chr(velocity + char_offset))
- txt += str(chr(channel + char_offset))
-
- if start_time > 0:
- for k in karaoke_events_matrix:
- if event[1] == k[1]:
- txt += str('=')
- txt += str(k[2])
- break
-
- if line_by_line_output:
- txt += chr(10)
- else:
- txt += chr(32)
-
- previous_event = copy.deepcopy(event)
-
- if not line_by_line_output:
- txt += chr(10)
-
- # Final processing code...
- # =======================================================================
-
- # Helper aux/backup function for Karaoke
- karaokez.sort(reverse=False, key=lambda x: x[1])
-
- # MuseNet sorting
- if musenet_encoding and not melody_conditioned_encoding and not karaoke:
- chords.sort(key=lambda x: (x[1], x[3]))
-
- # Final melody sort
- melody_list.sort()
-
- # auxs for future use
- aux1 = [None]
- aux2 = [None]
-
- return txt, melody_list, chords, bass_melody, karaokez, INTS, aux1, aux2 # aux1 and aux2 are not used atm
-
-###################################################################################
-
-def Optimus_TXT_to_Notes_Converter(Optimus_TXT_String,
- line_by_line_dataset = True,
- has_velocities = True,
- has_MIDI_channels = True,
- dataset_MIDI_events_time_denominator = 1,
- char_encoding_offset = 30000,
- save_only_first_composition = True,
- simulate_velocity=True,
- karaoke=False,
- zero_token=False):
-
- '''Project Los Angeles
- Tegridy Code 2020'''
-
- print('Tegridy Optimus TXT to Notes Converter')
- print('Converting TXT to Notes list...Please wait...')
-
- song_name = ''
-
- if line_by_line_dataset:
- input_string = Optimus_TXT_String.split('\n')
- else:
- input_string = Optimus_TXT_String.split(' ')
-
- if line_by_line_dataset:
- name_string = Optimus_TXT_String.split('\n')[0].split('=')
- else:
- name_string = Optimus_TXT_String.split(' ')[0].split('=')
-
- # Zero token
- zt = ''
-
- zt += chr(char_encoding_offset) + chr(char_encoding_offset)
-
- if has_MIDI_channels:
- zt += chr(char_encoding_offset)
-
- if has_velocities:
- zt += chr(char_encoding_offset) + chr(char_encoding_offset)
-
- else:
- zt += chr(char_encoding_offset)
-
- if zero_token:
- if name_string[0] == zt:
- song_name = name_string[1]
-
- else:
- if name_string[0] == 'SONG':
- song_name = name_string[1]
-
- output_list = []
- st = 0
-
- for i in range(2, len(input_string)-1):
-
- if save_only_first_composition:
- if zero_token:
- if input_string[i].split('=')[0] == zt:
-
- song_name = name_string[1]
- break
-
- else:
- if input_string[i].split('=')[0] == 'SONG':
-
- song_name = name_string[1]
- break
- try:
- istring = input_string[i]
-
- if has_MIDI_channels == False:
- step = 4
-
- if has_MIDI_channels == True:
- step = 5
-
- if has_velocities == False:
- step -= 1
-
- st += int(ord(istring[0]) - char_encoding_offset) * dataset_MIDI_events_time_denominator
-
- if not karaoke:
- for s in range(0, len(istring), step):
- if has_MIDI_channels==True:
- if step > 3 and len(istring) > 2:
- out = []
- out.append('note')
-
- out.append(st) # Start time
-
- out.append(int(ord(istring[s+1]) - char_encoding_offset) * dataset_MIDI_events_time_denominator) # Duration
-
- if has_velocities:
- out.append(int(ord(istring[s+4]) - char_encoding_offset)) # Channel
- else:
- out.append(int(ord(istring[s+3]) - char_encoding_offset)) # Channel
-
- out.append(int(ord(istring[s+2]) - char_encoding_offset)) # Pitch
-
- if simulate_velocity:
- if s == 0:
- sim_vel = int(ord(istring[s+2]) - char_encoding_offset)
- out.append(sim_vel) # Simulated Velocity (= highest note's pitch)
- else:
- out.append(int(ord(istring[s+3]) - char_encoding_offset)) # Velocity
-
- if has_MIDI_channels==False:
- if step > 3 and len(istring) > 2:
- out = []
- out.append('note')
-
- out.append(st) # Start time
- out.append(int(ord(istring[s+1]) - char_encoding_offset) * dataset_MIDI_events_time_denominator) # Duration
- out.append(0) # Channel
- out.append(int(ord(istring[s+2]) - char_encoding_offset)) # Pitch
-
- if simulate_velocity:
- if s == 0:
- sim_vel = int(ord(istring[s+2]) - char_encoding_offset)
- out.append(sim_vel) # Simulated Velocity (= highest note's pitch)
- else:
- out.append(int(ord(istring[s+3]) - char_encoding_offset)) # Velocity
-
- if step == 3 and len(istring) > 2:
- out = []
- out.append('note')
-
- out.append(st) # Start time
- out.append(int(ord(istring[s+1]) - char_encoding_offset) * dataset_MIDI_events_time_denominator) # Duration
- out.append(0) # Channel
- out.append(int(ord(istring[s+2]) - char_encoding_offset)) # Pitch
-
- out.append(int(ord(istring[s+2]) - char_encoding_offset)) # Velocity = Pitch
-
- output_list.append(out)
-
- if karaoke:
- try:
- out = []
- out.append('note')
-
- out.append(st) # Start time
- out.append(int(ord(istring[1]) - char_encoding_offset) * dataset_MIDI_events_time_denominator) # Duration
- out.append(int(ord(istring[4]) - char_encoding_offset)) # Channel
- out.append(int(ord(istring[2]) - char_encoding_offset)) # Pitch
-
- if simulate_velocity:
- if s == 0:
- sim_vel = int(ord(istring[2]) - char_encoding_offset)
- out.append(sim_vel) # Simulated Velocity (= highest note's pitch)
- else:
- out.append(int(ord(istring[3]) - char_encoding_offset)) # Velocity
- output_list.append(out)
- out = []
- if istring.split('=')[1] != '':
- out.append('lyric')
- out.append(st)
- out.append(istring.split('=')[1])
- output_list.append(out)
- except:
- continue
-
-
- except:
- print('Bad note string:', istring)
- continue
-
- # Simple error control just in case
- S = []
- for x in output_list:
- if len(x) == 6 or len(x) == 3:
- S.append(x)
-
- output_list.clear()
- output_list = copy.deepcopy(S)
-
-
- print('Task complete! Enjoy! :)')
-
- return output_list, song_name
-
-###################################################################################
-
-def Optimus_Data2TXT_Converter(data,
- dataset_time_denominator=1,
- transpose_by = 0,
- char_offset = 33,
- line_by_line_output = True,
- output_velocity = False,
- output_MIDI_channels = False):
-
-
- '''Input: data as a flat chords list of flat chords lists
-
- Output: TXT string
- INTs
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- txt = ''
- TXT = ''
-
- quit = False
- counter = 0
-
- INTs = []
- INTs_f = []
-
- for d in tqdm.tqdm(sorted(data)):
-
- if quit == True:
- break
-
- txt = 'SONG=' + str(counter)
- counter += 1
-
- if line_by_line_output:
- txt += chr(10)
- else:
- txt += chr(32)
-
- INTs = []
-
- # TXT Stuff
- previous_event = copy.deepcopy(d[0])
- for event in sorted(d):
-
- # Computing events details
- start_time = int(abs(event[1] - previous_event[1]) / dataset_time_denominator)
-
- duration = int(previous_event[2] / dataset_time_denominator)
-
- channel = int(previous_event[3])
-
- pitch = int(previous_event[4] + transpose_by)
-
- velocity = int(previous_event[5])
-
- INTs.append([start_time, duration, pitch])
-
- # Converting to TXT if possible...
- try:
- txt += str(chr(start_time + char_offset))
- txt += str(chr(duration + char_offset))
- txt += str(chr(pitch + char_offset))
- if output_velocity:
- txt += str(chr(velocity + char_offset))
- if output_MIDI_channels:
- txt += str(chr(channel + char_offset))
-
- if line_by_line_output:
- txt += chr(10)
- else:
- txt += chr(32)
-
- previous_event = copy.deepcopy(event)
- except KeyboardInterrupt:
- quit = True
- break
- except:
- print('Problematic MIDI data. Skipping...')
- continue
-
- if not line_by_line_output:
- txt += chr(10)
-
- TXT += txt
- INTs_f.extend(INTs)
-
- return TXT, INTs_f
-
-###################################################################################
-
-def Optimus_Squash(chords_list, simulate_velocity=True, mono_compression=False):
-
- '''Input: Flat chords list
- Simulate velocity or not
- Mono-compression enabled or disabled
-
- Default is almost lossless 25% compression, otherwise, lossy 50% compression (mono-compression)
-
- Output: Squashed chords list
- Resulting compression level
-
- Please note that if drums are passed through as is
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- output = []
- ptime = 0
- vel = 0
- boost = 15
- stptc = []
- ocount = 0
- rcount = 0
-
- for c in chords_list:
-
- cc = copy.deepcopy(c)
- ocount += 1
-
- if [cc[1], cc[3], (cc[4] % 12) + 60] not in stptc:
- stptc.append([cc[1], cc[3], (cc[4] % 12) + 60])
-
- if cc[3] != 9:
- cc[4] = (c[4] % 12) + 60
-
- if simulate_velocity and c[1] != ptime:
- vel = c[4] + boost
-
- if cc[3] != 9:
- cc[5] = vel
-
- if mono_compression:
- if c[1] != ptime:
- output.append(cc)
- rcount += 1
- else:
- output.append(cc)
- rcount += 1
-
- ptime = c[1]
-
- output.sort(key=lambda x: (x[1], x[4]))
-
- comp_level = 100 - int((rcount * 100) / ocount)
-
- return output, comp_level
-
-###################################################################################
-
-def Optimus_Signature(chords_list, calculate_full_signature=False):
-
- '''Optimus Signature
-
- ---In the name of the search for a perfect score slice signature---
-
- Input: Flat chords list to evaluate
-
- Output: Full Optimus Signature as a list
- Best/recommended Optimus Signature as a list
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- # Pitches
-
- ## StDev
- if calculate_full_signature:
- psd = statistics.stdev([int(y[4]) for y in chords_list])
- else:
- psd = 0
-
- ## Median
- pmh = statistics.median_high([int(y[4]) for y in chords_list])
- pm = statistics.median([int(y[4]) for y in chords_list])
- pml = statistics.median_low([int(y[4]) for y in chords_list])
-
- ## Mean
- if calculate_full_signature:
- phm = statistics.harmonic_mean([int(y[4]) for y in chords_list])
- else:
- phm = 0
-
- # Durations
- dur = statistics.median([int(y[2]) for y in chords_list])
-
- # Velocities
-
- vel = statistics.median([int(y[5]) for y in chords_list])
-
- # Beats
- mtds = statistics.median([int(abs(chords_list[i-1][1]-chords_list[i][1])) for i in range(1, len(chords_list))])
- if calculate_full_signature:
- hmtds = statistics.harmonic_mean([int(abs(chords_list[i-1][1]-chords_list[i][1])) for i in range(1, len(chords_list))])
- else:
- hmtds = 0
-
- # Final Optimus signatures
- full_Optimus_signature = [round(psd), round(pmh), round(pm), round(pml), round(phm), round(dur), round(vel), round(mtds), round(hmtds)]
- ######################## PStDev PMedianH PMedian PMedianL PHarmoMe Duration Velocity Beat HarmoBeat
-
- best_Optimus_signature = [round(pmh), round(pm), round(pml), round(dur, -1), round(vel, -1), round(mtds, -1)]
- ######################## PMedianH PMedian PMedianL Duration Velocity Beat
-
- # Return...
- return full_Optimus_signature, best_Optimus_signature
-
-
-###################################################################################
-#
-# TMIDI 2.0 Helper functions
-#
-###################################################################################
-
-def Tegridy_FastSearch(needle, haystack, randomize = False):
-
- '''
-
- Input: Needle iterable
- Haystack iterable
- Randomize search range (this prevents determinism)
-
- Output: Start index of the needle iterable in a haystack iterable
- If nothing found, -1 is returned
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- need = copy.deepcopy(needle)
-
- try:
- if randomize:
- idx = haystack.index(need, secrets.randbelow(len(haystack)-len(need)))
- else:
- idx = haystack.index(need)
-
- except KeyboardInterrupt:
- return -1
-
- except:
- return -1
-
- return idx
-
-###################################################################################
-
-def Tegridy_Chord_Match(chord1, chord2, match_type=2):
-
- '''Tegridy Chord Match
-
- Input: Two chords to evaluate
- Match type: 2 = duration, channel, pitch, velocity
- 3 = channel, pitch, velocity
- 4 = pitch, velocity
- 5 = velocity
-
- Output: Match rating (0-100)
- NOTE: Match rating == -1 means identical source chords
- NOTE: Match rating == 100 means mutual shortest chord
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- match_rating = 0
-
- if chord1 == []:
- return 0
- if chord2 == []:
- return 0
-
- if chord1 == chord2:
- return -1
-
- else:
- zipped_pairs = list(zip(chord1, chord2))
- zipped_diff = abs(len(chord1) - len(chord2))
-
- short_match = [False]
- for pair in zipped_pairs:
- cho1 = ' '.join([str(y) for y in pair[0][match_type:]])
- cho2 = ' '.join([str(y) for y in pair[1][match_type:]])
- if cho1 == cho2:
- short_match.append(True)
- else:
- short_match.append(False)
-
- if True in short_match:
- return 100
-
- pairs_ratings = []
-
- for pair in zipped_pairs:
- cho1 = ' '.join([str(y) for y in pair[0][match_type:]])
- cho2 = ' '.join([str(y) for y in pair[1][match_type:]])
- pairs_ratings.append(SM(None, cho1, cho2).ratio())
-
- match_rating = sum(pairs_ratings) / len(pairs_ratings) * 100
-
- return match_rating
-
-###################################################################################
-
-def Tegridy_Last_Chord_Finder(chords_list):
-
- '''Tegridy Last Chord Finder
-
- Input: Flat chords list
-
- Output: Last detected chord of the chords list
- Last chord start index in the original chords list
- First chord end index in the original chords list
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- chords = []
- cho = []
-
- ptime = 0
-
- i = 0
-
- pc_idx = 0
- fc_idx = 0
-
- chords_list.sort(reverse=False, key=lambda x: x[1])
-
- for cc in chords_list:
-
- if cc[1] == ptime:
-
- cho.append(cc)
-
- ptime = cc[1]
-
- else:
- if pc_idx == 0:
- fc_idx = chords_list.index(cc)
- pc_idx = chords_list.index(cc)
-
- chords.append(cho)
-
- cho = []
-
- cho.append(cc)
-
- ptime = cc[1]
-
- i += 1
-
- if cho != []:
- chords.append(cho)
- i += 1
-
- return chords_list[pc_idx:], pc_idx, fc_idx
-
-###################################################################################
-
-def Tegridy_Chords_Generator(chords_list, shuffle_pairs = True, remove_single_notes=False):
-
- '''Tegridy Score Chords Pairs Generator
-
- Input: Flat chords list
- Shuffle pairs (recommended)
-
- Output: List of chords
-
- Average time(ms) per chord
- Average time(ms) per pitch
- Average chords delta time
-
- Average duration
- Average channel
- Average pitch
- Average velocity
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- chords = []
- cho = []
-
- i = 0
-
- # Sort by start time
- chords_list.sort(reverse=False, key=lambda x: x[1])
-
- # Main loop
- pcho = chords_list[0]
- for cc in chords_list:
- if cc[1] == pcho[1]:
-
- cho.append(cc)
- pcho = copy.deepcopy(cc)
-
- else:
- if not remove_single_notes:
- chords.append(cho)
- cho = []
- cho.append(cc)
- pcho = copy.deepcopy(cc)
-
- i += 1
- else:
- if len(cho) > 1:
- chords.append(cho)
- cho = []
- cho.append(cc)
- pcho = copy.deepcopy(cc)
-
- i += 1
-
- # Averages
- t0 = chords[0][0][1]
- t1 = chords[-1][-1][1]
- tdel = abs(t1 - t0)
- avg_ms_per_chord = int(tdel / i)
- avg_ms_per_pitch = int(tdel / len(chords_list))
-
- # Delta time
- tds = [int(abs(chords_list[i-1][1]-chords_list[i][1]) / 1) for i in range(1, len(chords_list))]
- if len(tds) != 0: avg_delta_time = int(sum(tds) / len(tds))
-
- # Chords list attributes
- p = int(sum([int(y[4]) for y in chords_list]) / len(chords_list))
- d = int(sum([int(y[2]) for y in chords_list]) / len(chords_list))
- c = int(sum([int(y[3]) for y in chords_list]) / len(chords_list))
- v = int(sum([int(y[5]) for y in chords_list]) / len(chords_list))
-
- # Final shuffle
- if shuffle_pairs:
- random.shuffle(chords)
-
- return chords, [avg_ms_per_chord, avg_ms_per_pitch, avg_delta_time], [d, c, p, v]
-
-###################################################################################
-
-def Tegridy_Chords_List_Music_Features(chords_list, st_dur_div = 1, pitch_div = 1, vel_div = 1):
-
- '''Tegridy Chords List Music Features
-
- Input: Flat chords list
-
- Output: A list of the extracted chords list's music features
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- chords_list1 = [x for x in chords_list if x]
- chords_list1.sort(reverse=False, key=lambda x: x[1])
-
- # Features extraction code
-
- melody_list = []
- bass_melody = []
- melody_chords = []
- mel_avg_tds = []
- mel_chrd_avg_tds = []
- bass_melody_avg_tds = []
-
- #print('Grouping by start time. This will take a while...')
- values = set(map(lambda x:x[1], chords_list1)) # Non-multithreaded function version just in case
-
- groups = [[y for y in chords_list1 if y[1]==x and len(y) == 6] for x in values] # Grouping notes into chords while discarting bad notes...
-
- #print('Sorting events...')
- for items in groups:
- items.sort(reverse=True, key=lambda x: x[4]) # Sorting events by pitch
- melody_list.append(items[0]) # Creating final melody list
- melody_chords.append(items) # Creating final chords list
- bass_melody.append(items[-1]) # Creating final bass melody list
-
- #print('Final sorting by start time...')
- melody_list.sort(reverse=False, key=lambda x: x[1]) # Sorting events by start time
- melody_chords.sort(reverse=False, key=lambda x: x[0][1]) # Sorting events by start time
- bass_melody.sort(reverse=False, key=lambda x: x[1]) # Sorting events by start time
-
- # Extracting music features from the chords list
-
- # Melody features
- mel_avg_pitch = int(sum([y[4] for y in melody_list]) / len(melody_list) / pitch_div)
- mel_avg_dur = int(sum([int(y[2] / st_dur_div) for y in melody_list]) / len(melody_list))
- mel_avg_vel = int(sum([int(y[5] / vel_div) for y in melody_list]) / len(melody_list))
- mel_avg_chan = int(sum([int(y[3]) for y in melody_list]) / len(melody_list))
-
- mel_tds = [int(abs(melody_list[i-1][1]-melody_list[i][1])) for i in range(1, len(melody_list))]
- if len(mel_tds) != 0: mel_avg_tds = int(sum(mel_tds) / len(mel_tds) / st_dur_div)
-
- melody_features = [mel_avg_tds, mel_avg_dur, mel_avg_chan, mel_avg_pitch, mel_avg_vel]
-
- # Chords list features
- mel_chrd_avg_pitch = int(sum([y[4] for y in chords_list1]) / len(chords_list1) / pitch_div)
- mel_chrd_avg_dur = int(sum([int(y[2] / st_dur_div) for y in chords_list1]) / len(chords_list1))
- mel_chrd_avg_vel = int(sum([int(y[5] / vel_div) for y in chords_list1]) / len(chords_list1))
- mel_chrd_avg_chan = int(sum([int(y[3]) for y in chords_list1]) / len(chords_list1))
-
- mel_chrd_tds = [int(abs(chords_list1[i-1][1]-chords_list1[i][1])) for i in range(1, len(chords_list1))]
- if len(mel_tds) != 0: mel_chrd_avg_tds = int(sum(mel_chrd_tds) / len(mel_chrd_tds) / st_dur_div)
-
- chords_list_features = [mel_chrd_avg_tds, mel_chrd_avg_dur, mel_chrd_avg_chan, mel_chrd_avg_pitch, mel_chrd_avg_vel]
-
- # Bass melody features
- bass_melody_avg_pitch = int(sum([y[4] for y in bass_melody]) / len(bass_melody) / pitch_div)
- bass_melody_avg_dur = int(sum([int(y[2] / st_dur_div) for y in bass_melody]) / len(bass_melody))
- bass_melody_avg_vel = int(sum([int(y[5] / vel_div) for y in bass_melody]) / len(bass_melody))
- bass_melody_avg_chan = int(sum([int(y[3]) for y in bass_melody]) / len(bass_melody))
-
- bass_melody_tds = [int(abs(bass_melody[i-1][1]-bass_melody[i][1])) for i in range(1, len(bass_melody))]
- if len(bass_melody_tds) != 0: bass_melody_avg_tds = int(sum(bass_melody_tds) / len(bass_melody_tds) / st_dur_div)
-
- bass_melody_features = [bass_melody_avg_tds, bass_melody_avg_dur, bass_melody_avg_chan, bass_melody_avg_pitch, bass_melody_avg_vel]
-
- # A list to return all features
- music_features = []
-
- music_features.extend([len(chords_list1)]) # Count of the original chords list notes
-
- music_features.extend(melody_features) # Extracted melody features
- music_features.extend(chords_list_features) # Extracted chords list features
- music_features.extend(bass_melody_features) # Extracted bass melody features
- music_features.extend([sum([y[4] for y in chords_list1])]) # Sum of all pitches in the original chords list
-
- return music_features
-
-###################################################################################
-
-def Tegridy_Transform(chords_list, to_pitch=60, to_velocity=-1):
-
- '''Tegridy Transform
-
- Input: Flat chords list
- Desired average pitch (-1 == no change)
- Desired average velocity (-1 == no change)
-
- Output: Transformed flat chords list
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- transformed_chords_list = []
-
- chords_list.sort(reverse=False, key=lambda x: x[1])
-
- chords_list_features = Optimus_Signature(chords_list)[1]
-
- pitch_diff = int((chords_list_features[0] + chords_list_features[1] + chords_list_features[2]) / 3) - to_pitch
- velocity_diff = chords_list_features[4] - to_velocity
-
- for c in chords_list:
- cc = copy.deepcopy(c)
- if c[3] != 9: # Except the drums
- if to_pitch != -1:
- cc[4] = c[4] - pitch_diff
-
- if to_velocity != -1:
- cc[5] = c[5] - velocity_diff
-
- transformed_chords_list.append(cc)
-
- return transformed_chords_list
-
-###################################################################################
-
-def Tegridy_MIDI_Zip_Notes_Summarizer(chords_list, match_type = 4):
-
- '''Tegridy MIDI Zip Notes Summarizer
-
- Input: Flat chords list / SONG
- Match type according to 'note' event of MIDI.py
-
- Output: Summarized chords list
- Number of summarized notes
- Number of dicarted notes
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- i = 0
- j = 0
- out1 = []
- pout = []
-
-
- for o in chords_list:
-
- # MIDI Zip
-
- if o[match_type:] not in pout:
- pout.append(o[match_type:])
-
- out1.append(o)
- j += 1
-
- else:
- i += 1
-
- return out1, i
-
-###################################################################################
-
-def Tegridy_Score_Chords_Pairs_Generator(chords_list, shuffle_pairs = True, remove_single_notes=False):
-
- '''Tegridy Score Chords Pairs Generator
-
- Input: Flat chords list
- Shuffle pairs (recommended)
-
- Output: Score chords pairs list
- Number of created pairs
- Number of detected chords
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- chords = []
- cho = []
-
- i = 0
- j = 0
-
- chords_list.sort(reverse=False, key=lambda x: x[1])
- pcho = chords_list[0]
- for cc in chords_list:
- if cc[1] == pcho[1]:
-
- cho.append(cc)
- pcho = copy.deepcopy(cc)
-
- else:
- if not remove_single_notes:
- chords.append(cho)
- cho = []
- cho.append(cc)
- pcho = copy.deepcopy(cc)
-
- i += 1
- else:
- if len(cho) > 1:
- chords.append(cho)
- cho = []
- cho.append(cc)
- pcho = copy.deepcopy(cc)
-
- i += 1
-
- chords_pairs = []
- for i in range(len(chords)-1):
- chords_pairs.append([chords[i], chords[i+1]])
- j += 1
- if shuffle_pairs: random.shuffle(chords_pairs)
-
- return chords_pairs, j, i
-
-###################################################################################
-
-def Tegridy_Sliced_Score_Pairs_Generator(chords_list, number_of_miliseconds_per_slice=2000, shuffle_pairs = False):
-
- '''Tegridy Sliced Score Pairs Generator
-
- Input: Flat chords list
- Number of miliseconds per slice
-
- Output: Sliced score pairs list
- Number of created slices
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- chords = []
- cho = []
-
- time = number_of_miliseconds_per_slice
-
- i = 0
-
- chords_list1 = [x for x in chords_list if x]
- chords_list1.sort(reverse=False, key=lambda x: x[1])
- pcho = chords_list1[0]
- for cc in chords_list1[1:]:
-
- if cc[1] <= time:
-
- cho.append(cc)
-
- else:
- if cho != [] and pcho != []: chords.append([pcho, cho])
- pcho = copy.deepcopy(cho)
- cho = []
- cho.append(cc)
- time += number_of_miliseconds_per_slice
- i += 1
-
- if cho != [] and pcho != []:
- chords.append([pcho, cho])
- pcho = copy.deepcopy(cho)
- i += 1
-
- if shuffle_pairs: random.shuffle(chords)
-
- return chords, i
-
-###################################################################################
-
-def Tegridy_Timings_Converter(chords_list,
- max_delta_time = 1000,
- fixed_start_time = 250,
- start_time = 0,
- start_time_multiplier = 1,
- durations_multiplier = 1):
-
- '''Tegridy Timings Converter
-
- Input: Flat chords list
- Max delta time allowed between notes
- Fixed start note time for excessive gaps
-
- Output: Converted flat chords list
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- song = chords_list
-
- song1 = []
-
- p = song[0]
-
- p[1] = start_time
-
- time = start_time
-
- delta = [0]
-
- for i in range(len(song)):
- if song[i][0] == 'note':
- ss = copy.deepcopy(song[i])
- if song[i][1] != p[1]:
-
- if abs(song[i][1] - p[1]) > max_delta_time:
- time += fixed_start_time
- else:
- time += abs(song[i][1] - p[1])
- delta.append(abs(song[i][1] - p[1]))
-
- ss[1] = int(round(time * start_time_multiplier, -1))
- ss[2] = int(round(song[i][2] * durations_multiplier, -1))
- song1.append(ss)
-
- p = copy.deepcopy(song[i])
- else:
-
- ss[1] = int(round(time * start_time_multiplier, -1))
- ss[2] = int(round(song[i][2] * durations_multiplier, -1))
- song1.append(ss)
-
- p = copy.deepcopy(song[i])
-
- else:
- ss = copy.deepcopy(song[i])
- ss[1] = time
- song1.append(ss)
-
- average_delta_st = int(sum(delta) / len(delta))
- average_duration = int(sum([y[2] for y in song1 if y[0] == 'note']) / len([y[2] for y in song1 if y[0] == 'note']))
-
- song1.sort(reverse=False, key=lambda x: x[1])
-
- return song1, time, average_delta_st, average_duration
-
-###################################################################################
-
-def Tegridy_Score_Slicer(chords_list, number_of_miliseconds_per_slice=2000, overlap_notes = 0, overlap_chords=False):
-
- '''Tegridy Score Slicer
-
- Input: Flat chords list
- Number of miliseconds per slice
-
- Output: Sliced chords list
- Number of created slices
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- chords = []
- cho = []
-
- time = number_of_miliseconds_per_slice
- ptime = 0
-
- i = 0
-
- pc_idx = 0
-
- chords_list.sort(reverse=False, key=lambda x: x[1])
-
- for cc in chords_list:
-
- if cc[1] <= time:
-
- cho.append(cc)
-
- if ptime != cc[1]:
- pc_idx = cho.index(cc)
-
- ptime = cc[1]
-
-
- else:
-
- if overlap_chords:
- chords.append(cho)
- cho.extend(chords[-1][pc_idx:])
-
- else:
- chords.append(cho[:pc_idx])
-
- cho = []
-
- cho.append(cc)
-
- time += number_of_miliseconds_per_slice
- ptime = cc[1]
-
- i += 1
-
- if cho != []:
- chords.append(cho)
- i += 1
-
- return [x for x in chords if x], i
-
-###################################################################################
-
-def Tegridy_TXT_Tokenizer(input_TXT_string, line_by_line_TXT_string=True):
-
- '''Tegridy TXT Tokenizer
-
- Input: TXT String
-
- Output: Tokenized TXT string + forward and reverse dics
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- print('Tegridy TXT Tokenizer')
-
- if line_by_line_TXT_string:
- T = input_TXT_string.split()
- else:
- T = input_TXT_string.split(' ')
-
- DIC = dict(zip(T, range(len(T))))
- RDIC = dict(zip(range(len(T)), T))
-
- TXTT = ''
-
- for t in T:
- try:
- TXTT += chr(DIC[t])
- except:
- print('Error. Could not finish.')
- return TXTT, DIC, RDIC
-
- print('Done!')
-
- return TXTT, DIC, RDIC
-
-###################################################################################
-
-def Tegridy_TXT_DeTokenizer(input_Tokenized_TXT_string, RDIC):
-
- '''Tegridy TXT Tokenizer
-
- Input: Tokenized TXT String
-
-
- Output: DeTokenized TXT string
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- print('Tegridy TXT DeTokenizer')
-
- Q = list(input_Tokenized_TXT_string)
- c = 0
- RTXT = ''
- for q in Q:
- try:
- RTXT += RDIC[ord(q)] + chr(10)
- except:
- c+=1
-
- print('Number of errors:', c)
-
- print('Done!')
-
- return RTXT
-
-###################################################################################
-
-def Tegridy_List_Slicer(input_list, slices_length_in_notes=20):
-
- '''Input: List to slice
- Desired slices length in notes
-
- Output: Sliced list of lists
-
- Project Los Angeles
- Tegridy Code 2021'''
-
- for i in range(0, len(input_list), slices_length_in_notes):
- yield input_list[i:i + slices_length_in_notes]
-
-###################################################################################
-
-def Tegridy_Split_List(list_to_split, split_value=0):
-
- # src courtesy of www.geeksforgeeks.org
-
- # using list comprehension + zip() + slicing + enumerate()
- # Split list into lists by particular value
- size = len(list_to_split)
- idx_list = [idx + 1 for idx, val in
- enumerate(list_to_split) if val == split_value]
-
-
- res = [list_to_split[i: j] for i, j in
- zip([0] + idx_list, idx_list +
- ([size] if idx_list[-1] != size else []))]
-
- # print result
- # print("The list after splitting by a value : " + str(res))
-
- return res
-
-###################################################################################
-
-# This is the end of the TMIDI X Python module
-
-###################################################################################
diff --git a/spaces/atimughal662/InfoFusion/src/enums.py b/spaces/atimughal662/InfoFusion/src/enums.py
deleted file mode 100644
index c6013be6c9e773834cfb23e8c6cd856839856782..0000000000000000000000000000000000000000
--- a/spaces/atimughal662/InfoFusion/src/enums.py
+++ /dev/null
@@ -1,225 +0,0 @@
-from enum import Enum
-
-
-class PromptType(Enum):
- custom = -1
- plain = 0
- instruct = 1
- quality = 2
- human_bot = 3
- dai_faq = 4
- summarize = 5
- simple_instruct = 6
- instruct_vicuna = 7
- instruct_with_end = 8
- human_bot_orig = 9
- prompt_answer = 10
- open_assistant = 11
- wizard_lm = 12
- wizard_mega = 13
- instruct_vicuna2 = 14
- instruct_vicuna3 = 15
- wizard2 = 16
- wizard3 = 17
- instruct_simple = 18
- wizard_vicuna = 19
- openai = 20
- openai_chat = 21
- gptj = 22
- prompt_answer_openllama = 23
- vicuna11 = 24
- mptinstruct = 25
- mptchat = 26
- falcon = 27
- guanaco = 28
- llama2 = 29
- beluga = 30
- wizard3nospace = 31
- one_shot = 32
- falcon_chat = 33
-
-
-class DocumentSubset(Enum):
- Relevant = 0
- RelSources = 1
- TopKSources = 2
-
-
-non_query_commands = [
- DocumentSubset.RelSources.name,
- DocumentSubset.TopKSources.name
-]
-
-
-class DocumentChoice(Enum):
- ALL = 'All'
-
-
-class LangChainMode(Enum):
- """LangChain mode"""
-
- DISABLED = "Disabled"
- LLM = "LLM"
- WIKI = "wiki"
- WIKI_FULL = "wiki_full"
- USER_DATA = "UserData"
- MY_DATA = "MyData"
- GITHUB_H2OGPT = "github h2oGPT"
- H2O_DAI_DOCS = "DriverlessAI docs"
-
-
-class LangChainTypes(Enum):
- SHARED = 'shared'
- PERSONAL = 'personal'
- EITHER = 'either' # used when user did not pass which one, so need to try both
-
-
-# modes should not be removed from visible list or added by name
-langchain_modes_intrinsic = [LangChainMode.DISABLED.value,
- LangChainMode.LLM.value,
- LangChainMode.MY_DATA.value]
-
-langchain_modes_non_db = [LangChainMode.DISABLED.value,
- LangChainMode.LLM.value]
-
-
-class LangChainAction(Enum):
- """LangChain action"""
-
- QUERY = "Query"
- # WIP:
- # SUMMARIZE_MAP = "Summarize_map_reduce"
- SUMMARIZE_MAP = "Summarize"
- SUMMARIZE_ALL = "Summarize_all"
- SUMMARIZE_REFINE = "Summarize_refine"
-
-
-class LangChainAgent(Enum):
- """LangChain agents"""
-
- SEARCH = "Search"
- COLLECTION = "Collection"
- PYTHON = "Python"
- CSV = "CSV"
- PANDAS = "Pandas"
- JSON = 'JSON'
-
-
-no_server_str = no_lora_str = no_model_str = '[None/Remove]'
-
-# from site-packages/langchain/llms/openai.py
-# but needed since ChatOpenAI doesn't have this information
-model_token_mapping = {
- "gpt-4": 8192,
- "gpt-4-0314": 8192,
- "gpt-4-32k": 32768,
- "gpt-4-32k-0314": 32768,
- "gpt-3.5-turbo": 4096,
- "gpt-3.5-turbo-16k": 16 * 1024,
- "gpt-3.5-turbo-0301": 4096,
- "text-ada-001": 2049,
- "ada": 2049,
- "text-babbage-001": 2040,
- "babbage": 2049,
- "text-curie-001": 2049,
- "curie": 2049,
- "davinci": 2049,
- "text-davinci-003": 4097,
- "text-davinci-002": 4097,
- "code-davinci-002": 8001,
- "code-davinci-001": 8001,
- "code-cushman-002": 2048,
- "code-cushman-001": 2048,
-}
-
-font_size = 2
-head_acc = 40 # 40 for 6-way
-source_prefix = "Sources [Score | Link]:"
-source_postfix = "End Sources<p>"
-
-super_source_prefix = f"""<details><summary><font size="{font_size}">Sources</font></summary><font size="{font_size}"><font size="{font_size}">Sources [Score | Link]:"""
-super_source_postfix = f"""End Sources<p></font></font></details>"""
-
-
-def t5_type(model_name):
- return 't5' == model_name.lower() or \
- 't5-' in model_name.lower() or \
- 'flan-' in model_name.lower() or \
- 'fastchat-t5' in model_name.lower()
-
-
-def get_langchain_prompts(pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary,
- model_name, inference_server, model_path_llama):
- if model_name and ('falcon' in model_name or
- 'Llama-2'.lower() in model_name.lower() or
- model_path_llama and 'llama-2' in model_path_llama.lower()) or \
- model_name in [None, '']:
- # use when no model, like no --base_model
- pre_prompt_query1 = "Pay attention and remember the information below, which will help to answer the question or imperative after the context ends.\n"
- prompt_query1 = "According to only the information in the document sources provided within the context above, "
- elif inference_server and inference_server.startswith('openai'):
- pre_prompt_query1 = "Pay attention and remember the information below, which will help to answer the question or imperative after the context ends. If the answer cannot be primarily obtained from information within the context, then respond that the answer does not appear in the context of the documents.\n"
- prompt_query1 = "According to (primarily) the information in the document sources provided within context above, "
- else:
- pre_prompt_query1 = ""
- prompt_query1 = ""
-
- pre_prompt_summary1 = """In order to write a concise single-paragraph or bulleted list summary, pay attention to the following text\n"""
- prompt_summary1 = "Using only the information in the document sources above, write a condensed and concise summary of key results (preferably as bullet points):\n"
-
- if pre_prompt_query is None:
- pre_prompt_query = pre_prompt_query1
- if prompt_query is None:
- prompt_query = prompt_query1
- if pre_prompt_summary is None:
- pre_prompt_summary = pre_prompt_summary1
- if prompt_summary is None:
- prompt_summary = prompt_summary1
-
- return pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary
-
-
-def gr_to_lg(image_loaders,
- pdf_loaders,
- url_loaders,
- **kwargs,
- ):
- if image_loaders is None:
- image_loaders = kwargs['image_loaders_options0']
- if pdf_loaders is None:
- pdf_loaders = kwargs['pdf_loaders_options0']
- if url_loaders is None:
- url_loaders = kwargs['url_loaders_options0']
- # translate:
- # 'auto' wouldn't be used here
- ret = dict(
- # urls
- use_unstructured='Unstructured' in url_loaders,
- use_playwright='PlayWright' in url_loaders,
- use_selenium='Selenium' in url_loaders,
-
- # pdfs
- use_pymupdf='on' if 'PyMuPDF' in pdf_loaders else 'off',
- use_unstructured_pdf='on' if 'Unstructured' in pdf_loaders else 'off',
- use_pypdf='on' if 'PyPDF' in pdf_loaders else 'off',
- enable_pdf_ocr='on' if 'OCR' in pdf_loaders else 'off',
- enable_pdf_doctr='on' if 'DocTR' in pdf_loaders else 'off',
- try_pdf_as_html='on' if 'TryHTML' in pdf_loaders else 'off',
-
- # images
- enable_ocr='OCR' in image_loaders,
- enable_doctr='DocTR' in image_loaders,
- enable_pix2struct='Pix2Struct' in image_loaders,
- enable_captions='Caption' in image_loaders or 'CaptionBlip2' in image_loaders,
- )
- if 'CaptionBlip2' in image_loaders:
- # just override, don't actually do both even if user chose both
- captions_model = "Salesforce/blip2-flan-t5-xl"
- else:
- captions_model = kwargs['captions_model']
- return ret, captions_model
-
-
-invalid_key_msg = 'Invalid Access Key, request access key from sales@h2o.ai or jon.mckinney@h2o.ai'
-
-docs_ordering_types = ['best_first', 'best_near_prompt', 'reverse_ucurve_sort']
diff --git a/spaces/aus10powell/TwitterAccounts/scripts/__init__.py b/spaces/aus10powell/TwitterAccounts/scripts/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/awacke1/ASRGenerateStory/README.md b/spaces/awacke1/ASRGenerateStory/README.md
deleted file mode 100644
index 56b5a8a11809f7e19031b3c96a95a86daf20d6ec..0000000000000000000000000000000000000000
--- a/spaces/awacke1/ASRGenerateStory/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: 🗣️ASR Speech 2 Sentiment 2 Save 2 Story 2 Image 2 Video🎥
-emoji: 🗣️ASR❤️🖼️🎥
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.0.24
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/Games-In-Python/README.md b/spaces/awacke1/Games-In-Python/README.md
deleted file mode 100644
index 1076340e83db50cdfcc002fe43297968fed3f852..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Games-In-Python/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Games In Python
-emoji: 😻
-colorFrom: yellow
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/acw-dr-llama-7b-chat/backupapp.py b/spaces/awacke1/acw-dr-llama-7b-chat/backupapp.py
deleted file mode 100644
index 413ce04e1cbd0f1a5980b82775afe2791422f4e8..0000000000000000000000000000000000000000
--- a/spaces/awacke1/acw-dr-llama-7b-chat/backupapp.py
+++ /dev/null
@@ -1,728 +0,0 @@
-# Imports
-import base64
-import glob
-import json
-import math
-import openai
-import os
-import pytz
-import re
-import requests
-import streamlit as st
-import textract
-import time
-import zipfile
-import huggingface_hub
-import dotenv
-from audio_recorder_streamlit import audio_recorder
-from bs4 import BeautifulSoup
-from collections import deque
-from datetime import datetime
-from dotenv import load_dotenv
-from huggingface_hub import InferenceClient
-from io import BytesIO
-from langchain.chat_models import ChatOpenAI
-from langchain.chains import ConversationalRetrievalChain
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.memory import ConversationBufferMemory
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import FAISS
-from openai import ChatCompletion
-from PyPDF2 import PdfReader
-from templates import bot_template, css, user_template
-from xml.etree import ElementTree as ET
-import streamlit.components.v1 as components # Import Streamlit Components for HTML5
-
-
-st.set_page_config(page_title="🐪Llama Whisperer🦙 Voice Chat🌟", layout="wide")
-
-
-def add_Med_Licensing_Exam_Dataset():
- import streamlit as st
- from datasets import load_dataset
- dataset = load_dataset("augtoma/usmle_step_1")['test'] # Using 'test' split
- st.title("USMLE Step 1 Dataset Viewer")
- if len(dataset) == 0:
- st.write("😢 The dataset is empty.")
- else:
- st.write("""
- 🔍 Use the search box to filter questions or use the grid to scroll through the dataset.
- """)
-
- # 👩🔬 Search Box
- search_term = st.text_input("Search for a specific question:", "")
-
- # 🎛 Pagination
- records_per_page = 100
- num_records = len(dataset)
- num_pages = max(int(num_records / records_per_page), 1)
-
- # Skip generating the slider if num_pages is 1 (i.e., all records fit in one page)
- if num_pages > 1:
- page_number = st.select_slider("Select page:", options=list(range(1, num_pages + 1)))
- else:
- page_number = 1 # Only one page
-
- # 📊 Display Data
- start_idx = (page_number - 1) * records_per_page
- end_idx = start_idx + records_per_page
-
- # 🧪 Apply the Search Filter
- filtered_data = []
- for record in dataset[start_idx:end_idx]:
- if isinstance(record, dict) and 'text' in record and 'id' in record:
- if search_term:
- if search_term.lower() in record['text'].lower():
- filtered_data.append(record)
- else:
- filtered_data.append(record)
-
- # 🌐 Render the Grid
- for record in filtered_data:
- st.write(f"## Question ID: {record['id']}")
- st.write(f"### Question:")
- st.write(f"{record['text']}")
- st.write(f"### Answer:")
- st.write(f"{record['answer']}")
- st.write("---")
-
- st.write(f"😊 Total Records: {num_records} | 📄 Displaying {start_idx+1} to {min(end_idx, num_records)}")
-
-# 1. Constants and Top Level UI Variables
-
-# My Inference API Copy
-# API_URL = 'https://qe55p8afio98s0u3.us-east-1.aws.endpoints.huggingface.cloud' # Dr Llama
-# Original:
-API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"
-API_KEY = os.getenv('API_KEY')
-MODEL1="meta-llama/Llama-2-7b-chat-hf"
-MODEL1URL="https://huggingface.co/meta-llama/Llama-2-7b-chat-hf"
-HF_KEY = os.getenv('HF_KEY')
-headers = {
- "Authorization": f"Bearer {HF_KEY}",
- "Content-Type": "application/json"
-}
-key = os.getenv('OPENAI_API_KEY')
-prompt = f"Write instructions to teach anyone to write a discharge plan. List the entities, features and relationships to CCDA and FHIR objects in boldface."
-should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.")
-
-# 2. Prompt label button demo for LLM
-def add_witty_humor_buttons():
- with st.expander("Wit and Humor 🤣", expanded=True):
- # Tip about the Dromedary family
- st.markdown("🔬 **Fun Fact**: Dromedaries, part of the camel family, have a single hump and are adapted to arid environments. Their 'superpowers' include the ability to survive without water for up to 7 days, thanks to their specialized blood cells and water storage in their hump.")
-
- # Define button descriptions
- descriptions = {
- "Generate Limericks 😂": "Write ten random adult limericks based on quotes that are tweet length and make you laugh 🎭",
- "Wise Quotes 🧙": "Generate ten wise quotes that are tweet length 🦉",
- "Funny Rhymes 🎤": "Create ten funny rhymes that are tweet length 🎶",
- "Medical Jokes 💉": "Create ten medical jokes that are tweet length 🏥",
- "Minnesota Humor ❄️": "Create ten jokes about Minnesota that are tweet length 🌨️",
- "Top Funny Stories 📖": "Create ten funny stories that are tweet length 📚",
- "More Funny Rhymes 🎙️": "Create ten more funny rhymes that are tweet length 🎵"
- }
-
- # Create columns
- col1, col2, col3 = st.columns([1, 1, 1], gap="small")
-
- # Add buttons to columns
- if col1.button("Generate Limericks 😂"):
- StreamLLMChatResponse(descriptions["Generate Limericks 😂"])
-
- if col2.button("Wise Quotes 🧙"):
- StreamLLMChatResponse(descriptions["Wise Quotes 🧙"])
-
- if col3.button("Funny Rhymes 🎤"):
- StreamLLMChatResponse(descriptions["Funny Rhymes 🎤"])
-
- col4, col5, col6 = st.columns([1, 1, 1], gap="small")
-
- if col4.button("Medical Jokes 💉"):
- StreamLLMChatResponse(descriptions["Medical Jokes 💉"])
-
- if col5.button("Minnesota Humor ❄️"):
- StreamLLMChatResponse(descriptions["Minnesota Humor ❄️"])
-
- if col6.button("Top Funny Stories 📖"):
- StreamLLMChatResponse(descriptions["Top Funny Stories 📖"])
-
- col7 = st.columns(1, gap="small")
-
- if col7[0].button("More Funny Rhymes 🎙️"):
- StreamLLMChatResponse(descriptions["More Funny Rhymes 🎙️"])
-
-def SpeechSynthesis(result):
- documentHTML5='''
- <!DOCTYPE html>
- <html>
- <head>
- <title>Read It Aloud
-
-
-
- 🔊 Read It Aloud
-
- 🔊 Read Aloud
-
-
