{
// 获取包含Hugging Face文本的span元素
const spans = link.querySelectorAll('span.whitespace-nowrap, span.hidden.whitespace-nowrap');
spans.forEach(span => {
if (span.textContent && span.textContent.trim().match(/Hugging\s*Face/i)) {
span.textContent = 'AI快站';
}
});
});
// 替换logo图片的alt属性
document.querySelectorAll('img[alt*="Hugging"], img[alt*="Face"]').forEach(img => {
if (img.alt.match(/Hugging\s*Face/i)) {
img.alt = 'AI快站 logo';
}
});
}
// 替换导航栏中的链接
function replaceNavigationLinks() {
// 已替换标记，防止重复运行
if (window._navLinksReplaced) {
return;
}
// 已经替换过的链接集合，防止重复替换
const replacedLinks = new Set();
// 只在导航栏区域查找和替换链接
const headerArea = document.querySelector('header') || document.querySelector('nav');
if (!headerArea) {
return;
}
// 在导航区域内查找链接
const navLinks = headerArea.querySelectorAll('a');
navLinks.forEach(link => {
// 如果已经替换过，跳过
if (replacedLinks.has(link)) return;
const linkText = link.textContent.trim();
const linkHref = link.getAttribute('href') || '';
// 替换Spaces链接 - 仅替换一次
if (
(linkHref.includes('/spaces') || linkHref === '/spaces' ||
linkText === 'Spaces' || linkText.match(/^s*Spacess*$/i)) &&
linkText !== 'PDF TO Markdown' &&
linkText !== 'PDF TO Markdown'
) {
link.textContent = 'PDF TO Markdown';
link.href = 'https://fast360.xyz';
link.setAttribute('target', '_blank');
link.setAttribute('rel', 'noopener noreferrer');
replacedLinks.add(link);
}
// 删除Posts链接
else if (
(linkHref.includes('/posts') || linkHref === '/posts' ||
linkText === 'Posts' || linkText.match(/^s*Postss*$/i))
) {
if (link.parentNode) {
link.parentNode.removeChild(link);
}
replacedLinks.add(link);
}
// 替换Docs链接 - 仅替换一次
else if (
(linkHref.includes('/docs') || linkHref === '/docs' ||
linkText === 'Docs' || linkText.match(/^s*Docss*$/i)) &&
linkText !== 'Voice Cloning'
) {
link.textContent = 'Voice Cloning';
link.href = 'https://vibevoice.info/';
replacedLinks.add(link);
}
// 删除Enterprise链接
else if (
(linkHref.includes('/enterprise') || linkHref === '/enterprise' ||
linkText === 'Enterprise' || linkText.match(/^s*Enterprises*$/i))
) {
if (link.parentNode) {
link.parentNode.removeChild(link);
}
replacedLinks.add(link);
}
});
// 查找可能嵌套的Spaces和Posts文本
const textNodes = [];
function findTextNodes(element) {
if (element.nodeType === Node.TEXT_NODE) {
const text = element.textContent.trim();
if (text === 'Spaces' || text === 'Posts' || text === 'Enterprise') {
textNodes.push(element);
}
} else {
for (const child of element.childNodes) {
findTextNodes(child);
}
}
}
// 只在导航区域内查找文本节点
findTextNodes(headerArea);
// 替换找到的文本节点
textNodes.forEach(node => {
const text = node.textContent.trim();
if (text === 'Spaces') {
node.textContent = node.textContent.replace(/Spaces/g, 'PDF TO Markdown');
} else if (text === 'Posts') {
// 删除Posts文本节点
if (node.parentNode) {
node.parentNode.removeChild(node);
}
} else if (text === 'Enterprise') {
// 删除Enterprise文本节点
if (node.parentNode) {
node.parentNode.removeChild(node);
}
}
});
// 标记已替换完成
window._navLinksReplaced = true;
}
// 替换代码区域中的域名
function replaceCodeDomains() {
// 特别处理span.hljs-string和span.njs-string元素
document.querySelectorAll('span.hljs-string, span.njs-string, span[class*="hljs-string"], span[class*="njs-string"]').forEach(span => {
if (span.textContent && span.textContent.includes('huggingface.co')) {
span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com');
}
});
// 替换hljs-string类的span中的域名（移除多余的转义符号）
document.querySelectorAll('span.hljs-string, span[class*="hljs-string"]').forEach(span => {
if (span.textContent && span.textContent.includes('huggingface.co')) {
span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com');
}
});
// 替换pre和code标签中包含git clone命令的域名
document.querySelectorAll('pre, code').forEach(element => {
if (element.textContent && element.textContent.includes('git clone')) {
const text = element.innerHTML;
if (text.includes('huggingface.co')) {
element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com');
}
}
});
// 处理特定的命令行示例
document.querySelectorAll('pre, code').forEach(element => {
const text = element.innerHTML;
if (text.includes('huggingface.co')) {
// 针对git clone命令的专门处理
if (text.includes('git clone') || text.includes('GIT_LFS_SKIP_SMUDGE=1')) {
element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com');
}
}
});
// 特别处理模型下载页面上的代码片段
document.querySelectorAll('.flex.border-t, .svelte_hydrator, .inline-block').forEach(container => {
const content = container.innerHTML;
if (content && content.includes('huggingface.co')) {
container.innerHTML = content.replace(/huggingface.co/g, 'aifasthub.com');
}
});
// 特别处理模型仓库克隆对话框中的代码片段
try {
// 查找包含"Clone this model repository"标题的对话框
const cloneDialog = document.querySelector('.svelte_hydration_boundary, [data-target="MainHeader"]');
if (cloneDialog) {
// 查找对话框中所有的代码片段和命令示例
const codeElements = cloneDialog.querySelectorAll('pre, code, span');
codeElements.forEach(element => {
if (element.textContent && element.textContent.includes('huggingface.co')) {
if (element.innerHTML.includes('huggingface.co')) {
element.innerHTML = element.innerHTML.replace(/huggingface.co/g, 'aifasthub.com');
} else {
element.textContent = element.textContent.replace(/huggingface.co/g, 'aifasthub.com');
}
}
});
}
// 更精确地定位克隆命令中的域名
document.querySelectorAll('[data-target]').forEach(container => {
const codeBlocks = container.querySelectorAll('pre, code, span.hljs-string');
codeBlocks.forEach(block => {
if (block.textContent && block.textContent.includes('huggingface.co')) {
if (block.innerHTML.includes('huggingface.co')) {
block.innerHTML = block.innerHTML.replace(/huggingface.co/g, 'aifasthub.com');
} else {
block.textContent = block.textContent.replace(/huggingface.co/g, 'aifasthub.com');
}
}
});
});
} catch (e) {
// 错误处理但不打印日志
}
}
// 当DOM加载完成后执行替换
if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', () => {
replaceHeaderBranding();
replaceNavigationLinks();
replaceCodeDomains();
// 只在必要时执行替换 - 3秒后再次检查
setTimeout(() => {
if (!window._navLinksReplaced) {
console.log('[Client] 3秒后重新检查导航链接');
replaceNavigationLinks();
}
}, 3000);
});
} else {
replaceHeaderBranding();
replaceNavigationLinks();
replaceCodeDomains();
// 只在必要时执行替换 - 3秒后再次检查
setTimeout(() => {
if (!window._navLinksReplaced) {
console.log('[Client] 3秒后重新检查导航链接');
replaceNavigationLinks();
}
}, 3000);
}
// 增加一个MutationObserver来处理可能的动态元素加载
const observer = new MutationObserver(mutations => {
// 检查是否导航区域有变化
const hasNavChanges = mutations.some(mutation => {
// 检查是否存在header或nav元素变化
return Array.from(mutation.addedNodes).some(node => {
if (node.nodeType === Node.ELEMENT_NODE) {
// 检查是否是导航元素或其子元素
if (node.tagName === 'HEADER' || node.tagName === 'NAV' ||
node.querySelector('header, nav')) {
return true;
}
// 检查是否在导航元素内部
let parent = node.parentElement;
while (parent) {
if (parent.tagName === 'HEADER' || parent.tagName === 'NAV') {
return true;
}
parent = parent.parentElement;
}
}
return false;
});
});
// 只在导航区域有变化时执行替换
if (hasNavChanges) {
// 重置替换状态，允许再次替换
window._navLinksReplaced = false;
replaceHeaderBranding();
replaceNavigationLinks();
}
});
// 开始观察document.body的变化，包括子节点
if (document.body) {
observer.observe(document.body, { childList: true, subtree: true });
} else {
document.addEventListener('DOMContentLoaded', () => {
observer.observe(document.body, { childList: true, subtree: true });
});
}
})();
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|---|---|---|---|---|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TableTransformerConfig",
"TableTransformerOnnxConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TableTransformerForObjectDetection",
"TableTransformerModel",
"TableTransformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_table_transformer import (
TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TableTransformerConfig,
TableTransformerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_table_transformer import (
TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TableTransformerForObjectDetection,
TableTransformerModel,
TableTransformerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 308 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def lowercase__ ( lowerCamelCase : Tuple , lowerCamelCase : Dict=False , lowerCamelCase : Any=False ) -> Union[str, Any]:
lowerCAmelCase__ : str = "backbone." if is_semantic else ""
lowerCAmelCase__ : str = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append(
(F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append(
(F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
(F"{prefix}cls_token", "beit.embeddings.cls_token"),
(F"{prefix}patch_embed.proj.weight", "beit.embeddings.patch_embeddings.projection.weight"),
(F"{prefix}patch_embed.proj.bias", "beit.embeddings.patch_embeddings.projection.bias"),
(F"{prefix}pos_embed", "beit.embeddings.position_embeddings"),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
("mask_token", "beit.embeddings.mask_token"),
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
("fc_norm.weight", "beit.pooler.layernorm.weight"),
("fc_norm.bias", "beit.pooler.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
return rename_keys
def lowercase__ ( lowerCamelCase : int , lowerCamelCase : Tuple , lowerCamelCase : int=False , lowerCamelCase : Union[str, Any]=False ) -> List[str]:
for i in range(config.num_hidden_layers ):
lowerCAmelCase__ : Optional[int] = "backbone." if is_semantic else ""
# queries, keys and values
lowerCAmelCase__ : Any = state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight" )
lowerCAmelCase__ : List[Any] = state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias" )
lowerCAmelCase__ : Dict = state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias" )
lowerCAmelCase__ : Tuple = in_proj_weight[
: config.hidden_size, :
]
lowerCAmelCase__ : Optional[Any] = q_bias
lowerCAmelCase__ : Optional[int] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCAmelCase__ : Union[str, Any] = in_proj_weight[
-config.hidden_size :, :
]
lowerCAmelCase__ : List[Any] = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
lowerCAmelCase__ : Optional[Any] = state_dict.pop(F"{prefix}blocks.{i}.gamma_1" )
lowerCAmelCase__ : Optional[Any] = state_dict.pop(F"{prefix}blocks.{i}.gamma_2" )
lowerCAmelCase__ : Union[str, Any] = gamma_a
lowerCAmelCase__ : Optional[Any] = gamma_a
def lowercase__ ( lowerCamelCase : int , lowerCamelCase : Union[str, Any] , lowerCamelCase : Tuple ) -> List[Any]:
lowerCAmelCase__ : Dict = dct.pop(lowerCamelCase )
lowerCAmelCase__ : Union[str, Any] = val
def lowercase__ ( ) -> Any:
lowerCAmelCase__ : List[str] = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCAmelCase__ : Tuple = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw )
return im
@torch.no_grad()
def lowercase__ ( lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : List[str]=False ) -> int:
lowerCAmelCase__ : Optional[int] = False if "rvlcdip" in checkpoint_url else True
lowerCAmelCase__ : Any = BeitConfig(use_absolute_position_embeddings=lowerCamelCase , use_mask_token=lowerCamelCase )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
lowerCAmelCase__ : Optional[Any] = 1_0_2_4
lowerCAmelCase__ : Any = 4_0_9_6
lowerCAmelCase__ : int = 2_4
lowerCAmelCase__ : Tuple = 1_6
# labels
if "rvlcdip" in checkpoint_url:
lowerCAmelCase__ : Optional[Any] = 1_6
lowerCAmelCase__ : str = "huggingface/label-files"
lowerCAmelCase__ : List[str] = "rvlcdip-id2label.json"
lowerCAmelCase__ : Tuple = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="dataset" ) , "r" ) )
lowerCAmelCase__ : Tuple = {int(lowerCamelCase ): v for k, v in idalabel.items()}
lowerCAmelCase__ : Optional[Any] = idalabel
lowerCAmelCase__ : Any = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
lowerCAmelCase__ : int = torch.hub.load_state_dict_from_url(lowerCamelCase , map_location="cpu" )["model"]
lowerCAmelCase__ : Union[str, Any] = create_rename_keys(lowerCamelCase , has_lm_head=lowerCamelCase )
for src, dest in rename_keys:
rename_key(lowerCamelCase , lowerCamelCase , lowerCamelCase )
read_in_q_k_v(lowerCamelCase , lowerCamelCase , has_lm_head=lowerCamelCase )
# load HuggingFace model
lowerCAmelCase__ : Union[str, Any] = BeitForMaskedImageModeling(lowerCamelCase ) if has_lm_head else BeitForImageClassification(lowerCamelCase )
model.eval()
model.load_state_dict(lowerCamelCase )
# Check outputs on an image
lowerCAmelCase__ : Dict = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=lowerCamelCase )
lowerCAmelCase__ : str = prepare_img()
lowerCAmelCase__ : List[str] = image_processor(images=lowerCamelCase , return_tensors="pt" )
lowerCAmelCase__ : Any = encoding["pixel_values"]
lowerCAmelCase__ : Optional[Any] = model(lowerCamelCase )
lowerCAmelCase__ : Union[str, Any] = outputs.logits
# verify logits
lowerCAmelCase__ : str = [1, 1_6] if "rvlcdip" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
assert logits.shape == torch.Size(lowerCamelCase ), "Shape of logits not as expected"
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(lowerCamelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(lowerCamelCase )
if push_to_hub:
if has_lm_head:
lowerCAmelCase__ : List[Any] = "dit-base" if "base" in checkpoint_url else "dit-large"
else:
lowerCAmelCase__ : Tuple = "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
image_processor.push_to_hub(
repo_path_or_name=Path(lowerCamelCase , lowerCamelCase ) , organization="nielsr" , commit_message="Add image processor" , use_temp_dir=lowerCamelCase , )
model.push_to_hub(
repo_path_or_name=Path(lowerCamelCase , lowerCamelCase ) , organization="nielsr" , commit_message="Add model" , use_temp_dir=lowerCamelCase , )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
type=str,
help="URL to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
__UpperCAmelCase = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 308 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,unittest.TestCase ):
'''simple docstring'''
UpperCamelCase__ =AltDiffusionPipeline
UpperCamelCase__ =TEXT_TO_IMAGE_PARAMS
UpperCamelCase__ =TEXT_TO_IMAGE_BATCH_PARAMS
UpperCamelCase__ =TEXT_TO_IMAGE_IMAGE_PARAMS
UpperCamelCase__ =TEXT_TO_IMAGE_IMAGE_PARAMS
def UpperCAmelCase__ ( self : List[Any] ) -> Any:
torch.manual_seed(0 )
__magic_name__ : int = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
__magic_name__ : Optional[Any] = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' , clip_sample=__lowerCAmelCase , set_alpha_to_one=__lowerCAmelCase , )
torch.manual_seed(0 )
__magic_name__ : Tuple = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
# TODO: address the non-deterministic text encoder (fails for save-load tests)
# torch.manual_seed(0)
# text_encoder_config = RobertaSeriesConfig(
# hidden_size=32,
# project_dim=32,
# intermediate_size=37,
# layer_norm_eps=1e-05,
# num_attention_heads=4,
# num_hidden_layers=5,
# vocab_size=5002,
# )
# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
torch.manual_seed(0 )
__magic_name__ : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5002 , )
__magic_name__ : Dict = CLIPTextModel(__lowerCAmelCase )
__magic_name__ : Optional[int] = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' )
__magic_name__ : Tuple = 77
__magic_name__ : Tuple = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def UpperCAmelCase__ ( self : List[Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : Any=0 ) -> Dict:
if str(__lowerCAmelCase ).startswith('''mps''' ):
__magic_name__ : List[str] = torch.manual_seed(__lowerCAmelCase )
else:
__magic_name__ : str = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
__magic_name__ : List[str] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def UpperCAmelCase__ ( self : int ) -> Any:
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def UpperCAmelCase__ ( self : Optional[int] ) -> Tuple:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def UpperCAmelCase__ ( self : Optional[Any] ) -> Any:
__magic_name__ : Any = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__magic_name__ : List[str] = self.get_dummy_components()
torch.manual_seed(0 )
__magic_name__ : Optional[int] = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
# TODO: remove after fixing the non-deterministic text encoder
__magic_name__ : Dict = RobertaSeriesModelWithTransformation(__lowerCAmelCase )
__magic_name__ : Any = text_encoder
__magic_name__ : int = AltDiffusionPipeline(**__lowerCAmelCase )
__magic_name__ : Optional[Any] = alt_pipe.to(__lowerCAmelCase )
alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
__magic_name__ : int = self.get_dummy_inputs(__lowerCAmelCase )
__magic_name__ : List[Any] = '''A photo of an astronaut'''
__magic_name__ : List[Any] = alt_pipe(**__lowerCAmelCase )
__magic_name__ : Tuple = output.images
__magic_name__ : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__magic_name__ : Optional[int] = np.array(
[0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase__ ( self : Optional[int] ) -> str:
__magic_name__ : Optional[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__magic_name__ : List[Any] = self.get_dummy_components()
__magic_name__ : str = PNDMScheduler(skip_prk_steps=__lowerCAmelCase )
torch.manual_seed(0 )
__magic_name__ : List[Any] = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
# TODO: remove after fixing the non-deterministic text encoder
__magic_name__ : Any = RobertaSeriesModelWithTransformation(__lowerCAmelCase )
__magic_name__ : Any = text_encoder
__magic_name__ : int = AltDiffusionPipeline(**__lowerCAmelCase )
__magic_name__ : int = alt_pipe.to(__lowerCAmelCase )
alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
__magic_name__ : str = self.get_dummy_inputs(__lowerCAmelCase )
__magic_name__ : Optional[int] = alt_pipe(**__lowerCAmelCase )
__magic_name__ : Union[str, Any] = output.images
__magic_name__ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__magic_name__ : List[Any] = np.array(
[0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase__ ( self : str ) -> Dict:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self : str ) -> Tuple:
__magic_name__ : Union[str, Any] = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , safety_checker=__lowerCAmelCase )
__magic_name__ : int = alt_pipe.to(__lowerCAmelCase )
alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
__magic_name__ : Any = '''A painting of a squirrel eating a burger'''
__magic_name__ : Optional[int] = torch.manual_seed(0 )
__magic_name__ : List[Any] = alt_pipe([prompt] , generator=__lowerCAmelCase , guidance_scale=6.0 , num_inference_steps=20 , output_type='''np''' )
__magic_name__ : List[Any] = output.images
__magic_name__ : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__magic_name__ : Tuple = np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[int]:
__magic_name__ : Dict = DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''' , subfolder='''scheduler''' )
__magic_name__ : List[Any] = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , scheduler=__lowerCAmelCase , safety_checker=__lowerCAmelCase )
__magic_name__ : List[Any] = alt_pipe.to(__lowerCAmelCase )
alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
__magic_name__ : Dict = '''A painting of a squirrel eating a burger'''
__magic_name__ : Union[str, Any] = torch.manual_seed(0 )
__magic_name__ : Optional[Any] = alt_pipe([prompt] , generator=__lowerCAmelCase , num_inference_steps=2 , output_type='''numpy''' )
__magic_name__ : Tuple = output.images
__magic_name__ : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__magic_name__ : Any = np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 707 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def lowercase__ ( __A: str ):
'''simple docstring'''
return 1.0 / (1.0 + np.exp(-_outputs ))
def lowercase__ ( __A: str ):
'''simple docstring'''
__magic_name__ : List[str] = np.max(_outputs ,axis=-1 ,keepdims=__A )
__magic_name__ : int = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 ,keepdims=__A )
class lowerCamelCase ( _lowerCamelCase ):
'''simple docstring'''
UpperCamelCase__ ='''sigmoid'''
UpperCamelCase__ ='''softmax'''
UpperCamelCase__ ='''none'''
@add_end_docstrings(
_lowerCamelCase ,R'''
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `"default"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `"sigmoid"`: Applies the sigmoid function on the output.
- `"softmax"`: Applies the softmax function on the output.
- `"none"`: Does not apply any function on the output.
''' ,)
class lowerCamelCase ( _lowerCamelCase ):
'''simple docstring'''
UpperCamelCase__ =False
UpperCamelCase__ =ClassificationFunction.NONE
def __init__( self : Tuple , **lowerCamelCase_ : Tuple ) -> List[Any]:
super().__init__(**lowerCamelCase_ )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def UpperCAmelCase__ ( self : Optional[int] , lowerCamelCase_ : Tuple=None , lowerCamelCase_ : Dict=None , lowerCamelCase_ : Optional[int]="" , **lowerCamelCase_ : int ) -> str:
# Using "" as default argument because we're going to use `top_k=None` in user code to declare
# "No top_k"
__magic_name__ : Dict = tokenizer_kwargs
__magic_name__ : Dict = {}
if hasattr(self.model.config , '''return_all_scores''' ) and return_all_scores is None:
__magic_name__ : List[str] = self.model.config.return_all_scores
if isinstance(lowerCamelCase_ , lowerCamelCase_ ) or top_k is None:
__magic_name__ : Dict = top_k
__magic_name__ : List[Any] = False
elif return_all_scores is not None:
warnings.warn(
'''`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of'''
''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.''' , lowerCamelCase_ , )
if return_all_scores:
__magic_name__ : str = None
else:
__magic_name__ : int = 1
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
__magic_name__ : str = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
__magic_name__ : List[str] = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self : Optional[Any] , *lowerCamelCase_ : Union[str, Any] , **lowerCamelCase_ : Union[str, Any] ) -> Optional[int]:
__magic_name__ : Optional[int] = super().__call__(*lowerCamelCase_ , **lowerCamelCase_ )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
__magic_name__ : int = '''top_k''' not in kwargs
if isinstance(args[0] , lowerCamelCase_ ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def UpperCAmelCase__ ( self : Optional[Any] , lowerCamelCase_ : Any , **lowerCamelCase_ : int ) -> Dict[str, GenericTensor]:
__magic_name__ : Union[str, Any] = self.framework
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
return self.tokenizer(**lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
elif isinstance(lowerCamelCase_ , lowerCamelCase_ ) and len(lowerCamelCase_ ) == 1 and isinstance(inputs[0] , lowerCamelCase_ ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
elif isinstance(lowerCamelCase_ , lowerCamelCase_ ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
'''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.''' )
return self.tokenizer(lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
def UpperCAmelCase__ ( self : str , lowerCamelCase_ : Dict ) -> Union[str, Any]:
return self.model(**lowerCamelCase_ )
def UpperCAmelCase__ ( self : Dict , lowerCamelCase_ : Dict , lowerCamelCase_ : Dict=None , lowerCamelCase_ : List[str]=1 , lowerCamelCase_ : Any=True ) -> int:
# `_legacy` is used to determine if we're running the naked pipeline and in backward
# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
# the more natural result containing the list.
# Default value before `set_parameters`
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
__magic_name__ : Union[str, Any] = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
__magic_name__ : str = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , '''function_to_apply''' ) and function_to_apply is None:
__magic_name__ : Dict = self.model.config.function_to_apply
else:
__magic_name__ : Optional[Any] = ClassificationFunction.NONE
__magic_name__ : Any = model_outputs['''logits'''][0]
__magic_name__ : Optional[int] = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
__magic_name__ : List[str] = sigmoid(lowerCamelCase_ )
elif function_to_apply == ClassificationFunction.SOFTMAX:
__magic_name__ : Optional[int] = softmax(lowerCamelCase_ )
elif function_to_apply == ClassificationFunction.NONE:
__magic_name__ : Optional[int] = outputs
else:
raise ValueError(F'''Unrecognized `function_to_apply` argument: {function_to_apply}''' )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
__magic_name__ : Union[str, Any] = [
{'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(lowerCamelCase_ )
]
if not _legacy:
dict_scores.sort(key=lambda lowerCamelCase_ : x["score"] , reverse=lowerCamelCase_ )
if top_k is not None:
__magic_name__ : Dict = dict_scores[:top_k]
return dict_scores
| 501 | 0 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowerCamelCase = logging.get_logger(__name__)
class _snake_case ( __lowerCamelCase ):
"""simple docstring"""
def __init__( self , a=None , **a ) -> Dict:
"""simple docstring"""
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , a__ , )
super().__init__(args=a__ , **a__ )
| 317 |
'''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase__ ( __lowerCamelCase ):
"""simple docstring"""
UpperCamelCase__ = '''EncodecFeatureExtractor'''
UpperCamelCase__ = ('''T5Tokenizer''', '''T5TokenizerFast''')
def __init__( self : int ,a__ : int ,a__ : Optional[Any] ):
super().__init__(a__ ,a__ )
a__ = self.feature_extractor
a__ = False
def lowerCAmelCase_ ( self : Optional[int] ,a__ : List[Any]=None ,a__ : str=None ,a__ : int=True ):
return self.tokenizer.get_decoder_prompt_ids(task=a__ ,language=a__ ,no_timestamps=a__ )
def __call__( self : Any ,*a__ : Union[str, Any] ,**a__ : Any ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*a__ ,**a__ )
a__ = kwargs.pop("audio" ,a__ )
a__ = kwargs.pop("sampling_rate" ,a__ )
a__ = kwargs.pop("text" ,a__ )
if len(a__ ) > 0:
a__ = args[0]
a__ = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if text is not None:
a__ = self.tokenizer(a__ ,**a__ )
if audio is not None:
a__ = self.feature_extractor(a__ ,*a__ ,sampling_rate=a__ ,**a__ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
a__ = audio_inputs["input_values"]
if "padding_mask" in audio_inputs:
a__ = audio_inputs["padding_mask"]
return inputs
def lowerCAmelCase_ ( self : str ,*a__ : Union[str, Any] ,**a__ : Optional[Any] ):
a__ = kwargs.pop("audio" ,a__ )
a__ = kwargs.pop("padding_mask" ,a__ )
if len(a__ ) > 0:
a__ = args[0]
a__ = args[1:]
if audio_values is not None:
return self._decode_audio(a__ ,padding_mask=a__ )
else:
return self.tokenizer.batch_decode(*a__ ,**a__ )
def lowerCAmelCase_ ( self : Any ,*a__ : List[str] ,**a__ : Tuple ):
return self.tokenizer.decode(*a__ ,**a__ )
def lowerCAmelCase_ ( self : Optional[int] ,a__ : Optional[Any] ,a__ : Optional = None ):
a__ = to_numpy(a__ )
a__ , a__ , a__ = audio_values.shape
if padding_mask is None:
return list(a__ )
a__ = to_numpy(a__ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
a__ = seq_len - padding_mask.shape[-1]
a__ = 1 - self.feature_extractor.padding_value
a__ = np.pad(a__ ,((0, 0), (0, difference)) ,"constant" ,constant_values=a__ )
a__ = audio_values.tolist()
for i in range(a__ ):
a__ = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
a__ = sliced_audio.reshape(a__ ,-1 )
return audio_values
| 331 | 0 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCamelCase_ ( _lowercase , _lowercase , _lowercase = 10**-10 ) -> float:
__A : Any = a
while True:
__A : List[Any] = Decimal(_lowercase ) - (
Decimal(eval(_lowercase ) ) / Decimal(eval(str(diff(_lowercase ) ) ) ) # noqa: S307
)
# This number dictates the accuracy of the answer
if abs(eval(_lowercase ) ) < precision: # noqa: S307
return float(_lowercase )
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
# Find root of polynomial
print(F'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
# Find Square Root of 5
print(F'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
# Exponential Roots
print(F'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')
| 387 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class _a ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Union[str, Any] = """gpt_neox"""
def __init__( self , __UpperCAmelCase=50_432 , __UpperCAmelCase=6_144 , __UpperCAmelCase=44 , __UpperCAmelCase=64 , __UpperCAmelCase=24_576 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.25 , __UpperCAmelCase=10_000 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.1 , __UpperCAmelCase=2_048 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1e-5 , __UpperCAmelCase=True , __UpperCAmelCase=0 , __UpperCAmelCase=2 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=None , **__UpperCAmelCase , ):
super().__init__(bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
__A : Optional[int] = vocab_size
__A : List[Any] = max_position_embeddings
__A : Any = hidden_size
__A : str = num_hidden_layers
__A : List[str] = num_attention_heads
__A : Dict = intermediate_size
__A : List[Any] = hidden_act
__A : Tuple = rotary_pct
__A : Optional[int] = rotary_emb_base
__A : int = attention_dropout
__A : Optional[int] = hidden_dropout
__A : List[Any] = classifier_dropout
__A : Optional[Any] = initializer_range
__A : Optional[int] = layer_norm_eps
__A : str = use_cache
__A : Optional[int] = tie_word_embeddings
__A : Any = use_parallel_residual
__A : List[Any] = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
"The hidden size is not divisble by the number of attention heads! Make sure to update them!" )
def __UpperCAmelCase( self ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __UpperCAmelCase ) or len(self.rope_scaling ) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
F"got {self.rope_scaling}" )
__A : Dict = self.rope_scaling.get("type" , __UpperCAmelCase )
__A : Dict = self.rope_scaling.get("factor" , __UpperCAmelCase )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" )
if rope_scaling_factor is None or not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 387 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
a : Tuple = logging.getLogger(__name__)
@dataclass(frozen=_lowercase )
class lowercase:
__snake_case: Optional[Any] = 42
__snake_case: List[str] = 42
__snake_case: Union[str, Any] = None
__snake_case: Tuple = None
__snake_case: Tuple = None
@dataclass(frozen=_lowercase )
class lowercase:
__snake_case: Tuple = 42
__snake_case: str = None
__snake_case: Optional[Any] = None
__snake_case: Optional[Any] = None
__snake_case: List[Any] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class lowercase(_lowercase ):
__snake_case: Any = 42
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE = False , ) -> Dict:
"""simple docstring"""
a__ = hans_processors[task]()
a__ = os.path.join(
SCREAMING_SNAKE_CASE__ , 'cached_{}_{}_{}_{}'.format(
'dev' if evaluate else 'train' , tokenizer.__class__.__name__ , str(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ , ) , )
a__ = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
a__ = label_list[2], label_list[1]
a__ = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
a__ = cached_features_file + '.lock'
with FileLock(SCREAMING_SNAKE_CASE__ ):
if os.path.exists(SCREAMING_SNAKE_CASE__ ) and not overwrite_cache:
logger.info(f'Loading features from cached file {cached_features_file}' )
a__ = torch.load(SCREAMING_SNAKE_CASE__ )
else:
logger.info(f'Creating features from dataset file at {data_dir}' )
a__ = (
processor.get_dev_examples(SCREAMING_SNAKE_CASE__ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE__ )
)
logger.info('Training examples: %s' , len(SCREAMING_SNAKE_CASE__ ) )
a__ = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
logger.info('Saving features into cached file %s' , SCREAMING_SNAKE_CASE__ )
torch.save(self.features , SCREAMING_SNAKE_CASE__ )
def __len__( self ) -> Optional[int]:
"""simple docstring"""
return len(self.features )
def __getitem__( self , __SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
return self.features[i]
def lowercase__ ( self ) -> Optional[int]:
"""simple docstring"""
return self.label_list
if is_tf_available():
import tensorflow as tf
class lowercase:
__snake_case: str = 42
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 1_2_8 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE = False , ) -> Optional[Any]:
"""simple docstring"""
a__ = hans_processors[task]()
a__ = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
a__ = label_list[2], label_list[1]
a__ = label_list
a__ = processor.get_dev_examples(SCREAMING_SNAKE_CASE__ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE__ )
a__ = hans_convert_examples_to_features(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='convert examples to features' ):
if ex_index % 1_0_0_0_0 == 0:
logger.info('Writing example %d of %d' % (ex_index, len(SCREAMING_SNAKE_CASE__ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
a__ = tf.data.Dataset.from_generator(
SCREAMING_SNAKE_CASE__ , (
{
'example_id': tf.intaa,
'input_ids': tf.intaa,
'attention_mask': tf.intaa,
'token_type_ids': tf.intaa,
},
tf.intaa,
) , (
{
'example_id': tf.TensorShape([] ),
'input_ids': tf.TensorShape([None, None] ),
'attention_mask': tf.TensorShape([None, None] ),
'token_type_ids': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def lowercase__ ( self ) -> Any:
"""simple docstring"""
return self.dataset
def __len__( self ) -> str:
"""simple docstring"""
return len(self.features )
def __getitem__( self , __SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
return self.features[i]
def lowercase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
return self.label_list
class lowercase(_lowercase ):
def lowercase__ ( self , __SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE__ , 'heuristics_train_set.txt' ) ) , 'train' )
def lowercase__ ( self , __SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE__ , 'heuristics_evaluation_set.txt' ) ) , 'dev' )
def lowercase__ ( self ) -> List[str]:
"""simple docstring"""
return ["contradiction", "entailment", "neutral"]
def lowercase__ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
a__ = []
for i, line in enumerate(SCREAMING_SNAKE_CASE__ ):
if i == 0:
continue
a__ = '%s-%s' % (set_type, line[0])
a__ = line[5]
a__ = line[6]
a__ = line[7][2:] if line[7].startswith('ex' ) else line[7]
a__ = line[0]
examples.append(InputExample(guid=SCREAMING_SNAKE_CASE__ , text_a=SCREAMING_SNAKE_CASE__ , text_b=SCREAMING_SNAKE_CASE__ , label=SCREAMING_SNAKE_CASE__ , pairID=SCREAMING_SNAKE_CASE__ ) )
return examples
def __magic_name__ ( UpperCamelCase : List[InputExample] , UpperCamelCase : List[str] , UpperCamelCase : int , UpperCamelCase : PreTrainedTokenizer , ) -> Dict:
a__ = {label: i for i, label in enumerate(__A )}
a__ = []
for ex_index, example in tqdm.tqdm(enumerate(__A ) , desc='convert examples to features' ):
if ex_index % 10000 == 0:
logger.info('Writing example %d' % (ex_index) )
a__ = tokenizer(
example.text_a , example.text_b , add_special_tokens=__A , max_length=__A , padding='max_length' , truncation=__A , return_overflowing_tokens=__A , )
a__ = label_map[example.label] if example.label in label_map else 0
a__ = int(example.pairID )
features.append(InputFeatures(**__A , label=__A , pairID=__A ) )
for i, example in enumerate(examples[:5] ):
logger.info('*** Example ***' )
logger.info(f'guid: {example}' )
logger.info(f'features: {features[i]}' )
return features
a : Any = {
'hans': 3,
}
a : Any = {
'hans': HansProcessor,
}
| 273 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__UpperCAmelCase = TypeVar("KEY")
__UpperCAmelCase = TypeVar("VAL")
@dataclass(frozen=snake_case , slots=snake_case )
class SCREAMING_SNAKE_CASE ( Generic[KEY, VAL] ):
'''simple docstring'''
__UpperCamelCase = 42
__UpperCamelCase = 42
class SCREAMING_SNAKE_CASE ( _Item ):
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __bool__( self ):
'''simple docstring'''
return False
__UpperCAmelCase = _DeletedItem()
class SCREAMING_SNAKE_CASE ( MutableMapping[KEY, VAL] ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE__ = 8 , SCREAMING_SNAKE_CASE__ = 0.75 ):
'''simple docstring'''
snake_case: str = initial_block_size
snake_case: list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
snake_case: List[Any] = capacity_factor
snake_case: int = 0
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return hash(SCREAMING_SNAKE_CASE__ ) % len(self._buckets )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Tuple = self._buckets[ind]
if not stored:
snake_case: Optional[Any] = _Item(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self._len += 1
return True
elif stored.key == key:
snake_case: List[str] = _Item(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return True
else:
return False
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
snake_case: Union[str, Any] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Optional[Any] = self._buckets
snake_case: Optional[Any] = [None] * new_size
snake_case: Optional[Any] = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _UpperCamelCase ( self ):
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def _UpperCamelCase ( self ):
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Tuple = self._get_bucket_index(SCREAMING_SNAKE_CASE__ )
for _ in range(len(self._buckets ) ):
yield ind
snake_case: int = self._get_next_ind(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE__ ):
if self._try_set(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
break
def __setitem__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __delitem__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE__ ):
snake_case: List[str] = self._buckets[ind]
if item is None:
raise KeyError(SCREAMING_SNAKE_CASE__ )
if item is _deleted:
continue
if item.key == key:
snake_case: str = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE__ ):
snake_case: Union[str, Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(SCREAMING_SNAKE_CASE__ )
def __len__( self ):
'''simple docstring'''
return self._len
def __iter__( self ):
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self ):
'''simple docstring'''
snake_case: Union[str, Any] = ' ,'.join(
F"""{item.key}: {item.val}""" for item in self._buckets if item )
return F"""HashMap({val_string})"""
| 329 | 0 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
UpperCAmelCase__ =get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
UpperCAmelCase__ =12_8022
UpperCAmelCase__ =12_8028
@require_sentencepiece
class lowerCamelCase__ ( _a , unittest.TestCase ):
a : Dict = MaMaaaTokenizer
a : Optional[int] = False
a : str = False
a : Any = True
def SCREAMING_SNAKE_CASE_ ( self : int ):
'''simple docstring'''
super().setUp()
__lowercase = ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
__lowercase = dict(zip(A_ , range(len(A_ ) ) ) )
__lowercase = Path(self.tmpdirname )
save_json(A_ , save_dir / VOCAB_FILES_NAMES["""vocab_file"""] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(A_ , save_dir / VOCAB_FILES_NAMES["""spm_file"""] )
__lowercase = MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , **A_ : List[Any] ):
'''simple docstring'''
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **A_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , A_ : Optional[int] ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
__lowercase = """</s>"""
__lowercase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_ ) , A_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_ ) , A_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = self.get_tokenizer()
__lowercase = list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """</s>""" )
self.assertEqual(vocab_keys[1] , """<unk>""" )
self.assertEqual(vocab_keys[-1] , """<s>""" )
self.assertEqual(len(A_ ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip("""Skip this test while all models are still to be uploaded.""" )
def SCREAMING_SNAKE_CASE_ ( self : int ):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self : Any ):
'''simple docstring'''
__lowercase = self.get_tokenizer()
__lowercase = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(A_ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A_ ) , [2, 3, 4, 5, 6] , )
__lowercase = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(A_ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
__lowercase = tokenizer.convert_tokens_to_string(A_ )
self.assertEqual(A_ , """This is a test""" )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
'''simple docstring'''
__lowercase = {"""input_ids""": [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A_ , model_name="""facebook/m2m100_418M""" , revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e""" , )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase__ ( unittest.TestCase ):
a : Optional[int] = """facebook/m2m100_418M"""
a : str = [
"""In my opinion, there are two levels of response from the French government.""",
"""NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
]
a : int = [
"""Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
"""L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
]
# fmt: off
a : Optional[int] = [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : str ):
'''simple docstring'''
__lowercase = MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""en""" , tgt_lang="""fr""" )
__lowercase = 1
return cls
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
self.assertEqual(self.tokenizer.get_lang_id("""ar""" ) , 1_2_8_0_0_6 )
self.assertEqual(self.tokenizer.get_lang_id("""en""" ) , 1_2_8_0_2_2 )
self.assertEqual(self.tokenizer.get_lang_id("""ro""" ) , 1_2_8_0_7_6 )
self.assertEqual(self.tokenizer.get_lang_id("""mr""" ) , 1_2_8_0_6_3 )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
__lowercase = self.tokenizer.get_vocab()
self.assertEqual(len(A_ ) , self.tokenizer.vocab_size )
self.assertEqual(vocab["""<unk>"""] , 3 )
self.assertIn(self.tokenizer.get_lang_token("""en""" ) , A_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = """en"""
__lowercase = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , A_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
self.assertIn(A_ , self.tokenizer.all_special_ids )
# fmt: off
__lowercase = [FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
# fmt: on
__lowercase = self.tokenizer.decode(A_ , skip_special_tokens=A_ )
__lowercase = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=A_ )
self.assertEqual(A_ , A_ )
self.assertNotIn(self.tokenizer.eos_token , A_ )
def SCREAMING_SNAKE_CASE_ ( self : int ):
'''simple docstring'''
__lowercase = tempfile.mkdtemp()
__lowercase = self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(A_ )
__lowercase = MaMaaaTokenizer.from_pretrained(A_ )
self.assertDictEqual(new_tok.lang_token_to_id , A_ )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
__lowercase = """en"""
__lowercase = """fr"""
__lowercase = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=A_ , return_tensors="""pt""" )
__lowercase = shift_tokens_right(
batch["""labels"""] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
__lowercase = batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : int ):
'''simple docstring'''
__lowercase = """mr"""
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("""mr""" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
__lowercase = """zh"""
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("""zh""" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = """mr"""
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("""mr""" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
__lowercase = """zh"""
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("""zh""" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self : int ):
'''simple docstring'''
__lowercase = self.tokenizer._build_translation_inputs("""A test""" , return_tensors="""pt""" , src_lang="""en""" , tgt_lang="""ar""" )
self.assertEqual(
nested_simplify(A_ ) , {
# en_XX, A, test, EOS
"""input_ids""": [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 1_2_8_0_0_6,
} , )
| 442 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase_ ( UpperCamelCase__ : str ):
"""simple docstring"""
def decorator(UpperCamelCase__ : Tuple ):
__lowercase = getattr(UpperCamelCase__ , """handle_key""" , [] )
handle += [key]
setattr(UpperCamelCase__ , """handle_key""" , UpperCamelCase__ )
return func
return decorator
def lowerCAmelCase_ ( *UpperCamelCase__ : List[str] ):
"""simple docstring"""
def decorator(UpperCamelCase__ : Tuple ):
__lowercase = getattr(UpperCamelCase__ , """handle_key""" , [] )
handle += keys
setattr(UpperCamelCase__ , """handle_key""" , UpperCamelCase__ )
return func
return decorator
class lowerCamelCase__ ( _a ):
def __new__( cls : str , A_ : Optional[Any] , A_ : Union[str, Any] , A_ : int ):
'''simple docstring'''
__lowercase = super().__new__(cls , A_ , A_ , A_ )
if not hasattr(A_ , """key_handler""" ):
setattr(A_ , """key_handler""" , {} )
setattr(A_ , """handle_input""" , KeyHandler.handle_input )
for value in attrs.values():
__lowercase = getattr(A_ , """handle_key""" , [] )
for key in handled_keys:
__lowercase = value
return new_cls
@staticmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict ):
'''simple docstring'''
__lowercase = get_character()
if char != KEYMAP["undefined"]:
__lowercase = ord(A_ )
__lowercase = cls.key_handler.get(A_ )
if handler:
__lowercase = char
return handler(cls )
else:
return None
def lowerCAmelCase_ ( cls : int ):
"""simple docstring"""
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 442 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
_a : Optional[Any] = logging.getLogger(__name__)
_a : str = tf.data.AUTOTUNE
def _lowerCAmelCase ( ) -> Tuple:
__lowerCAmelCase = argparse.ArgumentParser(description="""Train a masked language model on TPU.""" )
parser.add_argument(
"""--pretrained_model_config""" , type=lowercase , default="""roberta-base""" , help="""The model config to use. Note that we don't copy the model's weights, only the config!""" , )
parser.add_argument(
"""--tokenizer""" , type=lowercase , default="""unigram-tokenizer-wikitext""" , help="""The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size.""" , )
parser.add_argument(
"""--per_replica_batch_size""" , type=lowercase , default=8 , help="""Batch size per TPU core.""" , )
parser.add_argument(
"""--no_tpu""" , action="""store_true""" , help="""If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances.""" , )
parser.add_argument(
"""--tpu_name""" , type=lowercase , help="""Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs.""" , default="""local""" , )
parser.add_argument(
"""--tpu_zone""" , type=lowercase , help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.""" , )
parser.add_argument(
"""--gcp_project""" , type=lowercase , help="""Google cloud project name. Only used for non-Colab TPU nodes.""" )
parser.add_argument(
"""--bfloat16""" , action="""store_true""" , help="""Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.""" , )
parser.add_argument(
"""--train_dataset""" , type=lowercase , help="""Path to training dataset to load. If the path begins with `gs://`"""
""" then the dataset will be loaded from a Google Cloud Storage bucket.""" , )
parser.add_argument(
"""--shuffle_buffer_size""" , type=lowercase , default=2**18 , help="""Size of the shuffle buffer (in samples)""" , )
parser.add_argument(
"""--eval_dataset""" , type=lowercase , help="""Path to evaluation dataset to load. If the path begins with `gs://`"""
""" then the dataset will be loaded from a Google Cloud Storage bucket.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowercase , default=1 , help="""Number of epochs to train for.""" , )
parser.add_argument(
"""--learning_rate""" , type=lowercase , default=1e-4 , help="""Learning rate to use for training.""" , )
parser.add_argument(
"""--weight_decay_rate""" , type=lowercase , default=1e-3 , help="""Weight decay rate to use for training.""" , )
parser.add_argument(
"""--max_length""" , type=lowercase , default=512 , help="""Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py""" , )
parser.add_argument(
"""--mlm_probability""" , type=lowercase , default=0.15 , help="""Fraction of tokens to mask during training.""" , )
parser.add_argument("""--output_dir""" , type=lowercase , required=lowercase , help="""Path to save model checkpoints to.""" )
parser.add_argument("""--hub_model_id""" , type=lowercase , help="""Model ID to upload to on the Hugging Face Hub.""" )
__lowerCAmelCase = parser.parse_args()
return args
def _lowerCAmelCase ( lowercase ) -> int:
try:
if args.tpu_name:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver(
args.tpu_name , zone=args.tpu_zone , project=args.gcp_project )
else:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
raise RuntimeError(
"""Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or """
"""--gcp_project. When running on a TPU VM, use --tpu_name local.""" )
tf.config.experimental_connect_to_cluster(lowercase )
tf.tpu.experimental.initialize_tpu_system(lowercase )
return tpu
def _lowerCAmelCase ( lowercase ) -> List[str]:
__lowerCAmelCase = 0
for file in file_list:
__lowerCAmelCase = file.split("""/""" )[-1]
__lowerCAmelCase = re.search(R"""-\d+-(\d+)\.tfrecord""" , lowercase ).group(1 )
__lowerCAmelCase = int(lowercase )
num_samples += sample_count
return num_samples
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase , lowercase=None ) -> Any:
__lowerCAmelCase = count_samples(lowercase )
__lowerCAmelCase = tf.data.Dataset.from_tensor_slices(lowercase )
if shuffle:
__lowerCAmelCase = dataset.shuffle(len(lowercase ) )
__lowerCAmelCase = tf.data.TFRecordDataset(lowercase , num_parallel_reads=lowercase )
# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
__lowerCAmelCase = dataset.apply(tf.data.experimental.assert_cardinality(lowercase ) )
__lowerCAmelCase = dataset.map(lowercase , num_parallel_calls=lowercase )
if shuffle:
assert shuffle_buffer_size is not None
__lowerCAmelCase = dataset.shuffle(args.shuffle_buffer_size )
__lowerCAmelCase = dataset.batch(lowercase , drop_remainder=lowercase )
__lowerCAmelCase = dataset.map(lowercase , num_parallel_calls=lowercase )
__lowerCAmelCase = dataset.prefetch(lowercase )
return dataset
def _lowerCAmelCase ( lowercase ) -> int:
if not args.no_tpu:
__lowerCAmelCase = initialize_tpu(lowercase )
__lowerCAmelCase = tf.distribute.TPUStrategy(lowercase )
else:
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device="""/gpu:0""" )
if args.bfloataa:
tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16""" )
__lowerCAmelCase = AutoTokenizer.from_pretrained(args.tokenizer )
__lowerCAmelCase = AutoConfig.from_pretrained(args.pretrained_model_config )
__lowerCAmelCase = tokenizer.vocab_size
__lowerCAmelCase = tf.io.gfile.glob(os.path.join(args.train_dataset , """*.tfrecord""" ) )
if not training_records:
raise ValueError(f'No .tfrecord files found in {args.train_dataset}.' )
__lowerCAmelCase = tf.io.gfile.glob(os.path.join(args.eval_dataset , """*.tfrecord""" ) )
if not eval_records:
raise ValueError(f'No .tfrecord files found in {args.eval_dataset}.' )
__lowerCAmelCase = count_samples(lowercase )
__lowerCAmelCase = num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
__lowerCAmelCase = steps_per_epoch * args.num_epochs
with strategy.scope():
__lowerCAmelCase = TFAutoModelForMaskedLM.from_config(lowercase )
model(model.dummy_inputs ) # Pass some dummy inputs through the model to ensure all the weights are built
__lowerCAmelCase , __lowerCAmelCase = create_optimizer(
num_train_steps=lowercase , num_warmup_steps=total_train_steps // 20 , init_lr=args.learning_rate , weight_decay_rate=args.weight_decay_rate , )
# Transformers models compute the right loss for their task by default when labels are passed, and will
# use this for training unless you specify your own loss function in compile().
model.compile(optimizer=lowercase , metrics=["""accuracy"""] )
def decode_fn(lowercase ):
__lowerCAmelCase = {
"""input_ids""": tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
"""attention_mask""": tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
}
return tf.io.parse_single_example(lowercase , lowercase )
# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
# use their methods in our data pipeline.
__lowerCAmelCase = DataCollatorForLanguageModeling(
tokenizer=lowercase , mlm_probability=args.mlm_probability , mlm=lowercase , return_tensors="""tf""" )
def mask_with_collator(lowercase ):
# TF really needs an isin() function
__lowerCAmelCase = (
~tf.cast(batch["""attention_mask"""] , tf.bool )
| (batch["""input_ids"""] == tokenizer.cls_token_id)
| (batch["""input_ids"""] == tokenizer.sep_token_id)
)
__lowerCAmelCase , __lowerCAmelCase = data_collator.tf_mask_tokens(
batch["""input_ids"""] , vocab_size=len(lowercase ) , mask_token_id=tokenizer.mask_token_id , special_tokens_mask=lowercase , )
return batch
__lowerCAmelCase = args.per_replica_batch_size * strategy.num_replicas_in_sync
__lowerCAmelCase = prepare_dataset(
lowercase , decode_fn=lowercase , mask_fn=lowercase , batch_size=lowercase , shuffle=lowercase , shuffle_buffer_size=args.shuffle_buffer_size , )
__lowerCAmelCase = prepare_dataset(
lowercase , decode_fn=lowercase , mask_fn=lowercase , batch_size=lowercase , shuffle=lowercase , )
__lowerCAmelCase = []
if args.hub_model_id:
callbacks.append(
PushToHubCallback(output_dir=args.output_dir , hub_model_id=args.hub_model_id , tokenizer=lowercase ) )
model.fit(
lowercase , validation_data=lowercase , epochs=args.num_epochs , callbacks=lowercase , )
model.save_pretrained(args.output_dir )
if __name__ == "__main__":
_a : Any = parse_args()
main(args)
| 689 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_a : Optional[int] = logging.get_logger(__name__)
_a : int = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_a : str = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _UpperCAmelCase :
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Model type selected in the list: """ + """, """.join(lowerCAmelCase_ )} )
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""} )
a : int =field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : int =field(
default=1_28 , metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""} , )
a : int =field(
default=64 , metadata={
"""help""": (
"""The maximum number of tokens for the question. Questions longer than this will """
"""be truncated to this length."""
)
} , )
a : int =field(
default=30 , metadata={
"""help""": (
"""The maximum length of an answer that can be generated. This is needed because the start """
"""and end predictions are not conditioned on one another."""
)
} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""} )
a : float =field(
default=0.0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a : int =field(
default=20 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a : int =field(
default=0 , metadata={
"""help""": (
"""language id of input for language-specific xlm models (see"""
""" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
)
} , )
a : int =field(default=1 , metadata={"""help""": """multiple threads for converting example to features"""} )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Optional[Any] ="""train"""
a : Optional[int] ="""dev"""
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : SquadDataTrainingArguments
a : List[SquadFeatures]
a : Split
a : bool
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = Split.train,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = "pt",):
'''simple docstring'''
__lowerCAmelCase = args
__lowerCAmelCase = is_language_sensitive
__lowerCAmelCase = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
try:
__lowerCAmelCase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
__lowerCAmelCase = mode
# Load data features from cache or dataset file
__lowerCAmelCase = """v2""" if args.version_2_with_negative else """v1"""
__lowerCAmelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir,f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}',)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(__SCREAMING_SNAKE_CASE ):
if os.path.exists(__SCREAMING_SNAKE_CASE ) and not args.overwrite_cache:
__lowerCAmelCase = time.time()
__lowerCAmelCase = torch.load(__SCREAMING_SNAKE_CASE )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
__lowerCAmelCase = self.old_features["""features"""]
__lowerCAmelCase = self.old_features.get("""dataset""",__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.old_features.get("""examples""",__SCREAMING_SNAKE_CASE )
logger.info(
f'Loading features from cached file {cached_features_file} [took %.3f s]',time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
""" future run""" )
else:
if mode == Split.dev:
__lowerCAmelCase = self.processor.get_dev_examples(args.data_dir )
else:
__lowerCAmelCase = self.processor.get_train_examples(args.data_dir )
__lowerCAmelCase , __lowerCAmelCase = squad_convert_examples_to_features(
examples=self.examples,tokenizer=__SCREAMING_SNAKE_CASE,max_seq_length=args.max_seq_length,doc_stride=args.doc_stride,max_query_length=args.max_query_length,is_training=mode == Split.train,threads=args.threads,return_dataset=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples},__SCREAMING_SNAKE_CASE,)
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.features[i]
__lowerCAmelCase = torch.tensor(feature.input_ids,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.attention_mask,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.token_type_ids,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.cls_index,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.p_mask,dtype=torch.float )
__lowerCAmelCase = torch.tensor(feature.is_impossible,dtype=torch.float )
__lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape,dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
__lowerCAmelCase = torch.tensor(feature.start_position,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.end_position,dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 689 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class __UpperCamelCase ( lowercase__ , unittest.TestCase ):
lowercase : Optional[int] = VideoToVideoSDPipeline
lowercase : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'video'} ) - {'image', 'width', 'height'}
lowercase : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'video'} ) - {'image'}
lowercase : str = PipelineTesterMixin.required_optional_params - {'latents'}
lowercase : Optional[int] = False
# No `output_type`.
lowercase : List[str] = frozenset(
[
'num_inference_steps',
'generator',
'latents',
'return_dict',
'callback',
'callback_steps',
] )
def a__ ( self :Tuple ):
torch.manual_seed(0 )
snake_case_ : Any = UNetaDConditionModel(
block_out_channels=(3_2, 6_4, 6_4, 6_4) ,layers_per_block=2 ,sample_size=3_2 ,in_channels=4 ,out_channels=4 ,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""") ,up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""") ,cross_attention_dim=3_2 ,attention_head_dim=4 ,)
snake_case_ : List[Any] = DDIMScheduler(
beta_start=0.0_00_85 ,beta_end=0.0_12 ,beta_schedule="""scaled_linear""" ,clip_sample=__a ,set_alpha_to_one=__a ,)
torch.manual_seed(0 )
snake_case_ : List[str] = AutoencoderKL(
block_out_channels=[3_2, 6_4] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,sample_size=1_2_8 ,)
torch.manual_seed(0 )
snake_case_ : Tuple = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=3_2 ,intermediate_size=3_7 ,layer_norm_eps=1E-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1_0_0_0 ,hidden_act="""gelu""" ,projection_dim=5_1_2 ,)
snake_case_ : List[Any] = CLIPTextModel(__a )
snake_case_ : Optional[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
snake_case_ : Dict = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def a__ ( self :List[Any] ,_UpperCamelCase :List[str] ,_UpperCamelCase :str=0 ):
snake_case_ : Dict = floats_tensor((1, 3, 3, 3_2, 3_2) ,rng=random.Random(__a ) ).to(__a )
if str(__a ).startswith("""mps""" ):
snake_case_ : int = torch.manual_seed(__a )
else:
snake_case_ : Any = torch.Generator(device=__a ).manual_seed(__a )
snake_case_ : int = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""video""": video,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def a__ ( self :str ):
snake_case_ : Union[str, Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case_ : Any = self.get_dummy_components()
snake_case_ : List[str] = VideoToVideoSDPipeline(**__a )
snake_case_ : Union[str, Any] = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
snake_case_ : Dict = self.get_dummy_inputs(__a )
snake_case_ : Optional[Any] = """np"""
snake_case_ : List[str] = sd_pipe(**__a ).frames
snake_case_ : int = frames[0][-3:, -3:, -1]
assert frames[0].shape == (3_2, 3_2, 3)
snake_case_ : Optional[Any] = np.array([1_0_6, 1_1_7, 1_1_3, 1_7_4, 1_3_7, 1_1_2, 1_4_8, 1_5_1, 1_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def a__ ( self :Union[str, Any] ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__a ,expected_max_diff=5E-3 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def a__ ( self :List[str] ):
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def a__ ( self :Optional[int] ):
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def a__ ( self :Tuple ):
pass
def a__ ( self :Optional[Any] ):
return super().test_progress_bar()
@slow
@skip_mps
class __UpperCamelCase ( unittest.TestCase ):
def a__ ( self :List[Any] ):
snake_case_ : Union[str, Any] = VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL""" ,torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
snake_case_ : str = torch.Generator(device="""cpu""" ).manual_seed(0 )
snake_case_ : str = torch.randn((1, 1_0, 3, 1_0_2_4, 5_7_6) ,generator=__a )
snake_case_ : int = video.to("""cuda""" )
snake_case_ : List[Any] = """Spiderman is surfing"""
snake_case_ : Union[str, Any] = pipe(__a ,video=__a ,generator=__a ,num_inference_steps=3 ,output_type="""pt""" ).frames
snake_case_ : int = np.array([-1.0_45_89_84, -1.1_27_92_97, -0.9_66_30_86, -0.91_50_39_06, -0.75_09_76_56] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1E-2
| 712 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__A : List[Any] = logging.get_logger(__name__)
def UpperCAmelCase ( lowerCamelCase_ :nn.ModuleList , lowerCamelCase_ :nn.ModuleList , lowerCamelCase_ :List[int] ):
'''simple docstring'''
snake_case_ : Optional[Any] = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(lowerCamelCase_ ) == len(lowerCamelCase_ ), F'''{len(lowerCamelCase_ )} != {len(lowerCamelCase_ )}'''
dest_layers.load_state_dict(layers_to_copy.state_dict() )
__A : Optional[int] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__A : Dict = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def UpperCAmelCase ( lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :int ):
'''simple docstring'''
try:
snake_case_ : int = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
F''' {n_student}''' )
return list(range(lowerCamelCase_ ) )
def UpperCAmelCase ( lowerCamelCase_ :List[Any] , lowerCamelCase_ :List[Any] ):
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}''' )
elif n_teacher == n_student:
return list(range(lowerCamelCase_ ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def UpperCAmelCase ( lowerCamelCase_ :Union[str, PreTrainedModel] , lowerCamelCase_ :Union[str, Path] = "student" , lowerCamelCase_ :Union[int, None] = None , lowerCamelCase_ :Union[int, None] = None , lowerCamelCase_ :List[str]=False , lowerCamelCase_ :Optional[int]=None , lowerCamelCase_ :Optional[Any]=None , **lowerCamelCase_ :Dict , ):
'''simple docstring'''
snake_case_ : Optional[Any] = """encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
assert (e is not None) or (d is not None), _msg
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
AutoTokenizer.from_pretrained(lowerCamelCase_ ).save_pretrained(lowerCamelCase_ ) # purely for convenience
snake_case_ : List[Any] = AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_ ).eval()
else:
assert isinstance(lowerCamelCase_ , lowerCamelCase_ ), F'''teacher must be a model or string got type {type(lowerCamelCase_ )}'''
snake_case_ : Any = teacher.config.to_diff_dict()
try:
snake_case_ , snake_case_ : List[str] = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
snake_case_ : Dict = teacher_e
if d is None:
snake_case_ : List[Any] = teacher_d
init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d} )
except AttributeError: # T5
if hasattr(teacher.config , """num_encoder_layers""" ):
snake_case_ , snake_case_ : Tuple = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
snake_case_ , snake_case_ : Union[str, Any] = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
snake_case_ : Optional[int] = teacher_e
if d is None:
snake_case_ : List[str] = teacher_d
if hasattr(teacher.config , """num_encoder_layers""" ):
init_kwargs.update({"""num_encoder_layers""": e, """num_decoder_layers""": d} )
else:
init_kwargs.update({"""num_layers""": e, """num_decoder_layers""": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(lowerCamelCase_ )
# Copy weights
snake_case_ : List[Any] = teacher.config_class(**lowerCamelCase_ )
snake_case_ : Tuple = AutoModelForSeqaSeqLM.from_config(lowerCamelCase_ )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
snake_case_ : Tuple = student.load_state_dict(teacher.state_dict() , strict=lowerCamelCase_ )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
snake_case_ , snake_case_ : List[str] = list(range(lowerCamelCase_ ) ), list(range(lowerCamelCase_ ) )
logger.info(
F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
F''' {save_path}''' )
student.save_pretrained(lowerCamelCase_ )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
snake_case_ : List[int] = pick_layers_to_copy(lowerCamelCase_ , lowerCamelCase_ )
if d_layers_to_copy is None:
snake_case_ : List[int] = pick_layers_to_copy(lowerCamelCase_ , lowerCamelCase_ )
try:
if hasattr(
lowerCamelCase_ , """prophetnet""" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , lowerCamelCase_ )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , lowerCamelCase_ )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , lowerCamelCase_ )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , lowerCamelCase_ )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , lowerCamelCase_ )
copy_layers(teacher.decoder.block , student.decoder.block , lowerCamelCase_ )
logger.info(
F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''' )
snake_case_ : Any = {
"""teacher_type""": teacher.config.model_type,
"""copied_encoder_layers""": e_layers_to_copy,
"""copied_decoder_layers""": d_layers_to_copy,
}
student.save_pretrained(lowerCamelCase_ )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 267 | 0 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__SCREAMING_SNAKE_CASE =False
class UpperCamelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
lowercase_ : str = VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' ,torch_dtype=torch.floataa )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
lowercase_ : Tuple = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
lowercase_ : int = torch.manual_seed(0 )
lowercase_ : Optional[int] = pipe.dual_guided(
prompt='first prompt' ,image=__UpperCamelCase ,text_to_image_strength=0.75 ,generator=__UpperCamelCase ,guidance_scale=7.5 ,num_inference_steps=2 ,output_type='numpy' ,).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__UpperCamelCase )
lowercase_ : str = VersatileDiffusionPipeline.from_pretrained(__UpperCamelCase ,torch_dtype=torch.floataa )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
lowercase_ : List[Any] = generator.manual_seed(0 )
lowercase_ : Union[str, Any] = pipe.dual_guided(
prompt='first prompt' ,image=__UpperCamelCase ,text_to_image_strength=0.75 ,generator=__UpperCamelCase ,guidance_scale=7.5 ,num_inference_steps=2 ,output_type='numpy' ,).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def _UpperCAmelCase ( self ) -> Dict:
'''simple docstring'''
lowercase_ : Any = VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' ,torch_dtype=torch.floataa )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
lowercase_ : int = 'cyberpunk 2077'
lowercase_ : Union[str, Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
lowercase_ : Optional[Any] = torch.manual_seed(0 )
lowercase_ : int = pipe.dual_guided(
prompt=__UpperCamelCase ,image=__UpperCamelCase ,text_to_image_strength=0.75 ,generator=__UpperCamelCase ,guidance_scale=7.5 ,num_inference_steps=50 ,output_type='numpy' ,).images
lowercase_ : int = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
lowercase_ : Union[str, Any] = np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
lowercase_ : Optional[Any] = 'A painting of a squirrel eating a burger '
lowercase_ : Optional[Any] = torch.manual_seed(0 )
lowercase_ : Dict = pipe.text_to_image(
prompt=__UpperCamelCase ,generator=__UpperCamelCase ,guidance_scale=7.5 ,num_inference_steps=50 ,output_type='numpy' ).images
lowercase_ : Tuple = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
lowercase_ : Tuple = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
lowercase_ : Dict = pipe.image_variation(__UpperCamelCase ,generator=__UpperCamelCase ,output_type='numpy' ).images
lowercase_ : Union[str, Any] = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
lowercase_ : Union[str, Any] = np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 425 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class UpperCamelCase ( lowercase_ , lowercase_ ):
@register_to_config
def __init__( self ,__UpperCamelCase = 768 ,) -> str:
'''simple docstring'''
super().__init__()
lowercase_ : Union[str, Any] = nn.Parameter(torch.zeros(1 ,__UpperCamelCase ) )
lowercase_ : Optional[Any] = nn.Parameter(torch.ones(1 ,__UpperCamelCase ) )
def _UpperCAmelCase ( self ,__UpperCamelCase = None ,__UpperCamelCase = None ,) -> Tuple:
'''simple docstring'''
lowercase_ : Tuple = nn.Parameter(self.mean.to(__UpperCamelCase ).to(__UpperCamelCase ) )
lowercase_ : str = nn.Parameter(self.std.to(__UpperCamelCase ).to(__UpperCamelCase ) )
return self
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> str:
'''simple docstring'''
lowercase_ : Dict = (embeds - self.mean) * 1.0 / self.std
return embeds
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> Dict:
'''simple docstring'''
lowercase_ : Any = (embeds * self.std) + self.mean
return embeds
| 425 | 1 |
'''simple docstring'''
# Copyright 2022 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.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
_UpperCAmelCase : Union[str, Any] = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
def lowerCAmelCase_ (lowercase__ : Optional[int]=None ) -> List[str]:
'''simple docstring'''
if subparsers is not None:
lowerCAmelCase__ = subparsers.add_parser('''tpu-config''' , description=_description )
else:
lowerCAmelCase__ = argparse.ArgumentParser('''Accelerate tpu-config command''' , description=_description )
# Core arguments
lowerCAmelCase__ = parser.add_argument_group(
'''Config Arguments''' , '''Arguments that can be configured through `accelerate config`.''' )
config_args.add_argument(
'''--config_file''' , type=lowercase__ , default=lowercase__ , help='''Path to the config file to use for accelerate.''' , )
config_args.add_argument(
'''--tpu_name''' , default=lowercase__ , help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''' , )
config_args.add_argument(
'''--tpu_zone''' , default=lowercase__ , help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''' , )
lowerCAmelCase__ = parser.add_argument_group('''TPU Arguments''' , '''Arguments for options ran inside the TPU.''' )
pod_args.add_argument(
'''--use_alpha''' , action='''store_true''' , help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''' , )
pod_args.add_argument(
'''--command_file''' , default=lowercase__ , help='''The path to the file containing the commands to run on the pod on startup.''' , )
pod_args.add_argument(
'''--command''' , action='''append''' , nargs='''+''' , help='''A command to run on the pod. Can be passed multiple times.''' , )
pod_args.add_argument(
'''--install_accelerate''' , action='''store_true''' , help='''Whether to install accelerate on the pod. Defaults to False.''' , )
pod_args.add_argument(
'''--accelerate_version''' , default='''latest''' , help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''' , )
pod_args.add_argument(
'''--debug''' , action='''store_true''' , help='''If set, will print the command that would be run instead of running it.''' )
if subparsers is not None:
parser.set_defaults(func=lowercase__ )
return parser
def lowerCAmelCase_ (lowercase__ : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase__ = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(lowercase__ ):
lowerCAmelCase__ = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
lowerCAmelCase__ = defaults.command_file
if not args.command and defaults.commands is not None:
lowerCAmelCase__ = defaults.commands
if not args.tpu_name:
lowerCAmelCase__ = defaults.tpu_name
if not args.tpu_zone:
lowerCAmelCase__ = defaults.tpu_zone
if args.accelerate_version == "dev":
lowerCAmelCase__ = '''git+https://github.com/huggingface/accelerate.git'''
elif args.accelerate_version == "latest":
lowerCAmelCase__ = '''accelerate -U'''
elif isinstance(parse(args.accelerate_version ) , lowercase__ ):
lowerCAmelCase__ = f'accelerate=={args.accelerate_version}'
if not args.command_file and not args.command:
raise ValueError('''You must specify either a command file or a command to run on the pod.''' )
if args.command_file:
with open(args.command_file , '''r''' ) as f:
lowerCAmelCase__ = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , lowercase__ ):
lowerCAmelCase__ = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
lowerCAmelCase__ = ['''cd /usr/share''']
if args.install_accelerate:
new_cmd += [f'pip install {args.accelerate_version}']
new_cmd += args.command
lowerCAmelCase__ = '''; '''.join(lowercase__ )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
lowerCAmelCase__ = ['''gcloud''']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'Running {" ".join(lowercase__ )}' )
return
subprocess.run(lowercase__ )
print('''Successfully setup pod.''' )
def lowerCAmelCase_ () -> str:
'''simple docstring'''
lowerCAmelCase__ = tpu_command_parser()
lowerCAmelCase__ = parser.parse_args()
tpu_command_launcher(lowercase__ )
| 717 |
from __future__ import annotations
def lowerCAmelCase_ (lowercase__ : list[int] ) -> bool:
'''simple docstring'''
return len(set(lowercase__ ) ) == len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 288 | 0 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__=True, UpperCAmelCase__="pt" ) -> Optional[Any]:
A_ = {"""add_prefix_space""": True} if isinstance(UpperCAmelCase__, UpperCAmelCase__ ) and not line.startswith(""" """ ) else {}
A_ = padding_side
return tokenizer(
[line], max_length=UpperCAmelCase__, padding="""max_length""" if pad_to_max_length else None, truncation=UpperCAmelCase__, return_tensors=UpperCAmelCase__, add_special_tokens=UpperCAmelCase__, **UpperCAmelCase__, )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__=None, ) -> str:
A_ = input_ids.ne(UpperCAmelCase__ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class A__ ( _snake_case ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__="train" , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__="" , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
A_ = Path(UpperCamelCase__ ).joinpath(type_path + """.source""" )
A_ = Path(UpperCamelCase__ ).joinpath(type_path + """.target""" )
A_ = self.get_char_lens(self.src_file )
A_ = max_source_length
A_ = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
A_ = tokenizer
A_ = prefix
if n_obs is not None:
A_ = self.src_lens[:n_obs]
A_ = src_lang
A_ = tgt_lang
def __len__( self ) -> List[Any]:
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self , UpperCamelCase__ ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
A_ = index + 1 # linecache starts at 1
A_ = self.prefix + linecache.getline(str(self.src_file ) , UpperCamelCase__ ).rstrip("""\n""" )
A_ = linecache.getline(str(self.tgt_file ) , UpperCamelCase__ ).rstrip("""\n""" )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , UpperCamelCase__ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
A_ = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , UpperCamelCase__ ) else self.tokenizer
)
A_ = self.tokenizer.generator if isinstance(self.tokenizer , UpperCamelCase__ ) else self.tokenizer
A_ = encode_line(UpperCamelCase__ , UpperCamelCase__ , self.max_source_length , """right""" )
A_ = encode_line(UpperCamelCase__ , UpperCamelCase__ , self.max_target_length , """right""" )
A_ = source_inputs["""input_ids"""].squeeze()
A_ = target_inputs["""input_ids"""].squeeze()
A_ = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def snake_case_ ( UpperCamelCase__ ) -> Optional[int]:
'''simple docstring'''
return [len(UpperCamelCase__ ) for x in Path(UpperCamelCase__ ).open().readlines()]
def snake_case_ ( self , UpperCamelCase__ ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
A_ = torch.stack([x["""input_ids"""] for x in batch] )
A_ = torch.stack([x["""attention_mask"""] for x in batch] )
A_ = torch.stack([x["""decoder_input_ids"""] for x in batch] )
A_ = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , UpperCamelCase__ )
else self.tokenizer.pad_token_id
)
A_ = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , UpperCamelCase__ )
else self.tokenizer.pad_token_id
)
A_ = trim_batch(UpperCamelCase__ , UpperCamelCase__ )
A_ , A_ = trim_batch(UpperCamelCase__ , UpperCamelCase__ , attention_mask=UpperCamelCase__ )
A_ = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__lowerCamelCase = getLogger(__name__)
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Optional[int]:
return list(itertools.chain.from_iterable(UpperCAmelCase__ ) )
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> None:
A_ = get_git_info()
save_json(UpperCAmelCase__, os.path.join(UpperCAmelCase__, """git_log.json""" ) )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__=4, **UpperCAmelCase__ ) -> Dict:
with open(UpperCAmelCase__, """w""" ) as f:
json.dump(UpperCAmelCase__, UpperCAmelCase__, indent=UpperCAmelCase__, **UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Tuple:
with open(UpperCAmelCase__ ) as f:
return json.load(UpperCAmelCase__ )
def UpperCAmelCase__ ( ) -> Tuple:
A_ = git.Repo(search_parent_directories=UpperCAmelCase__ )
A_ = {
"""repo_id""": str(UpperCAmelCase__ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> List:
return list(map(UpperCAmelCase__, UpperCAmelCase__ ) )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Optional[Any]:
with open(UpperCAmelCase__, """wb""" ) as f:
return pickle.dump(UpperCAmelCase__, UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> List[str]:
def remove_articles(UpperCAmelCase__ ):
return re.sub(r"""\b(a|an|the)\b""", """ """, UpperCAmelCase__ )
def white_space_fix(UpperCAmelCase__ ):
return " ".join(text.split() )
def remove_punc(UpperCAmelCase__ ):
A_ = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(UpperCAmelCase__ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__ ) ) ) )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Union[str, Any]:
A_ = normalize_answer(UpperCAmelCase__ ).split()
A_ = normalize_answer(UpperCAmelCase__ ).split()
A_ = Counter(UpperCAmelCase__ ) & Counter(UpperCAmelCase__ )
A_ = sum(common.values() )
if num_same == 0:
return 0
A_ = 1.0 * num_same / len(UpperCAmelCase__ )
A_ = 1.0 * num_same / len(UpperCAmelCase__ )
A_ = (2 * precision * recall) / (precision + recall)
return fa
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> List[str]:
return normalize_answer(UpperCAmelCase__ ) == normalize_answer(UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Dict:
assert len(UpperCAmelCase__ ) == len(UpperCAmelCase__ )
A_ = 0
for hypo, pred in zip(UpperCAmelCase__, UpperCAmelCase__ ):
em += exact_match_score(UpperCAmelCase__, UpperCAmelCase__ )
if len(UpperCAmelCase__ ) > 0:
em /= len(UpperCAmelCase__ )
return {"em": em}
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Any:
return model_prefix.startswith("""rag""" )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Optional[Any]:
A_ = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
A_ = """dropout_rate"""
for p in extra_params:
if getattr(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ):
if not hasattr(UpperCAmelCase__, UpperCAmelCase__ ) and not hasattr(UpperCAmelCase__, equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(UpperCAmelCase__ ) )
delattr(UpperCAmelCase__, UpperCAmelCase__ )
continue
A_ = p if hasattr(UpperCAmelCase__, UpperCAmelCase__ ) else equivalent_param[p]
setattr(UpperCAmelCase__, UpperCAmelCase__, getattr(UpperCAmelCase__, UpperCAmelCase__ ) )
delattr(UpperCAmelCase__, UpperCAmelCase__ )
return hparams, config
| 288 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class A__ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=12 , UpperCamelCase__=7 , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=99 , UpperCamelCase__=32 , UpperCamelCase__=32 , UpperCamelCase__=2 , UpperCamelCase__=4 , UpperCamelCase__=37 , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=512 , UpperCamelCase__=0.02 , UpperCamelCase__=0 , UpperCamelCase__=None , ) -> Union[str, Any]:
'''simple docstring'''
A_ = parent
A_ = batch_size
A_ = seq_length
A_ = is_training
A_ = use_input_mask
A_ = use_labels
A_ = vocab_size
A_ = hidden_size
A_ = projection_dim
A_ = num_hidden_layers
A_ = num_attention_heads
A_ = intermediate_size
A_ = dropout
A_ = attention_dropout
A_ = max_position_embeddings
A_ = initializer_range
A_ = scope
A_ = bos_token_id
def snake_case_ ( self ) -> Dict:
'''simple docstring'''
A_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ = None
if self.use_input_mask:
A_ = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
A_ = input_mask.numpy()
A_ , A_ = input_mask.shape
A_ = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(UpperCamelCase__ ):
A_ = 1
A_ = 0
A_ = self.get_config()
return config, input_ids, tf.convert_to_tensor(UpperCamelCase__ )
def snake_case_ ( self ) -> Optional[int]:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
A_ = TFBlipTextModel(config=UpperCamelCase__ )
A_ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , training=UpperCamelCase__ )
A_ = model(UpperCamelCase__ , training=UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = self.prepare_config_and_inputs()
A_ , A_ , A_ = config_and_inputs
A_ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class A__ ( _snake_case , unittest.TestCase ):
lowercase = (TFBlipTextModel,) if is_tf_available() else ()
lowercase = False
lowercase = False
lowercase = False
def snake_case_ ( self ) -> Optional[int]:
'''simple docstring'''
A_ = BlipTextModelTester(self )
A_ = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=37 )
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case_ ( self ) -> str:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
pass
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""Blip does not use inputs_embeds""" )
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING""" )
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING""" )
def snake_case_ ( self ) -> Any:
'''simple docstring'''
pass
@slow
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ = TFBlipTextModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__=True ) -> str:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__ )
| 288 | 1 |
"""simple docstring"""
from __future__ import annotations
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = str(lowerCamelCase__ )
return len(lowerCamelCase__ ) == 9 and set(lowerCamelCase__ ) == set("""123456789""" )
def _UpperCAmelCase ( ):
"""simple docstring"""
for base_num in range(9999 , 4999 , -1 ):
lowerCAmelCase__ = 10_0002 * base_num
if is_9_pandigital(lowerCamelCase__ ):
return candidate
for base_num in range(333 , 99 , -1 ):
lowerCAmelCase__ = 100_2003 * base_num
if is_9_pandigital(lowerCamelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"{solution() = }")
| 674 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : str = LayoutLMTokenizer
UpperCamelCase_ : List[Any] = LayoutLMTokenizerFast
UpperCamelCase_ : Dict = True
UpperCamelCase_ : Any = True
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
lowerCAmelCase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowerCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def _SCREAMING_SNAKE_CASE ( self : int , **snake_case__ : Union[str, Any] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Tuple ):
lowerCAmelCase__ = """UNwant\u00E9d,running"""
lowerCAmelCase__ = """unwanted, running"""
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.tokenizer_class(self.vocab_file )
lowerCAmelCase__ = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(snake_case__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , [7, 4, 5, 10, 8, 9] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
pass
| 674 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowercase__ ( __lowercase : str , __lowercase : Union[str, Any] , __lowercase : List[str] , __lowercase : str , ) -> list[float]:
"""simple docstring"""
__UpperCamelCase = coefficient_matrix.shape
__UpperCamelCase = constant_matrix.shape
if rowsa != colsa:
__UpperCamelCase = F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
raise ValueError(A__ )
if colsa != 1:
__UpperCamelCase = F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
raise ValueError(A__ )
if rowsa != rowsa:
__UpperCamelCase = (
'Coefficient and constant matrices dimensions must be nxn and nx1 but '
F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
)
raise ValueError(A__ )
if len(A__ ) != rowsa:
__UpperCamelCase = (
'Number of initial values must be equal to number of rows in coefficient '
F'''matrix but received {len(A__ )} and {rowsa}'''
)
raise ValueError(A__ )
if iterations <= 0:
raise ValueError('Iterations must be at least 1' )
__UpperCamelCase = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
__UpperCamelCase = table.shape
strictly_diagonally_dominant(A__ )
# Iterates the whole matrix for given number of times
for _ in range(A__ ):
__UpperCamelCase = []
for row in range(A__ ):
__UpperCamelCase = 0
for col in range(A__ ):
if col == row:
__UpperCamelCase = table[row][col]
elif col == cols - 1:
__UpperCamelCase = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
__UpperCamelCase = (temp + val) / denom
new_val.append(A__ )
__UpperCamelCase = new_val
return [float(A__ ) for i in new_val]
def lowercase__ ( __lowercase : Optional[int] ) -> bool:
"""simple docstring"""
__UpperCamelCase = table.shape
__UpperCamelCase = True
for i in range(0 , A__ ):
__UpperCamelCase = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError('Coefficient matrix is not strictly diagonally dominant' )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 399 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
lowercase : int = get_tests_dir("""fixtures""")
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
a__ : List[str] = mock.Mock()
a__ : Any = 500
a__ : List[Any] = {}
a__ : List[str] = HTTPError
a__ : Optional[Any] = {}
# Download this model to make sure it's in the cache.
a__ : Dict = ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit')
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=lowercase) as mock_head:
a__ : str = ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit')
# This check we did call the fake head request
mock_head.assert_called()
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ : Optional[Any] = ViTImageProcessor.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json')
def __lowercase ( self) -> int:
'''simple docstring'''
with self.assertRaises(lowercase):
# config is in subfolder, the following should not work without specifying the subfolder
a__ : Optional[int] = AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants')
a__ : int = AutoImageProcessor.from_pretrained(
'hf-internal-testing/stable-diffusion-all-variants' , subfolder='feature_extractor')
self.assertIsNotNone(lowercase)
@is_staging_test
class A__ ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def __lowercase ( cls) -> Dict:
'''simple docstring'''
a__ : Union[str, Any] = TOKEN
HfFolder.save_token(lowercase)
@classmethod
def __lowercase ( cls) -> Optional[Any]:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-image-processor')
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-image-processor-org')
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-image-processor')
except HTTPError:
pass
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : str = ViTImageProcessor.from_pretrained(lowercase)
image_processor.push_to_hub('test-image-processor' , use_auth_token=self._token)
a__ : Dict = ViTImageProcessor.from_pretrained(F'{USER}/test-image-processor')
for k, v in image_processor.__dict__.items():
self.assertEqual(lowercase , getattr(lowercase , lowercase))
# Reset repo
delete_repo(token=self._token , repo_id='test-image-processor')
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
lowercase , repo_id='test-image-processor' , push_to_hub=lowercase , use_auth_token=self._token)
a__ : List[str] = ViTImageProcessor.from_pretrained(F'{USER}/test-image-processor')
for k, v in image_processor.__dict__.items():
self.assertEqual(lowercase , getattr(lowercase , lowercase))
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
a__ : int = ViTImageProcessor.from_pretrained(lowercase)
image_processor.push_to_hub('valid_org/test-image-processor' , use_auth_token=self._token)
a__ : Any = ViTImageProcessor.from_pretrained('valid_org/test-image-processor')
for k, v in image_processor.__dict__.items():
self.assertEqual(lowercase , getattr(lowercase , lowercase))
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-image-processor')
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
lowercase , repo_id='valid_org/test-image-processor-org' , push_to_hub=lowercase , use_auth_token=self._token)
a__ : int = ViTImageProcessor.from_pretrained('valid_org/test-image-processor-org')
for k, v in image_processor.__dict__.items():
self.assertEqual(lowercase , getattr(lowercase , lowercase))
def __lowercase ( self) -> List[str]:
'''simple docstring'''
CustomImageProcessor.register_for_auto_class()
a__ : List[Any] = CustomImageProcessor.from_pretrained(lowercase)
image_processor.push_to_hub('test-dynamic-image-processor' , use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'AutoImageProcessor': 'custom_image_processing.CustomImageProcessor'} , )
a__ : str = AutoImageProcessor.from_pretrained(
F'{USER}/test-dynamic-image-processor' , trust_remote_code=lowercase)
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , 'CustomImageProcessor')
| 302 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase__ : str = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
'SwiftFormerOnnxConfig',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : Optional[int] = [
'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'SwiftFormerForImageClassification',
'SwiftFormerModel',
'SwiftFormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 703 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class _A ( UpperCamelCase ):
'''simple docstring'''
_lowercase = 42
_lowercase = 42
class _A ( nn.Module ):
'''simple docstring'''
_lowercase = 42
_lowercase = (16, 32, 96, 256)
_lowercase = jnp.floataa
def __lowerCAmelCase ( self : Tuple )-> Any:
snake_case__ : Union[str, Any] = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
snake_case__ : List[str] = []
for i in range(len(self.block_out_channels ) - 1 ):
snake_case__ : List[str] = self.block_out_channels[i]
snake_case__ : Union[str, Any] = self.block_out_channels[i + 1]
snake_case__ : Optional[int] = nn.Conv(
lowerCamelCase , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(lowerCamelCase )
snake_case__ : int = nn.Conv(
lowerCamelCase , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(lowerCamelCase )
snake_case__ : Any = blocks
snake_case__ : Union[str, Any] = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self : Union[str, Any] , lowerCamelCase : Any )-> Tuple:
snake_case__ : int = self.conv_in(lowerCamelCase )
snake_case__ : Dict = nn.silu(lowerCamelCase )
for block in self.blocks:
snake_case__ : Dict = block(lowerCamelCase )
snake_case__ : str = nn.silu(lowerCamelCase )
snake_case__ : Union[str, Any] = self.conv_out(lowerCamelCase )
return embedding
@flax_register_to_config
class _A ( nn.Module , UpperCamelCase , UpperCamelCase ):
'''simple docstring'''
_lowercase = 32
_lowercase = 4
_lowercase = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_lowercase = False
_lowercase = (320, 640, 1280, 1280)
_lowercase = 2
_lowercase = 8
_lowercase = None
_lowercase = 1280
_lowercase = 0.0
_lowercase = False
_lowercase = jnp.floataa
_lowercase = True
_lowercase = 0
_lowercase = "rgb"
_lowercase = (16, 32, 96, 256)
def __lowerCAmelCase ( self : Optional[int] , lowerCamelCase : jax.random.KeyArray )-> FrozenDict:
# init input tensors
snake_case__ : Union[str, Any] = (1, self.in_channels, self.sample_size, self.sample_size)
snake_case__ : Dict = jnp.zeros(lowerCamelCase , dtype=jnp.floataa )
snake_case__ : str = jnp.ones((1,) , dtype=jnp.intaa )
snake_case__ : str = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
snake_case__ : Dict = (1, 3, self.sample_size * 8, self.sample_size * 8)
snake_case__ : Optional[Any] = jnp.zeros(lowerCamelCase , dtype=jnp.floataa )
snake_case__ , snake_case__ : List[Any] = jax.random.split(lowerCamelCase )
snake_case__ : Optional[Any] = {"""params""": params_rng, """dropout""": dropout_rng}
return self.init(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )["params"]
def __lowerCAmelCase ( self : int )-> int:
snake_case__ : List[Any] = self.block_out_channels
snake_case__ : Union[str, Any] = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
snake_case__ : Any = self.num_attention_heads or self.attention_head_dim
# input
snake_case__ : Any = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
snake_case__ : List[str] = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
snake_case__ : Union[str, Any] = FlaxTimestepEmbedding(lowerCamelCase , dtype=self.dtype )
snake_case__ : Tuple = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
snake_case__ : Union[str, Any] = self.only_cross_attention
if isinstance(lowerCamelCase , lowerCamelCase ):
snake_case__ : Dict = (only_cross_attention,) * len(self.down_block_types )
if isinstance(lowerCamelCase , lowerCamelCase ):
snake_case__ : Union[str, Any] = (num_attention_heads,) * len(self.down_block_types )
# down
snake_case__ : Optional[Any] = []
snake_case__ : str = []
snake_case__ : Tuple = block_out_channels[0]
snake_case__ : Tuple = nn.Conv(
lowerCamelCase , kernel_size=(1, 1) , padding="""VALID""" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(lowerCamelCase )
for i, down_block_type in enumerate(self.down_block_types ):
snake_case__ : Optional[int] = output_channel
snake_case__ : int = block_out_channels[i]
snake_case__ : Union[str, Any] = i == len(lowerCamelCase ) - 1
if down_block_type == "CrossAttnDownBlock2D":
snake_case__ : int = FlaxCrossAttnDownBlockaD(
in_channels=lowerCamelCase , out_channels=lowerCamelCase , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
snake_case__ : Any = FlaxDownBlockaD(
in_channels=lowerCamelCase , out_channels=lowerCamelCase , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(lowerCamelCase )
for _ in range(self.layers_per_block ):
snake_case__ : str = nn.Conv(
lowerCamelCase , kernel_size=(1, 1) , padding="""VALID""" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(lowerCamelCase )
if not is_final_block:
snake_case__ : Tuple = nn.Conv(
lowerCamelCase , kernel_size=(1, 1) , padding="""VALID""" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(lowerCamelCase )
snake_case__ : Optional[Any] = down_blocks
snake_case__ : List[str] = controlnet_down_blocks
# mid
snake_case__ : Union[str, Any] = block_out_channels[-1]
snake_case__ : Optional[int] = FlaxUNetMidBlockaDCrossAttn(
in_channels=lowerCamelCase , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
snake_case__ : Tuple = nn.Conv(
lowerCamelCase , kernel_size=(1, 1) , padding="""VALID""" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self : Optional[int] , lowerCamelCase : int , lowerCamelCase : List[Any] , lowerCamelCase : Dict , lowerCamelCase : Optional[int] , lowerCamelCase : float = 1.0 , lowerCamelCase : bool = True , lowerCamelCase : bool = False , )-> Union[FlaxControlNetOutput, Tuple]:
snake_case__ : int = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
snake_case__ : Union[str, Any] = jnp.flip(lowerCamelCase , axis=1 )
# 1. time
if not isinstance(lowerCamelCase , jnp.ndarray ):
snake_case__ : List[Any] = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(lowerCamelCase , jnp.ndarray ) and len(timesteps.shape ) == 0:
snake_case__ : Optional[int] = timesteps.astype(dtype=jnp.floataa )
snake_case__ : Optional[int] = jnp.expand_dims(lowerCamelCase , 0 )
snake_case__ : Any = self.time_proj(lowerCamelCase )
snake_case__ : List[Any] = self.time_embedding(lowerCamelCase )
# 2. pre-process
snake_case__ : Dict = jnp.transpose(lowerCamelCase , (0, 2, 3, 1) )
snake_case__ : Any = self.conv_in(lowerCamelCase )
snake_case__ : Dict = jnp.transpose(lowerCamelCase , (0, 2, 3, 1) )
snake_case__ : str = self.controlnet_cond_embedding(lowerCamelCase )
sample += controlnet_cond
# 3. down
snake_case__ : Any = (sample,)
for down_block in self.down_blocks:
if isinstance(lowerCamelCase , lowerCamelCase ):
snake_case__ , snake_case__ : List[str] = down_block(lowerCamelCase , lowerCamelCase , lowerCamelCase , deterministic=not train )
else:
snake_case__ , snake_case__ : List[str] = down_block(lowerCamelCase , lowerCamelCase , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
snake_case__ : Optional[Any] = self.mid_block(lowerCamelCase , lowerCamelCase , lowerCamelCase , deterministic=not train )
# 5. contronet blocks
snake_case__ : List[str] = ()
for down_block_res_sample, controlnet_block in zip(lowerCamelCase , self.controlnet_down_blocks ):
snake_case__ : List[str] = controlnet_block(lowerCamelCase )
controlnet_down_block_res_samples += (down_block_res_sample,)
snake_case__ : Optional[Any] = controlnet_down_block_res_samples
snake_case__ : Optional[int] = self.controlnet_mid_block(lowerCamelCase )
# 6. scaling
snake_case__ : Optional[Any] = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=lowerCamelCase , mid_block_res_sample=lowerCamelCase )
| 172 | 0 |
import math
def _a ( ) -> Tuple:
"""simple docstring"""
lowerCamelCase__ : Dict = input('''Enter message: ''' )
lowerCamelCase__ : List[str] = int(input(f"Enter key [2-{len(A__ ) - 1}]: " ) )
lowerCamelCase__ : List[Any] = input('''Encryption/Decryption [e/d]: ''' )
if mode.lower().startswith('''e''' ):
lowerCamelCase__ : List[Any] = encrypt_message(A__ , A__ )
elif mode.lower().startswith('''d''' ):
lowerCamelCase__ : Tuple = decrypt_message(A__ , A__ )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(f"Output:\n{text + '|'}" )
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase__ : int = [''''''] * key
for col in range(A__ ):
lowerCamelCase__ : Union[str, Any] = col
while pointer < len(A__ ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(A__ )
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase__ : int = math.ceil(len(A__ ) / key )
lowerCamelCase__ : Optional[int] = key
lowerCamelCase__ : int = (num_cols * num_rows) - len(A__ )
lowerCamelCase__ : List[str] = [''''''] * num_cols
lowerCamelCase__ : Tuple = 0
lowerCamelCase__ : int = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
lowerCamelCase__ : Any = 0
row += 1
return "".join(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 315 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
a__ : List[str] = logging.get_logger("transformers.models.encodec")
a__ : Optional[Any] = {
"quantizer.vq.layers.*._codebook.inited": "quantizer.layers.*.codebook.inited",
"quantizer.vq.layers.*._codebook.cluster_size": "quantizer.layers.*.codebook.cluster_size",
"quantizer.vq.layers.*._codebook.embed": "quantizer.layers.*.codebook.embed",
"quantizer.vq.layers.*._codebook.embed_avg": "quantizer.layers.*.codebook.embed_avg",
}
a__ : int = {
"encoder.model.0.conv.conv": "encoder.layers.0.conv",
"encoder.model.1.block.1.conv.conv": "encoder.layers.1.block.1.conv",
"encoder.model.1.block.3.conv.conv": "encoder.layers.1.block.3.conv",
"encoder.model.1.shortcut.conv.conv": "encoder.layers.1.shortcut.conv",
"encoder.model.3.conv.conv": "encoder.layers.3.conv",
"encoder.model.4.block.1.conv.conv": "encoder.layers.4.block.1.conv",
"encoder.model.4.block.3.conv.conv": "encoder.layers.4.block.3.conv",
"encoder.model.4.shortcut.conv.conv": "encoder.layers.4.shortcut.conv",
"encoder.model.6.conv.conv": "encoder.layers.6.conv",
"encoder.model.7.block.1.conv.conv": "encoder.layers.7.block.1.conv",
"encoder.model.7.block.3.conv.conv": "encoder.layers.7.block.3.conv",
"encoder.model.7.shortcut.conv.conv": "encoder.layers.7.shortcut.conv",
"encoder.model.9.conv.conv": "encoder.layers.9.conv",
"encoder.model.10.block.1.conv.conv": "encoder.layers.10.block.1.conv",
"encoder.model.10.block.3.conv.conv": "encoder.layers.10.block.3.conv",
"encoder.model.10.shortcut.conv.conv": "encoder.layers.10.shortcut.conv",
"encoder.model.12.conv.conv": "encoder.layers.12.conv",
"encoder.model.13.lstm": "encoder.layers.13.lstm",
"encoder.model.15.conv.conv": "encoder.layers.15.conv",
}
a__ : int = {
"encoder.model.0.conv.norm": "encoder.layers.0.norm",
"encoder.model.1.block.1.conv.norm": "encoder.layers.1.block.1.norm",
"encoder.model.1.block.3.conv.norm": "encoder.layers.1.block.3.norm",
"encoder.model.1.shortcut.conv.norm": "encoder.layers.1.shortcut.norm",
"encoder.model.3.conv.norm": "encoder.layers.3.norm",
"encoder.model.4.block.1.conv.norm": "encoder.layers.4.block.1.norm",
"encoder.model.4.block.3.conv.norm": "encoder.layers.4.block.3.norm",
"encoder.model.4.shortcut.conv.norm": "encoder.layers.4.shortcut.norm",
"encoder.model.6.conv.norm": "encoder.layers.6.norm",
"encoder.model.7.block.1.conv.norm": "encoder.layers.7.block.1.norm",
"encoder.model.7.block.3.conv.norm": "encoder.layers.7.block.3.norm",
"encoder.model.7.shortcut.conv.norm": "encoder.layers.7.shortcut.norm",
"encoder.model.9.conv.norm": "encoder.layers.9.norm",
"encoder.model.10.block.1.conv.norm": "encoder.layers.10.block.1.norm",
"encoder.model.10.block.3.conv.norm": "encoder.layers.10.block.3.norm",
"encoder.model.10.shortcut.conv.norm": "encoder.layers.10.shortcut.norm",
"encoder.model.12.conv.norm": "encoder.layers.12.norm",
"encoder.model.15.conv.norm": "encoder.layers.15.norm",
}
a__ : Dict = {
"decoder.model.0.conv.conv": "decoder.layers.0.conv",
"decoder.model.1.lstm": "decoder.layers.1.lstm",
"decoder.model.3.convtr.convtr": "decoder.layers.3.conv",
"decoder.model.4.block.1.conv.conv": "decoder.layers.4.block.1.conv",
"decoder.model.4.block.3.conv.conv": "decoder.layers.4.block.3.conv",
"decoder.model.4.shortcut.conv.conv": "decoder.layers.4.shortcut.conv",
"decoder.model.6.convtr.convtr": "decoder.layers.6.conv",
"decoder.model.7.block.1.conv.conv": "decoder.layers.7.block.1.conv",
"decoder.model.7.block.3.conv.conv": "decoder.layers.7.block.3.conv",
"decoder.model.7.shortcut.conv.conv": "decoder.layers.7.shortcut.conv",
"decoder.model.9.convtr.convtr": "decoder.layers.9.conv",
"decoder.model.10.block.1.conv.conv": "decoder.layers.10.block.1.conv",
"decoder.model.10.block.3.conv.conv": "decoder.layers.10.block.3.conv",
"decoder.model.10.shortcut.conv.conv": "decoder.layers.10.shortcut.conv",
"decoder.model.12.convtr.convtr": "decoder.layers.12.conv",
"decoder.model.13.block.1.conv.conv": "decoder.layers.13.block.1.conv",
"decoder.model.13.block.3.conv.conv": "decoder.layers.13.block.3.conv",
"decoder.model.13.shortcut.conv.conv": "decoder.layers.13.shortcut.conv",
"decoder.model.15.conv.conv": "decoder.layers.15.conv",
}
a__ : Any = {
"decoder.model.0.conv.norm": "decoder.layers.0.norm",
"decoder.model.3.convtr.norm": "decoder.layers.3.norm",
"decoder.model.4.block.1.conv.norm": "decoder.layers.4.block.1.norm",
"decoder.model.4.block.3.conv.norm": "decoder.layers.4.block.3.norm",
"decoder.model.4.shortcut.conv.norm": "decoder.layers.4.shortcut.norm",
"decoder.model.6.convtr.norm": "decoder.layers.6.norm",
"decoder.model.7.block.1.conv.norm": "decoder.layers.7.block.1.norm",
"decoder.model.7.block.3.conv.norm": "decoder.layers.7.block.3.norm",
"decoder.model.7.shortcut.conv.norm": "decoder.layers.7.shortcut.norm",
"decoder.model.9.convtr.norm": "decoder.layers.9.norm",
"decoder.model.10.block.1.conv.norm": "decoder.layers.10.block.1.norm",
"decoder.model.10.block.3.conv.norm": "decoder.layers.10.block.3.norm",
"decoder.model.10.shortcut.conv.norm": "decoder.layers.10.shortcut.norm",
"decoder.model.12.convtr.norm": "decoder.layers.12.norm",
"decoder.model.13.block.1.conv.norm": "decoder.layers.13.block.1.norm",
"decoder.model.13.block.3.conv.norm": "decoder.layers.13.block.3.norm",
"decoder.model.13.shortcut.conv.norm": "decoder.layers.13.shortcut.norm",
"decoder.model.15.conv.norm": "decoder.layers.15.norm",
}
a__ : Tuple = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
a__ : List[str] = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
a__ : str = []
a__ : str = []
def _lowerCAmelCase ( A__ , A__ , A__ , A__ , A__ ):
for attribute in key.split('.' ):
lowercase__ = getattr(A__ , A__ )
if weight_type is not None:
lowercase__ = getattr(A__ , A__ ).shape
else:
lowercase__ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}''' )
if weight_type == "weight":
lowercase__ = value
elif weight_type == "weight_g":
lowercase__ = value
elif weight_type == "weight_v":
lowercase__ = value
elif weight_type == "bias":
lowercase__ = value
elif weight_type == "running_mean":
lowercase__ = value
elif weight_type == "running_var":
lowercase__ = value
elif weight_type == "num_batches_tracked":
lowercase__ = value
elif weight_type == "weight_ih_l0":
lowercase__ = value
elif weight_type == "weight_hh_l0":
lowercase__ = value
elif weight_type == "bias_ih_l0":
lowercase__ = value
elif weight_type == "bias_hh_l0":
lowercase__ = value
elif weight_type == "weight_ih_l1":
lowercase__ = value
elif weight_type == "weight_hh_l1":
lowercase__ = value
elif weight_type == "bias_ih_l1":
lowercase__ = value
elif weight_type == "bias_hh_l1":
lowercase__ = value
else:
lowercase__ = value
logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.''' )
def _lowerCAmelCase ( A__ , A__ ):
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
lowercase__, lowercase__ = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def _lowerCAmelCase ( A__ , A__ , A__ ):
lowercase__ = []
if model_name == "encodec_24khz" or "encodec_32khz":
lowercase__ = MAPPING_24K
elif model_name == "encodec_48khz":
lowercase__ = MAPPING_48K
else:
raise ValueError(F'''Unsupported model: {model_name}''' )
for name, value in orig_dict.items():
if should_ignore(A__ , A__ ):
logger.info(F'''{name} was ignored''' )
continue
lowercase__ = False
for key, mapped_key in MAPPING.items():
if "*" in key:
lowercase__, lowercase__ = key.split('.*.' )
if prefix in name and suffix in name:
lowercase__ = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('embed' ) and name.endswith('embed_avg' ):
continue
lowercase__ = True
if "*" in mapped_key:
lowercase__ = name.split(A__ )[0].split('.' )[-2]
lowercase__ = mapped_key.replace('*' , A__ )
if "weight_g" in name:
lowercase__ = 'weight_g'
elif "weight_v" in name:
lowercase__ = 'weight_v'
elif "weight_ih_l0" in name:
lowercase__ = 'weight_ih_l0'
elif "weight_hh_l0" in name:
lowercase__ = 'weight_hh_l0'
elif "bias_ih_l0" in name:
lowercase__ = 'bias_ih_l0'
elif "bias_hh_l0" in name:
lowercase__ = 'bias_hh_l0'
elif "weight_ih_l1" in name:
lowercase__ = 'weight_ih_l1'
elif "weight_hh_l1" in name:
lowercase__ = 'weight_hh_l1'
elif "bias_ih_l1" in name:
lowercase__ = 'bias_ih_l1'
elif "bias_hh_l1" in name:
lowercase__ = 'bias_hh_l1'
elif "bias" in name:
lowercase__ = 'bias'
elif "weight" in name:
lowercase__ = 'weight'
elif "running_mean" in name:
lowercase__ = 'running_mean'
elif "running_var" in name:
lowercase__ = 'running_var'
elif "num_batches_tracked" in name:
lowercase__ = 'num_batches_tracked'
else:
lowercase__ = None
set_recursively(A__ , A__ , A__ , A__ , A__ )
continue
if not is_used:
unused_weights.append(A__ )
logger.warning(F'''Unused weights: {unused_weights}''' )
@torch.no_grad()
def _lowerCAmelCase ( A__ , A__ , A__ , A__=None , A__=None , ):
if config_path is not None:
lowercase__ = EncodecConfig.from_pretrained(A__ )
else:
lowercase__ = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
lowercase__ = [8, 5, 4, 4]
lowercase__ = [2.2]
lowercase__ = 64
lowercase__ = 32_000
lowercase__ = 2_048
lowercase__ = False
lowercase__ = False
lowercase__ = False
elif model_name == "encodec_48khz":
lowercase__ = [8, 5, 4, 2]
lowercase__ = [3.0, 6.0, 12.0, 24.0]
lowercase__ = 48_000
lowercase__ = 2
lowercase__ = False
lowercase__ = 'time_group_norm'
lowercase__ = True
lowercase__ = 1.0
lowercase__ = 0.01
else:
raise ValueError(F'''Unknown model name: {model_name}''' )
lowercase__ = EncodecModel(A__ )
lowercase__ = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(A__ )
lowercase__ = torch.load(A__ )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
lowercase__ = original_checkpoint['best_state']
recursively_load_weights(A__ , A__ , A__ )
model.save_pretrained(A__ )
if repo_id:
print('Pushing to the hub...' )
feature_extractor.push_to_hub(A__ )
model.push_to_hub(A__ )
if __name__ == "__main__":
a__ : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--model",
default="encodec_24khz",
type=str,
help="The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
a__ : Optional[int] = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 622 | 0 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class a_ ( a__ ):
"""simple docstring"""
@slow
@require_torch
def __lowerCAmelCase ( self ) ->int:
SCREAMING_SNAKE_CASE : int = EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' , '''prajjwal1/bert-tiny''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = BertTokenizer.from_pretrained('''bert-base-uncased''' )
SCREAMING_SNAKE_CASE : Optional[Any] = bertabert.config.encoder.vocab_size
SCREAMING_SNAKE_CASE : Tuple = tokenizer.sep_token_id
SCREAMING_SNAKE_CASE : List[str] = tokenizer.cls_token_id
SCREAMING_SNAKE_CASE : int = 128
SCREAMING_SNAKE_CASE : List[Any] = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''train[:1%]''' )
SCREAMING_SNAKE_CASE : Optional[Any] = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''validation[:1%]''' )
SCREAMING_SNAKE_CASE : List[str] = train_dataset.select(range(32 ) )
SCREAMING_SNAKE_CASE : str = val_dataset.select(range(16 ) )
SCREAMING_SNAKE_CASE : Optional[int] = 4
def _map_to_encoder_decoder_inputs(_lowerCamelCase ):
# Tokenizer will automatically set [BOS] <text> [EOS]
SCREAMING_SNAKE_CASE : List[Any] = tokenizer(batch['''article'''] , padding='''max_length''' , truncation=_lowerCamelCase , max_length=512 )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer(batch['''highlights'''] , padding='''max_length''' , truncation=_lowerCamelCase , max_length=128 )
SCREAMING_SNAKE_CASE : Dict = inputs.input_ids
SCREAMING_SNAKE_CASE : Union[str, Any] = inputs.attention_mask
SCREAMING_SNAKE_CASE : Tuple = outputs.input_ids
SCREAMING_SNAKE_CASE : Any = outputs.input_ids.copy()
SCREAMING_SNAKE_CASE : Tuple = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
]
SCREAMING_SNAKE_CASE : int = outputs.attention_mask
assert all(len(_lowerCamelCase ) == 512 for x in inputs.input_ids )
assert all(len(_lowerCamelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(_lowerCamelCase ):
SCREAMING_SNAKE_CASE : Optional[Any] = pred.label_ids
SCREAMING_SNAKE_CASE : str = pred.predictions
# all unnecessary tokens are removed
SCREAMING_SNAKE_CASE : int = tokenizer.batch_decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.batch_decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase )
SCREAMING_SNAKE_CASE : Optional[Any] = sum([int(pred_str[i] == label_str[i] ) for i in range(len(_lowerCamelCase ) )] ) / len(_lowerCamelCase )
return {"accuracy": accuracy}
# map train dataset
SCREAMING_SNAKE_CASE : List[Any] = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=_lowerCamelCase , batch_size=_lowerCamelCase , remove_columns=['''article''', '''highlights'''] , )
train_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
# same for validation dataset
SCREAMING_SNAKE_CASE : Any = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=_lowerCamelCase , batch_size=_lowerCamelCase , remove_columns=['''article''', '''highlights'''] , )
val_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE : List[Any] = SeqaSeqTrainingArguments(
output_dir=_lowerCamelCase , per_device_train_batch_size=_lowerCamelCase , per_device_eval_batch_size=_lowerCamelCase , predict_with_generate=_lowerCamelCase , evaluation_strategy='''steps''' , do_train=_lowerCamelCase , do_eval=_lowerCamelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
SCREAMING_SNAKE_CASE : int = SeqaSeqTrainer(
model=_lowerCamelCase , args=_lowerCamelCase , compute_metrics=_compute_metrics , train_dataset=_lowerCamelCase , eval_dataset=_lowerCamelCase , tokenizer=_lowerCamelCase , )
# start training
trainer.train()
| 333 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = torch.load(a__ , map_location='''cpu''' )
SCREAMING_SNAKE_CASE : List[str] = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
SCREAMING_SNAKE_CASE : Optional[Any] = {}
for k, v in state_dict.items():
if "pred_layer" in k:
SCREAMING_SNAKE_CASE : str = v
else:
SCREAMING_SNAKE_CASE : Dict = v
SCREAMING_SNAKE_CASE : int = chkpt['''params''']
SCREAMING_SNAKE_CASE : Tuple = {n: v for n, v in config.items() if not isinstance(a__ , (torch.FloatTensor, numpy.ndarray) )}
SCREAMING_SNAKE_CASE : List[str] = chkpt['''dico_word2id''']
SCREAMING_SNAKE_CASE : Optional[Any] = {s + '''</w>''' if s.find('''@@''' ) == -1 and i > 13 else s.replace('''@@''' , '''''' ): i for s, i in vocab.items()}
# Save pytorch-model
SCREAMING_SNAKE_CASE : List[Any] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
SCREAMING_SNAKE_CASE : Optional[Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
SCREAMING_SNAKE_CASE : List[Any] = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']
print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(a__ , a__ )
print(F"""Save configuration file to {pytorch_config_dump_path}""" )
with open(a__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(a__ , indent=2 ) + '''\n''' )
print(F"""Save vocab file to {pytorch_config_dump_path}""" )
with open(a__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(a__ , indent=2 ) + '''\n''' )
if __name__ == "__main__":
a__ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
a__ : int = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 333 | 1 |
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> Tuple:
def count_of_possible_combinations(snake_case ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(SCREAMING_SNAKE_CASE__ )
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> str:
def count_of_possible_combinations_with_dp_array(
snake_case , snake_case ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
_UpperCAmelCase = sum(
count_of_possible_combinations_with_dp_array(target - item , SCREAMING_SNAKE_CASE__ )
for item in array )
_UpperCAmelCase = answer
return answer
_UpperCAmelCase = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = [0] * (target + 1)
_UpperCAmelCase = 1
for i in range(1 , target + 1 ):
for j in range(SCREAMING_SNAKE_CASE__ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
a = 3
a = 5
a = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 518 |
from __future__ import annotations
def _A ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : int ):
UpperCamelCase :list[list[int]] = []
UpperCamelCase :list[int] = []
UpperCamelCase :List[str] = 0
UpperCamelCase :Any = sum(SCREAMING_SNAKE_CASE__ )
create_state_space_tree(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return result
def _A ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[list[int]] , SCREAMING_SNAKE_CASE__ : int , ):
if sum(SCREAMING_SNAKE_CASE__ ) > max_sum or (remaining_nums_sum + sum(SCREAMING_SNAKE_CASE__ )) < max_sum:
return
if sum(SCREAMING_SNAKE_CASE__ ) == max_sum:
result.append(SCREAMING_SNAKE_CASE__ )
return
for index in range(SCREAMING_SNAKE_CASE__ , len(SCREAMING_SNAKE_CASE__ ) ):
create_state_space_tree(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , index + 1 , [*path, nums[index]] , SCREAMING_SNAKE_CASE__ , remaining_nums_sum - nums[index] , )
__snake_case = [3, 34, 4, 12, 5, 2]
__snake_case = 9
__snake_case = generate_sum_of_subsets_soln(nums, max_sum)
print(*result)
| 658 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class __lowerCAmelCase :
_UpperCamelCase : Tuple = BlenderbotConfig
_UpperCamelCase : Optional[Any] = {}
_UpperCamelCase : List[Any] = """gelu"""
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=False , snake_case=99 , snake_case=32 , snake_case=2 , snake_case=4 , snake_case=37 , snake_case=0.1 , snake_case=0.1 , snake_case=20 , snake_case=2 , snake_case=1 , snake_case=0 , ) -> Optional[int]:
"""simple docstring"""
a__ : Any = parent
a__ : Optional[Any] = batch_size
a__ : Tuple = seq_length
a__ : Dict = is_training
a__ : Union[str, Any] = use_labels
a__ : int = vocab_size
a__ : Any = hidden_size
a__ : Any = num_hidden_layers
a__ : List[Any] = num_attention_heads
a__ : List[str] = intermediate_size
a__ : Optional[int] = hidden_dropout_prob
a__ : List[Any] = attention_probs_dropout_prob
a__ : Tuple = max_position_embeddings
a__ : List[Any] = eos_token_id
a__ : List[str] = pad_token_id
a__ : List[Any] = bos_token_id
def _snake_case ( self ) -> str:
"""simple docstring"""
a__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
a__ : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
a__ : str = tf.concat([input_ids, eos_tensor] , axis=1 )
a__ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ : Dict = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
a__ : Union[str, Any] = prepare_blenderbot_inputs_dict(snake_case , snake_case , snake_case )
return config, inputs_dict
def _snake_case ( self , snake_case , snake_case ) -> Dict:
"""simple docstring"""
a__ : Optional[int] = TFBlenderbotModel(config=snake_case ).get_decoder()
a__ : Optional[int] = inputs_dict["input_ids"]
a__ : Tuple = input_ids[:1, :]
a__ : str = inputs_dict["attention_mask"][:1, :]
a__ : Optional[int] = inputs_dict["head_mask"]
a__ : Any = 1
# first forward pass
a__ : str = model(snake_case , attention_mask=snake_case , head_mask=snake_case , use_cache=snake_case )
a__ , a__ : Any = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
a__ : str = ids_tensor((self.batch_size, 3) , config.vocab_size )
a__ : Dict = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
a__ : Optional[int] = tf.concat([input_ids, next_tokens] , axis=-1 )
a__ : str = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
a__ : int = model(snake_case , attention_mask=snake_case )[0]
a__ : Tuple = model(snake_case , attention_mask=snake_case , past_key_values=snake_case )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
a__ : int = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
a__ : Optional[int] = output_from_no_past[:, -3:, random_slice_idx]
a__ : Any = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(snake_case , snake_case , rtol=1E-3 )
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , ):
if attention_mask is None:
a__ : List[Any] = tf.cast(tf.math.not_equal(lowerCamelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
a__ : Union[str, Any] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
a__ : Union[str, Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
a__ : Dict = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
a__ : List[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __lowerCAmelCase ( _UpperCamelCase ,_UpperCamelCase ,unittest.TestCase ):
_UpperCamelCase : str = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
_UpperCamelCase : str = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
_UpperCamelCase : Dict = (
{
"""conversational""": TFBlenderbotForConditionalGeneration,
"""feature-extraction""": TFBlenderbotModel,
"""summarization""": TFBlenderbotForConditionalGeneration,
"""text2text-generation""": TFBlenderbotForConditionalGeneration,
"""translation""": TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
_UpperCamelCase : Any = True
_UpperCamelCase : Union[str, Any] = False
_UpperCamelCase : Dict = False
def _snake_case ( self ) -> Dict:
"""simple docstring"""
a__ : str = TFBlenderbotModelTester(self )
a__ : Optional[int] = ConfigTester(self , config_class=snake_case )
def _snake_case ( self ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def _snake_case ( self ) -> int:
"""simple docstring"""
a__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*snake_case )
@require_tokenizers
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
_UpperCamelCase : Dict = ["""My friends are cool but they eat too many carbs."""]
_UpperCamelCase : List[Any] = """facebook/blenderbot-400M-distill"""
@cached_property
def _snake_case ( self ) -> List[Any]:
"""simple docstring"""
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def _snake_case ( self ) -> Tuple:
"""simple docstring"""
a__ : int = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def _snake_case ( self ) -> Optional[Any]:
"""simple docstring"""
a__ : Optional[Any] = self.tokenizer(self.src_text , return_tensors="tf" )
a__ : Optional[Any] = self.model.generate(
model_inputs.input_ids , )
a__ : Union[str, Any] = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=snake_case )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 629 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : str = {
"""configuration_distilbert""": [
"""DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""DistilBertConfig""",
"""DistilBertOnnxConfig""",
],
"""tokenization_distilbert""": ["""DistilBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""DistilBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Tuple = [
"""DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DistilBertForMaskedLM""",
"""DistilBertForMultipleChoice""",
"""DistilBertForQuestionAnswering""",
"""DistilBertForSequenceClassification""",
"""DistilBertForTokenClassification""",
"""DistilBertModel""",
"""DistilBertPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[Any] = [
"""TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDistilBertForMaskedLM""",
"""TFDistilBertForMultipleChoice""",
"""TFDistilBertForQuestionAnswering""",
"""TFDistilBertForSequenceClassification""",
"""TFDistilBertForTokenClassification""",
"""TFDistilBertMainLayer""",
"""TFDistilBertModel""",
"""TFDistilBertPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Any = [
"""FlaxDistilBertForMaskedLM""",
"""FlaxDistilBertForMultipleChoice""",
"""FlaxDistilBertForQuestionAnswering""",
"""FlaxDistilBertForSequenceClassification""",
"""FlaxDistilBertForTokenClassification""",
"""FlaxDistilBertModel""",
"""FlaxDistilBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 629 | 1 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( ) -> int:
_UpperCAmelCase = 1_0
_UpperCAmelCase = datasets.Features(
{
"""tokens""": datasets.Sequence(datasets.Value("""string""" ) ),
"""labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ),
"""answers""": datasets.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
"""id""": datasets.Value("""int64""" ),
} )
_UpperCAmelCase = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
"""id""": list(range(__snake_case ) ),
} , features=__snake_case , )
return dataset
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> int:
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" )
dataset.map(cache_file_name=__snake_case )
return filename
# FILE_CONTENT + files
__a: int = '''\
Text data.
Second line of data.'''
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Dict:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """file.txt"""
_UpperCAmelCase = FILE_CONTENT
with open(__snake_case , """w""" ) as f:
f.write(__snake_case )
return filename
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Optional[int]:
import bza
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2"""
_UpperCAmelCase = bytes(__snake_case , """utf-8""" )
with bza.open(__snake_case , """wb""" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> int:
import gzip
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" )
_UpperCAmelCase = bytes(__snake_case , """utf-8""" )
with gzip.open(__snake_case , """wb""" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Optional[int]:
if datasets.config.LZ4_AVAILABLE:
import lza.frame
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4"""
_UpperCAmelCase = bytes(__snake_case , """utf-8""" )
with lza.frame.open(__snake_case , """wb""" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> List[Any]:
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z"""
with pyazr.SevenZipFile(__snake_case , """w""" ) as archive:
archive.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> Tuple:
import tarfile
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar"""
with tarfile.TarFile(__snake_case , """w""" ) as f:
f.add(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Union[str, Any]:
import lzma
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz"""
_UpperCAmelCase = bytes(__snake_case , """utf-8""" )
with lzma.open(__snake_case , """wb""" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> Dict:
import zipfile
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip"""
with zipfile.ZipFile(__snake_case , """w""" ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Tuple:
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst"""
_UpperCAmelCase = bytes(__snake_case , """utf-8""" )
with zstd.open(__snake_case , """wb""" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> str:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """file.xml"""
_UpperCAmelCase = textwrap.dedent(
"""\
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>
<tmx version=\"1.4\">
<header segtype=\"sentence\" srclang=\"ca\" />
<body>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>""" )
with open(__snake_case , """w""" ) as f:
f.write(__snake_case )
return filename
__a: Dict = [
{'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
__a: Tuple = [
{'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
{'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
__a: List[str] = {
'''col_1''': ['''0''', '''1''', '''2''', '''3'''],
'''col_2''': [0, 1, 2, 3],
'''col_3''': [0.0, 1.0, 2.0, 3.0],
}
__a: int = [
{'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
{'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]
__a: Tuple = [
{'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> str:
_UpperCAmelCase = datasets.Dataset.from_dict(__snake_case )
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" )
dataset.map(cache_file_name=__snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Optional[int]:
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" )
with contextlib.closing(sqlitea.connect(__snake_case ) ) as con:
_UpperCAmelCase = con.cursor()
cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" )
for item in DATA:
cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> str:
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" )
with open(__snake_case , """w""" , newline="""""" ) as f:
_UpperCAmelCase = csv.DictWriter(__snake_case , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(__snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Tuple:
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" )
with open(__snake_case , """w""" , newline="""""" ) as f:
_UpperCAmelCase = csv.DictWriter(__snake_case , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(__snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> Tuple:
import bza
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2"""
with open(__snake_case , """rb""" ) as f:
_UpperCAmelCase = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(__snake_case , """wb""" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case ) -> List[Any]:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(__snake_case , """w""" ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case ) -> Optional[Any]:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(__snake_case , """w""" ) as f:
f.write(__snake_case , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) )
f.write(__snake_case , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case ) -> int:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(__snake_case , """w""" ) as f:
f.write(__snake_case , arcname=os.path.join("""main_dir""" , os.path.basename(__snake_case ) ) )
f.write(__snake_case , arcname=os.path.join("""main_dir""" , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Union[str, Any]:
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" )
_UpperCAmelCase = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
} )
with open(__snake_case , """wb""" ) as f:
_UpperCAmelCase = pq.ParquetWriter(__snake_case , schema=__snake_case )
_UpperCAmelCase = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(__snake_case ) )] for k in DATA[0]} , schema=__snake_case )
writer.write_table(__snake_case )
writer.close()
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Dict:
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
_UpperCAmelCase = {"""data""": DATA}
with open(__snake_case , """w""" ) as f:
json.dump(__snake_case , __snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Dict:
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
_UpperCAmelCase = {"""data""": DATA_DICT_OF_LISTS}
with open(__snake_case , """w""" ) as f:
json.dump(__snake_case , __snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Tuple:
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" )
with open(__snake_case , """w""" ) as f:
for item in DATA:
f.write(json.dumps(__snake_case ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Any:
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" )
with open(__snake_case , """w""" ) as f:
for item in DATA:
f.write(json.dumps(__snake_case ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Union[str, Any]:
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" )
with open(__snake_case , """w""" ) as f:
for item in DATA_312:
f.write(json.dumps(__snake_case ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Dict:
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" )
with open(__snake_case , """w""" ) as f:
for item in DATA_STR:
f.write(json.dumps(__snake_case ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> str:
import gzip
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" )
with open(__snake_case , """rb""" ) as orig_file:
with gzip.open(__snake_case , """wb""" ) as zipped_file:
zipped_file.writelines(__snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> str:
import gzip
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" )
with open(__snake_case , """rb""" ) as orig_file:
with gzip.open(__snake_case , """wb""" ) as zipped_file:
zipped_file.writelines(__snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case ) -> Dict:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip"""
with zipfile.ZipFile(__snake_case , """w""" ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case , __snake_case ) -> Tuple:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(__snake_case , """w""" ) as f:
f.write(__snake_case , arcname=os.path.join("""nested""" , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case ) -> Union[str, Any]:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(__snake_case , """w""" ) as f:
f.write(__snake_case , arcname=os.path.join("""main_dir""" , os.path.basename(__snake_case ) ) )
f.write(__snake_case , arcname=os.path.join("""main_dir""" , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case ) -> int:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar"""
with tarfile.TarFile(__snake_case , """w""" ) as f:
f.add(__snake_case , arcname=os.path.basename(__snake_case ) )
f.add(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case , __snake_case ) -> int:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(__snake_case , """w""" ) as f:
f.add(__snake_case , arcname=os.path.join("""nested""" , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Union[str, Any]:
_UpperCAmelCase = ["""0""", """1""", """2""", """3"""]
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" )
with open(__snake_case , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Union[str, Any]:
_UpperCAmelCase = ["""0""", """1""", """2""", """3"""]
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" )
with open(__snake_case , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> str:
_UpperCAmelCase = ["""0""", """1""", """2""", """3"""]
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset.abc"""
with open(__snake_case , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case ) -> Optional[Any]:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip"""
with zipfile.ZipFile(__snake_case , """w""" ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case ) -> Any:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(__snake_case , """w""" ) as f:
f.write(__snake_case , arcname=os.path.join("""main_dir""" , os.path.basename(__snake_case ) ) )
f.write(__snake_case , arcname=os.path.join("""main_dir""" , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case ) -> str:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip"""
with zipfile.ZipFile(__snake_case , """w""" ) as f:
f.write(__snake_case , arcname=os.path.basename("""unsupported.ext""" ) )
f.write(__snake_case , arcname=os.path.basename("""unsupported_2.ext""" ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> str:
_UpperCAmelCase = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] )
_UpperCAmelCase = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" )
with open(__snake_case , """w""" , encoding="""utf-8""" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( ) -> Union[str, Any]:
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" )
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" )
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> Optional[Any]:
_UpperCAmelCase = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip"""
with zipfile.ZipFile(__snake_case , """w""" ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
f.write(__snake_case , arcname=os.path.basename(__snake_case ).replace(""".jpg""" , """2.jpg""" ) )
return path
@pytest.fixture(scope="""session""" )
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> str:
_UpperCAmelCase = tmp_path_factory.mktemp("""data_dir""" )
(data_dir / "subdir").mkdir()
with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
return data_dir
| 108 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : Optional[int] ):
"""simple docstring"""
snake_case_ : List[Any] = args.pruning_method
snake_case_ : Any = args.threshold
snake_case_ : Optional[Any] = args.model_name_or_path.rstrip("""/""" )
snake_case_ : Optional[Any] = args.target_model_path
print(f'Load fine-pruned model from {model_name_or_path}' )
snake_case_ : str = torch.load(os.path.join(SCREAMING_SNAKE_CASE__ , """pytorch_model.bin""" ) )
snake_case_ : Optional[int] = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
snake_case_ : Dict = tensor
print(f'Copied layer {name}' )
elif "classifier" in name or "qa_output" in name:
snake_case_ : List[Any] = tensor
print(f'Copied layer {name}' )
elif "bias" in name:
snake_case_ : Tuple = tensor
print(f'Copied layer {name}' )
else:
if pruning_method == "magnitude":
snake_case_ : List[Any] = MagnitudeBinarizer.apply(inputs=SCREAMING_SNAKE_CASE__ , threshold=SCREAMING_SNAKE_CASE__ )
snake_case_ : int = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
snake_case_ : List[str] = name[:-6]
snake_case_ : int = model[f'{prefix_}mask_scores']
snake_case_ : Optional[Any] = TopKBinarizer.apply(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case_ : Optional[int] = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
snake_case_ : str = name[:-6]
snake_case_ : str = model[f'{prefix_}mask_scores']
snake_case_ : List[str] = ThresholdBinarizer.apply(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
snake_case_ : Dict = tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
snake_case_ : List[Any] = name[:-6]
snake_case_ : Optional[int] = model[f'{prefix_}mask_scores']
snake_case_ , snake_case_ : List[str] = -0.1, 1.1
snake_case_ : Optional[int] = torch.sigmoid(SCREAMING_SNAKE_CASE__ )
snake_case_ : Optional[int] = s * (r - l) + l
snake_case_ : Tuple = s_bar.clamp(min=0.0 , max=1.0 )
snake_case_ : List[str] = tensor * mask
print(f'Pruned layer {name}' )
else:
raise ValueError("""Unknown pruning method""" )
if target_model_path is None:
snake_case_ : int = os.path.join(
os.path.dirname(SCREAMING_SNAKE_CASE__ ) , f'bertarized_{os.path.basename(SCREAMING_SNAKE_CASE__ )}' )
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
shutil.copytree(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
print(f'\nCreated folder {target_model_path}' )
torch.save(SCREAMING_SNAKE_CASE__ , os.path.join(SCREAMING_SNAKE_CASE__ , """pytorch_model.bin""" ) )
print("""\nPruned model saved! See you later!""" )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'''--pruning_method''',
choices=['''l0''', '''magnitude''', '''topK''', '''sigmoied_threshold'''],
type=str,
required=True,
help=(
'''Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'''
''' sigmoied_threshold = Soft movement pruning)'''
),
)
parser.add_argument(
'''--threshold''',
type=float,
required=False,
help=(
'''For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
'''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
'''Not needed for `l0`'''
),
)
parser.add_argument(
'''--model_name_or_path''',
type=str,
required=True,
help='''Folder containing the model that was previously fine-pruned''',
)
parser.add_argument(
'''--target_model_path''',
default=None,
type=str,
required=False,
help='''Folder containing the model that was previously fine-pruned''',
)
a_ = parser.parse_args()
main(args)
| 480 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def __lowerCamelCase ( __snake_case : str, __snake_case : Optional[int]=False ) -> Tuple:
"""simple docstring"""
A__ : Optional[Any] =[]
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append(
(f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
("""module.cls_token""", """vit.embeddings.cls_token"""),
("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""module.norm.weight""", """layernorm.weight"""),
("""module.norm.bias""", """layernorm.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
A__ : Dict =[(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def __lowerCamelCase ( __snake_case : Dict, __snake_case : Optional[Any], __snake_case : Any=False ) -> int:
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
A__ : Optional[int] =""""""
else:
A__ : int ="""vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
A__ : List[Any] =state_dict.pop(f"module.blocks.{i}.attn.qkv.weight" )
A__ : Dict =state_dict.pop(f"module.blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
A__ : List[Any] =in_proj_weight[
: config.hidden_size, :
]
A__ : Any =in_proj_bias[: config.hidden_size]
A__ : int =in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
A__ : Dict =in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
A__ : Tuple =in_proj_weight[
-config.hidden_size :, :
]
A__ : Any =in_proj_bias[-config.hidden_size :]
def __lowerCamelCase ( __snake_case : Tuple ) -> List[str]:
"""simple docstring"""
A__ : List[Any] =["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( __snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
A__ : int =[
"""module.fc.fc1.weight""",
"""module.fc.fc1.bias""",
"""module.fc.bn1.weight""",
"""module.fc.bn1.bias""",
"""module.fc.bn1.running_mean""",
"""module.fc.bn1.running_var""",
"""module.fc.bn1.num_batches_tracked""",
"""module.fc.fc2.weight""",
"""module.fc.fc2.bias""",
"""module.fc.bn2.weight""",
"""module.fc.bn2.bias""",
"""module.fc.bn2.running_mean""",
"""module.fc.bn2.running_var""",
"""module.fc.bn2.num_batches_tracked""",
"""module.fc.fc3.weight""",
"""module.fc.fc3.bias""",
]
for k in ignore_keys:
state_dict.pop(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( __snake_case : int, __snake_case : int, __snake_case : Dict ) -> Optional[int]:
"""simple docstring"""
A__ : Any =dct.pop(_SCREAMING_SNAKE_CASE )
A__ : Optional[int] =val
def __lowerCamelCase ( __snake_case : str, __snake_case : Any ) -> List[str]:
"""simple docstring"""
A__ : int =ViTMSNConfig()
A__ : Optional[int] =1_000
A__ : str ="""datasets/huggingface/label-files"""
A__ : Optional[int] ="""imagenet-1k-id2label.json"""
A__ : List[Any] =json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE ), """r""" ) )
A__ : Dict ={int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
A__ : Optional[Any] =idalabel
A__ : Dict ={v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
A__ : str =384
A__ : str =1_536
A__ : List[str] =6
elif "l16" in checkpoint_url:
A__ : int =1_024
A__ : Tuple =4_096
A__ : Tuple =24
A__ : Optional[int] =16
A__ : List[Any] =0.1
elif "b4" in checkpoint_url:
A__ : List[str] =4
elif "l7" in checkpoint_url:
A__ : Tuple =7
A__ : str =1_024
A__ : int =4_096
A__ : int =24
A__ : int =16
A__ : List[str] =0.1
A__ : Optional[Any] =ViTMSNModel(_SCREAMING_SNAKE_CASE )
A__ : Any =torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE, map_location="""cpu""" )["""target_encoder"""]
A__ : Tuple =ViTImageProcessor(size=config.image_size )
remove_projection_head(_SCREAMING_SNAKE_CASE )
A__ : Union[str, Any] =create_rename_keys(_SCREAMING_SNAKE_CASE, base_model=_SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE )
read_in_q_k_v(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, base_model=_SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
model.eval()
A__ : int ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
A__ : Tuple =Image.open(requests.get(_SCREAMING_SNAKE_CASE, stream=_SCREAMING_SNAKE_CASE ).raw )
A__ : Dict =ViTImageProcessor(
size=config.image_size, image_mean=_SCREAMING_SNAKE_CASE, image_std=_SCREAMING_SNAKE_CASE )
A__ : int =image_processor(images=_SCREAMING_SNAKE_CASE, return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
A__ : int =model(**_SCREAMING_SNAKE_CASE )
A__ : str =outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
A__ : List[str] =torch.tensor([[-1.09_15, -1.48_76, -1.18_09]] )
elif "b16" in checkpoint_url:
A__ : Optional[Any] =torch.tensor([[14.28_89, -18.90_45, 11.72_81]] )
elif "l16" in checkpoint_url:
A__ : List[str] =torch.tensor([[41.50_28, -22.86_81, 45.64_75]] )
elif "b4" in checkpoint_url:
A__ : List[Any] =torch.tensor([[-4.38_68, 5.29_32, -0.41_37]] )
else:
A__ : Union[str, Any] =torch.tensor([[-0.17_92, -0.64_65, 2.42_63]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3], _SCREAMING_SNAKE_CASE, atol=1E-4 )
print(f"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__snake_case : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar',
type=str,
help='URL of the checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
__snake_case : Any = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 702 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowercase__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowercase__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
A__ : Any =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
A__ : Optional[Any] =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
A__ : Optional[int] ="""xvjiarui/stable-diffusion-2-inpainting"""
A__ , A__ : List[str] =FlaxStableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase_ , safety_checker=lowerCAmelCase_ )
A__ : List[str] ="""Face of a yellow cat, high resolution, sitting on a park bench"""
A__ : Optional[Any] =jax.random.PRNGKey(0 )
A__ : List[str] =50
A__ : List[str] =jax.device_count()
A__ : List[str] =num_samples * [prompt]
A__ : List[str] =num_samples * [init_image]
A__ : Tuple =num_samples * [mask_image]
A__ , A__ , A__ : List[Any] =pipeline.prepare_inputs(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# shard inputs and rng
A__ : Dict =replicate(lowerCAmelCase_ )
A__ : Union[str, Any] =jax.random.split(lowerCAmelCase_ , jax.device_count() )
A__ : List[Any] =shard(lowerCAmelCase_ )
A__ : Union[str, Any] =shard(lowerCAmelCase_ )
A__ : str =shard(lowerCAmelCase_ )
A__ : List[str] =pipeline(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , jit=lowerCAmelCase_ )
A__ : List[Any] =output.images.reshape(lowerCAmelCase_ , 5_12 , 5_12 , 3 )
A__ : str =images[0, 2_53:2_56, 2_53:2_56, -1]
A__ : Tuple =jnp.asarray(jax.device_get(image_slice.flatten() ) )
A__ : Optional[int] =jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f"output_slice: {output_slice}" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 687 | 0 |
'''simple docstring'''
import unittest
import numpy as np
def __a ( lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : np.ndarray | None = None , ):
a__ : Union[str, Any] = np.shape(lowerCAmelCase__ )
a__ : Tuple = np.shape(lowerCAmelCase__ )
a__ : List[Any] = np.shape(lowerCAmelCase__ )
if shape_a[0] != shape_b[0]:
a__ : List[Any] = (
'''Expected the same number of rows for A and B. '''
F'Instead found A of size {shape_a} and B of size {shape_b}'
)
raise ValueError(lowerCAmelCase__ )
if shape_b[1] != shape_c[1]:
a__ : Optional[int] = (
'''Expected the same number of columns for B and C. '''
F'Instead found B of size {shape_b} and C of size {shape_c}'
)
raise ValueError(lowerCAmelCase__ )
a__ : List[Any] = pseudo_inv
if a_inv is None:
try:
a__ : int = np.linalg.inv(lowerCAmelCase__ )
except np.linalg.LinAlgError:
raise ValueError(
'''Input matrix A is not invertible. Cannot compute Schur complement.''' )
return mat_c - mat_b.T @ a_inv @ mat_b
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Any ) -> None:
'''simple docstring'''
a__ : int = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
a__ : Union[str, Any] = np.array([[0, 3], [3, 0], [2, 3]] )
a__ : List[str] = np.array([[2, 1], [6, 3]] )
a__ : int = schur_complement(A__ , A__ , A__ )
a__ : List[str] = np.block([[a, b], [b.T, c]] )
a__ : Tuple = np.linalg.det(A__ )
a__ : Dict = np.linalg.det(A__ )
a__ : List[str] = np.linalg.det(A__ )
self.assertAlmostEqual(A__ , det_a * det_s )
def __lowerCAmelCase ( self : Union[str, Any] ) -> None:
'''simple docstring'''
a__ : List[str] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
a__ : int = np.array([[0, 3], [3, 0], [2, 3]] )
a__ : Tuple = np.array([[2, 1], [6, 3]] )
with self.assertRaises(A__ ):
schur_complement(A__ , A__ , A__ )
def __lowerCAmelCase ( self : Tuple ) -> None:
'''simple docstring'''
a__ : Tuple = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
a__ : List[str] = np.array([[0, 3], [3, 0], [2, 3]] )
a__ : Any = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(A__ ):
schur_complement(A__ , A__ , A__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 688 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCAmelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = LxmertTokenizer
__UpperCamelCase = LxmertTokenizerFast
__UpperCamelCase = True
__UpperCamelCase = True
def __lowerCAmelCase ( self : str ) -> str:
'''simple docstring'''
super().setUp()
a__ : Dict = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
a__ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __lowerCAmelCase ( self : int , A__ : int ) -> int:
'''simple docstring'''
a__ : List[Any] = '''UNwant\u00E9d,running'''
a__ : Optional[int] = '''unwanted, running'''
return input_text, output_text
def __lowerCAmelCase ( self : int ) -> Dict:
'''simple docstring'''
a__ : Optional[int] = self.tokenizer_class(self.vocab_file )
a__ : List[Any] = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(A__ , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , [7, 4, 5, 1_0, 8, 9] )
def __lowerCAmelCase ( self : Any ) -> Dict:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
a__ : Union[str, Any] = self.get_tokenizer()
a__ : Union[str, Any] = self.get_rust_tokenizer()
a__ : str = '''I was born in 92000, and this is falsé.'''
a__ : Tuple = tokenizer.tokenize(A__ )
a__ : Tuple = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
a__ : Optional[int] = tokenizer.encode(A__ , add_special_tokens=A__ )
a__ : Optional[Any] = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
a__ : List[str] = self.get_rust_tokenizer()
a__ : str = tokenizer.encode(A__ )
a__ : int = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
| 688 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _a ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Path , _SCREAMING_SNAKE_CASE : str = None , _SCREAMING_SNAKE_CASE : str = None , _SCREAMING_SNAKE_CASE : str = None , ):
if config_name_or_path is None:
_SCREAMING_SNAKE_CASE = "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"
if generator_tokenizer_name_or_path is None:
_SCREAMING_SNAKE_CASE = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
_SCREAMING_SNAKE_CASE = question_encoder_name_or_path
_SCREAMING_SNAKE_CASE = RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration
# Save model.
_SCREAMING_SNAKE_CASE = RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE = gen_config
_SCREAMING_SNAKE_CASE = question_encoder_config
_SCREAMING_SNAKE_CASE = model_class.from_pretrained_question_encoder_generator(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , config=_SCREAMING_SNAKE_CASE )
rag_model.save_pretrained(_SCREAMING_SNAKE_CASE )
# Sanity check.
model_class.from_pretrained(_SCREAMING_SNAKE_CASE )
# Save tokenizers.
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/" )
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/" )
if __name__ == "__main__":
_snake_case : Any = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""",
choices=["""rag_sequence""", """rag_token"""],
required=True,
type=str,
help="""RAG model type: rag_sequence, rag_token""",
)
parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
parser.add_argument(
"""--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
)
parser.add_argument(
"""--generator_tokenizer_name_or_path""",
type=str,
help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
)
parser.add_argument(
"""--question_encoder_tokenizer_name_or_path""",
type=str,
help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
)
parser.add_argument(
"""--config_name_or_path""",
type=str,
help=(
"""Identifier of the model config to use, if not provided, resolves to a base config for a given"""
""" ``model_type``"""
),
)
_snake_case : List[str] = parser.parse_args()
_snake_case : int = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 493 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_snake_case : Optional[Any] = {
"""configuration_rag""": ["""RagConfig"""],
"""retrieval_rag""": ["""RagRetriever"""],
"""tokenization_rag""": ["""RagTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : int = [
"""RagModel""",
"""RagPreTrainedModel""",
"""RagSequenceForGeneration""",
"""RagTokenForGeneration""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : Dict = [
"""TFRagModel""",
"""TFRagPreTrainedModel""",
"""TFRagSequenceForGeneration""",
"""TFRagTokenForGeneration""",
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
_snake_case : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 493 | 1 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __magic_name__ ( _A ):
UpperCAmelCase ="char"
UpperCAmelCase ="bpe"
UpperCAmelCase ="wp"
lowercase =(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class __magic_name__ ( _A ):
UpperCAmelCase =["image_processor", "char_tokenizer"]
UpperCAmelCase ="ViTImageProcessor"
UpperCAmelCase ="MgpstrTokenizer"
def __init__( self , snake_case=None , snake_case=None , **snake_case) -> Any:
'''simple docstring'''
_UpperCAmelCase : Dict =None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , snake_case , )
_UpperCAmelCase : int =kwargs.pop('feature_extractor')
_UpperCAmelCase : int =image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.')
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.')
_UpperCAmelCase : Union[str, Any] =tokenizer
_UpperCAmelCase : Any =AutoTokenizer.from_pretrained('gpt2')
_UpperCAmelCase : str =AutoTokenizer.from_pretrained('bert-base-uncased')
super().__init__(snake_case , snake_case)
def __call__( self , snake_case=None , snake_case=None , snake_case=None , **snake_case) -> Dict:
'''simple docstring'''
if images is None and text is None:
raise ValueError('You need to specify either an `images` or `text` input to process.')
if images is not None:
_UpperCAmelCase : Tuple =self.image_processor(snake_case , return_tensors=snake_case , **snake_case)
if text is not None:
_UpperCAmelCase : Optional[Any] =self.char_tokenizer(snake_case , return_tensors=snake_case , **snake_case)
if text is None:
return inputs
elif images is None:
return encodings
else:
_UpperCAmelCase : str =encodings["input_ids"]
return inputs
def lowerCAmelCase ( self , snake_case) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : int =sequences
_UpperCAmelCase : int =char_preds.size(0)
_UpperCAmelCase : Dict =self._decode_helper(snake_case , 'char')
_UpperCAmelCase : List[str] =self._decode_helper(snake_case , 'bpe')
_UpperCAmelCase : List[Any] =self._decode_helper(snake_case , 'wp')
_UpperCAmelCase : Any =[]
_UpperCAmelCase : Optional[int] =[]
for i in range(snake_case):
_UpperCAmelCase : Any =[char_scores[i], bpe_scores[i], wp_scores[i]]
_UpperCAmelCase : Tuple =[char_strs[i], bpe_strs[i], wp_strs[i]]
_UpperCAmelCase : Dict =scores.index(max(snake_case))
final_strs.append(strs[max_score_index])
final_scores.append(scores[max_score_index])
_UpperCAmelCase : Optional[int] ={}
_UpperCAmelCase : List[str] =final_strs
_UpperCAmelCase : Union[str, Any] =final_scores
_UpperCAmelCase : Union[str, Any] =char_strs
_UpperCAmelCase : Optional[Any] =bpe_strs
_UpperCAmelCase : Union[str, Any] =wp_strs
return out
def lowerCAmelCase ( self , snake_case , snake_case) -> Optional[int]:
'''simple docstring'''
if format == DecodeType.CHARACTER:
_UpperCAmelCase : Dict =self.char_decode
_UpperCAmelCase : Dict =1
_UpperCAmelCase : List[Any] ="[s]"
elif format == DecodeType.BPE:
_UpperCAmelCase : Optional[Any] =self.bpe_decode
_UpperCAmelCase : Optional[Any] =2
_UpperCAmelCase : Dict ="#"
elif format == DecodeType.WORDPIECE:
_UpperCAmelCase : int =self.wp_decode
_UpperCAmelCase : Union[str, Any] =1_0_2
_UpperCAmelCase : Optional[int] ="[SEP]"
else:
raise ValueError(f"Format {format} is not supported.")
_UpperCAmelCase : List[str] =[], []
_UpperCAmelCase : List[str] =pred_logits.size(0)
_UpperCAmelCase : Optional[int] =pred_logits.size(1)
_UpperCAmelCase : Dict =pred_logits.topk(1 , dim=-1 , largest=snake_case , sorted=snake_case)
_UpperCAmelCase : Optional[Any] =preds_index.view(-1 , snake_case)[:, 1:]
_UpperCAmelCase : Optional[int] =decoder(snake_case)
_UpperCAmelCase : List[Any] =torch.nn.functional.softmax(snake_case , dim=2).max(dim=2)
_UpperCAmelCase : str =preds_max_prob[:, 1:]
for index in range(snake_case):
_UpperCAmelCase : Optional[Any] =preds_str[index].find(snake_case)
_UpperCAmelCase : Tuple =preds_str[index][:pred_eos]
_UpperCAmelCase : Dict =preds_index[index].cpu().tolist()
_UpperCAmelCase : Dict =pred_index.index(snake_case) if eos_token in pred_index else -1
_UpperCAmelCase : str =preds_max_prob[index][: pred_eos_index + 1]
_UpperCAmelCase : Union[str, Any] =pred_max_prob.cumprod(dim=0)[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(snake_case)
conf_scores.append(snake_case)
return dec_strs, conf_scores
def lowerCAmelCase ( self , snake_case) -> int:
'''simple docstring'''
_UpperCAmelCase : str =[seq.replace(' ' , '') for seq in self.char_tokenizer.batch_decode(snake_case)]
return decode_strs
def lowerCAmelCase ( self , snake_case) -> Optional[Any]:
'''simple docstring'''
return self.bpe_tokenizer.batch_decode(snake_case)
def lowerCAmelCase ( self , snake_case) -> Dict:
'''simple docstring'''
_UpperCAmelCase : Optional[int] =[seq.replace(' ' , '') for seq in self.wp_tokenizer.batch_decode(snake_case)]
return decode_strs
| 446 |
from math import factorial, radians
def lowercase_ ( __snake_case : float , __snake_case : int = 18 , __snake_case : int = 10 ) -> float:
'''simple docstring'''
snake_case__ :Optional[int] = angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)
# Converting from degrees to radians
snake_case__ :Optional[int] = radians(__snake_case )
snake_case__ :Optional[Any] = angle_in_radians
snake_case__ :Optional[int] = 3
snake_case__ :Union[str, Any] = -1
for _ in range(__snake_case ):
result += (b * (angle_in_radians**a)) / factorial(__snake_case )
snake_case__ :Optional[int] = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(__snake_case , __snake_case )
if __name__ == "__main__":
__import__("doctest").testmod()
| 241 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( A__ : Optional[Any] , A__ : Union[str, Any] , A__ : Optional[int] , A__ : Optional[Any] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
__lowerCamelCase = (l + r) // 2
if v[m] >= key:
__lowerCamelCase = m
else:
__lowerCamelCase = m # noqa: E741
return r
def lowerCamelCase__ ( A__ : int ):
'''simple docstring'''
if len(_lowercase ) == 0:
return 0
__lowerCamelCase = [0] * len(_lowercase )
__lowerCamelCase = 1
__lowerCamelCase = v[0]
for i in range(1 , len(_lowercase ) ):
if v[i] < tail[0]:
__lowerCamelCase = v[i]
elif v[i] > tail[length - 1]:
__lowerCamelCase = v[i]
length += 1
else:
__lowerCamelCase = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 718 |
import os
from collections.abc import Iterator
def lowerCamelCase__ ( A__ : str = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(A__ ):
__lowerCamelCase = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(A__ )[1] in (".py", ".ipynb"):
yield os.path.join(A__ , A__ ).lstrip("""./""" )
def lowerCamelCase__ ( A__ : Optional[int] ):
'''simple docstring'''
return f'{i * " "}*' if i else "\n##"
def lowerCamelCase__ ( A__ : str , A__ : str ):
'''simple docstring'''
__lowerCamelCase = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(A__ ) or old_parts[i] != new_part) and new_part:
print(f'{md_prefix(A__ )} {new_part.replace("_" , " " ).title()}' )
return new_path
def lowerCamelCase__ ( A__ : str = "." ):
'''simple docstring'''
__lowerCamelCase = """"""
for filepath in sorted(good_file_paths(A__ ) ):
__lowerCamelCase, __lowerCamelCase = os.path.split(A__ )
if filepath != old_path:
__lowerCamelCase = print_path(A__ , A__ )
__lowerCamelCase = (filepath.count(os.sep ) + 1) if filepath else 0
__lowerCamelCase = f'{filepath}/{filename}'.replace(""" """ , """%20""" )
__lowerCamelCase = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(f'{md_prefix(A__ )} [{filename}]({url})' )
if __name__ == "__main__":
print_directory_md('.')
| 80 | 0 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
a : Optional[int] = {
"169M": 12,
"430M": 24,
"1B5": 24,
"3B": 32,
"7B": 32,
"14B": 40,
}
a : Any = {
"169M": 768,
"430M": 1_024,
"1B5": 2_048,
"3B": 2_560,
"7B": 4_096,
"14B": 5_120,
}
def lowerCamelCase__ ( __lowerCamelCase : Optional[Any] ):
__UpperCAmelCase : Optional[Any] = list(state_dict.keys() )
for name in state_dict_keys:
__UpperCAmelCase : Optional[int] = state_dict.pop(__lowerCamelCase )
# emb -> embedding
if name.startswith("""emb.""" ):
__UpperCAmelCase : Optional[int] = name.replace("""emb.""" , """embeddings.""" )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith("""blocks.0.ln0""" ):
__UpperCAmelCase : Optional[int] = name.replace("""blocks.0.ln0""" , """blocks.0.pre_ln""" )
# att -> attention
__UpperCAmelCase : Tuple = re.sub(R"""blocks\.(\d+)\.att""" , R"""blocks.\1.attention""" , __lowerCamelCase )
# ffn -> feed_forward
__UpperCAmelCase : Tuple = re.sub(R"""blocks\.(\d+)\.ffn""" , R"""blocks.\1.feed_forward""" , __lowerCamelCase )
# time_mix_k -> time_mix_key and reshape
if name.endswith(""".time_mix_k""" ):
__UpperCAmelCase : Optional[Any] = name.replace(""".time_mix_k""" , """.time_mix_key""" )
# time_mix_v -> time_mix_value and reshape
if name.endswith(""".time_mix_v""" ):
__UpperCAmelCase : Any = name.replace(""".time_mix_v""" , """.time_mix_value""" )
# time_mix_r -> time_mix_key and reshape
if name.endswith(""".time_mix_r""" ):
__UpperCAmelCase : Dict = name.replace(""".time_mix_r""" , """.time_mix_receptance""" )
if name != "head.weight":
__UpperCAmelCase : Union[str, Any] = """rwkv.""" + name
__UpperCAmelCase : Tuple = weight
return state_dict
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : Optional[int] , __lowerCamelCase : Dict , __lowerCamelCase : str=None , __lowerCamelCase : List[Any]=None , __lowerCamelCase : Union[str, Any]=False , __lowerCamelCase : Tuple=None ):
# 1. If possible, build the tokenizer.
if tokenizer_file is None:
print("""No `--tokenizer_file` provided, we will use the default tokenizer.""" )
__UpperCAmelCase : Tuple = 50277
__UpperCAmelCase : Tuple = AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b""" )
else:
__UpperCAmelCase : Any = PreTrainedTokenizerFast(tokenizer_file=__lowerCamelCase )
__UpperCAmelCase : List[str] = len(__lowerCamelCase )
tokenizer.save_pretrained(__lowerCamelCase )
# 2. Build the config
__UpperCAmelCase : Union[str, Any] = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
__UpperCAmelCase : Optional[int] = candidate
break
if size is None:
raise ValueError("""Could not infer the size, please provide it with the `--size` argument.""" )
if size not in possible_sizes:
raise ValueError(f"""`size` should be one of {possible_sizes}, got {size}.""" )
__UpperCAmelCase : List[str] = RwkvConfig(
vocab_size=__lowerCamelCase , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(__lowerCamelCase )
# 3. Download model file then convert state_dict
__UpperCAmelCase : Tuple = hf_hub_download(__lowerCamelCase , __lowerCamelCase )
__UpperCAmelCase : Dict = torch.load(__lowerCamelCase , map_location="""cpu""" )
__UpperCAmelCase : Dict = convert_state_dict(__lowerCamelCase )
# 4. Split in shards and save
__UpperCAmelCase , __UpperCAmelCase : List[str] = shard_checkpoint(__lowerCamelCase )
for shard_file, shard in shards.items():
torch.save(__lowerCamelCase , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
if index is not None:
__UpperCAmelCase : Optional[Any] = os.path.join(__lowerCamelCase , __lowerCamelCase )
# Save the index as well
with open(__lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
__UpperCAmelCase : Dict = json.dumps(__lowerCamelCase , indent=2 , sort_keys=__lowerCamelCase ) + """\n"""
f.write(__lowerCamelCase )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
"""Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model.""" )
__UpperCAmelCase : List[str] = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
__UpperCAmelCase : Dict = torch.load(os.path.join(__lowerCamelCase , __lowerCamelCase ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError("""Please provide a `model_name` to push the model to the Hub.""" )
__UpperCAmelCase : Tuple = AutoModelForCausalLM.from_pretrained(__lowerCamelCase )
model.push_to_hub(__lowerCamelCase , max_shard_size="""2GB""" )
tokenizer.push_to_hub(__lowerCamelCase )
if __name__ == "__main__":
a : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--repo_id", default=None, type=str, required=True, help="Repo ID from which to pull the checkpoint."
)
parser.add_argument(
"--checkpoint_file", default=None, type=str, required=True, help="Name of the checkpoint file in the repo."
)
parser.add_argument(
"--output_dir", default=None, type=str, required=True, help="Where to save the converted model."
)
parser.add_argument(
"--tokenizer_file",
default=None,
type=str,
help="Path to the tokenizer file to use (if not provided, only the model is converted).",
)
parser.add_argument(
"--size",
default=None,
type=str,
help="Size of the model. Will be inferred from the `checkpoint_file` if not passed.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Push to the Hub the converted model.",
)
parser.add_argument(
"--model_name",
default=None,
type=str,
help="Name of the pushed model on the Hub, including the username / organization.",
)
a : Tuple = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 63 |
def UpperCamelCase( __UpperCamelCase : Any ):
if not head:
return True
# split the list to two parts
lowerCAmelCase_ , lowerCAmelCase_ : Any = head.next, head
while fast and fast.next:
lowerCAmelCase_ : List[Any] = fast.next.next
lowerCAmelCase_ : Union[str, Any] = slow.next
lowerCAmelCase_ : Union[str, Any] = slow.next
lowerCAmelCase_ : List[Any] = None # Don't forget here! But forget still works!
# reverse the second part
lowerCAmelCase_ : str = None
while second:
lowerCAmelCase_ : List[str] = second.next
lowerCAmelCase_ : List[Any] = node
lowerCAmelCase_ : Tuple = second
lowerCAmelCase_ : Optional[int] = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
lowerCAmelCase_ : Union[str, Any] = node.next
lowerCAmelCase_ : str = head.next
return True
def UpperCamelCase( __UpperCamelCase : str ):
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
lowerCAmelCase_ : Any = head
while fast and fast.next:
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = fast.next.next, slow.next
# 2. Push the second half into the stack
lowerCAmelCase_ : List[str] = [slow.val]
while slow.next:
lowerCAmelCase_ : List[str] = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
lowerCAmelCase_ : Optional[int] = cur.next
return True
def UpperCamelCase( __UpperCamelCase : Any ):
if not head or not head.next:
return True
lowerCAmelCase_ : Optional[int] = {}
lowerCAmelCase_ : List[Any] = 0
while head:
if head.val in d:
d[head.val].append(__UpperCamelCase )
else:
lowerCAmelCase_ : Tuple = [pos]
lowerCAmelCase_ : Tuple = head.next
pos += 1
lowerCAmelCase_ : int = pos - 1
lowerCAmelCase_ : int = 0
for v in d.values():
if len(__UpperCamelCase ) % 2 != 0:
middle += 1
else:
lowerCAmelCase_ : Any = 0
for i in range(0 ,len(__UpperCamelCase ) ):
if v[i] + v[len(__UpperCamelCase ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 171 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
"""TableTransformerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TableTransformerForObjectDetection""",
"""TableTransformerModel""",
"""TableTransformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_table_transformer import (
TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TableTransformerConfig,
TableTransformerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_table_transformer import (
TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TableTransformerForObjectDetection,
TableTransformerModel,
TableTransformerPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 701 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
a_ = """true"""
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : List[str]=82 ,_UpperCamelCase : Optional[Any]=16 ):
set_seed(42 )
__lowerCamelCase = RegressionModel()
__lowerCamelCase = deepcopy(_UpperCamelCase )
__lowerCamelCase = RegressionDataset(length=_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=_UpperCamelCase )
model.to(accelerator.device )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return model, ddp_model, dataloader
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : str=False ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' ,split='''validation''' )
def tokenize_function(_UpperCamelCase : int ):
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
with accelerator.main_process_first():
__lowerCamelCase = dataset.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
if use_longest:
return tokenizer.pad(_UpperCamelCase ,padding='''longest''' ,return_tensors='''pt''' )
return tokenizer.pad(_UpperCamelCase ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt''' )
return DataLoader(_UpperCamelCase ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=16 )
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : List[str] ):
__lowerCamelCase = Accelerator(dispatch_batches=_UpperCamelCase ,split_batches=_UpperCamelCase )
__lowerCamelCase = get_dataloader(_UpperCamelCase ,not dispatch_batches )
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
'''hf-internal-testing/mrpc-bert-base-cased''' ,return_dict=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = []
for batch in dataloader:
__lowerCamelCase ,__lowerCamelCase = batch.values()
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__lowerCamelCase ,__lowerCamelCase = [], []
for logit, targ in logits_and_targets:
logits.append(_UpperCamelCase )
targs.append(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch.cat(_UpperCamelCase ), torch.cat(_UpperCamelCase )
return logits, targs
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : List[Any]=82 ,_UpperCamelCase : str=False ,_UpperCamelCase : List[str]=False ,_UpperCamelCase : Optional[int]=16 ):
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = get_basic_setup(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = generate_predictions(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
assert (
len(_UpperCamelCase ) == num_samples
), F"""Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_UpperCamelCase )}"""
def a__ ( _UpperCamelCase : bool = False ,_UpperCamelCase : bool = False ):
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
__lowerCamelCase ,__lowerCamelCase = get_mrpc_setup(_UpperCamelCase ,_UpperCamelCase )
# First do baseline
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''no''']
model.to(_UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(_UpperCamelCase )
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=_UpperCamelCase ,references=batch['''labels'''] )
__lowerCamelCase = metric.compute()
# Then do distributed
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''ddp''']
model.eval()
for batch in dataloader:
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase = batch['''labels''']
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=_UpperCamelCase ,references=_UpperCamelCase )
__lowerCamelCase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] ,distributed[key] ), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""
def a__ ( ):
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('''**Testing gather_for_metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`""" )
test_mrpc(_UpperCamelCase ,_UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test torch metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""" )
test_torch_metrics(_UpperCamelCase ,99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test last batch is not dropped when perfectly divisible**''' )
__lowerCamelCase = Accelerator()
test_torch_metrics(_UpperCamelCase ,5_12 )
accelerator.state._reset_state()
def a__ ( _UpperCamelCase : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 622 | 0 |
from typing import Dict
from .base import GenericTensor, Pipeline
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def UpperCamelCase_ ( self : Tuple , lowerCAmelCase : List[Any]=None , lowerCAmelCase : int=None , lowerCAmelCase : int=None , **lowerCAmelCase : Tuple) -> Optional[int]:
"""simple docstring"""
if tokenize_kwargs is None:
_snake_case : Tuple = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
"""truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)""")
_snake_case : str = truncation
_snake_case : Dict = tokenize_kwargs
_snake_case : List[Any] = {}
if return_tensors is not None:
_snake_case : int = return_tensors
return preprocess_params, {}, postprocess_params
def UpperCamelCase_ ( self : Tuple , lowerCAmelCase : Tuple , **lowerCAmelCase : Optional[Any]) -> Dict[str, GenericTensor]:
"""simple docstring"""
_snake_case : int = self.framework
_snake_case : Optional[Any] = self.tokenizer(lowerCAmelCase , return_tensors=lowerCAmelCase , **lowerCAmelCase)
return model_inputs
def UpperCamelCase_ ( self : Any , lowerCAmelCase : List[str]) -> int:
"""simple docstring"""
_snake_case : List[str] = self.model(**lowerCAmelCase)
return model_outputs
def UpperCamelCase_ ( self : List[str] , lowerCAmelCase : Optional[int] , lowerCAmelCase : Any=False) -> Tuple:
"""simple docstring"""
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self : Dict , *lowerCAmelCase : str , **lowerCAmelCase : Any) -> Dict:
"""simple docstring"""
return super().__call__(*lowerCAmelCase , **lowerCAmelCase)
| 477 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case_ : Optional[int] = ["""image_processor""", """tokenizer"""]
snake_case_ : Optional[int] = """LayoutLMv2ImageProcessor"""
snake_case_ : Dict = ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")
def __init__( self : Optional[int] , lowerCAmelCase : List[str]=None , lowerCAmelCase : List[Any]=None , **lowerCAmelCase : List[str]) -> Optional[Any]:
"""simple docstring"""
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , lowerCAmelCase , )
_snake_case : Tuple = kwargs.pop("""feature_extractor""")
_snake_case : List[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""")
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""")
super().__init__(lowerCAmelCase , lowerCAmelCase)
def __call__( self : List[str] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowerCAmelCase : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , lowerCAmelCase : Union[List[List[int]], List[List[List[int]]]] = None , lowerCAmelCase : Optional[Union[List[int], List[List[int]]]] = None , lowerCAmelCase : bool = True , lowerCAmelCase : Union[bool, str, PaddingStrategy] = False , lowerCAmelCase : Union[bool, str, TruncationStrategy] = None , lowerCAmelCase : Optional[int] = None , lowerCAmelCase : int = 0 , lowerCAmelCase : Optional[int] = None , lowerCAmelCase : Optional[bool] = None , lowerCAmelCase : Optional[bool] = None , lowerCAmelCase : bool = False , lowerCAmelCase : bool = False , lowerCAmelCase : bool = False , lowerCAmelCase : bool = False , lowerCAmelCase : bool = True , lowerCAmelCase : Optional[Union[str, TensorType]] = None , **lowerCAmelCase : Optional[int] , ) -> BatchEncoding:
"""simple docstring"""
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"""You cannot provide bounding boxes """
"""if you initialized the image processor with apply_ocr set to True.""")
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"""You cannot provide word labels if you initialized the image processor with apply_ocr set to True.""")
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping.""")
# first, apply the image processor
_snake_case : str = self.image_processor(images=lowerCAmelCase , return_tensors=lowerCAmelCase)
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(lowerCAmelCase , lowerCAmelCase):
_snake_case : Optional[Any] = [text] # add batch dimension (as the image processor always adds a batch dimension)
_snake_case : Optional[Any] = features["""words"""]
_snake_case : Tuple = self.tokenizer(
text=text if text is not None else features["""words"""] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["""boxes"""] , word_labels=lowerCAmelCase , add_special_tokens=lowerCAmelCase , padding=lowerCAmelCase , truncation=lowerCAmelCase , max_length=lowerCAmelCase , stride=lowerCAmelCase , pad_to_multiple_of=lowerCAmelCase , return_token_type_ids=lowerCAmelCase , return_attention_mask=lowerCAmelCase , return_overflowing_tokens=lowerCAmelCase , return_special_tokens_mask=lowerCAmelCase , return_offsets_mapping=lowerCAmelCase , return_length=lowerCAmelCase , verbose=lowerCAmelCase , return_tensors=lowerCAmelCase , **lowerCAmelCase , )
# add pixel values
_snake_case : Optional[int] = features.pop("""pixel_values""")
if return_overflowing_tokens is True:
_snake_case : Tuple = self.get_overflowing_images(lowerCAmelCase , encoded_inputs["""overflow_to_sample_mapping"""])
_snake_case : int = images
return encoded_inputs
def UpperCamelCase_ ( self : Tuple , lowerCAmelCase : str , lowerCAmelCase : Tuple) -> Tuple:
"""simple docstring"""
_snake_case : int = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx])
if len(lowerCAmelCase) != len(lowerCAmelCase):
raise ValueError(
"""Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
F''' {len(lowerCAmelCase)} and {len(lowerCAmelCase)}''')
return images_with_overflow
def UpperCamelCase_ ( self : List[Any] , *lowerCAmelCase : Union[str, Any] , **lowerCAmelCase : Optional[int]) -> int:
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase , **lowerCAmelCase)
def UpperCamelCase_ ( self : Dict , *lowerCAmelCase : Any , **lowerCAmelCase : int) -> List[str]:
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase , **lowerCAmelCase)
@property
def UpperCamelCase_ ( self : Tuple) -> Any:
"""simple docstring"""
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def UpperCamelCase_ ( self : Any) -> Optional[int]:
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , lowerCAmelCase , )
return self.image_processor_class
@property
def UpperCamelCase_ ( self : Optional[int]) -> List[Any]:
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , lowerCAmelCase , )
return self.image_processor
| 477 | 1 |
class __snake_case :
def __init__( self : List[Any] ) -> str:
'''simple docstring'''
_lowerCAmelCase : str = {}
def SCREAMING_SNAKE_CASE ( self : Dict ) -> None:
'''simple docstring'''
print(self.vertex )
for i in self.vertex:
print(_UpperCAmelCase , """ -> """ , """ -> """.join([str(_UpperCAmelCase ) for j in self.vertex[i]] ) )
def SCREAMING_SNAKE_CASE ( self : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> None:
'''simple docstring'''
if from_vertex in self.vertex:
self.vertex[from_vertex].append(_UpperCAmelCase )
else:
# else make a new vertex
_lowerCAmelCase : Optional[int] = [to_vertex]
def SCREAMING_SNAKE_CASE ( self : Dict ) -> None:
'''simple docstring'''
_lowerCAmelCase : int = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Any , _UpperCAmelCase : int , _UpperCAmelCase : list ) -> None:
'''simple docstring'''
_lowerCAmelCase : List[str] = True
print(_UpperCAmelCase , end=""" """ )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(_UpperCAmelCase , _UpperCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : Optional[Any] = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("DFS:")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 196 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __snake_case :
def __init__( self : List[Any] , _UpperCAmelCase : Any ) -> Dict:
'''simple docstring'''
_lowerCAmelCase : Any = data
_lowerCAmelCase : Node | None = None
class __snake_case :
def __init__( self : Tuple ) -> Tuple:
'''simple docstring'''
_lowerCAmelCase : str = None
_lowerCAmelCase : List[Any] = None
def __iter__( self : str ) -> Iterator[Any]:
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.head
while self.head:
yield node.data
_lowerCAmelCase : List[str] = node.next
if node == self.head:
break
def __len__( self : Union[str, Any] ) -> int:
'''simple docstring'''
return sum(1 for _ in self )
def __repr__( self : Optional[int] ) -> str:
'''simple docstring'''
return "->".join(str(_UpperCAmelCase ) for item in iter(self ) )
def SCREAMING_SNAKE_CASE ( self : Any , _UpperCAmelCase : Any ) -> None:
'''simple docstring'''
self.insert_nth(len(self ) , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Dict , _UpperCAmelCase : Any ) -> None:
'''simple docstring'''
self.insert_nth(0 , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : Any ) -> None:
'''simple docstring'''
if index < 0 or index > len(self ):
raise IndexError("""list index out of range.""" )
_lowerCAmelCase : Optional[int] = Node(_UpperCAmelCase )
if self.head is None:
_lowerCAmelCase : Tuple = new_node # first node points itself
_lowerCAmelCase : int = new_node
elif index == 0: # insert at head
_lowerCAmelCase : Tuple = self.head
_lowerCAmelCase : int = new_node
else:
_lowerCAmelCase : Union[str, Any] = self.head
for _ in range(index - 1 ):
_lowerCAmelCase : Optional[Any] = temp.next
_lowerCAmelCase : Optional[int] = temp.next
_lowerCAmelCase : Optional[int] = new_node
if index == len(self ) - 1: # insert at tail
_lowerCAmelCase : Optional[Any] = new_node
def SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
'''simple docstring'''
return self.delete_nth(0 )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Any:
'''simple docstring'''
return self.delete_nth(len(self ) - 1 )
def SCREAMING_SNAKE_CASE ( self : str , _UpperCAmelCase : int = 0 ) -> Any:
'''simple docstring'''
if not 0 <= index < len(self ):
raise IndexError("""list index out of range.""" )
_lowerCAmelCase : Optional[Any] = self.head
if self.head == self.tail: # just one node
_lowerCAmelCase : Optional[Any] = None
elif index == 0: # delete head node
_lowerCAmelCase : str = self.tail.next.next
_lowerCAmelCase : List[str] = self.head.next
else:
_lowerCAmelCase : List[str] = self.head
for _ in range(index - 1 ):
_lowerCAmelCase : List[Any] = temp.next
_lowerCAmelCase : List[str] = temp.next
_lowerCAmelCase : Dict = temp.next.next
if index == len(self ) - 1: # delete at tail
_lowerCAmelCase : Optional[int] = temp
return delete_node.data
def SCREAMING_SNAKE_CASE ( self : int ) -> bool:
'''simple docstring'''
return len(self ) == 0
def _UpperCAmelCase ():
'''simple docstring'''
_lowerCAmelCase : Dict = CircularLinkedList()
assert len(UpperCamelCase_ ) == 0
assert circular_linked_list.is_empty() is True
assert str(UpperCamelCase_ ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(UpperCamelCase_ ) == i
circular_linked_list.insert_nth(UpperCamelCase_ , i + 1 )
assert str(UpperCamelCase_ ) == "->".join(str(UpperCamelCase_ ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(UpperCamelCase_ ) == "->".join(str(UpperCamelCase_ ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(UpperCamelCase_ ) == "->".join(str(UpperCamelCase_ ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(UpperCamelCase_ ) == "->".join(str(UpperCamelCase_ ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(UpperCamelCase_ ) == "->".join(str(UpperCamelCase_ ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 196 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( __a):
__a : List[str] = """WhisperFeatureExtractor"""
__a : Optional[Any] = """WhisperTokenizer"""
def __init__( self , _A , _A ) -> int:
'''simple docstring'''
super().__init__(_A , _A )
_UpperCAmelCase : Optional[int] = self.feature_extractor
_UpperCAmelCase : Optional[Any] = False
def __snake_case ( self , _A=None , _A=None , _A=True ) -> List[Any]:
'''simple docstring'''
return self.tokenizer.get_decoder_prompt_ids(task=_A , language=_A , no_timestamps=_A )
def __call__( self , *_A , **_A ) -> List[Any]:
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*_A , **_A )
_UpperCAmelCase : Dict = kwargs.pop("""audio""" , _A )
_UpperCAmelCase : Dict = kwargs.pop("""sampling_rate""" , _A )
_UpperCAmelCase : str = kwargs.pop("""text""" , _A )
if len(_A ) > 0:
_UpperCAmelCase : Any = args[0]
_UpperCAmelCase : Optional[int] = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
_UpperCAmelCase : int = self.feature_extractor(_A , *_A , sampling_rate=_A , **_A )
if text is not None:
_UpperCAmelCase : Optional[int] = self.tokenizer(_A , **_A )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_UpperCAmelCase : Optional[int] = encodings["""input_ids"""]
return inputs
def __snake_case ( self , *_A , **_A ) -> Optional[int]:
'''simple docstring'''
return self.tokenizer.batch_decode(*_A , **_A )
def __snake_case ( self , *_A , **_A ) -> Union[str, Any]:
'''simple docstring'''
return self.tokenizer.decode(*_A , **_A )
def __snake_case ( self , _A , _A="np" ) -> Tuple:
'''simple docstring'''
return self.tokenizer.get_prompt_ids(_A , return_tensors=_A )
| 238 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
lowerCamelCase__ : str = logging.getLogger(__name__)
@dataclass
class _UpperCAmelCase :
__a : str = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
__a : Optional[str] = field(
default=__a , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__a : bool = field(
default=__a , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
__a : str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__a : bool = field(
default=__a , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
@dataclass
class _UpperCAmelCase :
__a : Optional[str] = field(default=__a , metadata={"""help""": """The input training data file (a text file)."""})
__a : Optional[str] = field(
default=__a , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
__a : bool = field(
default=__a , metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
__a : Optional[int] = field(
default=__a , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
__a : Optional[int] = field(
default=__a , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. If passed, sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__a : bool = field(
default=__a , metadata={
"""help""": (
"""Whether to pad all samples to the maximum sentence length. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch. More """
"""efficient on GPU but very bad for TPU."""
)
} , )
__a : Optional[int] = field(
default=__a , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__a : Optional[int] = field(
default=__a , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def __snake_case ( self ) -> Union[str, Any]:
'''simple docstring'''
if self.train_file is not None:
_UpperCAmelCase : Any = self.train_file.split(""".""" )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
_UpperCAmelCase : int = self.validation_file.split(""".""" )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class _UpperCAmelCase :
__a : PreTrainedTokenizerBase
__a : Union[bool, str, PaddingStrategy] = True
__a : Optional[int] = None
__a : Optional[int] = None
def __call__( self , _A ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = """label""" if """label""" in features[0].keys() else """labels"""
_UpperCAmelCase : Any = [feature.pop(_A ) for feature in features]
_UpperCAmelCase : Optional[int] = len(_A )
_UpperCAmelCase : Union[str, Any] = len(features[0]["""input_ids"""] )
_UpperCAmelCase : str = [
[{k: v[i] for k, v in feature.items()} for i in range(_A )] for feature in features
]
_UpperCAmelCase : List[str] = list(chain(*_A ) )
_UpperCAmelCase : int = self.tokenizer.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="""pt""" , )
# Un-flatten
_UpperCAmelCase : Optional[int] = {k: v.view(_A , _A , -1 ) for k, v in batch.items()}
# Add back labels
_UpperCAmelCase : Any = torch.tensor(_A , dtype=torch.intaa )
return batch
def UpperCamelCase ( ) -> Dict:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase : Optional[int] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Optional[Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : List[str] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_swag""", _lowerCAmelCase, _lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", handlers=[logging.StreamHandler(sys.stdout )], )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_UpperCAmelCase : Union[str, Any] = training_args.get_process_log_level()
logger.setLevel(_lowerCAmelCase )
datasets.utils.logging.set_verbosity(_lowerCAmelCase )
transformers.utils.logging.set_verbosity(_lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
_UpperCAmelCase : List[Any] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase : Optional[int] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
_UpperCAmelCase : Dict = {}
if data_args.train_file is not None:
_UpperCAmelCase : Optional[int] = data_args.train_file
if data_args.validation_file is not None:
_UpperCAmelCase : Optional[int] = data_args.validation_file
_UpperCAmelCase : int = data_args.train_file.split(""".""" )[-1]
_UpperCAmelCase : List[Any] = load_dataset(
_lowerCAmelCase, data_files=_lowerCAmelCase, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
else:
# Downloading and loading the swag dataset from the hub.
_UpperCAmelCase : List[str] = load_dataset(
"""swag""", """regular""", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase : Tuple = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
_UpperCAmelCase : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
_UpperCAmelCase : List[str] = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path, from_tf=bool(""".ckpt""" in model_args.model_name_or_path ), config=_lowerCAmelCase, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
_UpperCAmelCase : str = [f'''ending{i}''' for i in range(4 )]
_UpperCAmelCase : List[Any] = """sent1"""
_UpperCAmelCase : str = """sent2"""
if data_args.max_seq_length is None:
_UpperCAmelCase : Tuple = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
"""The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"""
""" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"""
""" override this default with `--block_size xxx`.""" )
_UpperCAmelCase : List[str] = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
f'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''' )
_UpperCAmelCase : Tuple = min(data_args.max_seq_length, tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(_lowerCAmelCase : str ):
_UpperCAmelCase : int = [[context] * 4 for context in examples[context_name]]
_UpperCAmelCase : List[Any] = examples[question_header_name]
_UpperCAmelCase : List[str] = [
[f'''{header} {examples[end][i]}''' for end in ending_names] for i, header in enumerate(_lowerCAmelCase )
]
# Flatten out
_UpperCAmelCase : Any = list(chain(*_lowerCAmelCase ) )
_UpperCAmelCase : Any = list(chain(*_lowerCAmelCase ) )
# Tokenize
_UpperCAmelCase : Dict = tokenizer(
_lowerCAmelCase, _lowerCAmelCase, truncation=_lowerCAmelCase, max_length=_lowerCAmelCase, padding="""max_length""" if data_args.pad_to_max_length else False, )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0, len(_lowerCAmelCase ), 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
_UpperCAmelCase : Any = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
_UpperCAmelCase : List[Any] = min(len(_lowerCAmelCase ), data_args.max_train_samples )
_UpperCAmelCase : List[Any] = train_dataset.select(range(_lowerCAmelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
_UpperCAmelCase : List[Any] = train_dataset.map(
_lowerCAmelCase, batched=_lowerCAmelCase, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
_UpperCAmelCase : Optional[Any] = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
_UpperCAmelCase : int = min(len(_lowerCAmelCase ), data_args.max_eval_samples )
_UpperCAmelCase : List[str] = eval_dataset.select(range(_lowerCAmelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
_UpperCAmelCase : List[str] = eval_dataset.map(
_lowerCAmelCase, batched=_lowerCAmelCase, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
# Data collator
_UpperCAmelCase : List[Any] = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=_lowerCAmelCase, pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(_lowerCAmelCase : List[Any] ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = eval_predictions
_UpperCAmelCase : Dict = np.argmax(_lowerCAmelCase, axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
_UpperCAmelCase : Union[str, Any] = Trainer(
model=_lowerCAmelCase, args=_lowerCAmelCase, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=_lowerCAmelCase, data_collator=_lowerCAmelCase, compute_metrics=_lowerCAmelCase, )
# Training
if training_args.do_train:
_UpperCAmelCase : Dict = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase : Optional[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase : int = last_checkpoint
_UpperCAmelCase : Union[str, Any] = trainer.train(resume_from_checkpoint=_lowerCAmelCase )
trainer.save_model() # Saves the tokenizer too for easy upload
_UpperCAmelCase : List[Any] = train_result.metrics
_UpperCAmelCase : Dict = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_lowerCAmelCase )
)
_UpperCAmelCase : List[Any] = min(_lowerCAmelCase, len(_lowerCAmelCase ) )
trainer.log_metrics("""train""", _lowerCAmelCase )
trainer.save_metrics("""train""", _lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_UpperCAmelCase : Optional[Any] = trainer.evaluate()
_UpperCAmelCase : List[str] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_lowerCAmelCase )
_UpperCAmelCase : Optional[int] = min(_lowerCAmelCase, len(_lowerCAmelCase ) )
trainer.log_metrics("""eval""", _lowerCAmelCase )
trainer.save_metrics("""eval""", _lowerCAmelCase )
_UpperCAmelCase : int = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """multiple-choice""",
"""dataset_tags""": """swag""",
"""dataset_args""": """regular""",
"""dataset""": """SWAG""",
"""language""": """en""",
}
if training_args.push_to_hub:
trainer.push_to_hub(**_lowerCAmelCase )
else:
trainer.create_model_card(**_lowerCAmelCase )
def UpperCamelCase ( _lowerCAmelCase : Tuple ) -> Tuple:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 238 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase : List[str] = logging.get_logger(__name__)
_UpperCamelCase : Any = {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}
class _lowerCAmelCase( SCREAMING_SNAKE_CASE__):
"""simple docstring"""
lowerCamelCase__ = '''mvp'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , UpperCAmelCase=5_02_67 , UpperCAmelCase=10_24 , UpperCAmelCase=12 , UpperCAmelCase=40_96 , UpperCAmelCase=16 , UpperCAmelCase=12 , UpperCAmelCase=40_96 , UpperCAmelCase=16 , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase="gelu" , UpperCAmelCase=10_24 , UpperCAmelCase=0.1 , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase=0.02 , UpperCAmelCase=0.0 , UpperCAmelCase=False , UpperCAmelCase=True , UpperCAmelCase=1 , UpperCAmelCase=0 , UpperCAmelCase=2 , UpperCAmelCase=True , UpperCAmelCase=2 , UpperCAmelCase=2 , UpperCAmelCase=False , UpperCAmelCase=1_00 , UpperCAmelCase=8_00 , **UpperCAmelCase , )-> Optional[int]:
__A = vocab_size
__A = max_position_embeddings
__A = d_model
__A = encoder_ffn_dim
__A = encoder_layers
__A = encoder_attention_heads
__A = decoder_ffn_dim
__A = decoder_layers
__A = decoder_attention_heads
__A = dropout
__A = attention_dropout
__A = activation_dropout
__A = activation_function
__A = init_std
__A = encoder_layerdrop
__A = decoder_layerdrop
__A = classifier_dropout
__A = use_cache
__A = encoder_layers
__A = scale_embedding # scale factor will be sqrt(d_model) if True
__A = use_prompt
__A = prompt_length
__A = prompt_mid_dim
super().__init__(
pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , is_encoder_decoder=snake_case__ , decoder_start_token_id=snake_case__ , forced_eos_token_id=snake_case__ , **snake_case__ , )
if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''' , snake_case__ ):
__A = self.bos_token_id
warnings.warn(
f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
'''The config can simply be saved and uploaded again to be fixed.''' )
| 719 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_UpperCamelCase : str = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")
def __UpperCamelCase ( snake_case , snake_case , snake_case = 1_6_0_0_0 ) -> Tuple:
'''simple docstring'''
__A = int(round(sample_rate * max_length ) )
if len(snake_case ) <= sample_length:
return wav
__A = randint(0 , len(snake_case ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class _lowerCAmelCase:
"""simple docstring"""
lowerCamelCase__ = field(default=_a , metadata={'''help''': '''Name of a dataset from the datasets package'''})
lowerCamelCase__ = field(
default=_a , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''})
lowerCamelCase__ = field(
default=_a , metadata={'''help''': '''A file containing the training audio paths and labels.'''})
lowerCamelCase__ = field(
default=_a , metadata={'''help''': '''A file containing the validation audio paths and labels.'''})
lowerCamelCase__ = field(
default='''train''' , metadata={
'''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
} , )
lowerCamelCase__ = field(
default='''validation''' , metadata={
'''help''': (
'''The name of the training data set split to use (via the datasets library). Defaults to \'validation\''''
)
} , )
lowerCamelCase__ = field(
default='''audio''' , metadata={'''help''': '''The name of the dataset column containing the audio data. Defaults to \'audio\''''} , )
lowerCamelCase__ = field(
default='''label''' , metadata={'''help''': '''The name of the dataset column containing the labels. Defaults to \'label\''''})
lowerCamelCase__ = field(
default=_a , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
lowerCamelCase__ = field(
default=_a , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
lowerCamelCase__ = field(
default=20 , metadata={'''help''': '''Audio clips will be randomly cut to this length during training if the value is set.'''} , )
@dataclass
class _lowerCAmelCase:
"""simple docstring"""
lowerCamelCase__ = field(
default='''facebook/wav2vec2-base''' , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} , )
lowerCamelCase__ = field(
default=_a , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
lowerCamelCase__ = field(
default=_a , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from the Hub'''})
lowerCamelCase__ = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
lowerCamelCase__ = field(
default=_a , metadata={'''help''': '''Name or path of preprocessor config.'''})
lowerCamelCase__ = field(
default=_a , metadata={'''help''': '''Whether to freeze the feature encoder layers of the model.'''})
lowerCamelCase__ = field(
default=_a , metadata={'''help''': '''Whether to generate an attention mask in the feature extractor.'''})
lowerCamelCase__ = field(
default=_a , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
lowerCamelCase__ = field(
default=_a , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''})
lowerCamelCase__ = field(
default=_a , metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} , )
def SCREAMING_SNAKE_CASE__ ( self )-> List[str]:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , UpperCAmelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def __UpperCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
__A = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__A , __A , __A = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__A , __A , __A = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_audio_classification''' , snake_case , snake_case )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
__A = training_args.get_process_log_level()
logger.setLevel(snake_case )
transformers.utils.logging.set_verbosity(snake_case )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} "
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
__A = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__A = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
'''Use --overwrite_output_dir to train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset and prepare it for the audio classification task.
__A = DatasetDict()
__A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
__A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
F"{', '.join(raw_datasets['train'].column_names )}." )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
'''Make sure to set `--label_column_name` to the correct text column - one of '''
F"{', '.join(raw_datasets['train'].column_names )}." )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
__A = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
__A = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
__A = feature_extractor.model_input_names[0]
def train_transforms(snake_case ):
__A = []
for audio in batch[data_args.audio_column_name]:
__A = random_subsample(
audio['''array'''] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(snake_case )
__A = feature_extractor(snake_case , sampling_rate=feature_extractor.sampling_rate )
__A = {model_input_name: inputs.get(snake_case )}
__A = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(snake_case ):
__A = [audio['''array'''] for audio in batch[data_args.audio_column_name]]
__A = feature_extractor(snake_case , sampling_rate=feature_extractor.sampling_rate )
__A = {model_input_name: inputs.get(snake_case )}
__A = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
__A = raw_datasets['''train'''].features[data_args.label_column_name].names
__A , __A = {}, {}
for i, label in enumerate(snake_case ):
__A = str(snake_case )
__A = label
# Load the accuracy metric from the datasets package
__A = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(snake_case ):
__A = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=snake_case , references=eval_pred.label_ids )
__A = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(snake_case ) , labelaid=snake_case , idalabel=snake_case , finetuning_task='''audio-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__A = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=snake_case , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
__A = (
raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(snake_case , output_all_columns=snake_case )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
__A = (
raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(snake_case , output_all_columns=snake_case )
# Initialize our trainer
__A = Trainer(
model=snake_case , args=snake_case , train_dataset=raw_datasets['''train'''] if training_args.do_train else None , eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None , compute_metrics=snake_case , tokenizer=snake_case , )
# Training
if training_args.do_train:
__A = None
if training_args.resume_from_checkpoint is not None:
__A = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__A = last_checkpoint
__A = trainer.train(resume_from_checkpoint=snake_case )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
__A = trainer.evaluate()
trainer.log_metrics('''eval''' , snake_case )
trainer.save_metrics('''eval''' , snake_case )
# Write model card and (optionally) push to hub
__A = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''audio-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''audio-classification'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**snake_case )
else:
trainer.create_model_card(**snake_case )
if __name__ == "__main__":
main()
| 341 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
__A ='docs/source/en/_toctree.yml'
def _UpperCamelCase ( UpperCamelCase__ ):
UpperCAmelCase__ : Dict = defaultdict(UpperCamelCase__ )
UpperCAmelCase__ : Dict = []
UpperCAmelCase__ : Union[str, Any] = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(UpperCamelCase__ )
UpperCAmelCase__ : int = new_doc_list
UpperCAmelCase__ : List[str] = [key for key, value in counts.items() if value > 1]
UpperCAmelCase__ : Any = []
for duplicate_key in duplicates:
UpperCAmelCase__ : Union[str, Any] = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(UpperCamelCase__ ) > 1:
raise ValueError(
f'''{duplicate_key} is present several times in the documentation table of content at '''
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
UpperCAmelCase__ : Optional[int] = sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(UpperCamelCase__ ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(UpperCamelCase__ )
# Sort
return overview_doc
def _UpperCamelCase ( UpperCamelCase__=False ):
with open(UpperCamelCase__ , encoding="""utf-8""" ) as f:
UpperCAmelCase__ : Dict = yaml.safe_load(f.read() )
# Get to the API doc
UpperCAmelCase__ : Any = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCAmelCase__ : Union[str, Any] = content[api_idx]["""sections"""]
# Then to the model doc
UpperCAmelCase__ : Any = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
UpperCAmelCase__ : Any = api_doc[scheduler_idx]["""sections"""]
UpperCAmelCase__ : Union[str, Any] = clean_doc_toc(UpperCamelCase__ )
UpperCAmelCase__ : Union[str, Any] = False
if new_scheduler_doc != scheduler_doc:
UpperCAmelCase__ : int = True
if overwrite:
UpperCAmelCase__ : Union[str, Any] = new_scheduler_doc
if diff:
if overwrite:
UpperCAmelCase__ : str = api_doc
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(UpperCamelCase__ , allow_unicode=UpperCamelCase__ ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def _UpperCamelCase ( UpperCamelCase__=False ):
with open(UpperCamelCase__ , encoding="""utf-8""" ) as f:
UpperCAmelCase__ : Any = yaml.safe_load(f.read() )
# Get to the API doc
UpperCAmelCase__ : Tuple = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCAmelCase__ : List[Any] = content[api_idx]["""sections"""]
# Then to the model doc
UpperCAmelCase__ : int = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : Optional[int] = api_doc[pipeline_idx]["""sections"""]
UpperCAmelCase__ : Dict = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
UpperCAmelCase__ : Dict = pipeline_doc["""section"""]
UpperCAmelCase__ : Optional[int] = clean_doc_toc(UpperCamelCase__ )
if overwrite:
UpperCAmelCase__ : int = new_sub_pipeline_doc
new_pipeline_docs.append(UpperCamelCase__ )
# sort overall pipeline doc
UpperCAmelCase__ : Union[str, Any] = clean_doc_toc(UpperCamelCase__ )
if new_pipeline_docs != pipeline_docs:
UpperCAmelCase__ : List[Any] = True
if overwrite:
UpperCAmelCase__ : int = new_pipeline_docs
if diff:
if overwrite:
UpperCAmelCase__ : Dict = api_doc
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(UpperCamelCase__ , allow_unicode=UpperCamelCase__ ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
__A =argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__A =parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 407 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
__A ={
'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def _UpperCamelCase ( UpperCamelCase__ ):
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
if args.student_type == "roberta":
UpperCAmelCase__ : Optional[Any] = False
elif args.student_type == "gpt2":
UpperCAmelCase__ : str = False
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
if args.student_type == "roberta":
UpperCAmelCase__ : str = False
def _UpperCamelCase ( ):
UpperCAmelCase__ : Union[str, Any] = argparse.ArgumentParser(description="""Training""" )
parser.add_argument("""--force""" , action="""store_true""" , help="""Overwrite dump_path if it already exists.""" )
parser.add_argument(
"""--dump_path""" , type=UpperCamelCase__ , required=UpperCamelCase__ , help="""The output directory (log, checkpoints, parameters, etc.)""" )
parser.add_argument(
"""--data_file""" , type=UpperCamelCase__ , required=UpperCamelCase__ , help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence.""" , )
parser.add_argument(
"""--student_type""" , type=UpperCamelCase__ , choices=["""distilbert""", """roberta""", """gpt2"""] , required=UpperCamelCase__ , help="""The student type (DistilBERT, RoBERTa).""" , )
parser.add_argument("""--student_config""" , type=UpperCamelCase__ , required=UpperCamelCase__ , help="""Path to the student configuration.""" )
parser.add_argument(
"""--student_pretrained_weights""" , default=UpperCamelCase__ , type=UpperCamelCase__ , help="""Load student initialization checkpoint.""" )
parser.add_argument(
"""--teacher_type""" , choices=["""bert""", """roberta""", """gpt2"""] , required=UpperCamelCase__ , help="""Teacher type (BERT, RoBERTa).""" )
parser.add_argument("""--teacher_name""" , type=UpperCamelCase__ , required=UpperCamelCase__ , help="""The teacher model.""" )
parser.add_argument("""--temperature""" , default=2.0 , type=UpperCamelCase__ , help="""Temperature for the softmax temperature.""" )
parser.add_argument(
"""--alpha_ce""" , default=0.5 , type=UpperCamelCase__ , help="""Linear weight for the distillation loss. Must be >=0.""" )
parser.add_argument(
"""--alpha_mlm""" , default=0.0 , type=UpperCamelCase__ , help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.""" , )
parser.add_argument("""--alpha_clm""" , default=0.5 , type=UpperCamelCase__ , help="""Linear weight for the CLM loss. Must be >=0.""" )
parser.add_argument("""--alpha_mse""" , default=0.0 , type=UpperCamelCase__ , help="""Linear weight of the MSE loss. Must be >=0.""" )
parser.add_argument(
"""--alpha_cos""" , default=0.0 , type=UpperCamelCase__ , help="""Linear weight of the cosine embedding loss. Must be >=0.""" )
parser.add_argument(
"""--mlm""" , action="""store_true""" , help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.""" )
parser.add_argument(
"""--mlm_mask_prop""" , default=0.15 , type=UpperCamelCase__ , help="""Proportion of tokens for which we need to make a prediction.""" , )
parser.add_argument("""--word_mask""" , default=0.8 , type=UpperCamelCase__ , help="""Proportion of tokens to mask out.""" )
parser.add_argument("""--word_keep""" , default=0.1 , type=UpperCamelCase__ , help="""Proportion of tokens to keep.""" )
parser.add_argument("""--word_rand""" , default=0.1 , type=UpperCamelCase__ , help="""Proportion of tokens to randomly replace.""" )
parser.add_argument(
"""--mlm_smoothing""" , default=0.7 , type=UpperCamelCase__ , help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).""" , )
parser.add_argument("""--token_counts""" , type=UpperCamelCase__ , help="""The token counts in the data_file for MLM.""" )
parser.add_argument(
"""--restrict_ce_to_mask""" , action="""store_true""" , help="""If true, compute the distillation loss only the [MLM] prediction distribution.""" , )
parser.add_argument(
"""--freeze_pos_embs""" , action="""store_true""" , help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.""" , )
parser.add_argument(
"""--freeze_token_type_embds""" , action="""store_true""" , help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.""" , )
parser.add_argument("""--n_epoch""" , type=UpperCamelCase__ , default=3 , help="""Number of pass on the whole dataset.""" )
parser.add_argument("""--batch_size""" , type=UpperCamelCase__ , default=5 , help="""Batch size (for each process).""" )
parser.add_argument(
"""--group_by_size""" , action="""store_false""" , help="""If true, group sequences that have similar length into the same batch. Default is true.""" , )
parser.add_argument(
"""--gradient_accumulation_steps""" , type=UpperCamelCase__ , default=5_0 , help="""Gradient accumulation for larger training batches.""" , )
parser.add_argument("""--warmup_prop""" , default=0.05 , type=UpperCamelCase__ , help="""Linear warmup proportion.""" )
parser.add_argument("""--weight_decay""" , default=0.0 , type=UpperCamelCase__ , help="""Weight decay if we apply some.""" )
parser.add_argument("""--learning_rate""" , default=5e-4 , type=UpperCamelCase__ , help="""The initial learning rate for Adam.""" )
parser.add_argument("""--adam_epsilon""" , default=1e-6 , type=UpperCamelCase__ , help="""Epsilon for Adam optimizer.""" )
parser.add_argument("""--max_grad_norm""" , default=5.0 , type=UpperCamelCase__ , help="""Max gradient norm.""" )
parser.add_argument("""--initializer_range""" , default=0.02 , type=UpperCamelCase__ , help="""Random initialization range.""" )
parser.add_argument(
"""--fp16""" , action="""store_true""" , help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""" , )
parser.add_argument(
"""--fp16_opt_level""" , type=UpperCamelCase__ , default="""O1""" , help=(
"""For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
"""See details at https://nvidia.github.io/apex/amp.html"""
) , )
parser.add_argument("""--n_gpu""" , type=UpperCamelCase__ , default=1 , help="""Number of GPUs in the node.""" )
parser.add_argument("""--local_rank""" , type=UpperCamelCase__ , default=-1 , help="""Distributed training - Local rank""" )
parser.add_argument("""--seed""" , type=UpperCamelCase__ , default=5_6 , help="""Random seed""" )
parser.add_argument("""--log_interval""" , type=UpperCamelCase__ , default=5_0_0 , help="""Tensorboard logging interval.""" )
parser.add_argument("""--checkpoint_interval""" , type=UpperCamelCase__ , default=4_0_0_0 , help="""Checkpoint interval.""" )
UpperCAmelCase__ : Dict = parser.parse_args()
sanity_checks(UpperCamelCase__ )
# ARGS #
init_gpu_params(UpperCamelCase__ )
set_seed(UpperCamelCase__ )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
""" itUse `--force` if you want to overwrite it""" )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(f'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(f'''Param: {args}''' )
with open(os.path.join(args.dump_path , """parameters.json""" ) , """w""" ) as f:
json.dump(vars(UpperCamelCase__ ) , UpperCamelCase__ , indent=4 )
git_log(args.dump_path )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = MODEL_CLASSES[args.student_type]
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
UpperCAmelCase__ : Optional[Any] = teacher_tokenizer_class.from_pretrained(args.teacher_name )
UpperCAmelCase__ : Any = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
UpperCAmelCase__ : Any = tokenizer.all_special_tokens.index(UpperCamelCase__ )
UpperCAmelCase__ : str = tokenizer.all_special_ids[idx]
logger.info(f'''Special tokens {special_tok_ids}''' )
UpperCAmelCase__ : Any = special_tok_ids
UpperCAmelCase__ : str = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(f'''Loading data from {args.data_file}''' )
with open(args.data_file , """rb""" ) as fp:
UpperCAmelCase__ : str = pickle.load(UpperCamelCase__ )
if args.mlm:
logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts , """rb""" ) as fp:
UpperCAmelCase__ : int = pickle.load(UpperCamelCase__ )
UpperCAmelCase__ : Optional[Any] = np.maximum(UpperCamelCase__ , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
UpperCAmelCase__ : Tuple = 0.0 # do not predict special tokens
UpperCAmelCase__ : Any = torch.from_numpy(UpperCamelCase__ )
else:
UpperCAmelCase__ : Tuple = None
UpperCAmelCase__ : Optional[int] = LmSeqsDataset(params=UpperCamelCase__ , data=UpperCamelCase__ )
logger.info("""Data loader created.""" )
# STUDENT #
logger.info(f'''Loading student config from {args.student_config}''' )
UpperCAmelCase__ : int = student_config_class.from_pretrained(args.student_config )
UpperCAmelCase__ : Optional[int] = True
if args.student_pretrained_weights is not None:
logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}''' )
UpperCAmelCase__ : str = student_model_class.from_pretrained(args.student_pretrained_weights , config=UpperCamelCase__ )
else:
UpperCAmelCase__ : Any = student_model_class(UpperCamelCase__ )
if args.n_gpu > 0:
student.to(f'''cuda:{args.local_rank}''' )
logger.info("""Student loaded.""" )
# TEACHER #
UpperCAmelCase__ : int = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=UpperCamelCase__ )
if args.n_gpu > 0:
teacher.to(f'''cuda:{args.local_rank}''' )
logger.info(f'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(UpperCamelCase__ , UpperCamelCase__ )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(UpperCamelCase__ , UpperCamelCase__ )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
UpperCAmelCase__ : Optional[Any] = Distiller(
params=UpperCamelCase__ , dataset=UpperCamelCase__ , token_probs=UpperCamelCase__ , student=UpperCamelCase__ , teacher=UpperCamelCase__ )
distiller.train()
logger.info("""Let's go get some drinks.""" )
if __name__ == "__main__":
main()
| 407 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _a( UpperCamelCase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[int] =4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : int =4
SCREAMING_SNAKE_CASE__ : Any =4_8
SCREAMING_SNAKE_CASE__ : List[Any] ='''pixelshuffle_aux'''
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[int] =[6, 6, 6, 6]
SCREAMING_SNAKE_CASE__ : Tuple =6_0
SCREAMING_SNAKE_CASE__ : List[Any] =[6, 6, 6, 6]
SCREAMING_SNAKE_CASE__ : Tuple ='''pixelshuffledirect'''
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[Any] =4
SCREAMING_SNAKE_CASE__ : Any ='''nearest+conv'''
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =1
SCREAMING_SNAKE_CASE__ : int =1
SCREAMING_SNAKE_CASE__ : Any =1_2_6
SCREAMING_SNAKE_CASE__ : Dict =7
SCREAMING_SNAKE_CASE__ : Optional[Any] =2_5_5.0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =''''''
return config
def _a( UpperCamelCase__ : Tuple, UpperCamelCase__ : Dict ):
'''simple docstring'''
if "patch_embed.proj" in name and "layers" not in name:
SCREAMING_SNAKE_CASE__ : str =name.replace('''patch_embed.proj''', '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE__ : List[Any] =name.replace('''patch_embed.norm''', '''embeddings.patch_embeddings.layernorm''' )
if "layers" in name:
SCREAMING_SNAKE_CASE__ : List[str] =name.replace('''layers''', '''encoder.stages''' )
if "residual_group.blocks" in name:
SCREAMING_SNAKE_CASE__ : List[Any] =name.replace('''residual_group.blocks''', '''layers''' )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE__ : List[Any] =name.replace('''attn.proj''', '''attention.output.dense''' )
if "attn" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =name.replace('''attn''', '''attention.self''' )
if "norm1" in name:
SCREAMING_SNAKE_CASE__ : Any =name.replace('''norm1''', '''layernorm_before''' )
if "norm2" in name:
SCREAMING_SNAKE_CASE__ : str =name.replace('''norm2''', '''layernorm_after''' )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] =name.replace('''mlp.fc1''', '''intermediate.dense''' )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE__ : Optional[int] =name.replace('''mlp.fc2''', '''output.dense''' )
if "q_bias" in name:
SCREAMING_SNAKE_CASE__ : List[Any] =name.replace('''q_bias''', '''query.bias''' )
if "k_bias" in name:
SCREAMING_SNAKE_CASE__ : List[str] =name.replace('''k_bias''', '''key.bias''' )
if "v_bias" in name:
SCREAMING_SNAKE_CASE__ : List[str] =name.replace('''v_bias''', '''value.bias''' )
if "cpb_mlp" in name:
SCREAMING_SNAKE_CASE__ : int =name.replace('''cpb_mlp''', '''continuous_position_bias_mlp''' )
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE__ : Tuple =name.replace('''patch_embed.proj''', '''patch_embed.projection''' )
if name == "norm.weight":
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''layernorm.weight'''
if name == "norm.bias":
SCREAMING_SNAKE_CASE__ : Optional[int] ='''layernorm.bias'''
if "conv_first" in name:
SCREAMING_SNAKE_CASE__ : str =name.replace('''conv_first''', '''first_convolution''' )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
SCREAMING_SNAKE_CASE__ : Any =name.replace('''conv_last''', '''final_convolution''' )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
SCREAMING_SNAKE_CASE__ : str =name.replace('''conv_before_upsample.0''', '''conv_before_upsample''' )
if "upsample.0" in name:
SCREAMING_SNAKE_CASE__ : str =name.replace('''upsample.0''', '''upsample.convolution_0''' )
if "upsample.2" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =name.replace('''upsample.2''', '''upsample.convolution_1''' )
SCREAMING_SNAKE_CASE__ : Tuple ='''upsample.''' + name
elif config.upsampler == "pixelshuffledirect":
SCREAMING_SNAKE_CASE__ : int =name.replace('''upsample.0.weight''', '''upsample.conv.weight''' )
SCREAMING_SNAKE_CASE__ : str =name.replace('''upsample.0.bias''', '''upsample.conv.bias''' )
else:
pass
else:
SCREAMING_SNAKE_CASE__ : int ='''swin2sr.''' + name
return name
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : Tuple ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE__ : List[Any] =orig_state_dict.pop(UpperCamelCase__ )
if "qkv" in key:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =key.split('''.''' )
SCREAMING_SNAKE_CASE__ : List[Any] =int(key_split[1] )
SCREAMING_SNAKE_CASE__ : Dict =int(key_split[4] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =config.embed_dim
if "weight" in key:
SCREAMING_SNAKE_CASE__ : List[Any] =val[:dim, :]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE__ : Tuple =val[-dim:, :]
else:
SCREAMING_SNAKE_CASE__ : str =val[:dim]
SCREAMING_SNAKE_CASE__ : List[Any] =val[dim : dim * 2]
SCREAMING_SNAKE_CASE__ : Dict =val[-dim:]
pass
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =val
return orig_state_dict
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Optional[int], UpperCamelCase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =get_config(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Dict =SwinaSRForImageSuperResolution(UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =torch.hub.load_state_dict_from_url(UpperCamelCase__, map_location='''cpu''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =convert_state_dict(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] =model.load_state_dict(UpperCamelCase__, strict=UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
raise ValueError('''Missing keys when converting: {}'''.format(UpperCamelCase__ ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(f"Unexpected key {key} in state_dict" )
# verify values
SCREAMING_SNAKE_CASE__ : Any ='''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =Image.open(requests.get(UpperCamelCase__, stream=UpperCamelCase__ ).raw ).convert('''RGB''' )
SCREAMING_SNAKE_CASE__ : List[Any] =SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
SCREAMING_SNAKE_CASE__ : List[Any] =1_2_6 if '''Jpeg''' in checkpoint_url else 2_5_6
SCREAMING_SNAKE_CASE__ : str =Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6], std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
SCREAMING_SNAKE_CASE__ : int =transforms(UpperCamelCase__ ).unsqueeze(0 )
if config.num_channels == 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] =pixel_values[:, 0, :, :].unsqueeze(1 )
SCREAMING_SNAKE_CASE__ : List[str] =model(UpperCamelCase__ )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : int =torch.Size([1, 3, 5_1_2, 5_1_2] )
SCREAMING_SNAKE_CASE__ : Tuple =torch.tensor(
[[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Any =torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
SCREAMING_SNAKE_CASE__ : Dict =torch.tensor(
[[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
SCREAMING_SNAKE_CASE__ : Tuple =torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.tensor(
[[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : List[Any] =torch.Size([1, 3, 5_1_2, 5_1_2] )
SCREAMING_SNAKE_CASE__ : Tuple =torch.tensor(
[[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.tensor(
[[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]] )
assert (
outputs.reconstruction.shape == expected_shape
), f"Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3], UpperCamelCase__, atol=1e-3 )
print('''Looks ok!''' )
SCREAMING_SNAKE_CASE__ : List[str] ={
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''': (
'''swin2SR-classical-sr-x2-64'''
),
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth''': (
'''swin2SR-classical-sr-x4-64'''
),
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth''': (
'''swin2SR-compressed-sr-x4-48'''
),
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth''': (
'''swin2SR-lightweight-x2-64'''
),
'''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth''': (
'''swin2SR-realworld-sr-x4-64-bsrgan-psnr'''
),
}
SCREAMING_SNAKE_CASE__ : int =url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(UpperCamelCase__ )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(UpperCamelCase__ )
if push_to_hub:
model.push_to_hub(f"caidas/{model_name}" )
processor.push_to_hub(f"caidas/{model_name}" )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth',
type=str,
help='URL of the original Swin2SR checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the converted model to the hub.')
a_ = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 665 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')
# TF training parameters
a_ = False
a_ = False
def _a( UpperCamelCase__ : Namespace ):
'''simple docstring'''
return TrainCommand(UpperCamelCase__ )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
@staticmethod
def __magic_name__ ( __lowercase : ArgumentParser ) -> Any:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =parser.add_parser('''train''' , help='''CLI tool to train a model on a task.''' )
train_parser.add_argument(
'''--train_data''' , type=__lowercase , required=__lowercase , help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.''' , )
train_parser.add_argument(
'''--column_label''' , type=__lowercase , default=0 , help='''Column of the dataset csv file with example labels.''' )
train_parser.add_argument(
'''--column_text''' , type=__lowercase , default=1 , help='''Column of the dataset csv file with example texts.''' )
train_parser.add_argument(
'''--column_id''' , type=__lowercase , default=2 , help='''Column of the dataset csv file with example ids.''' )
train_parser.add_argument(
'''--skip_first_row''' , action='''store_true''' , help='''Skip the first row of the csv file (headers).''' )
train_parser.add_argument('''--validation_data''' , type=__lowercase , default='''''' , help='''path to validation dataset.''' )
train_parser.add_argument(
'''--validation_split''' , type=__lowercase , default=0.1 , help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.''' , )
train_parser.add_argument('''--output''' , type=__lowercase , default='''./''' , help='''path to saved the trained model.''' )
train_parser.add_argument(
'''--task''' , type=__lowercase , default='''text_classification''' , help='''Task to train the model on.''' )
train_parser.add_argument(
'''--model''' , type=__lowercase , default='''bert-base-uncased''' , help='''Model\'s name or path to stored model.''' )
train_parser.add_argument('''--train_batch_size''' , type=__lowercase , default=32 , help='''Batch size for training.''' )
train_parser.add_argument('''--valid_batch_size''' , type=__lowercase , default=64 , help='''Batch size for validation.''' )
train_parser.add_argument('''--learning_rate''' , type=__lowercase , default=3e-5 , help='''Learning rate.''' )
train_parser.add_argument('''--adam_epsilon''' , type=__lowercase , default=1e-08 , help='''Epsilon for Adam optimizer.''' )
train_parser.set_defaults(func=__lowercase )
def __init__( self : Tuple , __lowercase : Namespace ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Tuple =logging.get_logger('''transformers-cli/training''' )
SCREAMING_SNAKE_CASE__ : int ='''tf''' if is_tf_available() else '''torch'''
os.makedirs(args.output , exist_ok=__lowercase )
SCREAMING_SNAKE_CASE__ : Any =args.output
SCREAMING_SNAKE_CASE__ : str =args.column_label
SCREAMING_SNAKE_CASE__ : List[Any] =args.column_text
SCREAMING_SNAKE_CASE__ : Tuple =args.column_id
self.logger.info(F"Loading {args.task} pipeline for {args.model}" )
if args.task == "text_classification":
SCREAMING_SNAKE_CASE__ : List[str] =TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F"Loading dataset from {args.train_data}" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
if args.validation_data:
self.logger.info(F"Loading validation dataset from {args.validation_data}" )
SCREAMING_SNAKE_CASE__ : List[Any] =Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =args.validation_split
SCREAMING_SNAKE_CASE__ : List[Any] =args.train_batch_size
SCREAMING_SNAKE_CASE__ : Any =args.valid_batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =args.learning_rate
SCREAMING_SNAKE_CASE__ : int =args.adam_epsilon
def __magic_name__ ( self : Any ) -> str:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def __magic_name__ ( self : Optional[int] ) -> Tuple:
raise NotImplementedError
def __magic_name__ ( self : Dict ) -> List[Any]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 665 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 83 |
class __A :
def __init__( self : Dict , UpperCAmelCase_ : Any , UpperCAmelCase_ : int ):
lowerCAmelCase : Optional[Any] = name
lowerCAmelCase : int = val
def __str__( self : str ):
return f"{self.__class__.__name__}({self.name}, {self.val})"
def __lt__( self : Union[str, Any] , UpperCAmelCase_ : Dict ):
return self.val < other.val
class __A :
def __init__( self : Union[str, Any] , UpperCAmelCase_ : str ):
lowerCAmelCase : Optional[Any] = {}
lowerCAmelCase : Tuple = {}
lowerCAmelCase : Optional[Any] = self.build_heap(UpperCAmelCase_ )
def __getitem__( self : Union[str, Any] , UpperCAmelCase_ : str ):
return self.get_value(UpperCAmelCase_ )
def lowercase__ ( self : int , UpperCAmelCase_ : Any ):
return (idx - 1) // 2
def lowercase__ ( self : int , UpperCAmelCase_ : str ):
return idx * 2 + 1
def lowercase__ ( self : Optional[int] , UpperCAmelCase_ : Any ):
return idx * 2 + 2
def lowercase__ ( self : List[str] , UpperCAmelCase_ : List[Any] ):
return self.heap_dict[key]
def lowercase__ ( self : Optional[Any] , UpperCAmelCase_ : Optional[Any] ):
lowerCAmelCase : Optional[Any] = len(UpperCAmelCase_ ) - 1
lowerCAmelCase : Union[str, Any] = self.get_parent_idx(UpperCAmelCase_ )
for idx, i in enumerate(UpperCAmelCase_ ):
lowerCAmelCase : Any = idx
lowerCAmelCase : Union[str, Any] = i.val
for i in range(UpperCAmelCase_ , -1 , -1 ):
self.sift_down(UpperCAmelCase_ , UpperCAmelCase_ )
return array
def lowercase__ ( self : Tuple , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Any ):
while True:
lowerCAmelCase : Optional[int] = self.get_left_child_idx(UpperCAmelCase_ ) # noqa: E741
lowerCAmelCase : Union[str, Any] = self.get_right_child_idx(UpperCAmelCase_ )
lowerCAmelCase : Any = idx
if l < len(UpperCAmelCase_ ) and array[l] < array[idx]:
lowerCAmelCase : Tuple = l
if r < len(UpperCAmelCase_ ) and array[r] < array[smallest]:
lowerCAmelCase : Any = r
if smallest != idx:
lowerCAmelCase , lowerCAmelCase : Union[str, Any] = array[smallest], array[idx]
(
(
lowerCAmelCase
) , (
lowerCAmelCase
) ,
) : List[Any] = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
lowerCAmelCase : List[str] = smallest
else:
break
def lowercase__ ( self : Any , UpperCAmelCase_ : Optional[Any] ):
lowerCAmelCase : Optional[Any] = self.get_parent_idx(UpperCAmelCase_ )
while p >= 0 and self.heap[p] > self.heap[idx]:
lowerCAmelCase , lowerCAmelCase : Optional[int] = self.heap[idx], self.heap[p]
lowerCAmelCase , lowerCAmelCase : Union[str, Any] = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
lowerCAmelCase : Dict = p
lowerCAmelCase : Optional[Any] = self.get_parent_idx(UpperCAmelCase_ )
def lowercase__ ( self : str ):
return self.heap[0]
def lowercase__ ( self : int ):
lowerCAmelCase , lowerCAmelCase : str = self.heap[-1], self.heap[0]
lowerCAmelCase , lowerCAmelCase : Dict = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
lowerCAmelCase : Any = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def lowercase__ ( self : Any , UpperCAmelCase_ : Any ):
self.heap.append(UpperCAmelCase_ )
lowerCAmelCase : str = len(self.heap ) - 1
lowerCAmelCase : List[Any] = node.val
self.sift_up(len(self.heap ) - 1 )
def lowercase__ ( self : Optional[int] ):
return len(self.heap ) == 0
def lowercase__ ( self : Union[str, Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : int ):
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
lowerCAmelCase : Optional[int] = new_value
lowerCAmelCase : str = new_value
self.sift_up(self.idx_of_element[node] )
__A : Tuple = Node('''R''', -1)
__A : int = Node('''B''', 6)
__A : int = Node('''A''', 3)
__A : Optional[Any] = Node('''X''', 1)
__A : List[str] = Node('''E''', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
__A : Optional[int] = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
print(i)
print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 343 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 100 ) -> int:
_a = 0
_a = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(f"""{solution() = }""")
| 521 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def _lowerCamelCase ( lowercase : int = 100_0000 , lowercase : int = 10 ) -> int:
_a = defaultdict(lowercase )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
_a = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
_a = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(lowercase , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 521 | 1 |
"""simple docstring"""
from math import isqrt, loga
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = False
return [i for i in range(2 , UpperCamelCase_ ) if is_prime[i]]
def _lowerCAmelCase ( UpperCamelCase_ = 80_0800 , UpperCamelCase_ = 80_0800 ):
__SCREAMING_SNAKE_CASE = degree * loga(UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = int(UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = calculate_prime_numbers(UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = len(UpperCamelCase_ ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 155 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
__SCREAMING_SNAKE_CASE = str(bin(UpperCamelCase_ ) )
binary_number += "0" * shift_amount
return binary_number
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
__SCREAMING_SNAKE_CASE = str(bin(UpperCamelCase_ ) )[2:]
if shift_amount >= len(UpperCamelCase_ ):
return "0b0"
__SCREAMING_SNAKE_CASE = binary_number[: len(UpperCamelCase_ ) - shift_amount]
return "0b" + shifted_binary_number
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
if number >= 0: # Get binary representation of positive number
__SCREAMING_SNAKE_CASE = """0""" + str(bin(UpperCamelCase_ ) ).strip("""-""" )[2:]
else: # Get binary (2's complement) representation of negative number
__SCREAMING_SNAKE_CASE = len(bin(UpperCamelCase_ )[3:] ) # Find 2's complement of number
__SCREAMING_SNAKE_CASE = bin(abs(UpperCamelCase_ ) - (1 << binary_number_length) )[3:]
__SCREAMING_SNAKE_CASE = (
"""1""" + """0""" * (binary_number_length - len(UpperCamelCase_ )) + binary_number
)
if shift_amount >= len(UpperCamelCase_ ):
return "0b" + binary_number[0] * len(UpperCamelCase_ )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(UpperCamelCase_ ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 155 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = KandinskyVaaImgaImgPipeline
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """image"""]
snake_case_ = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
snake_case_ = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __magic_name__ ( self : List[str] ) -> Tuple:
return 32
@property
def __magic_name__ ( self : List[str] ) -> str:
return 32
@property
def __magic_name__ ( self : Any ) -> Optional[int]:
return self.time_input_dim
@property
def __magic_name__ ( self : List[Any] ) -> int:
return self.time_input_dim * 4
@property
def __magic_name__ ( self : Tuple ) -> Optional[int]:
return 1_00
@property
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
SCREAMING_SNAKE_CASE__ : Optional[int] =UNetaDConditionModel(**__lowercase )
return model
@property
def __magic_name__ ( self : Dict ) -> Any:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __magic_name__ ( self : Tuple ) -> Optional[Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] =VQModel(**self.dummy_movq_kwargs )
return model
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[str] =self.dummy_unet
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_movq
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
SCREAMING_SNAKE_CASE__ : str =DDIMScheduler(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any ={
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __magic_name__ ( self : str , __lowercase : Optional[Any] , __lowercase : Any=0 ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowercase )
# create init_image
SCREAMING_SNAKE_CASE__ : Optional[Any] =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ : Any =Image.fromarray(np.uinta(__lowercase ) ).convert('''RGB''' ).resize((2_56, 2_56) )
if str(__lowercase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ : Dict =torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ : str ={
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def __magic_name__ ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] ='''cpu'''
SCREAMING_SNAKE_CASE__ : Tuple =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =pipe(**self.get_dummy_inputs(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =output.images
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipe(
**self.get_dummy_inputs(__lowercase ) , return_dict=__lowercase , )[0]
SCREAMING_SNAKE_CASE__ : List[Any] =image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ : Tuple =np.array(
[0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : int ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : str =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
SCREAMING_SNAKE_CASE__ : List[Any] ='''A red cartoon frog, 4k'''
SCREAMING_SNAKE_CASE__ : Optional[int] =KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Dict =pipeline.to(__lowercase )
pipeline.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[str] =pipe_prior(
__lowercase , generator=__lowercase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
image=__lowercase , image_embeds=__lowercase , negative_image_embeds=__lowercase , generator=__lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , strength=0.2 , output_type='''np''' , )
SCREAMING_SNAKE_CASE__ : int =output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__lowercase , __lowercase )
| 710 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = JukeboxTokenizer
snake_case_ = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def __magic_name__ ( self : Optional[int] ) -> str:
import torch
SCREAMING_SNAKE_CASE__ : List[str] =JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
SCREAMING_SNAKE_CASE__ : str =tokenizer(**self.metas )['''input_ids''']
# fmt: off
SCREAMING_SNAKE_CASE__ : str =[
torch.tensor([[
0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def __magic_name__ ( self : Any ) -> List[str]:
import torch
SCREAMING_SNAKE_CASE__ : int =JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer(**self.metas )['''input_ids''']
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] =[
torch.tensor([[
0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
| 665 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowercase ( unittest.TestCase ):
def lowercase_ ( self ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = inspect.getfile(accelerate.test_utils )
lowerCAmelCase__ : Any = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
lowerCAmelCase__ : List[str] = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_distributed_data_loop.py'''] )
lowerCAmelCase__ : Optional[int] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_ops.py'''] )
@require_multi_gpu
def lowercase_ ( self ):
"""simple docstring"""
print(f'''Found {torch.cuda.device_count()} devices.''' )
lowerCAmelCase__ : List[Any] = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__SCREAMING_SNAKE_CASE , env=os.environ.copy() )
@require_multi_gpu
def lowercase_ ( self ):
"""simple docstring"""
print(f'''Found {torch.cuda.device_count()} devices.''' )
lowerCAmelCase__ : Union[str, Any] = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
print(f'''Command: {cmd}''' )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__SCREAMING_SNAKE_CASE , env=os.environ.copy() )
@require_multi_gpu
def lowercase_ ( self ):
"""simple docstring"""
lowerCAmelCase__ : Tuple = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__SCREAMING_SNAKE_CASE , env=os.environ.copy() )
@require_multi_gpu
def lowercase_ ( self ):
"""simple docstring"""
print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
lowerCAmelCase__ : Optional[Any] = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices='''0,1''' ):
execute_subprocess_async(__SCREAMING_SNAKE_CASE , env=os.environ.copy() )
if __name__ == "__main__":
A__ : Union[str, Any] = Accelerator()
A__ : List[Any] = (accelerator.state.process_index + 2, 1_0)
A__ : Tuple = torch.randint(0, 1_0, shape).to(accelerator.device)
A__ : List[Any] = ""
A__ : Any = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
A__ : Any = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
A__ : Optional[Any] = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 233 |
'''simple docstring'''
def UpperCamelCase__ ( __magic_name__ : List[Any] ) -> Tuple:
'''simple docstring'''
if not head:
return True
# split the list to two parts
snake_case__ , snake_case__ : Dict = head.next, head
while fast and fast.next:
snake_case__ : Any = fast.next.next
snake_case__ : int = slow.next
snake_case__ : Dict = slow.next
snake_case__ : List[str] = None # Don't forget here! But forget still works!
# reverse the second part
snake_case__ : Tuple = None
while second:
snake_case__ : Tuple = second.next
snake_case__ : Any = node
snake_case__ : str = second
snake_case__ : Optional[Any] = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
snake_case__ : List[Any] = node.next
snake_case__ : int = head.next
return True
def UpperCamelCase__ ( __magic_name__ : Any ) -> Optional[Any]:
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
snake_case__ : List[Any] = head
while fast and fast.next:
snake_case__ , snake_case__ : Any = fast.next.next, slow.next
# 2. Push the second half into the stack
snake_case__ : Tuple = [slow.val]
while slow.next:
snake_case__ : Optional[Any] = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
snake_case__ : str = cur.next
return True
def UpperCamelCase__ ( __magic_name__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
if not head or not head.next:
return True
snake_case__ : int = {}
snake_case__ : Union[str, Any] = 0
while head:
if head.val in d:
d[head.val].append(__magic_name__ )
else:
snake_case__ : Tuple = [pos]
snake_case__ : Optional[Any] = head.next
pos += 1
snake_case__ : int = pos - 1
snake_case__ : str = 0
for v in d.values():
if len(__magic_name__ ) % 2 != 0:
middle += 1
else:
snake_case__ : List[str] = 0
for i in range(0 , len(__magic_name__ ) ):
if v[i] + v[len(__magic_name__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 38 | 0 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _UpperCAmelCase ( lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : Optional[Any] = CodeGenTokenizer
_lowerCAmelCase : List[str] = CodeGenTokenizerFast
_lowerCAmelCase : List[str] = True
_lowerCAmelCase : List[str] = {"""add_prefix_space""": True}
_lowerCAmelCase : Tuple = False
def _snake_case ( self : List[Any] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ : Any = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
'''<|endoftext|>''',
]
snake_case_ : Union[str, Any] = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
snake_case_ : Optional[int] = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
snake_case_ : int = {'''unk_token''': '''<unk>'''}
snake_case_ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case_ : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowercase_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(lowercase_ ) )
def _snake_case ( self : Optional[Any] , **lowercase_ : str ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **lowercase_ )
def _snake_case ( self : int , **lowercase_ : str ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **lowercase_ )
def _snake_case ( self : int , lowercase_ : Any ):
snake_case_ : str = '''lower newer'''
snake_case_ : str = '''lower newer'''
return input_text, output_text
def _snake_case ( self : Dict ):
snake_case_ : List[str] = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ : List[Any] = '''lower newer'''
snake_case_ : int = ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
snake_case_ : Optional[Any] = tokenizer.tokenize(lowercase_ , add_prefix_space=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ : Optional[int] = tokens + [tokenizer.unk_token]
snake_case_ : Tuple = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ) , lowercase_ )
def _snake_case ( self : Optional[int] ):
if not self.test_rust_tokenizer:
return
snake_case_ : Dict = self.get_tokenizer()
snake_case_ : str = self.get_rust_tokenizer(add_prefix_space=lowercase_ )
snake_case_ : Union[str, Any] = '''lower newer'''
# Testing tokenization
snake_case_ : Tuple = tokenizer.tokenize(lowercase_ , add_prefix_space=lowercase_ )
snake_case_ : Union[str, Any] = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
# Testing conversion to ids without special tokens
snake_case_ : Optional[Any] = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ , add_prefix_space=lowercase_ )
snake_case_ : str = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
# Testing conversion to ids with special tokens
snake_case_ : Dict = self.get_rust_tokenizer(add_prefix_space=lowercase_ )
snake_case_ : Union[str, Any] = tokenizer.encode(lowercase_ , add_prefix_space=lowercase_ )
snake_case_ : List[Any] = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
# Testing the unknown token
snake_case_ : Dict = tokens + [rust_tokenizer.unk_token]
snake_case_ : int = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowercase_ ) , lowercase_ )
def _snake_case ( self : Any , *lowercase_ : Any , **lowercase_ : Optional[Any] ):
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def _snake_case ( self : Tuple , lowercase_ : Dict=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case_ : List[str] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
# Simple input
snake_case_ : Any = '''This is a simple input'''
snake_case_ : Tuple = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case_ : Optional[Any] = ('''This is a simple input''', '''This is a pair''')
snake_case_ : Optional[Any] = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(lowercase_ , tokenizer_r.encode , lowercase_ , max_length=lowercase_ , padding='''max_length''' )
# Simple input
self.assertRaises(lowercase_ , tokenizer_r.encode_plus , lowercase_ , max_length=lowercase_ , padding='''max_length''' )
# Simple input
self.assertRaises(
lowercase_ , tokenizer_r.batch_encode_plus , lowercase_ , max_length=lowercase_ , padding='''max_length''' , )
# Pair input
self.assertRaises(lowercase_ , tokenizer_r.encode , lowercase_ , max_length=lowercase_ , padding='''max_length''' )
# Pair input
self.assertRaises(lowercase_ , tokenizer_r.encode_plus , lowercase_ , max_length=lowercase_ , padding='''max_length''' )
# Pair input
self.assertRaises(
lowercase_ , tokenizer_r.batch_encode_plus , lowercase_ , max_length=lowercase_ , padding='''max_length''' , )
def _snake_case ( self : Union[str, Any] ):
snake_case_ : int = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='''<pad>''' )
# Simple input
snake_case_ : str = '''This is a simple input'''
snake_case_ : List[Any] = ['''This is a simple input looooooooong''', '''This is a simple input''']
snake_case_ : Dict = ('''This is a simple input''', '''This is a pair''')
snake_case_ : Union[str, Any] = [
('''This is a simple input loooooong''', '''This is a simple input'''),
('''This is a simple pair loooooong''', '''This is a simple pair'''),
]
snake_case_ : List[Any] = tokenizer.pad_token_id
snake_case_ : List[Any] = tokenizer(lowercase_ , padding='''max_length''' , max_length=30 , return_tensors='''np''' )
snake_case_ : Tuple = tokenizer(lowercase_ , padding=lowercase_ , truncate=lowercase_ , return_tensors='''np''' )
snake_case_ : Union[str, Any] = tokenizer(*lowercase_ , padding='''max_length''' , max_length=60 , return_tensors='''np''' )
snake_case_ : Optional[Any] = tokenizer(lowercase_ , padding=lowercase_ , truncate=lowercase_ , return_tensors='''np''' )
# s
# test single string max_length padding
self.assertEqual(out_s['''input_ids'''].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['''input_ids'''] )
self.assertTrue(0 in out_s['''attention_mask'''] )
# s2
# test automatic padding
self.assertEqual(out_sa['''input_ids'''].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['''input_ids'''][0] )
self.assertFalse(0 in out_sa['''attention_mask'''][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['''input_ids'''][1] )
self.assertTrue(0 in out_sa['''attention_mask'''][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['''input_ids'''].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['''input_ids'''] )
self.assertTrue(0 in out_p['''attention_mask'''] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['''input_ids'''].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['''input_ids'''][0] )
self.assertFalse(0 in out_pa['''attention_mask'''][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['''input_ids'''][1] )
self.assertTrue(0 in out_pa['''attention_mask'''][1] )
def _snake_case ( self : Dict ):
snake_case_ : Any = '''$$$'''
snake_case_ : str = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=lowercase_ , add_bos_token=lowercase_ )
snake_case_ : Optional[int] = '''This is a simple input'''
snake_case_ : str = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case_ : Any = tokenizer.bos_token_id
snake_case_ : List[Any] = tokenizer(lowercase_ )
snake_case_ : Optional[Any] = tokenizer(lowercase_ )
self.assertEqual(out_s.input_ids[0] , lowercase_ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
snake_case_ : List[str] = tokenizer.decode(out_s.input_ids )
snake_case_ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , lowercase_ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def _snake_case ( self : Optional[Any] ):
snake_case_ : List[Any] = CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono''' )
snake_case_ : List[str] = '''\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'''
snake_case_ : Dict = '''\nif len_a > len_b: result = a\nelse: result = b'''
snake_case_ : Tuple = tokenizer.encode(lowercase_ )
snake_case_ : List[str] = ['''^#''', re.escape('''<|endoftext|>''' ), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
snake_case_ : Any = tokenizer.decode(lowercase_ , truncate_before_pattern=lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
def _snake_case ( self : str ):
pass
| 485 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def __lowercase ( _a ):
return np.dot(_a , _a )
class _UpperCAmelCase :
def __init__( self : int , *,
lowercase_ : float = np.inf , lowercase_ : str = "linear" , lowercase_ : float = 0.0 , ):
snake_case_ : Optional[Any] = regularization
snake_case_ : Tuple = gamma
if kernel == "linear":
snake_case_ : int = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError('''rbf kernel requires gamma''' )
if not isinstance(self.gamma , (float, int) ):
raise ValueError('''gamma must be float or int''' )
if not self.gamma > 0:
raise ValueError('''gamma must be > 0''' )
snake_case_ : int = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
snake_case_ : List[Any] = f"Unknown kernel: {kernel}"
raise ValueError(lowercase_ )
def _snake_case ( self : int , lowercase_ : ndarray , lowercase_ : ndarray ):
return np.dot(lowercase_ , lowercase_ )
def _snake_case ( self : int , lowercase_ : ndarray , lowercase_ : ndarray ):
return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
def _snake_case ( self : Any , lowercase_ : list[ndarray] , lowercase_ : ndarray ):
snake_case_ : Union[str, Any] = observations
snake_case_ : int = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((snake_case_), ) : List[str] = np.shape(lowercase_ )
def to_minimize(lowercase_ : ndarray ) -> float:
snake_case_ : Tuple = 0
((snake_case_), ) : Optional[Any] = np.shape(lowercase_ )
for i in range(lowercase_ ):
for j in range(lowercase_ ):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j] )
)
return 1 / 2 * s - sum(lowercase_ )
snake_case_ : Optional[Any] = LinearConstraint(lowercase_ , 0 , 0 )
snake_case_ : str = Bounds(0 , self.regularization )
snake_case_ : int = minimize(
lowercase_ , np.ones(lowercase_ ) , bounds=lowercase_ , constraints=[ly_contraint] ).x
snake_case_ : Optional[Any] = l_star
# calculating mean offset of separation plane to points
snake_case_ : List[Any] = 0
for i in range(lowercase_ ):
for j in range(lowercase_ ):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j] )
snake_case_ : Union[str, Any] = s / n
def _snake_case ( self : List[str] , lowercase_ : ndarray ):
snake_case_ : int = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , lowercase_ )
for n in range(len(self.classes ) ) )
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 485 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
_snake_case : str = field(default='summarization' , metadata={'include_in_asdict_even_if_is_default': True} )
_snake_case : ClassVar[Features] = Features({'text': Value('string' )} )
_snake_case : ClassVar[Features] = Features({'summary': Value('string' )} )
_snake_case : str = "text"
_snake_case : str = "summary"
@property
def A ( self : Any )-> Dict[str, str]:
return {self.text_column: "text", self.summary_column: "summary"}
| 505 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
_A = logging.get_logger(__name__)
_A = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
# See all MVP models at https://huggingface.co/models?filter=mvp
_A = {
"vocab_file": {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
},
"added_tokens.json": {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
},
"merges_file": {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
},
"tokenizer_file": {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
},
}
_A = {
"RUCAIBox/mvp": 1_024,
}
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
_snake_case : Dict = VOCAB_FILES_NAMES
_snake_case : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
_snake_case : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case : Tuple = ['input_ids', 'attention_mask']
_snake_case : Any = MvpTokenizer
def __init__( self : str , A_ : int=None , A_ : List[Any]=None , A_ : Optional[Any]=None , A_ : int="replace" , A_ : int="<s>" , A_ : Any="</s>" , A_ : List[str]="</s>" , A_ : Optional[int]="<s>" , A_ : Optional[int]="<unk>" , A_ : Optional[int]="<pad>" , A_ : Union[str, Any]="<mask>" , A_ : str=False , A_ : List[str]=True , **A_ : Union[str, Any] , )-> Any:
super().__init__(
A_ , A_ , tokenizer_file=A_ , errors=A_ , bos_token=A_ , eos_token=A_ , sep_token=A_ , cls_token=A_ , unk_token=A_ , pad_token=A_ , mask_token=A_ , add_prefix_space=A_ , trim_offsets=A_ , **A_ , )
__UpperCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , A_ ) != add_prefix_space:
__UpperCamelCase = getattr(A_ , pre_tok_state.pop("type" ) )
__UpperCamelCase = add_prefix_space
__UpperCamelCase = pre_tok_class(**A_ )
__UpperCamelCase = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__UpperCamelCase = "post_processor"
__UpperCamelCase = getattr(self.backend_tokenizer , A_ , A_ )
if tokenizer_component_instance:
__UpperCamelCase = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__UpperCamelCase = tuple(state["sep"] )
if "cls" in state:
__UpperCamelCase = tuple(state["cls"] )
__UpperCamelCase = False
if state.get("add_prefix_space" , A_ ) != add_prefix_space:
__UpperCamelCase = add_prefix_space
__UpperCamelCase = True
if state.get("trim_offsets" , A_ ) != trim_offsets:
__UpperCamelCase = trim_offsets
__UpperCamelCase = True
if changes_to_apply:
__UpperCamelCase = getattr(A_ , state.pop("type" ) )
__UpperCamelCase = component_class(**A_ )
setattr(self.backend_tokenizer , A_ , A_ )
@property
def A ( self : List[str] )-> str:
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def A ( self : Any , A_ : List[Any] )-> List[Any]:
__UpperCamelCase = AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else value
__UpperCamelCase = value
def A ( self : str , *A_ : Dict , **A_ : Dict )-> BatchEncoding:
__UpperCamelCase = kwargs.get("is_split_into_words" , A_ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs." )
return super()._batch_encode_plus(*A_ , **A_ )
def A ( self : Tuple , *A_ : str , **A_ : List[str] )-> BatchEncoding:
__UpperCamelCase = kwargs.get("is_split_into_words" , A_ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs." )
return super()._encode_plus(*A_ , **A_ )
def A ( self : Optional[int] , A_ : str , A_ : Optional[str] = None )-> Tuple[str]:
__UpperCamelCase = self._tokenizer.model.save(A_ , name=A_ )
return tuple(A_ )
def A ( self : Any , A_ : Dict , A_ : Dict=None )-> Union[str, Any]:
__UpperCamelCase = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def A ( self : Optional[int] , A_ : List[int] , A_ : Optional[List[int]] = None )-> List[int]:
__UpperCamelCase = [self.sep_token_id]
__UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 505 | 1 |
def lowerCamelCase__ ( _lowerCamelCase = 1000 ) ->int:
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 592 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 592 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : Dict = {
"configuration_layoutlmv3": [
"LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP",
"LayoutLMv3Config",
"LayoutLMv3OnnxConfig",
],
"processing_layoutlmv3": ["LayoutLMv3Processor"],
"tokenization_layoutlmv3": ["LayoutLMv3Tokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Tuple = ["LayoutLMv3TokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Tuple = [
"LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
"LayoutLMv3ForQuestionAnswering",
"LayoutLMv3ForSequenceClassification",
"LayoutLMv3ForTokenClassification",
"LayoutLMv3Model",
"LayoutLMv3PreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : List[Any] = [
"TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLayoutLMv3ForQuestionAnswering",
"TFLayoutLMv3ForSequenceClassification",
"TFLayoutLMv3ForTokenClassification",
"TFLayoutLMv3Model",
"TFLayoutLMv3PreTrainedModel",
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Any = ["LayoutLMv3FeatureExtractor"]
__lowerCAmelCase : Tuple = ["LayoutLMv3ImageProcessor"]
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
__lowerCAmelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 509 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : Any = logging.get_logger(__name__)
__lowerCAmelCase : Optional[Any] = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "tokenizer_config.json",
"merges_file": "merges.txt",
}
__lowerCAmelCase : List[str] = {
"vocab_file": {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"
),
},
"tokenizer_config_file": {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"
),
},
"merges_file": {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"
),
},
}
__lowerCAmelCase : Union[str, Any] = "</w>"
__lowerCAmelCase : str = "@@ "
def UpperCAmelCase_ ( __lowerCAmelCase ) -> Tuple:
__lowercase : List[str] = set()
__lowercase : Tuple = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase : Tuple = char
return pairs
# Speech2Text2 has no max input length
__lowerCAmelCase : Optional[Any] = {"facebook/s2t-wav2vec2-large-en-de": 1_024}
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
A__ : Union[str, Any] = VOCAB_FILES_NAMES
A__ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
A__ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : List[Any] = ['''input_ids''', '''attention_mask''']
def __init__( self : Union[str, Any] , _snake_case : Optional[Any] , _snake_case : Optional[Any]="<s>" , _snake_case : Tuple="<pad>" , _snake_case : int="</s>" , _snake_case : Optional[Any]="<unk>" , _snake_case : Optional[Any]=False , _snake_case : List[Any]=None , **_snake_case : List[str] , ):
super().__init__(
unk_token=_snake_case , bos_token=_snake_case , eos_token=_snake_case , pad_token=_snake_case , do_lower_case=_snake_case , **_snake_case , )
__lowercase : Optional[int] = do_lower_case
with open(_snake_case , encoding='''utf-8''' ) as vocab_handle:
__lowercase : Optional[Any] = json.load(_snake_case )
__lowercase : List[str] = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(F'No merges files provided. {self.__class__.__name__} can only be used for decoding.' )
__lowercase : List[str] = None
__lowercase : Union[str, Any] = None
else:
with open(_snake_case , encoding='''utf-8''' ) as merges_handle:
__lowercase : Optional[int] = merges_handle.read().split('''\n''' )[:-1]
__lowercase : Any = [tuple(merge.split()[:2] ) for merge in merges]
__lowercase : Optional[int] = dict(zip(_snake_case , range(len(_snake_case ) ) ) )
__lowercase : List[Any] = {}
@property
def snake_case_ ( self : str ):
return len(self.decoder )
def snake_case_ ( self : Tuple ):
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case_ ( self : int , _snake_case : int ):
__lowercase : Optional[Any] = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
__lowercase : Any = get_pairs(_snake_case )
if not pairs:
return token
while True:
__lowercase : str = min(_snake_case , key=lambda _snake_case : self.bpe_ranks.get(_snake_case , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase : List[Any] = bigram
__lowercase : Optional[int] = []
__lowercase : List[str] = 0
while i < len(_snake_case ):
try:
__lowercase : int = word.index(_snake_case , _snake_case )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__lowercase : Union[str, Any] = j
if word[i] == first and i < len(_snake_case ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase : Union[str, Any] = tuple(_snake_case )
__lowercase : Dict = new_word
if len(_snake_case ) == 1:
break
else:
__lowercase : List[Any] = get_pairs(_snake_case )
__lowercase : List[Any] = ''' '''.join(_snake_case )
if word == "\n " + BPE_TOKEN_MERGES:
__lowercase : Optional[int] = '''\n''' + BPE_TOKEN_MERGES
if word.endswith(_snake_case ):
__lowercase : str = word.replace(_snake_case , '''''' )
__lowercase : List[str] = word.replace(''' ''' , _snake_case )
__lowercase : Union[str, Any] = word
return word
def snake_case_ ( self : List[str] , _snake_case : List[str] ):
if self.bpe_ranks is None:
raise ValueError(
'''This tokenizer was instantiated without a `merges.txt` file, so'''
''' that it can only be used for decoding, not for encoding.'''
'''Make sure to provide `merges.txt` file at instantiation to enable '''
'''encoding.''' )
if self.do_lower_case:
__lowercase : Dict = text.lower()
__lowercase : str = text.split()
__lowercase : Optional[int] = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(_snake_case ).split(''' ''' ) ) )
return split_tokens
def snake_case_ ( self : int , _snake_case : str ):
return self.encoder.get(_snake_case , self.encoder.get(self.unk_token ) )
def snake_case_ ( self : Optional[Any] , _snake_case : int ):
__lowercase : Dict = self.decoder.get(_snake_case , self.unk_token )
return result
def snake_case_ ( self : Union[str, Any] , _snake_case : List[str] ):
__lowercase : Tuple = ''' '''.join(_snake_case )
# make sure @@ tokens are concatenated
__lowercase : str = ''''''.join(string.split(_snake_case ) )
return string
def snake_case_ ( self : Tuple , _snake_case : str , _snake_case : Optional[str] = None ):
if not os.path.isdir(_snake_case ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__lowercase : int = os.path.join(
_snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
__lowercase : Optional[Any] = os.path.join(
_snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_snake_case , ensure_ascii=_snake_case ) + '''\n''' )
__lowercase : Any = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _snake_case : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'
''' Please check that the tokenizer is not corrupted!''' )
__lowercase : Optional[int] = token_index
writer.write(''' '''.join(_snake_case ) + '''\n''' )
index += 1
return (vocab_file, merges_file)
| 509 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _UpperCAmelCase ( snake_case ):
@staticmethod
@abstractmethod
def lowerCAmelCase__ ( a : ArgumentParser ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def lowerCAmelCase__ ( self : List[str] ):
'''simple docstring'''
raise NotImplementedError()
| 706 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
UpperCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCamelCase__ = '\n Examples:\n ```py\n >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n >>> pipe_prior.to("cuda")\n\n >>> prompt = "red cat, 4k photo"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> negative_image_emb = out.negative_image_embeds\n\n >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n >>> pipe.to("cuda")\n\n >>> image = pipe(\n ... prompt,\n ... image_embeds=image_emb,\n ... negative_image_embeds=negative_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... ).images\n\n >>> image[0].save("cat.png")\n ```\n'
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=8 ):
"""simple docstring"""
lowercase_ : int = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
lowercase_ : str = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class _UpperCAmelCase ( snake_case ):
def __init__( self : int , a : MultilingualCLIP , a : XLMRobertaTokenizer , a : UNetaDConditionModel , a : Union[DDIMScheduler, DDPMScheduler] , a : VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
text_encoder=a , tokenizer=a , unet=a , scheduler=a , movq=a , )
lowercase_ : Dict = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def lowerCAmelCase__ ( self : List[Any] , a : Tuple , a : List[str] , a : Optional[Any] , a : str , a : Tuple , a : List[str] ):
'''simple docstring'''
if latents is None:
lowercase_ : List[str] = randn_tensor(a , generator=a , device=a , dtype=a )
else:
if latents.shape != shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
lowercase_ : Optional[int] = latents.to(a )
lowercase_ : str = latents * scheduler.init_noise_sigma
return latents
def lowerCAmelCase__ ( self : Optional[Any] , a : List[str] , a : List[Any] , a : Union[str, Any] , a : str , a : Tuple=None , ):
'''simple docstring'''
lowercase_ : Tuple = len(a ) if isinstance(a , a ) else 1
# get prompt text embeddings
lowercase_ : Any = self.tokenizer(
a , padding="max_length" , truncation=a , max_length=7_7 , return_attention_mask=a , add_special_tokens=a , return_tensors="pt" , )
lowercase_ : Union[str, Any] = text_inputs.input_ids
lowercase_ : Tuple = self.tokenizer(a , padding="longest" , return_tensors="pt" ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(a , a ):
lowercase_ : Optional[int] = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
lowercase_ : List[str] = text_input_ids.to(a )
lowercase_ : int = text_inputs.attention_mask.to(a )
lowercase_ , lowercase_ : Optional[int] = self.text_encoder(
input_ids=a , attention_mask=a )
lowercase_ : str = prompt_embeds.repeat_interleave(a , dim=0 )
lowercase_ : int = text_encoder_hidden_states.repeat_interleave(a , dim=0 )
lowercase_ : int = text_mask.repeat_interleave(a , dim=0 )
if do_classifier_free_guidance:
lowercase_ : List[str]
if negative_prompt is None:
lowercase_ : int = [""] * batch_size
elif type(a ) is not type(a ):
raise TypeError(
f"""`negative_prompt` should be the same type to `prompt`, but got {type(a )} !="""
f""" {type(a )}.""" )
elif isinstance(a , a ):
lowercase_ : Tuple = [negative_prompt]
elif batch_size != len(a ):
raise ValueError(
f"""`negative_prompt`: {negative_prompt} has batch size {len(a )}, but `prompt`:"""
f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
" the batch size of `prompt`." )
else:
lowercase_ : Dict = negative_prompt
lowercase_ : str = self.tokenizer(
a , padding="max_length" , max_length=7_7 , truncation=a , return_attention_mask=a , add_special_tokens=a , return_tensors="pt" , )
lowercase_ : List[Any] = uncond_input.input_ids.to(a )
lowercase_ : Optional[int] = uncond_input.attention_mask.to(a )
lowercase_ , lowercase_ : int = self.text_encoder(
input_ids=a , attention_mask=a )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowercase_ : List[str] = negative_prompt_embeds.shape[1]
lowercase_ : Dict = negative_prompt_embeds.repeat(1 , a )
lowercase_ : Optional[Any] = negative_prompt_embeds.view(batch_size * num_images_per_prompt , a )
lowercase_ : Any = uncond_text_encoder_hidden_states.shape[1]
lowercase_ : List[Any] = uncond_text_encoder_hidden_states.repeat(1 , a , 1 )
lowercase_ : Tuple = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt , a , -1 )
lowercase_ : List[Any] = uncond_text_mask.repeat_interleave(a , dim=0 )
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowercase_ : Optional[int] = torch.cat([negative_prompt_embeds, prompt_embeds] )
lowercase_ : Tuple = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
lowercase_ : Any = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def lowerCAmelCase__ ( self : Tuple , a : Optional[Any]=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
lowercase_ : List[str] = torch.device(f"""cuda:{gpu_id}""" )
lowercase_ : str = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(a , a )
def lowerCAmelCase__ ( self : Union[str, Any] , a : List[str]=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(">=" , "0.17.0.dev0" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )
lowercase_ : List[str] = torch.device(f"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to("cpu" , silence_dtype_warnings=a )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowercase_ : List[str] = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
lowercase_ , lowercase_ : Optional[int] = cpu_offload_with_hook(a , a , prev_module_hook=a )
if self.safety_checker is not None:
lowercase_ , lowercase_ : Optional[int] = cpu_offload_with_hook(self.safety_checker , a , prev_module_hook=a )
# We'll offload the last model manually.
lowercase_ : Dict = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowerCAmelCase__ ( self : Tuple ):
'''simple docstring'''
if not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(a , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(a )
def __call__( self : Tuple , a : Union[str, List[str]] , a : Union[torch.FloatTensor, List[torch.FloatTensor]] , a : Union[torch.FloatTensor, List[torch.FloatTensor]] , a : Optional[Union[str, List[str]]] = None , a : int = 5_1_2 , a : int = 5_1_2 , a : int = 1_0_0 , a : float = 4.0 , a : int = 1 , a : Optional[Union[torch.Generator, List[torch.Generator]]] = None , a : Optional[torch.FloatTensor] = None , a : Optional[str] = "pil" , a : bool = True , ):
'''simple docstring'''
if isinstance(a , a ):
lowercase_ : List[str] = 1
elif isinstance(a , a ):
lowercase_ : int = len(a )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a )}""" )
lowercase_ : Tuple = self._execution_device
lowercase_ : Dict = batch_size * num_images_per_prompt
lowercase_ : Dict = guidance_scale > 1.0
lowercase_ , lowercase_ , lowercase_ : List[str] = self._encode_prompt(
a , a , a , a , a )
if isinstance(a , a ):
lowercase_ : Optional[int] = torch.cat(a , dim=0 )
if isinstance(a , a ):
lowercase_ : int = torch.cat(a , dim=0 )
if do_classifier_free_guidance:
lowercase_ : Optional[int] = image_embeds.repeat_interleave(a , dim=0 )
lowercase_ : int = negative_image_embeds.repeat_interleave(a , dim=0 )
lowercase_ : str = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(
dtype=prompt_embeds.dtype , device=a )
self.scheduler.set_timesteps(a , device=a )
lowercase_ : List[str] = self.scheduler.timesteps
lowercase_ : str = self.unet.config.in_channels
lowercase_ , lowercase_ : int = get_new_h_w(a , a , self.movq_scale_factor )
# create initial latent
lowercase_ : str = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , text_encoder_hidden_states.dtype , a , a , a , self.scheduler , )
for i, t in enumerate(self.progress_bar(a ) ):
# expand the latents if we are doing classifier free guidance
lowercase_ : Optional[int] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase_ : Optional[int] = {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
lowercase_ : Optional[int] = self.unet(
sample=a , timestep=a , encoder_hidden_states=a , added_cond_kwargs=a , return_dict=a , )[0]
if do_classifier_free_guidance:
lowercase_ , lowercase_ : Union[str, Any] = noise_pred.split(latents.shape[1] , dim=1 )
lowercase_ , lowercase_ : Optional[Any] = noise_pred.chunk(2 )
lowercase_ , lowercase_ : Any = variance_pred.chunk(2 )
lowercase_ : Dict = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowercase_ : int = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , "variance_type" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowercase_ , lowercase_ : str = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowercase_ : Tuple = self.scheduler.step(
a , a , a , generator=a , ).prev_sample
# post-processing
lowercase_ : Union[str, Any] = self.movq.decode(a , force_not_quantize=a )["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" )
if output_type in ["np", "pil"]:
lowercase_ : List[Any] = image * 0.5 + 0.5
lowercase_ : Optional[int] = image.clamp(0 , 1 )
lowercase_ : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase_ : List[str] = self.numpy_to_pil(a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=a )
| 640 | 0 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
snake_case = get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase ( UpperCamelCase_ , unittest.TestCase ):
A_ : Tuple = SpeechTaTokenizer
A_ : List[Any] = False
A_ : Optional[Any] = True
def _A ( self : Union[str, Any] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ : Any = SpeechTaTokenizer(a__ )
lowerCAmelCase__ : Optional[int] = AddedToken("<mask>" , lstrip=a__ , rstrip=a__ )
lowerCAmelCase__ : Tuple = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
tokenizer.save_pretrained(self.tmpdirname )
def _A ( self : Dict , a__ : str ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = "this is a test"
lowerCAmelCase__ : Optional[Any] = "this is a test"
return input_text, output_text
def _A ( self : Union[str, Any] , a__ : Any , a__ : Union[str, Any]=False , a__ : str=20 , a__ : Optional[Any]=5 ):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ : Tuple = self.get_input_output_texts(a__ )
lowerCAmelCase__ : List[str] = tokenizer.encode(a__ , add_special_tokens=a__ )
lowerCAmelCase__ : Union[str, Any] = tokenizer.decode(a__ , clean_up_tokenization_spaces=a__ )
return text, ids
def _A ( self : List[str] ):
'''simple docstring'''
lowerCAmelCase__ : Any = "<pad>"
lowerCAmelCase__ : List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__ ) , a__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__ ) , a__ )
def _A ( self : str ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-4] , "œ" )
self.assertEqual(vocab_keys[-2] , "<mask>" )
self.assertEqual(vocab_keys[-1] , "<ctc_blank>" )
self.assertEqual(len(a__ ) , 81 )
def _A ( self : List[Any] ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def _A ( self : List[str] ):
'''simple docstring'''
lowerCAmelCase__ : str = self.get_tokenizers(do_lower_case=a__ )
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
lowerCAmelCase__ : List[Any] = tokenizer.vocab_size
lowerCAmelCase__ : Any = len(a__ )
self.assertNotEqual(a__ , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
lowerCAmelCase__ : int = ["aaaaa bbbbbb", "cccccccccdddddddd"]
lowerCAmelCase__ : Tuple = tokenizer.add_tokens(a__ )
lowerCAmelCase__ : Tuple = tokenizer.vocab_size
lowerCAmelCase__ : str = len(a__ )
self.assertNotEqual(a__ , 0 )
self.assertEqual(a__ , a__ )
self.assertEqual(a__ , len(a__ ) )
self.assertEqual(a__ , all_size + len(a__ ) )
lowerCAmelCase__ : Optional[Any] = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l" , add_special_tokens=a__ )
self.assertGreaterEqual(len(a__ ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
lowerCAmelCase__ : Optional[Any] = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
lowerCAmelCase__ : List[Any] = tokenizer.add_special_tokens(a__ )
lowerCAmelCase__ : List[str] = tokenizer.vocab_size
lowerCAmelCase__ : Optional[int] = len(a__ )
self.assertNotEqual(a__ , 0 )
self.assertEqual(a__ , a__ )
self.assertEqual(a__ , len(a__ ) )
self.assertEqual(a__ , all_size_a + len(a__ ) )
lowerCAmelCase__ : Dict = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l" , add_special_tokens=a__ )
self.assertGreaterEqual(len(a__ ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def _A ( self : Any ):
'''simple docstring'''
pass
def _A ( self : List[str] ):
'''simple docstring'''
pass
def _A ( self : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = self.get_tokenizer()
lowerCAmelCase__ : int = tokenizer.tokenize("This is a test" )
# fmt: off
self.assertListEqual(a__ , [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(a__ ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
lowerCAmelCase__ : Optional[int] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
a__ , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
lowerCAmelCase__ : Optional[Any] = tokenizer.convert_tokens_to_ids(a__ )
# fmt: off
self.assertListEqual(a__ , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
lowerCAmelCase__ : List[str] = tokenizer.convert_ids_to_tokens(a__ )
self.assertListEqual(
a__ , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
@slow
def _A ( self : Optional[int] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = [
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
"general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
"Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
"models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
"conditioning on both left and right context in all layers.",
"The quick brown fox jumps over the lazy dog.",
]
# fmt: off
lowerCAmelCase__ : Tuple = {
"input_ids": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
"attention_mask": [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=a__ , model_name="microsoft/speecht5_asr" , revision="c5ef64c71905caeccde0e4462ef3f9077224c524" , sequences=a__ , )
| 378 |
'''simple docstring'''
from math import factorial
snake_case = {str(digit): factorial(digit) for digit in range(10)}
def UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise TypeError("Parameter number must be int" )
if number < 0:
raise ValueError("Parameter number must be greater than or equal to 0" )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(lowerCamelCase_ ) )
def UpperCAmelCase_ ( lowerCamelCase_ = 6_0 , lowerCamelCase_ = 1_0_0_0_0_0_0 ):
"""simple docstring"""
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ) or not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise TypeError("Parameters chain_length and number_limit must be int" )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
"Parameters chain_length and number_limit must be greater than 0" )
# the counter for the chains with the exact desired length
lowerCAmelCase__ : str = 0
# the cached sizes of the previous chains
lowerCAmelCase__ : dict[int, int] = {}
for start_chain_element in range(1 , lowerCamelCase_ ):
# The temporary set will contain the elements of the chain
lowerCAmelCase__ : Any = set()
lowerCAmelCase__ : int = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
lowerCAmelCase__ : Dict = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(lowerCamelCase_ )
chain_set_length += 1
lowerCAmelCase__ : Dict = digit_factorial_sum(lowerCamelCase_ )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
lowerCAmelCase__ : Optional[Any] = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'{solution()}')
| 378 | 1 |
import heapq
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> set[int]:
snake_case : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowercase ,[-1 * len(lowercase ), (key, value)] )
# chosen_vertices = set of chosen vertices
snake_case : List[Any] = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
snake_case : Union[str, Any] = heapq.heappop(lowercase )[1][0]
chosen_vertices.add(lowercase )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
snake_case : Any = elem[1][1].index(lowercase )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowercase )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase : Dict = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
| 684 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> str:
snake_case : int = []
for line in lines:
snake_case : Dict = re.sub(R"""#.*""" ,"""""" ,lowercase ) # remove comments
if line:
filtered_lines.append(lowercase )
snake_case : Optional[int] = """\n""".join(lowercase )
# Make a hash from all this code
snake_case : List[str] = full_str.encode("""utf-8""" )
return shaaaa(lowercase ).hexdigest()
# get importable module names and hash for caching
lowerCamelCase : Any = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
lowerCamelCase : Optional[int] = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
lowerCamelCase : Tuple = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
lowerCamelCase : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 684 | 1 |
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def a__ ( lowerCAmelCase__ ):
return getitem, k
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
return setitem, k, v
def a__ ( lowerCAmelCase__ ):
return delitem, k
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ ):
try:
return fun(lowerCAmelCase__ , *lowerCAmelCase__ ), None
except Exception as e:
return None, e
lowerCamelCase = (
_set("""key_a""", """val_a"""),
_set("""key_b""", """val_b"""),
)
lowerCamelCase = [
_set("""key_a""", """val_a"""),
_set("""key_a""", """val_b"""),
]
lowerCamelCase = [
_set("""key_a""", """val_a"""),
_set("""key_b""", """val_b"""),
_del("""key_a"""),
_del("""key_b"""),
_set("""key_a""", """val_a"""),
_del("""key_a"""),
]
lowerCamelCase = [
_get("""key_a"""),
_del("""key_a"""),
_set("""key_a""", """val_a"""),
_del("""key_a"""),
_del("""key_a"""),
_get("""key_a"""),
]
lowerCamelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCamelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("""key_a""", """val_b"""),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items" ),
pytest.param(_overwrite_items , id="overwrite items" ),
pytest.param(_delete_items , id="delete items" ),
pytest.param(_access_absent_items , id="access absent items" ),
pytest.param(_add_with_resize_up , id="add with resize up" ),
pytest.param(_add_with_resize_down , id="add with resize down" ),
) , )
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = HashMap(initial_block_size=4 )
UpperCAmelCase_ = {}
for _, (fun, *args) in enumerate(lowerCAmelCase__ ):
UpperCAmelCase_ , UpperCAmelCase_ = _run_operation(lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = _run_operation(lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ )
assert my_res == py_res
assert str(lowerCAmelCase__ ) == str(lowerCAmelCase__ )
assert set(lowerCAmelCase__ ) == set(lowerCAmelCase__ )
assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )
assert set(my.items() ) == set(py.items() )
def a__ ( ):
def is_public(lowerCAmelCase__ ) -> bool:
return not name.startswith("_" )
UpperCAmelCase_ = {name for name in dir({} ) if is_public(lowerCAmelCase__ )}
UpperCAmelCase_ = {name for name in dir(HashMap() ) if is_public(lowerCAmelCase__ )}
assert dict_public_names > hash_public_names
| 82 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
lowerCamelCase = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE )
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Union[str, Any] , **_UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
super().__init__(**_UpperCAmelCase )
if self.framework == "tf":
raise ValueError(F"""The {self.__class__} is only available in PyTorch.""" )
requires_backends(self , "vision" )
self.check_model_type(_UpperCAmelCase )
def __call__( self : int , _UpperCAmelCase : Union[str, "Image.Image", List[Dict[str, Any]]] , _UpperCAmelCase : Union[str, List[str]] = None , **_UpperCAmelCase : Optional[int] , ) -> List[Any]:
'''simple docstring'''
if "text_queries" in kwargs:
UpperCAmelCase_ = kwargs.pop("text_queries" )
if isinstance(_UpperCAmelCase , (str, Image.Image) ):
UpperCAmelCase_ = {"image": image, "candidate_labels": candidate_labels}
else:
UpperCAmelCase_ = image
UpperCAmelCase_ = super().__call__(_UpperCAmelCase , **_UpperCAmelCase )
return results
def lowercase__ ( self : str , **_UpperCAmelCase : List[Any] ) -> List[str]:
'''simple docstring'''
UpperCAmelCase_ = {}
if "threshold" in kwargs:
UpperCAmelCase_ = kwargs["threshold"]
if "top_k" in kwargs:
UpperCAmelCase_ = kwargs["top_k"]
return {}, {}, postprocess_params
def lowercase__ ( self : int , _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
UpperCAmelCase_ = load_image(inputs["image"] )
UpperCAmelCase_ = inputs["candidate_labels"]
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
UpperCAmelCase_ = candidate_labels.split("," )
UpperCAmelCase_ = torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(_UpperCAmelCase ):
UpperCAmelCase_ = self.tokenizer(_UpperCAmelCase , return_tensors=self.framework )
UpperCAmelCase_ = self.image_processor(_UpperCAmelCase , return_tensors=self.framework )
yield {
"is_last": i == len(_UpperCAmelCase ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def lowercase__ ( self : int , _UpperCAmelCase : List[Any] ) -> int:
'''simple docstring'''
UpperCAmelCase_ = model_inputs.pop("target_size" )
UpperCAmelCase_ = model_inputs.pop("candidate_label" )
UpperCAmelCase_ = model_inputs.pop("is_last" )
UpperCAmelCase_ = self.model(**_UpperCAmelCase )
UpperCAmelCase_ = {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
return model_outputs
def lowercase__ ( self : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : List[str]=None ) -> int:
'''simple docstring'''
UpperCAmelCase_ = []
for model_output in model_outputs:
UpperCAmelCase_ = model_output["candidate_label"]
UpperCAmelCase_ = BaseModelOutput(_UpperCAmelCase )
UpperCAmelCase_ = self.image_processor.post_process_object_detection(
outputs=_UpperCAmelCase , threshold=_UpperCAmelCase , target_sizes=model_output["target_size"] )[0]
for index in outputs["scores"].nonzero():
UpperCAmelCase_ = outputs["scores"][index].item()
UpperCAmelCase_ = self._get_bounding_box(outputs["boxes"][index][0] )
UpperCAmelCase_ = {"score": score, "label": label, "box": box}
results.append(_UpperCAmelCase )
UpperCAmelCase_ = sorted(_UpperCAmelCase , key=lambda _UpperCAmelCase : x["score"] , reverse=_UpperCAmelCase )
if top_k:
UpperCAmelCase_ = results[:top_k]
return results
def lowercase__ ( self : str , _UpperCAmelCase : "torch.Tensor" ) -> Dict[str, int]:
'''simple docstring'''
if self.framework != "pt":
raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch." )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = box.int().tolist()
UpperCAmelCase_ = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 82 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Any = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Any = [
'RagModel',
'RagPreTrainedModel',
'RagSequenceForGeneration',
'RagTokenForGeneration',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[int] = [
'TFRagModel',
'TFRagPreTrainedModel',
'TFRagSequenceForGeneration',
'TFRagTokenForGeneration',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
__UpperCamelCase : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 641 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class lowerCAmelCase__( snake_case__ ):
'''simple docstring'''
A_ : int = 'falcon'
A_ : int = ['past_key_values']
def __init__( self : Optional[Any] , __snake_case : Tuple=65_024 , __snake_case : List[str]=4_544 , __snake_case : Optional[Any]=32 , __snake_case : Any=71 , __snake_case : str=1E-5 , __snake_case : List[str]=0.02 , __snake_case : List[Any]=True , __snake_case : Dict=0.0 , __snake_case : Optional[Any]=0.0 , __snake_case : Any=None , __snake_case : List[Any]=False , __snake_case : Dict=False , __snake_case : Optional[int]=True , __snake_case : List[Any]=True , __snake_case : Optional[Any]=False , __snake_case : Dict=11 , __snake_case : List[str]=11 , **__snake_case : int , ):
'''simple docstring'''
UpperCAmelCase_ : int = vocab_size
# Backward compatibility with n_embed kwarg
UpperCAmelCase_ : Union[str, Any] = kwargs.pop('''n_embed''' , __snake_case )
UpperCAmelCase_ : str = hidden_size if n_embed is None else n_embed
UpperCAmelCase_ : Tuple = num_hidden_layers
UpperCAmelCase_ : Tuple = num_attention_heads
UpperCAmelCase_ : Optional[int] = layer_norm_epsilon
UpperCAmelCase_ : int = initializer_range
UpperCAmelCase_ : Optional[int] = use_cache
UpperCAmelCase_ : List[Any] = hidden_dropout
UpperCAmelCase_ : Any = attention_dropout
UpperCAmelCase_ : Tuple = bos_token_id
UpperCAmelCase_ : List[Any] = eos_token_id
UpperCAmelCase_ : Any = num_attention_heads if num_kv_heads is None else num_kv_heads
UpperCAmelCase_ : Optional[int] = alibi
UpperCAmelCase_ : Dict = new_decoder_architecture
UpperCAmelCase_ : List[Any] = multi_query # Ignored when new_decoder_architecture is True
UpperCAmelCase_ : Tuple = parallel_attn
UpperCAmelCase_ : List[Any] = bias
super().__init__(bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
@property
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
return self.hidden_size // self.num_attention_heads
@property
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
return not self.alibi
| 641 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( _A ):
a : List[str] = [0 for i in range(len(_SCREAMING_SNAKE_CASE ) )]
# initialize interval's left pointer and right pointer
a : List[str] = 0, 0
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
# case when current index is inside the interval
if i <= right_pointer:
a : Tuple = min(right_pointer - i + 1 , z_result[i - left_pointer] )
a : Optional[int] = min_edge
while go_next(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
a : Any = i, i + z_result[i] - 1
return z_result
def lowerCamelCase__ ( _A , _A , _A ):
return i + z_result[i] < len(_SCREAMING_SNAKE_CASE ) and s[z_result[i]] == s[i + z_result[i]]
def lowerCamelCase__ ( _A , _A ):
a : Tuple = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
a : Tuple = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(_SCREAMING_SNAKE_CASE ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 526 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
lowercase = datasets.logging.get_logger(__name__)
lowercase = '''\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
month = {November},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
pages = {909--918},
}
@inproceedings{rei-etal-2020-comet,
title = "{COMET}: A Neural Framework for {MT} Evaluation",
author = "Rei, Ricardo and
Stewart, Craig and
Farinha, Ana C and
Lavie, Alon",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
pages = "2685--2702",
}
'''
lowercase = '''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.
See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''
lowercase = '''
COMET score.
Args:
`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.
Returns:
`samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
`scores`: List of scores.
Examples:
>>> comet_metric = datasets.load_metric(\'comet\')
>>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\') # you can also choose which model to use
>>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
>>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
>>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
>>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
>>> print([round(v, 2) for v in results["scores"]])
[0.19, 0.92]
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCamelCase ( datasets.Metric ):
'''simple docstring'''
def a_ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="https://unbabel.github.io/COMET/html/index.html" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"sources": datasets.Value("string" , id="sequence" ),
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/Unbabel/COMET"] , reference_urls=[
"https://github.com/Unbabel/COMET",
"https://www.aclweb.org/anthology/2020.emnlp-main.213/",
"http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6",
] , )
def a_ ( self , a__ ):
if self.config_name == "default":
__SCREAMING_SNAKE_CASE : Union[str, Any] = comet.load_from_checkpoint(comet.download_model("wmt20-comet-da" ) )
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def a_ ( self , a__ , a__ , a__ , a__=None , a__=False ):
if gpus is None:
__SCREAMING_SNAKE_CASE : List[str] = 1 if torch.cuda.is_available() else 0
__SCREAMING_SNAKE_CASE : Any = {"src": sources, "mt": predictions, "ref": references}
__SCREAMING_SNAKE_CASE : int = [dict(zip(a__ , a__ ) ) for t in zip(*data.values() )]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.scorer.predict(a__ , gpus=a__ , progress_bar=a__ )
return {"mean_score": mean_score, "scores": scores}
| 211 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
lowerCamelCase : Any = 'true'
def lowercase__( A , A=8_2 , A=1_6 ):
set_seed(4_2 )
snake_case__ : Any = RegressionModel()
snake_case__ : List[Any] = deepcopy(A )
snake_case__ : Tuple = RegressionDataset(length=A )
snake_case__ : List[str] = DataLoader(A , batch_size=A )
model.to(accelerator.device )
snake_case__ , snake_case__ : Dict = accelerator.prepare(A , A )
return model, ddp_model, dataloader
def lowercase__( A , A=False ):
snake_case__ : str = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
snake_case__ : str = load_dataset('glue' , 'mrpc' , split='validation' )
def tokenize_function(A ):
snake_case__ : Optional[int] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=A , max_length=A )
return outputs
with accelerator.main_process_first():
snake_case__ : str = dataset.map(
A , batched=A , remove_columns=['idx', 'sentence1', 'sentence2'] , )
snake_case__ : Union[str, Any] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(A ):
if use_longest:
return tokenizer.pad(A , padding='longest' , return_tensors='pt' )
return tokenizer.pad(A , padding='max_length' , max_length=1_2_8 , return_tensors='pt' )
return DataLoader(A , shuffle=A , collate_fn=A , batch_size=1_6 )
def lowercase__( A , A ):
snake_case__ : Tuple = Accelerator(dispatch_batches=A , split_batches=A )
snake_case__ : int = get_dataloader(A , not dispatch_batches )
snake_case__ : str = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' , return_dict=A )
snake_case__ , snake_case__ : Optional[Any] = accelerator.prepare(A , A )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def lowercase__( A , A , A ):
snake_case__ : int = []
for batch in dataloader:
snake_case__ , snake_case__ : Optional[int] = batch.values()
with torch.no_grad():
snake_case__ : Optional[int] = model(A )
snake_case__ , snake_case__ : int = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
snake_case__ , snake_case__ : Dict = [], []
for logit, targ in logits_and_targets:
logits.append(A )
targs.append(A )
snake_case__ , snake_case__ : Union[str, Any] = torch.cat(A ), torch.cat(A )
return logits, targs
def lowercase__( A , A=8_2 , A=False , A=False , A=1_6 ):
snake_case__ , snake_case__ , snake_case__ : List[str] = get_basic_setup(A , A , A )
snake_case__ , snake_case__ : List[Any] = generate_predictions(A , A , A )
assert (
len(A ) == num_samples
), f'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(A )}'''
def lowercase__( A = False , A = False ):
snake_case__ : Optional[Any] = evaluate.load('glue' , 'mrpc' )
snake_case__ , snake_case__ : Optional[Any] = get_mrpc_setup(A , A )
# First do baseline
snake_case__ , snake_case__ , snake_case__ : Optional[Any] = setup['no']
model.to(A )
model.eval()
for batch in dataloader:
batch.to(A )
with torch.inference_mode():
snake_case__ : Optional[Any] = model(**A )
snake_case__ : Optional[Any] = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=A , references=batch['labels'] )
snake_case__ : str = metric.compute()
# Then do distributed
snake_case__ , snake_case__ , snake_case__ : Optional[Any] = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
snake_case__ : Union[str, Any] = model(**A )
snake_case__ : str = outputs.logits.argmax(dim=-1 )
snake_case__ : Optional[int] = batch['labels']
snake_case__ , snake_case__ : Tuple = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=A , references=A )
snake_case__ : str = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def lowercase__( ):
snake_case__ : Union[str, Any] = Accelerator(split_batches=A , dispatch_batches=A )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('**Testing gather_for_metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(A , A )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test torch metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
snake_case__ : Optional[int] = Accelerator(split_batches=A , dispatch_batches=A )
if accelerator.is_local_main_process:
print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(A , 9_9 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test last batch is not dropped when perfectly divisible**' )
snake_case__ : Dict = Accelerator()
test_torch_metrics(A , 5_1_2 )
accelerator.state._reset_state()
def lowercase__( A ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 303 |
from math import ceil
def lowercase__( A = 1_0_0_1 ):
snake_case__ : Dict = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
snake_case__ : str = 2 * i + 1
snake_case__ : Any = 2 * i
snake_case__ : List[Any] = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
lowerCamelCase : List[str] = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number')
| 303 | 1 |
'''simple docstring'''
class __UpperCAmelCase :
def __init__( self ):
"""simple docstring"""
_snake_case = {}
def lowerCamelCase ( self ):
"""simple docstring"""
print(self.vertex )
for i in self.vertex:
print(_UpperCAmelCase , ' -> ' , ' -> '.join([str(_UpperCAmelCase ) for j in self.vertex[i]] ) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
if from_vertex in self.vertex:
self.vertex[from_vertex].append(_UpperCAmelCase )
else:
# else make a new vertex
_snake_case = [to_vertex]
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = True
print(_UpperCAmelCase , end=' ' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(_UpperCAmelCase , _UpperCAmelCase )
if __name__ == "__main__":
lowercase : Union[str, Any] = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("DFS:")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 495 |
# Copyright 2021 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.
import warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class lowercase_ ( UpperCamelCase__):
"""simple docstring"""
def __init__( self , _UpperCAmelCase ):
"""simple docstring"""
a_ = data
def __iter__( self ):
"""simple docstring"""
for element in self.data:
yield element
def lowerCamelCase_ ( UpperCAmelCase__=True ):
"""simple docstring"""
a_ = Accelerator(even_batches=UpperCAmelCase__ )
assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
return accelerator
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = False ):
"""simple docstring"""
if iterable:
a_ = DummyIterableDataset(torch.as_tensor(range(UpperCAmelCase__ ) ) )
else:
a_ = TensorDataset(torch.as_tensor(range(UpperCAmelCase__ ) ) )
a_ = DataLoader(UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
a_ = accelerator.prepare(UpperCAmelCase__ )
return dl
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ):
"""simple docstring"""
a_ = create_dataloader(accelerator=UpperCAmelCase__ , dataset_size=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
a_ = [len(batch[0] ) for batch in dl]
if accelerator.process_index == 0:
assert batch_sizes == process_0_expected_batch_sizes
elif accelerator.process_index == 1:
assert batch_sizes == process_1_expected_batch_sizes
def lowerCamelCase_ ( ):
"""simple docstring"""
a_ = create_accelerator()
# without padding, we would expect a different number of batches
verify_dataloader_batch_sizes(
UpperCAmelCase__ , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1, 1] , )
# without padding, we would expect the same number of batches, but different sizes
verify_dataloader_batch_sizes(
UpperCAmelCase__ , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 2] , )
def lowerCamelCase_ ( ):
"""simple docstring"""
a_ = create_accelerator(even_batches=UpperCAmelCase__ )
verify_dataloader_batch_sizes(
UpperCAmelCase__ , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1] , )
verify_dataloader_batch_sizes(
UpperCAmelCase__ , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 1] , )
def lowerCamelCase_ ( ):
"""simple docstring"""
a_ = create_accelerator(even_batches=UpperCAmelCase__ )
a_ = torch.nn.Linear(1 , 1 )
a_ = accelerator.prepare(UpperCAmelCase__ )
a_ = create_dataloader(UpperCAmelCase__ , dataset_size=3 , batch_size=1 )
a_ = []
with accelerator.join_uneven_inputs([ddp_model] ):
for batch_idx, batch in enumerate(UpperCAmelCase__ ):
a_ = ddp_model(batch[0].float() )
a_ = output.sum()
loss.backward()
batch_idxs.append(UpperCAmelCase__ )
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
assert batch_idxs == [0, 1]
elif accelerator.process_index == 1:
assert batch_idxs == [0]
def lowerCamelCase_ ( UpperCAmelCase__ ):
"""simple docstring"""
with warnings.catch_warnings(record=UpperCAmelCase__ ) as w:
with accelerator.join_uneven_inputs([Mock()] ):
pass
assert issubclass(w[-1].category , UpperCAmelCase__ )
assert "only supported for multi-GPU" in str(w[-1].message )
def lowerCamelCase_ ( ):
"""simple docstring"""
a_ = True
a_ = False
a_ = create_accelerator(even_batches=UpperCAmelCase__ )
a_ = torch.nn.Linear(1 , 1 )
a_ = accelerator.prepare(UpperCAmelCase__ )
a_ = create_dataloader(UpperCAmelCase__ , dataset_size=3 , batch_size=1 )
a_ = create_dataloader(UpperCAmelCase__ , dataset_size=3 , batch_size=1 )
with accelerator.join_uneven_inputs([ddp_model] , even_batches=UpperCAmelCase__ ):
a_ = train_dl.batch_sampler.even_batches
a_ = valid_dl.batch_sampler.even_batches
assert train_dl_overridden_value == overridden_even_batches
assert valid_dl_overridden_value == overridden_even_batches
assert train_dl.batch_sampler.even_batches == default_even_batches
assert valid_dl.batch_sampler.even_batches == default_even_batches
def lowerCamelCase_ ( ):
"""simple docstring"""
a_ = True
a_ = False
a_ = create_accelerator(even_batches=UpperCAmelCase__ )
a_ = torch.nn.Linear(1 , 1 )
a_ = accelerator.prepare(UpperCAmelCase__ )
create_dataloader(UpperCAmelCase__ , dataset_size=3 , batch_size=1 , iterable=UpperCAmelCase__ )
a_ = create_dataloader(UpperCAmelCase__ , dataset_size=3 , batch_size=1 )
with warnings.catch_warnings():
warnings.filterwarnings("""ignore""" )
try:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=UpperCAmelCase__ ):
a_ = batch_dl.batch_sampler.even_batches
except AttributeError:
# ensure attribute error is not raised when processing iterable dl
raise AssertionError
assert batch_dl_overridden_value == overridden_even_batches
assert batch_dl.batch_sampler.even_batches == default_even_batches
def lowerCamelCase_ ( ):
"""simple docstring"""
a_ = create_accelerator()
a_ = torch.nn.Linear(1 , 1 )
a_ = accelerator.prepare(UpperCAmelCase__ )
create_dataloader(UpperCAmelCase__ , dataset_size=3 , batch_size=1 , iterable=UpperCAmelCase__ )
with warnings.catch_warnings(record=UpperCAmelCase__ ) as w:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=UpperCAmelCase__ ):
pass
assert issubclass(w[-1].category , UpperCAmelCase__ )
assert "only supported for map-style datasets" in str(w[-1].message )
def lowerCamelCase_ ( ):
"""simple docstring"""
a_ = create_accelerator()
accelerator.print("""Test that even_batches variable ensures uniform batches across processes""" )
test_default_ensures_even_batch_sizes()
accelerator.print("""Run tests with even_batches disabled""" )
test_can_disable_even_batches()
accelerator.print("""Test joining uneven inputs""" )
test_can_join_uneven_inputs()
accelerator.print("""Test overriding even_batches when joining uneven inputs""" )
test_join_can_override_even_batches()
accelerator.print("""Test overriding even_batches for mixed dataloader types""" )
test_join_can_override_for_mixed_type_dataloaders()
accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders""" )
test_join_raises_warning_for_iterable_when_overriding_even_batches()
accelerator.print("""Test join with non DDP distributed raises warning""" )
a_ = accelerator.state.distributed_type
a_ = DistributedType.FSDP
test_join_raises_warning_for_non_ddp_distributed(UpperCAmelCase__ )
a_ = original_state
if __name__ == "__main__":
main()
| 483 | 0 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__lowerCamelCase : str = None
try:
import msvcrt
except ImportError:
__lowerCamelCase : str = None
try:
import fcntl
except ImportError:
__lowerCamelCase : List[Any] = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
__lowerCamelCase : Union[str, Any] = OSError
# Data
# ------------------------------------------------
__lowerCamelCase : str = [
"""Timeout""",
"""BaseFileLock""",
"""WindowsFileLock""",
"""UnixFileLock""",
"""SoftFileLock""",
"""FileLock""",
]
__lowerCamelCase : Union[str, Any] = """3.0.12"""
__lowerCamelCase : Any = None
def A_ ( ) -> List[Any]:
global _logger
UpperCamelCase : Any = _logger or logging.getLogger(__name__ )
return _logger
class A__ ( __snake_case ):
def __init__( self , A_ ):
'''simple docstring'''
UpperCamelCase : Optional[int] = lock_file
return None
def __str__( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = F"""The file lock '{self.lock_file}' could not be acquired."""
return temp
class A__ :
def __init__( self , A_ ):
'''simple docstring'''
UpperCamelCase : Dict = lock
return None
def __enter__( self ):
'''simple docstring'''
return self.lock
def __exit__( self , A_ , A_ , A_ ):
'''simple docstring'''
self.lock.release()
return None
class A__ :
def __init__( self , A_ , A_=-1 , A_=None ):
'''simple docstring'''
UpperCamelCase : List[Any] = max_filename_length if max_filename_length is not None else 255
# Hash the filename if it's too long
UpperCamelCase : Dict = self.hash_filename_if_too_long(A_ , A_ )
# The path to the lock file.
UpperCamelCase : List[Any] = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
UpperCamelCase : Tuple = None
# The default timeout value.
UpperCamelCase : Optional[Any] = timeout
# We use this lock primarily for the lock counter.
UpperCamelCase : Union[str, Any] = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
UpperCamelCase : Dict = 0
return None
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return self._lock_file
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return self._timeout
@timeout.setter
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
UpperCamelCase : Dict = float(A_ )
return None
def __UpperCamelCase( self ):
'''simple docstring'''
raise NotImplementedError()
def __UpperCamelCase( self ):
'''simple docstring'''
raise NotImplementedError()
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return self._lock_file_fd is not None
def __UpperCamelCase( self , A_=None , A_=0.05 ):
'''simple docstring'''
if timeout is None:
UpperCamelCase : Optional[Any] = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
UpperCamelCase : Dict = id(self )
UpperCamelCase : List[str] = self._lock_file
UpperCamelCase : int = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(F"""Attempting to acquire lock {lock_id} on {lock_filename}""" )
self._acquire()
if self.is_locked:
logger().debug(F"""Lock {lock_id} acquired on {lock_filename}""" )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(F"""Timeout on acquiring lock {lock_id} on {lock_filename}""" )
raise Timeout(self._lock_file )
else:
logger().debug(
F"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...""" )
time.sleep(A_ )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
UpperCamelCase : List[Any] = max(0 , self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def __UpperCamelCase( self , A_=False ):
'''simple docstring'''
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
UpperCamelCase : List[Any] = id(self )
UpperCamelCase : Dict = self._lock_file
logger().debug(F"""Attempting to release lock {lock_id} on {lock_filename}""" )
self._release()
UpperCamelCase : Dict = 0
logger().debug(F"""Lock {lock_id} released on {lock_filename}""" )
return None
def __enter__( self ):
'''simple docstring'''
self.acquire()
return self
def __exit__( self , A_ , A_ , A_ ):
'''simple docstring'''
self.release()
return None
def __del__( self ):
'''simple docstring'''
self.release(force=A_ )
return None
def __UpperCamelCase( self , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Tuple = os.path.basename(A_ )
if len(A_ ) > max_length and max_length > 0:
UpperCamelCase : Optional[int] = os.path.dirname(A_ )
UpperCamelCase : int = str(hash(A_ ) )
UpperCamelCase : Any = filename[: max_length - len(A_ ) - 8] + "..." + hashed_filename + ".lock"
return os.path.join(A_ , A_ )
else:
return path
class A__ ( __snake_case ):
def __init__( self , A_ , A_=-1 , A_=None ):
'''simple docstring'''
from .file_utils import relative_to_absolute_path
super().__init__(A_ , timeout=A_ , max_filename_length=A_ )
UpperCamelCase : List[Any] = "\\\\?\\" + relative_to_absolute_path(self.lock_file )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
UpperCamelCase : str = os.open(self._lock_file , A_ )
except OSError:
pass
else:
try:
msvcrt.locking(A_ , msvcrt.LK_NBLCK , 1 )
except OSError:
os.close(A_ )
else:
UpperCamelCase : Optional[Any] = fd
return None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = self._lock_file_fd
UpperCamelCase : str = None
msvcrt.locking(A_ , msvcrt.LK_UNLCK , 1 )
os.close(A_ )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class A__ ( __snake_case ):
def __init__( self , A_ , A_=-1 , A_=None ):
'''simple docstring'''
UpperCamelCase : Tuple = os.statvfs(os.path.dirname(A_ ) ).f_namemax
super().__init__(A_ , timeout=A_ , max_filename_length=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = os.O_RDWR | os.O_CREAT | os.O_TRUNC
UpperCamelCase : int = os.open(self._lock_file , A_ )
try:
fcntl.flock(A_ , fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(A_ )
else:
UpperCamelCase : List[str] = fd
return None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = self._lock_file_fd
UpperCamelCase : List[Any] = None
fcntl.flock(A_ , fcntl.LOCK_UN )
os.close(A_ )
return None
class A__ ( __snake_case ):
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
UpperCamelCase : Optional[int] = os.open(self._lock_file , A_ )
except OSError:
pass
else:
UpperCamelCase : Tuple = fd
return None
def __UpperCamelCase( self ):
'''simple docstring'''
os.close(self._lock_file_fd )
UpperCamelCase : str = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
__lowerCamelCase : Dict = None
if msvcrt:
__lowerCamelCase : Any = WindowsFileLock
elif fcntl:
__lowerCamelCase : Any = UnixFileLock
else:
__lowerCamelCase : int = SoftFileLock
if warnings is not None:
warnings.warn("""only soft file lock is available""")
| 38 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lowerCamelCase : Union[str, Any] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
__lowerCamelCase : Dict = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
__lowerCamelCase : Tuple = {
"""facebook/blenderbot_small-90M""": 512,
}
class A__ ( __snake_case ):
_UpperCAmelCase :Union[str, Any] = VOCAB_FILES_NAMES
_UpperCAmelCase :Dict = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase :List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase :Optional[Any] = BlenderbotSmallTokenizer
def __init__( self , A_=None , A_=None , A_="<|endoftext|>" , A_="<|endoftext|>" , A_="<|endoftext|>" , A_=False , A_=True , **A_ , ):
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=A_ , merges=A_ , add_prefix_space=A_ , trim_offsets=A_ , ) , bos_token=A_ , eos_token=A_ , unk_token=A_ , **A_ , )
UpperCamelCase : Union[str, Any] = add_prefix_space
def __UpperCamelCase( self , A_ , A_=None ):
'''simple docstring'''
UpperCamelCase : Dict = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __UpperCamelCase( self , A_ , A_ = None ):
'''simple docstring'''
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 38 | 1 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : Dict = '''char'''
A : Any = '''bpe'''
A : Dict = '''wp'''
UpperCamelCase_ = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : List[Any] = ['''image_processor''', '''char_tokenizer''']
A : int = '''ViTImageProcessor'''
A : List[str] = '''MgpstrTokenizer'''
def __init__( self, A=None, A=None, **A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.', A, )
SCREAMING_SNAKE_CASE : str = kwargs.pop('feature_extractor' )
SCREAMING_SNAKE_CASE : Optional[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer
SCREAMING_SNAKE_CASE : Optional[int] = AutoTokenizer.from_pretrained('gpt2' )
SCREAMING_SNAKE_CASE : Optional[Any] = AutoTokenizer.from_pretrained('bert-base-uncased' )
super().__init__(A, A )
def __call__( self, A=None, A=None, A=None, **A ):
'''simple docstring'''
if images is None and text is None:
raise ValueError('You need to specify either an `images` or `text` input to process.' )
if images is not None:
SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processor(A, return_tensors=A, **A )
if text is not None:
SCREAMING_SNAKE_CASE : int = self.char_tokenizer(A, return_tensors=A, **A )
if text is None:
return inputs
elif images is None:
return encodings
else:
SCREAMING_SNAKE_CASE : Any = encodings['input_ids']
return inputs
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = sequences
SCREAMING_SNAKE_CASE : List[str] = char_preds.size(0 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = self._decode_helper(A, 'char' )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = self._decode_helper(A, 'bpe' )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = self._decode_helper(A, 'wp' )
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : Tuple = []
for i in range(A ):
SCREAMING_SNAKE_CASE : str = [char_scores[i], bpe_scores[i], wp_scores[i]]
SCREAMING_SNAKE_CASE : Dict = [char_strs[i], bpe_strs[i], wp_strs[i]]
SCREAMING_SNAKE_CASE : List[str] = scores.index(max(A ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
SCREAMING_SNAKE_CASE : List[Any] = {}
SCREAMING_SNAKE_CASE : int = final_strs
SCREAMING_SNAKE_CASE : Any = final_scores
SCREAMING_SNAKE_CASE : Dict = char_strs
SCREAMING_SNAKE_CASE : Any = bpe_strs
SCREAMING_SNAKE_CASE : Union[str, Any] = wp_strs
return out
def UpperCamelCase_ ( self, A, A ):
'''simple docstring'''
if format == DecodeType.CHARACTER:
SCREAMING_SNAKE_CASE : List[Any] = self.char_decode
SCREAMING_SNAKE_CASE : Optional[int] = 1
SCREAMING_SNAKE_CASE : str = '[s]'
elif format == DecodeType.BPE:
SCREAMING_SNAKE_CASE : str = self.bpe_decode
SCREAMING_SNAKE_CASE : str = 2
SCREAMING_SNAKE_CASE : List[str] = '#'
elif format == DecodeType.WORDPIECE:
SCREAMING_SNAKE_CASE : Any = self.wp_decode
SCREAMING_SNAKE_CASE : Tuple = 102
SCREAMING_SNAKE_CASE : List[Any] = '[SEP]'
else:
raise ValueError(F"Format {format} is not supported." )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = [], []
SCREAMING_SNAKE_CASE : Union[str, Any] = pred_logits.size(0 )
SCREAMING_SNAKE_CASE : Any = pred_logits.size(1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = pred_logits.topk(1, dim=-1, largest=A, sorted=A )
SCREAMING_SNAKE_CASE : Optional[int] = preds_index.view(-1, A )[:, 1:]
SCREAMING_SNAKE_CASE : List[Any] = decoder(A )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = torch.nn.functional.softmax(A, dim=2 ).max(dim=2 )
SCREAMING_SNAKE_CASE : Dict = preds_max_prob[:, 1:]
for index in range(A ):
SCREAMING_SNAKE_CASE : Optional[int] = preds_str[index].find(A )
SCREAMING_SNAKE_CASE : List[Any] = preds_str[index][:pred_eos]
SCREAMING_SNAKE_CASE : Dict = preds_index[index].cpu().tolist()
SCREAMING_SNAKE_CASE : Union[str, Any] = pred_index.index(A ) if eos_token in pred_index else -1
SCREAMING_SNAKE_CASE : Optional[int] = preds_max_prob[index][: pred_eos_index + 1]
SCREAMING_SNAKE_CASE : Optional[int] = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(A )
conf_scores.append(A )
return dec_strs, conf_scores
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = [seq.replace(' ', '' ) for seq in self.char_tokenizer.batch_decode(A )]
return decode_strs
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
return self.bpe_tokenizer.batch_decode(A )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [seq.replace(' ', '' ) for seq in self.wp_tokenizer.batch_decode(A )]
return decode_strs
| 28 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def _A ( A ,A ) -> str:
lowercase : Optional[int] = old_name
if "patch_embed" in old_name:
lowercase , lowercase , lowercase : Tuple = old_name.split("." )
if layer == "0":
lowercase : int = old_name.replace("0" ,"convolution1" )
elif layer == "1":
lowercase : List[str] = old_name.replace("1" ,"batchnorm_before" )
elif layer == "3":
lowercase : Dict = old_name.replace("3" ,"convolution2" )
else:
lowercase : Union[str, Any] = old_name.replace("4" ,"batchnorm_after" )
if "network" in old_name and re.search(r"\d\.\d" ,A ):
lowercase : List[str] = r"\b\d{2}\b"
if bool(re.search(A ,A ) ):
lowercase : str = re.search(r"\d\.\d\d." ,A ).group()
else:
lowercase : int = re.search(r"\d\.\d." ,A ).group()
if int(match[0] ) < 6:
lowercase : str = old_name.replace(A ,"" )
lowercase : List[str] = trimmed_name.replace("network" ,match[0] + ".meta4D_layers.blocks." + match[2:-1] )
lowercase : Optional[Any] = "intermediate_stages." + trimmed_name
else:
lowercase : str = old_name.replace(A ,"" )
if int(match[2] ) < num_meta4D_last_stage:
lowercase : Optional[int] = trimmed_name.replace("network" ,"meta4D_layers.blocks." + match[2] )
else:
lowercase : List[Any] = str(int(match[2] ) - num_meta4D_last_stage )
lowercase : List[Any] = trimmed_name.replace("network" ,"meta3D_layers.blocks." + layer_index )
if "norm1" in old_name:
lowercase : str = trimmed_name.replace("norm1" ,"layernorm1" )
elif "norm2" in old_name:
lowercase : Optional[Any] = trimmed_name.replace("norm2" ,"layernorm2" )
elif "fc1" in old_name:
lowercase : Optional[int] = trimmed_name.replace("fc1" ,"linear_in" )
elif "fc2" in old_name:
lowercase : str = trimmed_name.replace("fc2" ,"linear_out" )
lowercase : Dict = "last_stage." + trimmed_name
elif "network" in old_name and re.search(r".\d." ,A ):
lowercase : Union[str, Any] = old_name.replace("network" ,"intermediate_stages" )
if "fc" in new_name:
lowercase : Any = new_name.replace("fc" ,"convolution" )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
lowercase : Optional[Any] = new_name.replace("norm1" ,"batchnorm_before" )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
lowercase : List[str] = new_name.replace("norm2" ,"batchnorm_after" )
if "proj" in new_name:
lowercase : Optional[int] = new_name.replace("proj" ,"projection" )
if "dist_head" in new_name:
lowercase : Tuple = new_name.replace("dist_head" ,"distillation_classifier" )
elif "head" in new_name:
lowercase : Tuple = new_name.replace("head" ,"classifier" )
elif "patch_embed" in new_name:
lowercase : Optional[int] = "efficientformer." + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
lowercase : str = new_name.replace("norm" ,"layernorm" )
lowercase : List[Any] = "efficientformer." + new_name
else:
lowercase : Optional[Any] = "efficientformer.encoder." + new_name
return new_name
def _A ( A ,A ) -> Optional[Any]:
for key in checkpoint.copy().keys():
lowercase : List[str] = checkpoint.pop(A )
lowercase : int = val
return checkpoint
def _A ( ) -> Optional[int]:
lowercase : str = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowercase : Optional[Any] = Image.open(requests.get(A ,stream=A ).raw )
return image
def _A ( A ,A ,A ,A ) -> List[Any]:
lowercase : Optional[int] = torch.load(A ,map_location="cpu" )["model"]
lowercase : int = EfficientFormerConfig.from_json_file(A )
lowercase : Tuple = EfficientFormerForImageClassificationWithTeacher(A )
lowercase : int = "_".join(checkpoint_path.split("/" )[-1].split("." )[0].split("_" )[:-1] )
lowercase : Optional[int] = config.depths[-1] - config.num_metaad_blocks + 1
lowercase : int = convert_torch_checkpoint(A ,A )
model.load_state_dict(A )
model.eval()
lowercase : List[Any] = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
# prepare image
lowercase : Tuple = prepare_img()
lowercase : Optional[int] = 2_5_6
lowercase : str = 2_2_4
lowercase : List[str] = EfficientFormerImageProcessor(
size={"shortest_edge": image_size} ,crop_size={"height": crop_size, "width": crop_size} ,resample=pillow_resamplings["bicubic"] ,)
lowercase : Union[str, Any] = processor(images=A ,return_tensors="pt" ).pixel_values
# original processing pipeline
lowercase : Tuple = Compose(
[
Resize(A ,interpolation=pillow_resamplings["bicubic"] ),
CenterCrop(A ),
ToTensor(),
Normalize(A ,A ),
] )
lowercase : List[Any] = image_transforms(A ).unsqueeze(0 )
assert torch.allclose(A ,A )
lowercase : Union[str, Any] = model(A )
lowercase : Any = outputs.logits
lowercase : List[str] = (1, 1_0_0_0)
if "l1" in model_name:
lowercase : Any = torch.Tensor(
[-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328] )
assert torch.allclose(logits[0, :1_0] ,A ,atol=1e-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
lowercase : List[Any] = torch.Tensor(
[-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127] )
assert torch.allclose(logits[0, :1_0] ,A ,atol=1e-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
lowercase : Optional[int] = torch.Tensor(
[-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878] )
assert logits.shape == expected_shape
else:
raise ValueError(
F'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7''' )
# Save Checkpoints
Path(A ).mkdir(exist_ok=A )
model.save_pretrained(A )
print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
processor.save_pretrained(A )
print(F'''Processor successfuly saved at {pytorch_dump_path}''' )
if push_to_hub:
print("Pushing model to the hub..." )
model.push_to_hub(
repo_id=F'''Bearnardd/{pytorch_dump_path}''' ,commit_message="Add model" ,use_temp_dir=A ,)
processor.push_to_hub(
repo_id=F'''Bearnardd/{pytorch_dump_path}''' ,commit_message="Add image processor" ,use_temp_dir=A ,)
if __name__ == "__main__":
lowerCAmelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--pytorch_model_path""",
default=None,
type=str,
required=True,
help="""Path to EfficientFormer pytorch checkpoint.""",
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The json file for EfficientFormer model config.""",
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
parser.add_argument(
"""--no-push_to_hub""",
dest="""push_to_hub""",
action="""store_false""",
help="""Do not push model and image processor to the hub""",
)
parser.set_defaults(push_to_hub=True)
lowerCAmelCase : Optional[int] = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 372 | 0 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__a = 5_0_0_0_0_0
__a , __a = os.path.split(__file__)
__a = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def a ( snake_case__: datasets.Dataset , **snake_case__: Dict ):
'''simple docstring'''
lowercase_ = dataset.map(**snake_case__ )
@get_duration
def a ( snake_case__: datasets.Dataset , **snake_case__: Any ):
'''simple docstring'''
lowercase_ = dataset.filter(**snake_case__ )
def a ( ):
'''simple docstring'''
lowercase_ = {'''num examples''': SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ = datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
lowercase_ = generate_example_dataset(
os.path.join(snake_case__ , '''dataset.arrow''' ) , snake_case__ , num_examples=snake_case__ )
lowercase_ = transformers.AutoTokenizer.from_pretrained('''bert-base-cased''' , use_fast=snake_case__ )
def tokenize(snake_case__: List[str] ):
return tokenizer(examples['''text'''] )
lowercase_ = map(snake_case__ )
lowercase_ = map(snake_case__ , batched=snake_case__ )
lowercase_ = map(snake_case__ , function=lambda snake_case__ : None , batched=snake_case__ )
with dataset.formatted_as(type='''numpy''' ):
lowercase_ = map(snake_case__ , function=lambda snake_case__ : None , batched=snake_case__ )
with dataset.formatted_as(type='''pandas''' ):
lowercase_ = map(snake_case__ , function=lambda snake_case__ : None , batched=snake_case__ )
with dataset.formatted_as(type='''torch''' , columns='''numbers''' ):
lowercase_ = map(snake_case__ , function=lambda snake_case__ : None , batched=snake_case__ )
with dataset.formatted_as(type='''tensorflow''' , columns='''numbers''' ):
lowercase_ = map(snake_case__ , function=lambda snake_case__ : None , batched=snake_case__ )
lowercase_ = map(snake_case__ , function=snake_case__ , batched=snake_case__ )
lowercase_ = filter(snake_case__ )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(snake_case__ , '''wb''' ) as f:
f.write(json.dumps(snake_case__ ).encode('''utf-8''' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 409 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
# TODO Update this
__a = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :str = 'esm'
def __init__( self : int , SCREAMING_SNAKE_CASE_ : Any=None , SCREAMING_SNAKE_CASE_ : Tuple=None , SCREAMING_SNAKE_CASE_ : Any=None , SCREAMING_SNAKE_CASE_ : Any=7_6_8 , SCREAMING_SNAKE_CASE_ : List[str]=1_2 , SCREAMING_SNAKE_CASE_ : int=1_2 , SCREAMING_SNAKE_CASE_ : Dict=3_0_7_2 , SCREAMING_SNAKE_CASE_ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=1_0_2_6 , SCREAMING_SNAKE_CASE_ : List[str]=0.02 , SCREAMING_SNAKE_CASE_ : Optional[Any]=1e-12 , SCREAMING_SNAKE_CASE_ : int="absolute" , SCREAMING_SNAKE_CASE_ : Dict=True , SCREAMING_SNAKE_CASE_ : List[Any]=None , SCREAMING_SNAKE_CASE_ : Tuple=False , SCREAMING_SNAKE_CASE_ : Tuple=False , SCREAMING_SNAKE_CASE_ : Tuple=None , SCREAMING_SNAKE_CASE_ : int=None , **SCREAMING_SNAKE_CASE_ : List[str] , ) -> Union[str, Any]:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , mask_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = vocab_size
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = max_position_embeddings
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = position_embedding_type
lowercase_ = use_cache
lowercase_ = emb_layer_norm_before
lowercase_ = token_dropout
lowercase_ = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
lowercase_ = EsmFoldConfig()
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase_ = EsmFoldConfig(**SCREAMING_SNAKE_CASE_ )
lowercase_ = esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
lowercase_ = get_default_vocab_list()
else:
lowercase_ = vocab_list
else:
lowercase_ = None
lowercase_ = None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , SCREAMING_SNAKE_CASE_ ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def _lowercase ( self : Any ) -> str:
lowercase_ = super().to_dict()
if isinstance(self.esmfold_config , SCREAMING_SNAKE_CASE_ ):
lowercase_ = self.esmfold_config.to_dict()
return output
@dataclass
class lowercase__:
"""simple docstring"""
a :str = None
a :bool = True
a :bool = False
a :bool = False
a :bool = False
a :float = 0
a :bool = True
a :bool = False
a :int = 128
a :"TrunkConfig" = None
def _lowercase ( self : Optional[Any] ) -> List[str]:
if self.trunk is None:
lowercase_ = TrunkConfig()
elif isinstance(self.trunk , SCREAMING_SNAKE_CASE_ ):
lowercase_ = TrunkConfig(**self.trunk )
def _lowercase ( self : Dict ) -> int:
lowercase_ = asdict(self )
lowercase_ = self.trunk.to_dict()
return output
@dataclass
class lowercase__:
"""simple docstring"""
a :int = 48
a :int = 1_024
a :int = 128
a :int = 32
a :int = 32
a :int = 32
a :float = 0
a :float = 0
a :bool = False
a :int = 4
a :Optional[int] = 128
a :"StructureModuleConfig" = None
def _lowercase ( self : Tuple ) -> Dict:
if self.structure_module is None:
lowercase_ = StructureModuleConfig()
elif isinstance(self.structure_module , SCREAMING_SNAKE_CASE_ ):
lowercase_ = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
f''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
f''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
lowercase_ = self.sequence_state_dim // self.sequence_head_width
lowercase_ = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
f''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
f''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(f'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def _lowercase ( self : Any ) -> Dict:
lowercase_ = asdict(self )
lowercase_ = self.structure_module.to_dict()
return output
@dataclass
class lowercase__:
"""simple docstring"""
a :int = 384
a :int = 128
a :int = 16
a :int = 128
a :int = 12
a :int = 4
a :int = 8
a :float = 0.1
a :int = 8
a :int = 1
a :int = 2
a :int = 7
a :int = 10
a :float = 1e-8
a :float = 1e5
def _lowercase ( self : Tuple ) -> Tuple:
return asdict(self )
def a ( ):
'''simple docstring'''
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 409 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
SCREAMING_SNAKE_CASE :int = 16
SCREAMING_SNAKE_CASE :Any = 32
def UpperCAmelCase ( a_ , a_ = 1_6 ) -> List[Any]:
"""simple docstring"""
__A = AutoTokenizer.from_pretrained("bert-base-cased" )
__A = load_dataset("glue" , "mrpc" )
def tokenize_function(a_ ):
# max_length=None => use the model max length (it's actually the default)
__A = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=a_ , max_length=a_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__A = datasets.map(
a_ , batched=a_ , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__A = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(a_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__A = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__A = 1_6
elif accelerator.mixed_precision != "no":
__A = 8
else:
__A = None
return tokenizer.pad(
a_ , padding="longest" , max_length=a_ , pad_to_multiple_of=a_ , return_tensors="pt" , )
# Instantiate dataloaders.
__A = DataLoader(
tokenized_datasets["train"] , shuffle=a_ , collate_fn=a_ , batch_size=a_ )
__A = DataLoader(
tokenized_datasets["validation"] , shuffle=a_ , collate_fn=a_ , batch_size=a_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
SCREAMING_SNAKE_CASE :Any = mocked_dataloaders # noqa: F811
def UpperCAmelCase ( a_ , a_ ) -> str:
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" , a_ ) == "1":
__A = 2
# New Code #
__A = int(args.gradient_accumulation_steps )
__A = int(args.local_sgd_steps )
# Initialize accelerator
__A = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=a_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__A = config["lr"]
__A = int(config["num_epochs"] )
__A = int(config["seed"] )
__A = int(config["batch_size"] )
__A = evaluate.load("glue" , "mrpc" )
set_seed(a_ )
__A , __A = get_dataloaders(a_ , a_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__A = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=a_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__A = model.to(accelerator.device )
# Instantiate optimizer
__A = AdamW(params=model.parameters() , lr=a_ )
# Instantiate scheduler
__A = get_linear_schedule_with_warmup(
optimizer=a_ , num_warmup_steps=1_0_0 , num_training_steps=(len(a_ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__A , __A , __A , __A , __A = accelerator.prepare(
a_ , a_ , a_ , a_ , a_ )
# Now we train the model
for epoch in range(a_ ):
model.train()
with LocalSGD(
accelerator=a_ , model=a_ , local_sgd_steps=a_ , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(a_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(a_ ):
__A = model(**a_ )
__A = output.loss
accelerator.backward(a_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(a_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__A = model(**a_ )
__A = outputs.logits.argmax(dim=-1 )
__A , __A = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=a_ , references=a_ , )
__A = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , a_ )
def UpperCAmelCase ( ) -> int:
"""simple docstring"""
__A = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=a_ , default=a_ , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
# New Code #
parser.add_argument(
"--gradient_accumulation_steps" , type=a_ , default=1 , help="The number of minibatches to be ran before gradients are accumulated." , )
parser.add_argument(
"--local_sgd_steps" , type=a_ , default=8 , help="Number of local SGD steps or None to disable local SGD" )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
__A = parser.parse_args()
__A = {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
training_function(a_ , a_ )
if __name__ == "__main__":
main()
| 55 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
A_ = logging.get_logger(__name__)
A_ = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
"self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
"self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "ctc_proj",
"mask_emb": "masked_spec_embed",
}
A_ = [
"ctc_proj",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def _UpperCamelCase ( A , A , A , A , A ):
for attribute in key.split("." ):
UpperCamelCase_ =getattr(A , A )
if weight_type is not None:
UpperCamelCase_ =getattr(A , A ).shape
else:
UpperCamelCase_ =hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
UpperCamelCase_ =value
elif weight_type == "weight_g":
UpperCamelCase_ =value
elif weight_type == "weight_v":
UpperCamelCase_ =value
elif weight_type == "bias":
UpperCamelCase_ =value
else:
UpperCamelCase_ =value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def _UpperCamelCase ( A , A ):
UpperCamelCase_ =[]
UpperCamelCase_ =fairseq_model.state_dict()
UpperCamelCase_ =hf_model.feature_extractor
for name, value in fairseq_dict.items():
UpperCamelCase_ =False
if "conv_layers" in name:
load_conv_layer(
A , A , A , A , hf_model.config.feat_extract_norm == "group" , )
UpperCamelCase_ =True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
UpperCamelCase_ =True
if "*" in mapped_key:
UpperCamelCase_ =name.split(A )[0].split("." )[-2]
UpperCamelCase_ =mapped_key.replace("*" , A )
if "weight_g" in name:
UpperCamelCase_ ="weight_g"
elif "weight_v" in name:
UpperCamelCase_ ="weight_v"
elif "bias" in name and "relative_attention_bias" not in name:
UpperCamelCase_ ="bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
UpperCamelCase_ ="weight"
else:
UpperCamelCase_ =None
set_recursively(A , A , A , A , A )
continue
if not is_used:
unused_weights.append(A )
logger.warning(f"""Unused weights: {unused_weights}""" )
def _UpperCamelCase ( A , A , A , A , A ):
UpperCamelCase_ =full_name.split("conv_layers." )[-1]
UpperCamelCase_ =name.split("." )
UpperCamelCase_ =int(items[0] )
UpperCamelCase_ =int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
UpperCamelCase_ =value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
UpperCamelCase_ =value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
UpperCamelCase_ =value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
UpperCamelCase_ =value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(A )
@torch.no_grad()
def _UpperCamelCase ( A , A , A=None ):
# load the pre-trained checkpoints
UpperCamelCase_ =torch.load(A )
UpperCamelCase_ =WavLMConfigOrig(checkpoint["cfg"] )
UpperCamelCase_ =WavLMOrig(A )
model.load_state_dict(checkpoint["model"] )
model.eval()
if config_path is not None:
UpperCamelCase_ =WavLMConfig.from_pretrained(A )
else:
UpperCamelCase_ =WavLMConfig()
UpperCamelCase_ =WavLMModel(A )
recursively_load_weights(A , A )
hf_wavlm.save_pretrained(A )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
A_ = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 391 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class a__ ( _lowercase ):
__magic_name__ : Optional[Any] = "nllb-moe"
__magic_name__ : Optional[Any] = ["past_key_values"]
__magic_name__ : Optional[int] = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__(self : int, __UpperCAmelCase : Tuple=128112, __UpperCAmelCase : Any=1024, __UpperCAmelCase : Optional[Any]=12, __UpperCAmelCase : Optional[int]=4096, __UpperCAmelCase : Any=16, __UpperCAmelCase : Any=12, __UpperCAmelCase : Optional[Any]=4096, __UpperCAmelCase : Optional[int]=16, __UpperCAmelCase : List[Any]=0.05, __UpperCAmelCase : Dict=0.05, __UpperCAmelCase : Dict=True, __UpperCAmelCase : List[Any]=True, __UpperCAmelCase : Any="relu", __UpperCAmelCase : Union[str, Any]=1024, __UpperCAmelCase : Optional[int]=0.1, __UpperCAmelCase : Tuple=0.1, __UpperCAmelCase : List[Any]=0.0, __UpperCAmelCase : Optional[int]=0.02, __UpperCAmelCase : Tuple=2, __UpperCAmelCase : int=True, __UpperCAmelCase : int=False, __UpperCAmelCase : int="float32", __UpperCAmelCase : Optional[Any]=False, __UpperCAmelCase : List[str]=128, __UpperCAmelCase : Dict=64, __UpperCAmelCase : Dict=4, __UpperCAmelCase : Optional[Any]=4, __UpperCAmelCase : Optional[Any]=0.001, __UpperCAmelCase : Optional[Any]=0.001, __UpperCAmelCase : Optional[Any]="all", __UpperCAmelCase : List[str]=False, __UpperCAmelCase : Dict=False, __UpperCAmelCase : Any=1.0, __UpperCAmelCase : Dict=0.2, __UpperCAmelCase : int=1, __UpperCAmelCase : Union[str, Any]=0, __UpperCAmelCase : Any=2, __UpperCAmelCase : Union[str, Any]=False, **__UpperCAmelCase : Dict, ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : int = max_position_embeddings
SCREAMING_SNAKE_CASE : Optional[int] = d_model
SCREAMING_SNAKE_CASE : Dict = encoder_ffn_dim
SCREAMING_SNAKE_CASE : int = encoder_layers
SCREAMING_SNAKE_CASE : str = encoder_attention_heads
SCREAMING_SNAKE_CASE : Optional[Any] = decoder_ffn_dim
SCREAMING_SNAKE_CASE : str = decoder_layers
SCREAMING_SNAKE_CASE : Dict = decoder_attention_heads
SCREAMING_SNAKE_CASE : Dict = dropout
SCREAMING_SNAKE_CASE : Optional[Any] = attention_dropout
SCREAMING_SNAKE_CASE : Dict = activation_dropout
SCREAMING_SNAKE_CASE : List[str] = activation_function
SCREAMING_SNAKE_CASE : Union[str, Any] = init_std
SCREAMING_SNAKE_CASE : Any = encoder_layerdrop
SCREAMING_SNAKE_CASE : Optional[int] = decoder_layerdrop
SCREAMING_SNAKE_CASE : Dict = use_cache
SCREAMING_SNAKE_CASE : Optional[Any] = encoder_layers
SCREAMING_SNAKE_CASE : Optional[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE : List[str] = router_z_loss_coef
SCREAMING_SNAKE_CASE : List[str] = router_aux_loss_coef
SCREAMING_SNAKE_CASE : int = decoder_sparse_step
SCREAMING_SNAKE_CASE : Union[str, Any] = encoder_sparse_step
SCREAMING_SNAKE_CASE : Union[str, Any] = num_experts
SCREAMING_SNAKE_CASE : List[Any] = expert_capacity
SCREAMING_SNAKE_CASE : Optional[Any] = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = router_dtype
SCREAMING_SNAKE_CASE : int = router_ignore_padding_tokens
SCREAMING_SNAKE_CASE : Dict = batch_prioritized_routing
SCREAMING_SNAKE_CASE : Dict = second_expert_policy
SCREAMING_SNAKE_CASE : Tuple = normalize_router_prob_before_dropping
SCREAMING_SNAKE_CASE : List[str] = moe_eval_capacity_token_fraction
SCREAMING_SNAKE_CASE : List[Any] = moe_token_dropout
SCREAMING_SNAKE_CASE : List[Any] = output_router_logits
super().__init__(
pad_token_id=__UpperCAmelCase, bos_token_id=__UpperCAmelCase, eos_token_id=__UpperCAmelCase, is_encoder_decoder=__UpperCAmelCase, decoder_start_token_id=__UpperCAmelCase, **__UpperCAmelCase, )
| 355 |
'''simple docstring'''
import heapq
import sys
import numpy as np
snake_case_ = tuple[int, int]
class a__ :
def __init__(self : int ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Tuple = set()
def lowercase__ (self : Any ) -> Dict:
"""simple docstring"""
if not self.empty():
return self.elements[0][0]
else:
return float('''inf''' )
def lowercase__ (self : Dict ) -> Union[str, Any]:
"""simple docstring"""
return len(self.elements ) == 0
def lowercase__ (self : Dict, __UpperCAmelCase : int, __UpperCAmelCase : List[Any] ) -> Tuple:
"""simple docstring"""
if item not in self.set:
heapq.heappush(self.elements, (priority, item) )
self.set.add(__UpperCAmelCase )
else:
# update
# print("update", item)
SCREAMING_SNAKE_CASE : List[Any] = []
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) : Any = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) : Any = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements, (pro, xxx) )
def lowercase__ (self : Union[str, Any], __UpperCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
if item in self.set:
self.set.remove(__UpperCAmelCase )
SCREAMING_SNAKE_CASE : Optional[int] = []
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) : Any = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) : Optional[Any] = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements, (prito, yyy) )
def lowercase__ (self : Tuple ) -> Dict:
"""simple docstring"""
return self.elements[0][1]
def lowercase__ (self : Optional[Any] ) -> Tuple:
"""simple docstring"""
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) : Optional[int] = heapq.heappop(self.elements )
self.set.remove(__UpperCAmelCase )
return (priority, item)
def __lowercase (_SCREAMING_SNAKE_CASE :TPos , _SCREAMING_SNAKE_CASE :TPos ):
# euclidean distance
SCREAMING_SNAKE_CASE : str = np.array(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : List[Any] = np.array(_SCREAMING_SNAKE_CASE )
return np.linalg.norm(a - b )
def __lowercase (_SCREAMING_SNAKE_CASE :TPos , _SCREAMING_SNAKE_CASE :TPos ):
# integer division by time variable
return consistent_heuristic(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) // t
def __lowercase (_SCREAMING_SNAKE_CASE :TPos , _SCREAMING_SNAKE_CASE :TPos ):
# manhattan distance
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def __lowercase (_SCREAMING_SNAKE_CASE :TPos , _SCREAMING_SNAKE_CASE :int , _SCREAMING_SNAKE_CASE :TPos , _SCREAMING_SNAKE_CASE :dict[TPos, float] ):
SCREAMING_SNAKE_CASE : List[str] = g_function[start] + Wa * heuristics[i](_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return ans
def __lowercase (_SCREAMING_SNAKE_CASE :Optional[Any] , _SCREAMING_SNAKE_CASE :List[str] , _SCREAMING_SNAKE_CASE :Tuple ):
SCREAMING_SNAKE_CASE : Optional[int] = np.chararray((n, n) )
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE : Tuple = '''*'''
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAMING_SNAKE_CASE ):
if (j, (n - 1) - i) in blocks:
SCREAMING_SNAKE_CASE : Tuple = '''#'''
SCREAMING_SNAKE_CASE : List[Any] = '''-'''
SCREAMING_SNAKE_CASE : Any = back_pointer[goal]
while x != start:
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) : Optional[int] = x
# print(x)
SCREAMING_SNAKE_CASE : int = '''-'''
SCREAMING_SNAKE_CASE : Dict = back_pointer[x]
SCREAMING_SNAKE_CASE : int = '''-'''
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAMING_SNAKE_CASE ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=''' ''' )
print('''<-- End position''' , end=''' ''' )
else:
print(grid[i][j] , end=''' ''' )
print()
print('''^''' )
print('''Start position''' )
print()
print('''# is an obstacle''' )
print('''- is the path taken by algorithm''' )
print('''PATH TAKEN BY THE ALGORITHM IS:-''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = back_pointer[goal]
while x != start:
print(_SCREAMING_SNAKE_CASE , end=''' ''' )
SCREAMING_SNAKE_CASE : Optional[int] = back_pointer[x]
print(_SCREAMING_SNAKE_CASE )
sys.exit()
def __lowercase (_SCREAMING_SNAKE_CASE :TPos ):
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def __lowercase (_SCREAMING_SNAKE_CASE :Union[str, Any] , _SCREAMING_SNAKE_CASE :int , _SCREAMING_SNAKE_CASE :List[str] , _SCREAMING_SNAKE_CASE :List[str] , _SCREAMING_SNAKE_CASE :List[str] , _SCREAMING_SNAKE_CASE :Union[str, Any] , _SCREAMING_SNAKE_CASE :Tuple , _SCREAMING_SNAKE_CASE :Optional[int] , ):
for itera in range(_SCREAMING_SNAKE_CASE ):
open_list[itera].remove_element(_SCREAMING_SNAKE_CASE )
# print("s", s)
# print("j", j)
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) : Tuple = s
SCREAMING_SNAKE_CASE : List[Any] = (x - 1, y)
SCREAMING_SNAKE_CASE : Optional[Any] = (x + 1, y)
SCREAMING_SNAKE_CASE : List[Any] = (x, y + 1)
SCREAMING_SNAKE_CASE : Any = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(_SCREAMING_SNAKE_CASE ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : Optional[int] = -1
SCREAMING_SNAKE_CASE : List[str] = float('''inf''' )
if valid(_SCREAMING_SNAKE_CASE ) and g_function[neighbours] > g_function[s] + 1:
SCREAMING_SNAKE_CASE : List[str] = g_function[s] + 1
SCREAMING_SNAKE_CASE : Optional[Any] = s
if neighbours not in close_list_anchor:
open_list[0].put(_SCREAMING_SNAKE_CASE , key(_SCREAMING_SNAKE_CASE , 0 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
if neighbours not in close_list_inad:
for var in range(1 , _SCREAMING_SNAKE_CASE ):
if key(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) <= Wa * key(
_SCREAMING_SNAKE_CASE , 0 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
open_list[j].put(
_SCREAMING_SNAKE_CASE , key(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def __lowercase ():
SCREAMING_SNAKE_CASE : Optional[int] = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
snake_case_ = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
snake_case_ = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
snake_case_ = make_common_ground()
snake_case_ = blocks_blk
# hyper parameters
snake_case_ = 1
snake_case_ = 1
snake_case_ = 20
snake_case_ = 3 # one consistent and two other inconsistent
# start and end destination
snake_case_ = (0, 0)
snake_case_ = (n - 1, n - 1)
snake_case_ = 1
def __lowercase (_SCREAMING_SNAKE_CASE :TPos , _SCREAMING_SNAKE_CASE :TPos , _SCREAMING_SNAKE_CASE :int ):
SCREAMING_SNAKE_CASE : Any = {start: 0, goal: float('''inf''' )}
SCREAMING_SNAKE_CASE : Tuple = {start: -1, goal: -1}
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : Union[str, Any] = set()
for i in range(_SCREAMING_SNAKE_CASE ):
open_list.append(PriorityQueue() )
open_list[i].put(_SCREAMING_SNAKE_CASE , key(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
SCREAMING_SNAKE_CASE : list[int] = []
SCREAMING_SNAKE_CASE : list[int] = []
while open_list[0].minkey() < float('''inf''' ):
for i in range(1 , _SCREAMING_SNAKE_CASE ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float('''inf''' ):
do_something(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = open_list[i].top_show()
visited.add(_SCREAMING_SNAKE_CASE )
expand_state(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
close_list_inad.append(_SCREAMING_SNAKE_CASE )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float('''inf''' ):
do_something(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
SCREAMING_SNAKE_CASE : List[Any] = open_list[0].top_show()
visited.add(_SCREAMING_SNAKE_CASE )
expand_state(
_SCREAMING_SNAKE_CASE , 0 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
close_list_anchor.append(_SCREAMING_SNAKE_CASE )
print('''No path found to goal''' )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(_SCREAMING_SNAKE_CASE ):
if (j, i) in blocks:
print('''#''' , end=''' ''' )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print('''*''' , end=''' ''' )
else:
print('''-''' , end=''' ''' )
else:
print('''*''' , end=''' ''' )
if (j, i) == (n - 1, n - 1):
print('''<-- End position''' , end=''' ''' )
print()
print('''^''' )
print('''Start position''' )
print()
print('''# is an obstacle''' )
print('''- is the path taken by algorithm''' )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 355 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class lowerCamelCase ( _A , unittest.TestCase ):
snake_case_ = BertTokenizer
snake_case_ = BertTokenizerFast
snake_case_ = True
snake_case_ = True
snake_case_ = filter_non_english
def _lowerCamelCase ( self ):
super().setUp()
lowerCAmelCase : Union[str, Any] = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
lowerCAmelCase : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def _lowerCamelCase ( self , a_ ):
lowerCAmelCase : Tuple = "UNwant\u00E9d,running"
lowerCAmelCase : int = "unwanted, running"
return input_text, output_text
def _lowerCamelCase ( self ):
lowerCAmelCase : Optional[Any] = self.tokenizer_class(self.vocab_file )
lowerCAmelCase : Optional[int] = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(a_ , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(a_ ) , [9, 6, 7, 12, 10, 11] )
def _lowerCamelCase ( self ):
if not self.test_rust_tokenizer:
return
lowerCAmelCase : Union[str, Any] = self.get_tokenizer()
lowerCAmelCase : Union[str, Any] = self.get_rust_tokenizer()
lowerCAmelCase : Tuple = "UNwant\u00E9d,running"
lowerCAmelCase : Any = tokenizer.tokenize(a_ )
lowerCAmelCase : int = rust_tokenizer.tokenize(a_ )
self.assertListEqual(a_ , a_ )
lowerCAmelCase : Optional[int] = tokenizer.encode(a_ , add_special_tokens=a_ )
lowerCAmelCase : Dict = rust_tokenizer.encode(a_ , add_special_tokens=a_ )
self.assertListEqual(a_ , a_ )
lowerCAmelCase : Any = self.get_rust_tokenizer()
lowerCAmelCase : Optional[Any] = tokenizer.encode(a_ )
lowerCAmelCase : List[Any] = rust_tokenizer.encode(a_ )
self.assertListEqual(a_ , a_ )
# With lower casing
lowerCAmelCase : Any = self.get_tokenizer(do_lower_case=a_ )
lowerCAmelCase : int = self.get_rust_tokenizer(do_lower_case=a_ )
lowerCAmelCase : List[str] = "UNwant\u00E9d,running"
lowerCAmelCase : str = tokenizer.tokenize(a_ )
lowerCAmelCase : List[Any] = rust_tokenizer.tokenize(a_ )
self.assertListEqual(a_ , a_ )
lowerCAmelCase : Union[str, Any] = tokenizer.encode(a_ , add_special_tokens=a_ )
lowerCAmelCase : Tuple = rust_tokenizer.encode(a_ , add_special_tokens=a_ )
self.assertListEqual(a_ , a_ )
lowerCAmelCase : Tuple = self.get_rust_tokenizer()
lowerCAmelCase : str = tokenizer.encode(a_ )
lowerCAmelCase : List[Any] = rust_tokenizer.encode(a_ )
self.assertListEqual(a_ , a_ )
def _lowerCamelCase ( self ):
lowerCAmelCase : int = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def _lowerCamelCase ( self ):
lowerCAmelCase : int = BasicTokenizer(do_lower_case=a_ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _lowerCamelCase ( self ):
lowerCAmelCase : Dict = BasicTokenizer(do_lower_case=a_ , strip_accents=a_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def _lowerCamelCase ( self ):
lowerCAmelCase : Optional[Any] = BasicTokenizer(do_lower_case=a_ , strip_accents=a_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _lowerCamelCase ( self ):
lowerCAmelCase : Dict = BasicTokenizer(do_lower_case=a_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _lowerCamelCase ( self ):
lowerCAmelCase : Tuple = BasicTokenizer(do_lower_case=a_ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def _lowerCamelCase ( self ):
lowerCAmelCase : int = BasicTokenizer(do_lower_case=a_ , strip_accents=a_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def _lowerCamelCase ( self ):
lowerCAmelCase : Any = BasicTokenizer(do_lower_case=a_ , strip_accents=a_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def _lowerCamelCase ( self ):
lowerCAmelCase : Any = BasicTokenizer(do_lower_case=a_ , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def _lowerCamelCase ( self ):
lowerCAmelCase : str = BasicTokenizer()
lowerCAmelCase : Optional[Any] = "a\n'll !!to?'d of, can't."
lowerCAmelCase : Union[str, Any] = ["a", "'", "ll", "!", "!", "to", "?", "'", "d", "of", ",", "can", "'", "t", "."]
self.assertListEqual(tokenizer.tokenize(a_ ) , a_ )
def _lowerCamelCase ( self ):
lowerCAmelCase : Optional[Any] = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
lowerCAmelCase : Optional[Any] = {}
for i, token in enumerate(a_ ):
lowerCAmelCase : List[str] = i
lowerCAmelCase : List[Any] = WordpieceTokenizer(vocab=a_ , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("unwanted running" ) , ["un", "##want", "##ed", "runn", "##ing"] )
self.assertListEqual(tokenizer.tokenize("unwantedX running" ) , ["[UNK]", "runn", "##ing"] )
def _lowerCamelCase ( self ):
self.assertTrue(_is_whitespace(" " ) )
self.assertTrue(_is_whitespace("\t" ) )
self.assertTrue(_is_whitespace("\r" ) )
self.assertTrue(_is_whitespace("\n" ) )
self.assertTrue(_is_whitespace("\u00A0" ) )
self.assertFalse(_is_whitespace("A" ) )
self.assertFalse(_is_whitespace("-" ) )
def _lowerCamelCase ( self ):
self.assertTrue(_is_control("\u0005" ) )
self.assertFalse(_is_control("A" ) )
self.assertFalse(_is_control(" " ) )
self.assertFalse(_is_control("\t" ) )
self.assertFalse(_is_control("\r" ) )
def _lowerCamelCase ( self ):
self.assertTrue(_is_punctuation("-" ) )
self.assertTrue(_is_punctuation("$" ) )
self.assertTrue(_is_punctuation("`" ) )
self.assertTrue(_is_punctuation("." ) )
self.assertFalse(_is_punctuation("A" ) )
self.assertFalse(_is_punctuation(" " ) )
def _lowerCamelCase ( self ):
lowerCAmelCase : Dict = self.get_tokenizer()
lowerCAmelCase : Union[str, Any] = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(a_ ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
self.assertListEqual(
[rust_tokenizer.tokenize(a_ ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
@slow
def _lowerCamelCase ( self ):
lowerCAmelCase : Union[str, Any] = self.tokenizer_class.from_pretrained("bert-base-uncased" )
lowerCAmelCase : str = tokenizer.encode("sequence builders" , add_special_tokens=a_ )
lowerCAmelCase : int = tokenizer.encode("multi-sequence build" , add_special_tokens=a_ )
lowerCAmelCase : int = tokenizer.build_inputs_with_special_tokens(a_ )
lowerCAmelCase : List[Any] = tokenizer.build_inputs_with_special_tokens(a_ , a_ )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def _lowerCamelCase ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase : Tuple = self.rust_tokenizer_class.from_pretrained(a_ , **a_ )
lowerCAmelCase : List[Any] = F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
lowerCAmelCase : str = tokenizer_r.encode_plus(
a_ , return_attention_mask=a_ , return_token_type_ids=a_ , return_offsets_mapping=a_ , add_special_tokens=a_ , )
lowerCAmelCase : List[str] = tokenizer_r.do_lower_case if hasattr(a_ , "do_lower_case" ) else False
lowerCAmelCase : Optional[Any] = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"] )
def _lowerCamelCase ( self ):
lowerCAmelCase : Optional[int] = ["的", "人", "有"]
lowerCAmelCase : Optional[Any] = "".join(a_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase : List[Any] = True
lowerCAmelCase : int = self.tokenizer_class.from_pretrained(a_ , **a_ )
lowerCAmelCase : List[str] = self.rust_tokenizer_class.from_pretrained(a_ , **a_ )
lowerCAmelCase : Tuple = tokenizer_p.encode(a_ , add_special_tokens=a_ )
lowerCAmelCase : Any = tokenizer_r.encode(a_ , add_special_tokens=a_ )
lowerCAmelCase : Union[str, Any] = tokenizer_r.convert_ids_to_tokens(a_ )
lowerCAmelCase : int = tokenizer_p.convert_ids_to_tokens(a_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(a_ , a_ )
self.assertListEqual(a_ , a_ )
lowerCAmelCase : List[str] = False
lowerCAmelCase : List[Any] = self.rust_tokenizer_class.from_pretrained(a_ , **a_ )
lowerCAmelCase : Dict = self.tokenizer_class.from_pretrained(a_ , **a_ )
lowerCAmelCase : Tuple = tokenizer_r.encode(a_ , add_special_tokens=a_ )
lowerCAmelCase : List[str] = tokenizer_p.encode(a_ , add_special_tokens=a_ )
lowerCAmelCase : Union[str, Any] = tokenizer_r.convert_ids_to_tokens(a_ )
lowerCAmelCase : Dict = tokenizer_p.convert_ids_to_tokens(a_ )
# it is expected that only the first Chinese character is not preceded by "##".
lowerCAmelCase : Tuple = [
F'''##{token}''' if idx != 0 else token for idx, token in enumerate(a_ )
]
self.assertListEqual(a_ , a_ )
self.assertListEqual(a_ , a_ )
| 525 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCAmelCase = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 525 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a = logging.get_logger(__name__)
a = {
't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE__ ( _a ):
_a = 't5'
_a = ['past_key_values']
_a = {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
def __init__( self : Dict , lowerCAmelCase : Optional[Any]=3_2128 , lowerCAmelCase : Tuple=512 , lowerCAmelCase : int=64 , lowerCAmelCase : Dict=2048 , lowerCAmelCase : Any=6 , lowerCAmelCase : List[str]=None , lowerCAmelCase : Tuple=8 , lowerCAmelCase : int=32 , lowerCAmelCase : int=128 , lowerCAmelCase : Tuple=0.1 , lowerCAmelCase : Dict=1e-6 , lowerCAmelCase : int=1.0 , lowerCAmelCase : List[Any]="relu" , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : Any=True , lowerCAmelCase : Optional[Any]=0 , lowerCAmelCase : Optional[int]=1 , **lowerCAmelCase : List[Any] , ):
lowerCAmelCase = vocab_size
lowerCAmelCase = d_model
lowerCAmelCase = d_kv
lowerCAmelCase = d_ff
lowerCAmelCase = num_layers
lowerCAmelCase = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
lowerCAmelCase = num_heads
lowerCAmelCase = relative_attention_num_buckets
lowerCAmelCase = relative_attention_max_distance
lowerCAmelCase = dropout_rate
lowerCAmelCase = layer_norm_epsilon
lowerCAmelCase = initializer_factor
lowerCAmelCase = feed_forward_proj
lowerCAmelCase = use_cache
lowerCAmelCase = self.feed_forward_proj.split("""-""" )
lowerCAmelCase = act_info[-1]
lowerCAmelCase = act_info[0] == """gated"""
if len(lowerCAmelCase ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
lowerCAmelCase = """gelu_new"""
super().__init__(
pad_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , is_encoder_decoder=lowerCAmelCase , **lowerCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( _a ):
@property
def __lowercase ( self : str ):
lowerCAmelCase = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
lowerCAmelCase = """past_encoder_sequence + sequence"""
lowerCAmelCase = {0: """batch"""}
lowerCAmelCase = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
lowerCAmelCase = {0: """batch""", 1: """decoder_sequence"""}
lowerCAmelCase = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase , direction="""inputs""" )
return common_inputs
@property
def __lowercase ( self : Optional[Any] ):
return 13
| 529 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a = {
'configuration_owlvit': [
'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'OwlViTConfig',
'OwlViTOnnxConfig',
'OwlViTTextConfig',
'OwlViTVisionConfig',
],
'processing_owlvit': ['OwlViTProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = ['OwlViTFeatureExtractor']
a = ['OwlViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'OwlViTModel',
'OwlViTPreTrainedModel',
'OwlViTTextModel',
'OwlViTVisionModel',
'OwlViTForObjectDetection',
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 529 | 1 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : int=13 , SCREAMING_SNAKE_CASE__ : Any=7 , SCREAMING_SNAKE_CASE__ : int=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : int=True , SCREAMING_SNAKE_CASE__ : Optional[int]=True , SCREAMING_SNAKE_CASE__ : List[Any]=99 , SCREAMING_SNAKE_CASE__ : Optional[Any]=32 , SCREAMING_SNAKE_CASE__ : Dict=5 , SCREAMING_SNAKE_CASE__ : int=4 , SCREAMING_SNAKE_CASE__ : List[Any]=37 , SCREAMING_SNAKE_CASE__ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE__ : Dict=0.1 , SCREAMING_SNAKE_CASE__ : Optional[Any]=0.1 , SCREAMING_SNAKE_CASE__ : Optional[Any]=5_12 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=16 , SCREAMING_SNAKE_CASE__ : List[str]=2 , SCREAMING_SNAKE_CASE__ : List[str]=0.0_2 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=False , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : Optional[int]="None" , SCREAMING_SNAKE_CASE__ : Any=3 , SCREAMING_SNAKE_CASE__ : List[Any]=4 , SCREAMING_SNAKE_CASE__ : str=None , ) -> Optional[int]:
A : Union[str, Any] =parent
A : List[str] =batch_size
A : Tuple =seq_length
A : Optional[int] =is_training
A : Optional[Any] =use_input_mask
A : Dict =use_token_type_ids
A : int =use_labels
A : Dict =vocab_size
A : Any =hidden_size
A : int =num_hidden_layers
A : Any =num_attention_heads
A : Any =intermediate_size
A : Tuple =hidden_act
A : Tuple =hidden_dropout_prob
A : Optional[Any] =attention_probs_dropout_prob
A : Optional[int] =max_position_embeddings
A : Dict =type_vocab_size
A : Any =type_sequence_label_size
A : str =initializer_range
A : List[str] =num_labels
A : Dict =num_choices
A : Optional[Any] =relative_attention
A : Union[str, Any] =position_biased_input
A : Tuple =pos_att_type
A : Any =scope
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[Any]:
A : Optional[Any] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A : List[str] =None
if self.use_input_mask:
A : List[Any] =ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
A : List[Any] =None
if self.use_token_type_ids:
A : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A : Union[str, Any] =None
A : str =None
A : List[str] =None
if self.use_labels:
A : Optional[int] =ids_tensor([self.batch_size] , self.type_sequence_label_size )
A : Optional[int] =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A : Dict =ids_tensor([self.batch_size] , self.num_choices )
A : List[str] =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Union[str, Any]:
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Optional[Any]:
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def SCREAMING_SNAKE_CASE_ ( self : int , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Optional[int]:
A : Optional[Any] =DebertaVaModel(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
A : Tuple =model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ )[0]
A : List[str] =model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ )[0]
A : Union[str, Any] =model(lowerCAmelCase_ )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def SCREAMING_SNAKE_CASE_ ( self : Dict , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> str:
A : Tuple =DebertaVaForMaskedLM(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
A : Optional[Any] =model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_ ( self : int , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[str] ) -> Tuple:
A : List[Any] =self.num_labels
A : Any =DebertaVaForSequenceClassification(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
A : Optional[int] =model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Dict:
A : str =self.num_labels
A : Optional[Any] =DebertaVaForTokenClassification(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
A : Tuple =model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[Any] ) -> List[Any]:
A : List[str] =DebertaVaForQuestionAnswering(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
A : Dict =model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , start_positions=lowerCAmelCase_ , end_positions=lowerCAmelCase_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : int ) -> str:
A : List[Any] =DebertaVaForMultipleChoice(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
A : Optional[int] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A : Optional[int] =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A : str =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A : List[str] =model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Optional[Any]:
A : Tuple =self.prepare_config_and_inputs()
(
(
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) ,
) : List[Any] =config_and_inputs
A : str ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
lowercase : int = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
lowercase : List[Any] = (
{
'''feature-extraction''': DebertaVaModel,
'''fill-mask''': DebertaVaForMaskedLM,
'''question-answering''': DebertaVaForQuestionAnswering,
'''text-classification''': DebertaVaForSequenceClassification,
'''token-classification''': DebertaVaForTokenClassification,
'''zero-shot''': DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Optional[Any] = True
lowercase : Optional[Any] = False
lowercase : Union[str, Any] = False
lowercase : List[str] = False
lowercase : Optional[Any] = False
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Dict:
A : int =DebertaVaModelTester(self )
A : Optional[int] =ConfigTester(self , config_class=lowerCAmelCase_ , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Dict:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self : int ) -> str:
A : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> List[Any]:
A : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[int]:
A : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Optional[Any]:
A : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> List[Any]:
A : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> List[str]:
A : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCAmelCase_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[int]:
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A : Optional[int] =DebertaVaModel.from_pretrained(lowerCAmelCase_ )
self.assertIsNotNone(lowerCAmelCase_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason='Model not available yet' )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> int:
pass
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ) -> Tuple:
A : int =DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
A : Optional[Any] =torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
A : Any =torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
A : Union[str, Any] =model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ )[0]
# compare the actual values for a slice.
A : List[str] =torch.tensor(
[[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , lowerCAmelCase_ , atol=1e-4 ) , f'{output[:, 1:4, 1:4]}' )
| 305 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class a ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase :List[Any] = IFPipeline
lowerCamelCase :Optional[Any] = TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
lowerCamelCase :str = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCamelCase :Optional[int] = PipelineTesterMixin.required_optional_params - {'''latents'''}
def UpperCAmelCase ( self ) -> List[Any]:
return self._get_dummy_components()
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=0 ) -> str:
if str(lowerCAmelCase_ ).startswith("""mps""" ):
_A = torch.manual_seed(lowerCAmelCase_ )
else:
_A = torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
_A = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def UpperCAmelCase ( self ) -> Union[str, Any]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def UpperCAmelCase ( self ) -> int:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def UpperCAmelCase ( self ) -> List[str]:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def UpperCAmelCase ( self ) -> Tuple:
self._test_save_load_local()
def UpperCAmelCase ( self ) -> Any:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def UpperCAmelCase ( self ) -> List[str]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> Dict:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase ( self ) -> Union[str, Any]:
# if
_A = IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa )
_A = IFSuperResolutionPipeline.from_pretrained(
"""DeepFloyd/IF-II-L-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa , text_encoder=lowerCAmelCase_ , tokenizer=lowerCAmelCase_ )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to("""cuda""" )
_A , _A = pipe_a.encode_prompt("""anime turtle""" , device="""cuda""" )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
_A = None
_A = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
_A = IFImgaImgPipeline(**pipe_a.components )
_A = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
_A = IFInpaintingPipeline(**pipe_a.components )
_A = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> str:
# pipeline 1
_start_torch_memory_measurement()
_A = torch.Generator(device="""cpu""" ).manual_seed(0 )
_A = pipe_a(
prompt_embeds=lowerCAmelCase_ , negative_prompt_embeds=lowerCAmelCase_ , num_inference_steps=2 , generator=lowerCAmelCase_ , output_type="""np""" , )
_A = output.images[0]
assert image.shape == (64, 64, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
_A = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""" )
assert_mean_pixel_difference(lowerCAmelCase_ , lowerCAmelCase_ )
# pipeline 2
_start_torch_memory_measurement()
_A = torch.Generator(device="""cpu""" ).manual_seed(0 )
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(lowerCAmelCase_ )
_A = pipe_a(
prompt_embeds=lowerCAmelCase_ , negative_prompt_embeds=lowerCAmelCase_ , image=lowerCAmelCase_ , generator=lowerCAmelCase_ , num_inference_steps=2 , output_type="""np""" , )
_A = output.images[0]
assert image.shape == (2_56, 2_56, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_A = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(lowerCAmelCase_ , lowerCAmelCase_ )
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> List[str]:
# pipeline 1
_start_torch_memory_measurement()
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(lowerCAmelCase_ )
_A = torch.Generator(device="""cpu""" ).manual_seed(0 )
_A = pipe_a(
prompt_embeds=lowerCAmelCase_ , negative_prompt_embeds=lowerCAmelCase_ , image=lowerCAmelCase_ , num_inference_steps=2 , generator=lowerCAmelCase_ , output_type="""np""" , )
_A = output.images[0]
assert image.shape == (64, 64, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_A = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""" )
assert_mean_pixel_difference(lowerCAmelCase_ , lowerCAmelCase_ )
# pipeline 2
_start_torch_memory_measurement()
_A = torch.Generator(device="""cpu""" ).manual_seed(0 )
_A = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(lowerCAmelCase_ )
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(lowerCAmelCase_ )
_A = pipe_a(
prompt_embeds=lowerCAmelCase_ , negative_prompt_embeds=lowerCAmelCase_ , image=lowerCAmelCase_ , original_image=lowerCAmelCase_ , generator=lowerCAmelCase_ , num_inference_steps=2 , output_type="""np""" , )
_A = output.images[0]
assert image.shape == (2_56, 2_56, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_A = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(lowerCAmelCase_ , lowerCAmelCase_ )
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Optional[int]:
# pipeline 1
_start_torch_memory_measurement()
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(lowerCAmelCase_ )
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(lowerCAmelCase_ )
_A = torch.Generator(device="""cpu""" ).manual_seed(0 )
_A = pipe_a(
prompt_embeds=lowerCAmelCase_ , negative_prompt_embeds=lowerCAmelCase_ , image=lowerCAmelCase_ , mask_image=lowerCAmelCase_ , num_inference_steps=2 , generator=lowerCAmelCase_ , output_type="""np""" , )
_A = output.images[0]
assert image.shape == (64, 64, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_A = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy""" )
assert_mean_pixel_difference(lowerCAmelCase_ , lowerCAmelCase_ )
# pipeline 2
_start_torch_memory_measurement()
_A = torch.Generator(device="""cpu""" ).manual_seed(0 )
_A = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(lowerCAmelCase_ )
_A = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(lowerCAmelCase_ )
_A = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(1 ) ).to(lowerCAmelCase_ )
_A = pipe_a(
prompt_embeds=lowerCAmelCase_ , negative_prompt_embeds=lowerCAmelCase_ , image=lowerCAmelCase_ , mask_image=lowerCAmelCase_ , original_image=lowerCAmelCase_ , generator=lowerCAmelCase_ , num_inference_steps=2 , output_type="""np""" , )
_A = output.images[0]
assert image.shape == (2_56, 2_56, 3)
_A = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_A = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(lowerCAmelCase_ , lowerCAmelCase_ )
def snake_case ( ) -> Tuple:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 401 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase :
def __init__( self , a__ , a__=1_3 , a__=3_2 , a__=2 , a__=3 , a__=1_6 , a__=[1, 2, 1] , a__=[2, 2, 4] , a__=2 , a__=2.0 , a__=True , a__=0.0 , a__=0.0 , a__=0.1 , a__="gelu" , a__=False , a__=True , a__=0.0_2 , a__=1e-5 , a__=True , a__=None , a__=True , a__=1_0 , a__=8 , ):
A__ = parent
A__ = batch_size
A__ = image_size
A__ = patch_size
A__ = num_channels
A__ = embed_dim
A__ = depths
A__ = num_heads
A__ = window_size
A__ = mlp_ratio
A__ = qkv_bias
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = drop_path_rate
A__ = hidden_act
A__ = use_absolute_embeddings
A__ = patch_norm
A__ = layer_norm_eps
A__ = initializer_range
A__ = is_training
A__ = scope
A__ = use_labels
A__ = type_sequence_label_size
A__ = encoder_stride
def snake_case_ ( self):
A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
A__ = self.get_config()
return config, pixel_values, labels
def snake_case_ ( self):
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def snake_case_ ( self , a__ , a__ , a__):
A__ = SwinvaModel(config=snake_case__)
model.to(snake_case__)
model.eval()
A__ = model(snake_case__)
A__ = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
A__ = int(config.embed_dim * 2 ** (len(config.depths) - 1))
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim))
def snake_case_ ( self , a__ , a__ , a__):
A__ = SwinvaForMaskedImageModeling(config=snake_case__)
model.to(snake_case__)
model.eval()
A__ = model(snake_case__)
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size))
# test greyscale images
A__ = 1
A__ = SwinvaForMaskedImageModeling(snake_case__)
model.to(snake_case__)
model.eval()
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
A__ = model(snake_case__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size))
def snake_case_ ( self , a__ , a__ , a__):
A__ = self.type_sequence_label_size
A__ = SwinvaForImageClassification(snake_case__)
model.to(snake_case__)
model.eval()
A__ = model(snake_case__ , labels=snake_case__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def snake_case_ ( self):
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ = config_and_inputs
A__ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( __a , __a , unittest.TestCase ):
UpperCamelCase__ = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
UpperCamelCase__ = (
{'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def snake_case_ ( self):
A__ = SwinvaModelTester(self)
A__ = ConfigTester(self , config_class=snake_case__ , embed_dim=3_7)
def snake_case_ ( self):
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def snake_case_ ( self):
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__)
@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.''')
def snake_case_ ( self):
pass
@unittest.skip(reason='''Swinv2 does not use inputs_embeds''')
def snake_case_ ( self):
pass
def snake_case_ ( self):
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(snake_case__)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case__ , nn.Linear))
def snake_case_ ( self):
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(snake_case__)
A__ = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , snake_case__)
def snake_case_ ( self):
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = True
for model_class in self.all_model_classes:
A__ = True
A__ = False
A__ = True
A__ = model_class(snake_case__)
model.to(snake_case__)
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(snake_case__ , snake_case__))
A__ = outputs.attentions
A__ = len(self.model_tester.depths)
self.assertEqual(len(snake_case__) , snake_case__)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
A__ = True
A__ = config.window_size**2
A__ = model_class(snake_case__)
model.to(snake_case__)
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(snake_case__ , snake_case__))
A__ = outputs.attentions
self.assertEqual(len(snake_case__) , snake_case__)
self.assertListEqual(
list(attentions[0].shape[-3:]) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
A__ = len(snake_case__)
# Check attention is always last and order is fine
A__ = True
A__ = True
A__ = model_class(snake_case__)
model.to(snake_case__)
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(snake_case__ , snake_case__))
if hasattr(self.model_tester , '''num_hidden_states_types'''):
A__ = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
A__ = 2
self.assertEqual(out_len + added_hidden_states , len(snake_case__))
A__ = outputs.attentions
self.assertEqual(len(snake_case__) , snake_case__)
self.assertListEqual(
list(self_attentions[0].shape[-3:]) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def snake_case_ ( self , a__ , a__ , a__ , a__):
A__ = model_class(snake_case__)
model.to(snake_case__)
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(snake_case__ , snake_case__))
A__ = outputs.hidden_states
A__ = getattr(
self.model_tester , '''expected_num_hidden_layers''' , len(self.model_tester.depths) + 1)
self.assertEqual(len(snake_case__) , snake_case__)
# Swinv2 has a different seq_length
A__ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
A__ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [num_patches, self.model_tester.embed_dim] , )
A__ = outputs.reshaped_hidden_states
self.assertEqual(len(snake_case__) , snake_case__)
A__ , A__ , A__ , A__ = reshaped_hidden_states[0].shape
A__ = (
reshaped_hidden_states[0].view(snake_case__ , snake_case__ , height * width).permute(0 , 2 , 1)
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:]) , [num_patches, self.model_tester.embed_dim] , )
def snake_case_ ( self):
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
A__ = True
self.check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ , snake_case__)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A__ = True
self.check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ , snake_case__)
def snake_case_ ( self):
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = 3
A__ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
A__ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
A__ = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
A__ = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
A__ = True
self.check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ , (padded_height, padded_width))
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A__ = True
self.check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ , (padded_height, padded_width))
def snake_case_ ( self):
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__)
def snake_case_ ( self):
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__)
@slow
def snake_case_ ( self):
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = SwinvaModel.from_pretrained(snake_case__)
self.assertIsNotNone(snake_case__)
def snake_case_ ( self):
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = _config_zero_init(snake_case__)
for model_class in self.all_model_classes:
A__ = model_class(config=snake_case__)
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , )
@require_vision
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
@cached_property
def snake_case_ ( self):
return (
AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''')
if is_vision_available()
else None
)
@slow
def snake_case_ ( self):
A__ = SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''').to(
snake_case__)
A__ = self.default_image_processor
A__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
A__ = image_processor(images=snake_case__ , return_tensors='''pt''').to(snake_case__)
# forward pass
with torch.no_grad():
A__ = model(**snake_case__)
# verify the logits
A__ = torch.Size((1, 1_0_0_0))
self.assertEqual(outputs.logits.shape , snake_case__)
A__ = torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]).to(snake_case__)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case__ , atol=1e-4))
| 706 |
def lowerCAmelCase__ ( UpperCamelCase_ : Optional[int] , UpperCamelCase_ : List[str] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : str )-> int:
if height >= 1:
move_tower(height - 1 , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
move_disk(UpperCamelCase_ , UpperCamelCase_ )
move_tower(height - 1 , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( UpperCamelCase_ : Optional[int] , UpperCamelCase_ : List[Any] )-> str:
print('''moving disk from''' , UpperCamelCase_ , '''to''' , UpperCamelCase_ )
def lowerCAmelCase__ ( )-> Tuple:
A__ = int(input('''Height of hanoi: ''' ).strip() )
move_tower(UpperCamelCase_ , '''A''' , '''B''' , '''C''' )
if __name__ == "__main__":
main()
| 526 | 0 |
def __lowercase( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase = 1, 1
lowerCamelCase = 2
while True:
lowerCamelCase = 0
lowerCamelCase = fa + fa
lowerCamelCase , lowerCamelCase = fa, f
index += 1
for _ in str(UpperCAmelCase__ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 623 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(UpperCAmelCase__ )
if n > 1:
factors.append(UpperCAmelCase__ )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 623 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
UpperCAmelCase__ : List[str] = get_logger(__name__)
class __lowercase :
def __init__( self , lowercase_ = None) -> List[Any]:
__snake_case = (
os.path.join(lowercase_ , config.EXTRACTED_DATASETS_DIR) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
__snake_case = Extractor
def _a ( self , lowercase_) -> str:
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
__snake_case = os.path.abspath(lowercase_)
return os.path.join(self.extract_dir , hash_url_to_filename(lowercase_))
def _a ( self , lowercase_ , lowercase_) -> bool:
return force_extract or (
not os.path.isfile(lowercase_) and not (os.path.isdir(lowercase_) and os.listdir(lowercase_))
)
def _a ( self , lowercase_ , lowercase_ = False) -> str:
__snake_case = self.extractor.infer_extractor_format(lowercase_)
if not extractor_format:
return input_path
__snake_case = self._get_output_path(lowercase_)
if self._do_extract(lowercase_ , lowercase_):
self.extractor.extract(lowercase_ , lowercase_ , lowercase_)
return output_path
class __lowercase ( lowerCamelCase__ ):
@classmethod
@abstractmethod
def _a ( cls , lowercase_ , **lowercase_) -> bool:
...
@staticmethod
@abstractmethod
def _a ( lowercase_ , lowercase_) -> None:
...
class __lowercase ( lowerCamelCase__ , lowerCamelCase__ ):
__UpperCAmelCase = []
@staticmethod
def _a ( lowercase_ , lowercase_) -> Optional[Any]:
with open(lowercase_ , 'rb') as f:
return f.read(lowercase_)
@classmethod
def _a ( cls , lowercase_ , lowercase_ = b"") -> bool:
if not magic_number:
__snake_case = max(len(lowercase_) for cls_magic_number in cls.magic_numbers)
try:
__snake_case = cls.read_magic_number(lowercase_ , lowercase_)
except OSError:
return False
return any(magic_number.startswith(lowercase_) for cls_magic_number in cls.magic_numbers)
class __lowercase ( lowerCamelCase__ ):
@classmethod
def _a ( cls , lowercase_ , **lowercase_) -> bool:
return tarfile.is_tarfile(lowercase_)
@staticmethod
def _a ( lowercase_ , lowercase_) -> Any:
def resolved(lowercase_) -> str:
return os.path.realpath(os.path.abspath(lowercase_))
def badpath(lowercase_ , lowercase_) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(lowercase_ , lowercase_)).startswith(lowercase_)
def badlink(lowercase_ , lowercase_) -> bool:
# Links are interpreted relative to the directory containing the link
__snake_case = resolved(os.path.join(lowercase_ , os.path.dirname(info.name)))
return badpath(info.linkname , base=lowercase_)
__snake_case = resolved(lowercase_)
for finfo in members:
if badpath(finfo.name , lowercase_):
logger.error(F"Extraction of {finfo.name} is blocked (illegal path)")
elif finfo.issym() and badlink(lowercase_ , lowercase_):
logger.error(F"Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}")
elif finfo.islnk() and badlink(lowercase_ , lowercase_):
logger.error(F"Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}")
else:
yield finfo
@staticmethod
def _a ( lowercase_ , lowercase_) -> None:
os.makedirs(lowercase_ , exist_ok=lowercase_)
__snake_case = tarfile.open(lowercase_)
tar_file.extractall(lowercase_ , members=TarExtractor.safemembers(lowercase_ , lowercase_))
tar_file.close()
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = [b'''\x1F\x8B''']
@staticmethod
def _a ( lowercase_ , lowercase_) -> None:
with gzip.open(lowercase_ , 'rb') as gzip_file:
with open(lowercase_ , 'wb') as extracted_file:
shutil.copyfileobj(lowercase_ , lowercase_)
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = [
b'''PK\x03\x04''',
b'''PK\x05\x06''', # empty archive
b'''PK\x07\x08''', # spanned archive
]
@classmethod
def _a ( cls , lowercase_ , lowercase_ = b"") -> bool:
if super().is_extractable(lowercase_ , magic_number=lowercase_):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(lowercase_ , 'rb') as fp:
__snake_case = _EndRecData(lowercase_)
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET]) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
__snake_case = fp.read(lowercase_) # CD is where we expect it to be
if len(lowercase_) == sizeCentralDir:
__snake_case = struct.unpack(lowercase_ , lowercase_) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def _a ( lowercase_ , lowercase_) -> None:
os.makedirs(lowercase_ , exist_ok=lowercase_)
with zipfile.ZipFile(lowercase_ , 'r') as zip_file:
zip_file.extractall(lowercase_)
zip_file.close()
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = [b'''\xFD\x37\x7A\x58\x5A\x00''']
@staticmethod
def _a ( lowercase_ , lowercase_) -> None:
with lzma.open(lowercase_) as compressed_file:
with open(lowercase_ , 'wb') as extracted_file:
shutil.copyfileobj(lowercase_ , lowercase_)
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = [b'''Rar!\x1a\x07\x00''', b'''Rar!\x1a\x07\x01\x00'''] # RAR_ID # RAR5_ID
@staticmethod
def _a ( lowercase_ , lowercase_) -> None:
if not config.RARFILE_AVAILABLE:
raise ImportError('Please pip install rarfile')
import rarfile
os.makedirs(lowercase_ , exist_ok=lowercase_)
__snake_case = rarfile.RarFile(lowercase_)
rf.extractall(lowercase_)
rf.close()
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = [b'''\x28\xb5\x2F\xFD''']
@staticmethod
def _a ( lowercase_ , lowercase_) -> None:
if not config.ZSTANDARD_AVAILABLE:
raise ImportError('Please pip install zstandard')
import zstandard as zstd
__snake_case = zstd.ZstdDecompressor()
with open(lowercase_ , 'rb') as ifh, open(lowercase_ , 'wb') as ofh:
dctx.copy_stream(lowercase_ , lowercase_)
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = [b'''\x42\x5A\x68''']
@staticmethod
def _a ( lowercase_ , lowercase_) -> None:
with bza.open(lowercase_ , 'rb') as compressed_file:
with open(lowercase_ , 'wb') as extracted_file:
shutil.copyfileobj(lowercase_ , lowercase_)
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = [b'''\x37\x7A\xBC\xAF\x27\x1C''']
@staticmethod
def _a ( lowercase_ , lowercase_) -> None:
if not config.PY7ZR_AVAILABLE:
raise ImportError('Please pip install py7zr')
import pyazr
os.makedirs(lowercase_ , exist_ok=lowercase_)
with pyazr.SevenZipFile(lowercase_ , 'r') as archive:
archive.extractall(lowercase_)
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = [b'''\x04\x22\x4D\x18''']
@staticmethod
def _a ( lowercase_ , lowercase_) -> None:
if not config.LZ4_AVAILABLE:
raise ImportError('Please pip install lz4')
import lza.frame
with lza.frame.open(lowercase_ , 'rb') as compressed_file:
with open(lowercase_ , 'wb') as extracted_file:
shutil.copyfileobj(lowercase_ , lowercase_)
class __lowercase :
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
__UpperCAmelCase = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def _a ( cls) -> int:
return max(
len(lowercase_)
for extractor in cls.extractors.values()
if issubclass(lowercase_ , lowercase_)
for extractor_magic_number in extractor.magic_numbers)
@staticmethod
def _a ( lowercase_ , lowercase_) -> Tuple:
try:
return MagicNumberBaseExtractor.read_magic_number(lowercase_ , magic_number_length=lowercase_)
except OSError:
return b""
@classmethod
def _a ( cls , lowercase_ , lowercase_ = False) -> bool:
warnings.warn(
'Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
'Use \'infer_extractor_format\' instead.' , category=lowercase_ , )
__snake_case = cls.infer_extractor_format(lowercase_)
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def _a ( cls , lowercase_) -> str: # <Added version="2.4.0"/>
__snake_case = cls._get_magic_number_max_length()
__snake_case = cls._read_magic_number(lowercase_ , lowercase_)
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(lowercase_ , magic_number=lowercase_):
return extractor_format
@classmethod
def _a ( cls , lowercase_ , lowercase_ , lowercase_ = None , lowercase_ = "deprecated" , ) -> None:
os.makedirs(os.path.dirname(lowercase_) , exist_ok=lowercase_)
# Prevent parallel extractions
__snake_case = str(Path(lowercase_).with_suffix('.lock'))
with FileLock(lowercase_):
shutil.rmtree(lowercase_ , ignore_errors=lowercase_)
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(lowercase_ , lowercase_): # passed as positional arg
warnings.warn(
'Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
'Use \'extractor_format\' instead.' , category=lowercase_ , )
__snake_case = extractor if extractor != 'deprecated' else extractor_format
else:
__snake_case = cls.extractors[extractor_format]
return extractor.extract(lowercase_ , lowercase_)
else:
warnings.warn(
'Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '
'exception in 3.0.0.' , category=lowercase_ , )
for extractor in cls.extractors.values():
if extractor.is_extractable(lowercase_):
return extractor.extract(lowercase_ , lowercase_)
| 676 |
import numpy as np
def A ( snake_case__ : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def A ( snake_case__ : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(snake_case__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 676 | 1 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger()
def UpperCamelCase ( snake_case__ : int ,snake_case__ : str ,snake_case__ : LevitConfig ,snake_case__ : Path ,snake_case__ : bool = True ):
'''simple docstring'''
print(f'''Converting {name}...''' )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
__snake_case :List[str] = timm.create_model("""levit_128s""" ,pretrained=__snake_case )
else:
__snake_case :Any = timm.create_model("""levit_128""" ,pretrained=__snake_case )
if hidden_sizes == 192:
__snake_case :str = timm.create_model("""levit_192""" ,pretrained=__snake_case )
if hidden_sizes == 256:
__snake_case :Tuple = timm.create_model("""levit_256""" ,pretrained=__snake_case )
if hidden_sizes == 384:
__snake_case :List[Any] = timm.create_model("""levit_384""" ,pretrained=__snake_case )
from_model.eval()
__snake_case :List[Any] = LevitForImageClassificationWithTeacher(__snake_case ).eval()
__snake_case :Union[str, Any] = OrderedDict()
__snake_case :Union[str, Any] = from_model.state_dict()
__snake_case :List[Any] = list(from_model.state_dict().keys() )
__snake_case :Any = list(our_model.state_dict().keys() )
print(len(__snake_case ) ,len(__snake_case ) )
for i in range(len(__snake_case ) ):
__snake_case :Optional[int] = weights[og_keys[i]]
our_model.load_state_dict(__snake_case )
__snake_case :Dict = torch.randn((2, 3, 224, 224) )
__snake_case :Optional[int] = from_model(__snake_case )
__snake_case :Optional[int] = our_model(__snake_case ).logits
assert torch.allclose(__snake_case ,__snake_case ), "The model logits don't match the original one."
__snake_case :str = name
print(__snake_case )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
__snake_case :Optional[int] = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(f'''Pushed {checkpoint_name}''' )
def UpperCamelCase ( snake_case__ : Path ,snake_case__ : str = None ,snake_case__ : bool = True ):
'''simple docstring'''
__snake_case :str = """imagenet-1k-id2label.json"""
__snake_case :Optional[Any] = 1000
__snake_case :Any = (1, num_labels)
__snake_case :int = """huggingface/label-files"""
__snake_case :Union[str, Any] = num_labels
__snake_case :int = json.load(open(hf_hub_download(__snake_case ,__snake_case ,repo_type="""dataset""" ) ,"""r""" ) )
__snake_case :List[str] = {int(__snake_case ): v for k, v in idalabel.items()}
__snake_case :str = idalabel
__snake_case :Optional[int] = {v: k for k, v in idalabel.items()}
__snake_case :int = partial(__snake_case ,num_labels=__snake_case ,idalabel=__snake_case ,labelaid=__snake_case )
__snake_case :Optional[Any] = {
"""levit-128S""": 128,
"""levit-128""": 128,
"""levit-192""": 192,
"""levit-256""": 256,
"""levit-384""": 384,
}
__snake_case :Optional[Any] = {
"""levit-128S""": ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] ,num_attention_heads=[4, 6, 8] ,depths=[2, 3, 4] ,key_dim=[16, 16, 16] ,drop_path_rate=0 ,),
"""levit-128""": ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] ,num_attention_heads=[4, 8, 12] ,depths=[4, 4, 4] ,key_dim=[16, 16, 16] ,drop_path_rate=0 ,),
"""levit-192""": ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] ,num_attention_heads=[3, 5, 6] ,depths=[4, 4, 4] ,key_dim=[32, 32, 32] ,drop_path_rate=0 ,),
"""levit-256""": ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] ,num_attention_heads=[4, 6, 8] ,depths=[4, 4, 4] ,key_dim=[32, 32, 32] ,drop_path_rate=0 ,),
"""levit-384""": ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] ,num_attention_heads=[6, 9, 12] ,depths=[4, 4, 4] ,key_dim=[32, 32, 32] ,drop_path_rate=0.1 ,),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] ,__snake_case ,names_to_config[model_name] ,__snake_case ,__snake_case )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
return config, expected_shape
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""levit-dump-folder/""",
type=Path,
required=False,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
parser.add_argument(
"""--no-push_to_hub""",
dest="""push_to_hub""",
action="""store_false""",
help="""Do not push model and image processor to the hub""",
)
lowerCamelCase__ = parser.parse_args()
lowerCamelCase__ = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 455 |
"""simple docstring"""
import numpy as np
def _snake_case ( __snake_case : np.ndarray ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def _snake_case ( __snake_case : np.ndarray ):
"""simple docstring"""
return vector * sigmoid(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 88 | 0 |
def snake_case__ ( lowerCamelCase_=28123 ):
A : Union[str, Any] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
A : str = set()
A : Any = 0
for n in range(1 , limit + 1 ):
if sum_divs[n] > n:
abundants.add(lowerCamelCase_ )
if not any((n - a in abundants) for a in abundants ):
res += n
return res
if __name__ == "__main__":
print(solution())
| 423 |
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
return x if y == 0 else greatest_common_divisor(lowerCamelCase_ , x % y )
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
return (x * y) // greatest_common_divisor(lowerCamelCase_ , lowerCamelCase_ )
def snake_case__ ( lowerCamelCase_ = 20 ):
A : Optional[Any] = 1
for i in range(1 , n + 1 ):
A : Dict = lcm(lowerCamelCase_ , lowerCamelCase_ )
return g
if __name__ == "__main__":
print(F"{solution() = }")
| 423 | 1 |
def _SCREAMING_SNAKE_CASE ( lowercase : int = 10 ):
'''simple docstring'''
if not isinstance(lowercase , lowercase ) or n < 0:
raise ValueError('Invalid input' )
lowerCamelCase_ = 10**n
lowerCamelCase_ = 2_84_33 * (pow(2 , 7_83_04_57 , lowercase )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(10) = }""")
| 70 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def _SCREAMING_SNAKE_CASE ( lowercase : str , lowercase : float | Decimal , lowercase : float = 10**-10 ):
'''simple docstring'''
lowerCamelCase_ = a
while True:
lowerCamelCase_ = Decimal(lowercase ) - (
Decimal(eval(lowercase ) ) / Decimal(eval(str(diff(lowercase ) ) ) ) # noqa: S307
)
# This number dictates the accuracy of the answer
if abs(eval(lowercase ) ) < precision: # noqa: S307
return float(lowercase )
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")
# Find root of polynomial
print(F"""The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}""")
# Find Square Root of 5
print(F"""The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}""")
# Exponential Roots
print(F"""The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}""")
| 70 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCamelCase = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")
@dataclass
class _lowerCamelCase :
"""simple docstring"""
snake_case = field(
default="cifar10" , metadata={"help": "Name of a dataset from the datasets package"} )
snake_case = field(
default=UpperCamelCase , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
snake_case = field(
default=UpperCamelCase , metadata={"help": "The column name of the images in the files."} )
snake_case = field(default=UpperCamelCase , metadata={"help": "A folder containing the training data."} )
snake_case = field(default=UpperCamelCase , metadata={"help": "A folder containing the validation data."} )
snake_case = field(
default=0.1_5 , metadata={"help": "Percent to split off of train for validation."} )
snake_case = field(
default=UpperCamelCase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
snake_case = field(
default=UpperCamelCase , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
def _snake_case ( self )->Union[str, Any]:
'''simple docstring'''
A_ : Union[str, Any] = {}
if self.train_dir is not None:
A_ : Tuple = self.train_dir
if self.validation_dir is not None:
A_ : Any = self.validation_dir
A_ : Any = data_files if data_files else None
@dataclass
class _lowerCamelCase :
"""simple docstring"""
snake_case = field(
default=UpperCamelCase , metadata={
"help": (
"The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
)
} , )
snake_case = field(
default=UpperCamelCase , metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"} )
snake_case = field(
default=UpperCamelCase , metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
} , )
snake_case = field(
default=UpperCamelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} )
snake_case = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
snake_case = field(default=UpperCamelCase , metadata={"help": "Name or path of preprocessor config."} )
snake_case = field(
default=UpperCamelCase , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
snake_case = field(
default=0.7_5 , metadata={"help": "The ratio of the number of masked tokens in the input sequence."} )
snake_case = field(
default=UpperCamelCase , metadata={"help": "Whether or not to train with normalized pixel values as target."} )
@dataclass
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = field(
default=1e-3 , metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."} )
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
A_ : Optional[int] = torch.stack([example['''pixel_values'''] for example in examples] )
return {"pixel_values": pixel_values}
def _SCREAMING_SNAKE_CASE ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
A_ : int = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
A_ , A_ , A_ : Dict = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
A_ , A_ , A_ : str = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_mae''' , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
A_ : int = training_args.get_process_log_level()
logger.setLevel(SCREAMING_SNAKE_CASE )
transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
A_ : Optional[int] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
A_ : List[str] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset.
A_ : Optional[int] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
A_ : Union[str, Any] = None if '''validation''' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , SCREAMING_SNAKE_CASE ) and data_args.train_val_split > 0.0:
A_ : Dict = ds['''train'''].train_test_split(data_args.train_val_split )
A_ : Tuple = split['''train''']
A_ : Tuple = split['''test''']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
A_ : Optional[Any] = {
'''cache_dir''': model_args.cache_dir,
'''revision''': model_args.model_revision,
'''use_auth_token''': True if model_args.use_auth_token else None,
}
if model_args.config_name:
A_ : Any = ViTMAEConfig.from_pretrained(model_args.config_name , **SCREAMING_SNAKE_CASE )
elif model_args.model_name_or_path:
A_ : Optional[int] = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **SCREAMING_SNAKE_CASE )
else:
A_ : List[str] = ViTMAEConfig()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.config_overrides is not None:
logger.info(f'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(f'''New config: {config}''' )
# adapt config
config.update(
{
'''mask_ratio''': model_args.mask_ratio,
'''norm_pix_loss''': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
A_ : int = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **SCREAMING_SNAKE_CASE )
elif model_args.model_name_or_path:
A_ : str = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **SCREAMING_SNAKE_CASE )
else:
A_ : int = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
A_ : Optional[int] = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('''Training new model from scratch''' )
A_ : int = ViTMAEForPreTraining(SCREAMING_SNAKE_CASE )
if training_args.do_train:
A_ : Optional[int] = ds['''train'''].column_names
else:
A_ : List[Any] = ds['''validation'''].column_names
if data_args.image_column_name is not None:
A_ : Optional[int] = data_args.image_column_name
elif "image" in column_names:
A_ : str = '''image'''
elif "img" in column_names:
A_ : str = '''img'''
else:
A_ : Optional[Any] = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
A_ : int = image_processor.size['''shortest_edge''']
else:
A_ : int = (image_processor.size['''height'''], image_processor.size['''width'''])
A_ : Union[str, Any] = Compose(
[
Lambda(lambda SCREAMING_SNAKE_CASE : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
RandomResizedCrop(SCREAMING_SNAKE_CASE , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(SCREAMING_SNAKE_CASE ):
A_ : Union[str, Any] = [transforms(SCREAMING_SNAKE_CASE ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('''--do_train requires a train dataset''' )
if data_args.max_train_samples is not None:
A_ : Optional[Any] = ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(SCREAMING_SNAKE_CASE )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('''--do_eval requires a validation dataset''' )
if data_args.max_eval_samples is not None:
A_ : int = (
ds['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(SCREAMING_SNAKE_CASE )
# Compute absolute learning rate
A_ : Union[str, Any] = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
A_ : Union[str, Any] = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
A_ : int = Trainer(
model=SCREAMING_SNAKE_CASE , args=SCREAMING_SNAKE_CASE , train_dataset=ds['''train'''] if training_args.do_train else None , eval_dataset=ds['''validation'''] if training_args.do_eval else None , tokenizer=SCREAMING_SNAKE_CASE , data_collator=SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
A_ : int = None
if training_args.resume_from_checkpoint is not None:
A_ : Tuple = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
A_ : Union[str, Any] = last_checkpoint
A_ : Dict = trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
A_ : List[Any] = trainer.evaluate()
trainer.log_metrics('''eval''' , SCREAMING_SNAKE_CASE )
trainer.save_metrics('''eval''' , SCREAMING_SNAKE_CASE )
# Write model card and (optionally) push to hub
A_ : Dict = {
'''tasks''': '''masked-auto-encoding''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''masked-auto-encoding'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**SCREAMING_SNAKE_CASE )
else:
trainer.create_model_card(**SCREAMING_SNAKE_CASE )
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 152 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
UpperCamelCase = """tiny-wmt19-en-ru"""
# Build
# borrowed from a test
UpperCamelCase = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
UpperCamelCase = dict(zip(vocab, range(len(vocab))))
UpperCamelCase = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase = Path(tmpdirname)
UpperCamelCase = build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
UpperCamelCase = build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
UpperCamelCase = build_dir / VOCAB_FILES_NAMES["""merges_file"""]
with open(src_vocab_file, """w""") as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, """w""") as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, """w""") as fp:
fp.write("""\n""".join(merges))
UpperCamelCase = FSMTTokenizer(
langs=["""en""", """ru"""],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
UpperCamelCase = FSMTConfig(
langs=["""ru""", """en"""],
src_vocab_size=1000,
tgt_vocab_size=1000,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
UpperCamelCase = FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')
# Test
UpperCamelCase = tokenizer(["""Making tiny model"""], return_tensors="""pt""")
UpperCamelCase = tiny_model(**batch)
print("""test output:""", len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 152 | 1 |
A_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def UpperCAmelCase ( )-> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = input('''Enter message: ''' )
SCREAMING_SNAKE_CASE_ = input('''Enter key [alphanumeric]: ''' )
SCREAMING_SNAKE_CASE_ = input('''Encrypt/Decrypt [e/d]: ''' )
if mode.lower().startswith('''e''' ):
SCREAMING_SNAKE_CASE_ = '''encrypt'''
SCREAMING_SNAKE_CASE_ = encrypt_message(UpperCAmelCase ,UpperCAmelCase )
elif mode.lower().startswith('''d''' ):
SCREAMING_SNAKE_CASE_ = '''decrypt'''
SCREAMING_SNAKE_CASE_ = decrypt_message(UpperCAmelCase ,UpperCAmelCase )
print(f'''\n{mode.title()}ed message:''' )
print(UpperCAmelCase )
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase )-> str:
'''simple docstring'''
return translate_message(UpperCAmelCase ,UpperCAmelCase ,'''encrypt''' )
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase )-> str:
'''simple docstring'''
return translate_message(UpperCAmelCase ,UpperCAmelCase ,'''decrypt''' )
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase )-> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = key.upper()
for symbol in message:
SCREAMING_SNAKE_CASE_ = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(UpperCAmelCase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ = 0
else:
translated.append(UpperCAmelCase )
return "".join(UpperCAmelCase )
if __name__ == "__main__":
main()
| 393 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
A_ = get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase=0 )-> List[str]:
'''simple docstring'''
os.makedirs(UpperCAmelCase ,exist_ok=UpperCAmelCase )
with FSDP.state_dict_type(
UpperCAmelCase ,fsdp_plugin.state_dict_type ,fsdp_plugin.state_dict_config ,fsdp_plugin.optim_state_dict_config ):
SCREAMING_SNAKE_CASE_ = model.state_dict()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
SCREAMING_SNAKE_CASE_ = f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
SCREAMING_SNAKE_CASE_ = os.path.join(UpperCAmelCase ,UpperCAmelCase )
if accelerator.process_index == 0:
logger.info(f'''Saving model to {output_model_file}''' )
torch.save(UpperCAmelCase ,UpperCAmelCase )
logger.info(f'''Model saved to {output_model_file}''' )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
SCREAMING_SNAKE_CASE_ = (
f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
if model_index == 0
else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
)
SCREAMING_SNAKE_CASE_ = os.path.join(UpperCAmelCase ,UpperCAmelCase )
logger.info(f'''Saving model to {output_model_file}''' )
torch.save(UpperCAmelCase ,UpperCAmelCase )
logger.info(f'''Model saved to {output_model_file}''' )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
SCREAMING_SNAKE_CASE_ = os.path.join(UpperCAmelCase ,f'''{MODEL_NAME}_{model_index}''' )
os.makedirs(UpperCAmelCase ,exist_ok=UpperCAmelCase )
logger.info(f'''Saving model to {ckpt_dir}''' )
SCREAMING_SNAKE_CASE_ = {'''model''': state_dict}
dist_cp.save_state_dict(
state_dict=UpperCAmelCase ,storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase ) ,planner=DefaultSavePlanner() ,)
logger.info(f'''Model saved to {ckpt_dir}''' )
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase=0 )-> List[str]:
'''simple docstring'''
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
UpperCAmelCase ,fsdp_plugin.state_dict_type ,fsdp_plugin.state_dict_config ,fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(UpperCAmelCase ) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
'''Set the `sync_module_states` flag to `True` so that model states are synced across processes when '''
'''initializing FSDP object''' )
return
SCREAMING_SNAKE_CASE_ = f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
SCREAMING_SNAKE_CASE_ = os.path.join(UpperCAmelCase ,UpperCAmelCase )
logger.info(f'''Loading model from {input_model_file}''' )
SCREAMING_SNAKE_CASE_ = torch.load(UpperCAmelCase )
logger.info(f'''Model loaded from {input_model_file}''' )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
SCREAMING_SNAKE_CASE_ = (
f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
if model_index == 0
else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
)
SCREAMING_SNAKE_CASE_ = os.path.join(UpperCAmelCase ,UpperCAmelCase )
logger.info(f'''Loading model from {input_model_file}''' )
SCREAMING_SNAKE_CASE_ = torch.load(UpperCAmelCase )
logger.info(f'''Model loaded from {input_model_file}''' )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
SCREAMING_SNAKE_CASE_ = (
os.path.join(UpperCAmelCase ,f'''{MODEL_NAME}_{model_index}''' )
if f'''{MODEL_NAME}''' not in input_dir
else input_dir
)
logger.info(f'''Loading model from {ckpt_dir}''' )
SCREAMING_SNAKE_CASE_ = {'''model''': model.state_dict()}
dist_cp.load_state_dict(
state_dict=UpperCAmelCase ,storage_reader=dist_cp.FileSystemReader(UpperCAmelCase ) ,planner=DefaultLoadPlanner() ,)
SCREAMING_SNAKE_CASE_ = state_dict['''model''']
logger.info(f'''Model loaded from {ckpt_dir}''' )
model.load_state_dict(UpperCAmelCase )
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase=0 )-> int:
'''simple docstring'''
os.makedirs(UpperCAmelCase ,exist_ok=UpperCAmelCase )
with FSDP.state_dict_type(
UpperCAmelCase ,fsdp_plugin.state_dict_type ,fsdp_plugin.state_dict_config ,fsdp_plugin.optim_state_dict_config ):
SCREAMING_SNAKE_CASE_ = FSDP.optim_state_dict(UpperCAmelCase ,UpperCAmelCase )
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
SCREAMING_SNAKE_CASE_ = (
f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
)
SCREAMING_SNAKE_CASE_ = os.path.join(UpperCAmelCase ,UpperCAmelCase )
logger.info(f'''Saving Optimizer state to {output_optimizer_file}''' )
torch.save(UpperCAmelCase ,UpperCAmelCase )
logger.info(f'''Optimizer state saved in {output_optimizer_file}''' )
else:
SCREAMING_SNAKE_CASE_ = os.path.join(UpperCAmelCase ,f'''{OPTIMIZER_NAME}_{optimizer_index}''' )
os.makedirs(UpperCAmelCase ,exist_ok=UpperCAmelCase )
logger.info(f'''Saving Optimizer state to {ckpt_dir}''' )
dist_cp.save_state_dict(
state_dict={'''optimizer''': optim_state} ,storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase ) ,planner=DefaultSavePlanner() ,)
logger.info(f'''Optimizer state saved in {ckpt_dir}''' )
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase=0 )-> Any:
'''simple docstring'''
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
UpperCAmelCase ,fsdp_plugin.state_dict_type ,fsdp_plugin.state_dict_config ,fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
SCREAMING_SNAKE_CASE_ = None
# below check should work but currently it isn't working (mostly opytorch issue),
# in the meantime disabling it at the cost of excess memory usage
# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
SCREAMING_SNAKE_CASE_ = (
f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
)
SCREAMING_SNAKE_CASE_ = os.path.join(UpperCAmelCase ,UpperCAmelCase )
logger.info(f'''Loading Optimizer state from {input_optimizer_file}''' )
SCREAMING_SNAKE_CASE_ = torch.load(UpperCAmelCase )
logger.info(f'''Optimizer state loaded from {input_optimizer_file}''' )
else:
SCREAMING_SNAKE_CASE_ = (
os.path.join(UpperCAmelCase ,f'''{OPTIMIZER_NAME}_{optimizer_index}''' )
if f'''{OPTIMIZER_NAME}''' not in input_dir
else input_dir
)
logger.info(f'''Loading Optimizer from {ckpt_dir}''' )
SCREAMING_SNAKE_CASE_ = load_sharded_optimizer_state_dict(
model_state_dict=model.state_dict() ,optimizer_key='''optimizer''' ,storage_reader=dist_cp.FileSystemReader(UpperCAmelCase ) ,)
SCREAMING_SNAKE_CASE_ = optim_state['''optimizer''']
logger.info(f'''Optimizer loaded from {ckpt_dir}''' )
SCREAMING_SNAKE_CASE_ = FSDP.optim_state_dict_to_load(UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase )
optimizer.load_state_dict(UpperCAmelCase )
| 393 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __snake_case ( pl.LightningModule ):
def __init__( self : Any , _snake_case : Any):
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = model
UpperCAmelCase_ = 2
UpperCAmelCase_ = nn.Linear(self.model.config.hidden_size , self.num_labels)
def lowerCamelCase ( self : Tuple):
"""simple docstring"""
pass
def A (__A : str , __A : str , __A : str ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = LongformerModel.from_pretrained(__A )
UpperCAmelCase_ = LightningModel(__A )
UpperCAmelCase_ = torch.load(__A , map_location=torch.device('''cpu''' ) )
lightning_model.load_state_dict(ckpt['''state_dict'''] )
# init longformer question answering model
UpperCAmelCase_ = LongformerForQuestionAnswering.from_pretrained(__A )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(__A )
print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
snake_case_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--longformer_model",
default=None,
type=str,
required=True,
help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
)
parser.add_argument(
"--longformer_question_answering_ckpt_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch Lightning Checkpoint.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
snake_case_ : List[Any] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 169 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class __snake_case ( a , a ):
@register_to_config
def __init__( self : List[Any] , _snake_case : int = 128 , _snake_case : int = 256 , _snake_case : float = 2_0_0_0.0 , _snake_case : int = 768 , _snake_case : int = 12 , _snake_case : int = 12 , _snake_case : int = 64 , _snake_case : int = 2048 , _snake_case : float = 0.1 , ):
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = nn.Sequential(
nn.Linear(_snake_case , d_model * 4 , bias=_snake_case) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=_snake_case) , nn.SiLU() , )
UpperCAmelCase_ = nn.Embedding(_snake_case , _snake_case)
UpperCAmelCase_ = False
UpperCAmelCase_ = nn.Linear(_snake_case , _snake_case , bias=_snake_case)
UpperCAmelCase_ = nn.Dropout(p=_snake_case)
UpperCAmelCase_ = nn.ModuleList()
for lyr_num in range(_snake_case):
# FiLM conditional T5 decoder
UpperCAmelCase_ = DecoderLayer(d_model=_snake_case , d_kv=_snake_case , num_heads=_snake_case , d_ff=_snake_case , dropout_rate=_snake_case)
self.decoders.append(_snake_case)
UpperCAmelCase_ = TaLayerNorm(_snake_case)
UpperCAmelCase_ = nn.Dropout(p=_snake_case)
UpperCAmelCase_ = nn.Linear(_snake_case , _snake_case , bias=_snake_case)
def lowerCamelCase ( self : Optional[int] , _snake_case : str , _snake_case : List[str]):
"""simple docstring"""
UpperCAmelCase_ = torch.mul(query_input.unsqueeze(-1) , key_input.unsqueeze(-2))
return mask.unsqueeze(-3)
def lowerCamelCase ( self : Dict , _snake_case : Any , _snake_case : Optional[int] , _snake_case : int):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
UpperCAmelCase_ = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype)
UpperCAmelCase_ = self.conditioning_emb(_snake_case).unsqueeze(1)
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
UpperCAmelCase_ = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
UpperCAmelCase_ = torch.broadcast_to(
torch.arange(_snake_case , device=decoder_input_tokens.device) , (batch, seq_length) , )
UpperCAmelCase_ = self.position_encoding(_snake_case)
UpperCAmelCase_ = self.continuous_inputs_projection(_snake_case)
inputs += position_encodings
UpperCAmelCase_ = self.dropout(_snake_case)
# decoder: No padding present.
UpperCAmelCase_ = torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype)
# Translate encoding masks to encoder-decoder masks.
UpperCAmelCase_ = [(x, self.encoder_decoder_mask(_snake_case , _snake_case)) for x, y in encodings_and_masks]
# cross attend style: concat encodings
UpperCAmelCase_ = torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1)
UpperCAmelCase_ = torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1)
for lyr in self.decoders:
UpperCAmelCase_ = lyr(
_snake_case , conditioning_emb=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , )[0]
UpperCAmelCase_ = self.decoder_norm(_snake_case)
UpperCAmelCase_ = self.post_dropout(_snake_case)
UpperCAmelCase_ = self.spec_out(_snake_case)
return spec_out
class __snake_case ( nn.Module ):
def __init__( self : Optional[Any] , _snake_case : Optional[Any] , _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : Any , _snake_case : List[Any] , _snake_case : Optional[int]=1e-6):
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=_snake_case , d_kv=_snake_case , num_heads=_snake_case , dropout_rate=_snake_case))
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=_snake_case , d_kv=_snake_case , num_heads=_snake_case , dropout_rate=_snake_case , layer_norm_epsilon=_snake_case , ))
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=_snake_case , d_ff=_snake_case , dropout_rate=_snake_case , layer_norm_epsilon=_snake_case))
def lowerCamelCase ( self : Optional[Any] , _snake_case : str , _snake_case : Union[str, Any]=None , _snake_case : Any=None , _snake_case : Any=None , _snake_case : Any=None , _snake_case : Tuple=None , ):
"""simple docstring"""
UpperCAmelCase_ = self.layer[0](
_snake_case , conditioning_emb=_snake_case , attention_mask=_snake_case , )
if encoder_hidden_states is not None:
UpperCAmelCase_ = torch.where(encoder_attention_mask > 0 , 0 , -1e10).to(
encoder_hidden_states.dtype)
UpperCAmelCase_ = self.layer[1](
_snake_case , key_value_states=_snake_case , attention_mask=_snake_case , )
# Apply Film Conditional Feed Forward layer
UpperCAmelCase_ = self.layer[-1](_snake_case , _snake_case)
return (hidden_states,)
class __snake_case ( nn.Module ):
def __init__( self : List[Any] , _snake_case : int , _snake_case : str , _snake_case : int , _snake_case : str):
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = TaLayerNorm(_snake_case)
UpperCAmelCase_ = TaFiLMLayer(in_features=d_model * 4 , out_features=_snake_case)
UpperCAmelCase_ = Attention(query_dim=_snake_case , heads=_snake_case , dim_head=_snake_case , out_bias=_snake_case , scale_qk=_snake_case)
UpperCAmelCase_ = nn.Dropout(_snake_case)
def lowerCamelCase ( self : str , _snake_case : str , _snake_case : Dict=None , _snake_case : List[str]=None , ):
"""simple docstring"""
UpperCAmelCase_ = self.layer_norm(_snake_case)
if conditioning_emb is not None:
UpperCAmelCase_ = self.FiLMLayer(_snake_case , _snake_case)
# Self-attention block
UpperCAmelCase_ = self.attention(_snake_case)
UpperCAmelCase_ = hidden_states + self.dropout(_snake_case)
return hidden_states
class __snake_case ( nn.Module ):
def __init__( self : Any , _snake_case : Any , _snake_case : Any , _snake_case : Tuple , _snake_case : int , _snake_case : List[str]):
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = Attention(query_dim=_snake_case , heads=_snake_case , dim_head=_snake_case , out_bias=_snake_case , scale_qk=_snake_case)
UpperCAmelCase_ = TaLayerNorm(_snake_case , eps=_snake_case)
UpperCAmelCase_ = nn.Dropout(_snake_case)
def lowerCamelCase ( self : List[str] , _snake_case : Optional[Any] , _snake_case : List[str]=None , _snake_case : Tuple=None , ):
"""simple docstring"""
UpperCAmelCase_ = self.layer_norm(_snake_case)
UpperCAmelCase_ = self.attention(
_snake_case , encoder_hidden_states=_snake_case , attention_mask=attention_mask.squeeze(1) , )
UpperCAmelCase_ = hidden_states + self.dropout(_snake_case)
return layer_output
class __snake_case ( nn.Module ):
def __init__( self : List[Any] , _snake_case : Tuple , _snake_case : Any , _snake_case : List[Any] , _snake_case : Union[str, Any]):
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = TaDenseGatedActDense(d_model=_snake_case , d_ff=_snake_case , dropout_rate=_snake_case)
UpperCAmelCase_ = TaFiLMLayer(in_features=d_model * 4 , out_features=_snake_case)
UpperCAmelCase_ = TaLayerNorm(_snake_case , eps=_snake_case)
UpperCAmelCase_ = nn.Dropout(_snake_case)
def lowerCamelCase ( self : Any , _snake_case : str , _snake_case : int=None):
"""simple docstring"""
UpperCAmelCase_ = self.layer_norm(_snake_case)
if conditioning_emb is not None:
UpperCAmelCase_ = self.film(_snake_case , _snake_case)
UpperCAmelCase_ = self.DenseReluDense(_snake_case)
UpperCAmelCase_ = hidden_states + self.dropout(_snake_case)
return hidden_states
class __snake_case ( nn.Module ):
def __init__( self : List[Any] , _snake_case : Union[str, Any] , _snake_case : Tuple , _snake_case : Union[str, Any]):
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = nn.Linear(_snake_case , _snake_case , bias=_snake_case)
UpperCAmelCase_ = nn.Linear(_snake_case , _snake_case , bias=_snake_case)
UpperCAmelCase_ = nn.Linear(_snake_case , _snake_case , bias=_snake_case)
UpperCAmelCase_ = nn.Dropout(_snake_case)
UpperCAmelCase_ = NewGELUActivation()
def lowerCamelCase ( self : List[str] , _snake_case : str):
"""simple docstring"""
UpperCAmelCase_ = self.act(self.wi_a(_snake_case))
UpperCAmelCase_ = self.wi_a(_snake_case)
UpperCAmelCase_ = hidden_gelu * hidden_linear
UpperCAmelCase_ = self.dropout(_snake_case)
UpperCAmelCase_ = self.wo(_snake_case)
return hidden_states
class __snake_case ( nn.Module ):
def __init__( self : Any , _snake_case : Optional[Any] , _snake_case : List[Any]=1e-6):
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = nn.Parameter(torch.ones(_snake_case))
UpperCAmelCase_ = eps
def lowerCamelCase ( self : Tuple , _snake_case : List[str]):
"""simple docstring"""
UpperCAmelCase_ = hidden_states.to(torch.floataa).pow(2).mean(-1 , keepdim=_snake_case)
UpperCAmelCase_ = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
UpperCAmelCase_ = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states
class __snake_case ( nn.Module ):
def lowerCamelCase ( self : Tuple , _snake_case : torch.Tensor):
"""simple docstring"""
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.0_4_4_7_1_5 * torch.pow(_snake_case , 3.0))))
class __snake_case ( nn.Module ):
def __init__( self : int , _snake_case : int , _snake_case : Optional[Any]):
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = nn.Linear(_snake_case , out_features * 2 , bias=_snake_case)
def lowerCamelCase ( self : Optional[Any] , _snake_case : Tuple , _snake_case : Union[str, Any]):
"""simple docstring"""
UpperCAmelCase_ = self.scale_bias(_snake_case)
UpperCAmelCase_ , UpperCAmelCase_ = torch.chunk(_snake_case , 2 , -1)
UpperCAmelCase_ = x * (1 + scale) + shift
return x
| 169 | 1 |
def _lowerCAmelCase ( lowerCAmelCase_ :int = 600_851_475_143 )->int:
'''simple docstring'''
try:
snake_case_ = int(lowerCAmelCase_ )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one." )
snake_case_ = 1
snake_case_ = 2
while i * i <= n:
while n % i == 0:
snake_case_ = i
n //= i
i += 1
if n > 1:
snake_case_ = n
return int(lowerCAmelCase_ )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 283 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Dict = {
'''vocab_file''': '''vocab.json''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
'''merges_file''': '''merges.txt''',
}
SCREAMING_SNAKE_CASE :Optional[int] = {
'''vocab_file''': {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
),
},
'''tokenizer_config_file''': {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
),
},
'''merges_file''': {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
),
},
}
SCREAMING_SNAKE_CASE :List[str] = '''</w>'''
SCREAMING_SNAKE_CASE :int = '''@@ '''
def _lowerCAmelCase ( lowerCAmelCase_ :int )->int:
'''simple docstring'''
snake_case_ = set()
snake_case_ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
snake_case_ = char
return pairs
# Speech2Text2 has no max input length
SCREAMING_SNAKE_CASE :Tuple = {'''facebook/s2t-wav2vec2-large-en-de''': 10_24}
class __lowerCAmelCase ( a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any]="<s>" , _lowerCAmelCase : Tuple="<pad>" , _lowerCAmelCase : Union[str, Any]="</s>" , _lowerCAmelCase : str="<unk>" , _lowerCAmelCase : Union[str, Any]=False , _lowerCAmelCase : List[str]=None , **_lowerCAmelCase : List[str] , ) -> List[str]:
"""simple docstring"""
super().__init__(
unk_token=_lowerCAmelCase , bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , do_lower_case=_lowerCAmelCase , **_lowerCAmelCase , )
snake_case_ = do_lower_case
with open(_lowerCAmelCase , encoding="utf-8" ) as vocab_handle:
snake_case_ = json.load(_lowerCAmelCase )
snake_case_ = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(F'''No merges files provided. {self.__class__.__name__} can only be used for decoding.''' )
snake_case_ = None
snake_case_ = None
else:
with open(_lowerCAmelCase , encoding="utf-8" ) as merges_handle:
snake_case_ = merges_handle.read().split("\n" )[:-1]
snake_case_ = [tuple(merge.split()[:2] ) for merge in merges]
snake_case_ = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
snake_case_ = {}
@property
def lowerCAmelCase__ ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return len(self.decoder )
def lowerCAmelCase__ ( self : Any ) -> Dict:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCAmelCase__ ( self : Union[str, Any] , _lowerCAmelCase : Any ) -> Tuple:
"""simple docstring"""
snake_case_ = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
snake_case_ = get_pairs(_lowerCAmelCase )
if not pairs:
return token
while True:
snake_case_ = min(_lowerCAmelCase , key=lambda _lowerCAmelCase : self.bpe_ranks.get(_lowerCAmelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
snake_case_ , snake_case_ = bigram
snake_case_ = []
snake_case_ = 0
while i < len(_lowerCAmelCase ):
try:
snake_case_ = word.index(_lowerCAmelCase , _lowerCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
snake_case_ = j
if word[i] == first and i < len(_lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
snake_case_ = tuple(_lowerCAmelCase )
snake_case_ = new_word
if len(_lowerCAmelCase ) == 1:
break
else:
snake_case_ = get_pairs(_lowerCAmelCase )
snake_case_ = " ".join(_lowerCAmelCase )
if word == "\n " + BPE_TOKEN_MERGES:
snake_case_ = "\n" + BPE_TOKEN_MERGES
if word.endswith(_lowerCAmelCase ):
snake_case_ = word.replace(_lowerCAmelCase , "" )
snake_case_ = word.replace(" " , _lowerCAmelCase )
snake_case_ = word
return word
def lowerCAmelCase__ ( self : List[Any] , _lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
if self.bpe_ranks is None:
raise ValueError(
"This tokenizer was instantiated without a `merges.txt` file, so"
" that it can only be used for decoding, not for encoding."
"Make sure to provide `merges.txt` file at instantiation to enable "
"encoding." )
if self.do_lower_case:
snake_case_ = text.lower()
snake_case_ = text.split()
snake_case_ = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(_lowerCAmelCase ).split(" " ) ) )
return split_tokens
def lowerCAmelCase__ ( self : str , _lowerCAmelCase : str ) -> int:
"""simple docstring"""
return self.encoder.get(_lowerCAmelCase , self.encoder.get(self.unk_token ) )
def lowerCAmelCase__ ( self : int , _lowerCAmelCase : int ) -> str:
"""simple docstring"""
snake_case_ = self.decoder.get(_lowerCAmelCase , self.unk_token )
return result
def lowerCAmelCase__ ( self : Union[str, Any] , _lowerCAmelCase : List[str] ) -> str:
"""simple docstring"""
snake_case_ = " ".join(_lowerCAmelCase )
# make sure @@ tokens are concatenated
snake_case_ = "".join(string.split(_lowerCAmelCase ) )
return string
def lowerCAmelCase__ ( self : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
snake_case_ = os.path.join(
_lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ = os.path.join(
_lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(_lowerCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCAmelCase , ensure_ascii=_lowerCAmelCase ) + "\n" )
snake_case_ = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(_lowerCAmelCase , "w" , encoding="utf-8" ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
" Please check that the tokenizer is not corrupted!" )
snake_case_ = token_index
writer.write(" ".join(_lowerCAmelCase ) + "\n" )
index += 1
return (vocab_file, merges_file)
| 283 | 1 |
def _UpperCamelCase ( lowercase__ , lowercase__ ):
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=1_0))
| 710 |
from heapq import heappop, heappush
import numpy as np
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = grid.shape
__SCREAMING_SNAKE_CASE : Any = [-1, 1, 0, 0]
__SCREAMING_SNAKE_CASE : Any = [0, 0, -1, 1]
if allow_diagonal:
dx += [-1, -1, 1, 1]
dy += [-1, 1, -1, 1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = [(0, source)], set()
__SCREAMING_SNAKE_CASE : Optional[Any] = np.full((rows, cols) , np.inf )
__SCREAMING_SNAKE_CASE : Tuple = 0
__SCREAMING_SNAKE_CASE : int = np.empty((rows, cols) , dtype=lowercase__ )
__SCREAMING_SNAKE_CASE : List[str] = None
while queue:
((__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE)) : List[str] = heappop(lowercase__ )
if (x, y) in visited:
continue
visited.add((x, y) )
if (x, y) == destination:
__SCREAMING_SNAKE_CASE : int = []
while (x, y) != source:
path.append((x, y) )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = predecessors[x, y]
path.append(lowercase__ ) # add the source manually
path.reverse()
return matrix[destination], path
for i in range(len(lowercase__ ) ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = x + dx[i], y + dy[i]
if 0 <= nx < rows and 0 <= ny < cols:
__SCREAMING_SNAKE_CASE : Optional[int] = grid[nx][ny]
if next_node == 1 and matrix[nx, ny] > dist + 1:
heappush(lowercase__ , (dist + 1, (nx, ny)) )
__SCREAMING_SNAKE_CASE : Optional[int] = dist + 1
__SCREAMING_SNAKE_CASE : Optional[Any] = (x, y)
return np.inf, []
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( snake_case_ ):
_snake_case : UNetaDModel
_snake_case : ScoreSdeVeScheduler
def __init__( self , lowerCamelCase , lowerCamelCase ):
super().__init__()
self.register_modules(unet=lowerCamelCase , scheduler=lowerCamelCase )
@torch.no_grad()
def __call__( self , lowerCamelCase = 1 , lowerCamelCase = 2000 , lowerCamelCase = None , lowerCamelCase = "pil" , lowerCamelCase = True , **lowerCamelCase , ):
__a = self.unet.config.sample_size
__a = (batch_size, 3, img_size, img_size)
__a = self.unet
__a = randn_tensor(lowerCamelCase , generator=lowerCamelCase ) * self.scheduler.init_noise_sigma
__a = sample.to(self.device )
self.scheduler.set_timesteps(lowerCamelCase )
self.scheduler.set_sigmas(lowerCamelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
__a = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
__a = self.unet(lowerCamelCase , lowerCamelCase ).sample
__a = self.scheduler.step_correct(lowerCamelCase , lowerCamelCase , generator=lowerCamelCase ).prev_sample
# prediction step
__a = model(lowerCamelCase , lowerCamelCase ).sample
__a = self.scheduler.step_pred(lowerCamelCase , lowerCamelCase , lowerCamelCase , generator=lowerCamelCase )
__a , __a = output.prev_sample, output.prev_sample_mean
__a = sample_mean.clamp(0 , 1 )
__a = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__a = self.numpy_to_pil(lowerCamelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=lowerCamelCase )
| 528 |
"""simple docstring"""
import os
def _lowerCamelCase( a = "matrix.txt" ):
with open(os.path.join(os.path.dirname(a ) , a ) ) as in_file:
__a = in_file.read()
__a = [[int(a ) for cell in row.split("," )] for row in data.strip().splitlines()]
__a = [[0 for cell in row] for row in grid]
__a = len(grid[0] )
__a = [[0 for i in range(a )] for j in range(a )]
__a = grid[0][0]
for i in range(1 , a ):
__a = grid[0][i] + dp[0][i - 1]
for i in range(1 , a ):
__a = grid[i][0] + dp[i - 1][0]
for i in range(1 , a ):
for j in range(1 , a ):
__a = grid[i][j] + min(dp[i - 1][j] , dp[i][j - 1] )
return dp[-1][-1]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 528 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
def count_of_possible_combinations(_SCREAMING_SNAKE_CASE : Optional[Any] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(__A )
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
def count_of_possible_combinations_with_dp_array(
_SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : List[Any] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
_UpperCAmelCase = sum(
count_of_possible_combinations_with_dp_array(target - item , __A )
for item in array )
_UpperCAmelCase = answer
return answer
_UpperCAmelCase = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(__A , __A )
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = [0] * (target + 1)
_UpperCAmelCase = 1
for i in range(1 , target + 1 ):
for j in range(__A ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
__A : List[str] = 3
__A : Dict = 5
__A : Tuple = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 701 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _a ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = StableDiffusionXLImgaImgPipeline
UpperCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
UpperCamelCase__ = PipelineTesterMixin.required_optional_params - {"""latents"""}
UpperCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCamelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCamelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowercase__ ( self : Tuple )->int:
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , attention_head_dim=(2, 4) , use_linear_projection=__UpperCamelCase , addition_embed_type='''text_time''' , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=8_0 , cross_attention_dim=6_4 , )
_UpperCAmelCase = EulerDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , steps_offset=1 , beta_schedule='''scaled_linear''' , timestep_spacing='''leading''' , )
torch.manual_seed(0 )
_UpperCAmelCase = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
_UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act='''gelu''' , projection_dim=3_2 , )
_UpperCAmelCase = CLIPTextModel(__UpperCamelCase )
_UpperCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCamelCase )
_UpperCAmelCase = CLIPTextModelWithProjection(__UpperCamelCase )
_UpperCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' , local_files_only=__UpperCamelCase )
_UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''text_encoder_2''': text_encoder_a,
'''tokenizer_2''': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def lowercase__ ( self : int , __UpperCamelCase : Optional[int] , __UpperCamelCase : int=0 )->Any:
_UpperCAmelCase = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
_UpperCAmelCase = image / 2 + 0.5
if str(__UpperCamelCase ).startswith('''mps''' ):
_UpperCAmelCase = torch.manual_seed(__UpperCamelCase )
else:
_UpperCAmelCase = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
_UpperCAmelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 5.0,
'''output_type''': '''numpy''',
'''strength''': 0.7_5,
}
return inputs
def lowercase__ ( self : Any )->int:
_UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = StableDiffusionXLImgaImgPipeline(**__UpperCamelCase )
_UpperCAmelCase = sd_pipe.to(__UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCAmelCase = self.get_dummy_inputs(__UpperCamelCase )
_UpperCAmelCase = sd_pipe(**__UpperCamelCase ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
_UpperCAmelCase = np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase__ ( self : Optional[Any] )->Optional[int]:
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )
def lowercase__ ( self : Optional[Any] )->List[Any]:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def lowercase__ ( self : List[str] )->str:
pass
def lowercase__ ( self : Dict )->List[str]:
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = StableDiffusionXLImgaImgPipeline(**__UpperCamelCase )
_UpperCAmelCase = sd_pipe.to(__UpperCamelCase )
_UpperCAmelCase = sd_pipe.to(__UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )
# forward without prompt embeds
_UpperCAmelCase = self.get_dummy_inputs(__UpperCamelCase )
_UpperCAmelCase = 3 * ['''this is a negative prompt''']
_UpperCAmelCase = negative_prompt
_UpperCAmelCase = 3 * [inputs['''prompt''']]
_UpperCAmelCase = sd_pipe(**__UpperCamelCase )
_UpperCAmelCase = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
_UpperCAmelCase = self.get_dummy_inputs(__UpperCamelCase )
_UpperCAmelCase = 3 * ['''this is a negative prompt''']
_UpperCAmelCase = 3 * [inputs.pop('''prompt''' )]
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = sd_pipe.encode_prompt(__UpperCamelCase , negative_prompt=__UpperCamelCase )
_UpperCAmelCase = sd_pipe(
**__UpperCamelCase , prompt_embeds=__UpperCamelCase , negative_prompt_embeds=__UpperCamelCase , pooled_prompt_embeds=__UpperCamelCase , negative_pooled_prompt_embeds=__UpperCamelCase , )
_UpperCAmelCase = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@slow
@require_torch_gpu
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : Union[str, Any] )->Any:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self : Optional[Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict="cpu" , __UpperCamelCase : int=torch.floataa , __UpperCamelCase : Optional[Any]=0 )->Union[str, Any]:
_UpperCAmelCase = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
_UpperCAmelCase = np.random.RandomState(__UpperCamelCase ).standard_normal((1, 4, 6_4, 6_4) )
_UpperCAmelCase = torch.from_numpy(__UpperCamelCase ).to(device=__UpperCamelCase , dtype=__UpperCamelCase )
_UpperCAmelCase = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self : Optional[Any] )->Optional[Any]:
_UpperCAmelCase = DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''' )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCAmelCase = self.get_inputs(__UpperCamelCase )
_UpperCAmelCase = pipe(**__UpperCamelCase ).images
_UpperCAmelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_UpperCAmelCase = np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6] )
assert np.abs(image_slice - expected_slice ).max() < 7e-3
| 95 | 0 |
'''simple docstring'''
def UpperCamelCase ( _lowerCamelCase : int = 50 ):
A__ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 440 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( _lowerCamelCase : list[int] ): # This function is recursive
A__ = len(_lowerCamelCase )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
A__ = array[0]
A__ = False
A__ = 1
A__ = []
while not is_found and i < array_length:
if array[i] < pivot:
A__ = True
A__ = [element for element in array[i:] if element >= array[i]]
A__ = longest_subsequence(_lowerCamelCase )
if len(_lowerCamelCase ) > len(_lowerCamelCase ):
A__ = temp_array
else:
i += 1
A__ = [element for element in array[1:] if element >= pivot]
A__ = [pivot, *longest_subsequence(_lowerCamelCase )]
if len(_lowerCamelCase ) > len(_lowerCamelCase ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 440 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class a ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = 'van'
def __init__( self : str , lowerCamelCase__ : Union[str, Any]=224 , lowerCamelCase__ : Optional[int]=3 , lowerCamelCase__ : List[Any]=[7, 3, 3, 3] , lowerCamelCase__ : List[str]=[4, 2, 2, 2] , lowerCamelCase__ : Any=[64, 128, 320, 512] , lowerCamelCase__ : str=[3, 3, 12, 3] , lowerCamelCase__ : Optional[int]=[8, 8, 4, 4] , lowerCamelCase__ : Dict="gelu" , lowerCamelCase__ : Union[str, Any]=0.0_2 , lowerCamelCase__ : str=1e-6 , lowerCamelCase__ : Union[str, Any]=1e-2 , lowerCamelCase__ : str=0.0 , lowerCamelCase__ : List[Any]=0.0 , **lowerCamelCase__ : Any , ) -> int:
"""simple docstring"""
super().__init__(**lowerCamelCase__ )
__lowercase = image_size
__lowercase = num_channels
__lowercase = patch_sizes
__lowercase = strides
__lowercase = hidden_sizes
__lowercase = depths
__lowercase = mlp_ratios
__lowercase = hidden_act
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = layer_scale_init_value
__lowercase = drop_path_rate
__lowercase = dropout_rate
| 362 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase_ : UNetaDModel
UpperCamelCase_ : KarrasVeScheduler
def __init__( self : Tuple , lowerCamelCase__ : UNetaDModel , lowerCamelCase__ : KarrasVeScheduler ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
self.register_modules(unet=lowerCamelCase__ , scheduler=lowerCamelCase__ )
@torch.no_grad()
def __call__( self : str , lowerCamelCase__ : int = 1 , lowerCamelCase__ : int = 50 , lowerCamelCase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCamelCase__ : Optional[str] = "pil" , lowerCamelCase__ : bool = True , **lowerCamelCase__ : int , ) -> Union[Tuple, ImagePipelineOutput]:
"""simple docstring"""
__lowercase = self.unet.config.sample_size
__lowercase = (batch_size, 3, img_size, img_size)
__lowercase = self.unet
# sample x_0 ~ N(0, sigma_0^2 * I)
__lowercase = randn_tensor(lowerCamelCase__ , generator=lowerCamelCase__ , device=self.device ) * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(lowerCamelCase__ )
for t in self.progress_bar(self.scheduler.timesteps ):
# here sigma_t == t_i from the paper
__lowercase = self.scheduler.schedule[t]
__lowercase = self.scheduler.schedule[t - 1] if t > 0 else 0
# 1. Select temporarily increased noise level sigma_hat
# 2. Add new noise to move from sample_i to sample_hat
__lowercase , __lowercase = self.scheduler.add_noise_to_input(lowerCamelCase__ , lowerCamelCase__ , generator=lowerCamelCase__ )
# 3. Predict the noise residual given the noise magnitude `sigma_hat`
# The model inputs and output are adjusted by following eq. (213) in [1].
__lowercase = (sigma_hat / 2) * model((sample_hat + 1) / 2 , sigma_hat / 2 ).sample
# 4. Evaluate dx/dt at sigma_hat
# 5. Take Euler step from sigma to sigma_prev
__lowercase = self.scheduler.step(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
if sigma_prev != 0:
# 6. Apply 2nd order correction
# The model inputs and output are adjusted by following eq. (213) in [1].
__lowercase = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2 , sigma_prev / 2 ).sample
__lowercase = self.scheduler.step_correct(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , step_output.prev_sample , step_output['''derivative'''] , )
__lowercase = step_output.prev_sample
__lowercase = (sample / 2 + 0.5).clamp(0 , 1 )
__lowercase = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__lowercase = self.numpy_to_pil(lowerCamelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCamelCase__ )
| 362 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"""bigscience/bloom""": """https://huggingface.co/bigscience/bloom/resolve/main/config.json""",
"""bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/config.json""",
"""bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json""",
"""bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json""",
"""bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/config.json""",
"""bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json""",
}
class __magic_name__ ( lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase__ = 'bloom'
lowerCamelCase__ = ['past_key_values']
lowerCamelCase__ = {
'num_hidden_layers': 'n_layer',
'num_attention_heads': 'n_head',
}
def __init__( self , lowerCamelCase=25_0880 , lowerCamelCase=64 , lowerCamelCase=2 , lowerCamelCase=8 , lowerCamelCase=1E-5 , lowerCamelCase=0.02 , lowerCamelCase=True , lowerCamelCase=1 , lowerCamelCase=2 , lowerCamelCase=False , lowerCamelCase=0.0 , lowerCamelCase=0.0 , lowerCamelCase=1 , lowerCamelCase=False , **lowerCamelCase , ):
'''simple docstring'''
__A : List[str] = vocab_size
# Backward compatibility with n_embed kwarg
__A : List[Any] = kwargs.pop("n_embed" , lowerCamelCase )
__A : int = hidden_size if n_embed is None else n_embed
__A : Optional[Any] = n_layer
__A : Union[str, Any] = n_head
__A : str = layer_norm_epsilon
__A : Union[str, Any] = initializer_range
__A : Union[str, Any] = use_cache
__A : Dict = pretraining_tp
__A : Optional[Any] = apply_residual_connection_post_layernorm
__A : Optional[Any] = hidden_dropout
__A : Union[str, Any] = attention_dropout
__A : List[str] = bos_token_id
__A : Tuple = eos_token_id
__A : Any = slow_but_exact
super().__init__(bos_token_id=lowerCamelCase , eos_token_id=lowerCamelCase , **lowerCamelCase )
class __magic_name__ ( lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase__ = version.parse('1.12' )
def __init__( self , lowerCamelCase , lowerCamelCase = "default" , lowerCamelCase = None , lowerCamelCase = False , ):
'''simple docstring'''
super().__init__(lowerCamelCase , task=lowerCamelCase , patching_specs=lowerCamelCase , use_past=lowerCamelCase )
if not getattr(self._config , "pad_token_id" , lowerCamelCase ):
# TODO: how to do that better?
__A : Dict = 0
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
__A : Optional[int] = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
self.fill_with_past_key_values_(lowerCamelCase , direction="inputs" , inverted_values_shape=lowerCamelCase )
__A : Any = {0: "batch", 1: "past_sequence + sequence"}
else:
__A : Tuple = {0: "batch", 1: "sequence"}
return common_inputs
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return self._config.n_layer
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return self._config.n_head
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return 1E-3
def lowerCAmelCase__ ( self , lowerCamelCase , lowerCamelCase = -1 , lowerCamelCase = -1 , lowerCamelCase = False , lowerCamelCase = None , ):
'''simple docstring'''
__A : Union[str, Any] = super(lowerCamelCase , self ).generate_dummy_inputs(
lowerCamelCase , batch_size=lowerCamelCase , seq_length=lowerCamelCase , is_pair=lowerCamelCase , framework=lowerCamelCase )
# We need to order the input in the way they appears in the forward()
__A : Optional[Any] = OrderedDict({"input_ids": common_inputs["input_ids"]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
__A ,__A : List[str] = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
__A : Optional[int] = seqlen + 2
__A : Optional[int] = self._config.hidden_size // self.num_attention_heads
__A : Union[str, Any] = (
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
__A : List[str] = (
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
__A : Optional[Any] = [
(torch.zeros(lowerCamelCase ), torch.zeros(lowerCamelCase )) for _ in range(self.num_layers )
]
__A : int = common_inputs["attention_mask"]
if self.use_past:
__A : Optional[int] = ordered_inputs["attention_mask"].dtype
__A : Any = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(lowerCamelCase , lowerCamelCase , dtype=lowerCamelCase )] , dim=1 )
return ordered_inputs
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return 13
| 111 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}." )
if tokenizer_name is None:
__A : Tuple = TOKENIZER_CLASSES
else:
__A : Dict = {tokenizer_name: getattr(SCREAMING_SNAKE_CASE , tokenizer_name + "Fast" )}
logger.info(f"Loading tokenizer classes: {tokenizer_names}" )
for tokenizer_name in tokenizer_names:
__A : int = TOKENIZER_CLASSES[tokenizer_name]
__A : Union[str, Any] = True
if checkpoint_name is None:
__A : Dict = list(tokenizer_class.max_model_input_sizes.keys() )
else:
__A : str = [checkpoint_name]
logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}" )
for checkpoint in checkpoint_names:
logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}" )
# Load tokenizer
__A : List[str] = tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE , force_download=SCREAMING_SNAKE_CASE )
# Save fast tokenizer
logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}" )
# For organization names we create sub-directories
if "/" in checkpoint:
__A ,__A : Optional[Any] = checkpoint.split("/" )
__A : str = os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
elif add_prefix:
__A : Optional[int] = checkpoint
__A : Any = dump_path
else:
__A : int = None
__A : Optional[int] = dump_path
logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}" )
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
__A : Any = list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
__A : Optional[int] = file_path.split(SCREAMING_SNAKE_CASE )[-1][0]
if next_char == "/":
__A : Union[str, Any] = os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__A : Union[str, Any] = None
logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}" )
__A : Tuple = tokenizer.save_pretrained(
SCREAMING_SNAKE_CASE , legacy_format=SCREAMING_SNAKE_CASE , filename_prefix=SCREAMING_SNAKE_CASE )
logger.info(f"=> File names {file_names}" )
for file_name in file_names:
if not file_name.endswith("tokenizer.json" ):
os.remove(SCREAMING_SNAKE_CASE )
logger.info(f"=> removing {file_name}" )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--dump_path""", default=None, type=str, required=True, help="""Path to output generated fast tokenizer files."""
)
parser.add_argument(
"""--tokenizer_name""",
default=None,
type=str,
help=(
F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
"""download and convert all the checkpoints from AWS."""
),
)
parser.add_argument(
"""--checkpoint_name""",
default=None,
type=str,
help="""Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.""",
)
parser.add_argument(
"""--force_download""",
action="""store_true""",
help="""Re-download checkpoints.""",
)
_UpperCamelCase = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| 111 | 1 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
__UpperCAmelCase = random.Random()
def __A ( lowerCamelCase_ , lowerCamelCase_=1.0 , lowerCamelCase_=None , lowerCamelCase_=None ):
"""simple docstring"""
if rng is None:
SCREAMING_SNAKE_CASE : Optional[Any] = global_rng
SCREAMING_SNAKE_CASE : Optional[int] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Optional[int]=7 , lowerCamelCase_ : Optional[int]=4_00 , lowerCamelCase_ : int=20_00 , lowerCamelCase_ : List[str]=20_48 , lowerCamelCase_ : Optional[Any]=1_28 , lowerCamelCase_ : Optional[Any]=1 , lowerCamelCase_ : str=5_12 , lowerCamelCase_ : Dict=30 , lowerCamelCase_ : Dict=4_41_00 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = parent
SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE : List[str] = min_seq_length
SCREAMING_SNAKE_CASE : Any = max_seq_length
SCREAMING_SNAKE_CASE : Optional[int] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE : int = spectrogram_length
SCREAMING_SNAKE_CASE : List[Any] = feature_size
SCREAMING_SNAKE_CASE : Any = num_audio_channels
SCREAMING_SNAKE_CASE : Tuple = hop_length
SCREAMING_SNAKE_CASE : str = chunk_length
SCREAMING_SNAKE_CASE : Dict = sampling_rate
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Tuple=False , lowerCamelCase_ : Any=False ):
'''simple docstring'''
def _flatten(lowerCamelCase_ : Dict ):
return list(itertools.chain(*lowerCamelCase_ ) )
if equal_length:
SCREAMING_SNAKE_CASE : Optional[Any] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE : Dict = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE : Optional[int] = [np.asarray(lowerCamelCase_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowercase_ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = TvltFeatureExtractor
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = TvltFeatureExtractionTester(self )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCamelCase_ , """spectrogram_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """feature_size""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """num_audio_channels""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """hop_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """chunk_length""" ) )
self.assertTrue(hasattr(lowerCamelCase_ , """sampling_rate""" ) )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Any = feat_extract_first.save_pretrained(lowerCamelCase_ )[0]
check_json_file_has_correct_format(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.feature_extraction_class.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : List[Any] = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : Optional[int] = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase_ , lowerCamelCase_ ) )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : int = os.path.join(lowerCamelCase_ , """feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.feature_extraction_class.from_json_file(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE : int = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE : List[str] = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE : Optional[Any] = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase_ , lowerCamelCase_ ) )
self.assertEqual(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE : Optional[int] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
SCREAMING_SNAKE_CASE : int = [np.asarray(lowerCamelCase_ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE : Union[str, Any] = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
SCREAMING_SNAKE_CASE : Optional[int] = feature_extractor(lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
SCREAMING_SNAKE_CASE : List[str] = feature_extractor(
lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 , mask_audio=lowerCamelCase_ ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE : Optional[int] = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
SCREAMING_SNAKE_CASE : int = np.asarray(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = feature_extractor(lowerCamelCase_ , return_tensors="""np""" , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE : Union[str, Any] = ds.sort("""id""" ).select(range(lowerCamelCase_ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE : Tuple = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE : List[Any] = feature_extractor(lowerCamelCase_ , return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 1_92, 1_28) )
SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , lowerCamelCase_ , atol=1e-4 ) )
| 79 |
'''simple docstring'''
import math
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : Tuple=0 ): # a graph with Node 0,1,...,N-1
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = n
SCREAMING_SNAKE_CASE : Optional[int] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # adjacency matrix for weight
SCREAMING_SNAKE_CASE : Union[str, Any] = [
[math.inf for j in range(0 , lowerCamelCase_ )] for i in range(0 , lowerCamelCase_ )
] # dp[i][j] stores minimum distance from i to j
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : str , lowerCamelCase_ : List[str] , lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = w
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
SCREAMING_SNAKE_CASE : Union[str, Any] = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
return self.dp[u][v]
if __name__ == "__main__":
__UpperCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 79 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :List[str] = """transfo-xl"""
__magic_name__ :Any = ["""mems"""]
__magic_name__ :List[str] = {
"""n_token""": """vocab_size""",
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , __UpperCAmelCase=2_6_7_7_3_5 , __UpperCAmelCase=[2_0_0_0_0, 4_0_0_0_0, 2_0_0_0_0_0] , __UpperCAmelCase=1_0_2_4 , __UpperCAmelCase=1_0_2_4 , __UpperCAmelCase=1_6 , __UpperCAmelCase=6_4 , __UpperCAmelCase=4_0_9_6 , __UpperCAmelCase=4 , __UpperCAmelCase=False , __UpperCAmelCase=1_8 , __UpperCAmelCase=1_6_0_0 , __UpperCAmelCase=1_0_0_0 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=0 , __UpperCAmelCase=-1 , __UpperCAmelCase=True , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=True , __UpperCAmelCase="normal" , __UpperCAmelCase=0.01 , __UpperCAmelCase=0.01 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-5 , __UpperCAmelCase=0 , **__UpperCAmelCase , ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = vocab_size
lowerCAmelCase__ :Optional[int] = []
self.cutoffs.extend(__UpperCAmelCase )
if proj_share_all_but_first:
lowerCAmelCase__ :int = [False] + [True] * len(self.cutoffs )
else:
lowerCAmelCase__ :List[Any] = [False] + [False] * len(self.cutoffs )
lowerCAmelCase__ :Union[str, Any] = d_model
lowerCAmelCase__ :str = d_embed
lowerCAmelCase__ :str = d_head
lowerCAmelCase__ :List[str] = d_inner
lowerCAmelCase__ :List[str] = div_val
lowerCAmelCase__ :Optional[int] = pre_lnorm
lowerCAmelCase__ :Union[str, Any] = n_layer
lowerCAmelCase__ :Optional[Any] = n_head
lowerCAmelCase__ :Optional[int] = mem_len
lowerCAmelCase__ :Optional[Any] = same_length
lowerCAmelCase__ :Any = attn_type
lowerCAmelCase__ :Union[str, Any] = clamp_len
lowerCAmelCase__ :List[Any] = sample_softmax
lowerCAmelCase__ :Any = adaptive
lowerCAmelCase__ :Union[str, Any] = dropout
lowerCAmelCase__ :Optional[Any] = dropatt
lowerCAmelCase__ :str = untie_r
lowerCAmelCase__ :Optional[int] = init
lowerCAmelCase__ :str = init_range
lowerCAmelCase__ :Union[str, Any] = proj_init_std
lowerCAmelCase__ :List[str] = init_std
lowerCAmelCase__ :int = layer_norm_epsilon
super().__init__(eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
@property
def snake_case ( self ):
'''simple docstring'''
logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit." )
return -1
@max_position_embeddings.setter
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
raise NotImplementedError(
F"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 93 |
"""simple docstring"""
# Copyright 2023 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.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 93 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class __UpperCAmelCase ( __lowerCAmelCase ):
"""simple docstring"""
def snake_case_ ( self ):
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def snake_case_ ( self ):
__a = {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
return Dataset.from_dict(lowerCamelCase__ )
def snake_case_ ( self ):
__a = self._create_example_records()
__a = Dataset.from_list(lowerCamelCase__ )
self.assertListEqual(dset.column_names , ["""col_1""", """col_2"""] )
for i, r in enumerate(lowerCamelCase__ ):
self.assertDictEqual(lowerCamelCase__ , example_records[i] )
def snake_case_ ( self ):
__a = self._create_example_records()
__a = Dataset.from_list(lowerCamelCase__ )
__a = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def snake_case_ ( self ): # checks what happens with missing columns
__a = [{'''col_1''': 1}, {'''col_2''': '''x'''}]
__a = Dataset.from_list(lowerCamelCase__ )
self.assertDictEqual(dset[0] , {"""col_1""": 1} )
self.assertDictEqual(dset[1] , {"""col_1""": None} ) # NB: first record is used for columns
def snake_case_ ( self ): # checks if the type can be inferred from the second record
__a = [{'''col_1''': []}, {'''col_1''': [1, 2]}]
__a = Dataset.from_list(lowerCamelCase__ )
self.assertEqual(dset.info.features["""col_1"""] , Sequence(Value("""int64""" ) ) )
def snake_case_ ( self ):
__a = Dataset.from_list([] )
self.assertEqual(len(lowerCamelCase__ ) , 0 )
self.assertListEqual(dset.column_names , [] )
| 718 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __UpperCAmelCase ( __A , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = LDMTextToImagePipeline
_lowerCamelCase = TEXT_TO_IMAGE_PARAMS - {
"""negative_prompt""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
"""prompt_embeds""",
}
_lowerCamelCase = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
_lowerCamelCase = TEXT_TO_IMAGE_BATCH_PARAMS
_lowerCamelCase = False
def snake_case_ ( self ):
torch.manual_seed(0 )
__a = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
__a = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=__A , set_alpha_to_one=__A , )
torch.manual_seed(0 )
__a = AutoencoderKL(
block_out_channels=(32, 64) , in_channels=3 , out_channels=3 , down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""") , up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""") , latent_channels=4 , )
torch.manual_seed(0 )
__a = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__a = CLIPTextModel(__A )
__a = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__a = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vqvae""": vae,
"""bert""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def snake_case_ ( self , __A , __A=0 ):
if str(__A ).startswith("""mps""" ):
__a = torch.manual_seed(__A )
else:
__a = torch.Generator(device=__A ).manual_seed(__A )
__a = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def snake_case_ ( self ):
__a = """cpu""" # ensure determinism for the device-dependent torch.Generator
__a = self.get_dummy_components()
__a = LDMTextToImagePipeline(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
__a = self.get_dummy_inputs(__A )
__a = pipe(**__A ).images
__a = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
__a = np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self , __A , __A=torch.floataa , __A=0 ):
__a = torch.manual_seed(__A )
__a = np.random.RandomState(__A ).standard_normal((1, 4, 32, 32) )
__a = torch.from_numpy(__A ).to(device=__A , dtype=__A )
__a = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def snake_case_ ( self ):
__a = LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256""" ).to(__A )
pipe.set_progress_bar_config(disable=__A )
__a = self.get_inputs(__A )
__a = pipe(**__A ).images
__a = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
__a = np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878] )
__a = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1E-3
@nightly
@require_torch_gpu
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self , __A , __A=torch.floataa , __A=0 ):
__a = torch.manual_seed(__A )
__a = np.random.RandomState(__A ).standard_normal((1, 4, 32, 32) )
__a = torch.from_numpy(__A ).to(device=__A , dtype=__A )
__a = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 50,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def snake_case_ ( self ):
__a = LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256""" ).to(__A )
pipe.set_progress_bar_config(disable=__A )
__a = self.get_inputs(__A )
__a = pipe(**__A ).images[0]
__a = load_numpy(
"""https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy""" )
__a = np.abs(expected_image - image ).max()
assert max_diff < 1E-3
| 209 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ )-> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowerCamelCase__ ( )-> None:
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 554 |
"""simple docstring"""
def lowerCamelCase__ ( UpperCAmelCase_ = 60_08_51_47_51_43 )-> int:
"""simple docstring"""
try:
UpperCamelCase = int(UpperCAmelCase_ )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one." )
UpperCamelCase = 2
UpperCamelCase = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
UpperCamelCase = i
while n % i == 0:
UpperCamelCase = n // i
i += 1
return int(UpperCAmelCase_ )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 554 | 1 |
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class A ( __snake_case ):
def __init__( self , SCREAMING_SNAKE_CASE = "▁" , SCREAMING_SNAKE_CASE = True , SCREAMING_SNAKE_CASE = "<unk>" , SCREAMING_SNAKE_CASE = "</s>" , SCREAMING_SNAKE_CASE = "<pad>" , ) -> Tuple:
"""simple docstring"""
A : Optional[int] = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
A : Tuple = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
A : List[str] = token_dict['''token''']
A : int = Tokenizer(Unigram() )
A : Union[str, Any] = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
A : List[str] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=SCREAMING_SNAKE_CASE , add_prefix_space=SCREAMING_SNAKE_CASE ),
pre_tokenizers.Digits(individual_digits=SCREAMING_SNAKE_CASE ),
pre_tokenizers.Punctuation(),
] )
A : List[Any] = decoders.Metaspace(replacement=SCREAMING_SNAKE_CASE , add_prefix_space=SCREAMING_SNAKE_CASE )
A : Union[str, Any] = TemplateProcessing(
single=F'$A {self.special_tokens["eos"]["token"]}' , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
A : str = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 8000 , SCREAMING_SNAKE_CASE = True , ) -> Union[str, Any]:
"""simple docstring"""
A : List[Any] = trainers.UnigramTrainer(
vocab_size=SCREAMING_SNAKE_CASE , special_tokens=self.special_tokens_list , show_progress=SCREAMING_SNAKE_CASE , )
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A : Optional[Any] = [files]
self._tokenizer.train(SCREAMING_SNAKE_CASE , trainer=SCREAMING_SNAKE_CASE )
self.add_unk_id()
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 8000 , SCREAMING_SNAKE_CASE = True , ) -> Any:
"""simple docstring"""
A : Union[str, Any] = trainers.UnigramTrainer(
vocab_size=SCREAMING_SNAKE_CASE , special_tokens=self.special_tokens_list , show_progress=SCREAMING_SNAKE_CASE , )
self._tokenizer.train_from_iterator(SCREAMING_SNAKE_CASE , trainer=SCREAMING_SNAKE_CASE )
self.add_unk_id()
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : Tuple = json.loads(self._tokenizer.to_str() )
A : Tuple = self.special_tokens['''unk''']['''id''']
A : Any = Tokenizer.from_str(json.dumps(SCREAMING_SNAKE_CASE ) )
| 343 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : Optional[Any] = tempfile.mkdtemp()
A : List[Any] = SamImageProcessor()
A : Optional[int] = SamProcessor(SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self , **SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE ).image_processor
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
A : List[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A : Tuple = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A : Optional[int] = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A : Any = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE , padding_value=1.0 )
A : int = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=SCREAMING_SNAKE_CASE , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
A : Optional[Any] = self.get_image_processor()
A : Tuple = SamProcessor(image_processor=SCREAMING_SNAKE_CASE )
A : Optional[Any] = self.prepare_image_inputs()
A : List[str] = image_processor(SCREAMING_SNAKE_CASE , return_tensors='''np''' )
A : Any = processor(images=SCREAMING_SNAKE_CASE , return_tensors='''np''' )
input_feat_extract.pop('''original_sizes''' ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop('''reshaped_input_sizes''' ) # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
@require_torch
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : str = self.get_image_processor()
A : Union[str, Any] = SamProcessor(image_processor=SCREAMING_SNAKE_CASE )
A : str = [torch.ones((1, 3, 5, 5) )]
A : Union[str, Any] = [[1764, 2646]]
A : Tuple = [[683, 1024]]
A : Tuple = processor.post_process_masks(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
A : Union[str, Any] = processor.post_process_masks(
SCREAMING_SNAKE_CASE , torch.tensor(SCREAMING_SNAKE_CASE ) , torch.tensor(SCREAMING_SNAKE_CASE ) )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
# should also work with np
A : Optional[Any] = [np.ones((1, 3, 5, 5) )]
A : Tuple = processor.post_process_masks(SCREAMING_SNAKE_CASE , np.array(SCREAMING_SNAKE_CASE ) , np.array(SCREAMING_SNAKE_CASE ) )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
A : List[str] = [[1, 0], [0, 1]]
with self.assertRaises(SCREAMING_SNAKE_CASE ):
A : Union[str, Any] = processor.post_process_masks(SCREAMING_SNAKE_CASE , np.array(SCREAMING_SNAKE_CASE ) , np.array(SCREAMING_SNAKE_CASE ) )
@require_vision
@require_tf
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
A : int = tempfile.mkdtemp()
A : Optional[int] = SamImageProcessor()
A : List[Any] = SamProcessor(SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self , **SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE ).image_processor
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : Union[str, Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A : Any = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
A : Optional[int] = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A : List[str] = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE , padding_value=1.0 )
A : Tuple = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=SCREAMING_SNAKE_CASE , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : Any = self.get_image_processor()
A : Union[str, Any] = SamProcessor(image_processor=SCREAMING_SNAKE_CASE )
A : Tuple = self.prepare_image_inputs()
A : Any = image_processor(SCREAMING_SNAKE_CASE , return_tensors='''np''' )
A : Union[str, Any] = processor(images=SCREAMING_SNAKE_CASE , return_tensors='''np''' )
input_feat_extract.pop('''original_sizes''' ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop('''reshaped_input_sizes''' ) # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
@require_tf
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : Any = self.get_image_processor()
A : Optional[int] = SamProcessor(image_processor=SCREAMING_SNAKE_CASE )
A : List[Any] = [tf.ones((1, 3, 5, 5) )]
A : Any = [[1764, 2646]]
A : str = [[683, 1024]]
A : List[Any] = processor.post_process_masks(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
A : List[Any] = processor.post_process_masks(
SCREAMING_SNAKE_CASE , tf.convert_to_tensor(SCREAMING_SNAKE_CASE ) , tf.convert_to_tensor(SCREAMING_SNAKE_CASE ) , return_tensors='''tf''' , )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
# should also work with np
A : Dict = [np.ones((1, 3, 5, 5) )]
A : List[str] = processor.post_process_masks(
SCREAMING_SNAKE_CASE , np.array(SCREAMING_SNAKE_CASE ) , np.array(SCREAMING_SNAKE_CASE ) , return_tensors='''tf''' )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
A : Union[str, Any] = [[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError ):
A : Union[str, Any] = processor.post_process_masks(
SCREAMING_SNAKE_CASE , np.array(SCREAMING_SNAKE_CASE ) , np.array(SCREAMING_SNAKE_CASE ) , return_tensors='''tf''' )
@require_vision
@require_torchvision
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : Tuple = tempfile.mkdtemp()
A : Tuple = SamImageProcessor()
A : Dict = SamProcessor(SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self , **SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE ).image_processor
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : Optional[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A : Tuple = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE , 0 , -1 ) ) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : Union[str, Any] = self.get_image_processor()
A : int = SamProcessor(image_processor=SCREAMING_SNAKE_CASE )
A : Dict = np.random.randint(0 , 2 , size=(1, 3, 5, 5) ).astype(np.floataa )
A : Tuple = [tf.convert_to_tensor(SCREAMING_SNAKE_CASE )]
A : Optional[Any] = [torch.tensor(SCREAMING_SNAKE_CASE )]
A : Dict = [[1764, 2646]]
A : List[Any] = [[683, 1024]]
A : List[Any] = processor.post_process_masks(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , return_tensors='''tf''' )
A : Optional[Any] = processor.post_process_masks(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy() ) )
@is_pt_tf_cross_test
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : Optional[int] = self.get_image_processor()
A : Union[str, Any] = SamProcessor(image_processor=SCREAMING_SNAKE_CASE )
A : Optional[Any] = self.prepare_image_inputs()
A : Dict = image_processor(SCREAMING_SNAKE_CASE , return_tensors='''pt''' )['''pixel_values'''].numpy()
A : str = processor(images=SCREAMING_SNAKE_CASE , return_tensors='''pt''' )['''pixel_values'''].numpy()
A : List[str] = image_processor(SCREAMING_SNAKE_CASE , return_tensors='''tf''' )['''pixel_values'''].numpy()
A : int = processor(images=SCREAMING_SNAKE_CASE , return_tensors='''tf''' )['''pixel_values'''].numpy()
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
| 343 | 1 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
UpperCAmelCase__ = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
print('''Torch version:''', None)
try:
import deepspeed
print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
print('''DeepSpeed version:''', None)
try:
import tensorflow as tf
print('''TensorFlow version:''', tf.__version__)
print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
print('''TensorFlow version:''', None)
| 351 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class snake_case_ :
"""simple docstring"""
def __init__(self: Optional[int] , __UpperCAmelCase: int , __UpperCAmelCase: List[Any]=13 , __UpperCAmelCase: Any=30 , __UpperCAmelCase: List[str]=2 , __UpperCAmelCase: Optional[int]=3 , __UpperCAmelCase: Tuple=True , __UpperCAmelCase: str=True , __UpperCAmelCase: int=32 , __UpperCAmelCase: Tuple=5 , __UpperCAmelCase: Optional[Any]=4 , __UpperCAmelCase: Dict=37 , __UpperCAmelCase: List[Any]="gelu" , __UpperCAmelCase: List[Any]=0.1 , __UpperCAmelCase: Any=0.1 , __UpperCAmelCase: Optional[Any]=10 , __UpperCAmelCase: str=0.02 , __UpperCAmelCase: Any=3 , __UpperCAmelCase: Tuple=0.6 , __UpperCAmelCase: str=None , ) -> List[Any]:
'''simple docstring'''
__a : int = parent
__a : Any = batch_size
__a : int = image_size
__a : Tuple = patch_size
__a : str = num_channels
__a : List[Any] = is_training
__a : Tuple = use_labels
__a : str = hidden_size
__a : List[Any] = num_hidden_layers
__a : str = num_attention_heads
__a : Any = intermediate_size
__a : str = hidden_act
__a : Union[str, Any] = hidden_dropout_prob
__a : Optional[int] = attention_probs_dropout_prob
__a : Tuple = type_sequence_label_size
__a : Any = initializer_range
__a : Dict = mask_ratio
__a : Union[str, Any] = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
__a : Any = (image_size // patch_size) ** 2
__a : Optional[int] = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def UpperCAmelCase__ (self: Any ) -> str:
'''simple docstring'''
__a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__a : Optional[Any] = None
if self.use_labels:
__a : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : Optional[int] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase__ (self: Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def UpperCAmelCase__ (self: List[Any] , __UpperCAmelCase: str , __UpperCAmelCase: Tuple , __UpperCAmelCase: List[str] ) -> List[str]:
'''simple docstring'''
__a : str = ViTMAEModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__a : int = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase__ (self: Tuple , __UpperCAmelCase: Dict , __UpperCAmelCase: Tuple , __UpperCAmelCase: Dict ) -> Dict:
'''simple docstring'''
__a : Optional[Any] = ViTMAEForPreTraining(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__a : List[str] = model(__UpperCAmelCase )
__a : Union[str, Any] = (self.image_size // self.patch_size) ** 2
__a : Optional[int] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
__a : Optional[Any] = 1
__a : int = ViTMAEForPreTraining(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__a : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__a : Optional[int] = model(__UpperCAmelCase )
__a : List[Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def UpperCAmelCase__ (self: Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__a : List[Any] = self.prepare_config_and_inputs()
__a , __a , __a : Dict = config_and_inputs
__a : Union[str, Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class snake_case_ ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
snake_case__ = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
snake_case__ = {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}
snake_case__ = False
snake_case__ = False
snake_case__ = False
snake_case__ = False
def UpperCAmelCase__ (self: int ) -> Dict:
'''simple docstring'''
__a : List[str] = ViTMAEModelTester(self )
__a : str = ConfigTester(self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase , hidden_size=37 )
def UpperCAmelCase__ (self: Tuple ) -> Dict:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def UpperCAmelCase__ (self: str ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ (self: Dict ) -> str:
'''simple docstring'''
__a , __a : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Tuple = model_class(__UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__a : str = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase , nn.Linear ) )
def UpperCAmelCase__ (self: Dict ) -> Tuple:
'''simple docstring'''
__a , __a : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Any = model_class(__UpperCAmelCase )
__a : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a : Optional[Any] = [*signature.parameters.keys()]
__a : int = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def UpperCAmelCase__ (self: Any ) -> Optional[int]:
'''simple docstring'''
__a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def UpperCAmelCase__ (self: List[str] ) -> Tuple:
'''simple docstring'''
__a : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase )
def UpperCAmelCase__ (self: Dict , __UpperCAmelCase: int , __UpperCAmelCase: Dict , __UpperCAmelCase: str ) -> Dict:
'''simple docstring'''
np.random.seed(2 )
__a : Union[str, Any] = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
__a : List[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
__a : Tuple = torch.from_numpy(__UpperCAmelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
__a : Union[str, Any] = pt_noise
super().check_pt_tf_models(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def UpperCAmelCase__ (self: str ) -> Dict:
'''simple docstring'''
__a , __a : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Union[str, Any] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
__a : Any = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__a : Dict = outputs[0].cpu().numpy()
__a : str = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__UpperCAmelCase )
__a : Any = model_class.from_pretrained(__UpperCAmelCase )
model.to(__UpperCAmelCase )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
__a : Optional[int] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
# Make sure we don't have nans
__a : Dict = after_outputs[0].cpu().numpy()
__a : str = 0
__a : str = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__UpperCAmelCase , 1E-5 )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def UpperCAmelCase__ (self: Union[str, Any] ) -> Any:
'''simple docstring'''
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def UpperCAmelCase__ (self: Optional[int] ) -> Dict:
'''simple docstring'''
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def UpperCAmelCase__ (self: Optional[Any] ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def UpperCAmelCase__ (self: Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def UpperCAmelCase__ (self: List[str] ) -> List[Any]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase__ (self: Tuple ) -> int:
'''simple docstring'''
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : Tuple = ViTMAEModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def a_ () -> Optional[Any]:
"""simple docstring"""
__a : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class snake_case_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase__ (self: Optional[int] ) -> List[str]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def UpperCAmelCase__ (self: str ) -> Union[str, Any]:
'''simple docstring'''
np.random.seed(2 )
__a : int = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(__UpperCAmelCase )
__a : Dict = self.default_image_processor
__a : List[str] = prepare_img()
__a : Tuple = image_processor(images=__UpperCAmelCase , return_tensors="pt" ).to(__UpperCAmelCase )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
__a : Optional[Any] = ViTMAEConfig()
__a : str = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
__a : int = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
__a : Union[str, Any] = model(**__UpperCAmelCase , noise=torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase ) )
# verify the logits
__a : Optional[Any] = torch.Size((1, 196, 768) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__a : Any = torch.tensor(
[[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(__UpperCAmelCase ) , atol=1E-4 ) )
| 351 | 1 |
'''simple docstring'''
from __future__ import annotations
def A ( _UpperCAmelCase : float ,_UpperCAmelCase : float ,_UpperCAmelCase : float ,) -> tuple:
'''simple docstring'''
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )
elif electron_conc < 0:
raise ValueError('Electron concentration cannot be negative in a semiconductor' )
elif hole_conc < 0:
raise ValueError('Hole concentration cannot be negative in a semiconductor' )
elif intrinsic_conc < 0:
raise ValueError(
'Intrinsic concentration cannot be negative in a semiconductor' )
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 123 |
'''simple docstring'''
def A ( _UpperCAmelCase : int = 1_0 ,_UpperCAmelCase : int = 1_0_0_0 ,_UpperCAmelCase : bool = True ) -> int:
'''simple docstring'''
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and isinstance(_UpperCAmelCase ,_UpperCAmelCase )
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError('Invalid value for min_val or max_val (min_value < max_value)' )
return min_val if option else max_val
def A ( _UpperCAmelCase : int ,_UpperCAmelCase : int ) -> int:
'''simple docstring'''
return int((number_a + number_a) / 2 )
def A ( _UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> None:
'''simple docstring'''
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and isinstance(_UpperCAmelCase ,_UpperCAmelCase )
), 'argument values must be type of "int"'
if lower > higher:
raise ValueError('argument value for lower and higher must be(lower > higher)' )
if not lower < to_guess < higher:
raise ValueError(
'guess value must be within the range of lower and higher value' )
def answer(_UpperCAmelCase : int ) -> str:
if number > to_guess:
return "high"
elif number < to_guess:
return "low"
else:
return "same"
print('started...' )
__lowerCAmelCase : Union[str, Any] = lower
__lowerCAmelCase : List[Any] = higher
__lowerCAmelCase : List[str] = []
while True:
__lowerCAmelCase : Union[str, Any] = get_avg(_UpperCAmelCase ,_UpperCAmelCase )
last_numbers.append(_UpperCAmelCase )
if answer(_UpperCAmelCase ) == "low":
__lowerCAmelCase : List[Any] = number
elif answer(_UpperCAmelCase ) == "high":
__lowerCAmelCase : Optional[Any] = number
else:
break
print(F"""guess the number : {last_numbers[-1]}""" )
print(F"""details : {last_numbers!s}""" )
def A ( ) -> None:
'''simple docstring'''
__lowerCAmelCase : int = int(input('Enter lower value : ' ).strip() )
__lowerCAmelCase : Optional[Any] = int(input('Enter high value : ' ).strip() )
__lowerCAmelCase : int = int(input('Enter value to guess : ' ).strip() )
guess_the_number(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
if __name__ == "__main__":
main()
| 123 | 1 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument(
'--original_config_file',
type=str,
required=True,
help='The YAML config file corresponding to the original architecture.',
)
parser.add_argument(
'--num_in_channels',
default=None,
type=int,
help='The number of input channels. If `None` number of input channels will be automatically inferred.',
)
parser.add_argument(
'--image_size',
default=5_12,
type=int,
help=(
'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
' Base. Use 768 for Stable Diffusion v2.'
),
)
parser.add_argument(
'--extract_ema',
action='store_true',
help=(
'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
),
)
parser.add_argument(
'--upcast_attention',
action='store_true',
help=(
'Whether the attention computation should always be upcasted. This is necessary when running stable'
' diffusion 2.1.'
),
)
parser.add_argument(
'--from_safetensors',
action='store_true',
help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
)
parser.add_argument(
'--to_safetensors',
action='store_true',
help='Whether to store pipeline in safetensors format or not.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
def __UpperCamelCase ( lowercase__ : str ) -> Union[str, Any]:
'''simple docstring'''
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(f'could not parse string as bool {string}' )
parser.add_argument(
'--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
)
parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)
__UpperCAmelCase = parser.parse_args()
__UpperCAmelCase = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 600 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __a ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
__snake_case : Optional[Any] = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def __UpperCamelCase ( ) -> Optional[Any]:
'''simple docstring'''
if os.name == "nt":
lowerCAmelCase_ : str = CursorInfo()
lowerCAmelCase_ : Dict = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(lowercase__ , ctypes.byref(lowercase__ ) )
lowerCAmelCase_ : str = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(lowercase__ , ctypes.byref(lowercase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def __UpperCamelCase ( ) -> int:
'''simple docstring'''
if os.name == "nt":
lowerCAmelCase_ : int = CursorInfo()
lowerCAmelCase_ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(lowercase__ , ctypes.byref(lowercase__ ) )
lowerCAmelCase_ : Tuple = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(lowercase__ , ctypes.byref(lowercase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def __UpperCamelCase ( ) -> List[Any]:
'''simple docstring'''
try:
hide_cursor()
yield
finally:
show_cursor()
| 600 | 1 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __A(lowerCAmelCase ) -> List[str]:
"""simple docstring"""
if "model" in orig_key:
_UpperCamelCase = orig_key.replace("""model.""" , """""" )
if "norm1" in orig_key:
_UpperCamelCase = orig_key.replace("""norm1""" , """attention.output.LayerNorm""" )
if "norm2" in orig_key:
_UpperCamelCase = orig_key.replace("""norm2""" , """output.LayerNorm""" )
if "norm" in orig_key:
_UpperCamelCase = orig_key.replace("""norm""" , """LayerNorm""" )
if "transformer" in orig_key:
_UpperCamelCase = orig_key.split(""".""" )[0].split("""_""" )[-1]
_UpperCamelCase = orig_key.replace(F'transformer_{layer_num}' , F'encoder.layer.{layer_num}' )
if "mha.attn" in orig_key:
_UpperCamelCase = orig_key.replace("""mha.attn""" , """attention.self""" )
if "mha" in orig_key:
_UpperCamelCase = orig_key.replace("""mha""" , """attention""" )
if "W_q" in orig_key:
_UpperCamelCase = orig_key.replace("""W_q""" , """self.query""" )
if "W_k" in orig_key:
_UpperCamelCase = orig_key.replace("""W_k""" , """self.key""" )
if "W_v" in orig_key:
_UpperCamelCase = orig_key.replace("""W_v""" , """self.value""" )
if "ff1" in orig_key:
_UpperCamelCase = orig_key.replace("""ff1""" , """intermediate.dense""" )
if "ff2" in orig_key:
_UpperCamelCase = orig_key.replace("""ff2""" , """output.dense""" )
if "ff" in orig_key:
_UpperCamelCase = orig_key.replace("""ff""" , """output.dense""" )
if "mlm_class" in orig_key:
_UpperCamelCase = orig_key.replace("""mlm.mlm_class""" , """cls.predictions.decoder""" )
if "mlm" in orig_key:
_UpperCamelCase = orig_key.replace("""mlm""" , """cls.predictions.transform""" )
if "cls" not in orig_key:
_UpperCamelCase = """yoso.""" + orig_key
return orig_key
def __A(lowerCAmelCase , lowerCAmelCase ) -> List[str]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_UpperCamelCase = orig_state_dict.pop(lowerCAmelCase )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
_UpperCamelCase = val
_UpperCamelCase = orig_state_dict["""cls.predictions.decoder.bias"""]
_UpperCamelCase = torch.arange(lowerCAmelCase ).expand((1, -1) ) + 2
return orig_state_dict
def __A(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = torch.load(lowerCAmelCase , map_location="""cpu""" )["""model_state_dict"""]
_UpperCamelCase = YosoConfig.from_json_file(lowerCAmelCase )
_UpperCamelCase = YosoForMaskedLM(lowerCAmelCase )
_UpperCamelCase = convert_checkpoint_helper(config.max_position_embeddings , lowerCAmelCase )
print(model.load_state_dict(lowerCAmelCase ) )
model.eval()
model.save_pretrained(lowerCAmelCase )
print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}' )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The json file for YOSO model config.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
lowerCamelCase__ = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 202 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger()
@dataclass
class lowerCAmelCase__ :
UpperCamelCase_ : nn.Module
UpperCamelCase_ : List[nn.Module] = field(default_factory=__lowercase )
UpperCamelCase_ : list = field(default_factory=__lowercase )
def A_ ( self , a , a , a ) -> str:
'''simple docstring'''
_UpperCamelCase = len(list(m.modules() ) ) == 1 or isinstance(a , nn.Convad ) or isinstance(a , nn.BatchNormad )
if has_not_submodules:
self.traced.append(a )
def __call__( self , a ) -> Optional[int]:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(a )
[x.remove() for x in self.handles]
return self
@property
def A_ ( self ) -> Optional[int]:
'''simple docstring'''
return list(filter(lambda a : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class lowerCAmelCase__ :
UpperCamelCase_ : nn.Module
UpperCamelCase_ : nn.Module
UpperCamelCase_ : int = 0
UpperCamelCase_ : List = field(default_factory=__lowercase )
UpperCamelCase_ : List = field(default_factory=__lowercase )
def __call__( self , a ) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = Tracker(self.dest )(a ).parametrized
_UpperCamelCase = Tracker(self.src )(a ).parametrized
_UpperCamelCase = list(filter(lambda a : type(a ) not in self.src_skip , a ) )
_UpperCamelCase = list(filter(lambda a : type(a ) not in self.dest_skip , a ) )
if len(a ) != len(a ):
raise Exception(
F'Numbers of operations are different. Source module has {len(a )} operations while'
F' destination module has {len(a )}.' )
for dest_m, src_m in zip(a , a ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'Transfered from={src_m} to={dest_m}' )
def __A(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = True ) -> Optional[Any]:
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
_UpperCamelCase = timm.create_model(lowerCAmelCase , pretrained=lowerCAmelCase ).eval()
_UpperCamelCase = ResNetForImageClassification(lowerCAmelCase ).eval()
_UpperCamelCase = ModuleTransfer(src=lowerCAmelCase , dest=lowerCAmelCase )
_UpperCamelCase = torch.randn((1, 3, 2_2_4, 2_2_4) )
module_transfer(lowerCAmelCase )
assert torch.allclose(from_model(lowerCAmelCase ) , our_model(lowerCAmelCase ).logits ), "The model logits don't match the original one."
_UpperCamelCase = F'resnet{"-".join(name.split("resnet" ) )}'
print(lowerCAmelCase )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add model""" , use_temp_dir=lowerCAmelCase , )
# we can use the convnext one
_UpperCamelCase = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add image processor""" , use_temp_dir=lowerCAmelCase , )
print(F'Pushed {checkpoint_name}' )
def __A(lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = True ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = """imagenet-1k-id2label.json"""
_UpperCamelCase = 1_0_0_0
_UpperCamelCase = (1, num_labels)
_UpperCamelCase = """huggingface/label-files"""
_UpperCamelCase = num_labels
_UpperCamelCase = json.load(open(hf_hub_download(lowerCAmelCase , lowerCAmelCase , repo_type="""dataset""" ) , """r""" ) )
_UpperCamelCase = {int(lowerCAmelCase ): v for k, v in idalabel.items()}
_UpperCamelCase = idalabel
_UpperCamelCase = {v: k for k, v in idalabel.items()}
_UpperCamelCase = partial(lowerCAmelCase , num_labels=lowerCAmelCase , idalabel=lowerCAmelCase , labelaid=lowerCAmelCase )
_UpperCamelCase = {
"""resnet18""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type="""basic""" ),
"""resnet26""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet34""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type="""basic""" ),
"""resnet50""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet101""": ImageNetPreTrainedConfig(
depths=[3, 4, 2_3, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
"""resnet152""": ImageNetPreTrainedConfig(
depths=[3, 8, 3_6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="""bottleneck""" ),
}
if model_name:
convert_weight_and_push(lowerCAmelCase , names_to_config[model_name] , lowerCAmelCase , lowerCAmelCase )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
return config, expected_shape
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
lowerCamelCase__ = parser.parse_args()
lowerCamelCase__ = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 202 | 1 |
def _SCREAMING_SNAKE_CASE ( __lowercase : Any , __lowercase : int ) -> int:
"""simple docstring"""
return number | (1 << position)
def _SCREAMING_SNAKE_CASE ( __lowercase : Any , __lowercase : Optional[int] ) -> int:
"""simple docstring"""
return number & ~(1 << position)
def _SCREAMING_SNAKE_CASE ( __lowercase : Optional[Any] , __lowercase : Any ) -> int:
"""simple docstring"""
return number ^ (1 << position)
def _SCREAMING_SNAKE_CASE ( __lowercase : Optional[int] , __lowercase : List[str] ) -> bool:
"""simple docstring"""
return ((number >> position) & 1) == 1
def _SCREAMING_SNAKE_CASE ( __lowercase : Optional[Any] , __lowercase : Dict ) -> int:
"""simple docstring"""
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 637 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
UpperCamelCase_ = {
'n_samples': 6_4,
'horizon': 3_2,
'num_inference_steps': 2_0,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use value network
'scale_grad_by_std': True,
'scale': 0.1,
'eta': 0.0,
't_grad_cutoff': 2,
'device': 'cpu',
}
if __name__ == "__main__":
UpperCamelCase_ = 'hopper-medium-v2'
UpperCamelCase_ = gym.make(env_name)
UpperCamelCase_ = ValueGuidedRLPipeline.from_pretrained(
'bglick13/hopper-medium-v2-value-function-hor32',
env=env,
)
env.seed(0)
UpperCamelCase_ = env.reset()
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = 1_0_0_0
UpperCamelCase_ = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
UpperCamelCase_ = pipeline(obs, planning_horizon=3_2)
# execute action in environment
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = env.step(denorm_actions)
UpperCamelCase_ = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
f' {total_score}'
)
# save observations for rendering
rollout.append(next_observation.copy())
UpperCamelCase_ = next_observation
except KeyboardInterrupt:
pass
print(f'Total reward: {total_reward}')
| 132 | 0 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class A_ ( _a ):
_UpperCAmelCase : jnp.ndarray
@flax_register_to_config
class A_ ( nn.Module , _a , _a ):
_UpperCAmelCase : int = 32
_UpperCAmelCase : int = 4
_UpperCAmelCase : int = 4
_UpperCAmelCase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_UpperCAmelCase : Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
_UpperCAmelCase : Union[bool, Tuple[bool]] = False
_UpperCAmelCase : Tuple[int] = (320, 640, 1_280, 1_280)
_UpperCAmelCase : int = 2
_UpperCAmelCase : Union[int, Tuple[int]] = 8
_UpperCAmelCase : Optional[Union[int, Tuple[int]]] = None
_UpperCAmelCase : int = 1_280
_UpperCAmelCase : float = 0.0
_UpperCAmelCase : bool = False
_UpperCAmelCase : jnp.dtype = jnp.floataa
_UpperCAmelCase : bool = True
_UpperCAmelCase : int = 0
_UpperCAmelCase : bool = False
def lowerCAmelCase ( self : List[str] ,SCREAMING_SNAKE_CASE__ : List[Any]):
# init input tensors
__lowerCamelCase : List[str] = (1, self.in_channels, self.sample_size, self.sample_size)
__lowerCamelCase : Optional[Any] = jnp.zeros(snake_case_ ,dtype=jnp.floataa)
__lowerCamelCase : Tuple = jnp.ones((1,) ,dtype=jnp.intaa)
__lowerCamelCase : Tuple = jnp.zeros((1, 1, self.cross_attention_dim) ,dtype=jnp.floataa)
__lowerCamelCase , __lowerCamelCase : int = jax.random.split(snake_case_)
__lowerCamelCase : str = {'params': params_rng, 'dropout': dropout_rng}
return self.init(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_)["params"]
def lowerCAmelCase ( self : List[Any]):
__lowerCamelCase : List[Any] = self.block_out_channels
__lowerCamelCase : str = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
'At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.')
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
__lowerCamelCase : Optional[int] = self.num_attention_heads or self.attention_head_dim
# input
__lowerCamelCase : List[str] = nn.Conv(
block_out_channels[0] ,kernel_size=(3, 3) ,strides=(1, 1) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
# time
__lowerCamelCase : Optional[int] = FlaxTimesteps(
block_out_channels[0] ,flip_sin_to_cos=self.flip_sin_to_cos ,freq_shift=self.config.freq_shift)
__lowerCamelCase : List[str] = FlaxTimestepEmbedding(snake_case_ ,dtype=self.dtype)
__lowerCamelCase : Optional[Any] = self.only_cross_attention
if isinstance(snake_case_ ,snake_case_):
__lowerCamelCase : Any = (only_cross_attention,) * len(self.down_block_types)
if isinstance(snake_case_ ,snake_case_):
__lowerCamelCase : int = (num_attention_heads,) * len(self.down_block_types)
# down
__lowerCamelCase : List[Any] = []
__lowerCamelCase : str = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types):
__lowerCamelCase : Dict = output_channel
__lowerCamelCase : Union[str, Any] = block_out_channels[i]
__lowerCamelCase : Any = i == len(snake_case_) - 1
if down_block_type == "CrossAttnDownBlock2D":
__lowerCamelCase : int = FlaxCrossAttnDownBlockaD(
in_channels=snake_case_ ,out_channels=snake_case_ ,dropout=self.dropout ,num_layers=self.layers_per_block ,num_attention_heads=num_attention_heads[i] ,add_downsample=not is_final_block ,use_linear_projection=self.use_linear_projection ,only_cross_attention=only_cross_attention[i] ,use_memory_efficient_attention=self.use_memory_efficient_attention ,dtype=self.dtype ,)
else:
__lowerCamelCase : List[Any] = FlaxDownBlockaD(
in_channels=snake_case_ ,out_channels=snake_case_ ,dropout=self.dropout ,num_layers=self.layers_per_block ,add_downsample=not is_final_block ,dtype=self.dtype ,)
down_blocks.append(snake_case_)
__lowerCamelCase : Any = down_blocks
# mid
__lowerCamelCase : int = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] ,dropout=self.dropout ,num_attention_heads=num_attention_heads[-1] ,use_linear_projection=self.use_linear_projection ,use_memory_efficient_attention=self.use_memory_efficient_attention ,dtype=self.dtype ,)
# up
__lowerCamelCase : Any = []
__lowerCamelCase : Dict = list(reversed(snake_case_))
__lowerCamelCase : Any = list(reversed(snake_case_))
__lowerCamelCase : Any = list(reversed(snake_case_))
__lowerCamelCase : Dict = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types):
__lowerCamelCase : Union[str, Any] = output_channel
__lowerCamelCase : str = reversed_block_out_channels[i]
__lowerCamelCase : str = reversed_block_out_channels[min(i + 1 ,len(snake_case_) - 1)]
__lowerCamelCase : int = i == len(snake_case_) - 1
if up_block_type == "CrossAttnUpBlock2D":
__lowerCamelCase : int = FlaxCrossAttnUpBlockaD(
in_channels=snake_case_ ,out_channels=snake_case_ ,prev_output_channel=snake_case_ ,num_layers=self.layers_per_block + 1 ,num_attention_heads=reversed_num_attention_heads[i] ,add_upsample=not is_final_block ,dropout=self.dropout ,use_linear_projection=self.use_linear_projection ,only_cross_attention=only_cross_attention[i] ,use_memory_efficient_attention=self.use_memory_efficient_attention ,dtype=self.dtype ,)
else:
__lowerCamelCase : Union[str, Any] = FlaxUpBlockaD(
in_channels=snake_case_ ,out_channels=snake_case_ ,prev_output_channel=snake_case_ ,num_layers=self.layers_per_block + 1 ,add_upsample=not is_final_block ,dropout=self.dropout ,dtype=self.dtype ,)
up_blocks.append(snake_case_)
__lowerCamelCase : Dict = output_channel
__lowerCamelCase : Any = up_blocks
# out
__lowerCamelCase : Tuple = nn.GroupNorm(num_groups=3_2 ,epsilon=1E-5)
__lowerCamelCase : List[str] = nn.Conv(
self.out_channels ,kernel_size=(3, 3) ,strides=(1, 1) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
def __call__( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : Dict ,SCREAMING_SNAKE_CASE__ : str ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Tuple=None ,SCREAMING_SNAKE_CASE__ : str=None ,SCREAMING_SNAKE_CASE__ : Optional[Any] = True ,SCREAMING_SNAKE_CASE__ : List[Any] = False ,):
# 1. time
if not isinstance(snake_case_ ,jnp.ndarray):
__lowerCamelCase : Optional[Any] = jnp.array([timesteps] ,dtype=jnp.intaa)
elif isinstance(snake_case_ ,jnp.ndarray) and len(timesteps.shape) == 0:
__lowerCamelCase : List[str] = timesteps.astype(dtype=jnp.floataa)
__lowerCamelCase : Optional[Any] = jnp.expand_dims(snake_case_ ,0)
__lowerCamelCase : Any = self.time_proj(snake_case_)
__lowerCamelCase : Dict = self.time_embedding(snake_case_)
# 2. pre-process
__lowerCamelCase : List[Any] = jnp.transpose(snake_case_ ,(0, 2, 3, 1))
__lowerCamelCase : List[Any] = self.conv_in(snake_case_)
# 3. down
__lowerCamelCase : Optional[Any] = (sample,)
for down_block in self.down_blocks:
if isinstance(snake_case_ ,snake_case_):
__lowerCamelCase , __lowerCamelCase : Dict = down_block(snake_case_ ,snake_case_ ,snake_case_ ,deterministic=not train)
else:
__lowerCamelCase , __lowerCamelCase : Tuple = down_block(snake_case_ ,snake_case_ ,deterministic=not train)
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
__lowerCamelCase : Tuple = ()
for down_block_res_sample, down_block_additional_residual in zip(
snake_case_ ,snake_case_):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
__lowerCamelCase : Union[str, Any] = new_down_block_res_samples
# 4. mid
__lowerCamelCase : Tuple = self.mid_block(snake_case_ ,snake_case_ ,snake_case_ ,deterministic=not train)
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
__lowerCamelCase : str = down_block_res_samples[-(self.layers_per_block + 1) :]
__lowerCamelCase : List[Any] = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(snake_case_ ,snake_case_):
__lowerCamelCase : List[str] = up_block(
snake_case_ ,temb=snake_case_ ,encoder_hidden_states=snake_case_ ,res_hidden_states_tuple=snake_case_ ,deterministic=not train ,)
else:
__lowerCamelCase : str = up_block(snake_case_ ,temb=snake_case_ ,res_hidden_states_tuple=snake_case_ ,deterministic=not train)
# 6. post-process
__lowerCamelCase : Optional[int] = self.conv_norm_out(snake_case_)
__lowerCamelCase : List[Any] = nn.silu(snake_case_)
__lowerCamelCase : Optional[Any] = self.conv_out(snake_case_)
__lowerCamelCase : str = jnp.transpose(snake_case_ ,(0, 3, 1, 2))
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=snake_case_)
| 720 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class A_ :
def lowerCAmelCase ( self : Tuple):
torch.manual_seed(0)
__lowerCamelCase : Optional[int] = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')
torch.manual_seed(0)
__lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')
torch.manual_seed(0)
__lowerCamelCase : List[str] = UNetaDConditionModel(
sample_size=3_2 ,layers_per_block=1 ,block_out_channels=[3_2, 6_4] ,down_block_types=[
'ResnetDownsampleBlock2D',
'SimpleCrossAttnDownBlock2D',
] ,mid_block_type='UNetMidBlock2DSimpleCrossAttn' ,up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'] ,in_channels=3 ,out_channels=6 ,cross_attention_dim=3_2 ,encoder_hid_dim=3_2 ,attention_head_dim=8 ,addition_embed_type='text' ,addition_embed_type_num_heads=2 ,cross_attention_norm='group_norm' ,resnet_time_scale_shift='scale_shift' ,act_fn='gelu' ,)
unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
torch.manual_seed(0)
__lowerCamelCase : Dict = DDPMScheduler(
num_train_timesteps=1_0_0_0 ,beta_schedule='squaredcos_cap_v2' ,beta_start=0.0001 ,beta_end=0.02 ,thresholding=SCREAMING_SNAKE_CASE__ ,dynamic_thresholding_ratio=0.95 ,sample_max_value=1.0 ,prediction_type='epsilon' ,variance_type='learned_range' ,)
torch.manual_seed(0)
__lowerCamelCase : List[Any] = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def lowerCAmelCase ( self : Any):
torch.manual_seed(0)
__lowerCamelCase : int = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')
torch.manual_seed(0)
__lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')
torch.manual_seed(0)
__lowerCamelCase : Any = UNetaDConditionModel(
sample_size=3_2 ,layers_per_block=[1, 2] ,block_out_channels=[3_2, 6_4] ,down_block_types=[
'ResnetDownsampleBlock2D',
'SimpleCrossAttnDownBlock2D',
] ,mid_block_type='UNetMidBlock2DSimpleCrossAttn' ,up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'] ,in_channels=6 ,out_channels=6 ,cross_attention_dim=3_2 ,encoder_hid_dim=3_2 ,attention_head_dim=8 ,addition_embed_type='text' ,addition_embed_type_num_heads=2 ,cross_attention_norm='group_norm' ,resnet_time_scale_shift='scale_shift' ,act_fn='gelu' ,class_embed_type='timestep' ,mid_block_scale_factor=1.414 ,time_embedding_act_fn='gelu' ,time_embedding_dim=3_2 ,)
unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
torch.manual_seed(0)
__lowerCamelCase : str = DDPMScheduler(
num_train_timesteps=1_0_0_0 ,beta_schedule='squaredcos_cap_v2' ,beta_start=0.0001 ,beta_end=0.02 ,thresholding=SCREAMING_SNAKE_CASE__ ,dynamic_thresholding_ratio=0.95 ,sample_max_value=1.0 ,prediction_type='epsilon' ,variance_type='learned_range' ,)
torch.manual_seed(0)
__lowerCamelCase : Union[str, Any] = DDPMScheduler(
num_train_timesteps=1_0_0_0 ,beta_schedule='squaredcos_cap_v2' ,beta_start=0.0001 ,beta_end=0.02 ,)
torch.manual_seed(0)
__lowerCamelCase : Any = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def lowerCAmelCase ( self : str):
__lowerCamelCase : Union[str, Any] = self.get_dummy_components()
__lowerCamelCase : Tuple = self.pipeline_class(**SCREAMING_SNAKE_CASE__)
pipe.to(SCREAMING_SNAKE_CASE__)
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : str = inputs['prompt']
__lowerCamelCase : str = inputs['generator']
__lowerCamelCase : List[Any] = inputs['num_inference_steps']
__lowerCamelCase : Optional[Any] = inputs['output_type']
if "image" in inputs:
__lowerCamelCase : Dict = inputs['image']
else:
__lowerCamelCase : Optional[Any] = None
if "mask_image" in inputs:
__lowerCamelCase : Optional[int] = inputs['mask_image']
else:
__lowerCamelCase : Dict = None
if "original_image" in inputs:
__lowerCamelCase : Dict = inputs['original_image']
else:
__lowerCamelCase : Optional[Any] = None
__lowerCamelCase , __lowerCamelCase : Optional[Any] = pipe.encode_prompt(SCREAMING_SNAKE_CASE__)
# inputs with prompt converted to embeddings
__lowerCamelCase : Union[str, Any] = {
'prompt_embeds': prompt_embeds,
'negative_prompt_embeds': negative_prompt_embeds,
'generator': generator,
'num_inference_steps': num_inference_steps,
'output_type': output_type,
}
if image is not None:
__lowerCamelCase : List[str] = image
if mask_image is not None:
__lowerCamelCase : List[Any] = mask_image
if original_image is not None:
__lowerCamelCase : Optional[Any] = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[Any] = pipe(**SCREAMING_SNAKE_CASE__)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE__)
pipe_loaded.to(SCREAMING_SNAKE_CASE__)
pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__) is None ,F"`{optional_component}` did not stay set to None after loading." ,)
__lowerCamelCase : str = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[int] = inputs['generator']
__lowerCamelCase : Any = inputs['num_inference_steps']
__lowerCamelCase : List[str] = inputs['output_type']
# inputs with prompt converted to embeddings
__lowerCamelCase : Any = {
'prompt_embeds': prompt_embeds,
'negative_prompt_embeds': negative_prompt_embeds,
'generator': generator,
'num_inference_steps': num_inference_steps,
'output_type': output_type,
}
if image is not None:
__lowerCamelCase : Optional[int] = image
if mask_image is not None:
__lowerCamelCase : int = mask_image
if original_image is not None:
__lowerCamelCase : int = original_image
__lowerCamelCase : List[Any] = pipe_loaded(**SCREAMING_SNAKE_CASE__)[0]
__lowerCamelCase : Dict = np.abs(to_np(SCREAMING_SNAKE_CASE__) - to_np(SCREAMING_SNAKE_CASE__)).max()
self.assertLess(SCREAMING_SNAKE_CASE__ ,1E-4)
def lowerCAmelCase ( self : List[Any]):
__lowerCamelCase : str = self.get_dummy_components()
__lowerCamelCase : Optional[int] = self.pipeline_class(**SCREAMING_SNAKE_CASE__)
pipe.to(SCREAMING_SNAKE_CASE__)
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = pipe(**SCREAMING_SNAKE_CASE__)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE__)
pipe_loaded.to(SCREAMING_SNAKE_CASE__)
pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests
__lowerCamelCase : str = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Union[str, Any] = pipe_loaded(**SCREAMING_SNAKE_CASE__)[0]
__lowerCamelCase : int = np.abs(to_np(SCREAMING_SNAKE_CASE__) - to_np(SCREAMING_SNAKE_CASE__)).max()
self.assertLess(SCREAMING_SNAKE_CASE__ ,1E-4)
| 337 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=18 , SCREAMING_SNAKE_CASE__=30 , SCREAMING_SNAKE_CASE__=400 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=True , ) -> Any:
A__ = size if size is not None else {"height": 18, "width": 18}
A__ = parent
A__ = batch_size
A__ = num_channels
A__ = image_size
A__ = min_resolution
A__ = max_resolution
A__ = do_resize
A__ = size
A__ = apply_ocr
def snake_case__ ( self ) -> List[Any]:
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class UpperCamelCase__ ( _lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
A__ : Any = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def snake_case__ ( self ) -> str:
A__ = LayoutLMvaImageProcessingTester(self )
@property
def snake_case__ ( self ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case__ ( self ) -> Optional[Any]:
A__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "do_resize" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "size" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , "apply_ocr" ) )
def snake_case__ ( self ) -> Dict:
A__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 18, "width": 18} )
A__ = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"height": 42, "width": 42} )
def snake_case__ ( self ) -> List[str]:
pass
def snake_case__ ( self ) -> Tuple:
# Initialize image_processing
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="pt" )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
self.assertIsInstance(encoding.words , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(encoding.boxes , SCREAMING_SNAKE_CASE__ )
# Test batched
A__ = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def snake_case__ ( self ) -> Optional[int]:
# Initialize image_processing
A__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE__ , numpify=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , np.ndarray )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
A__ = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def snake_case__ ( self ) -> Tuple:
# Initialize image_processing
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE__ , torchify=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , torch.Tensor )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
A__ = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def snake_case__ ( self ) -> int:
# with apply_OCR = True
A__ = LayoutLMvaImageProcessor()
from datasets import load_dataset
A__ = load_dataset("hf-internal-testing/fixtures_docvqa" , split="test" )
A__ = Image.open(ds[0]["file"] ).convert("RGB" )
A__ = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors="pt" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
A__ = [["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]] # noqa: E231
A__ = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(encoding.boxes , SCREAMING_SNAKE_CASE__ )
# with apply_OCR = False
A__ = LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE__ )
A__ = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors="pt" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
| 104 |
"""simple docstring"""
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
A__ : List[str] = "data2vec-audio"
def __init__( self , SCREAMING_SNAKE_CASE__=32 , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=3072 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.0_2 , SCREAMING_SNAKE_CASE__=1e-5 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 512, 512, 512) , SCREAMING_SNAKE_CASE__=(5, 2, 2, 2, 2, 2, 2) , SCREAMING_SNAKE_CASE__=(10, 3, 3, 3, 3, 2, 2) , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=19 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=0.0_5 , SCREAMING_SNAKE_CASE__=10 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=10 , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__="sum" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 1500) , SCREAMING_SNAKE_CASE__=(5, 3, 3, 1, 1) , SCREAMING_SNAKE_CASE__=(1, 2, 3, 1, 1) , SCREAMING_SNAKE_CASE__=512 , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ , ) -> Dict:
super().__init__(**SCREAMING_SNAKE_CASE__ , pad_token_id=SCREAMING_SNAKE_CASE__ , bos_token_id=SCREAMING_SNAKE_CASE__ , eos_token_id=SCREAMING_SNAKE_CASE__ )
A__ = hidden_size
A__ = feat_extract_activation
A__ = list(SCREAMING_SNAKE_CASE__ )
A__ = list(SCREAMING_SNAKE_CASE__ )
A__ = list(SCREAMING_SNAKE_CASE__ )
A__ = conv_bias
A__ = num_conv_pos_embeddings
A__ = num_conv_pos_embedding_groups
A__ = conv_pos_kernel_size
A__ = len(self.conv_dim )
A__ = num_hidden_layers
A__ = intermediate_size
A__ = hidden_act
A__ = num_attention_heads
A__ = hidden_dropout
A__ = attention_dropout
A__ = activation_dropout
A__ = feat_proj_dropout
A__ = final_dropout
A__ = layerdrop
A__ = layer_norm_eps
A__ = initializer_range
A__ = vocab_size
A__ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
A__ = mask_time_prob
A__ = mask_time_length
A__ = mask_time_min_masks
A__ = mask_feature_prob
A__ = mask_feature_length
A__ = mask_feature_min_masks
# ctc loss
A__ = ctc_loss_reduction
A__ = ctc_zero_infinity
# adapter
A__ = add_adapter
A__ = adapter_kernel_size
A__ = adapter_stride
A__ = num_adapter_layers
A__ = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
A__ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
A__ = list(SCREAMING_SNAKE_CASE__ )
A__ = list(SCREAMING_SNAKE_CASE__ )
A__ = list(SCREAMING_SNAKE_CASE__ )
A__ = xvector_output_dim
@property
def snake_case__ ( self ) -> List[str]:
return math.prod(self.conv_stride )
| 104 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 718 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
SCREAMING_SNAKE_CASE : List[Any] = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
SCREAMING_SNAKE_CASE : Optional[int] = (
subprocess.check_output(F"git diff --diff-filter=d --name-only {fork_point_sha}".split()).decode("utf-8").split()
)
SCREAMING_SNAKE_CASE : Any = "|".join(sys.argv[1:])
SCREAMING_SNAKE_CASE : Union[str, Any] = re.compile(rF"^({joined_dirs}).*?\.py$")
SCREAMING_SNAKE_CASE : List[Any] = [x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")
| 354 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
_UpperCAmelCase : str = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
_UpperCAmelCase : Tuple = {'''facebook/blenderbot-3B''': 1_28}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _SCREAMING_SNAKE_CASE ( ):
_A = (
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
_A = bs[:]
_A = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__snake_case )
cs.append(2**8 + n )
n += 1
_A = [chr(__snake_case ) for n in cs]
return dict(zip(__snake_case , __snake_case ) )
def _SCREAMING_SNAKE_CASE ( __snake_case : Any ):
_A = set()
_A = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_A = char
return pairs
class lowercase_ ( _UpperCamelCase ):
"""simple docstring"""
__lowerCAmelCase = VOCAB_FILES_NAMES
__lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase = ["input_ids", "attention_mask"]
def __init__( self : Optional[Any], UpperCamelCase__ : Any, UpperCamelCase__ : Dict, UpperCamelCase__ : Optional[int]="replace", UpperCamelCase__ : List[str]="<s>", UpperCamelCase__ : Any="</s>", UpperCamelCase__ : List[str]="</s>", UpperCamelCase__ : List[Any]="<s>", UpperCamelCase__ : Optional[Any]="<unk>", UpperCamelCase__ : Tuple="<pad>", UpperCamelCase__ : Optional[int]="<mask>", UpperCamelCase__ : List[str]=False, **UpperCamelCase__ : Optional[Any], ) -> Dict:
_A = AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__, UpperCamelCase__ ) else bos_token
_A = AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__, UpperCamelCase__ ) else eos_token
_A = AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__, UpperCamelCase__ ) else sep_token
_A = AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__, UpperCamelCase__ ) else cls_token
_A = AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__, UpperCamelCase__ ) else unk_token
_A = AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__, UpperCamelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__, UpperCamelCase__ ) else mask_token
super().__init__(
errors=UpperCamelCase__, bos_token=UpperCamelCase__, eos_token=UpperCamelCase__, unk_token=UpperCamelCase__, sep_token=UpperCamelCase__, cls_token=UpperCamelCase__, pad_token=UpperCamelCase__, mask_token=UpperCamelCase__, add_prefix_space=UpperCamelCase__, **UpperCamelCase__, )
with open(UpperCamelCase__, encoding='utf-8' ) as vocab_handle:
_A = json.load(UpperCamelCase__ )
_A = {v: k for k, v in self.encoder.items()}
_A = errors # how to handle errors in decoding
_A = bytes_to_unicode()
_A = {v: k for k, v in self.byte_encoder.items()}
with open(UpperCamelCase__, encoding='utf-8' ) as merges_handle:
_A = merges_handle.read().split('\n' )[1:-1]
_A = [tuple(merge.split() ) for merge in bpe_merges]
_A = dict(zip(UpperCamelCase__, range(len(UpperCamelCase__ ) ) ) )
_A = {}
_A = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_A = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def __UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
return len(self.encoder )
def __UpperCAmelCase ( self : List[Any] ) -> Tuple:
return dict(self.encoder, **self.added_tokens_encoder )
def __UpperCAmelCase ( self : str, UpperCamelCase__ : Optional[int] ) -> Any:
if token in self.cache:
return self.cache[token]
_A = tuple(UpperCamelCase__ )
_A = get_pairs(UpperCamelCase__ )
if not pairs:
return token
while True:
_A = min(UpperCamelCase__, key=lambda UpperCamelCase__ : self.bpe_ranks.get(UpperCamelCase__, float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
_A , _A = bigram
_A = []
_A = 0
while i < len(UpperCamelCase__ ):
try:
_A = word.index(UpperCamelCase__, UpperCamelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_A = j
if word[i] == first and i < len(UpperCamelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_A = tuple(UpperCamelCase__ )
_A = new_word
if len(UpperCamelCase__ ) == 1:
break
else:
_A = get_pairs(UpperCamelCase__ )
_A = ' '.join(UpperCamelCase__ )
_A = word
return word
def __UpperCAmelCase ( self : Dict, UpperCamelCase__ : int ) -> Optional[Any]:
_A = []
for token in re.findall(self.pat, UpperCamelCase__ ):
_A = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__ ).split(' ' ) )
return bpe_tokens
def __UpperCAmelCase ( self : int, UpperCamelCase__ : Any ) -> Dict:
return self.encoder.get(UpperCamelCase__, self.encoder.get(self.unk_token ) )
def __UpperCAmelCase ( self : List[Any], UpperCamelCase__ : Any ) -> Optional[int]:
return self.decoder.get(UpperCamelCase__ )
def __UpperCAmelCase ( self : int, UpperCamelCase__ : int ) -> Any:
_A = ''.join(UpperCamelCase__ )
_A = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8', errors=self.errors )
return text
def __UpperCAmelCase ( self : Any, UpperCamelCase__ : str, UpperCamelCase__ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(UpperCamelCase__ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_A = os.path.join(
UpperCamelCase__, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
_A = os.path.join(
UpperCamelCase__, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(UpperCamelCase__, 'w', encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=UpperCamelCase__, ensure_ascii=UpperCamelCase__ ) + '\n' )
_A = 0
with open(UpperCamelCase__, 'w', encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda UpperCamelCase__ : kv[1] ):
if index != token_index:
logger.warning(
f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
' Please check that the tokenizer is not corrupted!' )
_A = token_index
writer.write(' '.join(UpperCamelCase__ ) + '\n' )
index += 1
return vocab_file, merge_file
def __UpperCAmelCase ( self : Tuple, UpperCamelCase__ : List[int], UpperCamelCase__ : Optional[List[int]] = None, UpperCamelCase__ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase__, token_ids_a=UpperCamelCase__, already_has_special_tokens=UpperCamelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(UpperCamelCase__ )) + [1]
return [1] + ([0] * len(UpperCamelCase__ )) + [1, 1] + ([0] * len(UpperCamelCase__ )) + [1]
def __UpperCAmelCase ( self : Tuple, UpperCamelCase__ : List[int], UpperCamelCase__ : Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __UpperCAmelCase ( self : Dict, UpperCamelCase__ : Tuple, UpperCamelCase__ : List[Any]=False, **UpperCamelCase__ : int ) -> Tuple:
_A = kwargs.pop('add_prefix_space', self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__ ) > 0 and not text[0].isspace()):
_A = ' ' + text
return (text, kwargs)
def __UpperCAmelCase ( self : List[str], UpperCamelCase__ : List[int], UpperCamelCase__ : Optional[List[int]] = None ) -> Any:
return token_ids_a + [self.eos_token_id]
def __UpperCAmelCase ( self : Any, UpperCamelCase__ : "Conversation" ) -> List[int]:
_A = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(' ' + text )
else:
# Generated responses should contain them already.
inputs.append(UpperCamelCase__ )
_A = ' '.join(UpperCamelCase__ )
_A = self.encode(UpperCamelCase__ )
if len(UpperCamelCase__ ) > self.model_max_length:
_A = input_ids[-self.model_max_length :]
logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.' )
return input_ids
| 107 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
'''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}
class __lowercase( lowercase__ ):
'''simple docstring'''
__a : Any = 'encodec'
def __init__( self , __a=[1.5, 3.0, 6.0, 12.0, 24.0] , __a=24000 , __a=1 , __a=False , __a=None , __a=None , __a=128 , __a=32 , __a=1 , __a=[8, 5, 4, 2] , __a="weight_norm" , __a=7 , __a=7 , __a=3 , __a=2 , __a=True , __a="reflect" , __a=2 , __a=2 , __a=1.0 , __a=1024 , __a=None , __a=True , **__a , ):
__lowerCamelCase : Optional[int] = target_bandwidths
__lowerCamelCase : Dict = sampling_rate
__lowerCamelCase : Tuple = audio_channels
__lowerCamelCase : List[Any] = normalize
__lowerCamelCase : List[str] = chunk_length_s
__lowerCamelCase : Optional[int] = overlap
__lowerCamelCase : List[str] = hidden_size
__lowerCamelCase : Tuple = num_filters
__lowerCamelCase : Optional[Any] = num_residual_layers
__lowerCamelCase : List[Any] = upsampling_ratios
__lowerCamelCase : int = norm_type
__lowerCamelCase : str = kernel_size
__lowerCamelCase : Tuple = last_kernel_size
__lowerCamelCase : str = residual_kernel_size
__lowerCamelCase : Tuple = dilation_growth_rate
__lowerCamelCase : Any = use_causal_conv
__lowerCamelCase : str = pad_mode
__lowerCamelCase : List[str] = compress
__lowerCamelCase : int = num_lstm_layers
__lowerCamelCase : str = trim_right_ratio
__lowerCamelCase : Optional[int] = codebook_size
__lowerCamelCase : Any = codebook_dim if codebook_dim is not None else hidden_size
__lowerCamelCase : Tuple = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}''' )
super().__init__(**__a )
@property
def snake_case_ ( self ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def snake_case_ ( self ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def snake_case_ ( self ):
__lowerCamelCase : str = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def snake_case_ ( self ):
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 594 | 0 |
def __magic_name__( __UpperCAmelCase = 3 , __UpperCAmelCase = 7 , __UpperCAmelCase = 100_0000 ) -> int:
'''simple docstring'''
_lowerCamelCase = 0
_lowerCamelCase = 1
for current_denominator in range(1 , limit + 1 ):
_lowerCamelCase = current_denominator * numerator // denominator
if current_denominator % denominator == 0:
current_numerator -= 1
if current_numerator * max_denominator > current_denominator * max_numerator:
_lowerCamelCase = current_numerator
_lowerCamelCase = current_denominator
return max_numerator
if __name__ == "__main__":
print(solution(numerator=3, denominator=7, limit=100_0000))
| 708 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase = '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
_lowerCamelCase = dict(zip(A_ , range(len(A_ ) ) ) )
_lowerCamelCase = {
'''unk_token''': '''<unk>''',
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
}
_lowerCamelCase = {
'''feature_size''': 1,
'''padding_value''': 0.0,
'''sampling_rate''': 1_60_00,
'''return_attention_mask''': False,
'''do_normalize''': True,
}
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_lowerCamelCase = os.path.join(self.tmpdirname , A_ )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A_ ) + '''\n''' )
with open(self.feature_extraction_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A_ ) + '''\n''' )
# load decoder from hub
_lowerCamelCase = '''hf-internal-testing/ngram-beam-search-decoder'''
def UpperCamelCase_ ( self , **A_ ) -> str:
"""simple docstring"""
_lowerCamelCase = self.add_kwargs_tokens_map.copy()
kwargs.update(A_ )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **A_ )
def UpperCamelCase_ ( self , **A_ ) -> Optional[Any]:
"""simple docstring"""
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **A_ )
def UpperCamelCase_ ( self , **A_ ) -> int:
"""simple docstring"""
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **A_ )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def UpperCamelCase_ ( self ) -> Any:
"""simple docstring"""
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
processor.save_pretrained(self.tmpdirname )
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , A_ )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , A_ )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , A_ )
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
_lowerCamelCase = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['''xx'''] )
with self.assertRaisesRegex(A_ , '''include''' ):
WavaVecaProcessorWithLM(
tokenizer=A_ , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = floats_list((3, 10_00) )
_lowerCamelCase = feature_extractor(A_ , return_tensors='''np''' )
_lowerCamelCase = processor(A_ , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = '''This is a test string'''
_lowerCamelCase = processor(text=A_ )
_lowerCamelCase = tokenizer(A_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCamelCase_ ( self , A_=(2, 10, 16) , A_=77 ) -> Optional[Any]:
"""simple docstring"""
np.random.seed(A_ )
return np.random.rand(*A_ )
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = self._get_dummy_logits(shape=(10, 16) , seed=13 )
_lowerCamelCase = processor.decode(A_ )
_lowerCamelCase = decoder.decode_beams(A_ )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual('''</s> <s> </s>''' , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
def UpperCamelCase_ ( self , A_ ) -> int:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
_lowerCamelCase = processor.batch_decode(A_ )
else:
with get_context(A_ ).Pool() as pool:
_lowerCamelCase = processor.batch_decode(A_ , A_ )
_lowerCamelCase = list(A_ )
with get_context('''fork''' ).Pool() as p:
_lowerCamelCase = decoder.decode_beams_batch(A_ , A_ )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(A_ , decoded_processor.text )
self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''] , decoded_processor.text )
self.assertListEqual(A_ , decoded_processor.logit_score )
self.assertListEqual(A_ , decoded_processor.lm_score )
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = self._get_dummy_logits()
_lowerCamelCase = 15
_lowerCamelCase = -20.0
_lowerCamelCase = -4.0
_lowerCamelCase = processor.batch_decode(
A_ , beam_width=A_ , beam_prune_logp=A_ , token_min_logp=A_ , )
_lowerCamelCase = decoded_processor_out.text
_lowerCamelCase = list(A_ )
with get_context('''fork''' ).Pool() as pool:
_lowerCamelCase = decoder.decode_beams_batch(
A_ , A_ , beam_width=A_ , beam_prune_logp=A_ , token_min_logp=A_ , )
_lowerCamelCase = [d[0][0] for d in decoded_decoder_out]
_lowerCamelCase = [d[0][2] for d in decoded_decoder_out]
_lowerCamelCase = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(A_ , A_ )
self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''] , A_ )
self.assertTrue(np.array_equal(A_ , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , A_ , atol=1E-3 ) )
self.assertTrue(np.array_equal(A_ , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9474] , A_ , atol=1E-3 ) )
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = self._get_dummy_logits()
_lowerCamelCase = 2.0
_lowerCamelCase = 5.0
_lowerCamelCase = -20.0
_lowerCamelCase = True
_lowerCamelCase = processor.batch_decode(
A_ , alpha=A_ , beta=A_ , unk_score_offset=A_ , lm_score_boundary=A_ , )
_lowerCamelCase = decoded_processor_out.text
_lowerCamelCase = list(A_ )
decoder.reset_params(
alpha=A_ , beta=A_ , unk_score_offset=A_ , lm_score_boundary=A_ , )
with get_context('''fork''' ).Pool() as pool:
_lowerCamelCase = decoder.decode_beams_batch(
A_ , A_ , )
_lowerCamelCase = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(A_ , A_ )
self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''] , A_ )
_lowerCamelCase = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , A_ )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = processor.decoder.model_container[processor.decoder._model_key]
_lowerCamelCase = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
_lowerCamelCase = os.listdir(A_ )
_lowerCamelCase = ['''alphabet.json''', '''language_model''']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(A_ , A_ )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
_lowerCamelCase = snapshot_download('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained(A_ )
_lowerCamelCase = processor.decoder.model_container[processor.decoder._model_key]
_lowerCamelCase = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
_lowerCamelCase = os.listdir(A_ )
_lowerCamelCase = os.listdir(A_ )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(A_ , A_ )
def UpperCamelCase_ ( self ) -> int:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = floats_list((3, 10_00) )
_lowerCamelCase = processor_wavaveca(A_ , return_tensors='''np''' )
_lowerCamelCase = processor_auto(A_ , return_tensors='''np''' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
_lowerCamelCase = self._get_dummy_logits()
_lowerCamelCase = processor_wavaveca.batch_decode(A_ )
_lowerCamelCase = processor_auto.batch_decode(A_ )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='''`processor` and `feature_extractor` model input names do not match''' , )
@staticmethod
def UpperCamelCase_ ( A_ , A_ ) -> str:
"""simple docstring"""
_lowerCamelCase = [d[key] for d in offsets]
return retrieved_list
def UpperCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = self._get_dummy_logits()[0]
_lowerCamelCase = processor.decode(A_ , output_word_offsets=A_ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(A_ , A_ ) )
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''end_offset''' ) , [1, 3, 5] )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = self._get_dummy_logits()
_lowerCamelCase = processor.batch_decode(A_ , output_word_offsets=A_ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(A_ , A_ ) )
self.assertListEqual(
[''' '''.join(self.get_from_offsets(A_ , '''word''' ) ) for o in outputs['''word_offsets''']] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''end_offset''' ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def UpperCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
import torch
_lowerCamelCase = load_dataset('''common_voice''' , '''en''' , split='''train''' , streaming=A_ )
_lowerCamelCase = ds.cast_column('''audio''' , datasets.Audio(sampling_rate=1_60_00 ) )
_lowerCamelCase = iter(A_ )
_lowerCamelCase = next(A_ )
_lowerCamelCase = AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
_lowerCamelCase = WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
_lowerCamelCase = processor(sample['''audio''']['''array'''] , return_tensors='''pt''' ).input_values
with torch.no_grad():
_lowerCamelCase = model(A_ ).logits.cpu().numpy()
_lowerCamelCase = processor.decode(logits[0] , output_word_offsets=A_ )
_lowerCamelCase = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
_lowerCamelCase = [
{
'''start_time''': d['''start_offset'''] * time_offset,
'''end_time''': d['''end_offset'''] * time_offset,
'''word''': d['''word'''],
}
for d in output['''word_offsets''']
]
_lowerCamelCase = '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''
# output words
self.assertEqual(''' '''.join(self.get_from_offsets(A_ , '''word''' ) ) , A_ )
self.assertEqual(''' '''.join(self.get_from_offsets(A_ , '''word''' ) ) , output.text )
# output times
_lowerCamelCase = torch.tensor(self.get_from_offsets(A_ , '''start_time''' ) )
_lowerCamelCase = torch.tensor(self.get_from_offsets(A_ , '''end_time''' ) )
# fmt: off
_lowerCamelCase = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
_lowerCamelCase = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(A_ , A_ , atol=0.01 ) )
self.assertTrue(torch.allclose(A_ , A_ , atol=0.01 ) )
| 638 | 0 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def a_ ( __magic_name__ ) -> Dict:
"""simple docstring"""
snake_case : int = model.config
snake_case : Any = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
snake_case : str = MBartConfig(
is_decoder=__a , is_encoder_decoder=__a , add_cross_attention=__a , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=__a , add_final_layer_norm=__a , )
return encoder_config, decoder_config
def a_ ( __magic_name__ ) -> int:
"""simple docstring"""
if "encoder.model" in name:
snake_case : Tuple = name.replace('''encoder.model''' , '''encoder''' )
if "decoder.model" in name:
snake_case : int = name.replace('''decoder.model''' , '''decoder''' )
if "patch_embed.proj" in name:
snake_case : Optional[Any] = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
snake_case : List[Any] = name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if name.startswith('''encoder''' ):
if "layers" in name:
snake_case : str = '''encoder.''' + name
if "attn.proj" in name:
snake_case : Any = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name and "mask" not in name:
snake_case : Union[str, Any] = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
snake_case : Optional[Any] = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
snake_case : Union[str, Any] = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
snake_case : str = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
snake_case : int = name.replace('''mlp.fc2''' , '''output.dense''' )
if name == "encoder.norm.weight":
snake_case : Optional[int] = '''encoder.layernorm.weight'''
if name == "encoder.norm.bias":
snake_case : Optional[Any] = '''encoder.layernorm.bias'''
return name
def a_ ( __magic_name__ , __magic_name__ ) -> Dict:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
snake_case : List[str] = orig_state_dict.pop(__a )
if "qkv" in key:
snake_case : Any = key.split('''.''' )
snake_case : List[Any] = int(key_split[3] )
snake_case : int = int(key_split[5] )
snake_case : Optional[Any] = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
snake_case : Dict = val[:dim, :]
snake_case : Tuple = val[dim : dim * 2, :]
snake_case : Optional[Any] = val[-dim:, :]
else:
snake_case : Optional[Any] = val[:dim]
snake_case : Any = val[dim : dim * 2]
snake_case : Optional[Any] = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
snake_case : List[Any] = val
return orig_state_dict
def a_ ( __magic_name__ , __magic_name__=None , __magic_name__=False ) -> str:
"""simple docstring"""
snake_case : List[Any] = DonutModel.from_pretrained(__a ).eval()
# load HuggingFace model
snake_case : str = get_configs(__a )
snake_case : Optional[int] = DonutSwinModel(__a )
snake_case : Tuple = MBartForCausalLM(__a )
snake_case : int = VisionEncoderDecoderModel(encoder=__a , decoder=__a )
model.eval()
snake_case : Any = original_model.state_dict()
snake_case : Optional[int] = convert_state_dict(__a , __a )
model.load_state_dict(__a )
# verify results on scanned document
snake_case : Any = load_dataset('''hf-internal-testing/example-documents''' )
snake_case : str = dataset['''test'''][0]['''image'''].convert('''RGB''' )
snake_case : List[Any] = XLMRobertaTokenizerFast.from_pretrained(__a , from_slow=__a )
snake_case : Any = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
snake_case : List[Any] = DonutProcessor(__a , __a )
snake_case : Any = processor(__a , return_tensors='''pt''' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
snake_case : str = '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
snake_case : Tuple = '''When is the coffee break?'''
snake_case : List[Any] = task_prompt.replace('''{user_input}''' , __a )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
snake_case : Any = '''<s_rvlcdip>'''
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
snake_case : int = '''<s_cord>'''
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
snake_case : Any = '''s_cord-v2>'''
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
snake_case : str = '''<s_zhtrainticket>'''
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
snake_case : List[str] = '''hello world'''
else:
raise ValueError('''Model name not supported''' )
snake_case : Any = original_model.decoder.tokenizer(__a , add_special_tokens=__a , return_tensors='''pt''' )[
'''input_ids'''
]
snake_case : Tuple = original_model.encoder.model.patch_embed(__a )
snake_case : Dict = model.encoder.embeddings(__a )
assert torch.allclose(__a , __a , atol=1e-3 )
# verify encoder hidden states
snake_case : str = original_model.encoder(__a )
snake_case : Any = model.encoder(__a ).last_hidden_state
assert torch.allclose(__a , __a , atol=1e-2 )
# verify decoder hidden states
snake_case : int = original_model(__a , __a , __a ).logits
snake_case : Tuple = model(__a , decoder_input_ids=__a ).logits
assert torch.allclose(__a , __a , atol=1e-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(F"Saving model and processor to {pytorch_dump_folder_path}" )
model.save_pretrained(__a )
processor.save_pretrained(__a )
if push_to_hub:
model.push_to_hub('''nielsr/''' + model_name.split('''/''' )[-1] , commit_message='''Update model''' )
processor.push_to_hub('''nielsr/''' + model_name.split('''/''' )[-1] , commit_message='''Update model''' )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='naver-clova-ix/donut-base-finetuned-docvqa',
required=False,
type=str,
help='Name of the original model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
required=False,
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether or not to push the converted model and processor to the 🤗 hub.',
)
_a : List[Any] = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 598 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")
UpperCAmelCase_ : str = logging.getLogger(__name__)
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
__UpperCamelCase = field(
default=1_2_8 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
} , )
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."} )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Train language if it is different from the evaluation language."} )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"} , )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
__UpperCamelCase = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} , )
def _A () -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_xnli''' , __a )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ : int = training_args.get_process_log_level()
logger.setLevel(__a )
datasets.utils.logging.set_verbosity(__a )
transformers.utils.logging.set_verbosity(__a )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE_ : Tuple = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE_ : int = load_dataset(
'''xnli''' , model_args.language , split='''train''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE_ : Any = load_dataset(
'''xnli''' , model_args.train_language , split='''train''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE_ : Optional[Any] = train_dataset.features['''label'''].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE_ : Dict = load_dataset(
'''xnli''' , model_args.language , split='''validation''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE_ : Tuple = eval_dataset.features['''label'''].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE_ : Optional[int] = load_dataset(
'''xnli''' , model_args.language , split='''test''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE_ : str = predict_dataset.features['''label'''].names
# Labels
SCREAMING_SNAKE_CASE_ : List[Any] = len(__a )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE_ : Optional[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__a , idalabel={str(__a ): label for i, label in enumerate(__a )} , labelaid={label: i for i, label in enumerate(__a )} , finetuning_task='''xnli''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE_ : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE_ : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__a , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE_ : Any = '''max_length'''
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE_ : Union[str, Any] = False
def preprocess_function(__a ):
# Tokenize the texts
return tokenizer(
examples['''premise'''] , examples['''hypothesis'''] , padding=__a , max_length=data_args.max_seq_length , truncation=__a , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE_ : Optional[Any] = min(len(__a ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE_ : Optional[int] = train_dataset.select(range(__a ) )
with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = train_dataset.map(
__a , batched=__a , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on train dataset''' , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__a ) ) , 3 ):
logger.info(f'Sample {index} of the training set: {train_dataset[index]}.' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE_ : Dict = min(len(__a ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE_ : str = eval_dataset.select(range(__a ) )
with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = eval_dataset.map(
__a , batched=__a , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on validation dataset''' , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE_ : Dict = min(len(__a ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE_ : List[str] = predict_dataset.select(range(__a ) )
with training_args.main_process_first(desc='''prediction dataset map pre-processing''' ):
SCREAMING_SNAKE_CASE_ : int = predict_dataset.map(
__a , batched=__a , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on prediction dataset''' , )
# Get the metric function
SCREAMING_SNAKE_CASE_ : List[str] = evaluate.load('''xnli''' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__a ):
SCREAMING_SNAKE_CASE_ : Any = p.predictions[0] if isinstance(p.predictions , __a ) else p.predictions
SCREAMING_SNAKE_CASE_ : Tuple = np.argmax(__a , axis=1 )
return metric.compute(predictions=__a , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE_ : str = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE_ : List[Any] = DataCollatorWithPadding(__a , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE_ : List[str] = Trainer(
model=__a , args=__a , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__a , tokenizer=__a , data_collator=__a , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE_ : List[str] = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE_ : Dict = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE_ : List[str] = last_checkpoint
SCREAMING_SNAKE_CASE_ : Any = trainer.train(resume_from_checkpoint=__a )
SCREAMING_SNAKE_CASE_ : List[Any] = train_result.metrics
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__a )
)
SCREAMING_SNAKE_CASE_ : Optional[Any] = min(__a , len(__a ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('''train''' , __a )
trainer.save_metrics('''train''' , __a )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
SCREAMING_SNAKE_CASE_ : List[str] = trainer.evaluate(eval_dataset=__a )
SCREAMING_SNAKE_CASE_ : List[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__a )
SCREAMING_SNAKE_CASE_ : str = min(__a , len(__a ) )
trainer.log_metrics('''eval''' , __a )
trainer.save_metrics('''eval''' , __a )
# Prediction
if training_args.do_predict:
logger.info('''*** Predict ***''' )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[str] = trainer.predict(__a , metric_key_prefix='''predict''' )
SCREAMING_SNAKE_CASE_ : List[Any] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__a )
)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = min(__a , len(__a ) )
trainer.log_metrics('''predict''' , __a )
trainer.save_metrics('''predict''' , __a )
SCREAMING_SNAKE_CASE_ : Any = np.argmax(__a , axis=1 )
SCREAMING_SNAKE_CASE_ : List[Any] = os.path.join(training_args.output_dir , '''predictions.txt''' )
if trainer.is_world_process_zero():
with open(__a , '''w''' ) as writer:
writer.write('''index\tprediction\n''' )
for index, item in enumerate(__a ):
SCREAMING_SNAKE_CASE_ : str = label_list[item]
writer.write(f'{index}\t{item}\n' )
if __name__ == "__main__":
main()
| 512 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class _a ( unittest.TestCase ):
'''simple docstring'''
@slow
def _A ( self ):
"""simple docstring"""
a__ : int = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small" , return_dict=__UpperCAmelCase ).to(__UpperCAmelCase )
a__ : Any = AutoTokenizer.from_pretrained("google/mt5-small" )
a__ : Optional[int] = tokenizer("Hello there" , return_tensors="pt" ).input_ids
a__ : Tuple = tokenizer("Hi I am" , return_tensors="pt" ).input_ids
a__ : Dict = model(input_ids.to(__UpperCAmelCase ) , labels=labels.to(__UpperCAmelCase ) ).loss
a__ : Dict = -(labels.shape[-1] * loss.item())
a__ : Optional[int] = -8_4.9_1_2_7
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 717 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_decord,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class _a ( unittest.TestCase ):
'''simple docstring'''
A :Any = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def _A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
"""simple docstring"""
a__ : Tuple = hf_hub_download(
repo_id="nateraw/video-demo" , filename="archery.mp4" , repo_type="dataset" )
a__ : Dict = VideoClassificationPipeline(model=__UpperCAmelCase , image_processor=__UpperCAmelCase , top_k=2 )
a__ : Dict = [
example_video_filepath,
"https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
]
return video_classifier, examples
def _A ( self , __UpperCAmelCase , __UpperCAmelCase ):
"""simple docstring"""
for example in examples:
a__ : List[str] = video_classifier(__UpperCAmelCase )
self.assertEqual(
__UpperCAmelCase , [
{"score": ANY(__UpperCAmelCase ), "label": ANY(__UpperCAmelCase )},
{"score": ANY(__UpperCAmelCase ), "label": ANY(__UpperCAmelCase )},
] , )
@require_torch
def _A ( self ):
"""simple docstring"""
a__ : List[Any] = "hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
a__ : Dict = VideoMAEFeatureExtractor(
size={"shortest_edge": 10} , crop_size={"height": 10, "width": 10} )
a__ : Dict = pipeline(
"video-classification" , model=__UpperCAmelCase , feature_extractor=__UpperCAmelCase , frame_sampling_rate=4 )
a__ : List[str] = hf_hub_download(repo_id="nateraw/video-demo" , filename="archery.mp4" , repo_type="dataset" )
a__ : List[Any] = video_classifier(__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [{"score": 0.5_1_9_9, "label": "LABEL_0"}, {"score": 0.4_8_0_1, "label": "LABEL_1"}] , )
a__ : Optional[int] = video_classifier(
[
video_file_path,
video_file_path,
] , top_k=2 , )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[{"score": 0.5_1_9_9, "label": "LABEL_0"}, {"score": 0.4_8_0_1, "label": "LABEL_1"}],
[{"score": 0.5_1_9_9, "label": "LABEL_0"}, {"score": 0.4_8_0_1, "label": "LABEL_1"}],
] , )
@require_tf
def _A ( self ):
"""simple docstring"""
pass
| 207 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCAmelCase__ = '\\n\n'
lowerCAmelCase__ = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'
lowerCAmelCase__ = '\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 78.22\n >>> print(round(results["perplexities"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = datasets.load_dataset("wikitext",\n ... "wikitext-2-raw-v1",\n ... split="test")["text"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!=\'\']\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 60.35\n >>> print(round(results["perplexities"][0], 2))\n 81.12\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowercase (datasets.Metric ):
def __UpperCamelCase ( self : Optional[int]):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'input_texts': datasets.Value('string'),
}) , reference_urls=['https://huggingface.co/docs/transformers/perplexity'] , )
def __UpperCamelCase ( self : List[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int = 16 , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Union[str, Any]=None):
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
UpperCamelCase__ : Any = 'cuda'
else:
UpperCamelCase__ : Optional[int] = 'cuda' if torch.cuda.is_available() else 'cpu'
UpperCamelCase__ : List[Any] = AutoModelForCausalLM.from_pretrained(UpperCAmelCase_)
UpperCamelCase__ : List[str] = model.to(UpperCAmelCase_)
UpperCamelCase__ : Dict = AutoTokenizer.from_pretrained(UpperCAmelCase_)
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
UpperCamelCase__ : int = list(tokenizer.special_tokens_map_extended.values())
# check that the model already has at least one special token defined
assert (
len(UpperCAmelCase_) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'pad_token': existing_special_tokens[0]})
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
UpperCamelCase__ : Optional[Any] = model.config.max_length - 1
else:
UpperCamelCase__ : int = model.config.max_length
UpperCamelCase__ : Any = tokenizer(
UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ , max_length=UpperCAmelCase_ , return_tensors='pt' , return_attention_mask=UpperCAmelCase_ , ).to(UpperCAmelCase_)
UpperCamelCase__ : Tuple = encodings['input_ids']
UpperCamelCase__ : Optional[Any] = encodings['attention_mask']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1) , 1)), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1) , 2)), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
UpperCamelCase__ : Tuple = []
UpperCamelCase__ : Union[str, Any] = CrossEntropyLoss(reduction='none')
for start_index in logging.tqdm(range(0 , len(UpperCAmelCase_) , UpperCAmelCase_)):
UpperCamelCase__ : List[str] = min(start_index + batch_size , len(UpperCAmelCase_))
UpperCamelCase__ : Any = encoded_texts[start_index:end_index]
UpperCamelCase__ : Any = attn_masks[start_index:end_index]
if add_start_token:
UpperCamelCase__ : Any = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0)).to(UpperCAmelCase_)
UpperCamelCase__ : Optional[int] = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1)
UpperCamelCase__ : Dict = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa).to(UpperCAmelCase_), attn_mask] , dim=1)
UpperCamelCase__ : Union[str, Any] = encoded_batch
with torch.no_grad():
UpperCamelCase__ : Optional[int] = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_).logits
UpperCamelCase__ : Optional[int] = out_logits[..., :-1, :].contiguous()
UpperCamelCase__ : List[Any] = labels[..., 1:].contiguous()
UpperCamelCase__ : Any = attn_mask[..., 1:].contiguous()
UpperCamelCase__ : Dict = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2) , UpperCAmelCase_) * shift_attention_mask_batch).sum(1)
/ shift_attention_mask_batch.sum(1))
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(UpperCAmelCase_)}
| 596 |
'''simple docstring'''
lowerCAmelCase__ = 'Alexander Joslin'
import operator as op
from .stack import Stack
def __UpperCAmelCase ( lowerCamelCase_) -> int:
UpperCamelCase__ : List[str] = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
UpperCamelCase__ : Stack[int] = Stack()
UpperCamelCase__ : Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(lowerCamelCase_))
elif i in operators:
# RULE 2
operator_stack.push(lowerCamelCase_)
elif i == ")":
# RULE 4
UpperCamelCase__ : Optional[Any] = operator_stack.peek()
operator_stack.pop()
UpperCamelCase__ : Optional[Any] = operand_stack.peek()
operand_stack.pop()
UpperCamelCase__ : List[Any] = operand_stack.peek()
operand_stack.pop()
UpperCamelCase__ : List[Any] = operators[opr](lowerCamelCase_ , lowerCamelCase_)
operand_stack.push(lowerCamelCase_)
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
lowerCAmelCase__ = '(5 + ((4 * 2) * (2 + 3)))'
# answer = 45
print(f'''{equation} = {dijkstras_two_stack_algorithm(equation)}''')
| 596 | 1 |
'''simple docstring'''
import torch
from diffusers import StableDiffusionPipeline
UpperCAmelCase : int = 'path-to-your-trained-model'
UpperCAmelCase : Dict = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
UpperCAmelCase : Dict = 'A photo of sks dog in a bucket'
UpperCAmelCase : Optional[int] = pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]
image.save('dog-bucket.png')
| 707 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class lowerCamelCase (unittest.TestCase ):
@slow
def UpperCAmelCase_ ( self ) -> int:
"""simple docstring"""
_snake_case : Tuple = FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''' )
_snake_case : Any = AutoTokenizer.from_pretrained('''google/mt5-small''' )
_snake_case : List[str] = tokenizer('''Hello there''' , return_tensors='''np''' ).input_ids
_snake_case : Dict = tokenizer('''Hi I am''' , return_tensors='''np''' ).input_ids
_snake_case : Any = shift_tokens_right(lowercase__ , model.config.pad_token_id , model.config.decoder_start_token_id )
_snake_case : Any = model(lowercase__ , decoder_input_ids=lowercase__ ).logits
_snake_case : Tuple = optax.softmax_cross_entropy(lowercase__ , onehot(lowercase__ , logits.shape[-1] ) ).mean()
_snake_case : Tuple = -(labels.shape[-1] * loss.item())
_snake_case : Union[str, Any] = -84.9_127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 47 | 0 |
"""simple docstring"""
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class UpperCAmelCase_ :
@property
def _UpperCamelCase ( self : Any ) -> Any:
return self.get_dummy_input()
@property
def _UpperCamelCase ( self : List[str] ) -> Dict:
if self.block_type == "down":
return (4, 32, 16, 16)
elif self.block_type == "mid":
return (4, 32, 32, 32)
elif self.block_type == "up":
return (4, 32, 64, 64)
raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.''' )
def _UpperCamelCase ( self : int , __UpperCamelCase : List[str]=True , __UpperCamelCase : List[str]=False , __UpperCamelCase : int=False , __UpperCamelCase : List[Any]=False , ) -> List[Any]:
_UpperCamelCase = 4
_UpperCamelCase = 32
_UpperCamelCase = (32, 32)
_UpperCamelCase = torch.manual_seed(0 )
_UpperCamelCase = torch.device(__UpperCamelCase )
_UpperCamelCase = (batch_size, num_channels) + sizes
_UpperCamelCase = randn_tensor(__UpperCamelCase , generator=__UpperCamelCase , device=__UpperCamelCase )
_UpperCamelCase = {'''hidden_states''': hidden_states}
if include_temb:
_UpperCamelCase = 128
_UpperCamelCase = randn_tensor((batch_size, temb_channels) , generator=__UpperCamelCase , device=__UpperCamelCase )
if include_res_hidden_states_tuple:
_UpperCamelCase = torch.manual_seed(1 )
_UpperCamelCase = (randn_tensor(__UpperCamelCase , generator=__UpperCamelCase , device=__UpperCamelCase ),)
if include_encoder_hidden_states:
_UpperCamelCase = floats_tensor((batch_size, 32, 32) ).to(__UpperCamelCase )
if include_skip_sample:
_UpperCamelCase = randn_tensor(((batch_size, 3) + sizes) , generator=__UpperCamelCase , device=__UpperCamelCase )
return dummy_input
def _UpperCamelCase ( self : List[Any] ) -> int:
_UpperCamelCase = {
'''in_channels''': 32,
'''out_channels''': 32,
'''temb_channels''': 128,
}
if self.block_type == "up":
_UpperCamelCase = 32
if self.block_type == "mid":
init_dict.pop('''out_channels''' )
_UpperCamelCase = self.dummy_input
return init_dict, inputs_dict
def _UpperCamelCase ( self : Any , __UpperCamelCase : Optional[Any] ) -> List[str]:
_UpperCamelCase , _UpperCamelCase = self.prepare_init_args_and_inputs_for_common()
_UpperCamelCase = self.block_class(**__UpperCamelCase )
unet_block.to(__UpperCamelCase )
unet_block.eval()
with torch.no_grad():
_UpperCamelCase = unet_block(**__UpperCamelCase )
if isinstance(__UpperCamelCase , __UpperCamelCase ):
_UpperCamelCase = output[0]
self.assertEqual(output.shape , self.output_shape )
_UpperCamelCase = output[0, -1, -3:, -3:]
_UpperCamelCase = torch.tensor(__UpperCamelCase ).to(__UpperCamelCase )
assert torch_all_close(output_slice.flatten() , __UpperCamelCase , atol=5E-3 )
@unittest.skipIf(torch_device == '''mps''' , '''Training is not supported in mps''' )
def _UpperCamelCase ( self : Optional[int] ) -> List[str]:
_UpperCamelCase , _UpperCamelCase = self.prepare_init_args_and_inputs_for_common()
_UpperCamelCase = self.block_class(**__UpperCamelCase )
model.to(__UpperCamelCase )
model.train()
_UpperCamelCase = model(**__UpperCamelCase )
if isinstance(__UpperCamelCase , __UpperCamelCase ):
_UpperCamelCase = output[0]
_UpperCamelCase = torch.device(__UpperCamelCase )
_UpperCamelCase = randn_tensor(output.shape , device=__UpperCamelCase )
_UpperCamelCase = torch.nn.functional.mse_loss(__UpperCamelCase , __UpperCamelCase )
loss.backward()
| 420 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase ( a__ : dict , a__ : str , a__ : set , a__ : set , a__ : dict , a__ : dict , a__ : PriorityQueue , a__ : dict , a__ : float | int , ) -> float | int:
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
_UpperCamelCase = cst_fwd.get(a__ , np.inf )
_UpperCamelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
_UpperCamelCase = new_cost_f
_UpperCamelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
_UpperCamelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def lowercase ( a__ : str , a__ : str , a__ : dict , a__ : dict ) -> int:
_UpperCamelCase = -1
_UpperCamelCase = set()
_UpperCamelCase = set()
_UpperCamelCase = {source: 0}
_UpperCamelCase = {destination: 0}
_UpperCamelCase = {source: None}
_UpperCamelCase = {destination: None}
_UpperCamelCase = PriorityQueue()
_UpperCamelCase = PriorityQueue()
_UpperCamelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
_UpperCamelCase , _UpperCamelCase = queue_forward.get()
visited_forward.add(a__ )
_UpperCamelCase , _UpperCamelCase = queue_backward.get()
visited_backward.add(a__ )
_UpperCamelCase = pass_and_relaxation(
a__ , a__ , a__ , a__ , a__ , a__ , a__ , a__ , a__ , )
_UpperCamelCase = pass_and_relaxation(
a__ , a__ , a__ , a__ , a__ , a__ , a__ , a__ , a__ , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
_UpperCamelCase = shortest_distance
return shortest_path_distance
UpperCAmelCase = {
"""B""": [["""C""", 1]],
"""C""": [["""D""", 1]],
"""D""": [["""F""", 1]],
"""E""": [["""B""", 1], ["""G""", 2]],
"""F""": [],
"""G""": [["""F""", 1]],
}
UpperCAmelCase = {
"""B""": [["""E""", 1]],
"""C""": [["""B""", 1]],
"""D""": [["""C""", 1]],
"""F""": [["""D""", 1], ["""G""", 1]],
"""E""": [[None, np.inf]],
"""G""": [["""E""", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 420 | 1 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
snake_case = None
snake_case = logging.get_logger(__name__)
snake_case = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
snake_case = {
"""vocab_file""": {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
},
"""tokenizer_file""": {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
},
}
# TODO(PVP) - this should be removed in Transformers v5
snake_case = {
"""t5-small""": 5_12,
"""t5-base""": 5_12,
"""t5-large""": 5_12,
"""t5-3b""": 5_12,
"""t5-11b""": 5_12,
}
class lowerCAmelCase ( UpperCamelCase_ ):
A_ : Any = VOCAB_FILES_NAMES
A_ : str = PRETRAINED_VOCAB_FILES_MAP
A_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ : str = ["""input_ids""", """attention_mask"""]
A_ : Optional[Any] = TaTokenizer
A_ : List[int] = []
def __init__( self : Dict , a__ : Tuple=None , a__ : Union[str, Any]=None , a__ : Optional[int]="</s>" , a__ : Union[str, Any]="<unk>" , a__ : Union[str, Any]="<pad>" , a__ : Union[str, Any]=100 , a__ : Any=None , **a__ : Union[str, Any] , ):
'''simple docstring'''
if extra_ids > 0 and additional_special_tokens is None:
lowerCAmelCase__ : Tuple = [F'''<extra_id_{i}>''' for i in range(a__ )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
lowerCAmelCase__ : Union[str, Any] = len(set(filter(lambda a__ : bool("extra_id_" in str(a__ ) ) , a__ ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
" tokens" )
super().__init__(
a__ , tokenizer_file=a__ , eos_token=a__ , unk_token=a__ , pad_token=a__ , extra_ids=a__ , additional_special_tokens=a__ , **a__ , )
lowerCAmelCase__ : Dict = vocab_file
lowerCAmelCase__ : Dict = False if not self.vocab_file else True
lowerCAmelCase__ : int = extra_ids
@staticmethod
def _A ( a__ : Tuple , a__ : Optional[int] , a__ : str ):
'''simple docstring'''
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
lowerCAmelCase__ : Dict = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"This tokenizer was incorrectly instantiated with a model max length of"
F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
F''' {pretrained_model_name_or_path} automatically truncating your input to'''
F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
" instantiate this tokenizer with `model_max_length` set to your preferred value." , a__ , )
return max_model_length
def _A ( self : Tuple , a__ : str , a__ : Optional[str] = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(a__ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCAmelCase__ : Tuple = os.path.join(
a__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a__ ):
copyfile(self.vocab_file , a__ )
logger.info(F'''Copy vocab file to {out_vocab_file}''' )
return (out_vocab_file,)
def _A ( self : Union[str, Any] , a__ : List[int] , a__ : Optional[List[int]] = None ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
lowerCAmelCase__ : int = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def _A ( self : List[Any] , a__ : List[int] , a__ : Optional[List[int]] = None ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def _A ( self : Tuple ):
'''simple docstring'''
return list(
set(filter(lambda a__ : bool(re.search(r"<extra_id_\d+>" , a__ ) ) is not None , self.additional_special_tokens ) ) )
def _A ( self : str ):
'''simple docstring'''
return [self.convert_tokens_to_ids(a__ ) for token in self.get_sentinel_tokens()]
| 701 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class lowerCAmelCase ( unittest.TestCase ):
def _A ( self : List[str] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _A ( self : int ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = 1
lowerCAmelCase__ : Union[str, Any] = 3
lowerCAmelCase__ : Any = (32, 32)
lowerCAmelCase__ : Dict = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(a__ )
return image
@property
def _A ( self : Optional[int] ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ : Union[str, Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
return model
@property
def _A ( self : Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ : int = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
return model
@property
def _A ( self : Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ : List[str] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModel(a__ )
@property
def _A ( self : int ):
'''simple docstring'''
def extract(*a__ : List[Any] , **a__ : int ):
class lowerCAmelCase :
def __init__( self : Optional[int] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = torch.ones([0] )
def _A ( self : str , a__ : str ):
'''simple docstring'''
self.pixel_values.to(a__ )
return self
return Out()
return extract
def _A ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase__ : Union[str, Any] = self.dummy_cond_unet
lowerCAmelCase__ : Union[str, Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=a__ , set_alpha_to_one=a__ , )
lowerCAmelCase__ : Optional[Any] = self.dummy_vae
lowerCAmelCase__ : Tuple = self.dummy_text_encoder
lowerCAmelCase__ : Dict = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
# make sure here that pndm scheduler skips prk
lowerCAmelCase__ : List[Any] = StableDiffusionPipeline(
unet=a__ , scheduler=a__ , vae=a__ , text_encoder=a__ , tokenizer=a__ , safety_checker=a__ , feature_extractor=self.dummy_extractor , )
lowerCAmelCase__ : Dict = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
lowerCAmelCase__ : List[str] = "A painting of a squirrel eating a burger"
lowerCAmelCase__ : str = torch.Generator(device=a__ ).manual_seed(0 )
lowerCAmelCase__ : Optional[int] = sd_pipe([prompt] , generator=a__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" )
lowerCAmelCase__ : Optional[Any] = output.images
lowerCAmelCase__ : Union[str, Any] = torch.Generator(device=a__ ).manual_seed(0 )
lowerCAmelCase__ : Optional[int] = sd_pipe(
[prompt] , generator=a__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" , return_dict=a__ , )[0]
lowerCAmelCase__ : Any = image[0, -3:, -3:, -1]
lowerCAmelCase__ : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase__ : Any = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def _A ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : Dict = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase__ : int = self.dummy_cond_unet
lowerCAmelCase__ : Optional[int] = PNDMScheduler(skip_prk_steps=a__ )
lowerCAmelCase__ : Optional[int] = self.dummy_vae
lowerCAmelCase__ : Tuple = self.dummy_text_encoder
lowerCAmelCase__ : List[str] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
# make sure here that pndm scheduler skips prk
lowerCAmelCase__ : List[str] = StableDiffusionPipeline(
unet=a__ , scheduler=a__ , vae=a__ , text_encoder=a__ , tokenizer=a__ , safety_checker=a__ , feature_extractor=self.dummy_extractor , )
lowerCAmelCase__ : Optional[int] = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
lowerCAmelCase__ : Tuple = "A painting of a squirrel eating a burger"
lowerCAmelCase__ : str = torch.Generator(device=a__ ).manual_seed(0 )
lowerCAmelCase__ : Optional[int] = sd_pipe([prompt] , generator=a__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" )
lowerCAmelCase__ : Union[str, Any] = output.images
lowerCAmelCase__ : Tuple = torch.Generator(device=a__ ).manual_seed(0 )
lowerCAmelCase__ : Tuple = sd_pipe(
[prompt] , generator=a__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" , return_dict=a__ , )[0]
lowerCAmelCase__ : Optional[Any] = image[0, -3:, -3:, -1]
lowerCAmelCase__ : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase__ : Optional[Any] = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def _A ( self : str ):
'''simple docstring'''
lowerCAmelCase__ : str = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-lms-pipe" , safety_checker=a__ )
assert isinstance(a__ , a__ )
assert isinstance(pipe.scheduler , a__ )
assert pipe.safety_checker is None
lowerCAmelCase__ : List[Any] = pipe("example prompt" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(a__ )
lowerCAmelCase__ : List[Any] = StableDiffusionPipeline.from_pretrained(a__ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
lowerCAmelCase__ : List[Any] = pipe("example prompt" , num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU" )
def _A ( self : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = self.dummy_cond_unet
lowerCAmelCase__ : Tuple = PNDMScheduler(skip_prk_steps=a__ )
lowerCAmelCase__ : Dict = self.dummy_vae
lowerCAmelCase__ : Optional[int] = self.dummy_text_encoder
lowerCAmelCase__ : Optional[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
# put models in fp16
lowerCAmelCase__ : List[Any] = unet.half()
lowerCAmelCase__ : Dict = vae.half()
lowerCAmelCase__ : List[str] = bert.half()
# make sure here that pndm scheduler skips prk
lowerCAmelCase__ : List[str] = StableDiffusionPipeline(
unet=a__ , scheduler=a__ , vae=a__ , text_encoder=a__ , tokenizer=a__ , safety_checker=a__ , feature_extractor=self.dummy_extractor , )
lowerCAmelCase__ : str = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
lowerCAmelCase__ : Dict = "A painting of a squirrel eating a burger"
lowerCAmelCase__ : Any = sd_pipe([prompt] , num_inference_steps=2 , output_type="np" ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class lowerCAmelCase ( unittest.TestCase ):
def _A ( self : Dict ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _A ( self : List[Any] ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5" , safety_checker=a__ )
lowerCAmelCase__ : Any = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
lowerCAmelCase__ : Tuple = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
lowerCAmelCase__ : Any = (
"portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
" children from bahnhof zoo, detailed "
)
lowerCAmelCase__ : List[str] = 40_0366_0346
lowerCAmelCase__ : int = 7
# without safety guidance (sld_guidance_scale = 0)
lowerCAmelCase__ : Dict = torch.manual_seed(a__ )
lowerCAmelCase__ : List[str] = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="np" , width=512 , height=512 , sld_guidance_scale=0 , )
lowerCAmelCase__ : List[Any] = output.images
lowerCAmelCase__ : Optional[Any] = image[0, -3:, -3:, -1]
lowerCAmelCase__ : Union[str, Any] = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
# without safety guidance (strong configuration)
lowerCAmelCase__ : Tuple = torch.manual_seed(a__ )
lowerCAmelCase__ : Tuple = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="np" , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
lowerCAmelCase__ : List[Any] = output.images
lowerCAmelCase__ : Union[str, Any] = image[0, -3:, -3:, -1]
lowerCAmelCase__ : List[str] = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _A ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5" , safety_checker=a__ )
lowerCAmelCase__ : str = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
lowerCAmelCase__ : Optional[Any] = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
lowerCAmelCase__ : int = "padme amidala taking a bath artwork, safe for work, no nudity"
lowerCAmelCase__ : Union[str, Any] = 27_3497_1755
lowerCAmelCase__ : str = 7
lowerCAmelCase__ : Any = torch.manual_seed(a__ )
lowerCAmelCase__ : Dict = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="np" , width=512 , height=512 , sld_guidance_scale=0 , )
lowerCAmelCase__ : Optional[Any] = output.images
lowerCAmelCase__ : Any = image[0, -3:, -3:, -1]
lowerCAmelCase__ : Optional[Any] = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
lowerCAmelCase__ : Optional[int] = torch.manual_seed(a__ )
lowerCAmelCase__ : Dict = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="np" , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
lowerCAmelCase__ : Optional[int] = output.images
lowerCAmelCase__ : Union[str, Any] = image[0, -3:, -3:, -1]
lowerCAmelCase__ : Union[str, Any] = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _A ( self : List[str] ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5" )
lowerCAmelCase__ : List[str] = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
lowerCAmelCase__ : Dict = (
"the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
" leyendecker"
)
lowerCAmelCase__ : Union[str, Any] = 10_4435_5234
lowerCAmelCase__ : Optional[int] = 12
lowerCAmelCase__ : Tuple = torch.manual_seed(a__ )
lowerCAmelCase__ : str = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="np" , width=512 , height=512 , sld_guidance_scale=0 , )
lowerCAmelCase__ : Union[str, Any] = output.images
lowerCAmelCase__ : int = image[0, -3:, -3:, -1]
lowerCAmelCase__ : str = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-7
lowerCAmelCase__ : List[Any] = torch.manual_seed(a__ )
lowerCAmelCase__ : List[Any] = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="np" , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
lowerCAmelCase__ : List[Any] = output.images
lowerCAmelCase__ : Optional[Any] = image[0, -3:, -3:, -1]
lowerCAmelCase__ : int = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 568 | 0 |
import itertools
import string
from collections.abc import Generator, Iterable
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Iterable[str] ,lowerCAmelCase_ : int ) -> Generator[tuple[str, ...], None, None]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] =iter(lowerCAmelCase_ )
while True:
SCREAMING_SNAKE_CASE_ : Union[str, Any] =tuple(itertools.islice(lowerCAmelCase_ ,lowerCAmelCase_ ) )
if not chunk:
return
yield chunk
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int =''.join([c.upper() for c in dirty if c in string.ascii_letters] )
SCREAMING_SNAKE_CASE_ : List[str] =''
if len(lowerCAmelCase_ ) < 2:
return dirty
for i in range(len(lowerCAmelCase_ ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(lowerCAmelCase_ ) & 1:
clean += "X"
return clean
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ) -> list[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict ='ABCDEFGHIKLMNOPQRSTUVWXYZ'
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
SCREAMING_SNAKE_CASE_ : Any =[]
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(lowerCAmelCase_ )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(lowerCAmelCase_ )
return table
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ,lowerCAmelCase_ : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple =generate_table(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] =prepare_input(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Any =''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(lowerCAmelCase_ ,2 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[str] =divmod(table.index(lowerCAmelCase_ ) ,5 )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[int] =divmod(table.index(lowerCAmelCase_ ) ,5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ,lowerCAmelCase_ : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] =generate_table(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[Any] =''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(lowerCAmelCase_ ,2 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any =divmod(table.index(lowerCAmelCase_ ) ,5 )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int =divmod(table.index(lowerCAmelCase_ ) ,5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 220 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__SCREAMING_SNAKE_CASE = re.compile(r'\b(a|an|the)\b', re.UNICODE)
__SCREAMING_SNAKE_CASE = None
def SCREAMING_SNAKE_CASE__ ( ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] =argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.' )
parser.add_argument('data_file' ,metavar='data.json' ,help='Input data JSON file.' )
parser.add_argument('pred_file' ,metavar='pred.json' ,help='Model predictions.' )
parser.add_argument(
'--out-file' ,'-o' ,metavar='eval.json' ,help='Write accuracy metrics to file (default is stdout).' )
parser.add_argument(
'--na-prob-file' ,'-n' ,metavar='na_prob.json' ,help='Model estimates of probability of no answer.' )
parser.add_argument(
'--na-prob-thresh' ,'-t' ,type=lowerCAmelCase_ ,default=1.0 ,help='Predict "" if no-answer probability exceeds this (default = 1.0).' ,)
parser.add_argument(
'--out-image-dir' ,'-p' ,metavar='out_images' ,default=lowerCAmelCase_ ,help='Save precision-recall curves to directory.' )
parser.add_argument('--verbose' ,'-v' ,action='store_true' )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Optional[int] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] ={}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
SCREAMING_SNAKE_CASE_ : Optional[Any] =bool(qa['answers']['text'] )
return qid_to_has_ans
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Any ) -> List[Any]:
"""simple docstring"""
def remove_articles(lowerCAmelCase_ : Optional[Any] ):
return ARTICLES_REGEX.sub(' ' ,lowerCAmelCase_ )
def white_space_fix(lowerCAmelCase_ : Tuple ):
return " ".join(text.split() )
def remove_punc(lowerCAmelCase_ : Dict ):
SCREAMING_SNAKE_CASE_ : Optional[int] =set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowerCAmelCase_ : Optional[Any] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_ ) ) ) )
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Any ) -> int:
"""simple docstring"""
if not s:
return []
return normalize_answer(lowerCAmelCase_ ).split()
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Union[str, Any] ,lowerCAmelCase_ : str ) -> Any:
"""simple docstring"""
return int(normalize_answer(lowerCAmelCase_ ) == normalize_answer(lowerCAmelCase_ ) )
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Any ,lowerCAmelCase_ : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] =get_tokens(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] =get_tokens(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] =collections.Counter(lowerCAmelCase_ ) & collections.Counter(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : str =sum(common.values() )
if len(lowerCAmelCase_ ) == 0 or len(lowerCAmelCase_ ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
SCREAMING_SNAKE_CASE_ : int =1.0 * num_same / len(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : List[str] =1.0 * num_same / len(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =(2 * precision * recall) / (precision + recall)
return fa
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Optional[int] ,lowerCAmelCase_ : Optional[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any ={}
SCREAMING_SNAKE_CASE_ : Optional[int] ={}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
SCREAMING_SNAKE_CASE_ : List[Any] =qa['id']
SCREAMING_SNAKE_CASE_ : int =[t for t in qa['answers']['text'] if normalize_answer(lowerCAmelCase_ )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
SCREAMING_SNAKE_CASE_ : str =['']
if qid not in preds:
print(F"""Missing prediction for {qid}""" )
continue
SCREAMING_SNAKE_CASE_ : Optional[Any] =preds[qid]
# Take max over all gold answers
SCREAMING_SNAKE_CASE_ : Any =max(compute_exact(lowerCAmelCase_ ,lowerCAmelCase_ ) for a in gold_answers )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =max(compute_fa(lowerCAmelCase_ ,lowerCAmelCase_ ) for a in gold_answers )
return exact_scores, fa_scores
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ,lowerCAmelCase_ : int ,lowerCAmelCase_ : Optional[int] ,lowerCAmelCase_ : int ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] ={}
for qid, s in scores.items():
SCREAMING_SNAKE_CASE_ : Union[str, Any] =na_probs[qid] > na_prob_thresh
if pred_na:
SCREAMING_SNAKE_CASE_ : Optional[Any] =float(not qid_to_has_ans[qid] )
else:
SCREAMING_SNAKE_CASE_ : Dict =s
return new_scores
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Any ,lowerCAmelCase_ : str ,lowerCAmelCase_ : int=None ) -> Tuple:
"""simple docstring"""
if not qid_list:
SCREAMING_SNAKE_CASE_ : int =len(lowerCAmelCase_ )
return collections.OrderedDict(
[
('exact', 100.0 * sum(exact_scores.values() ) / total),
('f1', 100.0 * sum(fa_scores.values() ) / total),
('total', total),
] )
else:
SCREAMING_SNAKE_CASE_ : Dict =len(lowerCAmelCase_ )
return collections.OrderedDict(
[
('exact', 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
('f1', 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
('total', total),
] )
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Dict ,lowerCAmelCase_ : Any ,lowerCAmelCase_ : int ) -> Dict:
"""simple docstring"""
for k in new_eval:
SCREAMING_SNAKE_CASE_ : Dict =new_eval[k]
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Union[str, Any] ,lowerCAmelCase_ : Dict ,lowerCAmelCase_ : Optional[Any] ,lowerCAmelCase_ : Any ) -> str:
"""simple docstring"""
plt.step(lowerCAmelCase_ ,lowerCAmelCase_ ,color='b' ,alpha=0.2 ,where='post' )
plt.fill_between(lowerCAmelCase_ ,lowerCAmelCase_ ,step='post' ,alpha=0.2 ,color='b' )
plt.xlabel('Recall' )
plt.ylabel('Precision' )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(lowerCAmelCase_ )
plt.savefig(lowerCAmelCase_ )
plt.clf()
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : List[Any] ,lowerCAmelCase_ : int ,lowerCAmelCase_ : Union[str, Any] ,lowerCAmelCase_ : Any ,lowerCAmelCase_ : Tuple=None ,lowerCAmelCase_ : Tuple=None ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str =sorted(lowerCAmelCase_ ,key=lambda lowerCAmelCase_ : na_probs[k] )
SCREAMING_SNAKE_CASE_ : Optional[int] =0.0
SCREAMING_SNAKE_CASE_ : Tuple =1.0
SCREAMING_SNAKE_CASE_ : Dict =0.0
SCREAMING_SNAKE_CASE_ : Dict =[1.0]
SCREAMING_SNAKE_CASE_ : str =[0.0]
SCREAMING_SNAKE_CASE_ : Union[str, Any] =0.0
for i, qid in enumerate(lowerCAmelCase_ ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
SCREAMING_SNAKE_CASE_ : List[str] =true_pos / float(i + 1 )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =true_pos / float(lowerCAmelCase_ )
if i == len(lowerCAmelCase_ ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(lowerCAmelCase_ )
recalls.append(lowerCAmelCase_ )
if out_image:
plot_pr_curve(lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ )
return {"ap": 100.0 * avg_prec}
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : List[Any] ,lowerCAmelCase_ : List[str] ,lowerCAmelCase_ : Tuple ,lowerCAmelCase_ : str ,lowerCAmelCase_ : int ,lowerCAmelCase_ : Dict ) -> Any:
"""simple docstring"""
if out_image_dir and not os.path.exists(lowerCAmelCase_ ):
os.makedirs(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : List[str] =sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
SCREAMING_SNAKE_CASE_ : List[str] =make_precision_recall_eval(
lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,out_image=os.path.join(lowerCAmelCase_ ,'pr_exact.png' ) ,title='Precision-Recall curve for Exact Match score' ,)
SCREAMING_SNAKE_CASE_ : int =make_precision_recall_eval(
lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,out_image=os.path.join(lowerCAmelCase_ ,'pr_f1.png' ) ,title='Precision-Recall curve for F1 score' ,)
SCREAMING_SNAKE_CASE_ : str ={k: float(lowerCAmelCase_ ) for k, v in qid_to_has_ans.items()}
SCREAMING_SNAKE_CASE_ : List[Any] =make_precision_recall_eval(
lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,out_image=os.path.join(lowerCAmelCase_ ,'pr_oracle.png' ) ,title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)' ,)
merge_eval(lowerCAmelCase_ ,lowerCAmelCase_ ,'pr_exact' )
merge_eval(lowerCAmelCase_ ,lowerCAmelCase_ ,'pr_f1' )
merge_eval(lowerCAmelCase_ ,lowerCAmelCase_ ,'pr_oracle' )
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Any ,lowerCAmelCase_ : Tuple ,lowerCAmelCase_ : Optional[int] ,lowerCAmelCase_ : Optional[Any] ) -> List[str]:
"""simple docstring"""
if not qid_list:
return
SCREAMING_SNAKE_CASE_ : List[str] =[na_probs[k] for k in qid_list]
SCREAMING_SNAKE_CASE_ : Optional[int] =np.ones_like(lowerCAmelCase_ ) / float(len(lowerCAmelCase_ ) )
plt.hist(lowerCAmelCase_ ,weights=lowerCAmelCase_ ,bins=20 ,range=(0.0, 1.0) )
plt.xlabel('Model probability of no-answer' )
plt.ylabel('Proportion of dataset' )
plt.title(F"""Histogram of no-answer probability: {name}""" )
plt.savefig(os.path.join(lowerCAmelCase_ ,F"""na_prob_hist_{name}.png""" ) )
plt.clf()
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Dict ,lowerCAmelCase_ : Tuple ,lowerCAmelCase_ : Dict ,lowerCAmelCase_ : Dict ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] =sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
SCREAMING_SNAKE_CASE_ : Dict =num_no_ans
SCREAMING_SNAKE_CASE_ : int =cur_score
SCREAMING_SNAKE_CASE_ : int =0.0
SCREAMING_SNAKE_CASE_ : Optional[Any] =sorted(lowerCAmelCase_ ,key=lambda lowerCAmelCase_ : na_probs[k] )
for i, qid in enumerate(lowerCAmelCase_ ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
SCREAMING_SNAKE_CASE_ : List[Any] =scores[qid]
else:
if preds[qid]:
SCREAMING_SNAKE_CASE_ : int =-1
else:
SCREAMING_SNAKE_CASE_ : Optional[int] =0
cur_score += diff
if cur_score > best_score:
SCREAMING_SNAKE_CASE_ : Tuple =cur_score
SCREAMING_SNAKE_CASE_ : Optional[Any] =na_probs[qid]
return 100.0 * best_score / len(lowerCAmelCase_ ), best_thresh
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Union[str, Any] ,lowerCAmelCase_ : int ,lowerCAmelCase_ : Dict ,lowerCAmelCase_ : Union[str, Any] ,lowerCAmelCase_ : Tuple ,lowerCAmelCase_ : List[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : str =find_best_thresh(lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[Any] =find_best_thresh(lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Any =best_exact
SCREAMING_SNAKE_CASE_ : Dict =exact_thresh
SCREAMING_SNAKE_CASE_ : Tuple =best_fa
SCREAMING_SNAKE_CASE_ : Any =fa_thresh
def SCREAMING_SNAKE_CASE__ ( ) -> List[str]:
"""simple docstring"""
with open(OPTS.data_file ) as f:
SCREAMING_SNAKE_CASE_ : Optional[Any] =json.load(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Any =dataset_json['data']
with open(OPTS.pred_file ) as f:
SCREAMING_SNAKE_CASE_ : Any =json.load(lowerCAmelCase_ )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
SCREAMING_SNAKE_CASE_ : Optional[int] =json.load(lowerCAmelCase_ )
else:
SCREAMING_SNAKE_CASE_ : Optional[Any] ={k: 0.0 for k in preds}
SCREAMING_SNAKE_CASE_ : str =make_qid_to_has_ans(lowerCAmelCase_ ) # maps qid to True/False
SCREAMING_SNAKE_CASE_ : str =[k for k, v in qid_to_has_ans.items() if v]
SCREAMING_SNAKE_CASE_ : str =[k for k, v in qid_to_has_ans.items() if not v]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any =get_raw_scores(lowerCAmelCase_ ,lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =apply_no_ans_threshold(lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,OPTS.na_prob_thresh )
SCREAMING_SNAKE_CASE_ : Dict =apply_no_ans_threshold(lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,OPTS.na_prob_thresh )
SCREAMING_SNAKE_CASE_ : List[Any] =make_eval_dict(lowerCAmelCase_ ,lowerCAmelCase_ )
if has_ans_qids:
SCREAMING_SNAKE_CASE_ : List[Any] =make_eval_dict(lowerCAmelCase_ ,lowerCAmelCase_ ,qid_list=lowerCAmelCase_ )
merge_eval(lowerCAmelCase_ ,lowerCAmelCase_ ,'HasAns' )
if no_ans_qids:
SCREAMING_SNAKE_CASE_ : str =make_eval_dict(lowerCAmelCase_ ,lowerCAmelCase_ ,qid_list=lowerCAmelCase_ )
merge_eval(lowerCAmelCase_ ,lowerCAmelCase_ ,'NoAns' )
if OPTS.na_prob_file:
find_all_best_thresh(lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,OPTS.out_image_dir )
histogram_na_prob(lowerCAmelCase_ ,lowerCAmelCase_ ,OPTS.out_image_dir ,'hasAns' )
histogram_na_prob(lowerCAmelCase_ ,lowerCAmelCase_ ,OPTS.out_image_dir ,'noAns' )
if OPTS.out_file:
with open(OPTS.out_file ,'w' ) as f:
json.dump(lowerCAmelCase_ ,lowerCAmelCase_ )
else:
print(json.dumps(lowerCAmelCase_ ,indent=2 ) )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
main()
| 220 | 1 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor
if is_flax_available():
import jax
from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Tuple=1_00 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=13 , SCREAMING_SNAKE_CASE__ : Any=30 , SCREAMING_SNAKE_CASE__ : str=2 , SCREAMING_SNAKE_CASE__ : Any=3 , SCREAMING_SNAKE_CASE__ : Tuple=True , SCREAMING_SNAKE_CASE__ : List[Any]=True , SCREAMING_SNAKE_CASE__ : List[Any]=32 , SCREAMING_SNAKE_CASE__ : Optional[int]=5 , SCREAMING_SNAKE_CASE__ : List[str]=4 , SCREAMING_SNAKE_CASE__ : int=37 , SCREAMING_SNAKE_CASE__ : Any="gelu" , SCREAMING_SNAKE_CASE__ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE__ : Tuple=0.1 , SCREAMING_SNAKE_CASE__ : int=10 , SCREAMING_SNAKE_CASE__ : Optional[int]=0.02 , SCREAMING_SNAKE_CASE__ : int=3 , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = vocab_size
UpperCamelCase = batch_size
UpperCamelCase = image_size
UpperCamelCase = patch_size
UpperCamelCase = num_channels
UpperCamelCase = is_training
UpperCamelCase = use_labels
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCamelCase = (image_size // patch_size) ** 2
UpperCamelCase = num_patches + 1
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=SCREAMING_SNAKE_CASE__ , initializer_range=self.initializer_range , )
return config, pixel_values, labels
def __lowerCAmelCase ( self : List[str] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = FlaxBeitModel(config=SCREAMING_SNAKE_CASE__ )
UpperCamelCase = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self : int , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
UpperCamelCase = FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__ )
UpperCamelCase = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def __lowerCAmelCase ( self : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = self.type_sequence_label_size
UpperCamelCase = FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE__ )
UpperCamelCase = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCamelCase = 1
UpperCamelCase = FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE__ )
UpperCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase = model(SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_flax
class _lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int =(
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = FlaxBeitModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , has_text_modality=SCREAMING_SNAKE_CASE__ , hidden_size=37 )
def __lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(SCREAMING_SNAKE_CASE__ )
UpperCamelCase = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase = [*signature.parameters.keys()]
UpperCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase = self._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
UpperCamelCase = model_class(SCREAMING_SNAKE_CASE__ )
@jax.jit
def model_jitted(SCREAMING_SNAKE_CASE__ : int , **SCREAMING_SNAKE_CASE__ : int ):
return model(pixel_values=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
with self.subTest('JIT Enabled' ):
UpperCamelCase = model_jitted(**SCREAMING_SNAKE_CASE__ ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
UpperCamelCase = model_jitted(**SCREAMING_SNAKE_CASE__ ).to_tuple()
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , len(SCREAMING_SNAKE_CASE__ ) )
for jitted_output, output in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
self.assertEqual(jitted_output.shape , output.shape )
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Any ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def __lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
UpperCamelCase = model_class_name.from_pretrained('microsoft/beit-base-patch16-224' )
UpperCamelCase = model(np.ones((1, 3, 2_24, 2_24) ) )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( ) -> Optional[Any]:
UpperCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None
@slow
def __lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' )
UpperCamelCase = self.default_image_processor
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='np' ).pixel_values
# prepare bool_masked_pos
UpperCamelCase = np.ones((1, 1_96) , dtype=SCREAMING_SNAKE_CASE__ )
# forward pass
UpperCamelCase = model(pixel_values=SCREAMING_SNAKE_CASE__ , bool_masked_pos=SCREAMING_SNAKE_CASE__ )
UpperCamelCase = outputs.logits
# verify the logits
UpperCamelCase = (1, 1_96, 81_92)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE__ )
UpperCamelCase = np.array(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] )
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-2 ) )
@slow
def __lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' )
UpperCamelCase = self.default_image_processor
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='np' )
# forward pass
UpperCamelCase = model(**SCREAMING_SNAKE_CASE__ )
UpperCamelCase = outputs.logits
# verify the logits
UpperCamelCase = (1, 10_00)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE__ )
UpperCamelCase = np.array([-1.2385, -1.0987, -1.0108] )
self.assertTrue(np.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-4 ) )
UpperCamelCase = 2_81
self.assertEqual(logits.argmax(-1 ).item() , SCREAMING_SNAKE_CASE__ )
@slow
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' )
UpperCamelCase = self.default_image_processor
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='np' )
# forward pass
UpperCamelCase = model(**SCREAMING_SNAKE_CASE__ )
UpperCamelCase = outputs.logits
# verify the logits
UpperCamelCase = (1, 2_18_41)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE__ )
UpperCamelCase = np.array([1.6881, -0.2787, 0.5901] )
self.assertTrue(np.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-4 ) )
UpperCamelCase = 23_96
self.assertEqual(logits.argmax(-1 ).item() , SCREAMING_SNAKE_CASE__ )
| 718 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
_snake_case = logging.get_logger(__name__)
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
UpperCamelCase = question_encoder
UpperCamelCase = generator
UpperCamelCase = self.question_encoder
def __lowerCAmelCase ( self : str , SCREAMING_SNAKE_CASE__ : Dict ):
"""simple docstring"""
if os.path.isfile(SCREAMING_SNAKE_CASE__ ):
raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file' )
os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
UpperCamelCase = os.path.join(SCREAMING_SNAKE_CASE__ , 'question_encoder_tokenizer' )
UpperCamelCase = os.path.join(SCREAMING_SNAKE_CASE__ , 'generator_tokenizer' )
self.question_encoder.save_pretrained(SCREAMING_SNAKE_CASE__ )
self.generator.save_pretrained(SCREAMING_SNAKE_CASE__ )
@classmethod
def __lowerCAmelCase ( cls : Any , SCREAMING_SNAKE_CASE__ : List[str] , **SCREAMING_SNAKE_CASE__ : Dict ):
"""simple docstring"""
from ..auto.tokenization_auto import AutoTokenizer
UpperCamelCase = kwargs.pop('config' , SCREAMING_SNAKE_CASE__ )
if config is None:
UpperCamelCase = RagConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
UpperCamelCase = AutoTokenizer.from_pretrained(
SCREAMING_SNAKE_CASE__ , config=config.question_encoder , subfolder='question_encoder_tokenizer' )
UpperCamelCase = AutoTokenizer.from_pretrained(
SCREAMING_SNAKE_CASE__ , config=config.generator , subfolder='generator_tokenizer' )
return cls(question_encoder=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ )
def __call__( self : Tuple , *SCREAMING_SNAKE_CASE__ : List[Any] , **SCREAMING_SNAKE_CASE__ : List[Any] ):
"""simple docstring"""
return self.current_tokenizer(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : int , *SCREAMING_SNAKE_CASE__ : Dict , **SCREAMING_SNAKE_CASE__ : List[str] ):
"""simple docstring"""
return self.generator.batch_decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Any , *SCREAMING_SNAKE_CASE__ : Tuple , **SCREAMING_SNAKE_CASE__ : Optional[Any] ):
"""simple docstring"""
return self.generator.decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
UpperCamelCase = self.question_encoder
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
UpperCamelCase = self.generator
def __lowerCAmelCase ( self : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[List[str]] = None , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : str = "longest" , SCREAMING_SNAKE_CASE__ : str = None , SCREAMING_SNAKE_CASE__ : bool = True , **SCREAMING_SNAKE_CASE__ : Optional[Any] , ):
"""simple docstring"""
warnings.warn(
'`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
'details' , SCREAMING_SNAKE_CASE__ , )
if max_length is None:
UpperCamelCase = self.current_tokenizer.model_max_length
UpperCamelCase = self(
SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
UpperCamelCase = self.current_tokenizer.model_max_length
UpperCamelCase = self(
text_target=SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
UpperCamelCase = labels['input_ids']
return model_inputs
| 170 | 0 |
"""simple docstring"""
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def a ( __UpperCAmelCase : Any , __UpperCAmelCase : Tuple ) -> Any:
__magic_name__: Union[str, Any] = old_name
if "patch_embed" in old_name:
__magic_name__, __magic_name__, __magic_name__: List[str] = old_name.split(""".""" )
if layer == "0":
__magic_name__: Optional[int] = old_name.replace("""0""" , """convolution1""" )
elif layer == "1":
__magic_name__: str = old_name.replace("""1""" , """batchnorm_before""" )
elif layer == "3":
__magic_name__: Dict = old_name.replace("""3""" , """convolution2""" )
else:
__magic_name__: Dict = old_name.replace("""4""" , """batchnorm_after""" )
if "network" in old_name and re.search(R"""\d\.\d""" , __UpperCAmelCase ):
__magic_name__: int = R"""\b\d{2}\b"""
if bool(re.search(__UpperCAmelCase , __UpperCAmelCase ) ):
__magic_name__: Tuple = re.search(R"""\d\.\d\d.""" , __UpperCAmelCase ).group()
else:
__magic_name__: int = re.search(R"""\d\.\d.""" , __UpperCAmelCase ).group()
if int(match[0] ) < 6:
__magic_name__: List[Any] = old_name.replace(__UpperCAmelCase , """""" )
__magic_name__: str = trimmed_name.replace("""network""" , match[0] + """.meta4D_layers.blocks.""" + match[2:-1] )
__magic_name__: Optional[Any] = """intermediate_stages.""" + trimmed_name
else:
__magic_name__: Any = old_name.replace(__UpperCAmelCase , """""" )
if int(match[2] ) < num_meta4D_last_stage:
__magic_name__: Dict = trimmed_name.replace("""network""" , """meta4D_layers.blocks.""" + match[2] )
else:
__magic_name__: Union[str, Any] = str(int(match[2] ) - num_meta4D_last_stage )
__magic_name__: Union[str, Any] = trimmed_name.replace("""network""" , """meta3D_layers.blocks.""" + layer_index )
if "norm1" in old_name:
__magic_name__: Optional[int] = trimmed_name.replace("""norm1""" , """layernorm1""" )
elif "norm2" in old_name:
__magic_name__: Optional[Any] = trimmed_name.replace("""norm2""" , """layernorm2""" )
elif "fc1" in old_name:
__magic_name__: Any = trimmed_name.replace("""fc1""" , """linear_in""" )
elif "fc2" in old_name:
__magic_name__: int = trimmed_name.replace("""fc2""" , """linear_out""" )
__magic_name__: List[Any] = """last_stage.""" + trimmed_name
elif "network" in old_name and re.search(R""".\d.""" , __UpperCAmelCase ):
__magic_name__: Optional[int] = old_name.replace("""network""" , """intermediate_stages""" )
if "fc" in new_name:
__magic_name__: str = new_name.replace("""fc""" , """convolution""" )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
__magic_name__: Optional[int] = new_name.replace("""norm1""" , """batchnorm_before""" )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
__magic_name__: List[Any] = new_name.replace("""norm2""" , """batchnorm_after""" )
if "proj" in new_name:
__magic_name__: Optional[int] = new_name.replace("""proj""" , """projection""" )
if "dist_head" in new_name:
__magic_name__: int = new_name.replace("""dist_head""" , """distillation_classifier""" )
elif "head" in new_name:
__magic_name__: Dict = new_name.replace("""head""" , """classifier""" )
elif "patch_embed" in new_name:
__magic_name__: List[str] = """efficientformer.""" + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
__magic_name__: Tuple = new_name.replace("""norm""" , """layernorm""" )
__magic_name__: Dict = """efficientformer.""" + new_name
else:
__magic_name__: Union[str, Any] = """efficientformer.encoder.""" + new_name
return new_name
def a ( __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[int] ) -> Optional[Any]:
for key in checkpoint.copy().keys():
__magic_name__: List[str] = checkpoint.pop(__UpperCAmelCase )
__magic_name__: Union[str, Any] = val
return checkpoint
def a ( ) -> Dict:
__magic_name__: Optional[int] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__magic_name__: List[Any] = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw )
return image
def a ( __UpperCAmelCase : Path , __UpperCAmelCase : Path , __UpperCAmelCase : Path , __UpperCAmelCase : bool ) -> Any:
__magic_name__: Union[str, Any] = torch.load(__UpperCAmelCase , map_location="""cpu""" )["""model"""]
__magic_name__: List[Any] = EfficientFormerConfig.from_json_file(__UpperCAmelCase )
__magic_name__: Any = EfficientFormerForImageClassificationWithTeacher(__UpperCAmelCase )
__magic_name__: Dict = """_""".join(checkpoint_path.split("""/""" )[-1].split(""".""" )[0].split("""_""" )[:-1] )
__magic_name__: int = config.depths[-1] - config.num_metaad_blocks + 1
__magic_name__: int = convert_torch_checkpoint(__UpperCAmelCase , __UpperCAmelCase )
model.load_state_dict(__UpperCAmelCase )
model.eval()
__magic_name__: Optional[Any] = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
# prepare image
__magic_name__: Any = prepare_img()
__magic_name__: List[Any] = 2_5_6
__magic_name__: Optional[int] = 2_2_4
__magic_name__: List[str] = EfficientFormerImageProcessor(
size={"""shortest_edge""": image_size} , crop_size={"""height""": crop_size, """width""": crop_size} , resample=pillow_resamplings["""bicubic"""] , )
__magic_name__: int = processor(images=__UpperCAmelCase , return_tensors="""pt""" ).pixel_values
# original processing pipeline
__magic_name__: Optional[int] = Compose(
[
Resize(__UpperCAmelCase , interpolation=pillow_resamplings["""bicubic"""] ),
CenterCrop(__UpperCAmelCase ),
ToTensor(),
Normalize(__UpperCAmelCase , __UpperCAmelCase ),
] )
__magic_name__: List[str] = image_transforms(__UpperCAmelCase ).unsqueeze(0 )
assert torch.allclose(__UpperCAmelCase , __UpperCAmelCase )
__magic_name__: Optional[Any] = model(__UpperCAmelCase )
__magic_name__: Any = outputs.logits
__magic_name__: Any = (1, 1_0_0_0)
if "l1" in model_name:
__magic_name__: List[str] = torch.Tensor(
[-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28] )
assert torch.allclose(logits[0, :1_0] , __UpperCAmelCase , atol=1E-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
__magic_name__: List[Any] = torch.Tensor(
[-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27] )
assert torch.allclose(logits[0, :1_0] , __UpperCAmelCase , atol=1E-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
__magic_name__: Any = torch.Tensor(
[-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78] )
assert logits.shape == expected_shape
else:
raise ValueError(
f'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7' )
# Save Checkpoints
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
model.save_pretrained(__UpperCAmelCase )
print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}' )
processor.save_pretrained(__UpperCAmelCase )
print(f'Processor successfuly saved at {pytorch_dump_path}' )
if push_to_hub:
print("""Pushing model to the hub...""" )
model.push_to_hub(
repo_id=f'Bearnardd/{pytorch_dump_path}' , commit_message="""Add model""" , use_temp_dir=__UpperCAmelCase , )
processor.push_to_hub(
repo_id=f'Bearnardd/{pytorch_dump_path}' , commit_message="""Add image processor""" , use_temp_dir=__UpperCAmelCase , )
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--pytorch_model_path',
default=None,
type=str,
required=True,
help='Path to EfficientFormer pytorch checkpoint.',
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The json file for EfficientFormer model config.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
parser.add_argument(
'--no-push_to_hub',
dest='push_to_hub',
action='store_false',
help='Do not push model and image processor to the hub',
)
parser.set_defaults(push_to_hub=True)
__lowerCamelCase = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 96 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
__magic_name__ =logging.getLogger(__name__)
class _A ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ : Optional[Any] ="token-classification"
def __init__(self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
if type(SCREAMING_SNAKE_CASE_ ) == dict:
UpperCamelCase__ = Namespace(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = import_module('''tasks''' )
try:
UpperCamelCase__ = getattr(SCREAMING_SNAKE_CASE_ , hparams.task_type )
UpperCamelCase__ = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}" )
UpperCamelCase__ = self.token_classification_task.get_labels(hparams.labels )
UpperCamelCase__ = CrossEntropyLoss().ignore_index
super().__init__(SCREAMING_SNAKE_CASE_ , len(self.labels ) , self.mode )
def _a (self , **SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
return self.model(**SCREAMING_SNAKE_CASE_ )
def _a (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type != "distilbert":
UpperCamelCase__ = (
batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
) # XLM and RoBERTa don"t use token_type_ids
UpperCamelCase__ = self(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def _a (self ) -> int:
'''simple docstring'''
UpperCamelCase__ = self.hparams
for mode in ["train", "dev", "test"]:
UpperCamelCase__ = self._feature_file(SCREAMING_SNAKE_CASE_ )
if os.path.exists(SCREAMING_SNAKE_CASE_ ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.load(SCREAMING_SNAKE_CASE_ )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
UpperCamelCase__ = self.token_classification_task.read_examples_from_file(args.data_dir , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = self.token_classification_task.convert_examples_to_features(
SCREAMING_SNAKE_CASE_ , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ['''xlnet'''] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=SCREAMING_SNAKE_CASE_ , pad_on_left=bool(self.config.model_type in ['''xlnet'''] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info('''Saving features into cached file %s''' , SCREAMING_SNAKE_CASE_ )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _a (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False ) -> DataLoader:
'''simple docstring'''
UpperCamelCase__ = self._feature_file(SCREAMING_SNAKE_CASE_ )
logger.info('''Loading features from cached file %s''' , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.load(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
UpperCamelCase__ = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
UpperCamelCase__ = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
UpperCamelCase__ = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
UpperCamelCase__ = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , batch_size=SCREAMING_SNAKE_CASE_ )
def _a (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
"""Compute validation""" ""
UpperCamelCase__ = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type != "distilbert":
UpperCamelCase__ = (
batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
) # XLM and RoBERTa don"t use token_type_ids
UpperCamelCase__ = self(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ , UpperCamelCase__ = outputs[:2]
UpperCamelCase__ = logits.detach().cpu().numpy()
UpperCamelCase__ = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _a (self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ = torch.stack([x['''val_loss'''] for x in outputs] ).mean()
UpperCamelCase__ = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
UpperCamelCase__ = np.argmax(SCREAMING_SNAKE_CASE_ , axis=2 )
UpperCamelCase__ = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
UpperCamelCase__ = dict(enumerate(self.labels ) )
UpperCamelCase__ = [[] for _ in range(out_label_ids.shape[0] )]
UpperCamelCase__ = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
UpperCamelCase__ = {
'''val_loss''': val_loss_mean,
'''accuracy_score''': accuracy_score(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ),
'''precision''': precision_score(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ),
'''recall''': recall_score(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ),
'''f1''': fa_score(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ),
}
UpperCamelCase__ = dict(results.items() )
UpperCamelCase__ = results
return ret, preds_list, out_label_list
def _a (self , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = self._eval_end(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _a (self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = self._eval_end(SCREAMING_SNAKE_CASE_ )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
UpperCamelCase__ = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _a (SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
parser.add_argument(
'''--task_type''' , default='''NER''' , type=SCREAMING_SNAKE_CASE_ , help='''Task type to fine tune in training (e.g. NER, POS, etc)''' )
parser.add_argument(
'''--max_seq_length''' , default=128 , type=SCREAMING_SNAKE_CASE_ , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--labels''' , default='''''' , type=SCREAMING_SNAKE_CASE_ , help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=SCREAMING_SNAKE_CASE_ , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
if __name__ == "__main__":
__magic_name__ =argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
__magic_name__ =NERTransformer.add_model_specific_args(parser, os.getcwd())
__magic_name__ =parser.parse_args()
__magic_name__ =NERTransformer(args)
__magic_name__ =generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
__magic_name__ =sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
__magic_name__ =model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 415 | 0 |
def _lowerCAmelCase ( _lowerCAmelCase ) -> int:
'''simple docstring'''
__snake_case = abs(_lowerCAmelCase )
__snake_case = 0
while n > 0:
res += n % 10
n //= 10
return res
def _lowerCAmelCase ( _lowerCAmelCase ) -> int:
'''simple docstring'''
__snake_case = abs(_lowerCAmelCase )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def _lowerCAmelCase ( _lowerCAmelCase ) -> int:
'''simple docstring'''
return sum(int(_lowerCAmelCase ) for c in str(abs(_lowerCAmelCase ) ) )
def _lowerCAmelCase ( ) -> None:
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(_lowerCAmelCase , _lowerCAmelCase ) -> None:
__snake_case = F'''{func.__name__}({value})'''
__snake_case = timeit(F'''__main__.{call}''' , setup="import __main__" )
print(F'''{call:56} = {func(_lowerCAmelCase )} -- {timing:.4f} seconds''' )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(_lowerCAmelCase , _lowerCAmelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 715 |
class UpperCamelCase:
def __init__( self : Any ) -> Any:
'''simple docstring'''
__snake_case = 0
__snake_case = 0
__snake_case = {}
def SCREAMING_SNAKE_CASE_ ( self : Dict , SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
if vertex not in self.adjacency:
__snake_case = {}
self.num_vertices += 1
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
'''simple docstring'''
self.add_vertex(SCREAMING_SNAKE_CASE )
self.add_vertex(SCREAMING_SNAKE_CASE )
if head == tail:
return
__snake_case = weight
__snake_case = weight
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
__snake_case = self.get_edges()
for edge in edges:
__snake_case , __snake_case , __snake_case = edge
edges.remove((tail, head, weight) )
for i in range(len(SCREAMING_SNAKE_CASE ) ):
__snake_case = list(edges[i] )
edges.sort(key=lambda SCREAMING_SNAKE_CASE : e[2] )
for i in range(len(SCREAMING_SNAKE_CASE ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
__snake_case = edges[i][2] + 1
for edge in edges:
__snake_case , __snake_case , __snake_case = edge
__snake_case = weight
__snake_case = weight
def __str__( self : Tuple ) -> List[Any]:
'''simple docstring'''
__snake_case = ""
for tail in self.adjacency:
for head in self.adjacency[tail]:
__snake_case = self.adjacency[head][tail]
string += f'''{head} -> {tail} == {weight}\n'''
return string.rstrip("\n" )
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
__snake_case = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def SCREAMING_SNAKE_CASE_ ( self : Any ) -> List[Any]:
'''simple docstring'''
return self.adjacency.keys()
@staticmethod
def SCREAMING_SNAKE_CASE_ ( SCREAMING_SNAKE_CASE : Any=None , SCREAMING_SNAKE_CASE : List[Any]=None ) -> int:
'''simple docstring'''
__snake_case = Graph()
if vertices is None:
__snake_case = []
if edges is None:
__snake_case = []
for vertex in vertices:
g.add_vertex(SCREAMING_SNAKE_CASE )
for edge in edges:
g.add_edge(*SCREAMING_SNAKE_CASE )
return g
class UpperCamelCase:
def __init__( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case = {}
__snake_case = {}
def __len__( self : List[str] ) -> Dict:
'''simple docstring'''
return len(self.parent )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , SCREAMING_SNAKE_CASE : int ) -> List[str]:
'''simple docstring'''
if item in self.parent:
return self.find(SCREAMING_SNAKE_CASE )
__snake_case = item
__snake_case = 0
return item
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , SCREAMING_SNAKE_CASE : List[str] ) -> Any:
'''simple docstring'''
if item not in self.parent:
return self.make_set(SCREAMING_SNAKE_CASE )
if item != self.parent[item]:
__snake_case = self.find(self.parent[item] )
return self.parent[item]
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Dict:
'''simple docstring'''
__snake_case = self.find(SCREAMING_SNAKE_CASE )
__snake_case = self.find(SCREAMING_SNAKE_CASE )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
__snake_case = roota
return roota
if self.rank[roota] < self.rank[roota]:
__snake_case = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
__snake_case = roota
return roota
return None
@staticmethod
def SCREAMING_SNAKE_CASE_ ( SCREAMING_SNAKE_CASE : str ) -> Any:
'''simple docstring'''
__snake_case = graph.num_vertices
__snake_case = Graph.UnionFind()
__snake_case = []
while num_components > 1:
__snake_case = {}
for vertex in graph.get_vertices():
__snake_case = -1
__snake_case = graph.get_edges()
for edge in edges:
__snake_case , __snake_case , __snake_case = edge
edges.remove((tail, head, weight) )
for edge in edges:
__snake_case , __snake_case , __snake_case = edge
__snake_case = union_find.find(SCREAMING_SNAKE_CASE )
__snake_case = union_find.find(SCREAMING_SNAKE_CASE )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
__snake_case = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
__snake_case = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
__snake_case , __snake_case , __snake_case = cheap_edge[vertex]
if union_find.find(SCREAMING_SNAKE_CASE ) != union_find.find(SCREAMING_SNAKE_CASE ):
union_find.union(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
mst_edges.append(cheap_edge[vertex] )
__snake_case = num_components - 1
__snake_case = Graph.build(edges=SCREAMING_SNAKE_CASE )
return mst
| 473 | 0 |
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase_ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase_ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
re.sub('''<n>''' , '''''' , SCREAMING_SNAKE_CASE__ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE__ ) )
| 39 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
snake_case : List[Any] = logging.get_logger(__name__)
snake_case : Optional[int] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
snake_case : Union[str, Any] = {
'''vocab_file''': {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
'''roberta-large-openai-detector''': (
'''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
'''roberta-large-openai-detector''': (
'''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
),
},
'''tokenizer_file''': {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
'''roberta-base-openai-detector''': (
'''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
),
'''roberta-large-openai-detector''': (
'''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
),
},
}
snake_case : Optional[int] = {
'''roberta-base''': 5_12,
'''roberta-large''': 5_12,
'''roberta-large-mnli''': 5_12,
'''distilroberta-base''': 5_12,
'''roberta-base-openai-detector''': 5_12,
'''roberta-large-openai-detector''': 5_12,
}
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = ['input_ids', 'attention_mask']
SCREAMING_SNAKE_CASE__ = RobertaTokenizer
def __init__( self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase="replace" , _lowerCamelCase="<s>" , _lowerCamelCase="</s>" , _lowerCamelCase="</s>" , _lowerCamelCase="<s>" , _lowerCamelCase="<unk>" , _lowerCamelCase="<pad>" , _lowerCamelCase="<mask>" , _lowerCamelCase=False , _lowerCamelCase=True , **_lowerCamelCase , ):
super().__init__(
_lowerCamelCase , _lowerCamelCase , tokenizer_file=_lowerCamelCase , errors=_lowerCamelCase , bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , sep_token=_lowerCamelCase , cls_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token=_lowerCamelCase , add_prefix_space=_lowerCamelCase , trim_offsets=_lowerCamelCase , **_lowerCamelCase , )
a :List[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , _lowerCamelCase ) != add_prefix_space:
a :Optional[int] = getattr(_lowerCamelCase , pre_tok_state.pop('''type''' ) )
a :Union[str, Any] = add_prefix_space
a :Dict = pre_tok_class(**_lowerCamelCase )
a :Union[str, Any] = add_prefix_space
a :List[Any] = '''post_processor'''
a :List[str] = getattr(self.backend_tokenizer , _lowerCamelCase , _lowerCamelCase )
if tokenizer_component_instance:
a :Optional[Any] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
a :Optional[int] = tuple(state['''sep'''] )
if "cls" in state:
a :Union[str, Any] = tuple(state['''cls'''] )
a :Optional[int] = False
if state.get('''add_prefix_space''' , _lowerCamelCase ) != add_prefix_space:
a :int = add_prefix_space
a :Union[str, Any] = True
if state.get('''trim_offsets''' , _lowerCamelCase ) != trim_offsets:
a :int = trim_offsets
a :List[str] = True
if changes_to_apply:
a :Union[str, Any] = getattr(_lowerCamelCase , state.pop('''type''' ) )
a :str = component_class(**_lowerCamelCase )
setattr(self.backend_tokenizer , _lowerCamelCase , _lowerCamelCase )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
a :Union[str, Any] = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else value
a :int = value
def SCREAMING_SNAKE_CASE__ ( self , *_lowerCamelCase , **_lowerCamelCase ):
a :Union[str, Any] = kwargs.get('''is_split_into_words''' , _lowerCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , *_lowerCamelCase , **_lowerCamelCase ):
a :Any = kwargs.get('''is_split_into_words''' , _lowerCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = None ):
a :Any = self._tokenizer.model.save(_lowerCamelCase , name=_lowerCamelCase )
return tuple(_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase=None ):
a :Union[str, Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = None ):
a :Dict = [self.sep_token_id]
a :int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 445 | 0 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A__ ( __A ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A__ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
_lowerCamelCase : List[str] = [1, 2, 3]
with pytest.raises(__A ):
with parallel_backend("""unsupported backend""" ):
map_nested(__A , __A , num_proc=2 )
with pytest.raises(__A ):
with parallel_backend("""unsupported backend""" ):
map_nested(__A , __A , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" , [2, -1] )
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = [1, 2]
_lowerCamelCase : str = {"""a""": 1, """b""": 2}
_lowerCamelCase : Dict = {"""a""": [1, 2], """b""": [3, 4]}
_lowerCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
_lowerCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
_lowerCamelCase : Dict = [2, 3]
_lowerCamelCase : str = {"""a""": 2, """b""": 3}
_lowerCamelCase : Tuple = {"""a""": [2, 3], """b""": [4, 5]}
_lowerCamelCase : Union[str, Any] = {"""a""": {"""1""": 2}, """b""": 3}
_lowerCamelCase : List[Any] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(__A , __A , num_proc=__A ) == expected_map_nested_sa
assert map_nested(__A , __A , num_proc=__A ) == expected_map_nested_sa
assert map_nested(__A , __A , num_proc=__A ) == expected_map_nested_sa
assert map_nested(__A , __A , num_proc=__A ) == expected_map_nested_sa
assert map_nested(__A , __A , num_proc=__A ) == expected_map_nested_sa
| 15 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCAmelCase : Tuple =logging.get_logger(__name__)
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = ['pixel_values']
def __init__( self : Optional[Any] , _UpperCamelCase : bool = True , _UpperCamelCase : Dict[str, int] = None , _UpperCamelCase : PILImageResampling = PIL.Image.BICUBIC , _UpperCamelCase : bool = True , _UpperCamelCase : Dict[str, int] = None , _UpperCamelCase : Union[int, float] = 1 / 255 , _UpperCamelCase : bool = True , _UpperCamelCase : bool = True , _UpperCamelCase : Optional[Union[float, List[float]]] = None , _UpperCamelCase : Optional[Union[float, List[float]]] = None , **_UpperCamelCase : str , ) ->None:
"""simple docstring"""
super().__init__(**_UpperCamelCase)
_lowerCamelCase : Tuple = size if size is not None else {"""height""": 256, """width""": 256}
_lowerCamelCase : Optional[Any] = get_size_dict(_UpperCamelCase)
_lowerCamelCase : Any = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
_lowerCamelCase : Any = get_size_dict(_UpperCamelCase , param_name="""crop_size""")
_lowerCamelCase : int = do_resize
_lowerCamelCase : int = size
_lowerCamelCase : Optional[int] = resample
_lowerCamelCase : int = do_center_crop
_lowerCamelCase : Optional[Any] = crop_size
_lowerCamelCase : Union[str, Any] = do_rescale
_lowerCamelCase : List[str] = rescale_factor
_lowerCamelCase : List[Any] = do_normalize
_lowerCamelCase : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCamelCase : int = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _SCREAMING_SNAKE_CASE ( self : Any , _UpperCamelCase : np.ndarray , _UpperCamelCase : Dict[str, int] , _UpperCamelCase : PILImageResampling = PIL.Image.BICUBIC , _UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCamelCase : Union[str, Any] , ) ->np.ndarray:
"""simple docstring"""
_lowerCamelCase : Dict = get_size_dict(_UpperCamelCase)
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
return resize(
_UpperCamelCase , size=(size["""height"""], size["""width"""]) , resample=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : List[str] , _UpperCamelCase : np.ndarray , _UpperCamelCase : Dict[str, int] , _UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCamelCase : List[str] , ) ->np.ndarray:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = get_size_dict(_UpperCamelCase)
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
return center_crop(_UpperCamelCase , size=(size["""height"""], size["""width"""]) , data_format=_UpperCamelCase , **_UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , _UpperCamelCase : np.ndarray , _UpperCamelCase : Union[int, float] , _UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCamelCase : Union[str, Any] , ) ->str:
"""simple docstring"""
return rescale(_UpperCamelCase , scale=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : List[Any] , _UpperCamelCase : np.ndarray , _UpperCamelCase : Union[float, List[float]] , _UpperCamelCase : Union[float, List[float]] , _UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCamelCase : Union[str, Any] , ) ->np.ndarray:
"""simple docstring"""
return normalize(_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , _UpperCamelCase : ImageInput , _UpperCamelCase : bool = None , _UpperCamelCase : Dict[str, int] = None , _UpperCamelCase : Tuple=None , _UpperCamelCase : bool = None , _UpperCamelCase : Dict[str, int] = None , _UpperCamelCase : bool = None , _UpperCamelCase : float = None , _UpperCamelCase : bool = None , _UpperCamelCase : Optional[Union[float, List[float]]] = None , _UpperCamelCase : Optional[Union[float, List[float]]] = None , _UpperCamelCase : Optional[Union[str, TensorType]] = None , _UpperCamelCase : ChannelDimension = ChannelDimension.FIRST , **_UpperCamelCase : List[Any] , ) ->PIL.Image.Image:
"""simple docstring"""
_lowerCamelCase : Any = do_resize if do_resize is not None else self.do_resize
_lowerCamelCase : List[str] = resample if resample is not None else self.resample
_lowerCamelCase : Dict = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCamelCase : Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowerCamelCase : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCamelCase : Dict = do_normalize if do_normalize is not None else self.do_normalize
_lowerCamelCase : int = image_mean if image_mean is not None else self.image_mean
_lowerCamelCase : Dict = image_std if image_std is not None else self.image_std
_lowerCamelCase : Optional[Any] = size if size is not None else self.size
_lowerCamelCase : Optional[int] = get_size_dict(_UpperCamelCase)
_lowerCamelCase : List[str] = crop_size if crop_size is not None else self.crop_size
_lowerCamelCase : Dict = get_size_dict(_UpperCamelCase , param_name="""crop_size""")
_lowerCamelCase : int = make_list_of_images(_UpperCamelCase)
if not valid_images(_UpperCamelCase):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""")
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""")
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""")
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""")
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""")
# All transformations expect numpy arrays.
_lowerCamelCase : Union[str, Any] = [to_numpy_array(_UpperCamelCase) for image in images]
if do_resize:
_lowerCamelCase : Any = [self.resize(image=_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase) for image in images]
if do_center_crop:
_lowerCamelCase : str = [self.center_crop(image=_UpperCamelCase , size=_UpperCamelCase) for image in images]
if do_rescale:
_lowerCamelCase : Optional[int] = [self.rescale(image=_UpperCamelCase , scale=_UpperCamelCase) for image in images]
if do_normalize:
_lowerCamelCase : List[str] = [self.normalize(image=_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase) for image in images]
_lowerCamelCase : List[str] = [to_channel_dimension_format(_UpperCamelCase , _UpperCamelCase) for image in images]
_lowerCamelCase : int = {"""pixel_values""": images}
return BatchFeature(data=_UpperCamelCase , tensor_type=_UpperCamelCase)
| 15 | 1 |
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