{
// 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*$/i)) &&
linkText !== 'OCR模型免费转Markdown' &&
linkText !== 'OCR模型免费转Markdown'
) {
link.textContent = 'OCR模型免费转Markdown';
link.href = 'https://fast360.xyz';
link.setAttribute('target', '_blank');
link.setAttribute('rel', 'noopener noreferrer');
replacedLinks.add(link);
}
// 删除Posts链接
else if (
(linkHref.includes('/posts') || linkHref === '/posts' ||
linkText === 'Posts' || linkText.match(/^s*Postss*$/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*$/i)) &&
linkText !== '模型下载攻略'
) {
link.textContent = '模型下载攻略';
link.href = '/';
replacedLinks.add(link);
}
// 删除Enterprise链接
else if (
(linkHref.includes('/enterprise') || linkHref === '/enterprise' ||
linkText === 'Enterprise' || linkText.match(/^s*Enterprises*$/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, 'OCR模型免费转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'''
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_torchaudio,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class snake_case__ ( unittest.TestCase ):
A__ = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
A__ = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
def A_ ( self : Optional[int] , __a : int , __a : List[Any] , __a : List[str] ) -> str:
'''simple docstring'''
__snake_case : Tuple = AudioClassificationPipeline(model=__a , feature_extractor=__a )
# test with a raw waveform
__snake_case : List[Any] = np.zeros((34000,) )
__snake_case : Union[str, Any] = np.zeros((14000,) )
return audio_classifier, [audioa, audio]
def A_ ( self : Any , __a : Any , __a : Tuple ) -> int:
'''simple docstring'''
__snake_case , __snake_case : Tuple = examples
__snake_case : Optional[int] = audio_classifier(__a )
# by default a model is initialized with num_labels=2
self.assertEqual(
__a , [
{'score': ANY(__a ), 'label': ANY(__a )},
{'score': ANY(__a ), 'label': ANY(__a )},
] , )
__snake_case : Optional[Any] = audio_classifier(__a , top_k=1 )
self.assertEqual(
__a , [
{'score': ANY(__a ), 'label': ANY(__a )},
] , )
self.run_torchaudio(__a )
@require_torchaudio
def A_ ( self : Optional[Any] , __a : str ) -> str:
'''simple docstring'''
import datasets
# test with a local file
__snake_case : Any = datasets.load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
__snake_case : Tuple = dataset[0]['audio']['array']
__snake_case : Any = audio_classifier(__a )
self.assertEqual(
__a , [
{'score': ANY(__a ), 'label': ANY(__a )},
{'score': ANY(__a ), 'label': ANY(__a )},
] , )
@require_torch
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Any = 'anton-l/wav2vec2-random-tiny-classifier'
__snake_case : List[Any] = pipeline('audio-classification' , model=__a )
__snake_case : Optional[Any] = np.ones((8000,) )
__snake_case : Any = audio_classifier(__a , top_k=4 )
__snake_case : Tuple = [
{'score': 0.0_8_4_2, 'label': 'no'},
{'score': 0.0_8_3_8, 'label': 'up'},
{'score': 0.0_8_3_7, 'label': 'go'},
{'score': 0.0_8_3_4, 'label': 'right'},
]
__snake_case : int = [
{'score': 0.0_8_4_5, 'label': 'stop'},
{'score': 0.0_8_4_4, 'label': 'on'},
{'score': 0.0_8_4_1, 'label': 'right'},
{'score': 0.0_8_3_4, 'label': 'left'},
]
self.assertIn(nested_simplify(__a , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
__snake_case : Any = {'array': np.ones((8000,) ), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
__snake_case : str = audio_classifier(__a , top_k=4 )
self.assertIn(nested_simplify(__a , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
@require_torch
@slow
def A_ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
import datasets
__snake_case : int = 'superb/wav2vec2-base-superb-ks'
__snake_case : Optional[int] = pipeline('audio-classification' , model=__a )
__snake_case : str = datasets.load_dataset('anton-l/superb_dummy' , 'ks' , split='test' )
__snake_case : int = np.array(dataset[3]['speech'] , dtype=np.floataa )
__snake_case : Tuple = audio_classifier(__a , top_k=4 )
self.assertEqual(
nested_simplify(__a , decimals=3 ) , [
{'score': 0.9_8_1, 'label': 'go'},
{'score': 0.0_0_7, 'label': 'up'},
{'score': 0.0_0_6, 'label': '_unknown_'},
{'score': 0.0_0_1, 'label': 'down'},
] , )
@require_tf
@unittest.skip('Audio classification is not implemented for TF' )
def A_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
pass
| 0 |
'''simple docstring'''
from math import factorial
A__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def a_ ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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(_UpperCAmelCase ) )
def a_ ( _UpperCAmelCase : int = 60 ,_UpperCAmelCase : int = 1_00_00_00 ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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
__snake_case : List[str] = 0
# the cached sizes of the previous chains
__snake_case : dict[int, int] = {}
for start_chain_element in range(1 ,_UpperCAmelCase ):
# The temporary set will contain the elements of the chain
__snake_case : Optional[int] = set()
__snake_case : List[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__snake_case : str = 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(_UpperCAmelCase )
chain_set_length += 1
__snake_case : Tuple = digit_factorial_sum(_UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__snake_case : 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()}""")
| 0 | 1 |
'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class snake_case__ :
def __init__( self : List[Any] , __a : Any , __a : Dict , __a : bool = True , __a : bool = False ) -> str:
'''simple docstring'''
__snake_case : Union[str, Any] = scheduler
__snake_case : List[Any] = optimizers if isinstance(__a , (list, tuple) ) else [optimizers]
__snake_case : List[Any] = split_batches
__snake_case : str = step_with_optimizer
__snake_case : int = GradientState()
def A_ ( self : Optional[Any] , *__a : Tuple , **__a : Optional[Any] ) -> str:
'''simple docstring'''
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*__a , **__a )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*__a , **__a )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
__snake_case : Dict = AcceleratorState().num_processes
for _ in range(__a ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , 'total_steps' ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*__a , **__a )
else:
self.scheduler.step(*__a , **__a )
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
return self.scheduler.get_last_lr()
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
return self.scheduler.state_dict()
def A_ ( self : List[str] , __a : List[str] ) -> Optional[int]:
'''simple docstring'''
self.scheduler.load_state_dict(__a )
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
return self.scheduler.get_lr()
def A_ ( self : str , *__a : Union[str, Any] , **__a : int ) -> List[str]:
'''simple docstring'''
return self.scheduler.print_lr(*__a , **__a )
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int = 1_00 ) -> int:
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : Union[str, Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 0 | 1 |
'''simple docstring'''
def a_ ( ) -> str:
__snake_case : Optional[int] = 0
for i in range(1 ,10_01 ):
total += i**i
return str(_UpperCAmelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : int = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''',
'''GroupViTTextConfig''',
'''GroupViTVisionConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple = [
'''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GroupViTModel''',
'''GroupViTPreTrainedModel''',
'''GroupViTTextModel''',
'''GroupViTVisionModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFGroupViTModel''',
'''TFGroupViTPreTrainedModel''',
'''TFGroupViTTextModel''',
'''TFGroupViTVisionModel''',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class snake_case__ :
def __init__( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : str = ''
__snake_case : Optional[int] = ''
__snake_case : Union[str, Any] = []
__snake_case : Dict = 0
__snake_case : Tuple = 256
__snake_case : Optional[int] = 0
__snake_case : Optional[Any] = 0
__snake_case : Optional[int] = 0
__snake_case : Dict = 0
def A_ ( self : Union[str, Any] , __a : Any ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = cva.imread(__a , 0 )
__snake_case : Dict = copy.deepcopy(self.img )
__snake_case , __snake_case , __snake_case : Union[str, Any] = plt.hist(self.img.ravel() , 256 , [0, 256] , label='x' )
__snake_case : Optional[int] = np.sum(__a )
for i in range(len(__a ) ):
__snake_case : Optional[Any] = x[i] / self.k
self.sk += prk
__snake_case : Any = (self.L - 1) * self.sk
if self.rem != 0:
__snake_case : List[str] = int(last % last )
__snake_case : Optional[int] = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__a )
__snake_case : Tuple = int(np.ma.count(self.img ) / self.img[1].size )
__snake_case : int = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
__snake_case : int = self.img[j][i]
if num != self.last_list[num]:
__snake_case : Tuple = self.last_list[num]
cva.imwrite('output_data/output.jpg' , self.img )
def A_ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
cva.imshow('Output-Image' , self.img )
cva.imshow('Input-Image' , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
A__ : Union[str, Any] = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
A__ : Any = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ShapEPipeline
A__ = ['''prompt''']
A__ = ['''prompt''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return 32
@property
def A_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
return 8
@property
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__a )
@property
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Dict = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
__snake_case : Optional[Any] = PriorTransformer(**__a )
return model
@property
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Tuple = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
__snake_case : Optional[int] = ShapERenderer(**__a )
return model
def A_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : Union[str, Any] = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : Optional[Any] = self.dummy_renderer
__snake_case : List[Any] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=__a , clip_sample=__a , clip_sample_range=1.0 , )
__snake_case : int = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def A_ ( self : Union[str, Any] , __a : Dict , __a : int=0 ) -> Optional[Any]:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : Optional[Any] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : Optional[int] = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = 'cpu'
__snake_case : Dict = self.get_dummy_components()
__snake_case : int = self.pipeline_class(**__a )
__snake_case : str = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[Any] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : Dict = output.images[0]
__snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__snake_case : str = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
__snake_case : int = torch_device == 'cpu'
__snake_case : str = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__a , relax_max_difference=__a , )
def A_ ( self : List[str] ) -> Dict:
'''simple docstring'''
__snake_case : str = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Dict = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : int = 1
__snake_case : Tuple = 2
__snake_case : Tuple = self.get_dummy_inputs(__a )
for key in inputs.keys():
if key in self.batch_params:
__snake_case : Union[str, Any] = batch_size * [inputs[key]]
__snake_case : str = pipe(**__a , num_images_per_prompt=__a )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def A_ ( self : str ) -> Dict:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
__snake_case : Union[str, Any] = ShapEPipeline.from_pretrained('openai/shap-e' )
__snake_case : Any = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = torch.Generator(device=__a ).manual_seed(0 )
__snake_case : Union[str, Any] = pipe(
'a shark' , generator=__a , guidance_scale=1_5.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__a , __a )
| 0 | 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 snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''char'''
A__ = '''bpe'''
A__ = '''wp'''
A__ : List[Any] = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = ['''image_processor''', '''char_tokenizer''']
A__ = '''ViTImageProcessor'''
A__ = '''MgpstrTokenizer'''
def __init__( self : Any , __a : Dict=None , __a : Dict=None , **__a : Any ) -> Dict:
'''simple docstring'''
__snake_case : Optional[Any] = 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 , )
__snake_case : Any = kwargs.pop('feature_extractor' )
__snake_case : List[str] = 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`.' )
__snake_case : List[Any] = tokenizer
__snake_case : List[str] = AutoTokenizer.from_pretrained('gpt2' )
__snake_case : Union[str, Any] = AutoTokenizer.from_pretrained('bert-base-uncased' )
super().__init__(__a , __a )
def __call__( self : Tuple , __a : Dict=None , __a : int=None , __a : Dict=None , **__a : List[str] ) -> Any:
'''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:
__snake_case : Optional[int] = self.image_processor(__a , return_tensors=__a , **__a )
if text is not None:
__snake_case : int = self.char_tokenizer(__a , return_tensors=__a , **__a )
if text is None:
return inputs
elif images is None:
return encodings
else:
__snake_case : Optional[int] = encodings['input_ids']
return inputs
def A_ ( self : Tuple , __a : str ) -> List[str]:
'''simple docstring'''
__snake_case , __snake_case , __snake_case : Union[str, Any] = sequences
__snake_case : List[Any] = char_preds.size(0 )
__snake_case , __snake_case : Union[str, Any] = self._decode_helper(__a , 'char' )
__snake_case , __snake_case : Optional[int] = self._decode_helper(__a , 'bpe' )
__snake_case , __snake_case : Any = self._decode_helper(__a , 'wp' )
__snake_case : int = []
__snake_case : Tuple = []
for i in range(__a ):
__snake_case : Tuple = [char_scores[i], bpe_scores[i], wp_scores[i]]
__snake_case : List[str] = [char_strs[i], bpe_strs[i], wp_strs[i]]
__snake_case : Optional[int] = scores.index(max(__a ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
__snake_case : Optional[Any] = {}
__snake_case : int = final_strs
__snake_case : Dict = final_scores
__snake_case : int = char_strs
__snake_case : Dict = bpe_strs
__snake_case : Optional[Any] = wp_strs
return out
def A_ ( self : int , __a : Optional[int] , __a : Union[str, Any] ) -> Any:
'''simple docstring'''
if format == DecodeType.CHARACTER:
__snake_case : Dict = self.char_decode
__snake_case : Dict = 1
__snake_case : Optional[int] = '[s]'
elif format == DecodeType.BPE:
__snake_case : Any = self.bpe_decode
__snake_case : Dict = 2
__snake_case : Any = '#'
elif format == DecodeType.WORDPIECE:
__snake_case : Tuple = self.wp_decode
__snake_case : Optional[int] = 102
__snake_case : List[Any] = '[SEP]'
else:
raise ValueError(f'''Format {format} is not supported.''' )
__snake_case , __snake_case : Optional[int] = [], []
__snake_case : List[Any] = pred_logits.size(0 )
__snake_case : List[str] = pred_logits.size(1 )
__snake_case , __snake_case : Tuple = pred_logits.topk(1 , dim=-1 , largest=__a , sorted=__a )
__snake_case : Any = preds_index.view(-1 , __a )[:, 1:]
__snake_case : Optional[int] = decoder(__a )
__snake_case , __snake_case : Optional[int] = torch.nn.functional.softmax(__a , dim=2 ).max(dim=2 )
__snake_case : Dict = preds_max_prob[:, 1:]
for index in range(__a ):
__snake_case : Optional[int] = preds_str[index].find(__a )
__snake_case : str = preds_str[index][:pred_eos]
__snake_case : Any = preds_index[index].cpu().tolist()
__snake_case : Any = pred_index.index(__a ) if eos_token in pred_index else -1
__snake_case : Any = preds_max_prob[index][: pred_eos_index + 1]
__snake_case : Tuple = 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 A_ ( self : int , __a : Dict ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = [seq.replace(' ' , '' ) for seq in self.char_tokenizer.batch_decode(__a )]
return decode_strs
def A_ ( self : Dict , __a : List[str] ) -> Union[str, Any]:
'''simple docstring'''
return self.bpe_tokenizer.batch_decode(__a )
def A_ ( self : List[str] , __a : List[Any] ) -> Any:
'''simple docstring'''
__snake_case : List[str] = [seq.replace(' ' , '' ) for seq in self.wp_tokenizer.batch_decode(__a )]
return decode_strs
| 0 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
A__ : str = [8, 5, 9, 7]
A__ : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A__ : Dict = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class snake_case__ :
def __init__( self : Union[str, Any] , __a : list[int] , __a : list[list[int]] , __a : list[list[int]] , ) -> None:
'''simple docstring'''
__snake_case : int = claim_vector
__snake_case : Optional[int] = allocated_resources_table
__snake_case : List[str] = maximum_claim_table
def A_ ( self : str ) -> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def A_ ( self : int ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def A_ ( self : int ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def A_ ( self : str ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__a ): i for i in self.__need()}
def A_ ( self : Union[str, Any] , **__a : int ) -> None:
'''simple docstring'''
__snake_case : str = self.__need()
__snake_case : List[Any] = self.__allocated_resources_table
__snake_case : Optional[int] = self.__available_resources()
__snake_case : Union[str, Any] = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
__snake_case : Tuple = False
for each_need in need_list:
__snake_case : Any = True
for index, need in enumerate(__a ):
if need > available_resources[index]:
__snake_case : List[str] = False
break
if execution:
__snake_case : Union[str, Any] = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__snake_case : str = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(__a )
# update available/freed resources stack
__snake_case : Union[str, Any] = np.array(__a ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__a ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__a ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : str = logging.get_logger(__name__)
A__ : Tuple = {
'''huggingface/informer-tourism-monthly''': (
'''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'''
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''informer'''
A__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : Optional[Any] , __a : Optional[int] = None , __a : Optional[int] = None , __a : str = "student_t" , __a : str = "nll" , __a : int = 1 , __a : List[int] = None , __a : Optional[Union[str, bool]] = "mean" , __a : int = 0 , __a : int = 0 , __a : int = 0 , __a : int = 0 , __a : Optional[List[int]] = None , __a : Optional[List[int]] = None , __a : int = 64 , __a : int = 32 , __a : int = 32 , __a : int = 2 , __a : int = 2 , __a : int = 2 , __a : int = 2 , __a : bool = True , __a : str = "gelu" , __a : float = 0.0_5 , __a : float = 0.1 , __a : float = 0.1 , __a : float = 0.1 , __a : float = 0.1 , __a : int = 100 , __a : float = 0.0_2 , __a : Any=True , __a : str = "prob" , __a : int = 5 , __a : bool = True , **__a : int , ) -> List[Any]:
'''simple docstring'''
# time series specific configuration
__snake_case : Any = prediction_length
__snake_case : Union[str, Any] = context_length or prediction_length
__snake_case : Dict = distribution_output
__snake_case : Any = loss
__snake_case : Tuple = input_size
__snake_case : int = num_time_features
__snake_case : Optional[Any] = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
__snake_case : List[str] = scaling
__snake_case : List[str] = num_dynamic_real_features
__snake_case : int = num_static_real_features
__snake_case : List[Any] = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(__a ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
__snake_case : List[str] = cardinality
else:
__snake_case : Tuple = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(__a ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
__snake_case : Dict = embedding_dimension
else:
__snake_case : Optional[Any] = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
__snake_case : Union[str, Any] = num_parallel_samples
# Transformer architecture configuration
__snake_case : int = input_size * len(self.lags_sequence ) + self._number_of_features
__snake_case : Tuple = d_model
__snake_case : Dict = encoder_attention_heads
__snake_case : Any = decoder_attention_heads
__snake_case : Optional[Any] = encoder_ffn_dim
__snake_case : Dict = decoder_ffn_dim
__snake_case : Tuple = encoder_layers
__snake_case : Dict = decoder_layers
__snake_case : str = dropout
__snake_case : Any = attention_dropout
__snake_case : int = activation_dropout
__snake_case : Optional[int] = encoder_layerdrop
__snake_case : List[Any] = decoder_layerdrop
__snake_case : Optional[Any] = activation_function
__snake_case : Optional[int] = init_std
__snake_case : str = use_cache
# Informer
__snake_case : List[Any] = attention_type
__snake_case : Union[str, Any] = sampling_factor
__snake_case : List[str] = distil
super().__init__(is_encoder_decoder=__a , **__a )
@property
def A_ ( self : int ) -> int:
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
A__ : Union[str, Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ : List[Any] = {
'''vocab_file''': {
'''google/electra-small-generator''': (
'''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
),
'''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
'''google/electra-large-generator''': (
'''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
),
'''google/electra-small-discriminator''': (
'''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
),
'''google/electra-base-discriminator''': (
'''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
),
'''google/electra-large-discriminator''': (
'''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''google/electra-small-generator''': (
'''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
),
'''google/electra-base-generator''': (
'''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
),
'''google/electra-large-generator''': (
'''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
),
'''google/electra-small-discriminator''': (
'''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
),
'''google/electra-base-discriminator''': (
'''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
),
'''google/electra-large-discriminator''': (
'''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
),
},
}
A__ : List[Any] = {
'''google/electra-small-generator''': 5_1_2,
'''google/electra-base-generator''': 5_1_2,
'''google/electra-large-generator''': 5_1_2,
'''google/electra-small-discriminator''': 5_1_2,
'''google/electra-base-discriminator''': 5_1_2,
'''google/electra-large-discriminator''': 5_1_2,
}
A__ : Optional[Any] = {
'''google/electra-small-generator''': {'''do_lower_case''': True},
'''google/electra-base-generator''': {'''do_lower_case''': True},
'''google/electra-large-generator''': {'''do_lower_case''': True},
'''google/electra-small-discriminator''': {'''do_lower_case''': True},
'''google/electra-base-discriminator''': {'''do_lower_case''': True},
'''google/electra-large-discriminator''': {'''do_lower_case''': True},
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_INIT_CONFIGURATION
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = ElectraTokenizer
def __init__( self : int , __a : List[Any]=None , __a : int=None , __a : List[str]=True , __a : Any="[UNK]" , __a : Any="[SEP]" , __a : Union[str, Any]="[PAD]" , __a : Dict="[CLS]" , __a : List[Any]="[MASK]" , __a : str=True , __a : Optional[int]=None , **__a : Optional[int] , ) -> str:
'''simple docstring'''
super().__init__(
__a , tokenizer_file=__a , do_lower_case=__a , unk_token=__a , sep_token=__a , pad_token=__a , cls_token=__a , mask_token=__a , tokenize_chinese_chars=__a , strip_accents=__a , **__a , )
__snake_case : Tuple = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __a ) != do_lower_case
or normalizer_state.get('strip_accents' , __a ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __a ) != tokenize_chinese_chars
):
__snake_case : List[Any] = getattr(__a , normalizer_state.pop('type' ) )
__snake_case : str = do_lower_case
__snake_case : Optional[int] = strip_accents
__snake_case : Any = tokenize_chinese_chars
__snake_case : Union[str, Any] = normalizer_class(**__a )
__snake_case : Any = do_lower_case
def A_ ( self : Any , __a : List[str] , __a : Optional[Any]=None ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A_ ( self : List[Any] , __a : List[int] , __a : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
__snake_case : int = [self.sep_token_id]
__snake_case : List[Any] = [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 ) * [0] + len(token_ids_a + sep ) * [1]
def A_ ( self : Optional[int] , __a : str , __a : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
__snake_case : Tuple = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
| 0 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def a_ ( _UpperCAmelCase : Tuple ) -> Union[str, Any]:
__snake_case , __snake_case : Any = image.size
__snake_case , __snake_case : Optional[int] = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
__snake_case : int = image.resize((w, h) ,resample=PIL_INTERPOLATION['lanczos'] )
__snake_case : Dict = np.array(_UpperCAmelCase ).astype(np.floataa ) / 2_5_5.0
__snake_case : Tuple = image[None].transpose(0 ,3 ,1 ,2 )
__snake_case : int = torch.from_numpy(_UpperCAmelCase )
return 2.0 * image - 1.0
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : List[str] , __a : VQModel , __a : UNetaDModel , __a : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ) -> List[str]:
'''simple docstring'''
super().__init__()
self.register_modules(vqvae=__a , unet=__a , scheduler=__a )
@torch.no_grad()
def __call__( self : Tuple , __a : Union[torch.Tensor, PIL.Image.Image] = None , __a : Optional[int] = 1 , __a : Optional[int] = 100 , __a : Optional[float] = 0.0 , __a : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __a : Optional[str] = "pil" , __a : bool = True , ) -> Union[Tuple, ImagePipelineOutput]:
'''simple docstring'''
if isinstance(__a , PIL.Image.Image ):
__snake_case : Union[str, Any] = 1
elif isinstance(__a , torch.Tensor ):
__snake_case : str = image.shape[0]
else:
raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__a )}''' )
if isinstance(__a , PIL.Image.Image ):
__snake_case : Tuple = preprocess(__a )
__snake_case , __snake_case : Any = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
__snake_case : Dict = (batch_size, self.unet.config.in_channels // 2, height, width)
__snake_case : Optional[int] = next(self.unet.parameters() ).dtype
__snake_case : int = randn_tensor(__a , generator=__a , device=self.device , dtype=__a )
__snake_case : Union[str, Any] = image.to(device=self.device , dtype=__a )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(__a , device=self.device )
__snake_case : List[Any] = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
__snake_case : Optional[Any] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
__snake_case : str = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__snake_case : str = {}
if accepts_eta:
__snake_case : Optional[Any] = eta
for t in self.progress_bar(__a ):
# concat latents and low resolution image in the channel dimension.
__snake_case : int = torch.cat([latents, image] , dim=1 )
__snake_case : Optional[int] = self.scheduler.scale_model_input(__a , __a )
# predict the noise residual
__snake_case : List[str] = self.unet(__a , __a ).sample
# compute the previous noisy sample x_t -> x_t-1
__snake_case : List[Any] = self.scheduler.step(__a , __a , __a , **__a ).prev_sample
# decode the image latents with the VQVAE
__snake_case : Any = self.vqvae.decode(__a ).sample
__snake_case : List[Any] = torch.clamp(__a , -1.0 , 1.0 )
__snake_case : Union[str, Any] = image / 2 + 0.5
__snake_case : str = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__snake_case : str = self.numpy_to_pil(__a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__a )
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> bool:
__snake_case : Union[str, Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 0 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class snake_case__ :
def __init__( self : List[str] ) -> None:
'''simple docstring'''
__snake_case : list[Any] = []
__snake_case : int = 0
__snake_case : int = 0
def A_ ( self : Tuple ) -> bool:
'''simple docstring'''
return self.head == self.tail
def A_ ( self : int , __a : Any ) -> None:
'''simple docstring'''
self.data.append(__a )
__snake_case : Optional[int] = self.tail + 1
def A_ ( self : Dict ) -> Any:
'''simple docstring'''
__snake_case : Any = self.data[self.head]
__snake_case : Dict = self.head + 1
return ret
def A_ ( self : List[str] ) -> int:
'''simple docstring'''
return self.tail - self.head
def A_ ( self : Union[str, Any] ) -> None:
'''simple docstring'''
print(self.data )
print('**************' )
print(self.data[self.head : self.tail] )
class snake_case__ :
def __init__( self : List[Any] , __a : Any ) -> None:
'''simple docstring'''
__snake_case : List[str] = data
__snake_case : MyNode | None = None
__snake_case : MyNode | None = None
__snake_case : int = 1
def A_ ( self : Dict ) -> Any:
'''simple docstring'''
return self.data
def A_ ( self : List[str] ) -> MyNode | None:
'''simple docstring'''
return self.left
def A_ ( self : Optional[Any] ) -> MyNode | None:
'''simple docstring'''
return self.right
def A_ ( self : Optional[Any] ) -> int:
'''simple docstring'''
return self.height
def A_ ( self : Tuple , __a : Any ) -> None:
'''simple docstring'''
__snake_case : Dict = data
def A_ ( self : List[str] , __a : MyNode | None ) -> None:
'''simple docstring'''
__snake_case : List[str] = node
def A_ ( self : str , __a : MyNode | None ) -> None:
'''simple docstring'''
__snake_case : int = node
def A_ ( self : Tuple , __a : int ) -> None:
'''simple docstring'''
__snake_case : Tuple = height
def a_ ( _UpperCAmelCase : MyNode | None ) -> int:
if node is None:
return 0
return node.get_height()
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ) -> int:
if a > b:
return a
return b
def a_ ( _UpperCAmelCase : MyNode ) -> MyNode:
print('left rotation node:' ,node.get_data() )
__snake_case : Any = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(_UpperCAmelCase )
__snake_case : Optional[int] = my_max(get_height(node.get_right() ) ,get_height(node.get_left() ) ) + 1
node.set_height(_UpperCAmelCase )
__snake_case : Optional[Any] = my_max(get_height(ret.get_right() ) ,get_height(ret.get_left() ) ) + 1
ret.set_height(_UpperCAmelCase )
return ret
def a_ ( _UpperCAmelCase : MyNode ) -> MyNode:
print('right rotation node:' ,node.get_data() )
__snake_case : Dict = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(_UpperCAmelCase )
__snake_case : Union[str, Any] = my_max(get_height(node.get_right() ) ,get_height(node.get_left() ) ) + 1
node.set_height(_UpperCAmelCase )
__snake_case : List[str] = my_max(get_height(ret.get_right() ) ,get_height(ret.get_left() ) ) + 1
ret.set_height(_UpperCAmelCase )
return ret
def a_ ( _UpperCAmelCase : MyNode ) -> MyNode:
__snake_case : str = node.get_left()
assert left_child is not None
node.set_left(left_rotation(_UpperCAmelCase ) )
return right_rotation(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : MyNode ) -> MyNode:
__snake_case : int = node.get_right()
assert right_child is not None
node.set_right(right_rotation(_UpperCAmelCase ) )
return left_rotation(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : MyNode | None ,_UpperCAmelCase : Any ) -> MyNode | None:
if node is None:
return MyNode(_UpperCAmelCase )
if data < node.get_data():
node.set_left(insert_node(node.get_left() ,_UpperCAmelCase ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
__snake_case : List[str] = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
__snake_case : Any = right_rotation(_UpperCAmelCase )
else:
__snake_case : List[Any] = lr_rotation(_UpperCAmelCase )
else:
node.set_right(insert_node(node.get_right() ,_UpperCAmelCase ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
__snake_case : Dict = node.get_right()
assert right_child is not None
if data < right_child.get_data():
__snake_case : Tuple = rl_rotation(_UpperCAmelCase )
else:
__snake_case : int = left_rotation(_UpperCAmelCase )
__snake_case : Optional[Any] = my_max(get_height(node.get_right() ) ,get_height(node.get_left() ) ) + 1
node.set_height(_UpperCAmelCase )
return node
def a_ ( _UpperCAmelCase : MyNode ) -> Any:
while True:
__snake_case : int = root.get_right()
if right_child is None:
break
__snake_case : int = right_child
return root.get_data()
def a_ ( _UpperCAmelCase : MyNode ) -> Any:
while True:
__snake_case : List[Any] = root.get_left()
if left_child is None:
break
__snake_case : List[str] = left_child
return root.get_data()
def a_ ( _UpperCAmelCase : MyNode ,_UpperCAmelCase : Any ) -> MyNode | None:
__snake_case : Tuple = root.get_left()
__snake_case : int = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
__snake_case : Any = get_left_most(_UpperCAmelCase )
root.set_data(_UpperCAmelCase )
root.set_right(del_node(_UpperCAmelCase ,_UpperCAmelCase ) )
elif left_child is not None:
__snake_case : Tuple = left_child
elif right_child is not None:
__snake_case : str = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print('No such data' )
return root
else:
root.set_left(del_node(_UpperCAmelCase ,_UpperCAmelCase ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(_UpperCAmelCase ,_UpperCAmelCase ) )
if get_height(_UpperCAmelCase ) - get_height(_UpperCAmelCase ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
__snake_case : List[Any] = left_rotation(_UpperCAmelCase )
else:
__snake_case : Tuple = rl_rotation(_UpperCAmelCase )
elif get_height(_UpperCAmelCase ) - get_height(_UpperCAmelCase ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
__snake_case : List[Any] = right_rotation(_UpperCAmelCase )
else:
__snake_case : Any = lr_rotation(_UpperCAmelCase )
__snake_case : List[str] = my_max(get_height(root.get_right() ) ,get_height(root.get_left() ) ) + 1
root.set_height(_UpperCAmelCase )
return root
class snake_case__ :
def __init__( self : Dict ) -> None:
'''simple docstring'''
__snake_case : MyNode | None = None
def A_ ( self : str ) -> int:
'''simple docstring'''
return get_height(self.root )
def A_ ( self : List[str] , __a : Any ) -> None:
'''simple docstring'''
print('insert:' + str(__a ) )
__snake_case : Optional[Any] = insert_node(self.root , __a )
def A_ ( self : Union[str, Any] , __a : Any ) -> None:
'''simple docstring'''
print('delete:' + str(__a ) )
if self.root is None:
print('Tree is empty!' )
return
__snake_case : Optional[Any] = del_node(self.root , __a )
def __str__( self : Optional[Any] , ) -> str: # a level traversale, gives a more intuitive look on the tree
'''simple docstring'''
__snake_case : Union[str, Any] = ''
__snake_case : List[Any] = MyQueue()
q.push(self.root )
__snake_case : Dict = self.get_height()
if layer == 0:
return output
__snake_case : Any = 0
while not q.is_empty():
__snake_case : List[str] = q.pop()
__snake_case : List[Any] = ' ' * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(__a )
q.push(__a )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
__snake_case : Dict = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 , __a ) - 1:
__snake_case : Union[str, Any] = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def a_ ( ) -> None:
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
A__ : Optional[Any] = AVLtree()
A__ : Optional[Any] = list(range(1_0))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
A__ : Tuple = pytest.mark.integration
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Dict = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(__a ) for x in np.arange(30 ).tolist()]} )
return dset
def A_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
__snake_case : Dict = dset.map(
lambda __a , __a : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=__a , keep_in_memory=__a )
__snake_case : List[Any] = dset.add_faiss_index('vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
__snake_case , __snake_case : str = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(__a , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
__snake_case : Dataset = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : Any = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
__snake_case : Union[str, Any] = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=__a )
__snake_case , __snake_case : str = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : str ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
__snake_case : Dict = np.zeros(5 , dtype=np.floataa )
__snake_case : List[str] = 1
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertRaises(__a , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
__snake_case : List[str] = np.eye(5 , dtype=np.floataa )[::-1]
__snake_case , __snake_case : Dict = index.search_batch(__a )
self.assertRaises(__a , index.search_batch , queries[0] )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : List[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , __a )
def A_ ( self : int ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
__snake_case : List[str] = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(__a ):
__snake_case : Dict = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def A_ ( self : str ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Tuple = faiss.IndexFlat(5 )
__snake_case : List[Any] = FaissIndex(custom_index=__a )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
import faiss
__snake_case : Optional[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
index.save(tmp_file.name )
__snake_case : List[Any] = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
__snake_case : List[Any] = np.zeros(5 , dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : int = index.search(__a )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def a_ ( _UpperCAmelCase : str ) -> Optional[int]:
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
__snake_case : Dict = 'index.faiss'
__snake_case : Any = f'''mock://{index_name}'''
index.save(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = FaissIndex.load(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = np.zeros(5 ,dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : Tuple = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : int = Elasticsearch()
__snake_case : Dict = {'acknowledged': True}
__snake_case : List[Any] = ElasticSearchIndex(es_client=__a )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
__snake_case : Optional[Any] = 'foo'
__snake_case : int = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
__snake_case : Dict = 'foo'
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : Optional[Any] = index.search(__a , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
__snake_case : List[Any] = ['foo', 'bar', 'foobar']
__snake_case : str = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : Any = index.search_batch(__a )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
# batched queries with timeout
__snake_case : Tuple = ['foo', 'bar', 'foobar']
__snake_case : List[Any] = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : int = index.search_batch(__a , request_timeout=30 )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
| 0 | 1 |
'''simple docstring'''
from math import factorial
A__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def a_ ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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(_UpperCAmelCase ) )
def a_ ( _UpperCAmelCase : int = 60 ,_UpperCAmelCase : int = 1_00_00_00 ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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
__snake_case : List[str] = 0
# the cached sizes of the previous chains
__snake_case : dict[int, int] = {}
for start_chain_element in range(1 ,_UpperCAmelCase ):
# The temporary set will contain the elements of the chain
__snake_case : Optional[int] = set()
__snake_case : List[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__snake_case : str = 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(_UpperCAmelCase )
chain_set_length += 1
__snake_case : Tuple = digit_factorial_sum(_UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__snake_case : 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()}""")
| 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A__ : List[Any] = logging.get_logger(__name__)
A__ : Tuple = {
'''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 snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''t5'''
A__ = ['''past_key_values''']
A__ = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : str , __a : Dict=32128 , __a : Dict=512 , __a : Union[str, Any]=64 , __a : str=2048 , __a : Union[str, Any]=6 , __a : Any=None , __a : Any=8 , __a : List[Any]=32 , __a : Any=128 , __a : Tuple=0.1 , __a : str=1e-6 , __a : Dict=1.0 , __a : Tuple="relu" , __a : Dict=True , __a : Union[str, Any]=True , __a : Any=0 , __a : Dict=1 , **__a : Union[str, Any] , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = vocab_size
__snake_case : str = d_model
__snake_case : str = d_kv
__snake_case : List[Any] = d_ff
__snake_case : List[str] = num_layers
__snake_case : Tuple = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__snake_case : Union[str, Any] = num_heads
__snake_case : Tuple = relative_attention_num_buckets
__snake_case : Optional[int] = relative_attention_max_distance
__snake_case : Optional[Any] = dropout_rate
__snake_case : str = layer_norm_epsilon
__snake_case : List[str] = initializer_factor
__snake_case : int = feed_forward_proj
__snake_case : Optional[Any] = use_cache
__snake_case : Optional[Any] = self.feed_forward_proj.split('-' )
__snake_case : Dict = act_info[-1]
__snake_case : List[str] = act_info[0] == 'gated'
if len(__a ) > 1 and act_info[0] != "gated" or len(__a ) > 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":
__snake_case : Dict = 'gelu_new'
super().__init__(
pad_token_id=__a , eos_token_id=__a , is_encoder_decoder=__a , **__a , )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@property
def A_ ( self : str ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__snake_case : Union[str, Any] = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
__snake_case : Tuple = 'past_encoder_sequence + sequence'
__snake_case : Dict = {0: 'batch'}
__snake_case : Dict = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
__snake_case : Tuple = {0: 'batch', 1: 'decoder_sequence'}
__snake_case : int = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(__a , direction='inputs' )
return common_inputs
@property
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
return 13
| 0 | 1 |
'''simple docstring'''
import math
def a_ ( _UpperCAmelCase : int ) -> bool:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
__snake_case : Dict = range(3 ,int(math.sqrt(_UpperCAmelCase ) + 1 ) ,2 )
return not any(not number % i for i in odd_numbers )
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : List[str]=1 ,**_UpperCAmelCase : List[Any] ) -> int:
__snake_case : Union[str, Any] = factor * value
__snake_case : List[Any] = value
while not is_prime(_UpperCAmelCase ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 ,**_UpperCAmelCase )
return value
| 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : Optional[int] = {}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''llama'''
A__ = ['''past_key_values''']
def __init__( self : Any , __a : List[str]=32000 , __a : Union[str, Any]=4096 , __a : Optional[Any]=11008 , __a : Any=32 , __a : str=32 , __a : Optional[int]=None , __a : Dict="silu" , __a : Dict=2048 , __a : List[str]=0.0_2 , __a : Union[str, Any]=1e-6 , __a : Dict=True , __a : List[str]=0 , __a : Tuple=1 , __a : Tuple=2 , __a : Optional[Any]=1 , __a : Any=False , __a : Tuple=None , **__a : List[Any] , ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = vocab_size
__snake_case : List[str] = max_position_embeddings
__snake_case : List[Any] = hidden_size
__snake_case : Union[str, Any] = intermediate_size
__snake_case : Optional[int] = num_hidden_layers
__snake_case : List[Any] = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
__snake_case : Optional[int] = num_attention_heads
__snake_case : Optional[Any] = num_key_value_heads
__snake_case : int = hidden_act
__snake_case : Any = initializer_range
__snake_case : Any = rms_norm_eps
__snake_case : Union[str, Any] = pretraining_tp
__snake_case : Optional[int] = use_cache
__snake_case : Any = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , tie_word_embeddings=__a , **__a , )
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __a ) 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}''' )
__snake_case : Optional[Any] = self.rope_scaling.get('type' , __a )
__snake_case : Tuple = self.rope_scaling.get('factor' , __a )
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(__a , __a ) or rope_scaling_factor <= 1.0:
raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )
| 0 | 1 |
'''simple docstring'''
from __future__ import annotations
A__ : Optional[int] = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
class snake_case__ :
def __init__( self : Tuple , __a : dict[str, list[str]] , __a : str ) -> None:
'''simple docstring'''
__snake_case : Optional[Any] = graph
# mapping node to its parent in resulting breadth first tree
__snake_case : dict[str, str | None] = {}
__snake_case : Optional[int] = source_vertex
def A_ ( self : Tuple ) -> None:
'''simple docstring'''
__snake_case : Union[str, Any] = {self.source_vertex}
__snake_case : Any = None
__snake_case : Union[str, Any] = [self.source_vertex] # first in first out queue
while queue:
__snake_case : str = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(__a )
__snake_case : Tuple = vertex
queue.append(__a )
def A_ ( self : Optional[int] , __a : str ) -> str:
'''simple docstring'''
if target_vertex == self.source_vertex:
return self.source_vertex
__snake_case : str = self.parent.get(__a )
if target_vertex_parent is None:
__snake_case : Dict = (
f'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
)
raise ValueError(__a )
return self.shortest_path(__a ) + f'''->{target_vertex}'''
if __name__ == "__main__":
A__ : Any = Graph(graph, '''G''')
g.breath_first_search()
print(g.shortest_path('''D'''))
print(g.shortest_path('''G'''))
print(g.shortest_path('''Foo'''))
| 0 |
'''simple docstring'''
from __future__ import annotations
A__ : str = '''Muhammad Umer Farooq'''
A__ : int = '''MIT'''
A__ : Optional[int] = '''1.0.0'''
A__ : List[Any] = '''Muhammad Umer Farooq'''
A__ : Optional[Any] = '''[email protected]'''
A__ : Optional[Any] = '''Alpha'''
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Union[str, Any] , __a : str ) -> None:
'''simple docstring'''
super().__init__()
__snake_case : list[str] = []
__snake_case : Dict = domain
def A_ ( self : Dict , __a : str , __a : list[tuple[str, str | None]] ) -> None:
'''simple docstring'''
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__snake_case : Optional[Any] = parse.urljoin(self.domain , __a )
self.urls.append(__a )
def a_ ( _UpperCAmelCase : str ) -> str:
return ".".join(get_sub_domain_name(_UpperCAmelCase ).split('.' )[-2:] )
def a_ ( _UpperCAmelCase : str ) -> str:
return parse.urlparse(_UpperCAmelCase ).netloc
def a_ ( _UpperCAmelCase : str = "https://github.com" ) -> list[str]:
__snake_case : List[Any] = get_domain_name(_UpperCAmelCase )
# Initialize the parser
__snake_case : Tuple = Parser(_UpperCAmelCase )
try:
# Open URL
__snake_case : Any = requests.get(_UpperCAmelCase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__snake_case : Dict = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__snake_case : List[Any] = requests.get(_UpperCAmelCase )
# Get the valid email.
__snake_case : Optional[Any] = re.findall('[a-zA-Z0-9]+@' + domain ,read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_UpperCAmelCase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Tuple = emails_from_url('''https://github.com''')
print(F"""{len(emails)} emails found:""")
print('''\n'''.join(sorted(emails)))
| 0 | 1 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def a_ ( ) -> str:
__snake_case : int = ArgumentParser(
description=(
'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
) )
# Optional arguments for the launch helper
parser.add_argument('--num_cores' ,type=_UpperCAmelCase ,default=1 ,help='Number of TPU cores to use (1 or 8).' )
# positional
parser.add_argument(
'training_script' ,type=_UpperCAmelCase ,help=(
'The full path to the single TPU training '
'program/script to be launched in parallel, '
'followed by all the arguments for the '
'training script'
) ,)
# rest from the training program
parser.add_argument('training_script_args' ,nargs=_UpperCAmelCase )
return parser.parse_args()
def a_ ( ) -> Any:
__snake_case : Dict = parse_args()
# Import training_script as a module.
__snake_case : str = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
__snake_case : Any = script_fpath.stem
__snake_case : Union[str, Any] = importlib.import_module(_UpperCAmelCase )
# Patch sys.argv
__snake_case : Union[str, Any] = [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores )]
xmp.spawn(mod._mp_fn ,args=() ,nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
A__ : Dict = logging.getLogger()
def a_ ( ) -> Tuple:
__snake_case : List[Any] = argparse.ArgumentParser()
parser.add_argument('-f' )
__snake_case : Any = parser.parse_args()
return args.f
def a_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
__snake_case : Tuple = {}
__snake_case : Union[str, Any] = os.path.join(_UpperCAmelCase ,'all_results.json' )
if os.path.exists(_UpperCAmelCase ):
with open(_UpperCAmelCase ,'r' ) as f:
__snake_case : List[str] = json.load(_UpperCAmelCase )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
def a_ ( ) -> Union[str, Any]:
__snake_case : Union[str, Any] = torch.cuda.is_available() and torch_device == 'cuda'
return is_using_cuda and is_apex_available()
A__ : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@classmethod
def A_ ( cls : Any ) -> List[str]:
'''simple docstring'''
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__snake_case : Optional[int] = tempfile.mkdtemp()
__snake_case : Dict = os.path.join(cls.tmpdir , 'default_config.yml' )
write_basic_config(save_location=cls.configPath )
__snake_case : List[Any] = ['accelerate', 'launch', '--config_file', cls.configPath]
@classmethod
def A_ ( cls : List[str] ) -> List[str]:
'''simple docstring'''
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : List[Any] = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'glue_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
'''.split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertLess(result['perplexity'] , 100 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'clm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : int = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertLess(result['perplexity'] , 42 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'mlm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__snake_case : Any = 7 if get_gpu_count() > 1 else 2
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertLess(result['train_loss'] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'ner_no_trainer' ) ) )
@unittest.skip(reason='Fix me @muellerzr' )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : Tuple = f'''
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['eval_f1'] , 28 )
self.assertGreaterEqual(result['eval_exact'] , 28 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'qa_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : str = self.get_auto_remove_tmp_dir()
__snake_case : Any = f'''
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__a , 'swag_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : int = get_results(__a )
self.assertGreaterEqual(result['eval_rouge1'] , 10 )
self.assertGreaterEqual(result['eval_rouge2'] , 2 )
self.assertGreaterEqual(result['eval_rougeL'] , 7 )
self.assertGreaterEqual(result['eval_rougeLsum'] , 7 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'summarization_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_bleu'] , 30 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'translation_no_trainer' ) ) )
@slow
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = logging.StreamHandler(sys.stdout )
logger.addHandler(__a )
__snake_case : List[str] = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertGreaterEqual(result['eval_overall_accuracy'] , 0.1_0 )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Dict = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : Optional[int] = get_results(__a )
# The base model scores a 25%
self.assertGreaterEqual(result['eval_accuracy'] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__a , 'step_1' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'image_classification_no_trainer' ) ) )
| 0 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class snake_case__ ( unittest.TestCase ):
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Any = 'hf-internal-testing/tiny-random-t5'
__snake_case : List[Any] = AutoTokenizer.from_pretrained(__a )
__snake_case : Any = AutoModelForSeqaSeqLM.from_pretrained(__a )
__snake_case : str = tokenizer('This is me' , return_tensors='pt' )
__snake_case : Any = model.to_bettertransformer()
self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
__snake_case : Optional[int] = model.generate(**__a )
__snake_case : List[Any] = model.reverse_bettertransformer()
self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__a )
__snake_case : Union[str, Any] = AutoModelForSeqaSeqLM.from_pretrained(__a )
self.assertFalse(
any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
__snake_case : int = model_reloaded.generate(**__a )
self.assertTrue(torch.allclose(__a , __a ) )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Any = 'hf-internal-testing/tiny-random-t5'
__snake_case : int = AutoModelForSeqaSeqLM.from_pretrained(__a )
__snake_case : int = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(__a ):
model.save_pretrained(__a )
__snake_case : List[str] = model.reverse_bettertransformer()
model.save_pretrained(__a )
| 0 |
'''simple docstring'''
import math
def a_ ( _UpperCAmelCase : int ) -> list:
__snake_case : Optional[Any] = [True] * n
__snake_case : Optional[int] = False
__snake_case : Dict = False
__snake_case : List[Any] = True
for i in range(3 ,int(n**0.5 + 1 ) ,2 ):
__snake_case : Optional[int] = i * 2
while index < n:
__snake_case : Union[str, Any] = False
__snake_case : int = index + i
__snake_case : Dict = [2]
for i in range(3 ,_UpperCAmelCase ,2 ):
if is_prime[i]:
primes.append(_UpperCAmelCase )
return primes
def a_ ( _UpperCAmelCase : int = 99_99_66_66_33_33 ) -> int:
__snake_case : List[Any] = math.floor(math.sqrt(_UpperCAmelCase ) ) + 1_00
__snake_case : Tuple = prime_sieve(_UpperCAmelCase )
__snake_case : List[Any] = 0
__snake_case : List[Any] = 0
__snake_case : Optional[int] = primes[prime_index]
while (last_prime**2) <= limit:
__snake_case : Optional[int] = primes[prime_index + 1]
__snake_case : Union[str, Any] = last_prime**2
__snake_case : Dict = next_prime**2
# Get numbers divisible by lps(current)
__snake_case : Optional[Any] = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
__snake_case : Optional[Any] = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
__snake_case : List[str] = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
__snake_case : Dict = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 0 | 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
A__ : Tuple = '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''
def a_ ( _UpperCAmelCase : Optional[int]=None ) -> Any:
if subparsers is not None:
__snake_case : Any = subparsers.add_parser('tpu-config' ,description=_description )
else:
__snake_case : Union[str, Any] = argparse.ArgumentParser('Accelerate tpu-config command' ,description=_description )
# Core arguments
__snake_case : Optional[Any] = parser.add_argument_group(
'Config Arguments' ,'Arguments that can be configured through `accelerate config`.' )
config_args.add_argument(
'--config_file' ,type=_UpperCAmelCase ,default=_UpperCAmelCase ,help='Path to the config file to use for accelerate.' ,)
config_args.add_argument(
'--tpu_name' ,default=_UpperCAmelCase ,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=_UpperCAmelCase ,help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.' ,)
__snake_case : Union[str, Any] = 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=_UpperCAmelCase ,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=_UpperCAmelCase )
return parser
def a_ ( _UpperCAmelCase : Optional[Any] ) -> int:
__snake_case : Dict = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(_UpperCAmelCase ):
__snake_case : Union[str, Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
__snake_case : int = defaults.command_file
if not args.command and defaults.commands is not None:
__snake_case : Any = defaults.commands
if not args.tpu_name:
__snake_case : int = defaults.tpu_name
if not args.tpu_zone:
__snake_case : List[Any] = defaults.tpu_zone
if args.accelerate_version == "dev":
__snake_case : Optional[int] = 'git+https://github.com/huggingface/accelerate.git'
elif args.accelerate_version == "latest":
__snake_case : Dict = 'accelerate -U'
elif isinstance(parse(args.accelerate_version ) ,_UpperCAmelCase ):
__snake_case : Union[str, Any] = 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:
__snake_case : List[str] = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] ,_UpperCAmelCase ):
__snake_case : Optional[int] = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
__snake_case : Optional[Any] = ['cd /usr/share']
if args.install_accelerate:
new_cmd += [f'''pip install {args.accelerate_version}''']
new_cmd += args.command
__snake_case : Optional[Any] = '; '.join(_UpperCAmelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
__snake_case : Any = ['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(_UpperCAmelCase )}''' )
return
subprocess.run(_UpperCAmelCase )
print('Successfully setup pod.' )
def a_ ( ) -> List[str]:
__snake_case : Optional[Any] = tpu_command_parser()
__snake_case : Optional[int] = parser.parse_args()
tpu_command_launcher(_UpperCAmelCase )
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : float ,_UpperCAmelCase : float ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"""{price_plus_tax(1_0_0, 0.25) = }""")
print(F"""{price_plus_tax(1_25.50, 0.05) = }""")
| 0 | 1 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
__snake_case : Optional[int] = SMALL_MODEL_IDENTIFIER
__snake_case : str = 'pt'
__snake_case : Union[str, Any] = 'tf'
def A_ ( self : Dict , __a : Tuple ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(__a )
def A_ ( self : Any , __a : Optional[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Union[str, Any] = TFAutoModel.from_pretrained(self.test_model , from_pt=__a )
model_tf.save_pretrained(__a )
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = 'mock_framework'
# Framework provided - return whatever the user provides
__snake_case : int = FeaturesManager.determine_framework(self.test_model , __a )
self.assertEqual(__a , __a )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : List[Any] = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Union[str, Any] = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(__a ):
__snake_case : Optional[int] = FeaturesManager.determine_framework(__a )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ):
__snake_case : int = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_tf )
# Both in environment -> use PyTorch
__snake_case : Optional[Any] = MagicMock(return_value=__a )
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# Both not in environment -> raise error
__snake_case : str = MagicMock(return_value=__a )
__snake_case : List[Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
with self.assertRaises(__a ):
__snake_case : Tuple = FeaturesManager.determine_framework(self.test_model )
| 0 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
__snake_case : Optional[int] = SMALL_MODEL_IDENTIFIER
__snake_case : str = 'pt'
__snake_case : Union[str, Any] = 'tf'
def A_ ( self : Dict , __a : Tuple ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(__a )
def A_ ( self : Any , __a : Optional[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Union[str, Any] = TFAutoModel.from_pretrained(self.test_model , from_pt=__a )
model_tf.save_pretrained(__a )
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = 'mock_framework'
# Framework provided - return whatever the user provides
__snake_case : int = FeaturesManager.determine_framework(self.test_model , __a )
self.assertEqual(__a , __a )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : List[Any] = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Union[str, Any] = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(__a ):
__snake_case : Optional[int] = FeaturesManager.determine_framework(__a )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ):
__snake_case : int = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_tf )
# Both in environment -> use PyTorch
__snake_case : Optional[Any] = MagicMock(return_value=__a )
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# Both not in environment -> raise error
__snake_case : str = MagicMock(return_value=__a )
__snake_case : List[Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
with self.assertRaises(__a ):
__snake_case : Tuple = FeaturesManager.determine_framework(self.test_model )
| 0 | 1 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Any , __a : Optional[Any] , __a : int ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
self.register_modules(unet=__a , scheduler=__a )
def __call__( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : str = torch.randn(
(1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , )
__snake_case : Tuple = 1
__snake_case : Optional[Any] = self.unet(__a , __a ).sample
__snake_case : Any = self.scheduler.step(__a , __a , __a ).prev_sample
__snake_case : str = scheduler_output - scheduler_output + torch.ones_like(__a )
return result
| 0 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ProphetNetTokenizer
A__ = False
def A_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
super().setUp()
__snake_case : Dict = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__snake_case : Any = 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 A_ ( self : int , __a : Union[str, Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[int] = 'UNwant\u00E9d,running'
__snake_case : List[str] = 'unwanted, running'
return input_text, output_text
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Dict = self.tokenizer_class(self.vocab_file )
__snake_case : List[str] = 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 A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : List[str] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Optional[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 A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[Any] = 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 A_ ( self : int ) -> Any:
'''simple docstring'''
__snake_case : int = 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 A_ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = 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 A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
__snake_case : str = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[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 A_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__snake_case : List[Any] = {}
for i, token in enumerate(__a ):
__snake_case : List[str] = i
__snake_case : 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'] )
@require_torch
def A_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[Any] = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : int = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__snake_case : str = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
__snake_case : Union[str, Any] = tokenizer(__a , padding=__a , return_tensors='pt' )
self.assertIsInstance(__a , __a )
__snake_case : int = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__a , __a )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
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 A_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
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 A_ ( self : List[Any] ) -> int:
'''simple docstring'''
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(' ' ) )
@slow
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : Optional[int] = tokenizer.encode('sequence builders' , add_special_tokens=__a )
__snake_case : Optional[int] = tokenizer.encode('multi-sequence build' , add_special_tokens=__a )
__snake_case : Optional[Any] = tokenizer.build_inputs_with_special_tokens(__a )
__snake_case : List[Any] = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 0 | 1 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : List[str] ) -> List[str]:
# Load configuration defined in the metadata file
with open(_UpperCAmelCase ) as metadata_file:
__snake_case : Optional[Any] = json.load(_UpperCAmelCase )
__snake_case : Optional[Any] = LukeConfig(use_entity_aware_attention=_UpperCAmelCase ,**metadata['model_config'] )
# Load in the weights from the checkpoint_path
__snake_case : int = torch.load(_UpperCAmelCase ,map_location='cpu' )
# Load the entity vocab file
__snake_case : Optional[int] = load_entity_vocab(_UpperCAmelCase )
__snake_case : Tuple = RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )
# Add special tokens to the token vocabulary for downstream tasks
__snake_case : int = AddedToken('<ent>' ,lstrip=_UpperCAmelCase ,rstrip=_UpperCAmelCase )
__snake_case : str = AddedToken('<ent2>' ,lstrip=_UpperCAmelCase ,rstrip=_UpperCAmelCase )
tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f'''Saving tokenizer to {pytorch_dump_folder_path}''' )
tokenizer.save_pretrained(_UpperCAmelCase )
with open(os.path.join(_UpperCAmelCase ,LukeTokenizer.vocab_files_names['entity_vocab_file'] ) ,'w' ) as f:
json.dump(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : Optional[int] = LukeTokenizer.from_pretrained(_UpperCAmelCase )
# Initialize the embeddings of the special tokens
__snake_case : Dict = state_dict['embeddings.word_embeddings.weight']
__snake_case : List[str] = word_emb[tokenizer.convert_tokens_to_ids(['@'] )[0]].unsqueeze(0 )
__snake_case : str = word_emb[tokenizer.convert_tokens_to_ids(['#'] )[0]].unsqueeze(0 )
__snake_case : int = torch.cat([word_emb, ent_emb, enta_emb] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
__snake_case : Tuple = f'''encoder.layer.{layer_index}.attention.self.'''
__snake_case : Optional[Any] = state_dict[prefix + matrix_name]
__snake_case : List[Any] = state_dict[prefix + matrix_name]
__snake_case : int = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
__snake_case : Dict = state_dict['entity_embeddings.entity_embeddings.weight']
__snake_case : List[str] = entity_emb[entity_vocab['[MASK]']]
__snake_case : int = LukeModel(config=_UpperCAmelCase ).eval()
__snake_case , __snake_case : str = model.load_state_dict(_UpperCAmelCase ,strict=_UpperCAmelCase )
if not (len(_UpperCAmelCase ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(f'''Missing keys {", ".join(_UpperCAmelCase )}. Expected only missing embeddings.position_ids''' )
if not (all(key.startswith('entity_predictions' ) or key.startswith('lm_head' ) for key in unexpected_keys )):
raise ValueError(
'Unexpected keys'
f''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}''' )
# Check outputs
__snake_case : Optional[Any] = LukeTokenizer.from_pretrained(_UpperCAmelCase ,task='entity_classification' )
__snake_case : List[Any] = (
'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
' new world number one avoid a humiliating second- round exit at Wimbledon .'
)
__snake_case : str = (39, 42)
__snake_case : str = tokenizer(_UpperCAmelCase ,entity_spans=[span] ,add_prefix_space=_UpperCAmelCase ,return_tensors='pt' )
__snake_case : Tuple = model(**_UpperCAmelCase )
# Verify word hidden states
if model_size == "large":
__snake_case : List[str] = torch.Size((1, 42, 10_24) )
__snake_case : Tuple = torch.tensor(
[[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]] )
else: # base
__snake_case : List[str] = torch.Size((1, 42, 7_68) )
__snake_case : Union[str, Any] = torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,_UpperCAmelCase ,atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
__snake_case : Any = torch.Size((1, 1, 10_24) )
__snake_case : Optional[int] = torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]] )
else: # base
__snake_case : str = torch.Size((1, 1, 7_68) )
__snake_case : Tuple = torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]] )
if not (outputs.entity_last_hidden_state.shape != expected_shape):
raise ValueError(
f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
f''' {expected_shape}''' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,_UpperCAmelCase ,atol=1E-4 ):
raise ValueError
# Finally, save our PyTorch model and tokenizer
print('Saving PyTorch model to {}'.format(_UpperCAmelCase ) )
model.save_pretrained(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : List[Any] ) -> Any:
__snake_case : List[str] = {}
with open(_UpperCAmelCase ,'r' ,encoding='utf-8' ) as f:
for index, line in enumerate(_UpperCAmelCase ):
__snake_case , __snake_case : List[Any] = line.rstrip().split('\t' )
__snake_case : str = index
return entity_vocab
if __name__ == "__main__":
A__ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
A__ : str = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NllbMoeForConditionalGeneration''',
'''NllbMoeModel''',
'''NllbMoePreTrainedModel''',
'''NllbMoeTop2Router''',
'''NllbMoeSparseMLP''',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
A__ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
from math import pi
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ) -> float:
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
__snake_case : Optional[Any] = gray_code_sequence_string(_UpperCAmelCase )
#
# convert them to integers
for i in range(len(_UpperCAmelCase ) ):
__snake_case : Optional[Any] = int(sequence[i] ,2 )
return sequence
def a_ ( _UpperCAmelCase : int ) -> list:
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__snake_case : Dict = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__snake_case : Dict = gray_code_sequence_string(bit_count - 1 )
__snake_case : Any = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__snake_case : str = '0' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__snake_case : Any = '1' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
A__ : List[str] = logging.get_logger(__name__)
set_seed(7_7_0)
A__ : Optional[Any] = {
'''c_attn''': '''att_proj''',
'''c_proj''': '''out_proj''',
'''c_fc''': '''in_proj''',
'''transformer.''': '''''',
'''h.''': '''layers.''',
'''ln_1''': '''layernorm_1''',
'''ln_2''': '''layernorm_2''',
'''ln_f''': '''layernorm_final''',
'''wpe''': '''position_embeds_layer''',
'''wte''': '''input_embeds_layer''',
}
A__ : Tuple = {
'''text_small''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''text.pt''',
},
'''coarse_small''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''coarse.pt''',
},
'''fine_small''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''fine.pt''',
},
'''text''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''text_2.pt''',
},
'''coarse''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''coarse_2.pt''',
},
'''fine''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''fine_2.pt''',
},
}
A__ : str = os.path.dirname(os.path.abspath(__file__))
A__ : Tuple = os.path.join(os.path.expanduser('''~'''), '''.cache''')
A__ : List[Any] = os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : List[str]=False ) -> int:
__snake_case : int = model_type
if use_small:
key += "_small"
return os.path.join(_UpperCAmelCase ,REMOTE_MODEL_PATHS[key]['file_name'] )
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : List[str] ) -> Any:
os.makedirs(_UpperCAmelCase ,exist_ok=_UpperCAmelCase )
hf_hub_download(repo_id=_UpperCAmelCase ,filename=_UpperCAmelCase ,local_dir=_UpperCAmelCase )
def a_ ( _UpperCAmelCase : List[Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : int=False ,_UpperCAmelCase : str="text" ) -> Optional[Any]:
if model_type == "text":
__snake_case : Tuple = BarkSemanticModel
__snake_case : Optional[int] = BarkSemanticConfig
__snake_case : str = BarkSemanticGenerationConfig
elif model_type == "coarse":
__snake_case : List[Any] = BarkCoarseModel
__snake_case : Optional[int] = BarkCoarseConfig
__snake_case : Optional[int] = BarkCoarseGenerationConfig
elif model_type == "fine":
__snake_case : List[Any] = BarkFineModel
__snake_case : Union[str, Any] = BarkFineConfig
__snake_case : Union[str, Any] = BarkFineGenerationConfig
else:
raise NotImplementedError()
__snake_case : List[str] = f'''{model_type}_small''' if use_small else model_type
__snake_case : Tuple = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(_UpperCAmelCase ):
logger.info(f'''{model_type} model not found, downloading into `{CACHE_DIR}`.''' )
_download(model_info['repo_id'] ,model_info['file_name'] )
__snake_case : Union[str, Any] = torch.load(_UpperCAmelCase ,map_location=_UpperCAmelCase )
# this is a hack
__snake_case : List[str] = checkpoint['model_args']
if "input_vocab_size" not in model_args:
__snake_case : int = model_args['vocab_size']
__snake_case : List[str] = model_args['vocab_size']
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
__snake_case : Union[str, Any] = model_args.pop('n_head' )
__snake_case : Dict = model_args.pop('n_embd' )
__snake_case : Any = model_args.pop('n_layer' )
__snake_case : Tuple = ConfigClass(**checkpoint['model_args'] )
__snake_case : List[Any] = ModelClass(config=_UpperCAmelCase )
__snake_case : Any = GenerationConfigClass()
__snake_case : Tuple = model_generation_config
__snake_case : Any = checkpoint['model']
# fixup checkpoint
__snake_case : Union[str, Any] = '_orig_mod.'
for k, v in list(state_dict.items() ):
if k.startswith(_UpperCAmelCase ):
# replace part of the key with corresponding layer name in HF implementation
__snake_case : int = k[len(_UpperCAmelCase ) :]
for old_layer_name in new_layer_name_dict:
__snake_case : Dict = new_k.replace(_UpperCAmelCase ,new_layer_name_dict[old_layer_name] )
__snake_case : List[str] = state_dict.pop(_UpperCAmelCase )
__snake_case : Optional[int] = set(state_dict.keys() ) - set(model.state_dict().keys() )
__snake_case : Any = {k for k in extra_keys if not k.endswith('.attn.bias' )}
__snake_case : str = set(model.state_dict().keys() ) - set(state_dict.keys() )
__snake_case : Any = {k for k in missing_keys if not k.endswith('.attn.bias' )}
if len(_UpperCAmelCase ) != 0:
raise ValueError(f'''extra keys found: {extra_keys}''' )
if len(_UpperCAmelCase ) != 0:
raise ValueError(f'''missing keys: {missing_keys}''' )
model.load_state_dict(_UpperCAmelCase ,strict=_UpperCAmelCase )
__snake_case : List[str] = model.num_parameters(exclude_embeddings=_UpperCAmelCase )
__snake_case : Optional[Any] = checkpoint['best_val_loss'].item()
logger.info(f'''model loaded: {round(n_params/1E6 ,1 )}M params, {round(_UpperCAmelCase ,3 )} loss''' )
model.eval()
model.to(_UpperCAmelCase )
del checkpoint, state_dict
return model
def a_ ( _UpperCAmelCase : Dict ,_UpperCAmelCase : List[Any]=False ,_UpperCAmelCase : Optional[int]="text" ) -> Union[str, Any]:
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
__snake_case : int = 'cpu' # do conversion on cpu
__snake_case : List[Any] = _get_ckpt_path(_UpperCAmelCase ,use_small=_UpperCAmelCase )
__snake_case : Any = _load_model(_UpperCAmelCase ,_UpperCAmelCase ,model_type=_UpperCAmelCase ,use_small=_UpperCAmelCase )
# load bark initial model
__snake_case : Union[str, Any] = _bark_load_model(_UpperCAmelCase ,'cpu' ,model_type=_UpperCAmelCase ,use_small=_UpperCAmelCase )
if model_type == "text":
__snake_case : Optional[int] = bark_model['model']
if model.num_parameters(exclude_embeddings=_UpperCAmelCase ) != bark_model.get_num_params():
raise ValueError('initial and new models don\'t have the same number of parameters' )
# check if same output as the bark model
__snake_case : List[Any] = 5
__snake_case : str = 10
if model_type in ["text", "coarse"]:
__snake_case : Tuple = torch.randint(2_56 ,(batch_size, sequence_length) ,dtype=torch.int )
__snake_case : Optional[int] = bark_model(_UpperCAmelCase )[0]
__snake_case : List[str] = model(_UpperCAmelCase )
# take last logits
__snake_case : List[Any] = output_new_model_total.logits[:, [-1], :]
else:
__snake_case : Optional[Any] = 3
__snake_case : List[Any] = 8
__snake_case : List[str] = torch.randint(2_56 ,(batch_size, sequence_length, n_codes_total) ,dtype=torch.int )
__snake_case : str = model(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : List[str] = bark_model(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : str = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError('initial and new outputs don\'t have the same shape' )
if (output_new_model - output_old_model).abs().max().item() > 1E-3:
raise ValueError('initial and new outputs are not equal' )
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : str ,_UpperCAmelCase : Any ,) -> int:
__snake_case : int = os.path.join(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : Dict = BarkSemanticConfig.from_pretrained(os.path.join(_UpperCAmelCase ,'config.json' ) )
__snake_case : Dict = BarkCoarseConfig.from_pretrained(os.path.join(_UpperCAmelCase ,'config.json' ) )
__snake_case : List[Any] = BarkFineConfig.from_pretrained(os.path.join(_UpperCAmelCase ,'config.json' ) )
__snake_case : Dict = EncodecConfig.from_pretrained('facebook/encodec_24khz' )
__snake_case : List[Any] = BarkSemanticModel.from_pretrained(_UpperCAmelCase )
__snake_case : Dict = BarkCoarseModel.from_pretrained(_UpperCAmelCase )
__snake_case : Dict = BarkFineModel.from_pretrained(_UpperCAmelCase )
__snake_case : Any = EncodecModel.from_pretrained('facebook/encodec_24khz' )
__snake_case : Tuple = BarkConfig.from_sub_model_configs(
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : List[str] = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config ,coarseAcoustic.generation_config ,fineAcoustic.generation_config )
__snake_case : str = BarkModel(_UpperCAmelCase )
__snake_case : Tuple = semantic
__snake_case : Any = coarseAcoustic
__snake_case : Tuple = fineAcoustic
__snake_case : Any = codec
__snake_case : Optional[int] = bark_generation_config
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
bark.save_pretrained(_UpperCAmelCase ,repo_id=_UpperCAmelCase ,push_to_hub=_UpperCAmelCase )
if __name__ == "__main__":
A__ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')
A__ : Dict = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case__ ( unittest.TestCase ):
def A_ ( self : int ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = tempfile.mkdtemp()
# fmt: off
__snake_case : List[str] = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
# fmt: on
__snake_case : Any = 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] ) )
__snake_case : List[str] = {
'do_resize': True,
'size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.5, 0.5, 0.5],
'image_std': [0.5, 0.5, 0.5],
}
__snake_case : Optional[Any] = os.path.join(self.tmpdirname , __a )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(__a , __a )
def A_ ( self : Optional[int] , **__a : Dict ) -> int:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : int , **__a : Dict ) -> Tuple:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__snake_case : List[str] = [Image.fromarray(np.moveaxis(__a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : Dict = self.get_image_processor()
__snake_case : Any = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
processor.save_pretrained(self.tmpdirname )
__snake_case : Any = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[Any] = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__snake_case : Optional[Any] = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__snake_case : Tuple = self.get_image_processor(do_normalize=__a , padding_value=1.0 )
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : str = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = self.prepare_image_inputs()
__snake_case : List[str] = image_processor(__a , return_tensors='np' )
__snake_case : List[str] = processor(images=__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 A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Optional[int] = 'lower newer'
__snake_case : Dict = processor(text=__a )
__snake_case : List[Any] = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : int = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : List[Any] = 'lower newer'
__snake_case : Optional[Any] = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with self.assertRaises(__a ):
processor()
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_image_processor()
__snake_case : Any = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__snake_case : int = processor.batch_decode(__a )
__snake_case : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def A_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[str] = self.get_image_processor()
__snake_case : Dict = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Union[str, Any] = 'lower newer'
__snake_case : Tuple = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 0 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class snake_case__ ( unittest.TestCase ):
@parameterized.expand([(None,), ('foo.json',)] )
def A_ ( self : str , __a : Optional[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Dict = GenerationConfig(
do_sample=__a , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__a , config_name=__a )
__snake_case : Optional[int] = GenerationConfig.from_pretrained(__a , config_name=__a )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , __a )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , __a )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : str = AutoConfig.from_pretrained('gpt2' )
__snake_case : Optional[int] = GenerationConfig.from_model_config(__a )
__snake_case : str = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(__a , __a )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
__snake_case : Optional[int] = GenerationConfig()
__snake_case : Any = {
'max_new_tokens': 1024,
'foo': 'bar',
}
__snake_case : Optional[Any] = copy.deepcopy(__a )
__snake_case : Tuple = generation_config.update(**__a )
# update_kwargs was not modified (no side effects)
self.assertEqual(__a , __a )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(__a , {'foo': 'bar'} )
def A_ ( self : Dict ) -> str:
'''simple docstring'''
__snake_case : int = GenerationConfig()
__snake_case : Tuple = 'bar'
with tempfile.TemporaryDirectory('test-generation-config' ) as tmp_dir:
generation_config.save_pretrained(__a )
__snake_case : List[str] = GenerationConfig.from_pretrained(__a )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , 'bar' )
__snake_case : List[str] = GenerationConfig.from_model_config(__a )
assert not hasattr(__a , 'foo' ) # no new kwargs should be initialized if from config
def A_ ( self : Tuple ) -> str:
'''simple docstring'''
__snake_case : List[Any] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , __a )
self.assertEqual(default_config.num_beams , 1 )
__snake_case : List[Any] = GenerationConfig(
do_sample=__a , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , __a )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__a )
__snake_case : List[str] = GenerationConfig.from_pretrained(__a , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , __a )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class snake_case__ ( unittest.TestCase ):
@classmethod
def A_ ( cls : List[str] ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[str] = TOKEN
HfFolder.save_token(__a )
@classmethod
def A_ ( cls : Dict ) -> List[Any]:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-generation-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-generation-config-org' )
except HTTPError:
pass
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Optional[Any] = GenerationConfig(
do_sample=__a , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('test-generation-config' , use_auth_token=self._token )
__snake_case : str = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-generation-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__a , repo_id='test-generation-config' , push_to_hub=__a , use_auth_token=self._token )
__snake_case : List[str] = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
def A_ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[int] = GenerationConfig(
do_sample=__a , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('valid_org/test-generation-config-org' , use_auth_token=self._token )
__snake_case : Dict = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-generation-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__a , repo_id='valid_org/test-generation-config-org' , push_to_hub=__a , use_auth_token=self._token )
__snake_case : Dict = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
| 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a_ ( _UpperCAmelCase : List[Any] ) -> Tuple:
__snake_case : str = []
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
) )
return embed
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Optional[int] ) -> List[str]:
__snake_case : Tuple = []
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
) )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
return attention_weights
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Dict:
__snake_case : Union[str, Any] = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token') )
return token
def a_ ( ) -> Optional[Any]:
__snake_case : Any = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ) -> Tuple:
__snake_case : List[str] = 'imagenet-1k-id2label.json'
__snake_case : Dict = 10_00
__snake_case : Union[str, Any] = 'huggingface/label-files'
__snake_case : str = num_labels
__snake_case : str = json.load(open(cached_download(hf_hub_url(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset' ) ) ,'r' ) )
__snake_case : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__snake_case : Optional[Any] = idalabel
__snake_case : str = {v: k for k, v in idalabel.items()}
__snake_case : Dict = CvtConfig(num_labels=_UpperCAmelCase ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' ,1 )[-1][4:6] == "13":
__snake_case : Tuple = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' ,1 )[-1][4:6] == "21":
__snake_case : str = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__snake_case : Dict = [2, 2, 20]
__snake_case : Any = [3, 12, 16]
__snake_case : Tuple = [1_92, 7_68, 10_24]
__snake_case : str = CvtForImageClassification(_UpperCAmelCase )
__snake_case : List[Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
__snake_case : int = image_size
__snake_case : int = torch.load(_UpperCAmelCase ,map_location=torch.device('cpu' ) )
__snake_case : List[Any] = OrderedDict()
__snake_case : Union[str, Any] = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__snake_case : Optional[Any] = list_of_state_dict + cls_token(_UpperCAmelCase )
__snake_case : Tuple = list_of_state_dict + embeddings(_UpperCAmelCase )
for cnt in range(config.depth[idx] ):
__snake_case : Optional[int] = list_of_state_dict + attention(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
__snake_case : List[str] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
A__ : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=3_8_4,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
A__ : Tuple = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 0 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : List[Any] = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
'''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''markuplm'''
def __init__( self : Optional[int] , __a : Union[str, Any]=30522 , __a : Any=768 , __a : Dict=12 , __a : Tuple=12 , __a : Optional[int]=3072 , __a : Optional[int]="gelu" , __a : Any=0.1 , __a : Any=0.1 , __a : Union[str, Any]=512 , __a : Any=2 , __a : List[str]=0.0_2 , __a : Optional[Any]=1e-12 , __a : List[str]=0 , __a : List[Any]=0 , __a : Optional[Any]=2 , __a : Optional[Any]=256 , __a : Any=1024 , __a : Tuple=216 , __a : List[str]=1001 , __a : Optional[int]=32 , __a : str=50 , __a : Tuple="absolute" , __a : Dict=True , __a : Tuple=None , **__a : List[Any] , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a , )
__snake_case : str = vocab_size
__snake_case : List[str] = hidden_size
__snake_case : Optional[Any] = num_hidden_layers
__snake_case : int = num_attention_heads
__snake_case : List[Any] = hidden_act
__snake_case : Any = intermediate_size
__snake_case : Any = hidden_dropout_prob
__snake_case : Union[str, Any] = attention_probs_dropout_prob
__snake_case : List[Any] = max_position_embeddings
__snake_case : str = type_vocab_size
__snake_case : Any = initializer_range
__snake_case : Tuple = layer_norm_eps
__snake_case : str = position_embedding_type
__snake_case : str = use_cache
__snake_case : Union[str, Any] = classifier_dropout
# additional properties
__snake_case : List[str] = max_depth
__snake_case : int = max_xpath_tag_unit_embeddings
__snake_case : Dict = max_xpath_subs_unit_embeddings
__snake_case : Any = tag_pad_id
__snake_case : Optional[int] = subs_pad_id
__snake_case : List[str] = xpath_unit_hidden_size
| 0 |
'''simple docstring'''
from __future__ import annotations
A__ : List[Any] = list[list[int]]
# assigning initial values to the grid
A__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
A__ : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def a_ ( _UpperCAmelCase : Matrix ,_UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def a_ ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def a_ ( _UpperCAmelCase : Matrix ) -> Matrix | None:
if location := find_empty_location(_UpperCAmelCase ):
__snake_case , __snake_case : Optional[int] = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 ,10 ):
if is_safe(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : Union[str, Any] = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
__snake_case : Optional[Any] = 0
return None
def a_ ( _UpperCAmelCase : Matrix ) -> None:
for row in grid:
for cell in row:
print(_UpperCAmelCase ,end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('''\nExample grid:\n''' + '''=''' * 2_0)
print_solution(example_grid)
print('''\nExample grid solution:''')
A__ : List[str] = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('''Cannot find a solution.''')
| 0 | 1 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : List[str] = logging.get_logger(__name__)
A__ : Optional[Any] = {
'''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''autoformer'''
A__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : Union[str, Any] , __a : Optional[int] = None , __a : Optional[int] = None , __a : str = "student_t" , __a : str = "nll" , __a : int = 1 , __a : List[int] = [1, 2, 3, 4, 5, 6, 7] , __a : bool = True , __a : int = 0 , __a : int = 0 , __a : int = 0 , __a : int = 0 , __a : Optional[List[int]] = None , __a : Optional[List[int]] = None , __a : int = 64 , __a : int = 2 , __a : int = 2 , __a : int = 2 , __a : int = 2 , __a : int = 32 , __a : int = 32 , __a : str = "gelu" , __a : float = 0.1 , __a : float = 0.1 , __a : float = 0.1 , __a : float = 0.1 , __a : float = 0.1 , __a : int = 100 , __a : float = 0.0_2 , __a : bool = True , __a : int=True , __a : int = 10 , __a : int = 25 , __a : int = 3 , **__a : int , ) -> Optional[int]:
'''simple docstring'''
# time series specific configuration
__snake_case : Optional[int] = prediction_length
__snake_case : int = context_length if context_length is not None else prediction_length
__snake_case : Any = distribution_output
__snake_case : List[str] = loss
__snake_case : List[Any] = input_size
__snake_case : List[str] = num_time_features
__snake_case : int = lags_sequence
__snake_case : List[str] = scaling
__snake_case : Optional[int] = num_dynamic_real_features
__snake_case : Tuple = num_static_real_features
__snake_case : str = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(__a ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
__snake_case : List[Any] = cardinality
else:
__snake_case : List[Any] = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(__a ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
__snake_case : int = embedding_dimension
else:
__snake_case : Optional[Any] = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
__snake_case : Any = num_parallel_samples
# Transformer architecture configuration
__snake_case : List[str] = input_size * len(self.lags_sequence ) + self._number_of_features
__snake_case : Optional[int] = d_model
__snake_case : List[Any] = encoder_attention_heads
__snake_case : List[Any] = decoder_attention_heads
__snake_case : Any = encoder_ffn_dim
__snake_case : Optional[int] = decoder_ffn_dim
__snake_case : Tuple = encoder_layers
__snake_case : Any = decoder_layers
__snake_case : List[str] = dropout
__snake_case : Optional[int] = attention_dropout
__snake_case : Optional[Any] = activation_dropout
__snake_case : Optional[Any] = encoder_layerdrop
__snake_case : List[Any] = decoder_layerdrop
__snake_case : Optional[Any] = activation_function
__snake_case : Tuple = init_std
__snake_case : Optional[int] = use_cache
# Autoformer
__snake_case : List[str] = label_length
__snake_case : Optional[int] = moving_average
__snake_case : List[str] = autocorrelation_factor
super().__init__(is_encoder_decoder=__a , **__a )
@property
def A_ ( self : Optional[Any] ) -> int:
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = KandinskyVaaPriorPipeline
A__ = ['''prompt''']
A__ = ['''prompt''', '''negative_prompt''']
A__ = [
'''num_images_per_prompt''',
'''generator''',
'''num_inference_steps''',
'''latents''',
'''negative_prompt''',
'''guidance_scale''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Dict ) -> List[str]:
'''simple docstring'''
return 32
@property
def A_ ( self : Any ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return self.time_input_dim
@property
def A_ ( self : str ) -> int:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return 100
@property
def A_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
__snake_case : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__a )
@property
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Any = {
'num_attention_heads': 2,
'attention_head_dim': 12,
'embedding_dim': self.text_embedder_hidden_size,
'num_layers': 1,
}
__snake_case : List[Any] = PriorTransformer(**__a )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
__snake_case : Any = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
__snake_case : Optional[Any] = CLIPVisionModelWithProjection(__a )
return model
@property
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = CLIPImageProcessor(
crop_size=224 , do_center_crop=__a , do_normalize=__a , do_resize=__a , image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , resample=3 , size=224 , )
return image_processor
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : List[str] = self.dummy_image_encoder
__snake_case : str = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : List[str] = self.dummy_image_processor
__snake_case : Any = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='sample' , num_train_timesteps=1000 , clip_sample=__a , clip_sample_range=1_0.0 , )
__snake_case : str = {
'prior': prior,
'image_encoder': image_encoder,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'scheduler': scheduler,
'image_processor': image_processor,
}
return components
def A_ ( self : List[Any] , __a : Optional[Any] , __a : Tuple=0 ) -> Any:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : List[str] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : List[Any] = {
'prompt': 'horse',
'generator': generator,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def A_ ( self : str ) -> Dict:
'''simple docstring'''
__snake_case : str = 'cpu'
__snake_case : List[str] = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Optional[Any] = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : List[str] = output.image_embeds
__snake_case : str = pipe(
**self.get_dummy_inputs(__a ) , return_dict=__a , )[0]
__snake_case : Union[str, Any] = image[0, -10:]
__snake_case : Any = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
__snake_case : List[Any] = np.array(
[-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def A_ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = torch_device == 'cpu'
__snake_case : Dict = True
__snake_case : Union[str, Any] = False
self._test_inference_batch_single_identical(
test_max_difference=__a , relax_max_difference=__a , test_mean_pixel_difference=__a , )
@skip_mps
def A_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = torch_device == 'cpu'
__snake_case : Optional[Any] = False
self._test_attention_slicing_forward_pass(
test_max_difference=__a , test_mean_pixel_difference=__a , )
| 0 | 1 |
'''simple docstring'''
from __future__ import annotations
A__ : List[Any] = list[list[int]]
# assigning initial values to the grid
A__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
A__ : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def a_ ( _UpperCAmelCase : Matrix ,_UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def a_ ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def a_ ( _UpperCAmelCase : Matrix ) -> Matrix | None:
if location := find_empty_location(_UpperCAmelCase ):
__snake_case , __snake_case : Optional[int] = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 ,10 ):
if is_safe(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : Union[str, Any] = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
__snake_case : Optional[Any] = 0
return None
def a_ ( _UpperCAmelCase : Matrix ) -> None:
for row in grid:
for cell in row:
print(_UpperCAmelCase ,end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('''\nExample grid:\n''' + '''=''' * 2_0)
print_solution(example_grid)
print('''\nExample grid solution:''')
A__ : List[str] = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('''Cannot find a solution.''')
| 0 |
'''simple docstring'''
from math import factorial
A__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def a_ ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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(_UpperCAmelCase ) )
def a_ ( _UpperCAmelCase : int = 60 ,_UpperCAmelCase : int = 1_00_00_00 ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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
__snake_case : List[str] = 0
# the cached sizes of the previous chains
__snake_case : dict[int, int] = {}
for start_chain_element in range(1 ,_UpperCAmelCase ):
# The temporary set will contain the elements of the chain
__snake_case : Optional[int] = set()
__snake_case : List[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__snake_case : str = 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(_UpperCAmelCase )
chain_set_length += 1
__snake_case : Tuple = digit_factorial_sum(_UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__snake_case : 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()}""")
| 0 | 1 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> bool:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : str = f'''Input value of [number={number}] must be an integer'''
raise TypeError(_UpperCAmelCase )
if number < 0:
return False
__snake_case : Tuple = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int = 1_00 ) -> int:
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : Union[str, Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 0 | 1 |
'''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A__ : int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A__ : Optional[Any] = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F"""{len(upper_files)} files contain uppercase characters:""")
print('''\n'''.join(upper_files) + '''\n''')
A__ : Optional[Any] = [file for file in filepaths if ''' ''' in file]
if space_files:
print(F"""{len(space_files)} files contain space characters:""")
print('''\n'''.join(space_files) + '''\n''')
A__ : Optional[Any] = [file for file in filepaths if '''-''' in file]
if hyphen_files:
print(F"""{len(hyphen_files)} files contain hyphen characters:""")
print('''\n'''.join(hyphen_files) + '''\n''')
A__ : List[str] = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F"""{len(nodir_files)} files are not in a directory:""")
print('''\n'''.join(nodir_files) + '''\n''')
A__ : List[Any] = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : int = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''',
'''GroupViTTextConfig''',
'''GroupViTVisionConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple = [
'''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GroupViTModel''',
'''GroupViTPreTrainedModel''',
'''GroupViTTextModel''',
'''GroupViTVisionModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFGroupViTModel''',
'''TFGroupViTPreTrainedModel''',
'''TFGroupViTTextModel''',
'''TFGroupViTVisionModel''',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
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 a_ ( ) -> str:
__snake_case : str = 10
__snake_case : Tuple = 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' ),
} )
__snake_case : str = datasets.Dataset.from_dict(
{
'tokens': [['foo'] * 5] * n,
'labels': [[1] * 5] * n,
'answers': [{'answer_start': [97], 'text': ['1976']}] * 10,
'id': list(range(_UpperCAmelCase ) ),
} ,features=_UpperCAmelCase ,)
return dataset
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Dict ,_UpperCAmelCase : Optional[int] ) -> List[Any]:
__snake_case : Any = str(tmp_path_factory.mktemp('data' ) / 'file.arrow' )
dataset.map(cache_file_name=_UpperCAmelCase )
return filename
# FILE_CONTENT + files
A__ : Optional[int] = '''\
Text data.
Second line of data.'''
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Any ) -> Union[str, Any]:
__snake_case : int = tmp_path_factory.mktemp('data' ) / 'file.txt'
__snake_case : Union[str, Any] = FILE_CONTENT
with open(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase )
return filename
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Dict ) -> Tuple:
import bza
__snake_case : Dict = tmp_path_factory.mktemp('data' ) / 'file.txt.bz2'
__snake_case : Any = bytes(_UpperCAmelCase ,'utf-8' )
with bza.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : int ) -> str:
import gzip
__snake_case : Optional[int] = str(tmp_path_factory.mktemp('data' ) / 'file.txt.gz' )
__snake_case : Tuple = bytes(_UpperCAmelCase ,'utf-8' )
with gzip.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ) -> str:
if datasets.config.LZ4_AVAILABLE:
import lza.frame
__snake_case : int = tmp_path_factory.mktemp('data' ) / 'file.txt.lz4'
__snake_case : int = bytes(_UpperCAmelCase ,'utf-8' )
with lza.frame.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : List[Any] ) -> Optional[Any]:
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
__snake_case : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'file.txt.7z'
with pyazr.SevenZipFile(_UpperCAmelCase ,'w' ) as archive:
archive.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : Dict ) -> str:
import tarfile
__snake_case : int = tmp_path_factory.mktemp('data' ) / 'file.txt.tar'
with tarfile.TarFile(_UpperCAmelCase ,'w' ) as f:
f.add(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Any ) -> str:
import lzma
__snake_case : List[str] = tmp_path_factory.mktemp('data' ) / 'file.txt.xz'
__snake_case : Tuple = bytes(_UpperCAmelCase ,'utf-8' )
with lzma.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[Any] ,_UpperCAmelCase : Tuple ) -> Dict:
import zipfile
__snake_case : int = tmp_path_factory.mktemp('data' ) / 'file.txt.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ) -> Any:
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
__snake_case : int = tmp_path_factory.mktemp('data' ) / 'file.txt.zst'
__snake_case : int = bytes(_UpperCAmelCase ,'utf-8' )
with zstd.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[Any] ) -> Optional[int]:
__snake_case : Tuple = tmp_path_factory.mktemp('data' ) / 'file.xml'
__snake_case : str = textwrap.dedent(
'\\n <?xml version="1.0" encoding="UTF-8" ?>\n <tmx version="1.4">\n <header segtype="sentence" srclang="ca" />\n <body>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang="en"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang="en"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang="en"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang="en"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang="en"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>' )
with open(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase )
return filename
A__ : List[Any] = [
{'''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__ : Any = [
{'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
{'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
A__ : Union[str, Any] = {
'''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__ : Optional[Any] = [
{'''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 a_ ( ) -> List[Any]:
return DATA_DICT_OF_LISTS
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[str] ) -> Any:
__snake_case : Any = datasets.Dataset.from_dict(_UpperCAmelCase )
__snake_case : Union[str, Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset.arrow' )
dataset.map(cache_file_name=_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[Any] ) -> Optional[Any]:
__snake_case : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset.sqlite' )
with contextlib.closing(sqlitea.connect(_UpperCAmelCase ) ) as con:
__snake_case : str = 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 a_ ( _UpperCAmelCase : Optional[int] ) -> Optional[int]:
__snake_case : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset.csv' )
with open(_UpperCAmelCase ,'w' ,newline='' ) as f:
__snake_case : Optional[int] = csv.DictWriter(_UpperCAmelCase ,fieldnames=['col_1', 'col_2', 'col_3'] )
writer.writeheader()
for item in DATA:
writer.writerow(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Tuple ) -> Tuple:
__snake_case : List[str] = str(tmp_path_factory.mktemp('data' ) / 'dataset2.csv' )
with open(_UpperCAmelCase ,'w' ,newline='' ) as f:
__snake_case : Optional[int] = csv.DictWriter(_UpperCAmelCase ,fieldnames=['col_1', 'col_2', 'col_3'] )
writer.writeheader()
for item in DATA:
writer.writerow(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : str ) -> Optional[int]:
import bza
__snake_case : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'dataset.csv.bz2'
with open(_UpperCAmelCase ,'rb' ) as f:
__snake_case : List[Any] = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : List[Any] ,_UpperCAmelCase : Dict ) -> Any:
__snake_case : List[str] = tmp_path_factory.mktemp('data' ) / 'dataset.csv.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : Any ,_UpperCAmelCase : Optional[Any] ) -> Optional[int]:
__snake_case : Dict = tmp_path_factory.mktemp('data' ) / 'dataset.csv.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename(csv_path.replace('.csv' ,'.CSV' ) ) )
f.write(_UpperCAmelCase ,arcname=os.path.basename(csva_path.replace('.csv' ,'.CSV' ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : str ,_UpperCAmelCase : Tuple ) -> List[str]:
__snake_case : Optional[int] = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.csv.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.join('main_dir' ,os.path.basename(_UpperCAmelCase ) ) )
f.write(_UpperCAmelCase ,arcname=os.path.join('main_dir' ,os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : str ) -> Union[str, Any]:
__snake_case : List[str] = str(tmp_path_factory.mktemp('data' ) / 'dataset.parquet' )
__snake_case : Union[str, Any] = pa.schema(
{
'col_1': pa.string(),
'col_2': pa.intaa(),
'col_3': pa.floataa(),
} )
with open(_UpperCAmelCase ,'wb' ) as f:
__snake_case : Union[str, Any] = pq.ParquetWriter(_UpperCAmelCase ,schema=_UpperCAmelCase )
__snake_case : Dict = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(_UpperCAmelCase ) )] for k in DATA[0]} ,schema=_UpperCAmelCase )
writer.write_table(_UpperCAmelCase )
writer.close()
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> str:
__snake_case : int = str(tmp_path_factory.mktemp('data' ) / 'dataset.json' )
__snake_case : Optional[int] = {'data': DATA}
with open(_UpperCAmelCase ,'w' ) as f:
json.dump(_UpperCAmelCase ,_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Tuple ) -> Optional[Any]:
__snake_case : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset.json' )
__snake_case : str = {'data': DATA_DICT_OF_LISTS}
with open(_UpperCAmelCase ,'w' ) as f:
json.dump(_UpperCAmelCase ,_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Tuple ) -> Dict:
__snake_case : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset.jsonl' )
with open(_UpperCAmelCase ,'w' ) as f:
for item in DATA:
f.write(json.dumps(_UpperCAmelCase ) + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Any ) -> Tuple:
__snake_case : Union[str, Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset2.jsonl' )
with open(_UpperCAmelCase ,'w' ) as f:
for item in DATA:
f.write(json.dumps(_UpperCAmelCase ) + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ) -> Optional[int]:
__snake_case : int = str(tmp_path_factory.mktemp('data' ) / 'dataset_312.jsonl' )
with open(_UpperCAmelCase ,'w' ) as f:
for item in DATA_312:
f.write(json.dumps(_UpperCAmelCase ) + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Any ) -> List[str]:
__snake_case : List[str] = str(tmp_path_factory.mktemp('data' ) / 'dataset-str.jsonl' )
with open(_UpperCAmelCase ,'w' ) as f:
for item in DATA_STR:
f.write(json.dumps(_UpperCAmelCase ) + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Tuple ) -> str:
import gzip
__snake_case : List[str] = str(tmp_path_factory.mktemp('data' ) / 'dataset.txt.gz' )
with open(_UpperCAmelCase ,'rb' ) as orig_file:
with gzip.open(_UpperCAmelCase ,'wb' ) as zipped_file:
zipped_file.writelines(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : List[Any] ) -> Optional[int]:
import gzip
__snake_case : str = str(tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.gz' )
with open(_UpperCAmelCase ,'rb' ) as orig_file:
with gzip.open(_UpperCAmelCase ,'wb' ) as zipped_file:
zipped_file.writelines(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : str ,_UpperCAmelCase : int ) -> Union[str, Any]:
__snake_case : Any = tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : int ) -> Tuple:
__snake_case : List[str] = tmp_path_factory.mktemp('data' ) / 'dataset_nested.jsonl.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.join('nested' ,os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
__snake_case : Tuple = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.jsonl.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.join('main_dir' ,os.path.basename(_UpperCAmelCase ) ) )
f.write(_UpperCAmelCase ,arcname=os.path.join('main_dir' ,os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Any ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Any ) -> int:
__snake_case : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.tar'
with tarfile.TarFile(_UpperCAmelCase ,'w' ) as f:
f.add(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
f.add(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[Any] ,_UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Optional[Any] ) -> Dict:
__snake_case : List[str] = tmp_path_factory.mktemp('data' ) / 'dataset_nested.jsonl.tar'
with tarfile.TarFile(_UpperCAmelCase ,'w' ) as f:
f.add(_UpperCAmelCase ,arcname=os.path.join('nested' ,os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> int:
__snake_case : int = ['0', '1', '2', '3']
__snake_case : Dict = str(tmp_path_factory.mktemp('data' ) / 'dataset.txt' )
with open(_UpperCAmelCase ,'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[str] ) -> Dict:
__snake_case : Optional[int] = ['0', '1', '2', '3']
__snake_case : List[Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset2.txt' )
with open(_UpperCAmelCase ,'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[Any] ) -> Optional[int]:
__snake_case : Dict = ['0', '1', '2', '3']
__snake_case : str = tmp_path_factory.mktemp('data' ) / 'dataset.abc'
with open(_UpperCAmelCase ,'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Optional[int] ) -> Tuple:
__snake_case : Optional[int] = tmp_path_factory.mktemp('data' ) / 'dataset.text.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : Any ) -> Optional[int]:
__snake_case : str = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.text.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.join('main_dir' ,os.path.basename(_UpperCAmelCase ) ) )
f.write(_UpperCAmelCase ,arcname=os.path.join('main_dir' ,os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Dict ,_UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : int ) -> List[str]:
__snake_case : str = tmp_path_factory.mktemp('data' ) / 'dataset.ext.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename('unsupported.ext' ) )
f.write(_UpperCAmelCase ,arcname=os.path.basename('unsupported_2.ext' ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ) -> int:
__snake_case : List[Any] = '\n'.join(['First', 'Second\u2029with Unicode new line', 'Third'] )
__snake_case : List[str] = str(tmp_path_factory.mktemp('data' ) / 'dataset_with_unicode_new_lines.txt' )
with open(_UpperCAmelCase ,'w' ,encoding='utf-8' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( ) -> List[str]:
return os.path.join('tests' ,'features' ,'data' ,'test_image_rgb.jpg' )
@pytest.fixture(scope='session' )
def a_ ( ) -> int:
return os.path.join('tests' ,'features' ,'data' ,'test_audio_44100.wav' )
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Any ,_UpperCAmelCase : Optional[Any] ) -> Dict:
__snake_case : List[str] = tmp_path_factory.mktemp('data' ) / 'dataset.img.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ).replace('.jpg' ,'2.jpg' ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : int ) -> Optional[Any]:
__snake_case : Tuple = tmp_path_factory.mktemp('data_dir' )
(data_dir / "subdir").mkdir()
with open(data_dir / 'subdir' / 'train.txt' ,'w' ) as f:
f.write('foo\n' * 10 )
with open(data_dir / 'subdir' / 'test.txt' ,'w' ) as f:
f.write('bar\n' * 10 )
# hidden file
with open(data_dir / 'subdir' / '.test.txt' ,'w' ) as f:
f.write('bar\n' * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / '.subdir' / 'train.txt' ,'w' ) as f:
f.write('foo\n' * 10 )
with open(data_dir / '.subdir' / 'test.txt' ,'w' ) as f:
f.write('bar\n' * 10 )
return data_dir
| 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ShapEPipeline
A__ = ['''prompt''']
A__ = ['''prompt''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return 32
@property
def A_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
return 8
@property
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__a )
@property
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Dict = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
__snake_case : Optional[Any] = PriorTransformer(**__a )
return model
@property
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Tuple = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
__snake_case : Optional[int] = ShapERenderer(**__a )
return model
def A_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : Union[str, Any] = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : Optional[Any] = self.dummy_renderer
__snake_case : List[Any] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=__a , clip_sample=__a , clip_sample_range=1.0 , )
__snake_case : int = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def A_ ( self : Union[str, Any] , __a : Dict , __a : int=0 ) -> Optional[Any]:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : Optional[Any] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : Optional[int] = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = 'cpu'
__snake_case : Dict = self.get_dummy_components()
__snake_case : int = self.pipeline_class(**__a )
__snake_case : str = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[Any] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : Dict = output.images[0]
__snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__snake_case : str = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
__snake_case : int = torch_device == 'cpu'
__snake_case : str = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__a , relax_max_difference=__a , )
def A_ ( self : List[str] ) -> Dict:
'''simple docstring'''
__snake_case : str = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Dict = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : int = 1
__snake_case : Tuple = 2
__snake_case : Tuple = self.get_dummy_inputs(__a )
for key in inputs.keys():
if key in self.batch_params:
__snake_case : Union[str, Any] = batch_size * [inputs[key]]
__snake_case : str = pipe(**__a , num_images_per_prompt=__a )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def A_ ( self : str ) -> Dict:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
__snake_case : Union[str, Any] = ShapEPipeline.from_pretrained('openai/shap-e' )
__snake_case : Any = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = torch.Generator(device=__a ).manual_seed(0 )
__snake_case : Union[str, Any] = pipe(
'a shark' , generator=__a , guidance_scale=1_5.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__a , __a )
| 0 | 1 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : str ) -> str:
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
A__ : str = [8, 5, 9, 7]
A__ : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A__ : Dict = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class snake_case__ :
def __init__( self : Union[str, Any] , __a : list[int] , __a : list[list[int]] , __a : list[list[int]] , ) -> None:
'''simple docstring'''
__snake_case : int = claim_vector
__snake_case : Optional[int] = allocated_resources_table
__snake_case : List[str] = maximum_claim_table
def A_ ( self : str ) -> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def A_ ( self : int ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def A_ ( self : int ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def A_ ( self : str ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__a ): i for i in self.__need()}
def A_ ( self : Union[str, Any] , **__a : int ) -> None:
'''simple docstring'''
__snake_case : str = self.__need()
__snake_case : List[Any] = self.__allocated_resources_table
__snake_case : Optional[int] = self.__available_resources()
__snake_case : Union[str, Any] = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
__snake_case : Tuple = False
for each_need in need_list:
__snake_case : Any = True
for index, need in enumerate(__a ):
if need > available_resources[index]:
__snake_case : List[str] = False
break
if execution:
__snake_case : Union[str, Any] = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__snake_case : str = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(__a )
# update available/freed resources stack
__snake_case : Union[str, Any] = np.array(__a ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__a ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__a ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''simple docstring'''
from math import sqrt
def a_ ( _UpperCAmelCase : int ) -> bool:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
__snake_case : Tuple = True
# 0 and 1 are none primes.
if number <= 1:
__snake_case : Dict = False
for divisor in range(2 ,int(round(sqrt(_UpperCAmelCase ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
__snake_case : str = False
break
# precondition
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ), "'status' must been from type bool"
return status
def a_ ( _UpperCAmelCase : Optional[int] ) -> Dict:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
__snake_case : Union[str, Any] = list(range(2 ,n + 1 ) )
__snake_case : Tuple = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(_UpperCAmelCase ) ):
for j in range(i + 1 ,len(_UpperCAmelCase ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
__snake_case : Dict = 0
# filters actual prime numbers.
__snake_case : Optional[Any] = [x for x in begin_list if x != 0]
# precondition
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ), "'ans' must been from type list"
return ans
def a_ ( _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (n > 2), "'N' must been an int and > 2"
__snake_case : Union[str, Any] = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 ,n + 1 ):
if is_prime(_UpperCAmelCase ):
ans.append(_UpperCAmelCase )
# precondition
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ), "'ans' must been from type list"
return ans
def a_ ( _UpperCAmelCase : Any ) -> Union[str, Any]:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and number >= 0, "'number' must been an int and >= 0"
__snake_case : Any = [] # this list will be returns of the function.
# potential prime number factors.
__snake_case : Any = 2
__snake_case : Union[str, Any] = number
if number == 0 or number == 1:
ans.append(_UpperCAmelCase )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(_UpperCAmelCase ):
while quotient != 1:
if is_prime(_UpperCAmelCase ) and (quotient % factor == 0):
ans.append(_UpperCAmelCase )
quotient /= factor
else:
factor += 1
else:
ans.append(_UpperCAmelCase )
# precondition
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ), "'ans' must been from type list"
return ans
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Optional[int]:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (
number >= 0
), "'number' bust been an int and >= 0"
__snake_case : Optional[Any] = 0
# prime factorization of 'number'
__snake_case : Dict = prime_factorization(_UpperCAmelCase )
__snake_case : Union[str, Any] = max(_UpperCAmelCase )
# precondition
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ), "'ans' must been from type int"
return ans
def a_ ( _UpperCAmelCase : List[Any] ) -> str:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (
number >= 0
), "'number' bust been an int and >= 0"
__snake_case : List[str] = 0
# prime factorization of 'number'
__snake_case : int = prime_factorization(_UpperCAmelCase )
__snake_case : int = min(_UpperCAmelCase )
# precondition
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ), "'ans' must been from type int"
return ans
def a_ ( _UpperCAmelCase : List[str] ) -> Any:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ), "'number' must been an int"
assert isinstance(number % 2 == 0 ,_UpperCAmelCase ), "compare bust been from type bool"
return number % 2 == 0
def a_ ( _UpperCAmelCase : str ) -> Union[str, Any]:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ), "'number' must been an int"
assert isinstance(number % 2 != 0 ,_UpperCAmelCase ), "compare bust been from type bool"
return number % 2 != 0
def a_ ( _UpperCAmelCase : Tuple ) -> Tuple:
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (number > 2) and is_even(_UpperCAmelCase )
), "'number' must been an int, even and > 2"
__snake_case : Optional[Any] = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
__snake_case : List[str] = get_prime_numbers(_UpperCAmelCase )
__snake_case : Optional[Any] = len(_UpperCAmelCase )
# run variable for while-loops.
__snake_case : Any = 0
__snake_case : Tuple = None
# exit variable. for break up the loops
__snake_case : Optional[int] = True
while i < len_pn and loop:
__snake_case : Union[str, Any] = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
__snake_case : Optional[int] = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and (len(_UpperCAmelCase ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : List[Any] ) -> List[str]:
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
__snake_case : Optional[Any] = 0
while numbera != 0:
__snake_case : Tuple = numbera % numbera
__snake_case : Any = numbera
__snake_case : Tuple = rest
# precondition
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def a_ ( _UpperCAmelCase : Any ,_UpperCAmelCase : List[str] ) -> Any:
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
__snake_case : Any = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
__snake_case : Optional[int] = prime_factorization(_UpperCAmelCase )
__snake_case : Dict = prime_factorization(_UpperCAmelCase )
elif numbera == 1 or numbera == 1:
__snake_case : Union[str, Any] = []
__snake_case : Union[str, Any] = []
__snake_case : List[str] = max(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : Union[str, Any] = 0
__snake_case : str = 0
__snake_case : Optional[int] = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
__snake_case : Optional[Any] = prime_fac_a.count(_UpperCAmelCase )
__snake_case : Optional[int] = prime_fac_a.count(_UpperCAmelCase )
for _ in range(max(_UpperCAmelCase ,_UpperCAmelCase ) ):
ans *= n
else:
__snake_case : List[str] = prime_fac_a.count(_UpperCAmelCase )
for _ in range(_UpperCAmelCase ):
ans *= n
done.append(_UpperCAmelCase )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
__snake_case : List[Any] = prime_fac_a.count(_UpperCAmelCase )
for _ in range(_UpperCAmelCase ):
ans *= n
done.append(_UpperCAmelCase )
# precondition
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def a_ ( _UpperCAmelCase : Tuple ) -> Union[str, Any]:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (n >= 0), "'number' must been a positive int"
__snake_case : int = 0
__snake_case : Tuple = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(_UpperCAmelCase ):
ans += 1
# precondition
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and is_prime(
_UpperCAmelCase ), "'ans' must been a prime number and from type int"
return ans
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : List[Any] ) -> List[str]:
assert (
is_prime(_UpperCAmelCase ) and is_prime(_UpperCAmelCase ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
__snake_case : Any = p_number_a + 1 # jump to the next number
__snake_case : List[Any] = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(_UpperCAmelCase ):
number += 1
while number < p_number_a:
ans.append(_UpperCAmelCase )
number += 1
# fetch the next prime number.
while not is_prime(_UpperCAmelCase ):
number += 1
# precondition
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and ans[0] != p_number_a
and ans[len(_UpperCAmelCase ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def a_ ( _UpperCAmelCase : Any ) -> str:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (n >= 1), "'n' must been int and >= 1"
__snake_case : Union[str, Any] = [] # will be returned.
for divisor in range(1 ,n + 1 ):
if n % divisor == 0:
ans.append(_UpperCAmelCase )
# precondition
assert ans[0] == 1 and ans[len(_UpperCAmelCase ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def a_ ( _UpperCAmelCase : List[Any] ) -> List[str]:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (
number > 1
), "'number' must been an int and >= 1"
__snake_case : Tuple = get_divisors(_UpperCAmelCase )
# precondition
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and (divisors[0] == 1)
and (divisors[len(_UpperCAmelCase ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def a_ ( _UpperCAmelCase : List[Any] ,_UpperCAmelCase : List[str] ) -> Tuple:
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
__snake_case : Optional[int] = gcd(abs(_UpperCAmelCase ) ,abs(_UpperCAmelCase ) )
# precondition
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def a_ ( _UpperCAmelCase : int ) -> int:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (n >= 0), "'n' must been a int and >= 0"
__snake_case : List[str] = 1 # this will be return.
for factor in range(1 ,n + 1 ):
ans *= factor
return ans
def a_ ( _UpperCAmelCase : str ) -> Dict:
assert isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and (n >= 0), "'n' must been an int and >= 0"
__snake_case : int = 0
__snake_case : List[str] = 1
__snake_case : Optional[Any] = 1 # this will be return
for _ in range(n - 1 ):
__snake_case : Dict = ans
ans += fiba
__snake_case : Dict = tmp
return ans
| 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
A__ : Union[str, Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ : List[Any] = {
'''vocab_file''': {
'''google/electra-small-generator''': (
'''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
),
'''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
'''google/electra-large-generator''': (
'''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
),
'''google/electra-small-discriminator''': (
'''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
),
'''google/electra-base-discriminator''': (
'''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
),
'''google/electra-large-discriminator''': (
'''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''google/electra-small-generator''': (
'''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
),
'''google/electra-base-generator''': (
'''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
),
'''google/electra-large-generator''': (
'''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
),
'''google/electra-small-discriminator''': (
'''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
),
'''google/electra-base-discriminator''': (
'''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
),
'''google/electra-large-discriminator''': (
'''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
),
},
}
A__ : List[Any] = {
'''google/electra-small-generator''': 5_1_2,
'''google/electra-base-generator''': 5_1_2,
'''google/electra-large-generator''': 5_1_2,
'''google/electra-small-discriminator''': 5_1_2,
'''google/electra-base-discriminator''': 5_1_2,
'''google/electra-large-discriminator''': 5_1_2,
}
A__ : Optional[Any] = {
'''google/electra-small-generator''': {'''do_lower_case''': True},
'''google/electra-base-generator''': {'''do_lower_case''': True},
'''google/electra-large-generator''': {'''do_lower_case''': True},
'''google/electra-small-discriminator''': {'''do_lower_case''': True},
'''google/electra-base-discriminator''': {'''do_lower_case''': True},
'''google/electra-large-discriminator''': {'''do_lower_case''': True},
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_INIT_CONFIGURATION
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = ElectraTokenizer
def __init__( self : int , __a : List[Any]=None , __a : int=None , __a : List[str]=True , __a : Any="[UNK]" , __a : Any="[SEP]" , __a : Union[str, Any]="[PAD]" , __a : Dict="[CLS]" , __a : List[Any]="[MASK]" , __a : str=True , __a : Optional[int]=None , **__a : Optional[int] , ) -> str:
'''simple docstring'''
super().__init__(
__a , tokenizer_file=__a , do_lower_case=__a , unk_token=__a , sep_token=__a , pad_token=__a , cls_token=__a , mask_token=__a , tokenize_chinese_chars=__a , strip_accents=__a , **__a , )
__snake_case : Tuple = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __a ) != do_lower_case
or normalizer_state.get('strip_accents' , __a ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __a ) != tokenize_chinese_chars
):
__snake_case : List[Any] = getattr(__a , normalizer_state.pop('type' ) )
__snake_case : str = do_lower_case
__snake_case : Optional[int] = strip_accents
__snake_case : Any = tokenize_chinese_chars
__snake_case : Union[str, Any] = normalizer_class(**__a )
__snake_case : Any = do_lower_case
def A_ ( self : Any , __a : List[str] , __a : Optional[Any]=None ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A_ ( self : List[Any] , __a : List[int] , __a : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
__snake_case : int = [self.sep_token_id]
__snake_case : List[Any] = [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 ) * [0] + len(token_ids_a + sep ) * [1]
def A_ ( self : Optional[int] , __a : str , __a : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
__snake_case : Tuple = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
| 0 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : int = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''',
'''GroupViTTextConfig''',
'''GroupViTVisionConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple = [
'''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GroupViTModel''',
'''GroupViTPreTrainedModel''',
'''GroupViTTextModel''',
'''GroupViTVisionModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFGroupViTModel''',
'''TFGroupViTPreTrainedModel''',
'''TFGroupViTTextModel''',
'''TFGroupViTVisionModel''',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> bool:
__snake_case : Union[str, Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 0 | 1 |
'''simple docstring'''
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 (
ImageTextPipelineOutput,
UniDiffuserPipeline,
)
else:
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
| 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
A__ : Tuple = pytest.mark.integration
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Dict = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(__a ) for x in np.arange(30 ).tolist()]} )
return dset
def A_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
__snake_case : Dict = dset.map(
lambda __a , __a : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=__a , keep_in_memory=__a )
__snake_case : List[Any] = dset.add_faiss_index('vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
__snake_case , __snake_case : str = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(__a , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
__snake_case : Dataset = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : Any = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
__snake_case : Union[str, Any] = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=__a )
__snake_case , __snake_case : str = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : str ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
__snake_case : Dict = np.zeros(5 , dtype=np.floataa )
__snake_case : List[str] = 1
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertRaises(__a , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
__snake_case : List[str] = np.eye(5 , dtype=np.floataa )[::-1]
__snake_case , __snake_case : Dict = index.search_batch(__a )
self.assertRaises(__a , index.search_batch , queries[0] )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : List[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , __a )
def A_ ( self : int ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
__snake_case : List[str] = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(__a ):
__snake_case : Dict = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def A_ ( self : str ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Tuple = faiss.IndexFlat(5 )
__snake_case : List[Any] = FaissIndex(custom_index=__a )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
import faiss
__snake_case : Optional[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
index.save(tmp_file.name )
__snake_case : List[Any] = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
__snake_case : List[Any] = np.zeros(5 , dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : int = index.search(__a )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def a_ ( _UpperCAmelCase : str ) -> Optional[int]:
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
__snake_case : Dict = 'index.faiss'
__snake_case : Any = f'''mock://{index_name}'''
index.save(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = FaissIndex.load(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = np.zeros(5 ,dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : Tuple = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : int = Elasticsearch()
__snake_case : Dict = {'acknowledged': True}
__snake_case : List[Any] = ElasticSearchIndex(es_client=__a )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
__snake_case : Optional[Any] = 'foo'
__snake_case : int = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
__snake_case : Dict = 'foo'
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : Optional[Any] = index.search(__a , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
__snake_case : List[Any] = ['foo', 'bar', 'foobar']
__snake_case : str = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : Any = index.search_batch(__a )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
# batched queries with timeout
__snake_case : Tuple = ['foo', 'bar', 'foobar']
__snake_case : List[Any] = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : int = index.search_batch(__a , request_timeout=30 )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
| 0 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
A__ : str = [8, 5, 9, 7]
A__ : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A__ : Dict = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class snake_case__ :
def __init__( self : Union[str, Any] , __a : list[int] , __a : list[list[int]] , __a : list[list[int]] , ) -> None:
'''simple docstring'''
__snake_case : int = claim_vector
__snake_case : Optional[int] = allocated_resources_table
__snake_case : List[str] = maximum_claim_table
def A_ ( self : str ) -> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def A_ ( self : int ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def A_ ( self : int ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def A_ ( self : str ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__a ): i for i in self.__need()}
def A_ ( self : Union[str, Any] , **__a : int ) -> None:
'''simple docstring'''
__snake_case : str = self.__need()
__snake_case : List[Any] = self.__allocated_resources_table
__snake_case : Optional[int] = self.__available_resources()
__snake_case : Union[str, Any] = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
__snake_case : Tuple = False
for each_need in need_list:
__snake_case : Any = True
for index, need in enumerate(__a ):
if need > available_resources[index]:
__snake_case : List[str] = False
break
if execution:
__snake_case : Union[str, Any] = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__snake_case : str = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(__a )
# update available/freed resources stack
__snake_case : Union[str, Any] = np.array(__a ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__a ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__a ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A__ : List[Any] = logging.get_logger(__name__)
A__ : Tuple = {
'''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 snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''t5'''
A__ = ['''past_key_values''']
A__ = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : str , __a : Dict=32128 , __a : Dict=512 , __a : Union[str, Any]=64 , __a : str=2048 , __a : Union[str, Any]=6 , __a : Any=None , __a : Any=8 , __a : List[Any]=32 , __a : Any=128 , __a : Tuple=0.1 , __a : str=1e-6 , __a : Dict=1.0 , __a : Tuple="relu" , __a : Dict=True , __a : Union[str, Any]=True , __a : Any=0 , __a : Dict=1 , **__a : Union[str, Any] , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = vocab_size
__snake_case : str = d_model
__snake_case : str = d_kv
__snake_case : List[Any] = d_ff
__snake_case : List[str] = num_layers
__snake_case : Tuple = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__snake_case : Union[str, Any] = num_heads
__snake_case : Tuple = relative_attention_num_buckets
__snake_case : Optional[int] = relative_attention_max_distance
__snake_case : Optional[Any] = dropout_rate
__snake_case : str = layer_norm_epsilon
__snake_case : List[str] = initializer_factor
__snake_case : int = feed_forward_proj
__snake_case : Optional[Any] = use_cache
__snake_case : Optional[Any] = self.feed_forward_proj.split('-' )
__snake_case : Dict = act_info[-1]
__snake_case : List[str] = act_info[0] == 'gated'
if len(__a ) > 1 and act_info[0] != "gated" or len(__a ) > 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":
__snake_case : Dict = 'gelu_new'
super().__init__(
pad_token_id=__a , eos_token_id=__a , is_encoder_decoder=__a , **__a , )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@property
def A_ ( self : str ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__snake_case : Union[str, Any] = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
__snake_case : Tuple = 'past_encoder_sequence + sequence'
__snake_case : Dict = {0: 'batch'}
__snake_case : Dict = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
__snake_case : Tuple = {0: 'batch', 1: 'decoder_sequence'}
__snake_case : int = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(__a , direction='inputs' )
return common_inputs
@property
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
return 13
| 0 | 1 |
'''simple docstring'''
from __future__ import annotations
def a_ ( _UpperCAmelCase : list ,_UpperCAmelCase : int | None = None ,_UpperCAmelCase : int | None = None ) -> None:
if start is None:
__snake_case : Dict = 0
if end is None:
__snake_case : Optional[Any] = len(_UpperCAmelCase ) - 1
if start >= end:
return
__snake_case : List[str] = (start + end) // 2
slowsort(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
slowsort(_UpperCAmelCase ,mid + 1 ,_UpperCAmelCase )
if sequence[end] < sequence[mid]:
__snake_case , __snake_case : Union[str, Any] = sequence[mid], sequence[end]
slowsort(_UpperCAmelCase ,_UpperCAmelCase ,end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : Optional[int] = {}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''llama'''
A__ = ['''past_key_values''']
def __init__( self : Any , __a : List[str]=32000 , __a : Union[str, Any]=4096 , __a : Optional[Any]=11008 , __a : Any=32 , __a : str=32 , __a : Optional[int]=None , __a : Dict="silu" , __a : Dict=2048 , __a : List[str]=0.0_2 , __a : Union[str, Any]=1e-6 , __a : Dict=True , __a : List[str]=0 , __a : Tuple=1 , __a : Tuple=2 , __a : Optional[Any]=1 , __a : Any=False , __a : Tuple=None , **__a : List[Any] , ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = vocab_size
__snake_case : List[str] = max_position_embeddings
__snake_case : List[Any] = hidden_size
__snake_case : Union[str, Any] = intermediate_size
__snake_case : Optional[int] = num_hidden_layers
__snake_case : List[Any] = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
__snake_case : Optional[int] = num_attention_heads
__snake_case : Optional[Any] = num_key_value_heads
__snake_case : int = hidden_act
__snake_case : Any = initializer_range
__snake_case : Any = rms_norm_eps
__snake_case : Union[str, Any] = pretraining_tp
__snake_case : Optional[int] = use_cache
__snake_case : Any = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , tie_word_embeddings=__a , **__a , )
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __a ) 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}''' )
__snake_case : Optional[Any] = self.rope_scaling.get('type' , __a )
__snake_case : Tuple = self.rope_scaling.get('factor' , __a )
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(__a , __a ) or rope_scaling_factor <= 1.0:
raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )
| 0 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
A__ : Dict = logging.get_logger(__name__)
A__ : Dict = {
'''openai/whisper-base''': '''https://huggingface.co/openai/whisper-base/resolve/main/config.json''',
}
# fmt: off
A__ : List[Any] = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
A__ : str = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''whisper'''
A__ = ['''past_key_values''']
A__ = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self : Union[str, Any] , __a : Dict=51865 , __a : int=80 , __a : int=6 , __a : Tuple=4 , __a : Any=6 , __a : str=4 , __a : Dict=1536 , __a : List[Any]=1536 , __a : List[Any]=0.0 , __a : int=0.0 , __a : Any=50257 , __a : List[str]=True , __a : List[Any]=True , __a : List[str]="gelu" , __a : Union[str, Any]=256 , __a : List[str]=0.0 , __a : str=0.0 , __a : List[Any]=0.0 , __a : int=0.0_2 , __a : List[Any]=False , __a : List[str]=1500 , __a : Dict=448 , __a : Tuple=50256 , __a : List[str]=50256 , __a : Any=50256 , __a : Optional[int]=None , __a : Any=[220, 50256] , __a : Dict=False , __a : Dict=256 , __a : Dict=False , __a : Dict=0.0_5 , __a : Optional[int]=10 , __a : str=2 , __a : int=0.0 , __a : int=10 , __a : int=0 , __a : int=7 , **__a : Optional[Any] , ) -> Optional[int]:
'''simple docstring'''
__snake_case : Dict = vocab_size
__snake_case : List[str] = num_mel_bins
__snake_case : str = d_model
__snake_case : Dict = encoder_layers
__snake_case : Tuple = encoder_attention_heads
__snake_case : Dict = decoder_layers
__snake_case : int = decoder_attention_heads
__snake_case : List[Any] = decoder_ffn_dim
__snake_case : List[Any] = encoder_ffn_dim
__snake_case : Optional[Any] = dropout
__snake_case : Tuple = attention_dropout
__snake_case : str = activation_dropout
__snake_case : Tuple = activation_function
__snake_case : List[Any] = init_std
__snake_case : Dict = encoder_layerdrop
__snake_case : Tuple = decoder_layerdrop
__snake_case : Dict = use_cache
__snake_case : List[Any] = encoder_layers
__snake_case : Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
__snake_case : Any = max_source_positions
__snake_case : int = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
__snake_case : Any = classifier_proj_size
__snake_case : str = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__snake_case : Optional[Any] = apply_spec_augment
__snake_case : List[str] = mask_time_prob
__snake_case : List[str] = mask_time_length
__snake_case : Union[str, Any] = mask_time_min_masks
__snake_case : Optional[int] = mask_feature_prob
__snake_case : Union[str, Any] = mask_feature_length
__snake_case : Union[str, Any] = mask_feature_min_masks
__snake_case : Dict = median_filter_width
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , is_encoder_decoder=__a , decoder_start_token_id=__a , suppress_tokens=__a , begin_suppress_tokens=__a , **__a , )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@property
def A_ ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__snake_case : Any = OrderedDict(
[
('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
] )
if self.use_past:
__snake_case : List[str] = {0: 'batch'}
else:
__snake_case : Dict = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(__a , direction='inputs' )
return common_inputs
def A_ ( self : Optional[int] , __a : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , __a : int = -1 , __a : int = -1 , __a : bool = False , __a : Optional["TensorType"] = None , __a : int = 22050 , __a : float = 5.0 , __a : int = 220 , ) -> Mapping[str, Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = OrderedDict()
__snake_case : Any = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=__a , framework=__a , sampling_rate=__a , time_duration=__a , frequency=__a , )
__snake_case : Union[str, Any] = encoder_inputs['input_features'].shape[2]
__snake_case : Optional[Any] = encoder_sequence_length // 2 if self.use_past else seq_length
__snake_case : List[Any] = super().generate_dummy_inputs(
preprocessor.tokenizer , __a , __a , __a , __a )
__snake_case : Any = encoder_inputs.pop('input_features' )
__snake_case : Dict = decoder_inputs.pop('decoder_input_ids' )
if "past_key_values" in decoder_inputs:
__snake_case : Union[str, Any] = decoder_inputs.pop('past_key_values' )
return dummy_inputs
@property
def A_ ( self : Union[str, Any] ) -> float:
'''simple docstring'''
return 1e-3
| 0 |
'''simple docstring'''
from __future__ import annotations
A__ : str = '''Muhammad Umer Farooq'''
A__ : int = '''MIT'''
A__ : Optional[int] = '''1.0.0'''
A__ : List[Any] = '''Muhammad Umer Farooq'''
A__ : Optional[Any] = '''[email protected]'''
A__ : Optional[Any] = '''Alpha'''
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Union[str, Any] , __a : str ) -> None:
'''simple docstring'''
super().__init__()
__snake_case : list[str] = []
__snake_case : Dict = domain
def A_ ( self : Dict , __a : str , __a : list[tuple[str, str | None]] ) -> None:
'''simple docstring'''
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__snake_case : Optional[Any] = parse.urljoin(self.domain , __a )
self.urls.append(__a )
def a_ ( _UpperCAmelCase : str ) -> str:
return ".".join(get_sub_domain_name(_UpperCAmelCase ).split('.' )[-2:] )
def a_ ( _UpperCAmelCase : str ) -> str:
return parse.urlparse(_UpperCAmelCase ).netloc
def a_ ( _UpperCAmelCase : str = "https://github.com" ) -> list[str]:
__snake_case : List[Any] = get_domain_name(_UpperCAmelCase )
# Initialize the parser
__snake_case : Tuple = Parser(_UpperCAmelCase )
try:
# Open URL
__snake_case : Any = requests.get(_UpperCAmelCase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__snake_case : Dict = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__snake_case : List[Any] = requests.get(_UpperCAmelCase )
# Get the valid email.
__snake_case : Optional[Any] = re.findall('[a-zA-Z0-9]+@' + domain ,read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_UpperCAmelCase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Tuple = emails_from_url('''https://github.com''')
print(F"""{len(emails)} emails found:""")
print('''\n'''.join(sorted(emails)))
| 0 | 1 |
'''simple docstring'''
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
A__ : Optional[int] = pd.read_csv(
'''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
'''position_salaries.csv'''
)
A__ : Optional[int] = dataset.iloc[:, 1:2].values
A__ : Any = dataset.iloc[:, 2].values
A__ , A__ , A__ , A__ : Dict = train_test_split(X, y, test_size=0.2, random_state=0)
A__ : str = PolynomialFeatures(degree=4)
A__ : Optional[Any] = poly_reg.fit_transform(X)
A__ : Dict = LinearRegression()
pol_reg.fit(X_poly, y)
def a_ ( ) -> Dict:
plt.scatter(_UpperCAmelCase ,_UpperCAmelCase ,color='red' )
plt.plot(_UpperCAmelCase ,pol_reg.predict(poly_reg.fit_transform(_UpperCAmelCase ) ) ,color='blue' )
plt.title('Truth or Bluff (Linear Regression)' )
plt.xlabel('Position level' )
plt.ylabel('Salary' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
A__ : Dict = logging.getLogger()
def a_ ( ) -> Tuple:
__snake_case : List[Any] = argparse.ArgumentParser()
parser.add_argument('-f' )
__snake_case : Any = parser.parse_args()
return args.f
def a_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
__snake_case : Tuple = {}
__snake_case : Union[str, Any] = os.path.join(_UpperCAmelCase ,'all_results.json' )
if os.path.exists(_UpperCAmelCase ):
with open(_UpperCAmelCase ,'r' ) as f:
__snake_case : List[str] = json.load(_UpperCAmelCase )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
def a_ ( ) -> Union[str, Any]:
__snake_case : Union[str, Any] = torch.cuda.is_available() and torch_device == 'cuda'
return is_using_cuda and is_apex_available()
A__ : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@classmethod
def A_ ( cls : Any ) -> List[str]:
'''simple docstring'''
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__snake_case : Optional[int] = tempfile.mkdtemp()
__snake_case : Dict = os.path.join(cls.tmpdir , 'default_config.yml' )
write_basic_config(save_location=cls.configPath )
__snake_case : List[Any] = ['accelerate', 'launch', '--config_file', cls.configPath]
@classmethod
def A_ ( cls : List[str] ) -> List[str]:
'''simple docstring'''
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : List[Any] = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'glue_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
'''.split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertLess(result['perplexity'] , 100 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'clm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : int = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertLess(result['perplexity'] , 42 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'mlm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__snake_case : Any = 7 if get_gpu_count() > 1 else 2
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertLess(result['train_loss'] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'ner_no_trainer' ) ) )
@unittest.skip(reason='Fix me @muellerzr' )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : Tuple = f'''
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['eval_f1'] , 28 )
self.assertGreaterEqual(result['eval_exact'] , 28 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'qa_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : str = self.get_auto_remove_tmp_dir()
__snake_case : Any = f'''
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__a , 'swag_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : int = get_results(__a )
self.assertGreaterEqual(result['eval_rouge1'] , 10 )
self.assertGreaterEqual(result['eval_rouge2'] , 2 )
self.assertGreaterEqual(result['eval_rougeL'] , 7 )
self.assertGreaterEqual(result['eval_rougeLsum'] , 7 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'summarization_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_bleu'] , 30 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'translation_no_trainer' ) ) )
@slow
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = logging.StreamHandler(sys.stdout )
logger.addHandler(__a )
__snake_case : List[str] = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertGreaterEqual(result['eval_overall_accuracy'] , 0.1_0 )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Dict = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : Optional[int] = get_results(__a )
# The base model scores a 25%
self.assertGreaterEqual(result['eval_accuracy'] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__a , 'step_1' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'image_classification_no_trainer' ) ) )
| 0 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=SCREAMING_SNAKE_CASE_ ):
A__ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Union[str, Any] , *__a : List[str] , **__a : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def A_ ( cls : Tuple , *__a : List[str] , **__a : Optional[Any] ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def A_ ( cls : Any , *__a : List[str] , **__a : List[str] ) -> Any:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class snake_case__ ( metaclass=SCREAMING_SNAKE_CASE_ ):
A__ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : int , *__a : int , **__a : int ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def A_ ( cls : Any , *__a : Dict , **__a : List[str] ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def A_ ( cls : int , *__a : int , **__a : List[Any] ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class snake_case__ ( metaclass=SCREAMING_SNAKE_CASE_ ):
A__ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Any , *__a : str , **__a : int ) -> str:
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def A_ ( cls : Optional[int] , *__a : Union[str, Any] , **__a : Union[str, Any] ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def A_ ( cls : Optional[int] , *__a : List[Any] , **__a : Union[str, Any] ) -> int:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class snake_case__ ( metaclass=SCREAMING_SNAKE_CASE_ ):
A__ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Dict , *__a : Dict , **__a : Dict ) -> int:
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def A_ ( cls : List[Any] , *__a : int , **__a : Optional[Any] ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def A_ ( cls : int , *__a : List[Any] , **__a : List[Any] ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class snake_case__ ( metaclass=SCREAMING_SNAKE_CASE_ ):
A__ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Optional[int] , *__a : str , **__a : List[str] ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def A_ ( cls : Tuple , *__a : Optional[int] , **__a : List[str] ) -> Any:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def A_ ( cls : Dict , *__a : Optional[Any] , **__a : Tuple ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class snake_case__ ( metaclass=SCREAMING_SNAKE_CASE_ ):
A__ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Dict , *__a : Union[str, Any] , **__a : List[str] ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def A_ ( cls : List[Any] , *__a : Dict , **__a : str ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def A_ ( cls : Tuple , *__a : int , **__a : Any ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 0 |
'''simple docstring'''
import math
def a_ ( _UpperCAmelCase : int ) -> list:
__snake_case : Optional[Any] = [True] * n
__snake_case : Optional[int] = False
__snake_case : Dict = False
__snake_case : List[Any] = True
for i in range(3 ,int(n**0.5 + 1 ) ,2 ):
__snake_case : Optional[int] = i * 2
while index < n:
__snake_case : Union[str, Any] = False
__snake_case : int = index + i
__snake_case : Dict = [2]
for i in range(3 ,_UpperCAmelCase ,2 ):
if is_prime[i]:
primes.append(_UpperCAmelCase )
return primes
def a_ ( _UpperCAmelCase : int = 99_99_66_66_33_33 ) -> int:
__snake_case : List[Any] = math.floor(math.sqrt(_UpperCAmelCase ) ) + 1_00
__snake_case : Tuple = prime_sieve(_UpperCAmelCase )
__snake_case : List[Any] = 0
__snake_case : List[Any] = 0
__snake_case : Optional[int] = primes[prime_index]
while (last_prime**2) <= limit:
__snake_case : Optional[int] = primes[prime_index + 1]
__snake_case : Union[str, Any] = last_prime**2
__snake_case : Dict = next_prime**2
# Get numbers divisible by lps(current)
__snake_case : Optional[Any] = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
__snake_case : Optional[Any] = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
__snake_case : List[str] = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
__snake_case : Dict = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 0 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = 42
A__ = 42
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : float ,_UpperCAmelCase : float ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"""{price_plus_tax(1_0_0, 0.25) = }""")
print(F"""{price_plus_tax(1_25.50, 0.05) = }""")
| 0 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
A__ : Optional[int] = logging.get_logger(__name__)
def a_ ( _UpperCAmelCase : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(_UpperCAmelCase ,np.ndarray ):
return list(tensor.shape )
__snake_case : Optional[Any] = tf.shape(_UpperCAmelCase )
if tensor.shape == tf.TensorShape(_UpperCAmelCase ):
return dynamic
__snake_case : int = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(_UpperCAmelCase )]
def a_ ( _UpperCAmelCase : tf.Tensor ,_UpperCAmelCase : Optional[int] = None ,_UpperCAmelCase : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1E-9 ,axis=_UpperCAmelCase ,name=_UpperCAmelCase )
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : List[Any] ,_UpperCAmelCase : str ,_UpperCAmelCase : str=1E-5 ,_UpperCAmelCase : Union[str, Any]=-1 ) -> str:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.' )
# Get mean and variance on the axis to be normalized
__snake_case , __snake_case : Union[str, Any] = tf.nn.moments(_UpperCAmelCase ,axes=[axis] ,keepdims=_UpperCAmelCase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
__snake_case : Optional[Any] = [1] * inputs.shape.rank
__snake_case : str = shape_list(_UpperCAmelCase )[axis]
__snake_case : Any = tf.reshape(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : int = tf.reshape(_UpperCAmelCase ,_UpperCAmelCase )
# Compute layer normalization using the batch_normalization
# function.
__snake_case : int = tf.nn.batch_normalization(
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,offset=_UpperCAmelCase ,scale=_UpperCAmelCase ,variance_epsilon=_UpperCAmelCase ,)
return outputs
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : Dict=0 ,_UpperCAmelCase : int=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
__snake_case : Any = tf.shape(_UpperCAmelCase )
__snake_case : int = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
__snake_case : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] ,axis=0 )
return tf.reshape(_UpperCAmelCase ,_UpperCAmelCase )
def a_ ( _UpperCAmelCase : tf.Tensor ) -> tf.Tensor:
if not isinstance(_UpperCAmelCase ,tf.Tensor ):
__snake_case : List[str] = tf.convert_to_tensor(_UpperCAmelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
__snake_case : Dict = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
__snake_case : List[str] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
__snake_case : Any = (
tf.cast(1 ,encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def a_ ( _UpperCAmelCase : tf.Tensor ,_UpperCAmelCase : int ,_UpperCAmelCase : str = "input_ids" ) -> None:
tf.debugging.assert_less(
_UpperCAmelCase ,tf.cast(_UpperCAmelCase ,dtype=tensor.dtype ) ,message=(
f'''The maximum value of {tensor_name} ({tf.math.reduce_max(_UpperCAmelCase )}) must be smaller than the embedding '''
f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) ,)
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : int ,_UpperCAmelCase : Tuple ) -> List[str]:
__snake_case : Union[str, Any] = 6_45_12
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
__snake_case : Any = [x for x in data if len(_UpperCAmelCase ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
'The following attributes cannot be saved to HDF5 file because '
f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
f'''bytes: {bad_attributes}''' )
__snake_case : Tuple = np.asarray(_UpperCAmelCase )
__snake_case : List[Any] = 1
__snake_case : List[str] = np.array_split(_UpperCAmelCase ,_UpperCAmelCase )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
__snake_case : List[str] = np.array_split(_UpperCAmelCase ,_UpperCAmelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(_UpperCAmelCase ):
__snake_case : Dict = chunk_data
else:
__snake_case : Optional[int] = data
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
if name in group.attrs:
__snake_case : List[str] = [n.decode('utf8' ) if hasattr(_UpperCAmelCase ,'decode' ) else n for n in group.attrs[name]]
else:
__snake_case : int = []
__snake_case : Tuple = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('utf8' ) if hasattr(_UpperCAmelCase ,'decode' ) else n for n in group.attrs['%s%d' % (name, chunk_id)]] )
chunk_id += 1
return data
def a_ ( _UpperCAmelCase : Optional[int] ) -> List[str]:
def _expand_single_ad_tensor(_UpperCAmelCase : Tuple ):
if isinstance(_UpperCAmelCase ,tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(_UpperCAmelCase ,axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor ,_UpperCAmelCase )
| 0 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
__snake_case : Optional[int] = SMALL_MODEL_IDENTIFIER
__snake_case : str = 'pt'
__snake_case : Union[str, Any] = 'tf'
def A_ ( self : Dict , __a : Tuple ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(__a )
def A_ ( self : Any , __a : Optional[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Union[str, Any] = TFAutoModel.from_pretrained(self.test_model , from_pt=__a )
model_tf.save_pretrained(__a )
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = 'mock_framework'
# Framework provided - return whatever the user provides
__snake_case : int = FeaturesManager.determine_framework(self.test_model , __a )
self.assertEqual(__a , __a )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : List[Any] = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Union[str, Any] = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(__a ):
__snake_case : Optional[int] = FeaturesManager.determine_framework(__a )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ):
__snake_case : int = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_tf )
# Both in environment -> use PyTorch
__snake_case : Optional[Any] = MagicMock(return_value=__a )
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# Both not in environment -> raise error
__snake_case : str = MagicMock(return_value=__a )
__snake_case : List[Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
with self.assertRaises(__a ):
__snake_case : Tuple = FeaturesManager.determine_framework(self.test_model )
| 0 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case__ ( unittest.TestCase ):
def A_ ( self : int ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = tempfile.mkdtemp()
# fmt: off
__snake_case : List[str] = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
# fmt: on
__snake_case : Any = 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] ) )
__snake_case : List[str] = {
'do_resize': True,
'size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.5, 0.5, 0.5],
'image_std': [0.5, 0.5, 0.5],
}
__snake_case : Optional[Any] = os.path.join(self.tmpdirname , __a )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(__a , __a )
def A_ ( self : Optional[int] , **__a : Dict ) -> int:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : int , **__a : Dict ) -> Tuple:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__snake_case : List[str] = [Image.fromarray(np.moveaxis(__a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : Dict = self.get_image_processor()
__snake_case : Any = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
processor.save_pretrained(self.tmpdirname )
__snake_case : Any = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[Any] = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__snake_case : Optional[Any] = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__snake_case : Tuple = self.get_image_processor(do_normalize=__a , padding_value=1.0 )
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : str = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = self.prepare_image_inputs()
__snake_case : List[str] = image_processor(__a , return_tensors='np' )
__snake_case : List[str] = processor(images=__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 A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Optional[int] = 'lower newer'
__snake_case : Dict = processor(text=__a )
__snake_case : List[Any] = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : int = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : List[Any] = 'lower newer'
__snake_case : Optional[Any] = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with self.assertRaises(__a ):
processor()
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_image_processor()
__snake_case : Any = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__snake_case : int = processor.batch_decode(__a )
__snake_case : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def A_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[str] = self.get_image_processor()
__snake_case : Dict = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Union[str, Any] = 'lower newer'
__snake_case : Tuple = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 0 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ProphetNetTokenizer
A__ = False
def A_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
super().setUp()
__snake_case : Dict = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__snake_case : Any = 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 A_ ( self : int , __a : Union[str, Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[int] = 'UNwant\u00E9d,running'
__snake_case : List[str] = 'unwanted, running'
return input_text, output_text
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Dict = self.tokenizer_class(self.vocab_file )
__snake_case : List[str] = 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 A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : List[str] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Optional[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 A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[Any] = 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 A_ ( self : int ) -> Any:
'''simple docstring'''
__snake_case : int = 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 A_ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = 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 A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
__snake_case : str = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[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 A_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__snake_case : List[Any] = {}
for i, token in enumerate(__a ):
__snake_case : List[str] = i
__snake_case : 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'] )
@require_torch
def A_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[Any] = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : int = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__snake_case : str = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
__snake_case : Union[str, Any] = tokenizer(__a , padding=__a , return_tensors='pt' )
self.assertIsInstance(__a , __a )
__snake_case : int = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__a , __a )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
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 A_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
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 A_ ( self : List[Any] ) -> int:
'''simple docstring'''
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(' ' ) )
@slow
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : Optional[int] = tokenizer.encode('sequence builders' , add_special_tokens=__a )
__snake_case : Optional[int] = tokenizer.encode('multi-sequence build' , add_special_tokens=__a )
__snake_case : Optional[Any] = tokenizer.build_inputs_with_special_tokens(__a )
__snake_case : List[Any] = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 0 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A__ : Optional[Any] = logging.get_logger(__name__)
A__ : str = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ : Union[str, Any] = {
'''tokenizer_file''': {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
},
}
A__ : Any = {
'''gpt-neox-20b''': 2_0_4_8,
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = ['''input_ids''', '''attention_mask''']
def __init__( self : Union[str, Any] , __a : List[str]=None , __a : List[str]=None , __a : str=None , __a : Optional[int]="<|endoftext|>" , __a : Any="<|endoftext|>" , __a : Tuple="<|endoftext|>" , __a : str=False , **__a : Union[str, Any] , ) -> Optional[int]:
'''simple docstring'''
super().__init__(
__a , __a , tokenizer_file=__a , unk_token=__a , bos_token=__a , eos_token=__a , add_prefix_space=__a , **__a , )
__snake_case : Optional[int] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , __a ) != add_prefix_space:
__snake_case : Any = getattr(__a , pre_tok_state.pop('type' ) )
__snake_case : int = add_prefix_space
__snake_case : Optional[Any] = pre_tok_class(**__a )
__snake_case : Union[str, Any] = add_prefix_space
def A_ ( self : List[str] , __a : str , __a : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
__snake_case : Dict = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
def A_ ( self : str , __a : "Conversation" ) -> List[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__a , add_special_tokens=__a ) + [self.eos_token_id] )
if len(__a ) > self.model_max_length:
__snake_case : Tuple = input_ids[-self.model_max_length :]
return input_ids
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NllbMoeForConditionalGeneration''',
'''NllbMoeModel''',
'''NllbMoePreTrainedModel''',
'''NllbMoeTop2Router''',
'''NllbMoeSparseMLP''',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
A__ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class snake_case__ ( unittest.TestCase ):
def A_ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
# A mock response for an HTTP head request to emulate server down
__snake_case : List[Any] = mock.Mock()
__snake_case : Any = 500
__snake_case : Optional[int] = {}
__snake_case : Optional[int] = HTTPError
__snake_case : List[str] = {}
# Download this model to make sure it's in the cache.
__snake_case : Union[str, Any] = BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' , return_value=__a ) as mock_head:
__snake_case : Tuple = BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def A_ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
# A mock response for an HTTP head request to emulate server down
__snake_case : Union[str, Any] = mock.Mock()
__snake_case : Tuple = 500
__snake_case : int = {}
__snake_case : List[str] = HTTPError
__snake_case : List[Any] = {}
# Download this model to make sure it's in the cache.
__snake_case : str = GPTaTokenizerFast.from_pretrained('gpt2' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' , return_value=__a ) as mock_head:
__snake_case : Dict = GPTaTokenizerFast.from_pretrained('gpt2' )
# This check we did call the fake head request
mock_head.assert_called()
def A_ ( self : List[Any] ) -> str:
'''simple docstring'''
# This test is for deprecated behavior and can be removed in v5
try:
__snake_case : Optional[int] = tempfile.mktemp()
with open(__a , 'wb' ) as f:
http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' , __a )
__snake_case : Any = AlbertTokenizer.from_pretrained(__a )
finally:
os.remove(__a )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile('tokenizer.json' ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open('tokenizer.json' , 'wb' ) as f:
http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json' , __a )
__snake_case : Optional[int] = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size , 1000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove('tokenizer.json' )
def A_ ( self : List[str] ) -> Any:
'''simple docstring'''
# This test is for deprecated behavior and can be removed in v5
__snake_case : int = AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' )
@is_staging_test
class snake_case__ ( unittest.TestCase ):
A__ = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
@classmethod
def A_ ( cls : int ) -> Dict:
'''simple docstring'''
__snake_case : Union[str, Any] = TOKEN
HfFolder.save_token(__a )
@classmethod
def A_ ( cls : List[Any] ) -> Dict:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-tokenizer' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-tokenizer-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-tokenizer' )
except HTTPError:
pass
def A_ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case : Optional[Any] = os.path.join(__a , 'vocab.txt' )
with open(__a , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
__snake_case : int = BertTokenizer(__a )
tokenizer.push_to_hub('test-tokenizer' , use_auth_token=self._token )
__snake_case : Union[str, Any] = BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer''' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id='test-tokenizer' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__a , repo_id='test-tokenizer' , push_to_hub=__a , use_auth_token=self._token )
__snake_case : Union[str, Any] = BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer''' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case : str = os.path.join(__a , 'vocab.txt' )
with open(__a , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
__snake_case : Union[str, Any] = BertTokenizer(__a )
tokenizer.push_to_hub('valid_org/test-tokenizer-org' , use_auth_token=self._token )
__snake_case : List[str] = BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-tokenizer-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
__a , repo_id='valid_org/test-tokenizer-org' , push_to_hub=__a , use_auth_token=self._token )
__snake_case : Dict = BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
@require_tokenizers
def A_ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case : Union[str, Any] = os.path.join(__a , 'vocab.txt' )
with open(__a , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
__snake_case : Optional[Any] = CustomTokenizer(__a )
# No fast custom tokenizer
tokenizer.push_to_hub('test-dynamic-tokenizer' , use_auth_token=self._token )
__snake_case : Union[str, Any] = AutoTokenizer.from_pretrained(f'''{USER}/test-dynamic-tokenizer''' , trust_remote_code=__a )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , 'CustomTokenizer' )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case : Dict = os.path.join(__a , 'vocab.txt' )
with open(__a , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
__snake_case : Dict = BertTokenizerFast.from_pretrained(__a )
bert_tokenizer.save_pretrained(__a )
__snake_case : Optional[int] = CustomTokenizerFast.from_pretrained(__a )
tokenizer.push_to_hub('test-dynamic-tokenizer' , use_auth_token=self._token )
__snake_case : Union[str, Any] = AutoTokenizer.from_pretrained(f'''{USER}/test-dynamic-tokenizer''' , trust_remote_code=__a )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , 'CustomTokenizerFast' )
__snake_case : int = AutoTokenizer.from_pretrained(
f'''{USER}/test-dynamic-tokenizer''' , use_fast=__a , trust_remote_code=__a )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , 'CustomTokenizer' )
class snake_case__ ( unittest.TestCase ):
def A_ ( self : str ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Any = Trie()
trie.add('Hello 友達' )
self.assertEqual(trie.data , {'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}} )
trie.add('Hello' )
trie.data
self.assertEqual(trie.data , {'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}} )
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = Trie()
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) , ['[CLS] This is a extra_id_100'] )
trie.add('[CLS]' )
trie.add('extra_id_1' )
trie.add('extra_id_100' )
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) , ['[CLS]', ' This is a ', 'extra_id_100'] )
def A_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Any = Trie()
trie.add('A' )
self.assertEqual(trie.split('ABC' ) , ['A', 'BC'] )
self.assertEqual(trie.split('BCA' ) , ['BC', 'A'] )
def A_ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Dict = Trie()
trie.add('TOKEN]' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) , ['This is something ', '[SPECIAL_TOKEN]'] )
def A_ ( self : str ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = Trie()
trie.add('A' )
trie.add('P' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) , ['This is something ', '[SPECIAL_TOKEN]'] )
def A_ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = Trie()
trie.add('AB' )
trie.add('B' )
trie.add('C' )
self.assertEqual(trie.split('ABC' ) , ['AB', 'C'] )
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Any = Trie()
trie.add('ABC' )
trie.add('B' )
trie.add('CD' )
self.assertEqual(trie.split('ABCD' ) , ['ABC', 'D'] )
def A_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
# Even if the offsets are wrong, we necessarily output correct string
# parts.
__snake_case : Optional[Any] = Trie()
__snake_case : Dict = trie.cut_text('ABC' , [0, 0, 2, 1, 2, 3] )
self.assertEqual(__a , ['AB', 'C'] )
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
__snake_case : Optional[Any] = gray_code_sequence_string(_UpperCAmelCase )
#
# convert them to integers
for i in range(len(_UpperCAmelCase ) ):
__snake_case : Optional[Any] = int(sequence[i] ,2 )
return sequence
def a_ ( _UpperCAmelCase : int ) -> list:
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__snake_case : Dict = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__snake_case : Dict = gray_code_sequence_string(bit_count - 1 )
__snake_case : Any = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__snake_case : str = '0' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__snake_case : Any = '1' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. 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 ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''Salesforce/blip-image-captioning-base'''
A__ = (
'''This is a tool that generates a description of an image. It takes an input named `image` which should be the '''
'''image to caption, and returns a text that contains the description in English.'''
)
A__ = '''image_captioner'''
A__ = AutoModelForVisionaSeq
A__ = ['''image''']
A__ = ['''text''']
def __init__( self : Optional[Any] , *__a : List[Any] , **__a : List[str] ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['vision'] )
super().__init__(*__a , **__a )
def A_ ( self : Optional[Any] , __a : "Image" ) -> int:
'''simple docstring'''
return self.pre_processor(images=__a , return_tensors='pt' )
def A_ ( self : Union[str, Any] , __a : Union[str, Any] ) -> List[str]:
'''simple docstring'''
return self.model.generate(**__a )
def A_ ( self : Any , __a : List[str] ) -> Any:
'''simple docstring'''
return self.pre_processor.batch_decode(__a , skip_special_tokens=__a )[0].strip()
| 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case__ ( unittest.TestCase ):
def A_ ( self : int ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = tempfile.mkdtemp()
# fmt: off
__snake_case : List[str] = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
# fmt: on
__snake_case : Any = 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] ) )
__snake_case : List[str] = {
'do_resize': True,
'size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.5, 0.5, 0.5],
'image_std': [0.5, 0.5, 0.5],
}
__snake_case : Optional[Any] = os.path.join(self.tmpdirname , __a )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(__a , __a )
def A_ ( self : Optional[int] , **__a : Dict ) -> int:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : int , **__a : Dict ) -> Tuple:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__snake_case : List[str] = [Image.fromarray(np.moveaxis(__a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : Dict = self.get_image_processor()
__snake_case : Any = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
processor.save_pretrained(self.tmpdirname )
__snake_case : Any = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[Any] = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__snake_case : Optional[Any] = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__snake_case : Tuple = self.get_image_processor(do_normalize=__a , padding_value=1.0 )
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : str = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = self.prepare_image_inputs()
__snake_case : List[str] = image_processor(__a , return_tensors='np' )
__snake_case : List[str] = processor(images=__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 A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Optional[int] = 'lower newer'
__snake_case : Dict = processor(text=__a )
__snake_case : List[Any] = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : int = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : List[Any] = 'lower newer'
__snake_case : Optional[Any] = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with self.assertRaises(__a ):
processor()
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_image_processor()
__snake_case : Any = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__snake_case : int = processor.batch_decode(__a )
__snake_case : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def A_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[str] = self.get_image_processor()
__snake_case : Dict = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Union[str, Any] = 'lower newer'
__snake_case : Tuple = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 0 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a_ ( _UpperCAmelCase : List[str] ) -> Any:
if "cls_token" in name:
__snake_case : int = name.replace('cls_token' ,'vit.embeddings.cls_token' )
if "mask_token" in name:
__snake_case : Tuple = name.replace('mask_token' ,'decoder.mask_token' )
if "decoder_pos_embed" in name:
__snake_case : List[Any] = name.replace('decoder_pos_embed' ,'decoder.decoder_pos_embed' )
if "pos_embed" in name and "decoder" not in name:
__snake_case : Tuple = name.replace('pos_embed' ,'vit.embeddings.position_embeddings' )
if "patch_embed.proj" in name:
__snake_case : Tuple = name.replace('patch_embed.proj' ,'vit.embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
__snake_case : List[str] = name.replace('patch_embed.norm' ,'vit.embeddings.norm' )
if "decoder_blocks" in name:
__snake_case : Union[str, Any] = name.replace('decoder_blocks' ,'decoder.decoder_layers' )
if "blocks" in name:
__snake_case : Any = name.replace('blocks' ,'vit.encoder.layer' )
if "attn.proj" in name:
__snake_case : Optional[int] = name.replace('attn.proj' ,'attention.output.dense' )
if "attn" in name:
__snake_case : str = name.replace('attn' ,'attention.self' )
if "norm1" in name:
__snake_case : int = name.replace('norm1' ,'layernorm_before' )
if "norm2" in name:
__snake_case : List[Any] = name.replace('norm2' ,'layernorm_after' )
if "mlp.fc1" in name:
__snake_case : Union[str, Any] = name.replace('mlp.fc1' ,'intermediate.dense' )
if "mlp.fc2" in name:
__snake_case : List[str] = name.replace('mlp.fc2' ,'output.dense' )
if "decoder_embed" in name:
__snake_case : Optional[Any] = name.replace('decoder_embed' ,'decoder.decoder_embed' )
if "decoder_norm" in name:
__snake_case : str = name.replace('decoder_norm' ,'decoder.decoder_norm' )
if "decoder_pred" in name:
__snake_case : List[Any] = name.replace('decoder_pred' ,'decoder.decoder_pred' )
if "norm.weight" in name and "decoder" not in name:
__snake_case : Optional[Any] = name.replace('norm.weight' ,'vit.layernorm.weight' )
if "norm.bias" in name and "decoder" not in name:
__snake_case : Tuple = name.replace('norm.bias' ,'vit.layernorm.bias' )
return name
def a_ ( _UpperCAmelCase : Dict ,_UpperCAmelCase : Any ) -> Optional[int]:
for key in orig_state_dict.copy().keys():
__snake_case : int = orig_state_dict.pop(_UpperCAmelCase )
if "qkv" in key:
__snake_case : List[str] = key.split('.' )
__snake_case : Union[str, Any] = int(key_split[1] )
if "decoder_blocks" in key:
__snake_case : Union[str, Any] = config.decoder_hidden_size
__snake_case : List[Any] = 'decoder.decoder_layers.'
if "weight" in key:
__snake_case : Optional[Any] = val[:dim, :]
__snake_case : Dict = val[dim : dim * 2, :]
__snake_case : str = val[-dim:, :]
elif "bias" in key:
__snake_case : List[Any] = val[:dim]
__snake_case : Optional[int] = val[dim : dim * 2]
__snake_case : List[Any] = val[-dim:]
else:
__snake_case : str = config.hidden_size
__snake_case : Tuple = 'vit.encoder.layer.'
if "weight" in key:
__snake_case : int = val[:dim, :]
__snake_case : Optional[int] = val[dim : dim * 2, :]
__snake_case : List[Any] = val[-dim:, :]
elif "bias" in key:
__snake_case : Union[str, Any] = val[:dim]
__snake_case : int = val[dim : dim * 2]
__snake_case : Union[str, Any] = val[-dim:]
else:
__snake_case : str = val
return orig_state_dict
def a_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : Dict ) -> Optional[Any]:
__snake_case : Tuple = ViTMAEConfig()
if "large" in checkpoint_url:
__snake_case : Optional[int] = 10_24
__snake_case : List[Any] = 40_96
__snake_case : Tuple = 24
__snake_case : Union[str, Any] = 16
elif "huge" in checkpoint_url:
__snake_case : Optional[int] = 14
__snake_case : Tuple = 12_80
__snake_case : Tuple = 51_20
__snake_case : Tuple = 32
__snake_case : Optional[int] = 16
__snake_case : Tuple = ViTMAEForPreTraining(_UpperCAmelCase )
__snake_case : str = torch.hub.load_state_dict_from_url(_UpperCAmelCase ,map_location='cpu' )['model']
__snake_case : Union[str, Any] = ViTMAEImageProcessor(size=config.image_size )
__snake_case : str = convert_state_dict(_UpperCAmelCase ,_UpperCAmelCase )
model.load_state_dict(_UpperCAmelCase )
model.eval()
__snake_case : Any = 'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'
__snake_case : Union[str, Any] = Image.open(requests.get(_UpperCAmelCase ,stream=_UpperCAmelCase ).raw )
__snake_case : int = ViTMAEImageProcessor(size=config.image_size )
__snake_case : Optional[int] = image_processor(images=_UpperCAmelCase ,return_tensors='pt' )
# forward pass
torch.manual_seed(2 )
__snake_case : Optional[Any] = model(**_UpperCAmelCase )
__snake_case : Any = outputs.logits
if "large" in checkpoint_url:
__snake_case : Tuple = torch.tensor(
[[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]] )
elif "huge" in checkpoint_url:
__snake_case : List[Any] = torch.tensor(
[[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]] )
else:
__snake_case : Optional[Any] = torch.tensor(
[[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]] )
# verify logits
assert torch.allclose(logits[0, :3, :3] ,_UpperCAmelCase ,atol=1E-4 )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
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.'''
)
A__ : str = parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a_ ( _UpperCAmelCase : List[Any] ) -> Tuple:
__snake_case : str = []
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
) )
return embed
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Optional[int] ) -> List[str]:
__snake_case : Tuple = []
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
) )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
return attention_weights
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Dict:
__snake_case : Union[str, Any] = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token') )
return token
def a_ ( ) -> Optional[Any]:
__snake_case : Any = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ) -> Tuple:
__snake_case : List[str] = 'imagenet-1k-id2label.json'
__snake_case : Dict = 10_00
__snake_case : Union[str, Any] = 'huggingface/label-files'
__snake_case : str = num_labels
__snake_case : str = json.load(open(cached_download(hf_hub_url(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset' ) ) ,'r' ) )
__snake_case : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__snake_case : Optional[Any] = idalabel
__snake_case : str = {v: k for k, v in idalabel.items()}
__snake_case : Dict = CvtConfig(num_labels=_UpperCAmelCase ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' ,1 )[-1][4:6] == "13":
__snake_case : Tuple = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' ,1 )[-1][4:6] == "21":
__snake_case : str = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__snake_case : Dict = [2, 2, 20]
__snake_case : Any = [3, 12, 16]
__snake_case : Tuple = [1_92, 7_68, 10_24]
__snake_case : str = CvtForImageClassification(_UpperCAmelCase )
__snake_case : List[Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
__snake_case : int = image_size
__snake_case : int = torch.load(_UpperCAmelCase ,map_location=torch.device('cpu' ) )
__snake_case : List[Any] = OrderedDict()
__snake_case : Union[str, Any] = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__snake_case : Optional[Any] = list_of_state_dict + cls_token(_UpperCAmelCase )
__snake_case : Tuple = list_of_state_dict + embeddings(_UpperCAmelCase )
for cnt in range(config.depth[idx] ):
__snake_case : Optional[int] = list_of_state_dict + attention(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
__snake_case : List[str] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
A__ : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=3_8_4,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
A__ : Tuple = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 0 | 1 |
'''simple docstring'''
from math import ceil, sqrt
def a_ ( _UpperCAmelCase : int = 1_00_00_00 ) -> int:
__snake_case : Union[str, Any] = 0
for outer_width in range(3 ,(limit // 4) + 2 ):
if outer_width**2 > limit:
__snake_case : Tuple = max(ceil(sqrt(outer_width**2 - limit ) ) ,1 )
else:
__snake_case : Any = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F"""{solution() = }""")
| 0 |
'''simple docstring'''
from __future__ import annotations
A__ : List[Any] = list[list[int]]
# assigning initial values to the grid
A__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
A__ : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def a_ ( _UpperCAmelCase : Matrix ,_UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def a_ ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def a_ ( _UpperCAmelCase : Matrix ) -> Matrix | None:
if location := find_empty_location(_UpperCAmelCase ):
__snake_case , __snake_case : Optional[int] = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 ,10 ):
if is_safe(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : Union[str, Any] = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
__snake_case : Optional[Any] = 0
return None
def a_ ( _UpperCAmelCase : Matrix ) -> None:
for row in grid:
for cell in row:
print(_UpperCAmelCase ,end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('''\nExample grid:\n''' + '''=''' * 2_0)
print_solution(example_grid)
print('''\nExample grid solution:''')
A__ : List[str] = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('''Cannot find a solution.''')
| 0 | 1 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int = 10_00 ) -> int:
__snake_case , __snake_case : Union[str, Any] = 1, 1
__snake_case : Dict = []
for i in range(1 ,n + 1 ):
__snake_case : Optional[Any] = prev_numerator + 2 * prev_denominator
__snake_case : int = prev_numerator + prev_denominator
if len(str(_UpperCAmelCase ) ) > len(str(_UpperCAmelCase ) ):
result.append(_UpperCAmelCase )
__snake_case : int = numerator
__snake_case : Union[str, Any] = denominator
return len(_UpperCAmelCase )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = KandinskyVaaPriorPipeline
A__ = ['''prompt''']
A__ = ['''prompt''', '''negative_prompt''']
A__ = [
'''num_images_per_prompt''',
'''generator''',
'''num_inference_steps''',
'''latents''',
'''negative_prompt''',
'''guidance_scale''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Dict ) -> List[str]:
'''simple docstring'''
return 32
@property
def A_ ( self : Any ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return self.time_input_dim
@property
def A_ ( self : str ) -> int:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return 100
@property
def A_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
__snake_case : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__a )
@property
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Any = {
'num_attention_heads': 2,
'attention_head_dim': 12,
'embedding_dim': self.text_embedder_hidden_size,
'num_layers': 1,
}
__snake_case : List[Any] = PriorTransformer(**__a )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
__snake_case : Any = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
__snake_case : Optional[Any] = CLIPVisionModelWithProjection(__a )
return model
@property
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = CLIPImageProcessor(
crop_size=224 , do_center_crop=__a , do_normalize=__a , do_resize=__a , image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , resample=3 , size=224 , )
return image_processor
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : List[str] = self.dummy_image_encoder
__snake_case : str = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : List[str] = self.dummy_image_processor
__snake_case : Any = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='sample' , num_train_timesteps=1000 , clip_sample=__a , clip_sample_range=1_0.0 , )
__snake_case : str = {
'prior': prior,
'image_encoder': image_encoder,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'scheduler': scheduler,
'image_processor': image_processor,
}
return components
def A_ ( self : List[Any] , __a : Optional[Any] , __a : Tuple=0 ) -> Any:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : List[str] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : List[Any] = {
'prompt': 'horse',
'generator': generator,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def A_ ( self : str ) -> Dict:
'''simple docstring'''
__snake_case : str = 'cpu'
__snake_case : List[str] = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Optional[Any] = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : List[str] = output.image_embeds
__snake_case : str = pipe(
**self.get_dummy_inputs(__a ) , return_dict=__a , )[0]
__snake_case : Union[str, Any] = image[0, -10:]
__snake_case : Any = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
__snake_case : List[Any] = np.array(
[-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def A_ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = torch_device == 'cpu'
__snake_case : Dict = True
__snake_case : Union[str, Any] = False
self._test_inference_batch_single_identical(
test_max_difference=__a , relax_max_difference=__a , test_mean_pixel_difference=__a , )
@skip_mps
def A_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = torch_device == 'cpu'
__snake_case : Optional[Any] = False
self._test_attention_slicing_forward_pass(
test_max_difference=__a , test_mean_pixel_difference=__a , )
| 0 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A__ : List[Any] = {
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[Any] = ['''MobileViTFeatureExtractor''']
A__ : Optional[int] = ['''MobileViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Any = [
'''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileViTForImageClassification''',
'''MobileViTForSemanticSegmentation''',
'''MobileViTModel''',
'''MobileViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFMobileViTForImageClassification''',
'''TFMobileViTForSemanticSegmentation''',
'''TFMobileViTModel''',
'''TFMobileViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
A__ : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 |
'''simple docstring'''
from math import factorial
A__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def a_ ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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(_UpperCAmelCase ) )
def a_ ( _UpperCAmelCase : int = 60 ,_UpperCAmelCase : int = 1_00_00_00 ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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
__snake_case : List[str] = 0
# the cached sizes of the previous chains
__snake_case : dict[int, int] = {}
for start_chain_element in range(1 ,_UpperCAmelCase ):
# The temporary set will contain the elements of the chain
__snake_case : Optional[int] = set()
__snake_case : List[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__snake_case : str = 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(_UpperCAmelCase )
chain_set_length += 1
__snake_case : Tuple = digit_factorial_sum(_UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__snake_case : 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()}""")
| 0 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
A__ : Dict = '''platform'''
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class snake_case__ :
A__ = PegasusConfig
A__ = {}
A__ = '''gelu'''
def __init__( self : Any , __a : Dict , __a : Tuple=13 , __a : Optional[int]=7 , __a : str=True , __a : str=False , __a : Any=99 , __a : Tuple=32 , __a : str=5 , __a : int=4 , __a : Optional[int]=37 , __a : Tuple=0.1 , __a : str=0.1 , __a : Optional[int]=20 , __a : Optional[int]=2 , __a : Union[str, Any]=1 , __a : Optional[int]=0 , ) -> List[str]:
'''simple docstring'''
__snake_case : Tuple = parent
__snake_case : List[str] = batch_size
__snake_case : Union[str, Any] = seq_length
__snake_case : List[str] = is_training
__snake_case : List[Any] = use_labels
__snake_case : int = vocab_size
__snake_case : List[str] = hidden_size
__snake_case : List[Any] = num_hidden_layers
__snake_case : str = num_attention_heads
__snake_case : Dict = intermediate_size
__snake_case : str = hidden_dropout_prob
__snake_case : Dict = attention_probs_dropout_prob
__snake_case : List[Any] = max_position_embeddings
__snake_case : List[str] = eos_token_id
__snake_case : Any = pad_token_id
__snake_case : str = bos_token_id
def A_ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
__snake_case : int = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
__snake_case : List[str] = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
__snake_case : Any = np.concatenate([input_ids, eos_tensor] , axis=1 )
__snake_case : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case : List[Any] = 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 , )
__snake_case : Optional[Any] = prepare_pegasus_inputs_dict(__a , __a , __a )
return config, inputs_dict
def A_ ( self : Union[str, Any] , __a : Dict , __a : int , __a : int ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = 20
__snake_case : int = model_class_name(__a )
__snake_case : List[Any] = model.encode(inputs_dict['input_ids'] )
__snake_case , __snake_case : List[Any] = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
__snake_case : List[str] = model.init_cache(decoder_input_ids.shape[0] , __a , __a )
__snake_case : List[Any] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='i4' )
__snake_case : int = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
__snake_case : Dict = model.decode(
decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , )
__snake_case : Dict = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4' )
__snake_case : Optional[int] = model.decode(
decoder_input_ids[:, -1:] , __a , decoder_attention_mask=__a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__a , )
__snake_case : List[str] = model.decode(__a , __a )
__snake_case : Any = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
def A_ ( self : Any , __a : Any , __a : List[str] , __a : Union[str, Any] ) -> int:
'''simple docstring'''
__snake_case : Dict = 20
__snake_case : List[str] = model_class_name(__a )
__snake_case : Optional[Any] = model.encode(inputs_dict['input_ids'] )
__snake_case , __snake_case : Optional[int] = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
__snake_case : Union[str, Any] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
__snake_case : Dict = model.init_cache(decoder_input_ids.shape[0] , __a , __a )
__snake_case : Optional[Any] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
__snake_case : List[Any] = model.decode(
decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , )
__snake_case : Optional[Any] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4' )
__snake_case : Union[str, Any] = model.decode(
decoder_input_ids[:, -1:] , __a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__a , decoder_position_ids=__a , )
__snake_case : Optional[Any] = model.decode(__a , __a , decoder_attention_mask=__a )
__snake_case : Any = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : str ,_UpperCAmelCase : str=None ,_UpperCAmelCase : Optional[int]=None ,) -> Optional[int]:
if attention_mask is None:
__snake_case : Union[str, Any] = np.not_equal(_UpperCAmelCase ,config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
__snake_case : List[str] = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape ,dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] ,config.pad_token_id ).astype(np.inta ),
] ,axis=-1 ,)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
A__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
A__ = True
A__ = False
A__ = False
A__ = False
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : str = FlaxPegasusModelTester(self )
__snake_case : List[str] = ConfigTester(self , config_class=__a )
def A_ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
__snake_case , __snake_case : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(__a , __a , __a )
def A_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
__snake_case , __snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(__a , __a , __a )
def A_ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
__snake_case , __snake_case : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__snake_case : Dict = self._prepare_for_class(__a , __a )
__snake_case : List[str] = model_class(__a )
@jax.jit
def encode_jitted(__a : Union[str, Any] , __a : Optional[Any]=None , **__a : Any ):
return model.encode(input_ids=__a , attention_mask=__a )
with self.subTest('JIT Enabled' ):
__snake_case : int = encode_jitted(**__a ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
__snake_case : Dict = encode_jitted(**__a ).to_tuple()
self.assertEqual(len(__a ) , len(__a ) )
for jitted_output, output in zip(__a , __a ):
self.assertEqual(jitted_output.shape , output.shape )
def A_ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case , __snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__snake_case : Optional[int] = model_class(__a )
__snake_case : Tuple = model.encode(inputs_dict['input_ids'] , inputs_dict['attention_mask'] )
__snake_case : Optional[int] = {
'decoder_input_ids': inputs_dict['decoder_input_ids'],
'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
'encoder_outputs': encoder_outputs,
}
@jax.jit
def decode_jitted(__a : List[str] , __a : Dict , __a : int ):
return model.decode(
decoder_input_ids=__a , decoder_attention_mask=__a , encoder_outputs=__a , )
with self.subTest('JIT Enabled' ):
__snake_case : Tuple = decode_jitted(**__a ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
__snake_case : str = decode_jitted(**__a ).to_tuple()
self.assertEqual(len(__a ) , len(__a ) )
for jitted_output, output in zip(__a , __a ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def A_ ( self : Dict ) -> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__snake_case : int = model_class_name.from_pretrained('google/pegasus-large' , from_pt=__a )
__snake_case : List[Any] = np.ones((1, 1) )
__snake_case : Dict = model(__a )
self.assertIsNotNone(__a )
@slow
def A_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum' )
__snake_case : List[str] = PegasusTokenizer.from_pretrained('google/pegasus-xsum' )
__snake_case : str = [
' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
]
__snake_case : Optional[int] = [
'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
]
__snake_case : Dict = tokenizer(__a , return_tensors='np' , truncation=__a , max_length=512 , padding=__a )
__snake_case : Any = model.generate(**__a , num_beams=2 ).sequences
__snake_case : List[Any] = tokenizer.batch_decode(__a , skip_special_tokens=__a )
assert tgt_text == decoded
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int = 1_00 ) -> int:
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : Union[str, Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 0 | 1 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class snake_case__ :
def __init__( self : int , __a : list[tuple[float, float]] ) -> Dict:
'''simple docstring'''
__snake_case : List[Any] = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__snake_case : Union[str, Any] = len(__a ) - 1
def A_ ( self : str , __a : float ) -> list[float]:
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__snake_case : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , __a ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(__a ) , 5 ) == 1
return output_values
def A_ ( self : List[Any] , __a : float ) -> tuple[float, float]:
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__snake_case : List[Any] = self.basis_function(__a )
__snake_case : List[Any] = 0.0
__snake_case : List[Any] = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def A_ ( self : Any , __a : float = 0.0_1 ) -> str:
'''simple docstring'''
from matplotlib import pyplot as plt # type: ignore
__snake_case : list[float] = [] # x coordinates of points to plot
__snake_case : list[float] = [] # y coordinates of points to plot
__snake_case : Dict = 0.0
while t <= 1:
__snake_case : Optional[Any] = self.bezier_curve_function(__a )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
__snake_case : List[Any] = [i[0] for i in self.list_of_points]
__snake_case : Optional[int] = [i[1] for i in self.list_of_points]
plt.plot(
__a , __a , color='blue' , label='Curve of Degree ' + str(self.degree ) , )
plt.scatter(__a , __a , color='red' , label='Control Points' )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : int = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''',
'''GroupViTTextConfig''',
'''GroupViTVisionConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple = [
'''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GroupViTModel''',
'''GroupViTPreTrainedModel''',
'''GroupViTTextModel''',
'''GroupViTVisionModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFGroupViTModel''',
'''TFGroupViTPreTrainedModel''',
'''TFGroupViTTextModel''',
'''TFGroupViTVisionModel''',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
from __future__ import annotations
A__ : str = '''Muhammad Umer Farooq'''
A__ : int = '''MIT'''
A__ : Optional[int] = '''1.0.0'''
A__ : List[Any] = '''Muhammad Umer Farooq'''
A__ : Optional[Any] = '''[email protected]'''
A__ : Optional[Any] = '''Alpha'''
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Union[str, Any] , __a : str ) -> None:
'''simple docstring'''
super().__init__()
__snake_case : list[str] = []
__snake_case : Dict = domain
def A_ ( self : Dict , __a : str , __a : list[tuple[str, str | None]] ) -> None:
'''simple docstring'''
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__snake_case : Optional[Any] = parse.urljoin(self.domain , __a )
self.urls.append(__a )
def a_ ( _UpperCAmelCase : str ) -> str:
return ".".join(get_sub_domain_name(_UpperCAmelCase ).split('.' )[-2:] )
def a_ ( _UpperCAmelCase : str ) -> str:
return parse.urlparse(_UpperCAmelCase ).netloc
def a_ ( _UpperCAmelCase : str = "https://github.com" ) -> list[str]:
__snake_case : List[Any] = get_domain_name(_UpperCAmelCase )
# Initialize the parser
__snake_case : Tuple = Parser(_UpperCAmelCase )
try:
# Open URL
__snake_case : Any = requests.get(_UpperCAmelCase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__snake_case : Dict = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__snake_case : List[Any] = requests.get(_UpperCAmelCase )
# Get the valid email.
__snake_case : Optional[Any] = re.findall('[a-zA-Z0-9]+@' + domain ,read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_UpperCAmelCase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Tuple = emails_from_url('''https://github.com''')
print(F"""{len(emails)} emails found:""")
print('''\n'''.join(sorted(emails)))
| 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ShapEPipeline
A__ = ['''prompt''']
A__ = ['''prompt''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return 32
@property
def A_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
return 8
@property
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__a )
@property
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Dict = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
__snake_case : Optional[Any] = PriorTransformer(**__a )
return model
@property
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Tuple = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
__snake_case : Optional[int] = ShapERenderer(**__a )
return model
def A_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : Union[str, Any] = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : Optional[Any] = self.dummy_renderer
__snake_case : List[Any] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=__a , clip_sample=__a , clip_sample_range=1.0 , )
__snake_case : int = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def A_ ( self : Union[str, Any] , __a : Dict , __a : int=0 ) -> Optional[Any]:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : Optional[Any] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : Optional[int] = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = 'cpu'
__snake_case : Dict = self.get_dummy_components()
__snake_case : int = self.pipeline_class(**__a )
__snake_case : str = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[Any] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : Dict = output.images[0]
__snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__snake_case : str = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
__snake_case : int = torch_device == 'cpu'
__snake_case : str = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__a , relax_max_difference=__a , )
def A_ ( self : List[str] ) -> Dict:
'''simple docstring'''
__snake_case : str = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Dict = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : int = 1
__snake_case : Tuple = 2
__snake_case : Tuple = self.get_dummy_inputs(__a )
for key in inputs.keys():
if key in self.batch_params:
__snake_case : Union[str, Any] = batch_size * [inputs[key]]
__snake_case : str = pipe(**__a , num_images_per_prompt=__a )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def A_ ( self : str ) -> Dict:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
__snake_case : Union[str, Any] = ShapEPipeline.from_pretrained('openai/shap-e' )
__snake_case : Any = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = torch.Generator(device=__a ).manual_seed(0 )
__snake_case : Union[str, Any] = pipe(
'a shark' , generator=__a , guidance_scale=1_5.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__a , __a )
| 0 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Any = logging.get_logger(__name__)
A__ : List[Any] = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''glpn'''
def __init__( self : Tuple , __a : int=3 , __a : Union[str, Any]=4 , __a : List[Any]=[2, 2, 2, 2] , __a : Dict=[8, 4, 2, 1] , __a : str=[32, 64, 160, 256] , __a : Optional[Any]=[7, 3, 3, 3] , __a : Optional[int]=[4, 2, 2, 2] , __a : int=[1, 2, 5, 8] , __a : Any=[4, 4, 4, 4] , __a : Dict="gelu" , __a : List[Any]=0.0 , __a : Any=0.0 , __a : Optional[Any]=0.0_2 , __a : Optional[Any]=0.1 , __a : Union[str, Any]=1e-6 , __a : str=64 , __a : List[str]=10 , __a : Dict=-1 , **__a : Union[str, Any] , ) -> List[Any]:
'''simple docstring'''
super().__init__(**__a )
__snake_case : List[Any] = num_channels
__snake_case : Tuple = num_encoder_blocks
__snake_case : Optional[Any] = depths
__snake_case : Any = sr_ratios
__snake_case : int = hidden_sizes
__snake_case : Any = patch_sizes
__snake_case : Optional[int] = strides
__snake_case : Dict = mlp_ratios
__snake_case : int = num_attention_heads
__snake_case : List[Any] = hidden_act
__snake_case : Optional[int] = hidden_dropout_prob
__snake_case : Any = attention_probs_dropout_prob
__snake_case : Optional[int] = initializer_range
__snake_case : Union[str, Any] = drop_path_rate
__snake_case : Any = layer_norm_eps
__snake_case : Any = decoder_hidden_size
__snake_case : str = max_depth
__snake_case : List[Any] = head_in_index
| 0 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
A__ : str = [8, 5, 9, 7]
A__ : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A__ : Dict = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class snake_case__ :
def __init__( self : Union[str, Any] , __a : list[int] , __a : list[list[int]] , __a : list[list[int]] , ) -> None:
'''simple docstring'''
__snake_case : int = claim_vector
__snake_case : Optional[int] = allocated_resources_table
__snake_case : List[str] = maximum_claim_table
def A_ ( self : str ) -> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def A_ ( self : int ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def A_ ( self : int ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def A_ ( self : str ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__a ): i for i in self.__need()}
def A_ ( self : Union[str, Any] , **__a : int ) -> None:
'''simple docstring'''
__snake_case : str = self.__need()
__snake_case : List[Any] = self.__allocated_resources_table
__snake_case : Optional[int] = self.__available_resources()
__snake_case : Union[str, Any] = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
__snake_case : Tuple = False
for each_need in need_list:
__snake_case : Any = True
for index, need in enumerate(__a ):
if need > available_resources[index]:
__snake_case : List[str] = False
break
if execution:
__snake_case : Union[str, Any] = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__snake_case : str = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(__a )
# update available/freed resources stack
__snake_case : Union[str, Any] = np.array(__a ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__a ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__a ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : str ,_UpperCAmelCase : Union[str, Any]=[] ) -> str:
__snake_case : Any = size[0] - overlap_pixels * 2
__snake_case : List[str] = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
__snake_case : Any = np.ones((size_y, size_x) ,dtype=np.uinta ) * 2_55
__snake_case : Optional[Any] = np.pad(_UpperCAmelCase ,mode='linear_ramp' ,pad_width=_UpperCAmelCase ,end_values=0 )
if "l" in remove_borders:
__snake_case : Dict = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
__snake_case : Tuple = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
__snake_case : Any = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
__snake_case : Tuple = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def a_ ( _UpperCAmelCase : Dict ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Dict ) -> Union[str, Any]:
return max(_UpperCAmelCase ,min(_UpperCAmelCase ,_UpperCAmelCase ) )
def a_ ( _UpperCAmelCase : [int] ,_UpperCAmelCase : [int] ,_UpperCAmelCase : [int] ) -> Optional[Any]:
return (
clamp(rect[0] ,min[0] ,max[0] ),
clamp(rect[1] ,min[1] ,max[1] ),
clamp(rect[2] ,min[0] ,max[0] ),
clamp(rect[3] ,min[1] ,max[1] ),
)
def a_ ( _UpperCAmelCase : [int] ,_UpperCAmelCase : int ,_UpperCAmelCase : [int] ) -> Union[str, Any]:
__snake_case : List[Any] = list(_UpperCAmelCase )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
__snake_case : Tuple = clamp_rect(_UpperCAmelCase ,[0, 0] ,[image_size[0], image_size[1]] )
return rect
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[int] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Tuple ) -> List[str]:
__snake_case : Union[str, Any] = Image.new('RGB' ,(tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) ,Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) ,(0, 0) ,)
result.paste(_UpperCAmelCase ,(original_slice, 0) )
return result
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ) -> List[str]:
__snake_case : Tuple = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
__snake_case : Dict = tile.crop(_UpperCAmelCase )
return tile
def a_ ( _UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : str ) -> Tuple:
__snake_case : List[str] = n % d
return n - divisor
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Any , __a : AutoencoderKL , __a : CLIPTextModel , __a : CLIPTokenizer , __a : UNetaDConditionModel , __a : DDPMScheduler , __a : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , __a : int = 350 , ) -> Any:
'''simple docstring'''
super().__init__(
vae=__a , text_encoder=__a , tokenizer=__a , unet=__a , low_res_scheduler=__a , scheduler=__a , max_noise_level=__a , )
def A_ ( self : int , __a : Optional[int] , __a : str , __a : Any , __a : Optional[int] , __a : Optional[int] , __a : Tuple , __a : List[str] , **__a : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Dict = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
__snake_case : Tuple = add_overlap_rect(__a , __a , image.size )
__snake_case : Optional[Any] = image.crop(__a )
__snake_case : Optional[Any] = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
__snake_case : Optional[int] = translated_slice_x - (original_image_slice / 2)
__snake_case : Tuple = max(0 , __a )
__snake_case : Optional[int] = squeeze_tile(__a , __a , __a , __a )
__snake_case : Optional[int] = to_input.size
__snake_case : Optional[Any] = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
__snake_case : Union[str, Any] = super(__a , self ).__call__(image=__a , **__a ).images[0]
__snake_case : List[Any] = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
__snake_case : Optional[int] = unsqueeze_tile(__a , __a )
__snake_case : Optional[Any] = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
__snake_case : Dict = []
if x == 0:
remove_borders.append('l' )
elif crop_rect[2] == image.size[0]:
remove_borders.append('r' )
if y == 0:
remove_borders.append('t' )
elif crop_rect[3] == image.size[1]:
remove_borders.append('b' )
__snake_case : Optional[Any] = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=__a ) , mode='L' , )
final_image.paste(
__a , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , __a )
@torch.no_grad()
def __call__( self : List[Any] , __a : Union[str, List[str]] , __a : Union[PIL.Image.Image, List[PIL.Image.Image]] , __a : int = 75 , __a : float = 9.0 , __a : int = 50 , __a : Optional[Union[str, List[str]]] = None , __a : Optional[int] = 1 , __a : float = 0.0 , __a : Optional[torch.Generator] = None , __a : Optional[torch.FloatTensor] = None , __a : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __a : int = 1 , __a : int = 128 , __a : int = 32 , __a : int = 32 , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : str = Image.new('RGB' , (image.size[0] * 4, image.size[1] * 4) )
__snake_case : List[str] = math.ceil(image.size[0] / tile_size )
__snake_case : List[str] = math.ceil(image.size[1] / tile_size )
__snake_case : int = tcx * tcy
__snake_case : int = 0
for y in range(__a ):
for x in range(__a ):
self._process_tile(
__a , __a , __a , __a , __a , __a , __a , prompt=__a , num_inference_steps=__a , guidance_scale=__a , noise_level=__a , negative_prompt=__a , num_images_per_prompt=__a , eta=__a , generator=__a , latents=__a , )
current_count += 1
if callback is not None:
callback({'progress': current_count / total_tile_count, 'image': final_image} )
return final_image
def a_ ( ) -> int:
# Run a demo
__snake_case : Optional[Any] = 'stabilityai/stable-diffusion-x4-upscaler'
__snake_case : str = StableDiffusionTiledUpscalePipeline.from_pretrained(_UpperCAmelCase ,revision='fp16' ,torch_dtype=torch.floataa )
__snake_case : Tuple = pipe.to('cuda' )
__snake_case : Any = Image.open('../../docs/source/imgs/diffusers_library.jpg' )
def callback(_UpperCAmelCase : Union[str, Any] ):
print(f'''progress: {obj["progress"]:.4f}''' )
obj["image"].save('diffusers_library_progress.jpg' )
__snake_case : Tuple = pipe(image=_UpperCAmelCase ,prompt='Black font, white background, vector' ,noise_level=40 ,callback=_UpperCAmelCase )
final_image.save('diffusers_library.jpg' )
if __name__ == "__main__":
main()
| 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
A__ : Union[str, Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ : List[Any] = {
'''vocab_file''': {
'''google/electra-small-generator''': (
'''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
),
'''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
'''google/electra-large-generator''': (
'''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
),
'''google/electra-small-discriminator''': (
'''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
),
'''google/electra-base-discriminator''': (
'''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
),
'''google/electra-large-discriminator''': (
'''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''google/electra-small-generator''': (
'''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
),
'''google/electra-base-generator''': (
'''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
),
'''google/electra-large-generator''': (
'''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
),
'''google/electra-small-discriminator''': (
'''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
),
'''google/electra-base-discriminator''': (
'''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
),
'''google/electra-large-discriminator''': (
'''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
),
},
}
A__ : List[Any] = {
'''google/electra-small-generator''': 5_1_2,
'''google/electra-base-generator''': 5_1_2,
'''google/electra-large-generator''': 5_1_2,
'''google/electra-small-discriminator''': 5_1_2,
'''google/electra-base-discriminator''': 5_1_2,
'''google/electra-large-discriminator''': 5_1_2,
}
A__ : Optional[Any] = {
'''google/electra-small-generator''': {'''do_lower_case''': True},
'''google/electra-base-generator''': {'''do_lower_case''': True},
'''google/electra-large-generator''': {'''do_lower_case''': True},
'''google/electra-small-discriminator''': {'''do_lower_case''': True},
'''google/electra-base-discriminator''': {'''do_lower_case''': True},
'''google/electra-large-discriminator''': {'''do_lower_case''': True},
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_INIT_CONFIGURATION
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = ElectraTokenizer
def __init__( self : int , __a : List[Any]=None , __a : int=None , __a : List[str]=True , __a : Any="[UNK]" , __a : Any="[SEP]" , __a : Union[str, Any]="[PAD]" , __a : Dict="[CLS]" , __a : List[Any]="[MASK]" , __a : str=True , __a : Optional[int]=None , **__a : Optional[int] , ) -> str:
'''simple docstring'''
super().__init__(
__a , tokenizer_file=__a , do_lower_case=__a , unk_token=__a , sep_token=__a , pad_token=__a , cls_token=__a , mask_token=__a , tokenize_chinese_chars=__a , strip_accents=__a , **__a , )
__snake_case : Tuple = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __a ) != do_lower_case
or normalizer_state.get('strip_accents' , __a ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __a ) != tokenize_chinese_chars
):
__snake_case : List[Any] = getattr(__a , normalizer_state.pop('type' ) )
__snake_case : str = do_lower_case
__snake_case : Optional[int] = strip_accents
__snake_case : Any = tokenize_chinese_chars
__snake_case : Union[str, Any] = normalizer_class(**__a )
__snake_case : Any = do_lower_case
def A_ ( self : Any , __a : List[str] , __a : Optional[Any]=None ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A_ ( self : List[Any] , __a : List[int] , __a : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
__snake_case : int = [self.sep_token_id]
__snake_case : List[Any] = [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 ) * [0] + len(token_ids_a + sep ) * [1]
def A_ ( self : Optional[int] , __a : str , __a : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
__snake_case : Tuple = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
| 0 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class snake_case__ ( unittest.TestCase ):
A__ = ViTImageProcessor if is_vision_available() else None
@property
def A_ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Any = (3, 32, 128)
__snake_case : Tuple = tempfile.mkdtemp()
# fmt: off
__snake_case : Optional[int] = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
# fmt: on
__snake_case : Optional[int] = dict(zip(__a , range(len(__a ) ) ) )
__snake_case : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__a ) + '\n' )
__snake_case : Union[str, Any] = {
'do_normalize': False,
'do_resize': True,
'image_processor_type': 'ViTImageProcessor',
'resample': 3,
'size': {'height': 32, 'width': 128},
}
__snake_case : Dict = os.path.join(self.tmpdirname , __a )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(__a , __a )
def A_ ( self : str , **__a : Any ) -> str:
'''simple docstring'''
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : Optional[int] , **__a : Any ) -> Union[str, Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : str ) -> Dict:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self : int ) -> Dict:
'''simple docstring'''
__snake_case : List[Any] = np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )
__snake_case : Dict = Image.fromarray(np.moveaxis(__a , 0 , -1 ) )
return image_input
def A_ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = self.get_tokenizer()
__snake_case : List[str] = self.get_image_processor()
__snake_case : Optional[int] = MgpstrProcessor(tokenizer=__a , image_processor=__a )
processor.save_pretrained(self.tmpdirname )
__snake_case : Union[str, Any] = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=__a )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , __a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : str = self.get_tokenizer()
__snake_case : Optional[Any] = self.get_image_processor()
__snake_case : Optional[int] = MgpstrProcessor(tokenizer=__a , image_processor=__a )
processor.save_pretrained(self.tmpdirname )
__snake_case : str = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__snake_case : str = self.get_image_processor(do_normalize=__a , padding_value=1.0 )
__snake_case : Any = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , __a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Optional[Any] = self.get_image_processor()
__snake_case : Optional[Any] = self.get_tokenizer()
__snake_case : Optional[Any] = MgpstrProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Optional[Any] = self.prepare_image_inputs()
__snake_case : Any = image_processor(__a , return_tensors='np' )
__snake_case : Optional[int] = processor(images=__a , return_tensors='np' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def A_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : List[Any] = self.get_tokenizer()
__snake_case : int = MgpstrProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Tuple = 'test'
__snake_case : Tuple = processor(text=__a )
__snake_case : Tuple = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A_ ( self : int ) -> Dict:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : Optional[int] = self.get_tokenizer()
__snake_case : Optional[Any] = MgpstrProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Dict = 'test'
__snake_case : Tuple = self.prepare_image_inputs()
__snake_case : Optional[Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , ['pixel_values', 'labels'] )
# test if it raises when no input is passed
with pytest.raises(__a ):
processor()
def A_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = self.get_image_processor()
__snake_case : Optional[Any] = self.get_tokenizer()
__snake_case : Tuple = MgpstrProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Any = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
__snake_case : List[Any] = processor.char_decode(__a )
__snake_case : Any = tokenizer.batch_decode(__a )
__snake_case : List[Any] = [seq.replace(' ' , '' ) for seq in decoded_tok]
self.assertListEqual(__a , __a )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : List[str] = self.get_image_processor()
__snake_case : Optional[Any] = self.get_tokenizer()
__snake_case : Union[str, Any] = MgpstrProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Optional[int] = None
__snake_case : List[Any] = self.prepare_image_inputs()
__snake_case : Optional[Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[Any] = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : Optional[int] = MgpstrProcessor(tokenizer=__a , image_processor=__a )
__snake_case : str = torch.randn(1 , 27 , 38 )
__snake_case : int = torch.randn(1 , 27 , 50257 )
__snake_case : Optional[int] = torch.randn(1 , 27 , 30522 )
__snake_case : Tuple = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds'] )
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> bool:
__snake_case : Union[str, Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 0 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
A__ : Tuple = pytest.mark.integration
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Dict = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(__a ) for x in np.arange(30 ).tolist()]} )
return dset
def A_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
__snake_case : Dict = dset.map(
lambda __a , __a : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=__a , keep_in_memory=__a )
__snake_case : List[Any] = dset.add_faiss_index('vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
__snake_case , __snake_case : str = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(__a , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
__snake_case : Dataset = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : Any = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
__snake_case : Union[str, Any] = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=__a )
__snake_case , __snake_case : str = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : str ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
__snake_case : Dict = np.zeros(5 , dtype=np.floataa )
__snake_case : List[str] = 1
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertRaises(__a , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
__snake_case : List[str] = np.eye(5 , dtype=np.floataa )[::-1]
__snake_case , __snake_case : Dict = index.search_batch(__a )
self.assertRaises(__a , index.search_batch , queries[0] )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : List[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , __a )
def A_ ( self : int ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
__snake_case : List[str] = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(__a ):
__snake_case : Dict = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def A_ ( self : str ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Tuple = faiss.IndexFlat(5 )
__snake_case : List[Any] = FaissIndex(custom_index=__a )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
import faiss
__snake_case : Optional[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
index.save(tmp_file.name )
__snake_case : List[Any] = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
__snake_case : List[Any] = np.zeros(5 , dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : int = index.search(__a )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def a_ ( _UpperCAmelCase : str ) -> Optional[int]:
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
__snake_case : Dict = 'index.faiss'
__snake_case : Any = f'''mock://{index_name}'''
index.save(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = FaissIndex.load(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = np.zeros(5 ,dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : Tuple = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : int = Elasticsearch()
__snake_case : Dict = {'acknowledged': True}
__snake_case : List[Any] = ElasticSearchIndex(es_client=__a )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
__snake_case : Optional[Any] = 'foo'
__snake_case : int = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
__snake_case : Dict = 'foo'
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : Optional[Any] = index.search(__a , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
__snake_case : List[Any] = ['foo', 'bar', 'foobar']
__snake_case : str = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : Any = index.search_batch(__a )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
# batched queries with timeout
__snake_case : Tuple = ['foo', 'bar', 'foobar']
__snake_case : List[Any] = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : int = index.search_batch(__a , request_timeout=30 )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
| 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
A__ : Tuple = pytest.mark.integration
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Dict = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(__a ) for x in np.arange(30 ).tolist()]} )
return dset
def A_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
__snake_case : Dict = dset.map(
lambda __a , __a : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=__a , keep_in_memory=__a )
__snake_case : List[Any] = dset.add_faiss_index('vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
__snake_case , __snake_case : str = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(__a , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
__snake_case : Dataset = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : Any = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
__snake_case : Union[str, Any] = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=__a )
__snake_case , __snake_case : str = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : str ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
__snake_case : Dict = np.zeros(5 , dtype=np.floataa )
__snake_case : List[str] = 1
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertRaises(__a , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
__snake_case : List[str] = np.eye(5 , dtype=np.floataa )[::-1]
__snake_case , __snake_case : Dict = index.search_batch(__a )
self.assertRaises(__a , index.search_batch , queries[0] )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : List[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , __a )
def A_ ( self : int ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
__snake_case : List[str] = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(__a ):
__snake_case : Dict = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def A_ ( self : str ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Tuple = faiss.IndexFlat(5 )
__snake_case : List[Any] = FaissIndex(custom_index=__a )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
import faiss
__snake_case : Optional[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
index.save(tmp_file.name )
__snake_case : List[Any] = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
__snake_case : List[Any] = np.zeros(5 , dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : int = index.search(__a )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def a_ ( _UpperCAmelCase : str ) -> Optional[int]:
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
__snake_case : Dict = 'index.faiss'
__snake_case : Any = f'''mock://{index_name}'''
index.save(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = FaissIndex.load(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = np.zeros(5 ,dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : Tuple = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : int = Elasticsearch()
__snake_case : Dict = {'acknowledged': True}
__snake_case : List[Any] = ElasticSearchIndex(es_client=__a )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
__snake_case : Optional[Any] = 'foo'
__snake_case : int = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
__snake_case : Dict = 'foo'
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : Optional[Any] = index.search(__a , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
__snake_case : List[Any] = ['foo', 'bar', 'foobar']
__snake_case : str = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : Any = index.search_batch(__a )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
# batched queries with timeout
__snake_case : Tuple = ['foo', 'bar', 'foobar']
__snake_case : List[Any] = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : int = index.search_batch(__a , request_timeout=30 )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
| 0 | 1 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __get__( self : Optional[Any] , __a : List[str] , __a : List[Any]=None ) -> Union[str, Any]:
'''simple docstring'''
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError('unreadable attribute' )
__snake_case : Tuple = '__cached_' + self.fget.__name__
__snake_case : int = getattr(__a , __a , __a )
if cached is None:
__snake_case : Optional[int] = self.fget(__a )
setattr(__a , __a , __a )
return cached
def a_ ( _UpperCAmelCase : int ) -> List[Any]:
__snake_case : int = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'''invalid truth value {val!r}''' )
def a_ ( _UpperCAmelCase : List[Any] ) -> str:
if is_torch_fx_proxy(_UpperCAmelCase ):
return True
if is_torch_available():
import torch
if isinstance(_UpperCAmelCase ,torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(_UpperCAmelCase ,tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(_UpperCAmelCase ,(jnp.ndarray, Tracer) ):
return True
return isinstance(_UpperCAmelCase ,np.ndarray )
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
return isinstance(_UpperCAmelCase ,np.ndarray )
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Optional[int]:
return _is_numpy(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : List[str] ) -> List[Any]:
import torch
return isinstance(_UpperCAmelCase ,torch.Tensor )
def a_ ( _UpperCAmelCase : List[Any] ) -> List[Any]:
return False if not is_torch_available() else _is_torch(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
import torch
return isinstance(_UpperCAmelCase ,torch.device )
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Dict:
return False if not is_torch_available() else _is_torch_device(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : List[Any] ) -> Any:
import torch
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
if hasattr(_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : int = getattr(_UpperCAmelCase ,_UpperCAmelCase )
else:
return False
return isinstance(_UpperCAmelCase ,torch.dtype )
def a_ ( _UpperCAmelCase : str ) -> Optional[int]:
return False if not is_torch_available() else _is_torch_dtype(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : str ) -> Tuple:
import tensorflow as tf
return isinstance(_UpperCAmelCase ,tf.Tensor )
def a_ ( _UpperCAmelCase : Tuple ) -> str:
return False if not is_tf_available() else _is_tensorflow(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : str ) -> Any:
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(_UpperCAmelCase ,'is_symbolic_tensor' ):
return tf.is_symbolic_tensor(_UpperCAmelCase )
return type(_UpperCAmelCase ) == tf.Tensor
def a_ ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
return False if not is_tf_available() else _is_tf_symbolic_tensor(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : str ) -> Optional[int]:
import jax.numpy as jnp # noqa: F811
return isinstance(_UpperCAmelCase ,jnp.ndarray )
def a_ ( _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
return False if not is_flax_available() else _is_jax(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : Optional[Any] ) -> List[Any]:
if isinstance(_UpperCAmelCase ,(dict, UserDict) ):
return {k: to_py_obj(_UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(_UpperCAmelCase ,(list, tuple) ):
return [to_py_obj(_UpperCAmelCase ) for o in obj]
elif is_tf_tensor(_UpperCAmelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(_UpperCAmelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(_UpperCAmelCase ):
return np.asarray(_UpperCAmelCase ).tolist()
elif isinstance(_UpperCAmelCase ,(np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def a_ ( _UpperCAmelCase : Any ) -> Any:
if isinstance(_UpperCAmelCase ,(dict, UserDict) ):
return {k: to_numpy(_UpperCAmelCase ) for k, v in obj.items()}
elif isinstance(_UpperCAmelCase ,(list, tuple) ):
return np.array(_UpperCAmelCase )
elif is_tf_tensor(_UpperCAmelCase ):
return obj.numpy()
elif is_torch_tensor(_UpperCAmelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(_UpperCAmelCase ):
return np.asarray(_UpperCAmelCase )
else:
return obj
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
__snake_case : Dict = fields(self )
# Safety and consistency checks
if not len(__a ):
raise ValueError(f'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(f'''{self.__class__.__name__} should not have more than one required field.''' )
__snake_case : Dict = getattr(self , class_fields[0].name )
__snake_case : Any = all(getattr(self , field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(__a ):
if isinstance(__a , __a ):
__snake_case : Optional[int] = first_field.items()
__snake_case : List[Any] = True
else:
try:
__snake_case : Optional[int] = iter(__a )
__snake_case : List[str] = True
except TypeError:
__snake_case : Optional[int] = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__a ):
if (
not isinstance(__a , (list, tuple) )
or not len(__a ) == 2
or not isinstance(element[0] , __a )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
__snake_case : Union[str, Any] = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self , element[0] , element[1] )
if element[1] is not None:
__snake_case : Optional[int] = element[1]
elif first_field is not None:
__snake_case : Optional[int] = first_field
else:
for field in class_fields:
__snake_case : Optional[Any] = getattr(self , field.name )
if v is not None:
__snake_case : List[str] = v
def __delitem__( self : List[str] , *__a : Dict , **__a : Union[str, Any] ) -> Tuple:
'''simple docstring'''
raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def A_ ( self : Optional[int] , *__a : List[Any] , **__a : Any ) -> Union[str, Any]:
'''simple docstring'''
raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def A_ ( self : Union[str, Any] , *__a : Dict , **__a : Any ) -> Dict:
'''simple docstring'''
raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def A_ ( self : Dict , *__a : str , **__a : str ) -> Dict:
'''simple docstring'''
raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : str , __a : Any ) -> int:
'''simple docstring'''
if isinstance(__a , __a ):
__snake_case : List[Any] = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : List[Any] , __a : Union[str, Any] , __a : List[Any] ) -> int:
'''simple docstring'''
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__a , __a )
super().__setattr__(__a , __a )
def __setitem__( self : List[Any] , __a : Optional[Any] , __a : Any ) -> int:
'''simple docstring'''
# Will raise a KeyException if needed
super().__setitem__(__a , __a )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__a , __a )
def A_ ( self : Dict ) -> Tuple[Any]:
'''simple docstring'''
return tuple(self[k] for k in self.keys() )
class snake_case__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
@classmethod
def A_ ( cls : List[str] , __a : List[Any] ) -> Dict:
'''simple docstring'''
raise ValueError(
f'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''longest'''
A__ = '''max_length'''
A__ = '''do_not_pad'''
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''pt'''
A__ = '''tf'''
A__ = '''np'''
A__ = '''jax'''
class snake_case__ :
def __init__( self : Union[str, Any] , __a : List[ContextManager] ) -> List[str]:
'''simple docstring'''
__snake_case : Tuple = context_managers
__snake_case : Optional[int] = ExitStack()
def __enter__( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
for context_manager in self.context_managers:
self.stack.enter_context(__a )
def __exit__( self : Tuple , *__a : Any , **__a : Any ) -> List[Any]:
'''simple docstring'''
self.stack.__exit__(*__a , **__a )
def a_ ( _UpperCAmelCase : Optional[Any] ) -> Dict:
__snake_case : Any = infer_framework(_UpperCAmelCase )
if framework == "tf":
__snake_case : Optional[int] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
__snake_case : Any = inspect.signature(model_class.forward ) # PyTorch models
else:
__snake_case : Optional[Any] = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def a_ ( _UpperCAmelCase : Any ) -> Dict:
__snake_case : str = model_class.__name__
__snake_case : str = infer_framework(_UpperCAmelCase )
if framework == "tf":
__snake_case : Optional[Any] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
__snake_case : List[str] = inspect.signature(model_class.forward ) # PyTorch models
else:
__snake_case : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def a_ ( _UpperCAmelCase : MutableMapping ,_UpperCAmelCase : str = "" ,_UpperCAmelCase : str = "." ) -> int:
def _flatten_dict(_UpperCAmelCase : Tuple ,_UpperCAmelCase : str="" ,_UpperCAmelCase : Dict="." ):
for k, v in d.items():
__snake_case : str = str(_UpperCAmelCase ) + delimiter + str(_UpperCAmelCase ) if parent_key else k
if v and isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
yield from flatten_dict(_UpperCAmelCase ,_UpperCAmelCase ,delimiter=_UpperCAmelCase ).items()
else:
yield key, v
return dict(_flatten_dict(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ) )
@contextmanager
def a_ ( _UpperCAmelCase : Dict ,_UpperCAmelCase : bool = False ) -> List[Any]:
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : Tuple=None ) -> Any:
if is_numpy_array(_UpperCAmelCase ):
return np.transpose(_UpperCAmelCase ,axes=_UpperCAmelCase )
elif is_torch_tensor(_UpperCAmelCase ):
return array.T if axes is None else array.permute(*_UpperCAmelCase )
elif is_tf_tensor(_UpperCAmelCase ):
import tensorflow as tf
return tf.transpose(_UpperCAmelCase ,perm=_UpperCAmelCase )
elif is_jax_tensor(_UpperCAmelCase ):
return jnp.transpose(_UpperCAmelCase ,axes=_UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for transpose: {type(_UpperCAmelCase )}.''' )
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : Any ) -> int:
if is_numpy_array(_UpperCAmelCase ):
return np.reshape(_UpperCAmelCase ,_UpperCAmelCase )
elif is_torch_tensor(_UpperCAmelCase ):
return array.reshape(*_UpperCAmelCase )
elif is_tf_tensor(_UpperCAmelCase ):
import tensorflow as tf
return tf.reshape(_UpperCAmelCase ,_UpperCAmelCase )
elif is_jax_tensor(_UpperCAmelCase ):
return jnp.reshape(_UpperCAmelCase ,_UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for reshape: {type(_UpperCAmelCase )}.''' )
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : int=None ) -> Optional[int]:
if is_numpy_array(_UpperCAmelCase ):
return np.squeeze(_UpperCAmelCase ,axis=_UpperCAmelCase )
elif is_torch_tensor(_UpperCAmelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=_UpperCAmelCase )
elif is_tf_tensor(_UpperCAmelCase ):
import tensorflow as tf
return tf.squeeze(_UpperCAmelCase ,axis=_UpperCAmelCase )
elif is_jax_tensor(_UpperCAmelCase ):
return jnp.squeeze(_UpperCAmelCase ,axis=_UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for squeeze: {type(_UpperCAmelCase )}.''' )
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : int ) -> Any:
if is_numpy_array(_UpperCAmelCase ):
return np.expand_dims(_UpperCAmelCase ,_UpperCAmelCase )
elif is_torch_tensor(_UpperCAmelCase ):
return array.unsqueeze(dim=_UpperCAmelCase )
elif is_tf_tensor(_UpperCAmelCase ):
import tensorflow as tf
return tf.expand_dims(_UpperCAmelCase ,axis=_UpperCAmelCase )
elif is_jax_tensor(_UpperCAmelCase ):
return jnp.expand_dims(_UpperCAmelCase ,axis=_UpperCAmelCase )
else:
raise ValueError(f'''Type not supported for expand_dims: {type(_UpperCAmelCase )}.''' )
def a_ ( _UpperCAmelCase : Optional[Any] ) -> Dict:
if is_numpy_array(_UpperCAmelCase ):
return np.size(_UpperCAmelCase )
elif is_torch_tensor(_UpperCAmelCase ):
return array.numel()
elif is_tf_tensor(_UpperCAmelCase ):
import tensorflow as tf
return tf.size(_UpperCAmelCase )
elif is_jax_tensor(_UpperCAmelCase ):
return array.size
else:
raise ValueError(f'''Type not supported for expand_dims: {type(_UpperCAmelCase )}.''' )
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Any ) -> Dict:
for key, value in auto_map.items():
if isinstance(_UpperCAmelCase ,(tuple, list) ):
__snake_case : Any = [f'''{repo_id}--{v}''' if (v is not None and '--' not in v) else v for v in value]
elif value is not None and "--" not in value:
__snake_case : List[Any] = f'''{repo_id}--{value}'''
return auto_map
def a_ ( _UpperCAmelCase : Optional[int] ) -> List[str]:
for base_class in inspect.getmro(_UpperCAmelCase ):
__snake_case : Optional[Any] = base_class.__module__
__snake_case : Optional[int] = base_class.__name__
if module.startswith('tensorflow' ) or module.startswith('keras' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('torch' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('flax' ) or module.startswith('jax' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'''Could not infer framework from class {model_class}.''' )
| 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A__ : List[Any] = logging.get_logger(__name__)
A__ : Tuple = {
'''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 snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''t5'''
A__ = ['''past_key_values''']
A__ = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : str , __a : Dict=32128 , __a : Dict=512 , __a : Union[str, Any]=64 , __a : str=2048 , __a : Union[str, Any]=6 , __a : Any=None , __a : Any=8 , __a : List[Any]=32 , __a : Any=128 , __a : Tuple=0.1 , __a : str=1e-6 , __a : Dict=1.0 , __a : Tuple="relu" , __a : Dict=True , __a : Union[str, Any]=True , __a : Any=0 , __a : Dict=1 , **__a : Union[str, Any] , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = vocab_size
__snake_case : str = d_model
__snake_case : str = d_kv
__snake_case : List[Any] = d_ff
__snake_case : List[str] = num_layers
__snake_case : Tuple = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__snake_case : Union[str, Any] = num_heads
__snake_case : Tuple = relative_attention_num_buckets
__snake_case : Optional[int] = relative_attention_max_distance
__snake_case : Optional[Any] = dropout_rate
__snake_case : str = layer_norm_epsilon
__snake_case : List[str] = initializer_factor
__snake_case : int = feed_forward_proj
__snake_case : Optional[Any] = use_cache
__snake_case : Optional[Any] = self.feed_forward_proj.split('-' )
__snake_case : Dict = act_info[-1]
__snake_case : List[str] = act_info[0] == 'gated'
if len(__a ) > 1 and act_info[0] != "gated" or len(__a ) > 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":
__snake_case : Dict = 'gelu_new'
super().__init__(
pad_token_id=__a , eos_token_id=__a , is_encoder_decoder=__a , **__a , )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@property
def A_ ( self : str ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__snake_case : Union[str, Any] = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
__snake_case : Tuple = 'past_encoder_sequence + sequence'
__snake_case : Dict = {0: 'batch'}
__snake_case : Dict = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
__snake_case : Tuple = {0: 'batch', 1: 'decoder_sequence'}
__snake_case : int = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(__a , direction='inputs' )
return common_inputs
@property
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
return 13
| 0 | 1 |
'''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
A__ : Optional[int] = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_ )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Dict , **__a : Dict ) -> List[str]:
'''simple docstring'''
super().__init__(**__a )
if self.framework == "tf":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
requires_backends(self , 'vision' )
self.check_model_type(__a )
def __call__( self : Dict , __a : Union[str, "Image.Image", List[Dict[str, Any]]] , __a : Union[str, List[str]] = None , **__a : Any , ) -> str:
'''simple docstring'''
if "text_queries" in kwargs:
__snake_case : Tuple = kwargs.pop('text_queries' )
if isinstance(__a , (str, Image.Image) ):
__snake_case : List[str] = {'image': image, 'candidate_labels': candidate_labels}
else:
__snake_case : Optional[int] = image
__snake_case : Union[str, Any] = super().__call__(__a , **__a )
return results
def A_ ( self : List[str] , **__a : int ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = {}
if "threshold" in kwargs:
__snake_case : int = kwargs['threshold']
if "top_k" in kwargs:
__snake_case : int = kwargs['top_k']
return {}, {}, postprocess_params
def A_ ( self : List[str] , __a : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = load_image(inputs['image'] )
__snake_case : Optional[int] = inputs['candidate_labels']
if isinstance(__a , __a ):
__snake_case : Dict = candidate_labels.split(',' )
__snake_case : Tuple = torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(__a ):
__snake_case : str = self.tokenizer(__a , return_tensors=self.framework )
__snake_case : Tuple = self.image_processor(__a , return_tensors=self.framework )
yield {
"is_last": i == len(__a ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def A_ ( self : Optional[Any] , __a : Optional[int] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Any = model_inputs.pop('target_size' )
__snake_case : Tuple = model_inputs.pop('candidate_label' )
__snake_case : List[Any] = model_inputs.pop('is_last' )
__snake_case : Any = self.model(**__a )
__snake_case : Optional[int] = {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
return model_outputs
def A_ ( self : Union[str, Any] , __a : Optional[Any] , __a : List[Any]=0.1 , __a : Dict=None ) -> Optional[Any]:
'''simple docstring'''
__snake_case : str = []
for model_output in model_outputs:
__snake_case : Optional[Any] = model_output['candidate_label']
__snake_case : List[str] = BaseModelOutput(__a )
__snake_case : Optional[int] = self.image_processor.post_process_object_detection(
outputs=__a , threshold=__a , target_sizes=model_output['target_size'] )[0]
for index in outputs["scores"].nonzero():
__snake_case : Dict = outputs['scores'][index].item()
__snake_case : List[Any] = self._get_bounding_box(outputs['boxes'][index][0] )
__snake_case : Any = {'score': score, 'label': label, 'box': box}
results.append(__a )
__snake_case : str = sorted(__a , key=lambda __a : x["score"] , reverse=__a )
if top_k:
__snake_case : Any = results[:top_k]
return results
def A_ ( self : Optional[int] , __a : "torch.Tensor" ) -> Dict[str, int]:
'''simple docstring'''
if self.framework != "pt":
raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.' )
__snake_case , __snake_case , __snake_case , __snake_case : Dict = box.int().tolist()
__snake_case : str = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : Optional[int] = {}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''llama'''
A__ = ['''past_key_values''']
def __init__( self : Any , __a : List[str]=32000 , __a : Union[str, Any]=4096 , __a : Optional[Any]=11008 , __a : Any=32 , __a : str=32 , __a : Optional[int]=None , __a : Dict="silu" , __a : Dict=2048 , __a : List[str]=0.0_2 , __a : Union[str, Any]=1e-6 , __a : Dict=True , __a : List[str]=0 , __a : Tuple=1 , __a : Tuple=2 , __a : Optional[Any]=1 , __a : Any=False , __a : Tuple=None , **__a : List[Any] , ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = vocab_size
__snake_case : List[str] = max_position_embeddings
__snake_case : List[Any] = hidden_size
__snake_case : Union[str, Any] = intermediate_size
__snake_case : Optional[int] = num_hidden_layers
__snake_case : List[Any] = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
__snake_case : Optional[int] = num_attention_heads
__snake_case : Optional[Any] = num_key_value_heads
__snake_case : int = hidden_act
__snake_case : Any = initializer_range
__snake_case : Any = rms_norm_eps
__snake_case : Union[str, Any] = pretraining_tp
__snake_case : Optional[int] = use_cache
__snake_case : Any = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , tie_word_embeddings=__a , **__a , )
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __a ) 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}''' )
__snake_case : Optional[Any] = self.rope_scaling.get('type' , __a )
__snake_case : Tuple = self.rope_scaling.get('factor' , __a )
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(__a , __a ) or rope_scaling_factor <= 1.0:
raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )
| 0 | 1 |
'''simple docstring'''
A__ : Optional[Any] = tuple[float, float, float]
A__ : str = tuple[float, float, float]
def a_ ( _UpperCAmelCase : Pointad ,_UpperCAmelCase : Pointad ) -> Vectorad:
__snake_case : List[Any] = end_pointa[0] - end_pointa[0]
__snake_case : str = end_pointa[1] - end_pointa[1]
__snake_case : Optional[int] = end_pointa[2] - end_pointa[2]
return (x, y, z)
def a_ ( _UpperCAmelCase : Vectorad ,_UpperCAmelCase : Vectorad ) -> Vectorad:
__snake_case : Dict = ab[1] * ac[2] - ab[2] * ac[1] # *i
__snake_case : int = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
__snake_case : Any = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def a_ ( _UpperCAmelCase : Vectorad ,_UpperCAmelCase : int ) -> bool:
return tuple(round(_UpperCAmelCase ,_UpperCAmelCase ) for x in vector ) == (0, 0, 0)
def a_ ( _UpperCAmelCase : Pointad ,_UpperCAmelCase : Pointad ,_UpperCAmelCase : Pointad ,_UpperCAmelCase : int = 10 ) -> bool:
__snake_case : Any = create_vector(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : List[Any] = create_vector(_UpperCAmelCase ,_UpperCAmelCase )
return is_zero_vector(get_ad_vectors_cross(_UpperCAmelCase ,_UpperCAmelCase ) ,_UpperCAmelCase )
| 0 |
'''simple docstring'''
from __future__ import annotations
A__ : str = '''Muhammad Umer Farooq'''
A__ : int = '''MIT'''
A__ : Optional[int] = '''1.0.0'''
A__ : List[Any] = '''Muhammad Umer Farooq'''
A__ : Optional[Any] = '''[email protected]'''
A__ : Optional[Any] = '''Alpha'''
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Union[str, Any] , __a : str ) -> None:
'''simple docstring'''
super().__init__()
__snake_case : list[str] = []
__snake_case : Dict = domain
def A_ ( self : Dict , __a : str , __a : list[tuple[str, str | None]] ) -> None:
'''simple docstring'''
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__snake_case : Optional[Any] = parse.urljoin(self.domain , __a )
self.urls.append(__a )
def a_ ( _UpperCAmelCase : str ) -> str:
return ".".join(get_sub_domain_name(_UpperCAmelCase ).split('.' )[-2:] )
def a_ ( _UpperCAmelCase : str ) -> str:
return parse.urlparse(_UpperCAmelCase ).netloc
def a_ ( _UpperCAmelCase : str = "https://github.com" ) -> list[str]:
__snake_case : List[Any] = get_domain_name(_UpperCAmelCase )
# Initialize the parser
__snake_case : Tuple = Parser(_UpperCAmelCase )
try:
# Open URL
__snake_case : Any = requests.get(_UpperCAmelCase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__snake_case : Dict = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__snake_case : List[Any] = requests.get(_UpperCAmelCase )
# Get the valid email.
__snake_case : Optional[Any] = re.findall('[a-zA-Z0-9]+@' + domain ,read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_UpperCAmelCase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Tuple = emails_from_url('''https://github.com''')
print(F"""{len(emails)} emails found:""")
print('''\n'''.join(sorted(emails)))
| 0 | 1 |
'''simple docstring'''
import math
import tensorflow as tf
from packaging import version
def a_ ( _UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
__snake_case : Optional[Any] = tf.convert_to_tensor(_UpperCAmelCase )
__snake_case : str = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) ,x.dtype ) ))
return x * cdf
def a_ ( _UpperCAmelCase : List[Any] ) -> Tuple:
__snake_case : Dict = tf.convert_to_tensor(_UpperCAmelCase )
__snake_case : List[Any] = tf.cast(math.pi ,x.dtype )
__snake_case : List[str] = tf.cast(0.0_4_4_7_1_5 ,x.dtype )
__snake_case : Optional[Any] = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(_UpperCAmelCase ,3 )) ))
return x * cdf
def a_ ( _UpperCAmelCase : Optional[int] ) -> Optional[Any]:
__snake_case : Any = tf.convert_to_tensor(_UpperCAmelCase )
return x * tf.tanh(tf.math.softplus(_UpperCAmelCase ) )
def a_ ( _UpperCAmelCase : str ) -> Dict:
__snake_case : Optional[Any] = tf.convert_to_tensor(_UpperCAmelCase )
__snake_case : List[Any] = tf.cast(0.0_4_4_7_1_5 ,x.dtype )
__snake_case : List[str] = tf.cast(0.7_9_7_8_8_4_5_6_0_8 ,x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def a_ ( _UpperCAmelCase : Dict ) -> Optional[int]:
__snake_case : List[Any] = tf.convert_to_tensor(_UpperCAmelCase )
__snake_case : str = tf.cast(1.7_0_2 ,x.dtype )
return x * tf.math.sigmoid(coeff * x )
def a_ ( _UpperCAmelCase : List[Any] ) -> Tuple:
return tf.clip_by_value(_gelu(_UpperCAmelCase ) ,-10 ,10 )
def a_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : str=-1 ) -> Dict:
__snake_case , __snake_case : Dict = tf.split(_UpperCAmelCase ,2 ,axis=_UpperCAmelCase )
return a * tf.math.sigmoid(_UpperCAmelCase )
if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
def a_ ( _UpperCAmelCase : int ) -> Dict:
return tf.keras.activations.gelu(_UpperCAmelCase ,approximate=_UpperCAmelCase )
A__ : List[Any] = tf.keras.activations.gelu
A__ : str = approximate_gelu_wrap
else:
A__ : List[str] = _gelu
A__ : List[str] = _gelu_new
A__ : str = {
'''gelu''': gelu,
'''gelu_10''': gelu_aa,
'''gelu_fast''': gelu_fast,
'''gelu_new''': gelu_new,
'''glu''': glu,
'''mish''': mish,
'''quick_gelu''': quick_gelu,
'''relu''': tf.keras.activations.relu,
'''sigmoid''': tf.keras.activations.sigmoid,
'''silu''': tf.keras.activations.swish,
'''swish''': tf.keras.activations.swish,
'''tanh''': tf.keras.activations.tanh,
}
def a_ ( _UpperCAmelCase : int ) -> int:
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}''' )
| 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
A__ : Dict = logging.getLogger()
def a_ ( ) -> Tuple:
__snake_case : List[Any] = argparse.ArgumentParser()
parser.add_argument('-f' )
__snake_case : Any = parser.parse_args()
return args.f
def a_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
__snake_case : Tuple = {}
__snake_case : Union[str, Any] = os.path.join(_UpperCAmelCase ,'all_results.json' )
if os.path.exists(_UpperCAmelCase ):
with open(_UpperCAmelCase ,'r' ) as f:
__snake_case : List[str] = json.load(_UpperCAmelCase )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
def a_ ( ) -> Union[str, Any]:
__snake_case : Union[str, Any] = torch.cuda.is_available() and torch_device == 'cuda'
return is_using_cuda and is_apex_available()
A__ : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@classmethod
def A_ ( cls : Any ) -> List[str]:
'''simple docstring'''
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__snake_case : Optional[int] = tempfile.mkdtemp()
__snake_case : Dict = os.path.join(cls.tmpdir , 'default_config.yml' )
write_basic_config(save_location=cls.configPath )
__snake_case : List[Any] = ['accelerate', 'launch', '--config_file', cls.configPath]
@classmethod
def A_ ( cls : List[str] ) -> List[str]:
'''simple docstring'''
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : List[Any] = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'glue_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
'''.split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertLess(result['perplexity'] , 100 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'clm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : int = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertLess(result['perplexity'] , 42 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'mlm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__snake_case : Any = 7 if get_gpu_count() > 1 else 2
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertLess(result['train_loss'] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'ner_no_trainer' ) ) )
@unittest.skip(reason='Fix me @muellerzr' )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : Tuple = f'''
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['eval_f1'] , 28 )
self.assertGreaterEqual(result['eval_exact'] , 28 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'qa_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : str = self.get_auto_remove_tmp_dir()
__snake_case : Any = f'''
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__a , 'swag_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : int = get_results(__a )
self.assertGreaterEqual(result['eval_rouge1'] , 10 )
self.assertGreaterEqual(result['eval_rouge2'] , 2 )
self.assertGreaterEqual(result['eval_rougeL'] , 7 )
self.assertGreaterEqual(result['eval_rougeLsum'] , 7 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'summarization_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_bleu'] , 30 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'translation_no_trainer' ) ) )
@slow
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = logging.StreamHandler(sys.stdout )
logger.addHandler(__a )
__snake_case : List[str] = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertGreaterEqual(result['eval_overall_accuracy'] , 0.1_0 )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Dict = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : Optional[int] = get_results(__a )
# The base model scores a 25%
self.assertGreaterEqual(result['eval_accuracy'] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__a , 'step_1' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'image_classification_no_trainer' ) ) )
| 0 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
A__ : List[Any] = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : List[Any] = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : List[Any] = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[Any] = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
A__ : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 |
'''simple docstring'''
import math
def a_ ( _UpperCAmelCase : int ) -> list:
__snake_case : Optional[Any] = [True] * n
__snake_case : Optional[int] = False
__snake_case : Dict = False
__snake_case : List[Any] = True
for i in range(3 ,int(n**0.5 + 1 ) ,2 ):
__snake_case : Optional[int] = i * 2
while index < n:
__snake_case : Union[str, Any] = False
__snake_case : int = index + i
__snake_case : Dict = [2]
for i in range(3 ,_UpperCAmelCase ,2 ):
if is_prime[i]:
primes.append(_UpperCAmelCase )
return primes
def a_ ( _UpperCAmelCase : int = 99_99_66_66_33_33 ) -> int:
__snake_case : List[Any] = math.floor(math.sqrt(_UpperCAmelCase ) ) + 1_00
__snake_case : Tuple = prime_sieve(_UpperCAmelCase )
__snake_case : List[Any] = 0
__snake_case : List[Any] = 0
__snake_case : Optional[int] = primes[prime_index]
while (last_prime**2) <= limit:
__snake_case : Optional[int] = primes[prime_index + 1]
__snake_case : Union[str, Any] = last_prime**2
__snake_case : Dict = next_prime**2
# Get numbers divisible by lps(current)
__snake_case : Optional[Any] = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
__snake_case : Optional[Any] = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
__snake_case : List[str] = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
__snake_case : Dict = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 0 | 1 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
A__ : Union[str, Any] = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def a_ ( ) -> Optional[int]:
__snake_case : List[str] = Github(os.environ['GITHUB_TOKEN'] )
__snake_case : Any = g.get_repo('huggingface/accelerate' )
__snake_case : List[Any] = repo.get_issues(state='open' )
for issue in open_issues:
__snake_case : Tuple = sorted([comment for comment in issue.get_comments()] ,key=lambda _UpperCAmelCase : i.created_at ,reverse=_UpperCAmelCase )
__snake_case : Tuple = comments[0] if len(_UpperCAmelCase ) > 0 else None
__snake_case : Dict = dt.utcnow()
__snake_case : Dict = (current_time - issue.updated_at).days
__snake_case : str = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state='closed' )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main()
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : float ,_UpperCAmelCase : float ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"""{price_plus_tax(1_0_0, 0.25) = }""")
print(F"""{price_plus_tax(1_25.50, 0.05) = }""")
| 0 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
A__ : Tuple = logging.get_logger(__name__)
def a_ ( _UpperCAmelCase : str ) -> Any:
__snake_case : str = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
__snake_case : str = 1_28
elif "12-12" in model_name:
__snake_case : str = 12
__snake_case : List[str] = 12
elif "14-14" in model_name:
__snake_case : Union[str, Any] = 14
__snake_case : Dict = 14
elif "16-16" in model_name:
__snake_case : int = 16
__snake_case : List[Any] = 16
else:
raise ValueError('Model not supported' )
__snake_case : Optional[Any] = 'huggingface/label-files'
if "speech-commands" in model_name:
__snake_case : Optional[int] = 35
__snake_case : Union[str, Any] = 'speech-commands-v2-id2label.json'
else:
__snake_case : Union[str, Any] = 5_27
__snake_case : Tuple = 'audioset-id2label.json'
__snake_case : Optional[int] = json.load(open(hf_hub_download(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset' ) ,'r' ) )
__snake_case : int = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__snake_case : Tuple = idalabel
__snake_case : int = {v: k for k, v in idalabel.items()}
return config
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> List[str]:
if "module.v" in name:
__snake_case : str = name.replace('module.v' ,'audio_spectrogram_transformer' )
if "cls_token" in name:
__snake_case : List[Any] = name.replace('cls_token' ,'embeddings.cls_token' )
if "dist_token" in name:
__snake_case : List[Any] = name.replace('dist_token' ,'embeddings.distillation_token' )
if "pos_embed" in name:
__snake_case : Optional[int] = name.replace('pos_embed' ,'embeddings.position_embeddings' )
if "patch_embed.proj" in name:
__snake_case : List[Any] = name.replace('patch_embed.proj' ,'embeddings.patch_embeddings.projection' )
# transformer blocks
if "blocks" in name:
__snake_case : Dict = name.replace('blocks' ,'encoder.layer' )
if "attn.proj" in name:
__snake_case : Tuple = name.replace('attn.proj' ,'attention.output.dense' )
if "attn" in name:
__snake_case : Dict = name.replace('attn' ,'attention.self' )
if "norm1" in name:
__snake_case : Dict = name.replace('norm1' ,'layernorm_before' )
if "norm2" in name:
__snake_case : List[str] = name.replace('norm2' ,'layernorm_after' )
if "mlp.fc1" in name:
__snake_case : Union[str, Any] = name.replace('mlp.fc1' ,'intermediate.dense' )
if "mlp.fc2" in name:
__snake_case : Optional[int] = name.replace('mlp.fc2' ,'output.dense' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
__snake_case : int = name.replace('audio_spectrogram_transformer.norm' ,'audio_spectrogram_transformer.layernorm' )
# classifier head
if "module.mlp_head.0" in name:
__snake_case : Union[str, Any] = name.replace('module.mlp_head.0' ,'classifier.layernorm' )
if "module.mlp_head.1" in name:
__snake_case : Tuple = name.replace('module.mlp_head.1' ,'classifier.dense' )
return name
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Any ) -> int:
for key in orig_state_dict.copy().keys():
__snake_case : int = orig_state_dict.pop(_UpperCAmelCase )
if "qkv" in key:
__snake_case : Union[str, Any] = key.split('.' )
__snake_case : int = int(key_split[3] )
__snake_case : Union[str, Any] = config.hidden_size
if "weight" in key:
__snake_case : Optional[Any] = val[:dim, :]
__snake_case : Optional[int] = val[dim : dim * 2, :]
__snake_case : Any = val[-dim:, :]
else:
__snake_case : int = val[:dim]
__snake_case : Optional[Any] = val[dim : dim * 2]
__snake_case : str = val[-dim:]
else:
__snake_case : int = val
return orig_state_dict
def a_ ( _UpperCAmelCase : Dict ) -> Optional[int]:
__snake_case : List[Any] = [
'module.v.head.weight',
'module.v.head.bias',
'module.v.head_dist.weight',
'module.v.head_dist.bias',
]
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase ,_UpperCAmelCase )
@torch.no_grad()
def a_ ( _UpperCAmelCase : Any ,_UpperCAmelCase : Any ,_UpperCAmelCase : Dict=False ) -> List[str]:
__snake_case : Dict = get_audio_spectrogram_transformer_config(_UpperCAmelCase )
__snake_case : Union[str, Any] = {
'ast-finetuned-audioset-10-10-0.4593': (
'https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.450': (
'https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.448': (
'https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.448-v2': (
'https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'
),
'ast-finetuned-audioset-12-12-0.447': (
'https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'
),
'ast-finetuned-audioset-14-14-0.443': (
'https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'
),
'ast-finetuned-audioset-16-16-0.442': (
'https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'
),
'ast-finetuned-speech-commands-v2': (
'https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'
),
}
# load original state_dict
__snake_case : Any = model_name_to_url[model_name]
__snake_case : Dict = torch.hub.load_state_dict_from_url(_UpperCAmelCase ,map_location='cpu' )
# remove some keys
remove_keys(_UpperCAmelCase )
# rename some keys
__snake_case : List[Any] = convert_state_dict(_UpperCAmelCase ,_UpperCAmelCase )
# load 🤗 model
__snake_case : Any = ASTForAudioClassification(_UpperCAmelCase )
model.eval()
model.load_state_dict(_UpperCAmelCase )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
__snake_case : Dict = -4.2_6_7_7_3_9_3 if 'speech-commands' not in model_name else -6.8_4_5_9_7_8
__snake_case : Dict = 4.5_6_8_9_9_7_4 if 'speech-commands' not in model_name else 5.5_6_5_4_5_2_6
__snake_case : Tuple = 10_24 if 'speech-commands' not in model_name else 1_28
__snake_case : Tuple = ASTFeatureExtractor(mean=_UpperCAmelCase ,std=_UpperCAmelCase ,max_length=_UpperCAmelCase )
if "speech-commands" in model_name:
__snake_case : int = load_dataset('speech_commands' ,'v0.02' ,split='validation' )
__snake_case : Optional[int] = dataset[0]['audio']['array']
else:
__snake_case : List[str] = hf_hub_download(
repo_id='nielsr/audio-spectogram-transformer-checkpoint' ,filename='sample_audio.flac' ,repo_type='dataset' ,)
__snake_case , __snake_case : Optional[int] = torchaudio.load(_UpperCAmelCase )
__snake_case : Union[str, Any] = waveform.squeeze().numpy()
__snake_case : Optional[Any] = feature_extractor(_UpperCAmelCase ,sampling_rate=1_60_00 ,return_tensors='pt' )
# forward pass
__snake_case : Any = model(**_UpperCAmelCase )
__snake_case : str = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
__snake_case : Tuple = torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
__snake_case : Union[str, Any] = torch.tensor([-1.1_9_8_6, -7.0_9_0_3, -8.2_7_1_8] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
__snake_case : Dict = torch.tensor([-2.6_1_2_8, -8.0_0_8_0, -9.4_3_4_4] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
__snake_case : Optional[Any] = torch.tensor([-1.5_0_8_0, -7.4_5_3_4, -8.8_9_1_7] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
__snake_case : List[Any] = torch.tensor([-0.5_0_5_0, -6.5_8_3_3, -8.0_8_4_3] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
__snake_case : Optional[int] = torch.tensor([-0.3_8_2_6, -7.0_3_3_6, -8.2_4_1_3] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
__snake_case : List[Any] = torch.tensor([-1.2_1_1_3, -6.9_1_0_1, -8.3_4_7_0] )
elif model_name == "ast-finetuned-speech-commands-v2":
__snake_case : Dict = torch.tensor([6.1_5_8_9, -8.0_5_6_6, -8.7_9_8_4] )
else:
raise ValueError('Unknown model name' )
if not torch.allclose(logits[0, :3] ,_UpperCAmelCase ,atol=1E-4 ):
raise ValueError('Logits don\'t match' )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_UpperCAmelCase )
print(f'''Saving feature extractor to {pytorch_dump_folder_path}''' )
feature_extractor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
print('Pushing model and feature extractor to the hub...' )
model.push_to_hub(f'''MIT/{model_name}''' )
feature_extractor.push_to_hub(f'''MIT/{model_name}''' )
if __name__ == "__main__":
A__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''ast-finetuned-audioset-10-10-0.4593''',
type=str,
help='''Name of the Audio Spectrogram Transformer model 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 or not to push the converted model to the 🤗 hub.'''
)
A__ : Dict = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 0 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
__snake_case : Optional[int] = SMALL_MODEL_IDENTIFIER
__snake_case : str = 'pt'
__snake_case : Union[str, Any] = 'tf'
def A_ ( self : Dict , __a : Tuple ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(__a )
def A_ ( self : Any , __a : Optional[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Union[str, Any] = TFAutoModel.from_pretrained(self.test_model , from_pt=__a )
model_tf.save_pretrained(__a )
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = 'mock_framework'
# Framework provided - return whatever the user provides
__snake_case : int = FeaturesManager.determine_framework(self.test_model , __a )
self.assertEqual(__a , __a )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : List[Any] = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Union[str, Any] = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(__a ):
__snake_case : Optional[int] = FeaturesManager.determine_framework(__a )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ):
__snake_case : int = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_tf )
# Both in environment -> use PyTorch
__snake_case : Optional[Any] = MagicMock(return_value=__a )
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# Both not in environment -> raise error
__snake_case : str = MagicMock(return_value=__a )
__snake_case : List[Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
with self.assertRaises(__a ):
__snake_case : Tuple = FeaturesManager.determine_framework(self.test_model )
| 0 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class snake_case__ ( unittest.TestCase ):
def __init__( self : Optional[Any] , __a : str , __a : int=13 , __a : List[str]=3 , __a : int=224 , __a : str=30 , __a : Tuple=400 , __a : int=True , __a : Any=None , __a : List[str]=True , __a : Optional[Any]=[0.5, 0.5, 0.5] , __a : List[Any]=[0.5, 0.5, 0.5] , ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = size if size is not None else {'height': 18, 'width': 18}
__snake_case : List[Any] = parent
__snake_case : Optional[Any] = batch_size
__snake_case : str = num_channels
__snake_case : List[Any] = image_size
__snake_case : Optional[Any] = min_resolution
__snake_case : Optional[Any] = max_resolution
__snake_case : Dict = do_resize
__snake_case : Union[str, Any] = size
__snake_case : List[Any] = do_normalize
__snake_case : List[str] = image_mean
__snake_case : Any = image_std
def A_ ( self : Optional[int] ) -> str:
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ViTImageProcessor if is_vision_available() else None
def A_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
__snake_case : Optional[Any] = EfficientFormerImageProcessorTester(self )
@property
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
return self.image_proc_tester.prepare_image_processor_dict()
def A_ ( self : Tuple ) -> int:
'''simple docstring'''
__snake_case : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__a , 'image_mean' ) )
self.assertTrue(hasattr(__a , 'image_std' ) )
self.assertTrue(hasattr(__a , 'do_normalize' ) )
self.assertTrue(hasattr(__a , 'do_resize' ) )
self.assertTrue(hasattr(__a , 'size' ) )
def A_ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
pass
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
# Initialize image_processor
__snake_case : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__snake_case : Union[str, Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=__a )
for image in image_inputs:
self.assertIsInstance(__a , Image.Image )
# Test not batched input
__snake_case : int = image_processor(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
# Test batched
__snake_case : int = image_processor(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
# Initialize image_processor
__snake_case : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__snake_case : List[str] = prepare_image_inputs(self.image_proc_tester , equal_resolution=__a , numpify=__a )
for image in image_inputs:
self.assertIsInstance(__a , np.ndarray )
# Test not batched input
__snake_case : Optional[Any] = image_processor(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
# Test batched
__snake_case : Union[str, Any] = image_processor(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
def A_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
# Initialize image_processor
__snake_case : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__snake_case : Any = prepare_image_inputs(self.image_proc_tester , equal_resolution=__a , torchify=__a )
for image in image_inputs:
self.assertIsInstance(__a , torch.Tensor )
# Test not batched input
__snake_case : List[str] = image_processor(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
# Test batched
__snake_case : str = image_processor(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
| 0 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ProphetNetTokenizer
A__ = False
def A_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
super().setUp()
__snake_case : Dict = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__snake_case : Any = 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 A_ ( self : int , __a : Union[str, Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[int] = 'UNwant\u00E9d,running'
__snake_case : List[str] = 'unwanted, running'
return input_text, output_text
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Dict = self.tokenizer_class(self.vocab_file )
__snake_case : List[str] = 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 A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : List[str] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Optional[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 A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[Any] = 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 A_ ( self : int ) -> Any:
'''simple docstring'''
__snake_case : int = 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 A_ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = 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 A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
__snake_case : str = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[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 A_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__snake_case : List[Any] = {}
for i, token in enumerate(__a ):
__snake_case : List[str] = i
__snake_case : 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'] )
@require_torch
def A_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[Any] = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : int = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__snake_case : str = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
__snake_case : Union[str, Any] = tokenizer(__a , padding=__a , return_tensors='pt' )
self.assertIsInstance(__a , __a )
__snake_case : int = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__a , __a )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
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 A_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
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 A_ ( self : List[Any] ) -> int:
'''simple docstring'''
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(' ' ) )
@slow
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : Optional[int] = tokenizer.encode('sequence builders' , add_special_tokens=__a )
__snake_case : Optional[int] = tokenizer.encode('multi-sequence build' , add_special_tokens=__a )
__snake_case : Optional[Any] = tokenizer.build_inputs_with_special_tokens(__a )
__snake_case : List[Any] = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 0 | 1 |
'''simple docstring'''
import math
def a_ ( _UpperCAmelCase : int ) -> list:
__snake_case : Optional[Any] = [True] * n
__snake_case : Optional[int] = False
__snake_case : Dict = False
__snake_case : List[Any] = True
for i in range(3 ,int(n**0.5 + 1 ) ,2 ):
__snake_case : Optional[int] = i * 2
while index < n:
__snake_case : Union[str, Any] = False
__snake_case : int = index + i
__snake_case : Dict = [2]
for i in range(3 ,_UpperCAmelCase ,2 ):
if is_prime[i]:
primes.append(_UpperCAmelCase )
return primes
def a_ ( _UpperCAmelCase : int = 99_99_66_66_33_33 ) -> int:
__snake_case : List[Any] = math.floor(math.sqrt(_UpperCAmelCase ) ) + 1_00
__snake_case : Tuple = prime_sieve(_UpperCAmelCase )
__snake_case : List[Any] = 0
__snake_case : List[Any] = 0
__snake_case : Optional[int] = primes[prime_index]
while (last_prime**2) <= limit:
__snake_case : Optional[int] = primes[prime_index + 1]
__snake_case : Union[str, Any] = last_prime**2
__snake_case : Dict = next_prime**2
# Get numbers divisible by lps(current)
__snake_case : Optional[Any] = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
__snake_case : Optional[Any] = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
__snake_case : List[str] = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
__snake_case : Dict = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NllbMoeForConditionalGeneration''',
'''NllbMoeModel''',
'''NllbMoePreTrainedModel''',
'''NllbMoeTop2Router''',
'''NllbMoeSparseMLP''',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
A__ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a_ ( _UpperCAmelCase : List[Any] ) -> Tuple:
__snake_case : str = []
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
) )
return embed
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Optional[int] ) -> List[str]:
__snake_case : Tuple = []
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
) )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
return attention_weights
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Dict:
__snake_case : Union[str, Any] = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token') )
return token
def a_ ( ) -> Optional[Any]:
__snake_case : Any = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ) -> Tuple:
__snake_case : List[str] = 'imagenet-1k-id2label.json'
__snake_case : Dict = 10_00
__snake_case : Union[str, Any] = 'huggingface/label-files'
__snake_case : str = num_labels
__snake_case : str = json.load(open(cached_download(hf_hub_url(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset' ) ) ,'r' ) )
__snake_case : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__snake_case : Optional[Any] = idalabel
__snake_case : str = {v: k for k, v in idalabel.items()}
__snake_case : Dict = CvtConfig(num_labels=_UpperCAmelCase ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' ,1 )[-1][4:6] == "13":
__snake_case : Tuple = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' ,1 )[-1][4:6] == "21":
__snake_case : str = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__snake_case : Dict = [2, 2, 20]
__snake_case : Any = [3, 12, 16]
__snake_case : Tuple = [1_92, 7_68, 10_24]
__snake_case : str = CvtForImageClassification(_UpperCAmelCase )
__snake_case : List[Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
__snake_case : int = image_size
__snake_case : int = torch.load(_UpperCAmelCase ,map_location=torch.device('cpu' ) )
__snake_case : List[Any] = OrderedDict()
__snake_case : Union[str, Any] = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__snake_case : Optional[Any] = list_of_state_dict + cls_token(_UpperCAmelCase )
__snake_case : Tuple = list_of_state_dict + embeddings(_UpperCAmelCase )
for cnt in range(config.depth[idx] ):
__snake_case : Optional[int] = list_of_state_dict + attention(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
__snake_case : List[str] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
A__ : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=3_8_4,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
A__ : Tuple = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
__snake_case : Optional[Any] = gray_code_sequence_string(_UpperCAmelCase )
#
# convert them to integers
for i in range(len(_UpperCAmelCase ) ):
__snake_case : Optional[Any] = int(sequence[i] ,2 )
return sequence
def a_ ( _UpperCAmelCase : int ) -> list:
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__snake_case : Dict = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__snake_case : Dict = gray_code_sequence_string(bit_count - 1 )
__snake_case : Any = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__snake_case : str = '0' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__snake_case : Any = '1' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int = 10_00 ) -> int:
__snake_case : Tuple = 2**power
__snake_case : List[str] = str(_UpperCAmelCase )
__snake_case : Union[str, Any] = list(_UpperCAmelCase )
__snake_case : Optional[Any] = 0
for i in list_num:
sum_of_num += int(_UpperCAmelCase )
return sum_of_num
if __name__ == "__main__":
A__ : List[Any] = int(input('''Enter the power of 2: ''').strip())
print('''2 ^ ''', power, ''' = ''', 2**power)
A__ : Dict = solution(power)
print('''Sum of the digits is: ''', result)
| 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case__ ( unittest.TestCase ):
def A_ ( self : int ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = tempfile.mkdtemp()
# fmt: off
__snake_case : List[str] = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
# fmt: on
__snake_case : Any = 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] ) )
__snake_case : List[str] = {
'do_resize': True,
'size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.5, 0.5, 0.5],
'image_std': [0.5, 0.5, 0.5],
}
__snake_case : Optional[Any] = os.path.join(self.tmpdirname , __a )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(__a , __a )
def A_ ( self : Optional[int] , **__a : Dict ) -> int:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : int , **__a : Dict ) -> Tuple:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__snake_case : List[str] = [Image.fromarray(np.moveaxis(__a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : Dict = self.get_image_processor()
__snake_case : Any = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
processor.save_pretrained(self.tmpdirname )
__snake_case : Any = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[Any] = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__snake_case : Optional[Any] = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__snake_case : Tuple = self.get_image_processor(do_normalize=__a , padding_value=1.0 )
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : str = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = self.prepare_image_inputs()
__snake_case : List[str] = image_processor(__a , return_tensors='np' )
__snake_case : List[str] = processor(images=__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 A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Optional[int] = 'lower newer'
__snake_case : Dict = processor(text=__a )
__snake_case : List[Any] = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : int = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : List[Any] = 'lower newer'
__snake_case : Optional[Any] = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with self.assertRaises(__a ):
processor()
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_image_processor()
__snake_case : Any = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__snake_case : int = processor.batch_decode(__a )
__snake_case : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def A_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[str] = self.get_image_processor()
__snake_case : Dict = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Union[str, Any] = 'lower newer'
__snake_case : Tuple = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 0 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A__ : Union[str, Any] = logging.get_logger(__name__)
A__ : int = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''van'''
def __init__( self : Any , __a : int=224 , __a : Union[str, Any]=3 , __a : Union[str, Any]=[7, 3, 3, 3] , __a : Any=[4, 2, 2, 2] , __a : Union[str, Any]=[64, 128, 320, 512] , __a : List[str]=[3, 3, 12, 3] , __a : Optional[int]=[8, 8, 4, 4] , __a : Dict="gelu" , __a : Dict=0.0_2 , __a : Union[str, Any]=1e-6 , __a : Union[str, Any]=1e-2 , __a : List[str]=0.0 , __a : Optional[Any]=0.0 , **__a : List[str] , ) -> str:
'''simple docstring'''
super().__init__(**__a )
__snake_case : str = image_size
__snake_case : str = num_channels
__snake_case : Optional[Any] = patch_sizes
__snake_case : Dict = strides
__snake_case : Tuple = hidden_sizes
__snake_case : List[str] = depths
__snake_case : List[Any] = mlp_ratios
__snake_case : Tuple = hidden_act
__snake_case : Union[str, Any] = initializer_range
__snake_case : str = layer_norm_eps
__snake_case : List[str] = layer_scale_init_value
__snake_case : Optional[int] = drop_path_rate
__snake_case : Union[str, Any] = dropout_rate
| 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a_ ( _UpperCAmelCase : List[Any] ) -> Tuple:
__snake_case : str = []
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
) )
return embed
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Optional[int] ) -> List[str]:
__snake_case : Tuple = []
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
) )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
return attention_weights
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Dict:
__snake_case : Union[str, Any] = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token') )
return token
def a_ ( ) -> Optional[Any]:
__snake_case : Any = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ) -> Tuple:
__snake_case : List[str] = 'imagenet-1k-id2label.json'
__snake_case : Dict = 10_00
__snake_case : Union[str, Any] = 'huggingface/label-files'
__snake_case : str = num_labels
__snake_case : str = json.load(open(cached_download(hf_hub_url(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset' ) ) ,'r' ) )
__snake_case : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__snake_case : Optional[Any] = idalabel
__snake_case : str = {v: k for k, v in idalabel.items()}
__snake_case : Dict = CvtConfig(num_labels=_UpperCAmelCase ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' ,1 )[-1][4:6] == "13":
__snake_case : Tuple = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' ,1 )[-1][4:6] == "21":
__snake_case : str = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__snake_case : Dict = [2, 2, 20]
__snake_case : Any = [3, 12, 16]
__snake_case : Tuple = [1_92, 7_68, 10_24]
__snake_case : str = CvtForImageClassification(_UpperCAmelCase )
__snake_case : List[Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
__snake_case : int = image_size
__snake_case : int = torch.load(_UpperCAmelCase ,map_location=torch.device('cpu' ) )
__snake_case : List[Any] = OrderedDict()
__snake_case : Union[str, Any] = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__snake_case : Optional[Any] = list_of_state_dict + cls_token(_UpperCAmelCase )
__snake_case : Tuple = list_of_state_dict + embeddings(_UpperCAmelCase )
for cnt in range(config.depth[idx] ):
__snake_case : Optional[int] = list_of_state_dict + attention(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
__snake_case : List[str] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
A__ : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=3_8_4,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
A__ : Tuple = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 0 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def a_ ( _UpperCAmelCase : float ,_UpperCAmelCase : float ,_UpperCAmelCase : float ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance == 0:
return {"resistance": sqrt(pow(_UpperCAmelCase ,2 ) - pow(_UpperCAmelCase ,2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(_UpperCAmelCase ,2 ) - pow(_UpperCAmelCase ,2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(_UpperCAmelCase ,2 ) + pow(_UpperCAmelCase ,2 ) )}
else:
raise ValueError('Exactly one argument must be 0' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 |
'''simple docstring'''
from __future__ import annotations
A__ : List[Any] = list[list[int]]
# assigning initial values to the grid
A__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
A__ : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def a_ ( _UpperCAmelCase : Matrix ,_UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def a_ ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def a_ ( _UpperCAmelCase : Matrix ) -> Matrix | None:
if location := find_empty_location(_UpperCAmelCase ):
__snake_case , __snake_case : Optional[int] = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 ,10 ):
if is_safe(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : Union[str, Any] = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
__snake_case : Optional[Any] = 0
return None
def a_ ( _UpperCAmelCase : Matrix ) -> None:
for row in grid:
for cell in row:
print(_UpperCAmelCase ,end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('''\nExample grid:\n''' + '''=''' * 2_0)
print_solution(example_grid)
print('''\nExample grid solution:''')
A__ : List[str] = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('''Cannot find a solution.''')
| 0 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
A__ : Union[str, Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : tuple ,_UpperCAmelCase : Path ,_UpperCAmelCase : Any ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Dict ,_UpperCAmelCase : str=False ,) -> Dict:
output_path.parent.mkdir(parents=_UpperCAmelCase ,exist_ok=_UpperCAmelCase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
_UpperCAmelCase ,_UpperCAmelCase ,f=output_path.as_posix() ,input_names=_UpperCAmelCase ,output_names=_UpperCAmelCase ,dynamic_axes=_UpperCAmelCase ,do_constant_folding=_UpperCAmelCase ,use_external_data_format=_UpperCAmelCase ,enable_onnx_checker=_UpperCAmelCase ,opset_version=_UpperCAmelCase ,)
else:
export(
_UpperCAmelCase ,_UpperCAmelCase ,f=output_path.as_posix() ,input_names=_UpperCAmelCase ,output_names=_UpperCAmelCase ,dynamic_axes=_UpperCAmelCase ,do_constant_folding=_UpperCAmelCase ,opset_version=_UpperCAmelCase ,)
@torch.no_grad()
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : str ,_UpperCAmelCase : int ,_UpperCAmelCase : bool = False ) -> List[Any]:
__snake_case : List[Any] = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__snake_case : Union[str, Any] = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
__snake_case : List[str] = 'cpu'
__snake_case : List[Any] = Path(_UpperCAmelCase )
# VAE DECODER
__snake_case : Optional[int] = AutoencoderKL.from_pretrained(model_path + '/vae' )
__snake_case : Any = vae_decoder.config.latent_channels
# forward only through the decoder part
__snake_case : str = vae_decoder.decode
onnx_export(
_UpperCAmelCase ,model_args=(
torch.randn(1 ,_UpperCAmelCase ,25 ,25 ).to(device=_UpperCAmelCase ,dtype=_UpperCAmelCase ),
False,
) ,output_path=output_path / 'vae_decoder' / 'model.onnx' ,ordered_input_names=['latent_sample', 'return_dict'] ,output_names=['sample'] ,dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} ,opset=_UpperCAmelCase ,)
del vae_decoder
if __name__ == "__main__":
A__ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
'''--model_path''',
type=str,
required=True,
help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
)
parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--opset''',
default=1_4,
type=int,
help='''The version of the ONNX operator set to use.''',
)
parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')
A__ : int = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print('''SD: Done: ONNX''')
| 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = KandinskyVaaPriorPipeline
A__ = ['''prompt''']
A__ = ['''prompt''', '''negative_prompt''']
A__ = [
'''num_images_per_prompt''',
'''generator''',
'''num_inference_steps''',
'''latents''',
'''negative_prompt''',
'''guidance_scale''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Dict ) -> List[str]:
'''simple docstring'''
return 32
@property
def A_ ( self : Any ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return self.time_input_dim
@property
def A_ ( self : str ) -> int:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return 100
@property
def A_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
__snake_case : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__a )
@property
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Any = {
'num_attention_heads': 2,
'attention_head_dim': 12,
'embedding_dim': self.text_embedder_hidden_size,
'num_layers': 1,
}
__snake_case : List[Any] = PriorTransformer(**__a )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
__snake_case : Any = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
__snake_case : Optional[Any] = CLIPVisionModelWithProjection(__a )
return model
@property
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = CLIPImageProcessor(
crop_size=224 , do_center_crop=__a , do_normalize=__a , do_resize=__a , image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , resample=3 , size=224 , )
return image_processor
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : List[str] = self.dummy_image_encoder
__snake_case : str = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : List[str] = self.dummy_image_processor
__snake_case : Any = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='sample' , num_train_timesteps=1000 , clip_sample=__a , clip_sample_range=1_0.0 , )
__snake_case : str = {
'prior': prior,
'image_encoder': image_encoder,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'scheduler': scheduler,
'image_processor': image_processor,
}
return components
def A_ ( self : List[Any] , __a : Optional[Any] , __a : Tuple=0 ) -> Any:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : List[str] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : List[Any] = {
'prompt': 'horse',
'generator': generator,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def A_ ( self : str ) -> Dict:
'''simple docstring'''
__snake_case : str = 'cpu'
__snake_case : List[str] = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Optional[Any] = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : List[str] = output.image_embeds
__snake_case : str = pipe(
**self.get_dummy_inputs(__a ) , return_dict=__a , )[0]
__snake_case : Union[str, Any] = image[0, -10:]
__snake_case : Any = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
__snake_case : List[Any] = np.array(
[-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def A_ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = torch_device == 'cpu'
__snake_case : Dict = True
__snake_case : Union[str, Any] = False
self._test_inference_batch_single_identical(
test_max_difference=__a , relax_max_difference=__a , test_mean_pixel_difference=__a , )
@skip_mps
def A_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = torch_device == 'cpu'
__snake_case : Optional[Any] = False
self._test_attention_slicing_forward_pass(
test_max_difference=__a , test_mean_pixel_difference=__a , )
| 0 | 1 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case__ :
def __init__( self : Tuple , __a : int , __a : Optional[Any]=13 , __a : Optional[Any]=7 , __a : Dict=True , __a : Optional[Any]=True , __a : Any=True , __a : Optional[int]=True , __a : Tuple=True , __a : Any=False , __a : int=False , __a : Dict=False , __a : Optional[int]=2 , __a : Optional[Any]=99 , __a : Tuple=0 , __a : str=32 , __a : List[Any]=5 , __a : List[Any]=4 , __a : Optional[int]=0.1 , __a : List[str]=0.1 , __a : Dict=512 , __a : str=2 , __a : List[str]=0.0_2 , __a : Any=2 , __a : str=4 , __a : int="last" , __a : Any=True , __a : Optional[int]=None , __a : Tuple=0 , ) -> str:
'''simple docstring'''
__snake_case : Union[str, Any] = parent
__snake_case : Any = batch_size
__snake_case : Optional[Any] = seq_length
__snake_case : List[str] = is_training
__snake_case : Tuple = use_input_lengths
__snake_case : List[Any] = use_token_type_ids
__snake_case : Optional[Any] = use_labels
__snake_case : List[Any] = gelu_activation
__snake_case : List[Any] = sinusoidal_embeddings
__snake_case : Any = causal
__snake_case : str = asm
__snake_case : int = n_langs
__snake_case : int = vocab_size
__snake_case : Tuple = n_special
__snake_case : Optional[Any] = hidden_size
__snake_case : str = num_hidden_layers
__snake_case : List[str] = num_attention_heads
__snake_case : Any = hidden_dropout_prob
__snake_case : Optional[Any] = attention_probs_dropout_prob
__snake_case : Union[str, Any] = max_position_embeddings
__snake_case : List[str] = type_sequence_label_size
__snake_case : Optional[Any] = initializer_range
__snake_case : Dict = num_labels
__snake_case : Dict = num_choices
__snake_case : Dict = summary_type
__snake_case : Dict = use_proj
__snake_case : List[str] = scope
__snake_case : Optional[int] = bos_token_id
def A_ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
__snake_case : Optional[int] = None
if self.use_input_lengths:
__snake_case : Dict = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__snake_case : int = None
if self.use_token_type_ids:
__snake_case : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__snake_case : List[Any] = None
__snake_case : str = None
__snake_case : Optional[int] = None
if self.use_labels:
__snake_case : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__snake_case : Union[str, Any] = ids_tensor([self.batch_size] , 2 ).float()
__snake_case : List[str] = ids_tensor([self.batch_size] , self.num_choices )
__snake_case : str = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def A_ ( self : Optional[Any] , __a : Dict , __a : Tuple , __a : str , __a : Dict , __a : Optional[Any] , __a : str , __a : List[Any] , __a : int , __a : Tuple , ) -> str:
'''simple docstring'''
__snake_case : Optional[int] = XLMModel(config=__a )
model.to(__a )
model.eval()
__snake_case : Optional[int] = model(__a , lengths=__a , langs=__a )
__snake_case : Dict = model(__a , langs=__a )
__snake_case : Union[str, Any] = model(__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A_ ( self : Any , __a : List[Any] , __a : List[str] , __a : Optional[Any] , __a : str , __a : int , __a : Union[str, Any] , __a : str , __a : Dict , __a : str , ) -> List[Any]:
'''simple docstring'''
__snake_case : str = XLMWithLMHeadModel(__a )
model.to(__a )
model.eval()
__snake_case : Union[str, Any] = model(__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A_ ( self : List[str] , __a : str , __a : Tuple , __a : Optional[int] , __a : Any , __a : Optional[Any] , __a : Union[str, Any] , __a : Union[str, Any] , __a : int , __a : List[str] , ) -> int:
'''simple docstring'''
__snake_case : Any = XLMForQuestionAnsweringSimple(__a )
model.to(__a )
model.eval()
__snake_case : Tuple = model(__a )
__snake_case : List[str] = model(__a , start_positions=__a , end_positions=__a )
__snake_case : Any = outputs
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 A_ ( self : str , __a : List[Any] , __a : Optional[Any] , __a : Any , __a : Union[str, Any] , __a : Dict , __a : Any , __a : str , __a : Tuple , __a : int , ) -> List[str]:
'''simple docstring'''
__snake_case : int = XLMForQuestionAnswering(__a )
model.to(__a )
model.eval()
__snake_case : Tuple = model(__a )
__snake_case : Tuple = model(
__a , start_positions=__a , end_positions=__a , cls_index=__a , is_impossible=__a , p_mask=__a , )
__snake_case : Tuple = model(
__a , start_positions=__a , end_positions=__a , cls_index=__a , is_impossible=__a , )
((__snake_case) , ) : Optional[int] = result_with_labels.to_tuple()
__snake_case : Tuple = model(__a , start_positions=__a , end_positions=__a )
((__snake_case) , ) : Optional[Any] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def A_ ( self : Union[str, Any] , __a : Union[str, Any] , __a : List[str] , __a : int , __a : Tuple , __a : Any , __a : List[Any] , __a : Tuple , __a : List[str] , __a : Tuple , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = XLMForSequenceClassification(__a )
model.to(__a )
model.eval()
__snake_case : Dict = model(__a )
__snake_case : Dict = model(__a , labels=__a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A_ ( self : Dict , __a : List[str] , __a : Any , __a : List[str] , __a : Dict , __a : str , __a : Tuple , __a : Optional[Any] , __a : List[Any] , __a : Union[str, Any] , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : str = self.num_labels
__snake_case : Any = XLMForTokenClassification(__a )
model.to(__a )
model.eval()
__snake_case : Tuple = model(__a , attention_mask=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : List[str] , __a : Optional[Any] , __a : Tuple , __a : Dict , __a : Optional[Any] , __a : Any , __a : List[Any] , __a : str , __a : Optional[Any] , __a : Optional[int] , ) -> List[Any]:
'''simple docstring'''
__snake_case : Tuple = self.num_choices
__snake_case : str = XLMForMultipleChoice(config=__a )
model.to(__a )
model.eval()
__snake_case : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__snake_case : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__snake_case : str = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__snake_case : Dict = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A_ ( self : int ) -> str:
'''simple docstring'''
__snake_case : List[Any] = self.prepare_config_and_inputs()
(
(
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) ,
) : Optional[Any] = config_and_inputs
__snake_case : Optional[int] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class snake_case__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
A__ = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
A__ = (
{
'''feature-extraction''': XLMModel,
'''fill-mask''': XLMWithLMHeadModel,
'''question-answering''': XLMForQuestionAnsweringSimple,
'''text-classification''': XLMForSequenceClassification,
'''text-generation''': XLMWithLMHeadModel,
'''token-classification''': XLMForTokenClassification,
'''zero-shot''': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def A_ ( self : Optional[int] , __a : Tuple , __a : Optional[int] , __a : List[str] , __a : List[Any] , __a : Any ) -> List[Any]:
'''simple docstring'''
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def A_ ( self : List[Any] , __a : Any , __a : Optional[Any] , __a : Any=False ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[int] = super()._prepare_for_class(__a , __a , return_labels=__a )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
__snake_case : Optional[int] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__a )
__snake_case : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__a )
return inputs_dict
def A_ ( self : Dict ) -> int:
'''simple docstring'''
__snake_case : Union[str, Any] = XLMModelTester(self )
__snake_case : List[str] = ConfigTester(self , config_class=__a , emb_dim=37 )
def A_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def A_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
__snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__a )
def A_ ( self : Optional[int] ) -> int:
'''simple docstring'''
__snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__a )
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__a )
def A_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
__snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__a )
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__a )
def A_ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
__snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__a )
def A_ ( self : Optional[int] ) -> int:
'''simple docstring'''
__snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__a )
def A_ ( self : int , __a : Union[str, Any] , __a : int , __a : int , __a : Optional[Any] , __a : Any , __a : Optional[Any]=False , __a : Union[str, Any]=1 ) -> Optional[Any]:
'''simple docstring'''
self.assertIsInstance(__a , __a )
self.assertListEqual(
[isinstance(__a , __a ) for iter_attentions in attentions] , [True] * len(__a ) )
self.assertEqual(len(__a ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__a ):
# adds PAD dummy token
__snake_case : Tuple = min_length + idx + 1
__snake_case : Optional[Any] = min_length + idx + 1
__snake_case : Optional[Any] = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(__a ) )
def A_ ( self : Optional[Any] , __a : List[str] , __a : Dict , __a : Optional[Any] , __a : List[str] , __a : List[str] , __a : List[str]=False , __a : Any=1 ) -> List[str]:
'''simple docstring'''
self.assertIsInstance(__a , __a )
self.assertListEqual(
[isinstance(__a , __a ) for iter_hidden_states in hidden_states] , [True] * len(__a ) , )
self.assertEqual(len(__a ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__a ):
# adds PAD dummy token
__snake_case : Optional[Any] = min_length + idx + 1
__snake_case : List[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(__a ) , )
pass
@slow
def A_ ( self : str ) -> str:
'''simple docstring'''
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Tuple = XLMModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
class snake_case__ ( unittest.TestCase ):
@slow
def A_ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : str = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(__a )
__snake_case : List[str] = torch.tensor([[14, 447]] , dtype=torch.long , device=__a ) # the president
__snake_case : List[str] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
__snake_case : int = model.generate(__a , do_sample=__a )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , __a )
| 0 |
'''simple docstring'''
from math import factorial
A__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def a_ ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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(_UpperCAmelCase ) )
def a_ ( _UpperCAmelCase : int = 60 ,_UpperCAmelCase : int = 1_00_00_00 ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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
__snake_case : List[str] = 0
# the cached sizes of the previous chains
__snake_case : dict[int, int] = {}
for start_chain_element in range(1 ,_UpperCAmelCase ):
# The temporary set will contain the elements of the chain
__snake_case : Optional[int] = set()
__snake_case : List[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__snake_case : str = 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(_UpperCAmelCase )
chain_set_length += 1
__snake_case : Tuple = digit_factorial_sum(_UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__snake_case : 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()}""")
| 0 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class snake_case__ :
def __init__( self : str , __a : Optional[int] , __a : str=3 , __a : Optional[int]=32 , __a : Optional[Any]=3 , __a : Optional[int]=10 , __a : List[Any]=[10, 20, 30, 40] , __a : Dict=[1, 1, 2, 1] , __a : Tuple=True , __a : Union[str, Any]=True , __a : Dict="relu" , __a : Any=3 , __a : str=None , ) -> int:
'''simple docstring'''
__snake_case : str = parent
__snake_case : Union[str, Any] = batch_size
__snake_case : List[str] = image_size
__snake_case : Tuple = num_channels
__snake_case : Any = embeddings_size
__snake_case : List[Any] = hidden_sizes
__snake_case : str = depths
__snake_case : int = is_training
__snake_case : List[str] = use_labels
__snake_case : Tuple = hidden_act
__snake_case : Any = num_labels
__snake_case : List[str] = scope
__snake_case : Dict = len(__a )
def A_ ( self : int ) -> Optional[int]:
'''simple docstring'''
__snake_case : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__snake_case : str = None
if self.use_labels:
__snake_case : Optional[int] = ids_tensor([self.batch_size] , self.num_labels )
__snake_case : Tuple = self.get_config()
return config, pixel_values, labels
def A_ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def A_ ( self : Optional[Any] , __a : Any , __a : Union[str, Any] , __a : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Union[str, Any] = TFRegNetModel(config=__a )
__snake_case : Union[str, Any] = model(__a , training=__a )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A_ ( self : List[str] , __a : Dict , __a : Union[str, Any] , __a : Optional[Any] ) -> Tuple:
'''simple docstring'''
__snake_case : List[str] = self.num_labels
__snake_case : List[Any] = TFRegNetForImageClassification(__a )
__snake_case : Optional[int] = model(__a , labels=__a , training=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A_ ( self : int ) -> List[Any]:
'''simple docstring'''
__snake_case : List[Any] = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case : Any = config_and_inputs
__snake_case : Any = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class snake_case__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
A__ = (
{'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
A__ = False
A__ = False
A__ = False
A__ = False
A__ = False
def A_ ( self : List[str] ) -> Tuple:
'''simple docstring'''
__snake_case : str = TFRegNetModelTester(self )
__snake_case : Optional[Any] = ConfigTester(self , config_class=__a , has_text_modality=__a )
def A_ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
return
@unittest.skip(reason='RegNet does not use inputs_embeds' )
def A_ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('GPU' ) ) == 0 , reason='TF does not support backprop for grouped convolutions on CPU.' , )
@slow
def A_ ( self : List[str] ) -> Any:
'''simple docstring'''
super().test_keras_fit()
@unittest.skip(reason='RegNet does not support input and output embeddings' )
def A_ ( self : str ) -> str:
'''simple docstring'''
pass
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
__snake_case , __snake_case : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case : Tuple = model_class(__a )
__snake_case : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__snake_case : Optional[int] = [*signature.parameters.keys()]
__snake_case : Any = ['pixel_values']
self.assertListEqual(arg_names[:1] , __a )
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def A_ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
def check_hidden_states_output(__a : List[str] , __a : List[Any] , __a : Optional[Any] ):
__snake_case : List[Any] = model_class(__a )
__snake_case : int = model(**self._prepare_for_class(__a , __a ) , training=__a )
__snake_case : Optional[int] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__snake_case : List[Any] = self.model_tester.num_stages
self.assertEqual(len(__a ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
__snake_case , __snake_case : int = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case : Optional[int] = ['basic', 'bottleneck']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__snake_case : List[str] = layer_type
__snake_case : Optional[Any] = True
check_hidden_states_output(__a , __a , __a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__snake_case : str = True
check_hidden_states_output(__a , __a , __a )
def A_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
__snake_case , __snake_case : int = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(__a : Optional[int] , __a : Tuple , __a : Union[str, Any] , __a : List[Any]={} ):
__snake_case : Optional[Any] = model(__a , return_dict=__a , **__a )
__snake_case : Tuple = model(__a , return_dict=__a , **__a ).to_tuple()
def recursive_check(__a : Tuple , __a : Optional[int] ):
if isinstance(__a , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(__a , __a ):
recursive_check(__a , __a )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(__a , __a ) ) , msg=(
'Tuple and dict output are not equal. Difference:'
f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}'''
) , )
recursive_check(__a , __a )
for model_class in self.all_model_classes:
__snake_case : List[str] = model_class(__a )
__snake_case : List[Any] = self._prepare_for_class(__a , __a )
__snake_case : str = self._prepare_for_class(__a , __a )
check_equivalence(__a , __a , __a )
__snake_case : List[str] = self._prepare_for_class(__a , __a , return_labels=__a )
__snake_case : Tuple = self._prepare_for_class(__a , __a , return_labels=__a )
check_equivalence(__a , __a , __a )
__snake_case : Union[str, Any] = self._prepare_for_class(__a , __a )
__snake_case : Union[str, Any] = self._prepare_for_class(__a , __a )
check_equivalence(__a , __a , __a , {'output_hidden_states': True} )
__snake_case : Optional[Any] = self._prepare_for_class(__a , __a , return_labels=__a )
__snake_case : Any = self._prepare_for_class(__a , __a , return_labels=__a )
check_equivalence(__a , __a , __a , {'output_hidden_states': True} )
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__a )
@slow
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Tuple = TFRegNetModel.from_pretrained(__a )
self.assertIsNotNone(__a )
def a_ ( ) -> Optional[int]:
__snake_case : Any = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class snake_case__ ( unittest.TestCase ):
@cached_property
def A_ ( self : List[Any] ) -> str:
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def A_ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[str] = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
__snake_case : str = self.default_image_processor
__snake_case : Union[str, Any] = prepare_img()
__snake_case : str = image_processor(images=__a , return_tensors='tf' )
# forward pass
__snake_case : Optional[int] = model(**__a , training=__a )
# verify the logits
__snake_case : List[str] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , __a )
__snake_case : List[str] = tf.constant([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6] )
tf.debugging.assert_near(outputs.logits[0, :3] , __a , atol=1e-4 )
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int = 1_00 ) -> int:
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : Union[str, Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 0 | 1 |
'''simple docstring'''
import numpy as np
def a_ ( _UpperCAmelCase : np.ndarray ,_UpperCAmelCase : np.ndarray ,_UpperCAmelCase : float = 1E-12 ,_UpperCAmelCase : int = 1_00 ,) -> tuple[float, np.ndarray]:
assert np.shape(_UpperCAmelCase )[0] == np.shape(_UpperCAmelCase )[1]
# Ensure proper dimensionality.
assert np.shape(_UpperCAmelCase )[0] == np.shape(_UpperCAmelCase )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(_UpperCAmelCase ) == np.iscomplexobj(_UpperCAmelCase )
__snake_case : Dict = np.iscomplexobj(_UpperCAmelCase )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(_UpperCAmelCase ,input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__snake_case : Union[str, Any] = False
__snake_case : Dict = 0
__snake_case : Any = 0
__snake_case : List[str] = 1E12
while not convergence:
# Multiple matrix by the vector.
__snake_case : Union[str, Any] = np.dot(_UpperCAmelCase ,_UpperCAmelCase )
# Normalize the resulting output vector.
__snake_case : int = w / np.linalg.norm(_UpperCAmelCase )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__snake_case : Dict = vector.conj().T if is_complex else vector.T
__snake_case : Optional[Any] = np.dot(_UpperCAmelCase ,np.dot(_UpperCAmelCase ,_UpperCAmelCase ) )
# Check convergence.
__snake_case : Any = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__snake_case : str = True
__snake_case : List[Any] = lambda_
if is_complex:
__snake_case : List[Any] = np.real(lambda_ )
return lambda_, vector
def a_ ( ) -> None:
__snake_case : Optional[int] = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
__snake_case : List[Any] = np.array([41, 4, 20] )
__snake_case : str = real_input_matrix.astype(np.complexaaa )
__snake_case : Optional[int] = np.triu(1J * complex_input_matrix ,1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__snake_case : int = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__snake_case : List[Any] = real_input_matrix
__snake_case : Optional[int] = real_vector
elif problem_type == "complex":
__snake_case : Dict = complex_input_matrix
__snake_case : Dict = complex_vector
# Our implementation.
__snake_case , __snake_case : Any = power_iteration(_UpperCAmelCase ,_UpperCAmelCase )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__snake_case , __snake_case : List[Any] = np.linalg.eigh(_UpperCAmelCase )
# Last eigenvalue is the maximum one.
__snake_case : str = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__snake_case : str = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(_UpperCAmelCase ) - np.abs(_UpperCAmelCase ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : int = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''',
'''GroupViTTextConfig''',
'''GroupViTVisionConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple = [
'''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GroupViTModel''',
'''GroupViTPreTrainedModel''',
'''GroupViTTextModel''',
'''GroupViTVisionModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFGroupViTModel''',
'''TFGroupViTPreTrainedModel''',
'''TFGroupViTTextModel''',
'''TFGroupViTVisionModel''',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : int ,_UpperCAmelCase : str=None ,**_UpperCAmelCase : str ) -> str:
__snake_case : List[str] = [x.strip() for x in open(_UpperCAmelCase ).readlines()]
__snake_case : List[str] = [x.strip() for x in open(_UpperCAmelCase ).readlines()][: len(_UpperCAmelCase )]
__snake_case : List[Any] = calculate_rouge(_UpperCAmelCase ,_UpperCAmelCase ,**_UpperCAmelCase )
if save_path is not None:
save_json(_UpperCAmelCase ,_UpperCAmelCase ,indent=_UpperCAmelCase )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ShapEPipeline
A__ = ['''prompt''']
A__ = ['''prompt''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return 32
@property
def A_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
return 8
@property
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__a )
@property
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Dict = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
__snake_case : Optional[Any] = PriorTransformer(**__a )
return model
@property
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Tuple = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
__snake_case : Optional[int] = ShapERenderer(**__a )
return model
def A_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : Union[str, Any] = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : Optional[Any] = self.dummy_renderer
__snake_case : List[Any] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=__a , clip_sample=__a , clip_sample_range=1.0 , )
__snake_case : int = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def A_ ( self : Union[str, Any] , __a : Dict , __a : int=0 ) -> Optional[Any]:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : Optional[Any] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : Optional[int] = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = 'cpu'
__snake_case : Dict = self.get_dummy_components()
__snake_case : int = self.pipeline_class(**__a )
__snake_case : str = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[Any] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : Dict = output.images[0]
__snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__snake_case : str = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
__snake_case : int = torch_device == 'cpu'
__snake_case : str = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__a , relax_max_difference=__a , )
def A_ ( self : List[str] ) -> Dict:
'''simple docstring'''
__snake_case : str = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Dict = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : int = 1
__snake_case : Tuple = 2
__snake_case : Tuple = self.get_dummy_inputs(__a )
for key in inputs.keys():
if key in self.batch_params:
__snake_case : Union[str, Any] = batch_size * [inputs[key]]
__snake_case : str = pipe(**__a , num_images_per_prompt=__a )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def A_ ( self : str ) -> Dict:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
__snake_case : Union[str, Any] = ShapEPipeline.from_pretrained('openai/shap-e' )
__snake_case : Any = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = torch.Generator(device=__a ).manual_seed(0 )
__snake_case : Union[str, Any] = pipe(
'a shark' , generator=__a , guidance_scale=1_5.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__a , __a )
| 0 | 1 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ) -> int:
return int((input_a, input_a).count(0 ) != 0 )
def a_ ( ) -> None:
assert nand_gate(0 ,0 ) == 1
assert nand_gate(0 ,1 ) == 1
assert nand_gate(1 ,0 ) == 1
assert nand_gate(1 ,1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 0 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
A__ : str = [8, 5, 9, 7]
A__ : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A__ : Dict = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class snake_case__ :
def __init__( self : Union[str, Any] , __a : list[int] , __a : list[list[int]] , __a : list[list[int]] , ) -> None:
'''simple docstring'''
__snake_case : int = claim_vector
__snake_case : Optional[int] = allocated_resources_table
__snake_case : List[str] = maximum_claim_table
def A_ ( self : str ) -> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def A_ ( self : int ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def A_ ( self : int ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def A_ ( self : str ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__a ): i for i in self.__need()}
def A_ ( self : Union[str, Any] , **__a : int ) -> None:
'''simple docstring'''
__snake_case : str = self.__need()
__snake_case : List[Any] = self.__allocated_resources_table
__snake_case : Optional[int] = self.__available_resources()
__snake_case : Union[str, Any] = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
__snake_case : Tuple = False
for each_need in need_list:
__snake_case : Any = True
for index, need in enumerate(__a ):
if need > available_resources[index]:
__snake_case : List[str] = False
break
if execution:
__snake_case : Union[str, Any] = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__snake_case : str = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(__a )
# update available/freed resources stack
__snake_case : Union[str, Any] = np.array(__a ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__a ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__a ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''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 snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = 42
A__ = 42
class snake_case__ ( nn.Module ):
A__ = 42
A__ = (16, 32, 96, 256)
A__ = jnp.floataa
def A_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
__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 : Tuple = []
for i in range(len(self.block_out_channels ) - 1 ):
__snake_case : str = self.block_out_channels[i]
__snake_case : Any = self.block_out_channels[i + 1]
__snake_case : Optional[int] = nn.Conv(
__a , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(__a )
__snake_case : Optional[int] = nn.Conv(
__a , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(__a )
__snake_case : List[Any] = blocks
__snake_case : List[str] = 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 : Any , __a : int ) -> List[str]:
'''simple docstring'''
__snake_case : int = self.conv_in(__a )
__snake_case : Dict = nn.silu(__a )
for block in self.blocks:
__snake_case : List[Any] = block(__a )
__snake_case : str = nn.silu(__a )
__snake_case : List[str] = self.conv_out(__a )
return embedding
@flax_register_to_config
class snake_case__ ( nn.Module , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__ = 32
A__ = 4
A__ = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
A__ = False
A__ = (320, 640, 1_280, 1_280)
A__ = 2
A__ = 8
A__ = None
A__ = 1_280
A__ = 0.0
A__ = False
A__ = jnp.floataa
A__ = True
A__ = 0
A__ = "rgb"
A__ = (16, 32, 96, 256)
def A_ ( self : Optional[Any] , __a : jax.random.KeyArray ) -> FrozenDict:
'''simple docstring'''
# init input tensors
__snake_case : Dict = (1, self.in_channels, self.sample_size, self.sample_size)
__snake_case : Union[str, Any] = jnp.zeros(__a , dtype=jnp.floataa )
__snake_case : Optional[int] = jnp.ones((1,) , dtype=jnp.intaa )
__snake_case : List[str] = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
__snake_case : Any = (1, 3, self.sample_size * 8, self.sample_size * 8)
__snake_case : int = jnp.zeros(__a , dtype=jnp.floataa )
__snake_case , __snake_case : Optional[Any] = jax.random.split(__a )
__snake_case : Dict = {'params': params_rng, 'dropout': dropout_rng}
return self.init(__a , __a , __a , __a , __a )["params"]
def A_ ( self : Tuple ) -> str:
'''simple docstring'''
__snake_case : str = self.block_out_channels
__snake_case : List[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 : int = self.num_attention_heads or self.attention_head_dim
# input
__snake_case : int = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
__snake_case : Optional[Any] = 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(__a , dtype=self.dtype )
__snake_case : Any = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
__snake_case : int = self.only_cross_attention
if isinstance(__a , __a ):
__snake_case : Optional[Any] = (only_cross_attention,) * len(self.down_block_types )
if isinstance(__a , __a ):
__snake_case : Any = (num_attention_heads,) * len(self.down_block_types )
# down
__snake_case : List[str] = []
__snake_case : Tuple = []
__snake_case : Tuple = block_out_channels[0]
__snake_case : Dict = nn.Conv(
__a , 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(__a )
for i, down_block_type in enumerate(self.down_block_types ):
__snake_case : int = output_channel
__snake_case : Union[str, Any] = block_out_channels[i]
__snake_case : str = i == len(__a ) - 1
if down_block_type == "CrossAttnDownBlock2D":
__snake_case : Union[str, Any] = FlaxCrossAttnDownBlockaD(
in_channels=__a , out_channels=__a , 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 : Tuple = FlaxDownBlockaD(
in_channels=__a , out_channels=__a , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(__a )
for _ in range(self.layers_per_block ):
__snake_case : Tuple = nn.Conv(
__a , 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(__a )
if not is_final_block:
__snake_case : int = nn.Conv(
__a , 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(__a )
__snake_case : Union[str, Any] = down_blocks
__snake_case : Any = controlnet_down_blocks
# mid
__snake_case : Optional[int] = block_out_channels[-1]
__snake_case : List[str] = FlaxUNetMidBlockaDCrossAttn(
in_channels=__a , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
__snake_case : List[Any] = nn.Conv(
__a , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self : str , __a : Tuple , __a : Any , __a : Optional[Any] , __a : List[Any] , __a : float = 1.0 , __a : bool = True , __a : bool = False , ) -> Union[FlaxControlNetOutput, Tuple]:
'''simple docstring'''
__snake_case : int = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
__snake_case : Tuple = jnp.flip(__a , axis=1 )
# 1. time
if not isinstance(__a , jnp.ndarray ):
__snake_case : List[str] = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(__a , jnp.ndarray ) and len(timesteps.shape ) == 0:
__snake_case : Any = timesteps.astype(dtype=jnp.floataa )
__snake_case : List[Any] = jnp.expand_dims(__a , 0 )
__snake_case : Dict = self.time_proj(__a )
__snake_case : List[Any] = self.time_embedding(__a )
# 2. pre-process
__snake_case : Dict = jnp.transpose(__a , (0, 2, 3, 1) )
__snake_case : Union[str, Any] = self.conv_in(__a )
__snake_case : str = jnp.transpose(__a , (0, 2, 3, 1) )
__snake_case : List[str] = self.controlnet_cond_embedding(__a )
sample += controlnet_cond
# 3. down
__snake_case : Union[str, Any] = (sample,)
for down_block in self.down_blocks:
if isinstance(__a , __a ):
__snake_case , __snake_case : Union[str, Any] = down_block(__a , __a , __a , deterministic=not train )
else:
__snake_case , __snake_case : Tuple = down_block(__a , __a , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
__snake_case : List[str] = self.mid_block(__a , __a , __a , deterministic=not train )
# 5. contronet blocks
__snake_case : Optional[Any] = ()
for down_block_res_sample, controlnet_block in zip(__a , self.controlnet_down_blocks ):
__snake_case : str = controlnet_block(__a )
controlnet_down_block_res_samples += (down_block_res_sample,)
__snake_case : Any = controlnet_down_block_res_samples
__snake_case : Any = self.controlnet_mid_block(__a )
# 6. scaling
__snake_case : Union[str, 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=__a , mid_block_res_sample=__a )
| 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
A__ : Union[str, Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ : List[Any] = {
'''vocab_file''': {
'''google/electra-small-generator''': (
'''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
),
'''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
'''google/electra-large-generator''': (
'''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
),
'''google/electra-small-discriminator''': (
'''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
),
'''google/electra-base-discriminator''': (
'''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
),
'''google/electra-large-discriminator''': (
'''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''google/electra-small-generator''': (
'''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
),
'''google/electra-base-generator''': (
'''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
),
'''google/electra-large-generator''': (
'''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
),
'''google/electra-small-discriminator''': (
'''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
),
'''google/electra-base-discriminator''': (
'''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
),
'''google/electra-large-discriminator''': (
'''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
),
},
}
A__ : List[Any] = {
'''google/electra-small-generator''': 5_1_2,
'''google/electra-base-generator''': 5_1_2,
'''google/electra-large-generator''': 5_1_2,
'''google/electra-small-discriminator''': 5_1_2,
'''google/electra-base-discriminator''': 5_1_2,
'''google/electra-large-discriminator''': 5_1_2,
}
A__ : Optional[Any] = {
'''google/electra-small-generator''': {'''do_lower_case''': True},
'''google/electra-base-generator''': {'''do_lower_case''': True},
'''google/electra-large-generator''': {'''do_lower_case''': True},
'''google/electra-small-discriminator''': {'''do_lower_case''': True},
'''google/electra-base-discriminator''': {'''do_lower_case''': True},
'''google/electra-large-discriminator''': {'''do_lower_case''': True},
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_INIT_CONFIGURATION
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = ElectraTokenizer
def __init__( self : int , __a : List[Any]=None , __a : int=None , __a : List[str]=True , __a : Any="[UNK]" , __a : Any="[SEP]" , __a : Union[str, Any]="[PAD]" , __a : Dict="[CLS]" , __a : List[Any]="[MASK]" , __a : str=True , __a : Optional[int]=None , **__a : Optional[int] , ) -> str:
'''simple docstring'''
super().__init__(
__a , tokenizer_file=__a , do_lower_case=__a , unk_token=__a , sep_token=__a , pad_token=__a , cls_token=__a , mask_token=__a , tokenize_chinese_chars=__a , strip_accents=__a , **__a , )
__snake_case : Tuple = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __a ) != do_lower_case
or normalizer_state.get('strip_accents' , __a ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __a ) != tokenize_chinese_chars
):
__snake_case : List[Any] = getattr(__a , normalizer_state.pop('type' ) )
__snake_case : str = do_lower_case
__snake_case : Optional[int] = strip_accents
__snake_case : Any = tokenize_chinese_chars
__snake_case : Union[str, Any] = normalizer_class(**__a )
__snake_case : Any = do_lower_case
def A_ ( self : Any , __a : List[str] , __a : Optional[Any]=None ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A_ ( self : List[Any] , __a : List[int] , __a : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
__snake_case : int = [self.sep_token_id]
__snake_case : List[Any] = [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 ) * [0] + len(token_ids_a + sep ) * [1]
def A_ ( self : Optional[int] , __a : str , __a : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
__snake_case : Tuple = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
| 0 | 1 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
__snake_case : Optional[Any] = gray_code_sequence_string(_UpperCAmelCase )
#
# convert them to integers
for i in range(len(_UpperCAmelCase ) ):
__snake_case : Optional[Any] = int(sequence[i] ,2 )
return sequence
def a_ ( _UpperCAmelCase : int ) -> list:
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__snake_case : Dict = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__snake_case : Dict = gray_code_sequence_string(bit_count - 1 )
__snake_case : Any = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__snake_case : str = '0' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__snake_case : Any = '1' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> bool:
__snake_case : Union[str, Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 0 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
A__ : Tuple = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq''': (
'''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wikisql-supervised''': (
'''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-tabfact''': (
'''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
),
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''tapas'''
def __init__( self : Optional[int] , __a : List[Any]=30522 , __a : int=768 , __a : int=12 , __a : Tuple=12 , __a : List[Any]=3072 , __a : str="gelu" , __a : Union[str, Any]=0.1 , __a : Union[str, Any]=0.1 , __a : Union[str, Any]=1024 , __a : int=[3, 256, 256, 2, 256, 256, 10] , __a : Dict=0.0_2 , __a : int=1e-12 , __a : str=0 , __a : Dict=1_0.0 , __a : Tuple=0 , __a : Dict=1.0 , __a : str=None , __a : List[Any]=1.0 , __a : Union[str, Any]=False , __a : Any=None , __a : Optional[Any]=1.0 , __a : Dict=1.0 , __a : Dict=False , __a : List[str]=False , __a : List[Any]="ratio" , __a : Tuple=None , __a : str=None , __a : Dict=64 , __a : str=32 , __a : List[Any]=False , __a : int=True , __a : List[str]=False , __a : Any=False , __a : Dict=True , __a : Dict=False , __a : int=None , __a : Optional[int]=None , **__a : List[Any] , ) -> Dict:
'''simple docstring'''
super().__init__(pad_token_id=__a , **__a )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
__snake_case : Optional[int] = vocab_size
__snake_case : str = hidden_size
__snake_case : Optional[int] = num_hidden_layers
__snake_case : Union[str, Any] = num_attention_heads
__snake_case : Any = hidden_act
__snake_case : str = intermediate_size
__snake_case : Tuple = hidden_dropout_prob
__snake_case : List[str] = attention_probs_dropout_prob
__snake_case : Dict = max_position_embeddings
__snake_case : List[str] = type_vocab_sizes
__snake_case : str = initializer_range
__snake_case : Tuple = layer_norm_eps
# Fine-tuning task hyperparameters
__snake_case : Optional[Any] = positive_label_weight
__snake_case : Dict = num_aggregation_labels
__snake_case : Optional[int] = aggregation_loss_weight
__snake_case : List[str] = use_answer_as_supervision
__snake_case : int = answer_loss_importance
__snake_case : List[Any] = use_normalized_answer_loss
__snake_case : Tuple = huber_loss_delta
__snake_case : Union[str, Any] = temperature
__snake_case : Optional[Any] = aggregation_temperature
__snake_case : Tuple = use_gumbel_for_cells
__snake_case : List[str] = use_gumbel_for_aggregation
__snake_case : str = average_approximation_function
__snake_case : Optional[int] = cell_selection_preference
__snake_case : Union[str, Any] = answer_loss_cutoff
__snake_case : Dict = max_num_rows
__snake_case : int = max_num_columns
__snake_case : Optional[Any] = average_logits_per_cell
__snake_case : Any = select_one_column
__snake_case : Any = allow_empty_column_selection
__snake_case : str = init_cell_selection_weights_to_zero
__snake_case : Union[str, Any] = reset_position_index_per_cell
__snake_case : Optional[Any] = disable_per_token_loss
# Aggregation hyperparameters
__snake_case : Any = aggregation_labels
__snake_case : int = no_aggregation_label_index
if isinstance(self.aggregation_labels , __a ):
__snake_case : int = {int(__a ): v for k, v in aggregation_labels.items()}
| 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
A__ : Tuple = pytest.mark.integration
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Dict = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(__a ) for x in np.arange(30 ).tolist()]} )
return dset
def A_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
__snake_case : Dict = dset.map(
lambda __a , __a : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=__a , keep_in_memory=__a )
__snake_case : List[Any] = dset.add_faiss_index('vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
__snake_case , __snake_case : str = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(__a , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
__snake_case : Dataset = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : Any = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
__snake_case : Union[str, Any] = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=__a )
__snake_case , __snake_case : str = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : str ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
__snake_case : Dict = np.zeros(5 , dtype=np.floataa )
__snake_case : List[str] = 1
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertRaises(__a , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
__snake_case : List[str] = np.eye(5 , dtype=np.floataa )[::-1]
__snake_case , __snake_case : Dict = index.search_batch(__a )
self.assertRaises(__a , index.search_batch , queries[0] )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : List[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , __a )
def A_ ( self : int ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
__snake_case : List[str] = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(__a ):
__snake_case : Dict = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def A_ ( self : str ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Tuple = faiss.IndexFlat(5 )
__snake_case : List[Any] = FaissIndex(custom_index=__a )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
import faiss
__snake_case : Optional[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
index.save(tmp_file.name )
__snake_case : List[Any] = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
__snake_case : List[Any] = np.zeros(5 , dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : int = index.search(__a )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def a_ ( _UpperCAmelCase : str ) -> Optional[int]:
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
__snake_case : Dict = 'index.faiss'
__snake_case : Any = f'''mock://{index_name}'''
index.save(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = FaissIndex.load(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = np.zeros(5 ,dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : Tuple = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : int = Elasticsearch()
__snake_case : Dict = {'acknowledged': True}
__snake_case : List[Any] = ElasticSearchIndex(es_client=__a )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
__snake_case : Optional[Any] = 'foo'
__snake_case : int = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
__snake_case : Dict = 'foo'
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : Optional[Any] = index.search(__a , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
__snake_case : List[Any] = ['foo', 'bar', 'foobar']
__snake_case : str = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : Any = index.search_batch(__a )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
# batched queries with timeout
__snake_case : Tuple = ['foo', 'bar', 'foobar']
__snake_case : List[Any] = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : int = index.search_batch(__a , request_timeout=30 )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
| 0 | 1 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> bool:
if num < 0:
return False
__snake_case : int = num
__snake_case : int = 0
while num > 0:
__snake_case : str = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A__ : List[Any] = logging.get_logger(__name__)
A__ : Tuple = {
'''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 snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''t5'''
A__ = ['''past_key_values''']
A__ = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : str , __a : Dict=32128 , __a : Dict=512 , __a : Union[str, Any]=64 , __a : str=2048 , __a : Union[str, Any]=6 , __a : Any=None , __a : Any=8 , __a : List[Any]=32 , __a : Any=128 , __a : Tuple=0.1 , __a : str=1e-6 , __a : Dict=1.0 , __a : Tuple="relu" , __a : Dict=True , __a : Union[str, Any]=True , __a : Any=0 , __a : Dict=1 , **__a : Union[str, Any] , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = vocab_size
__snake_case : str = d_model
__snake_case : str = d_kv
__snake_case : List[Any] = d_ff
__snake_case : List[str] = num_layers
__snake_case : Tuple = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__snake_case : Union[str, Any] = num_heads
__snake_case : Tuple = relative_attention_num_buckets
__snake_case : Optional[int] = relative_attention_max_distance
__snake_case : Optional[Any] = dropout_rate
__snake_case : str = layer_norm_epsilon
__snake_case : List[str] = initializer_factor
__snake_case : int = feed_forward_proj
__snake_case : Optional[Any] = use_cache
__snake_case : Optional[Any] = self.feed_forward_proj.split('-' )
__snake_case : Dict = act_info[-1]
__snake_case : List[str] = act_info[0] == 'gated'
if len(__a ) > 1 and act_info[0] != "gated" or len(__a ) > 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":
__snake_case : Dict = 'gelu_new'
super().__init__(
pad_token_id=__a , eos_token_id=__a , is_encoder_decoder=__a , **__a , )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@property
def A_ ( self : str ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__snake_case : Union[str, Any] = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
__snake_case : Tuple = 'past_encoder_sequence + sequence'
__snake_case : Dict = {0: 'batch'}
__snake_case : Dict = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
__snake_case : Tuple = {0: 'batch', 1: 'decoder_sequence'}
__snake_case : int = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(__a , direction='inputs' )
return common_inputs
@property
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
return 13
| 0 | 1 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A__ : List[Any] = logging.get_logger(__name__)
A__ : Tuple = {
'''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 snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''t5'''
A__ = ['''past_key_values''']
A__ = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : str , __a : Dict=32128 , __a : Dict=512 , __a : Union[str, Any]=64 , __a : str=2048 , __a : Union[str, Any]=6 , __a : Any=None , __a : Any=8 , __a : List[Any]=32 , __a : Any=128 , __a : Tuple=0.1 , __a : str=1e-6 , __a : Dict=1.0 , __a : Tuple="relu" , __a : Dict=True , __a : Union[str, Any]=True , __a : Any=0 , __a : Dict=1 , **__a : Union[str, Any] , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = vocab_size
__snake_case : str = d_model
__snake_case : str = d_kv
__snake_case : List[Any] = d_ff
__snake_case : List[str] = num_layers
__snake_case : Tuple = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__snake_case : Union[str, Any] = num_heads
__snake_case : Tuple = relative_attention_num_buckets
__snake_case : Optional[int] = relative_attention_max_distance
__snake_case : Optional[Any] = dropout_rate
__snake_case : str = layer_norm_epsilon
__snake_case : List[str] = initializer_factor
__snake_case : int = feed_forward_proj
__snake_case : Optional[Any] = use_cache
__snake_case : Optional[Any] = self.feed_forward_proj.split('-' )
__snake_case : Dict = act_info[-1]
__snake_case : List[str] = act_info[0] == 'gated'
if len(__a ) > 1 and act_info[0] != "gated" or len(__a ) > 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":
__snake_case : Dict = 'gelu_new'
super().__init__(
pad_token_id=__a , eos_token_id=__a , is_encoder_decoder=__a , **__a , )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@property
def A_ ( self : str ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__snake_case : Union[str, Any] = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
__snake_case : Tuple = 'past_encoder_sequence + sequence'
__snake_case : Dict = {0: 'batch'}
__snake_case : Dict = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
__snake_case : Tuple = {0: 'batch', 1: 'decoder_sequence'}
__snake_case : int = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(__a , direction='inputs' )
return common_inputs
@property
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
return 13
| 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : Optional[int] = {}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''llama'''
A__ = ['''past_key_values''']
def __init__( self : Any , __a : List[str]=32000 , __a : Union[str, Any]=4096 , __a : Optional[Any]=11008 , __a : Any=32 , __a : str=32 , __a : Optional[int]=None , __a : Dict="silu" , __a : Dict=2048 , __a : List[str]=0.0_2 , __a : Union[str, Any]=1e-6 , __a : Dict=True , __a : List[str]=0 , __a : Tuple=1 , __a : Tuple=2 , __a : Optional[Any]=1 , __a : Any=False , __a : Tuple=None , **__a : List[Any] , ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = vocab_size
__snake_case : List[str] = max_position_embeddings
__snake_case : List[Any] = hidden_size
__snake_case : Union[str, Any] = intermediate_size
__snake_case : Optional[int] = num_hidden_layers
__snake_case : List[Any] = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
__snake_case : Optional[int] = num_attention_heads
__snake_case : Optional[Any] = num_key_value_heads
__snake_case : int = hidden_act
__snake_case : Any = initializer_range
__snake_case : Any = rms_norm_eps
__snake_case : Union[str, Any] = pretraining_tp
__snake_case : Optional[int] = use_cache
__snake_case : Any = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , tie_word_embeddings=__a , **__a , )
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __a ) 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}''' )
__snake_case : Optional[Any] = self.rope_scaling.get('type' , __a )
__snake_case : Tuple = self.rope_scaling.get('factor' , __a )
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(__a , __a ) or rope_scaling_factor <= 1.0:
raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )
| 0 | 1 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A__ : Optional[int] = logging.get_logger(__name__)
A__ : Any = {'''vocab_file''': '''spiece.model'''}
A__ : str = {
'''vocab_file''': {
'''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
}
}
A__ : Tuple = {
'''AI-Sweden/gpt-sw3-126m''': 2_0_4_8,
'''AI-Sweden/gpt-sw3-350m''': 2_0_4_8,
'''AI-Sweden/gpt-sw3-1.6b''': 2_0_4_8,
'''AI-Sweden/gpt-sw3-6.7b''': 2_0_4_8,
'''AI-Sweden/gpt-sw3-20b''': 2_0_4_8,
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = ['''input_ids''', '''attention_mask''']
def __init__( self : Dict , __a : int , __a : List[str]=False , __a : int=False , __a : Union[str, Any]=False , __a : Union[str, Any]=None , __a : str=None , __a : Tuple=None , __a : Dict=None , __a : Optional[Dict[str, Any]] = None , **__a : Union[str, Any] , ) -> None:
'''simple docstring'''
__snake_case : int = {} if sp_model_kwargs is None else sp_model_kwargs
__snake_case : Dict = kwargs.get('name_or_path' )
if name_or_path is None:
logger.warning(
'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
' you are testing the model, this can safely be ignored' )
__snake_case : Tuple = 'None'
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__snake_case : str = '<|endoftext|>' if eos_token is None else eos_token
__snake_case : Any = '<unk>' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__snake_case : Tuple = unk_token if pad_token is None else pad_token
__snake_case : Union[str, Any] = eos_token if bos_token is None else bos_token
else:
__snake_case : List[str] = '<pad>' if pad_token is None else pad_token
__snake_case : List[Any] = '<s>' if bos_token is None else bos_token
super().__init__(
do_lower_case=__a , remove_space=__a , keep_accents=__a , bos_token=__a , eos_token=__a , unk_token=__a , pad_token=__a , sp_model_kwargs=self.sp_model_kwargs , **__a , )
__snake_case : Tuple = do_lower_case
__snake_case : Union[str, Any] = remove_space
__snake_case : List[Any] = keep_accents
__snake_case : Optional[Any] = vocab_file
__snake_case : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__a )
# Used for whitespace normalization in input texts
# fmt : off
__snake_case : Union[str, Any] = {' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '', ''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__snake_case : List[Any] = re.compile(
f'''[{"".join(map(__a , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(127 , 160 ) ) + [160, 173, 8203] ) )}]''' )
def __getstate__( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : List[Any] = self.__dict__.copy()
__snake_case : List[Any] = None
return state
def __setstate__( self : Dict , __a : List[Any] ) -> Any:
'''simple docstring'''
__snake_case : List[Any] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
__snake_case : str = {}
__snake_case : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def A_ ( self : int ) -> int:
'''simple docstring'''
return len(self.sp_model )
def A_ ( self : List[Any] , __a : str ) -> str:
'''simple docstring'''
__snake_case : Optional[Any] = self.non_printing_characters_re.sub('' , __a )
# Normalize whitespaces
__snake_case : Optional[int] = ''.join([char if char not in self.whitespaces else ' ' for char in text] )
# NFC Unicode normalization
__snake_case : Any = unicodedata.normalize('NFC' , __a )
return text
def A_ ( self : Dict , __a : str , **__a : Dict ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[int] = self.preprocess_text(__a )
return self.sp_model.encode(__a , out_type=__a )
def A_ ( self : List[str] , __a : str ) -> int:
'''simple docstring'''
return self.sp_model.PieceToId(__a )
def A_ ( self : List[str] , __a : int ) -> str:
'''simple docstring'''
return self.sp_model.IdToPiece(__a )
@staticmethod
def A_ ( __a : str ) -> str:
'''simple docstring'''
return out_string
def A_ ( self : str , __a : List[str] ) -> str:
'''simple docstring'''
__snake_case : List[str] = []
__snake_case : Optional[Any] = ''
__snake_case : Any = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__a ) + token
__snake_case : Tuple = True
__snake_case : List[str] = []
else:
current_sub_tokens.append(__a )
__snake_case : Optional[int] = False
out_string += self.sp_model.decode(__a )
return out_string
def A_ ( self : List[Any] ) -> Dict[str, int]:
'''simple docstring'''
__snake_case : Optional[int] = {self.convert_ids_to_tokens(__a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def A_ ( self : str , __a : str , __a : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(__a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__snake_case : int = 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 ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __a )
elif not os.path.isfile(self.vocab_file ):
with open(__a , 'wb' ) as fi:
__snake_case : List[Any] = self.sp_model.serialized_model_proto()
fi.write(__a )
return (out_vocab_file,)
def A_ ( self : str , __a : Union[str, List[str]] , __a : Union[str, bool] = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
'''simple docstring'''
if isinstance(__a , __a ):
__snake_case : Dict = self.preprocess_text(__a )
__snake_case : Tuple = self.sp_model.encode(__a )
else:
__snake_case : Tuple = [self.preprocess_text(__a ) for t in text]
__snake_case : Optional[Any] = self.sp_model.encode(__a )
if return_tensors is True or return_tensors == "pt":
__snake_case : Any = torch.tensor(__a )
return token_ids
def A_ ( self : List[Any] , __a : Union[int, List[int]] ) -> str:
'''simple docstring'''
return self.sp_model.decode(__a )
def A_ ( self : Optional[int] , __a : "Conversation" ) -> List[int]:
'''simple docstring'''
__snake_case : Any = [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
__snake_case : Optional[int] = (
f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(__a ) + f'''{self.bos_token}Bot:'''
)
return self.encode(text=__a )
| 0 |
'''simple docstring'''
from __future__ import annotations
A__ : str = '''Muhammad Umer Farooq'''
A__ : int = '''MIT'''
A__ : Optional[int] = '''1.0.0'''
A__ : List[Any] = '''Muhammad Umer Farooq'''
A__ : Optional[Any] = '''[email protected]'''
A__ : Optional[Any] = '''Alpha'''
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Union[str, Any] , __a : str ) -> None:
'''simple docstring'''
super().__init__()
__snake_case : list[str] = []
__snake_case : Dict = domain
def A_ ( self : Dict , __a : str , __a : list[tuple[str, str | None]] ) -> None:
'''simple docstring'''
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__snake_case : Optional[Any] = parse.urljoin(self.domain , __a )
self.urls.append(__a )
def a_ ( _UpperCAmelCase : str ) -> str:
return ".".join(get_sub_domain_name(_UpperCAmelCase ).split('.' )[-2:] )
def a_ ( _UpperCAmelCase : str ) -> str:
return parse.urlparse(_UpperCAmelCase ).netloc
def a_ ( _UpperCAmelCase : str = "https://github.com" ) -> list[str]:
__snake_case : List[Any] = get_domain_name(_UpperCAmelCase )
# Initialize the parser
__snake_case : Tuple = Parser(_UpperCAmelCase )
try:
# Open URL
__snake_case : Any = requests.get(_UpperCAmelCase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__snake_case : Dict = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__snake_case : List[Any] = requests.get(_UpperCAmelCase )
# Get the valid email.
__snake_case : Optional[Any] = re.findall('[a-zA-Z0-9]+@' + domain ,read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_UpperCAmelCase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Tuple = emails_from_url('''https://github.com''')
print(F"""{len(emails)} emails found:""")
print('''\n'''.join(sorted(emails)))
| 0 | 1 |
'''simple docstring'''
class snake_case__ :
def __init__( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = {}
def A_ ( self : Optional[int] ) -> None:
'''simple docstring'''
print(self.vertex )
for i in self.vertex:
print(__a , ' -> ' , ' -> '.join([str(__a ) for j in self.vertex[i]] ) )
def A_ ( self : Tuple , __a : int , __a : int ) -> None:
'''simple docstring'''
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(__a )
else:
# else make a new vertex
__snake_case : str = [to_vertex]
def A_ ( self : List[str] ) -> None:
'''simple docstring'''
# visited array for storing already visited nodes
__snake_case : Dict = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(__a , __a )
def A_ ( self : str , __a : int , __a : list ) -> None:
'''simple docstring'''
# mark start vertex as visited
__snake_case : Optional[Any] = True
print(__a , end=' ' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(__a , __a )
if __name__ == "__main__":
A__ : 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
| 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
A__ : Dict = logging.getLogger()
def a_ ( ) -> Tuple:
__snake_case : List[Any] = argparse.ArgumentParser()
parser.add_argument('-f' )
__snake_case : Any = parser.parse_args()
return args.f
def a_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
__snake_case : Tuple = {}
__snake_case : Union[str, Any] = os.path.join(_UpperCAmelCase ,'all_results.json' )
if os.path.exists(_UpperCAmelCase ):
with open(_UpperCAmelCase ,'r' ) as f:
__snake_case : List[str] = json.load(_UpperCAmelCase )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
def a_ ( ) -> Union[str, Any]:
__snake_case : Union[str, Any] = torch.cuda.is_available() and torch_device == 'cuda'
return is_using_cuda and is_apex_available()
A__ : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@classmethod
def A_ ( cls : Any ) -> List[str]:
'''simple docstring'''
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__snake_case : Optional[int] = tempfile.mkdtemp()
__snake_case : Dict = os.path.join(cls.tmpdir , 'default_config.yml' )
write_basic_config(save_location=cls.configPath )
__snake_case : List[Any] = ['accelerate', 'launch', '--config_file', cls.configPath]
@classmethod
def A_ ( cls : List[str] ) -> List[str]:
'''simple docstring'''
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : List[Any] = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'glue_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
'''.split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertLess(result['perplexity'] , 100 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'clm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : int = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertLess(result['perplexity'] , 42 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'mlm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__snake_case : Any = 7 if get_gpu_count() > 1 else 2
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertLess(result['train_loss'] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'ner_no_trainer' ) ) )
@unittest.skip(reason='Fix me @muellerzr' )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : Tuple = f'''
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['eval_f1'] , 28 )
self.assertGreaterEqual(result['eval_exact'] , 28 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'qa_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : str = self.get_auto_remove_tmp_dir()
__snake_case : Any = f'''
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__a , 'swag_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : int = get_results(__a )
self.assertGreaterEqual(result['eval_rouge1'] , 10 )
self.assertGreaterEqual(result['eval_rouge2'] , 2 )
self.assertGreaterEqual(result['eval_rougeL'] , 7 )
self.assertGreaterEqual(result['eval_rougeLsum'] , 7 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'summarization_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_bleu'] , 30 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'translation_no_trainer' ) ) )
@slow
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = logging.StreamHandler(sys.stdout )
logger.addHandler(__a )
__snake_case : List[str] = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertGreaterEqual(result['eval_overall_accuracy'] , 0.1_0 )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Dict = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : Optional[int] = get_results(__a )
# The base model scores a 25%
self.assertGreaterEqual(result['eval_accuracy'] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__a , 'step_1' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'image_classification_no_trainer' ) ) )
| 0 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
A__ : Optional[Any] = logging.get_logger(__name__)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Union[str, Any] , *__a : Optional[Any] , **__a : List[str] ) -> None:
'''simple docstring'''
warnings.warn(
'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use PerceiverImageProcessor instead.' , __a , )
super().__init__(*__a , **__a )
| 0 |
'''simple docstring'''
import math
def a_ ( _UpperCAmelCase : int ) -> list:
__snake_case : Optional[Any] = [True] * n
__snake_case : Optional[int] = False
__snake_case : Dict = False
__snake_case : List[Any] = True
for i in range(3 ,int(n**0.5 + 1 ) ,2 ):
__snake_case : Optional[int] = i * 2
while index < n:
__snake_case : Union[str, Any] = False
__snake_case : int = index + i
__snake_case : Dict = [2]
for i in range(3 ,_UpperCAmelCase ,2 ):
if is_prime[i]:
primes.append(_UpperCAmelCase )
return primes
def a_ ( _UpperCAmelCase : int = 99_99_66_66_33_33 ) -> int:
__snake_case : List[Any] = math.floor(math.sqrt(_UpperCAmelCase ) ) + 1_00
__snake_case : Tuple = prime_sieve(_UpperCAmelCase )
__snake_case : List[Any] = 0
__snake_case : List[Any] = 0
__snake_case : Optional[int] = primes[prime_index]
while (last_prime**2) <= limit:
__snake_case : Optional[int] = primes[prime_index + 1]
__snake_case : Union[str, Any] = last_prime**2
__snake_case : Dict = next_prime**2
# Get numbers divisible by lps(current)
__snake_case : Optional[Any] = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
__snake_case : Optional[Any] = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
__snake_case : List[str] = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
__snake_case : Dict = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 0 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
A__ : Dict = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
A__ : Optional[Any] = TaTokenizerFast
A__ : Tuple = {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : List[str] = [
'''MT5EncoderModel''',
'''MT5ForConditionalGeneration''',
'''MT5ForQuestionAnswering''',
'''MT5Model''',
'''MT5PreTrainedModel''',
'''MT5Stack''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[Any] = ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[Any] = ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
A__ : Tuple = _LazyModule(
__name__,
globals()['''__file__'''],
_import_structure,
extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
module_spec=__spec__,
)
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : float ,_UpperCAmelCase : float ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"""{price_plus_tax(1_0_0, 0.25) = }""")
print(F"""{price_plus_tax(1_25.50, 0.05) = }""")
| 0 | 1 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def a_ ( *_UpperCAmelCase : Any ,_UpperCAmelCase : Optional[Union[Dict, Any]] = None ,_UpperCAmelCase : str=True ,_UpperCAmelCase : str=2 ) -> Optional[int]:
from .. import __version__
__snake_case : Union[str, Any] = take_from
__snake_case : List[str] = ()
if not isinstance(args[0] ,_UpperCAmelCase ):
__snake_case : List[str] = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(_UpperCAmelCase ).base_version ) >= version.parse(_UpperCAmelCase ):
raise ValueError(
f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
f''' version {__version__} is >= {version_name}''' )
__snake_case : str = None
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(_UpperCAmelCase ),)
__snake_case : str = f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
elif hasattr(_UpperCAmelCase ,_UpperCAmelCase ):
values += (getattr(_UpperCAmelCase ,_UpperCAmelCase ),)
__snake_case : Optional[int] = f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
elif deprecated_kwargs is None:
__snake_case : Optional[Any] = f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''
if warning is not None:
__snake_case : List[str] = warning + ' ' if standard_warn else ''
warnings.warn(warning + message ,_UpperCAmelCase ,stacklevel=_UpperCAmelCase )
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and len(_UpperCAmelCase ) > 0:
__snake_case : Optional[int] = inspect.getouterframes(inspect.currentframe() )[1]
__snake_case : Union[str, Any] = call_frame.filename
__snake_case : Any = call_frame.lineno
__snake_case : Tuple = call_frame.function
__snake_case , __snake_case : Any = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`''' )
if len(_UpperCAmelCase ) == 0:
return
elif len(_UpperCAmelCase ) == 1:
return values[0]
return values
| 0 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
__snake_case : Optional[int] = SMALL_MODEL_IDENTIFIER
__snake_case : str = 'pt'
__snake_case : Union[str, Any] = 'tf'
def A_ ( self : Dict , __a : Tuple ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(__a )
def A_ ( self : Any , __a : Optional[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Union[str, Any] = TFAutoModel.from_pretrained(self.test_model , from_pt=__a )
model_tf.save_pretrained(__a )
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = 'mock_framework'
# Framework provided - return whatever the user provides
__snake_case : int = FeaturesManager.determine_framework(self.test_model , __a )
self.assertEqual(__a , __a )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : List[Any] = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Union[str, Any] = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(__a ):
__snake_case : Optional[int] = FeaturesManager.determine_framework(__a )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ):
__snake_case : int = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_tf )
# Both in environment -> use PyTorch
__snake_case : Optional[Any] = MagicMock(return_value=__a )
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# Both not in environment -> raise error
__snake_case : str = MagicMock(return_value=__a )
__snake_case : List[Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
with self.assertRaises(__a ):
__snake_case : Tuple = FeaturesManager.determine_framework(self.test_model )
| 0 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class snake_case__ :
def __init__( self : List[str] , __a : Optional[int] , __a : int=14 , __a : Dict=7 , __a : Any=True , __a : str=True , __a : Union[str, Any]=True , __a : Optional[Any]=True , __a : Tuple=True , __a : int=99 , __a : int=32 , __a : List[str]=5 , __a : int=4 , __a : Optional[Any]=37 , __a : Any="gelu" , __a : Optional[int]=0.1 , __a : str=0.1 , __a : Tuple=512 , __a : Tuple=16 , __a : List[str]=2 , __a : Optional[int]=0.0_2 , __a : Tuple=3 , __a : Union[str, Any]=4 , __a : Dict=None , ) -> Tuple:
'''simple docstring'''
__snake_case : int = parent
__snake_case : str = batch_size
__snake_case : List[Any] = seq_length
__snake_case : Dict = is_training
__snake_case : Optional[Any] = use_token_type_ids
__snake_case : Optional[int] = use_input_mask
__snake_case : Optional[int] = use_labels
__snake_case : Dict = use_mc_token_ids
__snake_case : List[Any] = vocab_size
__snake_case : List[str] = hidden_size
__snake_case : List[str] = num_hidden_layers
__snake_case : Optional[Any] = num_attention_heads
__snake_case : Any = intermediate_size
__snake_case : Union[str, Any] = hidden_act
__snake_case : Optional[Any] = hidden_dropout_prob
__snake_case : Optional[Any] = attention_probs_dropout_prob
__snake_case : int = max_position_embeddings
__snake_case : str = type_vocab_size
__snake_case : List[Any] = type_sequence_label_size
__snake_case : Optional[Any] = initializer_range
__snake_case : int = num_labels
__snake_case : Dict = num_choices
__snake_case : int = scope
__snake_case : Optional[int] = self.vocab_size - 1
def A_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case : Optional[int] = None
if self.use_input_mask:
__snake_case : Dict = random_attention_mask([self.batch_size, self.seq_length] )
__snake_case : Any = None
if self.use_token_type_ids:
__snake_case : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__snake_case : List[Any] = None
if self.use_mc_token_ids:
__snake_case : Dict = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
__snake_case : Optional[Any] = None
__snake_case : Tuple = None
__snake_case : List[Any] = None
if self.use_labels:
__snake_case : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__snake_case : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__snake_case : List[str] = ids_tensor([self.batch_size] , self.num_choices )
__snake_case : int = self.get_config()
__snake_case : str = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def A_ ( self : Any , __a : int , __a : Dict , __a : Union[str, Any] , __a : List[Any] , __a : Optional[Any] , *__a : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[str] = CTRLModel(config=__a )
model.to(__a )
model.eval()
model(__a , token_type_ids=__a , head_mask=__a )
model(__a , token_type_ids=__a )
__snake_case : Tuple = model(__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def A_ ( self : Any , __a : List[Any] , __a : str , __a : Tuple , __a : Optional[Any] , __a : List[Any] , *__a : List[str] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Optional[int] = CTRLLMHeadModel(__a )
model.to(__a )
model.eval()
__snake_case : List[Any] = model(__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Any = self.prepare_config_and_inputs()
(
(
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) ,
) : List[str] = config_and_inputs
__snake_case : Union[str, Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'head_mask': head_mask}
return config, inputs_dict
def A_ ( self : int , __a : int , __a : Optional[Any] , __a : Optional[Any] , __a : List[str] , *__a : List[Any] ) -> Tuple:
'''simple docstring'''
__snake_case : int = self.num_labels
__snake_case : Union[str, Any] = CTRLForSequenceClassification(__a )
model.to(__a )
model.eval()
__snake_case : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__snake_case : Optional[Any] = model(__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class snake_case__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
A__ = (CTRLLMHeadModel,) if is_torch_available() else ()
A__ = (
{
'''feature-extraction''': CTRLModel,
'''text-classification''': CTRLForSequenceClassification,
'''text-generation''': CTRLLMHeadModel,
'''zero-shot''': CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
A__ = True
A__ = False
A__ = False
def A_ ( self : Union[str, Any] , __a : Optional[Any] , __a : List[str] , __a : int , __a : int , __a : List[str] ) -> Union[str, Any]:
'''simple docstring'''
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def A_ ( self : Optional[int] ) -> str:
'''simple docstring'''
__snake_case : List[Any] = CTRLModelTester(self )
__snake_case : Tuple = ConfigTester(self , config_class=__a , n_embd=37 )
def A_ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def A_ ( self : Dict ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*__a )
def A_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__a )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def A_ ( self : Optional[int] ) -> int:
'''simple docstring'''
pass
@slow
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Tuple = CTRLModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@unittest.skip('The model doesn\'t support left padding' ) # and it's not used enough to be worth fixing :)
def A_ ( self : Any ) -> Any:
'''simple docstring'''
pass
@require_torch
class snake_case__ ( unittest.TestCase ):
def A_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__snake_case : Any = CTRLLMHeadModel.from_pretrained('ctrl' )
model.to(__a )
__snake_case : Optional[Any] = torch.tensor(
[[11859, 0, 1611, 8]] , dtype=torch.long , device=__a ) # Legal the president is
__snake_case : Optional[int] = [
11859,
0,
1611,
8,
5,
150,
26449,
2,
19,
348,
469,
3,
2595,
48,
20740,
246533,
246533,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
__snake_case : Any = model.generate(__a , do_sample=__a )
self.assertListEqual(output_ids[0].tolist() , __a )
| 0 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ProphetNetTokenizer
A__ = False
def A_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
super().setUp()
__snake_case : Dict = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__snake_case : Any = 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 A_ ( self : int , __a : Union[str, Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[int] = 'UNwant\u00E9d,running'
__snake_case : List[str] = 'unwanted, running'
return input_text, output_text
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Dict = self.tokenizer_class(self.vocab_file )
__snake_case : List[str] = 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 A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : List[str] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Optional[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 A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[Any] = 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 A_ ( self : int ) -> Any:
'''simple docstring'''
__snake_case : int = 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 A_ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = 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 A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
__snake_case : str = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[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 A_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__snake_case : List[Any] = {}
for i, token in enumerate(__a ):
__snake_case : List[str] = i
__snake_case : 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'] )
@require_torch
def A_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[Any] = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : int = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__snake_case : str = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
__snake_case : Union[str, Any] = tokenizer(__a , padding=__a , return_tensors='pt' )
self.assertIsInstance(__a , __a )
__snake_case : int = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__a , __a )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
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 A_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
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 A_ ( self : List[Any] ) -> int:
'''simple docstring'''
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(' ' ) )
@slow
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : Optional[int] = tokenizer.encode('sequence builders' , add_special_tokens=__a )
__snake_case : Optional[int] = tokenizer.encode('multi-sequence build' , add_special_tokens=__a )
__snake_case : Optional[Any] = tokenizer.build_inputs_with_special_tokens(__a )
__snake_case : List[Any] = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 0 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A__ : str = logging.get_logger(__name__)
A__ : List[str] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ : Tuple = {
'''vocab_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
},
'''merges_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
},
}
A__ : List[Any] = {
'''gpt2''': 1_0_2_4,
'''gpt2-medium''': 1_0_2_4,
'''gpt2-large''': 1_0_2_4,
'''gpt2-xl''': 1_0_2_4,
'''distilgpt2''': 1_0_2_4,
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = ['''input_ids''', '''attention_mask''']
A__ = GPTaTokenizer
def __init__( self : List[str] , __a : int=None , __a : Union[str, Any]=None , __a : List[str]=None , __a : Union[str, Any]="<|endoftext|>" , __a : Optional[int]="<|endoftext|>" , __a : List[Any]="<|endoftext|>" , __a : Dict=False , **__a : List[str] , ) -> Tuple:
'''simple docstring'''
super().__init__(
__a , __a , tokenizer_file=__a , unk_token=__a , bos_token=__a , eos_token=__a , add_prefix_space=__a , **__a , )
__snake_case : int = kwargs.pop('add_bos_token' , __a )
__snake_case : Any = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , __a ) != add_prefix_space:
__snake_case : Union[str, Any] = getattr(__a , pre_tok_state.pop('type' ) )
__snake_case : Optional[int] = add_prefix_space
__snake_case : int = pre_tok_class(**__a )
__snake_case : int = add_prefix_space
def A_ ( self : Dict , *__a : Dict , **__a : int ) -> BatchEncoding:
'''simple docstring'''
__snake_case : List[Any] = kwargs.get('is_split_into_words' , __a )
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(*__a , **__a )
def A_ ( self : List[Any] , *__a : str , **__a : Optional[int] ) -> BatchEncoding:
'''simple docstring'''
__snake_case : List[str] = kwargs.get('is_split_into_words' , __a )
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(*__a , **__a )
def A_ ( self : Dict , __a : str , __a : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
__snake_case : Dict = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
def A_ ( self : Dict , __a : "Conversation" ) -> List[int]:
'''simple docstring'''
__snake_case : Optional[Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__a , add_special_tokens=__a ) + [self.eos_token_id] )
if len(__a ) > self.model_max_length:
__snake_case : Tuple = input_ids[-self.model_max_length :]
return input_ids
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NllbMoeForConditionalGeneration''',
'''NllbMoeModel''',
'''NllbMoePreTrainedModel''',
'''NllbMoeTop2Router''',
'''NllbMoeSparseMLP''',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
A__ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : int = logging.get_logger(__name__)
A__ : int = {
'''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''timesformer'''
def __init__( self : List[str] , __a : List[Any]=224 , __a : Union[str, Any]=16 , __a : str=3 , __a : int=8 , __a : List[Any]=768 , __a : Dict=12 , __a : Optional[int]=12 , __a : Optional[int]=3072 , __a : Dict="gelu" , __a : Any=0.0 , __a : List[str]=0.0 , __a : Union[str, Any]=0.0_2 , __a : Tuple=1e-6 , __a : Union[str, Any]=True , __a : Optional[Any]="divided_space_time" , __a : Any=0 , **__a : int , ) -> int:
'''simple docstring'''
super().__init__(**__a )
__snake_case : Optional[Any] = image_size
__snake_case : Optional[int] = patch_size
__snake_case : int = num_channels
__snake_case : int = num_frames
__snake_case : Any = hidden_size
__snake_case : Union[str, Any] = num_hidden_layers
__snake_case : List[str] = num_attention_heads
__snake_case : Dict = intermediate_size
__snake_case : Optional[Any] = hidden_act
__snake_case : Union[str, Any] = hidden_dropout_prob
__snake_case : List[str] = attention_probs_dropout_prob
__snake_case : int = initializer_range
__snake_case : Any = layer_norm_eps
__snake_case : Optional[int] = qkv_bias
__snake_case : Optional[int] = attention_type
__snake_case : Optional[int] = drop_path_rate
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
__snake_case : Optional[Any] = gray_code_sequence_string(_UpperCAmelCase )
#
# convert them to integers
for i in range(len(_UpperCAmelCase ) ):
__snake_case : Optional[Any] = int(sequence[i] ,2 )
return sequence
def a_ ( _UpperCAmelCase : int ) -> list:
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__snake_case : Dict = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__snake_case : Dict = gray_code_sequence_string(bit_count - 1 )
__snake_case : Any = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__snake_case : str = '0' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__snake_case : Any = '1' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A__ : Optional[Any] = '''.'''
if __name__ == "__main__":
A__ : Optional[Any] = os.path.join(REPO_PATH, '''utils/documentation_tests.txt''')
A__ : int = []
A__ : str = []
with open(doctest_file_path) as fp:
for line in fp:
A__ : Union[str, Any] = line.strip()
A__ : Optional[Any] = os.path.join(REPO_PATH, line)
if not (os.path.isfile(path) or os.path.isdir(path)):
non_existent_paths.append(line)
all_paths.append(path)
if len(non_existent_paths) > 0:
A__ : Union[str, Any] = '''\n'''.join(non_existent_paths)
raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
if all_paths != sorted(all_paths):
raise ValueError('''Files in `utils/documentation_tests.txt` are not in alphabetical order.''')
| 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case__ ( unittest.TestCase ):
def A_ ( self : int ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = tempfile.mkdtemp()
# fmt: off
__snake_case : List[str] = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
# fmt: on
__snake_case : Any = 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] ) )
__snake_case : List[str] = {
'do_resize': True,
'size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.5, 0.5, 0.5],
'image_std': [0.5, 0.5, 0.5],
}
__snake_case : Optional[Any] = os.path.join(self.tmpdirname , __a )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(__a , __a )
def A_ ( self : Optional[int] , **__a : Dict ) -> int:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : int , **__a : Dict ) -> Tuple:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__snake_case : List[str] = [Image.fromarray(np.moveaxis(__a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : Dict = self.get_image_processor()
__snake_case : Any = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
processor.save_pretrained(self.tmpdirname )
__snake_case : Any = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[Any] = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__snake_case : Optional[Any] = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__snake_case : Tuple = self.get_image_processor(do_normalize=__a , padding_value=1.0 )
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : str = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = self.prepare_image_inputs()
__snake_case : List[str] = image_processor(__a , return_tensors='np' )
__snake_case : List[str] = processor(images=__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 A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Optional[int] = 'lower newer'
__snake_case : Dict = processor(text=__a )
__snake_case : List[Any] = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : int = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : List[Any] = 'lower newer'
__snake_case : Optional[Any] = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with self.assertRaises(__a ):
processor()
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_image_processor()
__snake_case : Any = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__snake_case : int = processor.batch_decode(__a )
__snake_case : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def A_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[str] = self.get_image_processor()
__snake_case : Dict = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Union[str, Any] = 'lower newer'
__snake_case : Tuple = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 0 | 1 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A__ : Optional[int] = logging.get_logger(__name__)
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : List[str]=False ) -> Dict:
__snake_case : Dict = OrderedDict()
for key, value in state_dict.items():
if encoder_only and not key.startswith('head' ):
__snake_case : Any = 'segformer.encoder.' + key
if key.startswith('backbone' ):
__snake_case : List[Any] = key.replace('backbone' ,'segformer.encoder' )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
__snake_case : Union[str, Any] = key[key.find('patch_embed' ) + len('patch_embed' )]
__snake_case : Optional[int] = key.replace(f'''patch_embed{idx}''' ,f'''patch_embeddings.{int(_UpperCAmelCase )-1}''' )
if "norm" in key:
__snake_case : List[str] = key.replace('norm' ,'layer_norm' )
if "segformer.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
__snake_case : Tuple = key[key.find('segformer.encoder.layer_norm' ) + len('segformer.encoder.layer_norm' )]
__snake_case : Union[str, Any] = key.replace(f'''layer_norm{idx}''' ,f'''layer_norm.{int(_UpperCAmelCase )-1}''' )
if "layer_norm1" in key:
__snake_case : str = key.replace('layer_norm1' ,'layer_norm_1' )
if "layer_norm2" in key:
__snake_case : int = key.replace('layer_norm2' ,'layer_norm_2' )
if "block" in key:
# replace for example block1 by block.0
__snake_case : Dict = key[key.find('block' ) + len('block' )]
__snake_case : List[str] = key.replace(f'''block{idx}''' ,f'''block.{int(_UpperCAmelCase )-1}''' )
if "attn.q" in key:
__snake_case : Dict = key.replace('attn.q' ,'attention.self.query' )
if "attn.proj" in key:
__snake_case : List[str] = key.replace('attn.proj' ,'attention.output.dense' )
if "attn" in key:
__snake_case : int = key.replace('attn' ,'attention.self' )
if "fc1" in key:
__snake_case : Optional[Any] = key.replace('fc1' ,'dense1' )
if "fc2" in key:
__snake_case : int = key.replace('fc2' ,'dense2' )
if "linear_pred" in key:
__snake_case : Optional[Any] = key.replace('linear_pred' ,'classifier' )
if "linear_fuse" in key:
__snake_case : Any = key.replace('linear_fuse.conv' ,'linear_fuse' )
__snake_case : List[str] = key.replace('linear_fuse.bn' ,'batch_norm' )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
__snake_case : int = key[key.find('linear_c' ) + len('linear_c' )]
__snake_case : List[str] = key.replace(f'''linear_c{idx}''' ,f'''linear_c.{int(_UpperCAmelCase )-1}''' )
if key.startswith('head' ):
__snake_case : Any = key.replace('head' ,'classifier' )
__snake_case : List[str] = value
return new_state_dict
def a_ ( _UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : List[str] ) -> List[Any]:
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
__snake_case : List[Any] = state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight''' )
__snake_case : int = state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.bias''' )
# next, add keys and values (in that order) to the state dict
__snake_case : int = kv_weight[
: config.hidden_sizes[i], :
]
__snake_case : List[str] = kv_bias[: config.hidden_sizes[i]]
__snake_case : Union[str, Any] = kv_weight[
config.hidden_sizes[i] :, :
]
__snake_case : Optional[Any] = kv_bias[
config.hidden_sizes[i] :
]
def a_ ( ) -> str:
__snake_case : int = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__snake_case : Optional[int] = Image.open(requests.get(_UpperCAmelCase ,stream=_UpperCAmelCase ).raw )
return image
@torch.no_grad()
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : str ,_UpperCAmelCase : Union[str, Any] ) -> List[str]:
__snake_case : List[Any] = SegformerConfig()
__snake_case : Optional[Any] = False
# set attributes based on model_name
__snake_case : int = 'huggingface/label-files'
if "segformer" in model_name:
__snake_case : Tuple = model_name[len('segformer.' ) : len('segformer.' ) + 2]
if "ade" in model_name:
__snake_case : Optional[Any] = 1_50
__snake_case : Optional[int] = 'ade20k-id2label.json'
__snake_case : Optional[Any] = (1, 1_50, 1_28, 1_28)
elif "city" in model_name:
__snake_case : Any = 19
__snake_case : List[Any] = 'cityscapes-id2label.json'
__snake_case : Tuple = (1, 19, 1_28, 1_28)
else:
raise ValueError(f'''Model {model_name} not supported''' )
elif "mit" in model_name:
__snake_case : Union[str, Any] = True
__snake_case : int = model_name[4:6]
__snake_case : Union[str, Any] = 10_00
__snake_case : str = 'imagenet-1k-id2label.json'
__snake_case : Any = (1, 10_00)
else:
raise ValueError(f'''Model {model_name} not supported''' )
# set config attributes
__snake_case : List[Any] = json.load(open(hf_hub_download(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset' ) ,'r' ) )
__snake_case : Optional[int] = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__snake_case : Dict = idalabel
__snake_case : List[Any] = {v: k for k, v in idalabel.items()}
if size == "b0":
pass
elif size == "b1":
__snake_case : List[Any] = [64, 1_28, 3_20, 5_12]
__snake_case : str = 2_56
elif size == "b2":
__snake_case : int = [64, 1_28, 3_20, 5_12]
__snake_case : str = 7_68
__snake_case : Optional[int] = [3, 4, 6, 3]
elif size == "b3":
__snake_case : Any = [64, 1_28, 3_20, 5_12]
__snake_case : int = 7_68
__snake_case : Tuple = [3, 4, 18, 3]
elif size == "b4":
__snake_case : Any = [64, 1_28, 3_20, 5_12]
__snake_case : Union[str, Any] = 7_68
__snake_case : Union[str, Any] = [3, 8, 27, 3]
elif size == "b5":
__snake_case : Any = [64, 1_28, 3_20, 5_12]
__snake_case : int = 7_68
__snake_case : List[str] = [3, 6, 40, 3]
else:
raise ValueError(f'''Size {size} not supported''' )
# load image processor (only resize + normalize)
__snake_case : Optional[Any] = SegformerImageProcessor(
image_scale=(5_12, 5_12) ,keep_ratio=_UpperCAmelCase ,align=_UpperCAmelCase ,do_random_crop=_UpperCAmelCase )
# prepare image
__snake_case : Dict = prepare_img()
__snake_case : List[str] = image_processor(images=_UpperCAmelCase ,return_tensors='pt' ).pixel_values
logger.info(f'''Converting model {model_name}...''' )
# load original state dict
if encoder_only:
__snake_case : List[Any] = torch.load(_UpperCAmelCase ,map_location=torch.device('cpu' ) )
else:
__snake_case : Optional[Any] = torch.load(_UpperCAmelCase ,map_location=torch.device('cpu' ) )['state_dict']
# rename keys
__snake_case : Optional[int] = rename_keys(_UpperCAmelCase ,encoder_only=_UpperCAmelCase )
if not encoder_only:
del state_dict["decode_head.conv_seg.weight"]
del state_dict["decode_head.conv_seg.bias"]
# key and value matrices need special treatment
read_in_k_v(_UpperCAmelCase ,_UpperCAmelCase )
# create HuggingFace model and load state dict
if encoder_only:
__snake_case : Dict = False
__snake_case : Union[str, Any] = SegformerForImageClassification(_UpperCAmelCase )
else:
__snake_case : Dict = SegformerForSemanticSegmentation(_UpperCAmelCase )
model.load_state_dict(_UpperCAmelCase )
model.eval()
# forward pass
__snake_case : List[str] = model(_UpperCAmelCase )
__snake_case : Tuple = outputs.logits
# set expected_slice based on model name
# ADE20k checkpoints
if model_name == "segformer.b0.512x512.ade.160k":
__snake_case : Tuple = torch.tensor(
[
[[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
[[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
[[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
] )
elif model_name == "segformer.b1.512x512.ade.160k":
__snake_case : str = torch.tensor(
[
[[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
[[-1_2.6_9_1_8, -1_3.8_9_9_4, -1_3.7_1_3_7], [-1_3.3_1_9_6, -1_5.7_5_2_3, -1_5.4_7_8_9], [-1_2.9_3_4_3, -1_4.8_7_5_7, -1_4.9_6_8_9]],
[[-1_1.1_9_1_1, -1_1.9_4_2_1, -1_1.3_2_4_3], [-1_1.3_3_4_2, -1_3.6_8_3_9, -1_3.3_5_8_1], [-1_0.3_9_0_9, -1_2.1_8_3_2, -1_2.4_8_5_8]],
] )
elif model_name == "segformer.b2.512x512.ade.160k":
__snake_case : Optional[Any] = torch.tensor(
[
[[-1_1.8_1_7_3, -1_4.3_8_5_0, -1_6.3_1_2_8], [-1_4.5_6_4_8, -1_6.5_8_0_4, -1_8.6_5_6_8], [-1_4.7_2_2_3, -1_5.7_3_8_7, -1_8.4_2_1_8]],
[[-1_5.7_2_9_0, -1_7.9_1_7_1, -1_9.4_4_2_3], [-1_8.3_1_0_5, -1_9.9_4_4_8, -2_1.4_6_6_1], [-1_7.9_2_9_6, -1_8.6_4_9_7, -2_0.7_9_1_0]],
[[-1_5.0_7_8_3, -1_7.0_3_3_6, -1_8.2_7_8_9], [-1_6.8_7_7_1, -1_8.6_8_7_0, -2_0.1_6_1_2], [-1_6.2_4_5_4, -1_7.1_4_2_6, -1_9.5_0_5_5]],
] )
elif model_name == "segformer.b3.512x512.ade.160k":
__snake_case : Union[str, Any] = torch.tensor(
[
[[-9.0_8_7_8, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3_1_4_4, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2_2_9_4, -1_0.3_8_5_5, -1_0.5_7_0_4]],
[[-1_2.2_3_1_6, -1_3.9_0_6_8, -1_3.6_1_0_2], [-1_2.9_1_6_1, -1_4.3_7_0_2, -1_4.3_2_3_5], [-1_2.5_2_3_3, -1_3.7_1_7_4, -1_3.7_9_3_2]],
[[-1_4.6_2_7_5, -1_5.2_4_9_0, -1_4.9_7_2_7], [-1_4.3_4_0_0, -1_5.9_6_8_7, -1_6.2_8_2_7], [-1_4.1_4_8_4, -1_5.4_0_3_3, -1_5.8_9_3_7]],
] )
elif model_name == "segformer.b4.512x512.ade.160k":
__snake_case : Dict = torch.tensor(
[
[[-1_2.3_1_4_4, -1_3.2_4_4_7, -1_4.0_8_0_2], [-1_3.3_6_1_4, -1_4.5_8_1_6, -1_5.6_1_1_7], [-1_3.3_3_4_0, -1_4.4_4_3_3, -1_6.2_2_1_9]],
[[-1_9.2_7_8_1, -2_0.4_1_2_8, -2_0.7_5_0_6], [-2_0.6_1_5_3, -2_1.6_5_6_6, -2_2.0_9_9_8], [-1_9.9_8_0_0, -2_1.0_4_3_0, -2_2.1_4_9_4]],
[[-1_8.8_7_3_9, -1_9.7_8_0_4, -2_1.1_8_3_4], [-2_0.1_2_3_3, -2_1.6_7_6_5, -2_3.2_9_4_4], [-2_0.0_3_1_5, -2_1.2_6_4_1, -2_3.6_9_4_4]],
] )
elif model_name == "segformer.b5.640x640.ade.160k":
__snake_case : Union[str, Any] = torch.tensor(
[
[[-9.5_5_2_4, -1_2.0_8_3_5, -1_1.7_3_4_8], [-1_0.5_2_2_9, -1_3.6_4_4_6, -1_4.5_6_6_2], [-9.5_8_4_2, -1_2.8_8_5_1, -1_3.9_4_1_4]],
[[-1_5.3_4_3_2, -1_7.5_3_2_3, -1_7.0_8_1_8], [-1_6.3_3_3_0, -1_8.9_2_5_5, -1_9.2_1_0_1], [-1_5.1_3_4_0, -1_7.7_8_4_8, -1_8.3_9_7_1]],
[[-1_2.6_0_7_2, -1_4.9_4_8_6, -1_4.6_6_3_1], [-1_3.7_6_2_9, -1_7.0_9_0_7, -1_7.7_7_4_5], [-1_2.7_8_9_9, -1_6.1_6_9_5, -1_7.1_6_7_1]],
] )
# Cityscapes checkpoints
elif model_name == "segformer.b0.1024x1024.city.160k":
__snake_case : List[str] = torch.tensor(
[
[[-1_1.9_2_9_5, -1_3.4_0_5_7, -1_4.8_1_0_6], [-1_3.3_4_3_1, -1_4.8_1_7_9, -1_5.3_7_8_1], [-1_4.2_8_3_6, -1_5.5_9_4_2, -1_6.1_5_8_8]],
[[-1_1.4_9_0_6, -1_2.8_0_6_7, -1_3.6_5_6_4], [-1_3.1_1_8_9, -1_4.0_5_0_0, -1_4.1_5_4_3], [-1_3.8_7_4_8, -1_4.5_1_3_6, -1_4.8_7_8_9]],
[[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
] )
elif model_name == "segformer.b0.512x1024.city.160k":
__snake_case : int = torch.tensor(
[
[[-7.8_2_1_7, -9.8_7_6_7, -1_0.1_7_1_7], [-9.4_4_3_8, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7_9_3_9, -1_2.3_4_9_5, -1_2.1_0_7_9]],
[[-7.1_5_1_4, -9.5_3_3_6, -1_0.0_8_6_0], [-9.7_7_7_6, -1_1.6_8_2_2, -1_1.8_4_3_9], [-1_0.1_4_1_1, -1_2.7_6_5_5, -1_2.8_9_7_2]],
[[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
] )
elif model_name == "segformer.b0.640x1280.city.160k":
__snake_case : int = torch.tensor(
[
[
[-1.1372E01, -1.2787E01, -1.3477E01],
[-1.2536E01, -1.4194E01, -1.4409E01],
[-1.3217E01, -1.4888E01, -1.5327E01],
],
[
[-1.4791E01, -1.7122E01, -1.8277E01],
[-1.7163E01, -1.9192E01, -1.9533E01],
[-1.7897E01, -1.9991E01, -2.0315E01],
],
[
[7.6723E-01, 4.1921E-01, -7.7878E-02],
[4.7772E-01, 9.5557E-03, -2.8082E-01],
[3.6032E-01, -2.4826E-01, -5.1168E-01],
],
] )
elif model_name == "segformer.b0.768x768.city.160k":
__snake_case : Optional[int] = torch.tensor(
[
[[-9.4_9_5_9, -1_1.3_0_8_7, -1_1.7_4_7_9], [-1_1.0_0_2_5, -1_2.6_5_4_0, -1_2.3_3_1_9], [-1_1.4_0_6_4, -1_3.0_4_8_7, -1_2.9_9_0_5]],
[[-9.8_9_0_5, -1_1.3_0_8_4, -1_2.0_8_5_4], [-1_1.1_7_2_6, -1_2.7_6_9_8, -1_2.9_5_8_3], [-1_1.5_9_8_5, -1_3.3_2_7_8, -1_4.1_7_7_4]],
[[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
] )
elif model_name == "segformer.b1.1024x1024.city.160k":
__snake_case : Tuple = torch.tensor(
[
[[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
[[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
[[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
] )
elif model_name == "segformer.b2.1024x1024.city.160k":
__snake_case : Optional[Any] = torch.tensor(
[
[[-1_6.0_9_7_6, -1_6.4_8_5_6, -1_7.3_9_6_2], [-1_6.6_2_3_4, -1_9.0_3_4_2, -1_9.7_6_8_5], [-1_6.0_9_0_0, -1_8.0_6_6_1, -1_9.1_1_8_0]],
[[-1_8.4_7_5_0, -1_8.8_4_8_8, -1_9.5_0_7_4], [-1_9.4_0_3_0, -2_2.1_5_7_0, -2_2.5_9_7_7], [-1_9.1_1_9_1, -2_0.8_4_8_6, -2_2.3_7_8_3]],
[[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
] )
elif model_name == "segformer.b3.1024x1024.city.160k":
__snake_case : Union[str, Any] = torch.tensor(
[
[[-1_4.2_0_8_1, -1_4.4_7_3_2, -1_4.1_9_7_7], [-1_4.5_8_6_7, -1_6.4_4_2_3, -1_6.6_3_5_6], [-1_3.4_4_4_1, -1_4.9_6_8_5, -1_6.8_6_9_6]],
[[-1_4.4_5_7_6, -1_4.7_0_7_3, -1_5.0_4_5_1], [-1_5.0_8_1_6, -1_7.6_2_3_7, -1_7.9_8_7_3], [-1_4.4_2_1_3, -1_6.0_1_9_9, -1_8.5_9_9_2]],
[[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
] )
elif model_name == "segformer.b4.1024x1024.city.160k":
__snake_case : Optional[int] = torch.tensor(
[
[[-1_1.7_7_3_7, -1_1.9_5_2_6, -1_1.3_2_7_3], [-1_3.6_6_9_2, -1_4.4_5_7_4, -1_3.8_8_7_8], [-1_3.8_9_3_7, -1_4.6_9_2_4, -1_5.9_3_4_5]],
[[-1_4.6_7_0_6, -1_4.5_3_3_0, -1_4.1_3_0_6], [-1_6.1_5_0_2, -1_6.8_1_8_0, -1_6.4_2_6_9], [-1_6.8_3_3_8, -1_7.8_9_3_9, -2_0.1_7_4_6]],
[[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
] )
elif model_name == "segformer.b5.1024x1024.city.160k":
__snake_case : int = torch.tensor(
[
[[-1_2.5_6_4_1, -1_3.4_7_7_7, -1_3.0_6_8_4], [-1_3.9_5_8_7, -1_5.8_9_8_3, -1_6.6_5_5_7], [-1_3.3_1_0_9, -1_5.7_3_5_0, -1_6.3_1_4_1]],
[[-1_4.7_0_7_4, -1_5.4_3_5_2, -1_4.5_9_4_4], [-1_6.6_3_5_3, -1_8.1_6_6_3, -1_8.6_1_2_0], [-1_5.1_7_0_2, -1_8.0_3_2_9, -1_8.1_5_4_7]],
[[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
] )
else:
__snake_case : Any = logits.argmax(-1 ).item()
print('Predicted class:' ,model.config.idalabel[predicted_class_idx] )
# verify logits
if not encoder_only:
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3, :3, :3] ,_UpperCAmelCase ,atol=1E-2 )
# finally, save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''segformer.b0.512x512.ade.160k''',
type=str,
help='''Name of the model you\'d like to convert.''',
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path 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.'''
)
A__ : Optional[int] = parser.parse_args()
convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a_ ( _UpperCAmelCase : List[Any] ) -> Tuple:
__snake_case : str = []
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
) )
return embed
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Optional[int] ) -> List[str]:
__snake_case : Tuple = []
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
) )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
return attention_weights
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Dict:
__snake_case : Union[str, Any] = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token') )
return token
def a_ ( ) -> Optional[Any]:
__snake_case : Any = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ) -> Tuple:
__snake_case : List[str] = 'imagenet-1k-id2label.json'
__snake_case : Dict = 10_00
__snake_case : Union[str, Any] = 'huggingface/label-files'
__snake_case : str = num_labels
__snake_case : str = json.load(open(cached_download(hf_hub_url(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset' ) ) ,'r' ) )
__snake_case : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__snake_case : Optional[Any] = idalabel
__snake_case : str = {v: k for k, v in idalabel.items()}
__snake_case : Dict = CvtConfig(num_labels=_UpperCAmelCase ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' ,1 )[-1][4:6] == "13":
__snake_case : Tuple = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' ,1 )[-1][4:6] == "21":
__snake_case : str = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__snake_case : Dict = [2, 2, 20]
__snake_case : Any = [3, 12, 16]
__snake_case : Tuple = [1_92, 7_68, 10_24]
__snake_case : str = CvtForImageClassification(_UpperCAmelCase )
__snake_case : List[Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
__snake_case : int = image_size
__snake_case : int = torch.load(_UpperCAmelCase ,map_location=torch.device('cpu' ) )
__snake_case : List[Any] = OrderedDict()
__snake_case : Union[str, Any] = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__snake_case : Optional[Any] = list_of_state_dict + cls_token(_UpperCAmelCase )
__snake_case : Tuple = list_of_state_dict + embeddings(_UpperCAmelCase )
for cnt in range(config.depth[idx] ):
__snake_case : Optional[int] = list_of_state_dict + attention(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
__snake_case : List[str] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
A__ : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=3_8_4,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
A__ : Tuple = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 0 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ShapEPipeline
A__ = ['''prompt''']
A__ = ['''prompt''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return 32
@property
def A_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
return 8
@property
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__a )
@property
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Dict = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
__snake_case : Optional[Any] = PriorTransformer(**__a )
return model
@property
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Tuple = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
__snake_case : Optional[int] = ShapERenderer(**__a )
return model
def A_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : Union[str, Any] = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : Optional[Any] = self.dummy_renderer
__snake_case : List[Any] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=__a , clip_sample=__a , clip_sample_range=1.0 , )
__snake_case : int = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def A_ ( self : Union[str, Any] , __a : Dict , __a : int=0 ) -> Optional[Any]:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : Optional[Any] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : Optional[int] = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = 'cpu'
__snake_case : Dict = self.get_dummy_components()
__snake_case : int = self.pipeline_class(**__a )
__snake_case : str = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[Any] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : Dict = output.images[0]
__snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__snake_case : str = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
__snake_case : int = torch_device == 'cpu'
__snake_case : str = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__a , relax_max_difference=__a , )
def A_ ( self : List[str] ) -> Dict:
'''simple docstring'''
__snake_case : str = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Dict = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : int = 1
__snake_case : Tuple = 2
__snake_case : Tuple = self.get_dummy_inputs(__a )
for key in inputs.keys():
if key in self.batch_params:
__snake_case : Union[str, Any] = batch_size * [inputs[key]]
__snake_case : str = pipe(**__a , num_images_per_prompt=__a )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def A_ ( self : str ) -> Dict:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
__snake_case : Union[str, Any] = ShapEPipeline.from_pretrained('openai/shap-e' )
__snake_case : Any = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = torch.Generator(device=__a ).manual_seed(0 )
__snake_case : Union[str, Any] = pipe(
'a shark' , generator=__a , guidance_scale=1_5.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__a , __a )
| 0 |
'''simple docstring'''
from __future__ import annotations
A__ : List[Any] = list[list[int]]
# assigning initial values to the grid
A__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
A__ : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def a_ ( _UpperCAmelCase : Matrix ,_UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def a_ ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def a_ ( _UpperCAmelCase : Matrix ) -> Matrix | None:
if location := find_empty_location(_UpperCAmelCase ):
__snake_case , __snake_case : Optional[int] = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 ,10 ):
if is_safe(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : Union[str, Any] = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
__snake_case : Optional[Any] = 0
return None
def a_ ( _UpperCAmelCase : Matrix ) -> None:
for row in grid:
for cell in row:
print(_UpperCAmelCase ,end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('''\nExample grid:\n''' + '''=''' * 2_0)
print_solution(example_grid)
print('''\nExample grid solution:''')
A__ : List[str] = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('''Cannot find a solution.''')
| 0 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = (PNDMScheduler,)
A__ = (('''num_inference_steps''', 50),)
def A_ ( self : Any , **__a : Any ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = {
'num_train_timesteps': 1000,
'beta_start': 0.0_0_0_1,
'beta_end': 0.0_2,
'beta_schedule': 'linear',
}
config.update(**__a )
return config
def A_ ( self : List[str] , __a : Dict=0 , **__a : str ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = dict(self.forward_default_kwargs )
__snake_case : List[Any] = kwargs.pop('num_inference_steps' , __a )
__snake_case : Dict = self.dummy_sample
__snake_case : Any = 0.1 * sample
__snake_case : str = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
__snake_case : str = self.get_scheduler_config(**__a )
__snake_case : List[str] = scheduler_class(**__a )
scheduler.set_timesteps(__a )
# copy over dummy past residuals
__snake_case : Dict = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__a )
__snake_case : Optional[int] = scheduler_class.from_pretrained(__a )
new_scheduler.set_timesteps(__a )
# copy over dummy past residuals
__snake_case : Optional[int] = dummy_past_residuals[:]
__snake_case : Optional[int] = scheduler.step_prk(__a , __a , __a , **__a ).prev_sample
__snake_case : Optional[Any] = new_scheduler.step_prk(__a , __a , __a , **__a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
__snake_case : Dict = scheduler.step_plms(__a , __a , __a , **__a ).prev_sample
__snake_case : Dict = new_scheduler.step_plms(__a , __a , __a , **__a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
pass
def A_ ( self : Any , __a : int=0 , **__a : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = dict(self.forward_default_kwargs )
__snake_case : Any = kwargs.pop('num_inference_steps' , __a )
__snake_case : Union[str, Any] = self.dummy_sample
__snake_case : Union[str, Any] = 0.1 * sample
__snake_case : Any = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
__snake_case : Optional[Any] = self.get_scheduler_config()
__snake_case : int = scheduler_class(**__a )
scheduler.set_timesteps(__a )
# copy over dummy past residuals (must be after setting timesteps)
__snake_case : Tuple = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__a )
__snake_case : int = scheduler_class.from_pretrained(__a )
# copy over dummy past residuals
new_scheduler.set_timesteps(__a )
# copy over dummy past residual (must be after setting timesteps)
__snake_case : str = dummy_past_residuals[:]
__snake_case : Dict = scheduler.step_prk(__a , __a , __a , **__a ).prev_sample
__snake_case : List[str] = new_scheduler.step_prk(__a , __a , __a , **__a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
__snake_case : List[Any] = scheduler.step_plms(__a , __a , __a , **__a ).prev_sample
__snake_case : List[str] = new_scheduler.step_plms(__a , __a , __a , **__a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def A_ ( self : Tuple , **__a : int ) -> Dict:
'''simple docstring'''
__snake_case : List[Any] = self.scheduler_classes[0]
__snake_case : Tuple = self.get_scheduler_config(**__a )
__snake_case : Any = scheduler_class(**__a )
__snake_case : int = 10
__snake_case : List[str] = self.dummy_model()
__snake_case : Any = self.dummy_sample_deter
scheduler.set_timesteps(__a )
for i, t in enumerate(scheduler.prk_timesteps ):
__snake_case : List[str] = model(__a , __a )
__snake_case : Optional[Any] = scheduler.step_prk(__a , __a , __a ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
__snake_case : Dict = model(__a , __a )
__snake_case : List[Any] = scheduler.step_plms(__a , __a , __a ).prev_sample
return sample
def A_ ( self : int ) -> str:
'''simple docstring'''
__snake_case : int = dict(self.forward_default_kwargs )
__snake_case : Any = kwargs.pop('num_inference_steps' , __a )
for scheduler_class in self.scheduler_classes:
__snake_case : List[Any] = self.get_scheduler_config()
__snake_case : Tuple = scheduler_class(**__a )
__snake_case : List[Any] = self.dummy_sample
__snake_case : Union[str, Any] = 0.1 * sample
if num_inference_steps is not None and hasattr(__a , 'set_timesteps' ):
scheduler.set_timesteps(__a )
elif num_inference_steps is not None and not hasattr(__a , 'set_timesteps' ):
__snake_case : Dict = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
__snake_case : str = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
__snake_case : Optional[Any] = dummy_past_residuals[:]
__snake_case : Optional[int] = scheduler.step_prk(__a , 0 , __a , **__a ).prev_sample
__snake_case : Union[str, Any] = scheduler.step_prk(__a , 1 , __a , **__a ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
__snake_case : List[Any] = scheduler.step_plms(__a , 0 , __a , **__a ).prev_sample
__snake_case : List[str] = scheduler.step_plms(__a , 1 , __a , **__a ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A_ ( self : str ) -> Optional[Any]:
'''simple docstring'''
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=__a )
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=__a )
__snake_case : str = self.scheduler_classes[0]
__snake_case : List[Any] = self.get_scheduler_config(steps_offset=1 )
__snake_case : int = scheduler_class(**__a )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def A_ ( self : int ) -> List[str]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1] , [0.0_0_2, 0.0_2] ):
self.check_over_configs(beta_start=__a , beta_end=__a )
def A_ ( self : Dict ) -> Any:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__a )
def A_ ( self : Optional[int] ) -> int:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a )
def A_ ( self : Tuple ) -> int:
'''simple docstring'''
for t in [1, 5, 10]:
self.check_over_forward(time_step=__a )
def A_ ( self : List[str] ) -> Any:
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=__a )
def A_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
__snake_case : Dict = 27
for scheduler_class in self.scheduler_classes:
__snake_case : int = self.dummy_sample
__snake_case : str = 0.1 * sample
__snake_case : Optional[Any] = self.get_scheduler_config()
__snake_case : Union[str, Any] = scheduler_class(**__a )
scheduler.set_timesteps(__a )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
__snake_case : int = scheduler.step_prk(__a , __a , __a ).prev_sample
def A_ ( self : Tuple ) -> str:
'''simple docstring'''
with self.assertRaises(__a ):
__snake_case : int = self.scheduler_classes[0]
__snake_case : int = self.get_scheduler_config()
__snake_case : List[Any] = scheduler_class(**__a )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def A_ ( self : Dict ) -> int:
'''simple docstring'''
__snake_case : Tuple = self.full_loop()
__snake_case : int = torch.sum(torch.abs(__a ) )
__snake_case : List[str] = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 1_9_8.1_3_1_8 ) < 1e-2
assert abs(result_mean.item() - 0.2_5_8_0 ) < 1e-3
def A_ ( self : List[str] ) -> int:
'''simple docstring'''
__snake_case : List[str] = self.full_loop(prediction_type='v_prediction' )
__snake_case : int = torch.sum(torch.abs(__a ) )
__snake_case : Union[str, Any] = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 6_7.3_9_8_6 ) < 1e-2
assert abs(result_mean.item() - 0.0_8_7_8 ) < 1e-3
def A_ ( self : Dict ) -> str:
'''simple docstring'''
# We specify different beta, so that the first alpha is 0.99
__snake_case : Any = self.full_loop(set_alpha_to_one=__a , beta_start=0.0_1 )
__snake_case : int = torch.sum(torch.abs(__a ) )
__snake_case : Optional[Any] = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 2_3_0.0_3_9_9 ) < 1e-2
assert abs(result_mean.item() - 0.2_9_9_5 ) < 1e-3
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
# We specify different beta, so that the first alpha is 0.99
__snake_case : str = self.full_loop(set_alpha_to_one=__a , beta_start=0.0_1 )
__snake_case : List[Any] = torch.sum(torch.abs(__a ) )
__snake_case : Any = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 1_8_6.9_4_8_2 ) < 1e-2
assert abs(result_mean.item() - 0.2_4_3_4 ) < 1e-3
| 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = KandinskyVaaPriorPipeline
A__ = ['''prompt''']
A__ = ['''prompt''', '''negative_prompt''']
A__ = [
'''num_images_per_prompt''',
'''generator''',
'''num_inference_steps''',
'''latents''',
'''negative_prompt''',
'''guidance_scale''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Dict ) -> List[str]:
'''simple docstring'''
return 32
@property
def A_ ( self : Any ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return self.time_input_dim
@property
def A_ ( self : str ) -> int:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return 100
@property
def A_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
__snake_case : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__a )
@property
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Any = {
'num_attention_heads': 2,
'attention_head_dim': 12,
'embedding_dim': self.text_embedder_hidden_size,
'num_layers': 1,
}
__snake_case : List[Any] = PriorTransformer(**__a )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
__snake_case : Any = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
__snake_case : Optional[Any] = CLIPVisionModelWithProjection(__a )
return model
@property
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = CLIPImageProcessor(
crop_size=224 , do_center_crop=__a , do_normalize=__a , do_resize=__a , image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , resample=3 , size=224 , )
return image_processor
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : List[str] = self.dummy_image_encoder
__snake_case : str = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : List[str] = self.dummy_image_processor
__snake_case : Any = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='sample' , num_train_timesteps=1000 , clip_sample=__a , clip_sample_range=1_0.0 , )
__snake_case : str = {
'prior': prior,
'image_encoder': image_encoder,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'scheduler': scheduler,
'image_processor': image_processor,
}
return components
def A_ ( self : List[Any] , __a : Optional[Any] , __a : Tuple=0 ) -> Any:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : List[str] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : List[Any] = {
'prompt': 'horse',
'generator': generator,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def A_ ( self : str ) -> Dict:
'''simple docstring'''
__snake_case : str = 'cpu'
__snake_case : List[str] = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Optional[Any] = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : List[str] = output.image_embeds
__snake_case : str = pipe(
**self.get_dummy_inputs(__a ) , return_dict=__a , )[0]
__snake_case : Union[str, Any] = image[0, -10:]
__snake_case : Any = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
__snake_case : List[Any] = np.array(
[-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def A_ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = torch_device == 'cpu'
__snake_case : Dict = True
__snake_case : Union[str, Any] = False
self._test_inference_batch_single_identical(
test_max_difference=__a , relax_max_difference=__a , test_mean_pixel_difference=__a , )
@skip_mps
def A_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = torch_device == 'cpu'
__snake_case : Optional[Any] = False
self._test_attention_slicing_forward_pass(
test_max_difference=__a , test_mean_pixel_difference=__a , )
| 0 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
A__ : Optional[int] = {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class snake_case__ ( unittest.TestCase ):
A__ = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
A__ = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
A__ = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
A__ = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def A_ ( self : Union[str, Any] , __a : str , __a : Optional[Any] , __a : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = ZeroShotClassificationPipeline(
model=__a , tokenizer=__a , candidate_labels=['polics', 'health'] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def A_ ( self : List[Any] , __a : Union[str, Any] , __a : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = classifier('Who are you voting for in 2020?' , candidate_labels='politics' )
self.assertEqual(__a , {'sequence': ANY(__a ), 'labels': [ANY(__a )], 'scores': [ANY(__a )]} )
# No kwarg
__snake_case : List[str] = classifier('Who are you voting for in 2020?' , ['politics'] )
self.assertEqual(__a , {'sequence': ANY(__a ), 'labels': [ANY(__a )], 'scores': [ANY(__a )]} )
__snake_case : int = classifier('Who are you voting for in 2020?' , candidate_labels=['politics'] )
self.assertEqual(__a , {'sequence': ANY(__a ), 'labels': [ANY(__a )], 'scores': [ANY(__a )]} )
__snake_case : Dict = classifier('Who are you voting for in 2020?' , candidate_labels='politics, public health' )
self.assertEqual(
__a , {'sequence': ANY(__a ), 'labels': [ANY(__a ), ANY(__a )], 'scores': [ANY(__a ), ANY(__a )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['scores'] ) ) , 1.0 )
__snake_case : Optional[Any] = classifier('Who are you voting for in 2020?' , candidate_labels=['politics', 'public health'] )
self.assertEqual(
__a , {'sequence': ANY(__a ), 'labels': [ANY(__a ), ANY(__a )], 'scores': [ANY(__a ), ANY(__a )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['scores'] ) ) , 1.0 )
__snake_case : Tuple = classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template='This text is about {}' )
self.assertEqual(__a , {'sequence': ANY(__a ), 'labels': [ANY(__a )], 'scores': [ANY(__a )]} )
# https://github.com/huggingface/transformers/issues/13846
__snake_case : List[Any] = classifier(['I am happy'] , ['positive', 'negative'] )
self.assertEqual(
__a , [
{'sequence': ANY(__a ), 'labels': [ANY(__a ), ANY(__a )], 'scores': [ANY(__a ), ANY(__a )]}
for i in range(1 )
] , )
__snake_case : Union[str, Any] = classifier(['I am happy', 'I am sad'] , ['positive', 'negative'] )
self.assertEqual(
__a , [
{'sequence': ANY(__a ), 'labels': [ANY(__a ), ANY(__a )], 'scores': [ANY(__a ), ANY(__a )]}
for i in range(2 )
] , )
with self.assertRaises(__a ):
classifier('' , candidate_labels='politics' )
with self.assertRaises(__a ):
classifier(__a , candidate_labels='politics' )
with self.assertRaises(__a ):
classifier('Who are you voting for in 2020?' , candidate_labels='' )
with self.assertRaises(__a ):
classifier('Who are you voting for in 2020?' , candidate_labels=__a )
with self.assertRaises(__a ):
classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template='Not formatting template' , )
with self.assertRaises(__a ):
classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template=__a , )
self.run_entailment_id(__a )
def A_ ( self : int , __a : Pipeline ) -> Dict:
'''simple docstring'''
__snake_case : List[str] = zero_shot_classifier.model.config
__snake_case : Union[str, Any] = config.labelaid
__snake_case : int = zero_shot_classifier.entailment_id
__snake_case : str = {'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
__snake_case : int = {'entailment': 0, 'neutral': 1, 'contradiction': 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
__snake_case : Optional[Any] = {'ENTAIL': 0, 'NON-ENTAIL': 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
__snake_case : Optional[int] = {'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
__snake_case : str = original_labelaid
self.assertEqual(__a , zero_shot_classifier.entailment_id )
@require_torch
def A_ ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
__snake_case : List[str] = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='pt' , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
'Who are you voting for in 2020?' * 100 , candidate_labels=['politics', 'public health', 'science'] )
@require_torch
def A_ ( self : List[Any] ) -> Any:
'''simple docstring'''
__snake_case : Optional[int] = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='pt' , )
__snake_case : str = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(__a ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['science', 'public health', 'politics'],
'scores': [0.3_3_3, 0.3_3_3, 0.3_3_3],
} , )
@require_tf
def A_ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : List[str] = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='tf' , )
__snake_case : str = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(__a ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['science', 'public health', 'politics'],
'scores': [0.3_3_3, 0.3_3_3, 0.3_3_3],
} , )
@slow
@require_torch
def A_ ( self : List[str] ) -> Any:
'''simple docstring'''
__snake_case : List[str] = pipeline('zero-shot-classification' , model='roberta-large-mnli' , framework='pt' )
__snake_case : List[str] = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(__a ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['politics', 'public health', 'science'],
'scores': [0.9_7_6, 0.0_1_5, 0.0_0_9],
} , )
__snake_case : str = zero_shot_classifier(
'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
' machine translation tasks show these models to be superior in quality while being more parallelizable'
' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
' English-to-German translation task, improving over the existing best results, including ensembles by'
' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
' fraction of the training costs of the best models from the literature. We show that the Transformer'
' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
' large and limited training data.' , candidate_labels=['machine learning', 'statistics', 'translation', 'vision'] , multi_label=__a , )
self.assertEqual(
nested_simplify(__a ) , {
'sequence': (
'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
' networks in an encoder-decoder configuration. The best performing models also connect the'
' encoder and decoder through an attention mechanism. We propose a new simple network'
' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
' superior in quality while being more parallelizable and requiring significantly less time to'
' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
' English-to-French translation task, our model establishes a new single-model state-of-the-art'
' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
' costs of the best models from the literature. We show that the Transformer generalizes well to'
' other tasks by applying it successfully to English constituency parsing both with large and'
' limited training data.'
),
'labels': ['translation', 'machine learning', 'vision', 'statistics'],
'scores': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
} , )
@slow
@require_tf
def A_ ( self : str ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = pipeline('zero-shot-classification' , model='roberta-large-mnli' , framework='tf' )
__snake_case : Tuple = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(__a ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['politics', 'public health', 'science'],
'scores': [0.9_7_6, 0.0_1_5, 0.0_0_9],
} , )
__snake_case : int = zero_shot_classifier(
'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
' machine translation tasks show these models to be superior in quality while being more parallelizable'
' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
' English-to-German translation task, improving over the existing best results, including ensembles by'
' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
' fraction of the training costs of the best models from the literature. We show that the Transformer'
' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
' large and limited training data.' , candidate_labels=['machine learning', 'statistics', 'translation', 'vision'] , multi_label=__a , )
self.assertEqual(
nested_simplify(__a ) , {
'sequence': (
'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
' networks in an encoder-decoder configuration. The best performing models also connect the'
' encoder and decoder through an attention mechanism. We propose a new simple network'
' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
' superior in quality while being more parallelizable and requiring significantly less time to'
' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
' English-to-French translation task, our model establishes a new single-model state-of-the-art'
' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
' costs of the best models from the literature. We show that the Transformer generalizes well to'
' other tasks by applying it successfully to English constituency parsing both with large and'
' limited training data.'
),
'labels': ['translation', 'machine learning', 'vision', 'statistics'],
'scores': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
} , )
| 0 |
'''simple docstring'''
from math import factorial
A__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def a_ ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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(_UpperCAmelCase ) )
def a_ ( _UpperCAmelCase : int = 60 ,_UpperCAmelCase : int = 1_00_00_00 ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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
__snake_case : List[str] = 0
# the cached sizes of the previous chains
__snake_case : dict[int, int] = {}
for start_chain_element in range(1 ,_UpperCAmelCase ):
# The temporary set will contain the elements of the chain
__snake_case : Optional[int] = set()
__snake_case : List[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__snake_case : str = 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(_UpperCAmelCase )
chain_set_length += 1
__snake_case : Tuple = digit_factorial_sum(_UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__snake_case : 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()}""")
| 0 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Any = logging.get_logger(__name__)
A__ : Optional[int] = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''decision_transformer'''
A__ = ['''past_key_values''']
A__ = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : str , __a : Union[str, Any]=17 , __a : Dict=4 , __a : str=128 , __a : Tuple=4096 , __a : str=True , __a : List[str]=1 , __a : Optional[Any]=1024 , __a : Any=3 , __a : List[str]=1 , __a : str=None , __a : Union[str, Any]="relu" , __a : Optional[Any]=0.1 , __a : str=0.1 , __a : List[str]=0.1 , __a : Any=1e-5 , __a : Dict=0.0_2 , __a : str=True , __a : str=True , __a : List[str]=50256 , __a : Any=50256 , __a : str=False , __a : List[str]=False , **__a : List[str] , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[Any] = state_dim
__snake_case : Dict = act_dim
__snake_case : Optional[int] = hidden_size
__snake_case : int = max_ep_len
__snake_case : Tuple = action_tanh
__snake_case : str = vocab_size
__snake_case : Tuple = n_positions
__snake_case : Optional[Any] = n_layer
__snake_case : int = n_head
__snake_case : List[str] = n_inner
__snake_case : List[Any] = activation_function
__snake_case : Optional[Any] = resid_pdrop
__snake_case : List[str] = embd_pdrop
__snake_case : List[Any] = attn_pdrop
__snake_case : Any = layer_norm_epsilon
__snake_case : Union[str, Any] = initializer_range
__snake_case : List[str] = scale_attn_weights
__snake_case : str = use_cache
__snake_case : List[Any] = scale_attn_by_inverse_layer_idx
__snake_case : Optional[int] = reorder_and_upcast_attn
__snake_case : Dict = bos_token_id
__snake_case : Tuple = eos_token_id
super().__init__(bos_token_id=__a , eos_token_id=__a , **__a )
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int = 1_00 ) -> int:
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : Union[str, Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 0 | 1 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : str = "The quick brown fox jumps over the lazy dog" ,) -> bool:
__snake_case : Optional[Any] = set()
# Replace all the whitespace in our sentence
__snake_case : int = input_str.replace(' ' ,'' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(_UpperCAmelCase ) == 26
def a_ ( _UpperCAmelCase : str = "The quick brown fox jumps over the lazy dog" ,) -> bool:
__snake_case : int = [False] * 26
for char in input_str:
if char.islower():
__snake_case : Optional[int] = True
elif char.isupper():
__snake_case : Union[str, Any] = True
return all(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : str = "The quick brown fox jumps over the lazy dog" ,) -> bool:
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def a_ ( ) -> None:
from timeit import timeit
__snake_case : Union[str, Any] = 'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
print(timeit('is_pangram()' ,setup=_UpperCAmelCase ) )
print(timeit('is_pangram_faster()' ,setup=_UpperCAmelCase ) )
print(timeit('is_pangram_fastest()' ,setup=_UpperCAmelCase ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : int = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''',
'''GroupViTTextConfig''',
'''GroupViTVisionConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple = [
'''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GroupViTModel''',
'''GroupViTPreTrainedModel''',
'''GroupViTTextModel''',
'''GroupViTVisionModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFGroupViTModel''',
'''TFGroupViTPreTrainedModel''',
'''TFGroupViTTextModel''',
'''TFGroupViTVisionModel''',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
A__ : Any = get_tests_dir('''fixtures/test_sentencepiece.model''')
A__ : Dict = {'''target_lang''': '''fi''', '''source_lang''': '''en'''}
A__ : Any = '''>>zh<<'''
A__ : List[Any] = '''Helsinki-NLP/'''
if is_torch_available():
A__ : Union[str, Any] = '''pt'''
elif is_tf_available():
A__ : Union[str, Any] = '''tf'''
else:
A__ : str = '''jax'''
@require_sentencepiece
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = MarianTokenizer
A__ = False
A__ = True
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
super().setUp()
__snake_case : List[str] = ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
__snake_case : Any = dict(zip(__a , range(len(__a ) ) ) )
__snake_case : List[str] = Path(self.tmpdirname )
save_json(__a , save_dir / VOCAB_FILES_NAMES['vocab'] )
save_json(__a , save_dir / VOCAB_FILES_NAMES['tokenizer_config_file'] )
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(__a , save_dir / VOCAB_FILES_NAMES['source_spm'] )
copyfile(__a , save_dir / VOCAB_FILES_NAMES['target_spm'] )
__snake_case : Union[str, Any] = MarianTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def A_ ( self : Any , **__a : int ) -> MarianTokenizer:
'''simple docstring'''
return MarianTokenizer.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : List[Any] , __a : Dict ) -> Any:
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : Optional[int] = '</s>'
__snake_case : List[str] = 0
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 : List[str] ) -> int:
'''simple docstring'''
__snake_case : Optional[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '</s>' )
self.assertEqual(vocab_keys[1] , '<unk>' )
self.assertEqual(vocab_keys[-1] , '<pad>' )
self.assertEqual(len(__a ) , 9 )
def A_ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 9 )
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : str = MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de''' )
__snake_case : Tuple = en_de_tokenizer(['I am a small frog'] , return_tensors=__a )
self.assertIsInstance(__a , __a )
__snake_case : Tuple = [38, 121, 14, 697, 38848, 0]
self.assertListEqual(__a , batch.input_ids[0] )
__snake_case : List[Any] = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(__a )
__snake_case : Optional[Any] = [x.name for x in Path(__a ).glob('*' )]
self.assertIn('source.spm' , __a )
MarianTokenizer.from_pretrained(__a )
def A_ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Any = self.get_tokenizer()
__snake_case : Dict = tok(
['I am a small frog' * 1000, 'I am a small frog'] , padding=__a , truncation=__a , return_tensors=__a )
self.assertIsInstance(__a , __a )
self.assertEqual(batch.input_ids.shape , (2, 512) )
def A_ ( self : Any ) -> Dict:
'''simple docstring'''
__snake_case : int = self.get_tokenizer()
__snake_case : List[str] = tok(['I am a tiny frog', 'I am a small frog'] , padding=__a , return_tensors=__a )
self.assertIsInstance(__a , __a )
self.assertEqual(batch_smaller.input_ids.shape , (2, 10) )
@slow
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
# fmt: off
__snake_case : str = {'input_ids': [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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='Helsinki-NLP/opus-mt-en-de' , revision='1a8c2263da11e68e50938f97e10cd57820bd504c' , decode_kwargs={'use_source_tokenizer': True} , )
def A_ ( self : Tuple ) -> int:
'''simple docstring'''
__snake_case : Union[str, Any] = MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs' )
__snake_case : List[Any] = 'Tämä on testi'
__snake_case : List[str] = 'This is a test'
__snake_case : Union[str, Any] = [76, 7, 2047, 2]
__snake_case : Tuple = [69, 12, 11, 940, 2]
__snake_case : Optional[Any] = tokenizer(__a ).input_ids
self.assertListEqual(__a , __a )
__snake_case : str = tokenizer(text_target=__a ).input_ids
self.assertListEqual(__a , __a )
__snake_case : Dict = tokenizer.decode(__a , skip_special_tokens=__a )
self.assertEqual(__a , __a )
| 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ShapEPipeline
A__ = ['''prompt''']
A__ = ['''prompt''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return 32
@property
def A_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
return 8
@property
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__a )
@property
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Dict = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
__snake_case : Optional[Any] = PriorTransformer(**__a )
return model
@property
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Tuple = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
__snake_case : Optional[int] = ShapERenderer(**__a )
return model
def A_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : Union[str, Any] = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : Optional[Any] = self.dummy_renderer
__snake_case : List[Any] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=__a , clip_sample=__a , clip_sample_range=1.0 , )
__snake_case : int = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def A_ ( self : Union[str, Any] , __a : Dict , __a : int=0 ) -> Optional[Any]:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : Optional[Any] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : Optional[int] = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = 'cpu'
__snake_case : Dict = self.get_dummy_components()
__snake_case : int = self.pipeline_class(**__a )
__snake_case : str = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[Any] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : Dict = output.images[0]
__snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__snake_case : str = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
__snake_case : int = torch_device == 'cpu'
__snake_case : str = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__a , relax_max_difference=__a , )
def A_ ( self : List[str] ) -> Dict:
'''simple docstring'''
__snake_case : str = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Dict = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : int = 1
__snake_case : Tuple = 2
__snake_case : Tuple = self.get_dummy_inputs(__a )
for key in inputs.keys():
if key in self.batch_params:
__snake_case : Union[str, Any] = batch_size * [inputs[key]]
__snake_case : str = pipe(**__a , num_images_per_prompt=__a )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def A_ ( self : str ) -> Dict:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
__snake_case : Union[str, Any] = ShapEPipeline.from_pretrained('openai/shap-e' )
__snake_case : Any = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = torch.Generator(device=__a ).manual_seed(0 )
__snake_case : Union[str, Any] = pipe(
'a shark' , generator=__a , guidance_scale=1_5.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__a , __a )
| 0 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
A__ : int = logging.getLogger(__name__)
require_version('''pytorch_lightning>=1.0.4''')
A__ : List[str] = {
'''base''': AutoModel,
'''sequence-classification''': AutoModelForSequenceClassification,
'''question-answering''': AutoModelForQuestionAnswering,
'''pretraining''': AutoModelForPreTraining,
'''token-classification''': AutoModelForTokenClassification,
'''language-modeling''': AutoModelWithLMHead,
'''summarization''': AutoModelForSeqaSeqLM,
'''translation''': AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
A__ : Any = {
'''linear''': get_linear_schedule_with_warmup,
'''cosine''': get_cosine_schedule_with_warmup,
'''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
'''polynomial''': get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
A__ : List[Any] = sorted(arg_to_scheduler.keys())
A__ : Optional[Any] = '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
class snake_case__ ( pl.LightningModule ):
def __init__( self : Tuple , __a : argparse.Namespace , __a : Dict=None , __a : List[str]="base" , __a : int=None , __a : Optional[Any]=None , __a : Dict=None , **__a : Union[str, Any] , ) -> List[str]:
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(__a )
__snake_case : Tuple = 0
__snake_case : Optional[Any] = Path(self.hparams.output_dir )
__snake_case : List[Any] = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
__snake_case : Optional[Any] = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({'num_labels': num_labels} if num_labels is not None else {}) , cache_dir=__a , **__a , )
else:
__snake_case : PretrainedConfig = config
__snake_case : Optional[Any] = ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
for p in extra_model_params:
if getattr(self.hparams , __a , __a ):
assert hasattr(self.config , __a ), f'''model config doesn\'t have a `{p}` attribute'''
setattr(self.config , __a , getattr(self.hparams , __a ) )
if tokenizer is None:
__snake_case : Dict = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=__a , )
else:
__snake_case : PreTrainedTokenizer = tokenizer
__snake_case : Dict = MODEL_MODES[mode]
if model is None:
__snake_case : Union[str, Any] = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool('.ckpt' in self.hparams.model_name_or_path ) , config=self.config , cache_dir=__a , )
else:
__snake_case : Optional[Any] = model
def A_ ( self : int , *__a : int , **__a : Any ) -> int:
'''simple docstring'''
__snake_case : List[Any] = self.model_type.from_pretrained(*__a , **__a )
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
__snake_case : List[str] = arg_to_scheduler[self.hparams.lr_scheduler]
__snake_case : Optional[Any] = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
__snake_case : Tuple = {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
return scheduler
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = self.model
__snake_case : str = ['bias', 'LayerNorm.weight']
__snake_case : Optional[Any] = [
{
'params': [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
'weight_decay': self.hparams.weight_decay,
},
{
'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
'weight_decay': 0.0,
},
]
if self.hparams.adafactor:
__snake_case : str = Adafactor(
__a , lr=self.hparams.learning_rate , scale_parameter=__a , relative_step=__a )
else:
__snake_case : Any = AdamW(
__a , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
__snake_case : Dict = optimizer
__snake_case : List[Any] = self.get_lr_scheduler()
return [optimizer], [scheduler]
def A_ ( self : Union[str, Any] , __a : str , __a : Tuple ) -> str:
'''simple docstring'''
return self.validation_step(__a , __a )
def A_ ( self : List[Any] , __a : Tuple ) -> List[Any]:
'''simple docstring'''
return self.validation_end(__a )
def A_ ( self : int ) -> int:
'''simple docstring'''
__snake_case : int = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
__snake_case : List[Any] = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def A_ ( self : Optional[int] , __a : Tuple ) -> int:
'''simple docstring'''
if stage == "test":
__snake_case : Union[str, Any] = len(self.test_dataloader().dataset )
else:
__snake_case : Dict = self.get_dataloader('train' , self.hparams.train_batch_size , shuffle=__a )
__snake_case : int = len(self.train_dataloader().dataset )
def A_ ( self : Union[str, Any] , __a : str , __a : int , __a : bool = False ) -> Union[str, Any]:
'''simple docstring'''
raise NotImplementedError('You must implement this for your task' )
def A_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
return self.train_loader
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return self.get_dataloader('dev' , self.hparams.eval_batch_size , shuffle=__a )
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
return self.get_dataloader('test' , self.hparams.eval_batch_size , shuffle=__a )
def A_ ( self : str , __a : Dict ) -> str:
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , 'cached_{}_{}_{}'.format(
__a , list(filter(__a , self.hparams.model_name_or_path.split('/' ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def A_ ( self : List[str] , __a : Dict[str, Any] ) -> None:
'''simple docstring'''
__snake_case : List[Any] = self.output_dir.joinpath('best_tfmr' )
__snake_case : Optional[int] = self.step_count
self.model.save_pretrained(__a )
self.tokenizer.save_pretrained(__a )
@staticmethod
def A_ ( __a : str , __a : Any ) -> Optional[Any]:
'''simple docstring'''
parser.add_argument(
'--model_name_or_path' , default=__a , type=__a , required=__a , help='Path to pretrained model or model identifier from huggingface.co/models' , )
parser.add_argument(
'--config_name' , default='' , type=__a , help='Pretrained config name or path if not the same as model_name' )
parser.add_argument(
'--tokenizer_name' , default=__a , type=__a , help='Pretrained tokenizer name or path if not the same as model_name' , )
parser.add_argument(
'--cache_dir' , default=str(Path(__a ).parent / 'test_run' / 'cache' ) , type=__a , help='Where do you want to store the pre-trained models downloaded from huggingface.co' , )
parser.add_argument(
'--encoder_layerdrop' , type=__a , help='Encoder layer dropout probability (Optional). Goes into model.config' , )
parser.add_argument(
'--decoder_layerdrop' , type=__a , help='Decoder layer dropout probability (Optional). Goes into model.config' , )
parser.add_argument(
'--dropout' , type=__a , help='Dropout probability (Optional). Goes into model.config' , )
parser.add_argument(
'--attention_dropout' , type=__a , help='Attention dropout probability (Optional). Goes into model.config' , )
parser.add_argument('--learning_rate' , default=5e-5 , type=__a , help='The initial learning rate for Adam.' )
parser.add_argument(
'--lr_scheduler' , default='linear' , choices=__a , metavar=__a , type=__a , help='Learning rate scheduler' , )
parser.add_argument('--weight_decay' , default=0.0 , type=__a , help='Weight decay if we apply some.' )
parser.add_argument('--adam_epsilon' , default=1e-8 , type=__a , help='Epsilon for Adam optimizer.' )
parser.add_argument('--warmup_steps' , default=0 , type=__a , help='Linear warmup over warmup_steps.' )
parser.add_argument('--num_workers' , default=4 , type=__a , help='kwarg passed to DataLoader' )
parser.add_argument('--num_train_epochs' , dest='max_epochs' , default=3 , type=__a )
parser.add_argument('--train_batch_size' , default=32 , type=__a )
parser.add_argument('--eval_batch_size' , default=32 , type=__a )
parser.add_argument('--adafactor' , action='store_true' )
class snake_case__ ( pl.Callback ):
def A_ ( self : int , __a : Optional[int] , __a : Optional[Any] ) -> Dict:
'''simple docstring'''
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class snake_case__ ( pl.Callback ):
def A_ ( self : Tuple , __a : List[str] , __a : Dict ) -> Any:
'''simple docstring'''
# print(pl_module.model.rag)
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(__a )
class snake_case__ ( pl.Callback ):
def A_ ( self : int , __a : int , __a : int ) -> str:
'''simple docstring'''
__snake_case : Optional[int] = trainer.lr_schedulers[0]['scheduler']
__snake_case : Optional[int] = {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(__a )
def A_ ( self : Optional[int] , __a : pl.Trainer , __a : pl.LightningModule ) -> Union[str, Any]:
'''simple docstring'''
rank_zero_info('***** Validation results *****' )
__snake_case : Union[str, Any] = trainer.callback_metrics
# Log results
for key in sorted(__a ):
if key not in ["log", "progress_bar"]:
rank_zero_info('{} = {}\n'.format(__a , str(metrics[key] ) ) )
def A_ ( self : str , __a : pl.Trainer , __a : pl.LightningModule ) -> Union[str, Any]:
'''simple docstring'''
rank_zero_info('***** Test results *****' )
__snake_case : Optional[int] = trainer.callback_metrics
# Log and save results to file
__snake_case : Optional[Any] = os.path.join(pl_module.hparams.output_dir , 'test_results.txt' )
with open(__a , 'w' ) as writer:
for key in sorted(__a ):
if key not in ["log", "progress_bar"]:
rank_zero_info('{} = {}\n'.format(__a , str(metrics[key] ) ) )
writer.write('{} = {}\n'.format(__a , str(metrics[key] ) ) )
def a_ ( _UpperCAmelCase : Dict ,_UpperCAmelCase : Dict ) -> None:
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
'--output_dir' ,default=str(Path(_UpperCAmelCase ).parent / 'test_run' / 'model_checkpoints' ) ,type=_UpperCAmelCase ,help='The output directory where the model predictions and checkpoints will be written.' ,)
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='O2' ,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_tpu_cores' ,dest='tpu_cores' ,type=_UpperCAmelCase )
parser.add_argument('--max_grad_norm' ,dest='gradient_clip_val' ,default=1.0 ,type=_UpperCAmelCase ,help='Max gradient norm' )
parser.add_argument('--do_train' ,action='store_true' ,help='Whether to run training.' )
parser.add_argument('--do_predict' ,action='store_true' ,help='Whether to run predictions on the test set.' )
parser.add_argument(
'--gradient_accumulation_steps' ,dest='accumulate_grad_batches' ,type=_UpperCAmelCase ,default=1 ,help='Number of updates steps to accumulate before performing a backward/update pass.' ,)
parser.add_argument('--seed' ,type=_UpperCAmelCase ,default=42 ,help='random seed for initialization' )
parser.add_argument(
'--data_dir' ,default=str(Path(_UpperCAmelCase ).parent / 'test_run' / 'dummy-train-data' ) ,type=_UpperCAmelCase ,help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.' ,)
def a_ ( _UpperCAmelCase : BaseTransformer ,_UpperCAmelCase : argparse.Namespace ,_UpperCAmelCase : List[Any]=None ,_UpperCAmelCase : str=True ,_UpperCAmelCase : Tuple=[] ,_UpperCAmelCase : List[str]=None ,_UpperCAmelCase : Union[str, Any]=None ,**_UpperCAmelCase : List[str] ,) -> Optional[Any]:
pl.seed_everything(args.seed )
# init model
__snake_case : Optional[Any] = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_UpperCAmelCase )
# add custom checkpoints
if checkpoint_callback is None:
__snake_case : Optional[int] = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir ,prefix='checkpoint' ,monitor='val_loss' ,mode='min' ,save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_UpperCAmelCase )
if logging_callback is None:
__snake_case : List[Any] = LoggingCallback()
__snake_case : List[str] = {}
if args.fpaa:
__snake_case : str = 16
if args.gpus > 1:
__snake_case : List[str] = 'auto'
__snake_case : Tuple = 'ddp'
__snake_case : Union[str, Any] = args.accumulate_grad_batches
__snake_case : List[Any] = None
__snake_case : Optional[int] = 'auto'
__snake_case : Union[str, Any] = pl.Trainer.from_argparse_args(
_UpperCAmelCase ,weights_summary=_UpperCAmelCase ,callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] ,logger=_UpperCAmelCase ,val_check_interval=1 ,num_sanity_val_steps=2 ,**_UpperCAmelCase ,)
if args.do_train:
trainer.fit(_UpperCAmelCase )
else:
print('RAG modeling tests with new set functions successfuly executed!' )
return trainer
| 0 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
A__ : str = [8, 5, 9, 7]
A__ : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A__ : Dict = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class snake_case__ :
def __init__( self : Union[str, Any] , __a : list[int] , __a : list[list[int]] , __a : list[list[int]] , ) -> None:
'''simple docstring'''
__snake_case : int = claim_vector
__snake_case : Optional[int] = allocated_resources_table
__snake_case : List[str] = maximum_claim_table
def A_ ( self : str ) -> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def A_ ( self : int ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def A_ ( self : int ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def A_ ( self : str ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__a ): i for i in self.__need()}
def A_ ( self : Union[str, Any] , **__a : int ) -> None:
'''simple docstring'''
__snake_case : str = self.__need()
__snake_case : List[Any] = self.__allocated_resources_table
__snake_case : Optional[int] = self.__available_resources()
__snake_case : Union[str, Any] = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
__snake_case : Tuple = False
for each_need in need_list:
__snake_case : Any = True
for index, need in enumerate(__a ):
if need > available_resources[index]:
__snake_case : List[str] = False
break
if execution:
__snake_case : Union[str, Any] = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
__snake_case : str = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(__a )
# update available/freed resources stack
__snake_case : Union[str, Any] = np.array(__a ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__a ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__a ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Tuple ) -> List[str]:
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : Tuple = np.full((len(_UpperCAmelCase ), sequence_length, 2) ,_UpperCAmelCase )
else:
__snake_case : int = np.full((len(_UpperCAmelCase ), sequence_length) ,_UpperCAmelCase )
for i, tensor in enumerate(_UpperCAmelCase ):
if padding_side == "right":
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : Dict = tensor[:sequence_length]
else:
__snake_case : List[Any] = tensor[:sequence_length]
else:
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : str = tensor[:sequence_length]
else:
__snake_case : int = tensor[:sequence_length]
return out_tensor.tolist()
def a_ ( _UpperCAmelCase : Any ) -> Optional[int]:
__snake_case : List[Any] = ord(_UpperCAmelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
return True
__snake_case : Dict = unicodedata.category(_UpperCAmelCase )
if cat.startswith('P' ):
return True
return False
@dataclass
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = 42
A__ = True
A__ = None
A__ = None
A__ = -100
A__ = "pt"
def A_ ( self : Any , __a : Dict ) -> List[str]:
'''simple docstring'''
import torch
__snake_case : Optional[int] = 'label' if 'label' in features[0].keys() else 'labels'
__snake_case : int = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__snake_case : Union[str, Any] = self.tokenizer.pad(
__a , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='pt' if labels is None else None , )
if labels is None:
return batch
__snake_case : Union[str, Any] = torch.tensor(batch['entity_ids'] ).shape[1]
__snake_case : Union[str, Any] = self.tokenizer.padding_side
if padding_side == "right":
__snake_case : Tuple = [
list(__a ) + [self.label_pad_token_id] * (sequence_length - len(__a )) for label in labels
]
else:
__snake_case : str = [
[self.label_pad_token_id] * (sequence_length - len(__a )) + list(__a ) for label in labels
]
__snake_case : Optional[Any] = [feature['ner_tags'] for feature in features]
__snake_case : List[Any] = padding_tensor(__a , -1 , __a , __a )
__snake_case : List[Any] = [feature['original_entity_spans'] for feature in features]
__snake_case : List[str] = padding_tensor(__a , (-1, -1) , __a , __a )
__snake_case : Optional[Any] = {k: torch.tensor(__a , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
A__ : Union[str, Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ : List[Any] = {
'''vocab_file''': {
'''google/electra-small-generator''': (
'''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
),
'''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
'''google/electra-large-generator''': (
'''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
),
'''google/electra-small-discriminator''': (
'''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
),
'''google/electra-base-discriminator''': (
'''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
),
'''google/electra-large-discriminator''': (
'''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''google/electra-small-generator''': (
'''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
),
'''google/electra-base-generator''': (
'''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
),
'''google/electra-large-generator''': (
'''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
),
'''google/electra-small-discriminator''': (
'''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
),
'''google/electra-base-discriminator''': (
'''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
),
'''google/electra-large-discriminator''': (
'''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
),
},
}
A__ : List[Any] = {
'''google/electra-small-generator''': 5_1_2,
'''google/electra-base-generator''': 5_1_2,
'''google/electra-large-generator''': 5_1_2,
'''google/electra-small-discriminator''': 5_1_2,
'''google/electra-base-discriminator''': 5_1_2,
'''google/electra-large-discriminator''': 5_1_2,
}
A__ : Optional[Any] = {
'''google/electra-small-generator''': {'''do_lower_case''': True},
'''google/electra-base-generator''': {'''do_lower_case''': True},
'''google/electra-large-generator''': {'''do_lower_case''': True},
'''google/electra-small-discriminator''': {'''do_lower_case''': True},
'''google/electra-base-discriminator''': {'''do_lower_case''': True},
'''google/electra-large-discriminator''': {'''do_lower_case''': True},
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_INIT_CONFIGURATION
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = ElectraTokenizer
def __init__( self : int , __a : List[Any]=None , __a : int=None , __a : List[str]=True , __a : Any="[UNK]" , __a : Any="[SEP]" , __a : Union[str, Any]="[PAD]" , __a : Dict="[CLS]" , __a : List[Any]="[MASK]" , __a : str=True , __a : Optional[int]=None , **__a : Optional[int] , ) -> str:
'''simple docstring'''
super().__init__(
__a , tokenizer_file=__a , do_lower_case=__a , unk_token=__a , sep_token=__a , pad_token=__a , cls_token=__a , mask_token=__a , tokenize_chinese_chars=__a , strip_accents=__a , **__a , )
__snake_case : Tuple = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __a ) != do_lower_case
or normalizer_state.get('strip_accents' , __a ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __a ) != tokenize_chinese_chars
):
__snake_case : List[Any] = getattr(__a , normalizer_state.pop('type' ) )
__snake_case : str = do_lower_case
__snake_case : Optional[int] = strip_accents
__snake_case : Any = tokenize_chinese_chars
__snake_case : Union[str, Any] = normalizer_class(**__a )
__snake_case : Any = do_lower_case
def A_ ( self : Any , __a : List[str] , __a : Optional[Any]=None ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A_ ( self : List[Any] , __a : List[int] , __a : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
__snake_case : int = [self.sep_token_id]
__snake_case : List[Any] = [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 ) * [0] + len(token_ids_a + sep ) * [1]
def A_ ( self : Optional[int] , __a : str , __a : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
__snake_case : Tuple = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
| 0 | 1 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = BertJapaneseTokenizer
A__ = False
A__ = True
def A_ ( self : List[str] ) -> Tuple:
'''simple docstring'''
super().setUp()
__snake_case : List[str] = [
'[UNK]',
'[CLS]',
'[SEP]',
'こんにちは',
'こん',
'にちは',
'ばんは',
'##こん',
'##にちは',
'##ばんは',
'世界',
'##世界',
'、',
'##、',
'。',
'##。',
]
__snake_case : 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 A_ ( self : List[str] , __a : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Dict = 'こんにちは、世界。 \nこんばんは、世界。'
__snake_case : Tuple = 'こんにちは 、 世界 。 こんばんは 、 世界 。'
return input_text, output_text
def A_ ( self : List[str] , __a : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case , __snake_case : Dict = self.get_input_output_texts(__a )
__snake_case : List[str] = tokenizer.encode(__a , add_special_tokens=__a )
__snake_case : int = tokenizer.decode(__a , clean_up_tokenization_spaces=__a )
return text, ids
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
pass # TODO add if relevant
def A_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
pass # TODO add if relevant
def A_ ( self : str ) -> int:
'''simple docstring'''
pass # TODO add if relevant
def A_ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = self.tokenizer_class(self.vocab_file )
__snake_case : Tuple = tokenizer.tokenize('こんにちは、世界。\nこんばんは、世界。' )
self.assertListEqual(__a , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : int = self.tokenizer_class(self.vocab_file , word_tokenizer_type='mecab' )
self.assertIsNotNone(__a )
__snake_case : Optional[Any] = 'こんにちは、世界。\nこんばんは、世界。'
__snake_case : List[str] = tokenizer.tokenize(__a )
self.assertListEqual(__a , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
__snake_case : List[Any] = os.path.join(self.tmpdirname , 'tokenizer.bin' )
with open(__a , 'wb' ) as handle:
pickle.dump(__a , __a )
with open(__a , 'rb' ) as handle:
__snake_case : Any = pickle.load(__a )
__snake_case : Dict = tokenizer_new.tokenize(__a )
self.assertListEqual(__a , __a )
def A_ ( self : Optional[int] ) -> str:
'''simple docstring'''
__snake_case : List[Any] = MecabTokenizer(mecab_dic='ipadic' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップルストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def A_ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
try:
__snake_case : int = MecabTokenizer(mecab_dic='unidic_lite' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def A_ ( self : List[Any] ) -> str:
'''simple docstring'''
try:
__snake_case : Dict = MecabTokenizer(mecab_dic='unidic' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def A_ ( self : int ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = MecabTokenizer(do_lower_case=__a , mecab_dic='ipadic' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップルストア', 'で', 'iphone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def A_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
try:
__snake_case : Union[str, Any] = MecabTokenizer(
do_lower_case=__a , normalize_text=__a , mecab_option='-d /usr/local/lib/mecab/dic/jumandic' )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れた', '\u3000', '。'] , )
def A_ ( self : Dict ) -> Any:
'''simple docstring'''
__snake_case : str = MecabTokenizer(normalize_text=__a , mecab_dic='ipadic' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れ', 'た', ' ', '。'] , )
@require_sudachi
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[Any] = self.tokenizer_class(self.vocab_file , word_tokenizer_type='sudachi' )
self.assertIsNotNone(__a )
__snake_case : Union[str, Any] = 'こんにちは、世界。\nこんばんは、世界。'
__snake_case : Optional[Any] = tokenizer.tokenize(__a )
self.assertListEqual(__a , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
__snake_case : List[str] = os.path.join(self.tmpdirname , 'tokenizer.bin' )
with open(__a , 'wb' ) as handle:
pickle.dump(__a , __a )
with open(__a , 'rb' ) as handle:
__snake_case : List[str] = pickle.load(__a )
__snake_case : int = tokenizer_new.tokenize(__a )
self.assertListEqual(__a , __a )
@require_sudachi
def A_ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : List[Any] = SudachiTokenizer(sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , [' ', '\t', 'アップル', 'ストア', 'で', 'iPhone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' '] , )
@require_sudachi
def A_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[int] = SudachiTokenizer(sudachi_dict_type='core' , sudachi_split_mode='A' )
self.assertListEqual(tokenizer.tokenize('外国人参政権' ) , ['外国', '人', '参政', '権'] )
@require_sudachi
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[int] = SudachiTokenizer(sudachi_dict_type='core' , sudachi_split_mode='B' )
self.assertListEqual(tokenizer.tokenize('外国人参政権' ) , ['外国人', '参政権'] )
@require_sudachi
def A_ ( self : str ) -> List[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = SudachiTokenizer(sudachi_dict_type='core' , sudachi_split_mode='C' )
self.assertListEqual(tokenizer.tokenize('外国人参政権' ) , ['外国人参政権'] )
@require_sudachi
def A_ ( self : Tuple ) -> int:
'''simple docstring'''
__snake_case : List[Any] = SudachiTokenizer(do_lower_case=__a , sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , [' ', '\t', 'アップル', 'ストア', 'で', 'iphone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' '] , )
@require_sudachi
def A_ ( self : Optional[int] ) -> int:
'''simple docstring'''
__snake_case : Dict = SudachiTokenizer(normalize_text=__a , sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , [' ', '\t', 'ｱｯﾌﾟﾙ', 'ストア', 'で', 'iPhone', '８', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', '\u3000', '。', ' ', ' '] , )
@require_sudachi
def A_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = SudachiTokenizer(trim_whitespace=__a , sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
@require_jumanpp
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
__snake_case : int = self.tokenizer_class(self.vocab_file , word_tokenizer_type='jumanpp' )
self.assertIsNotNone(__a )
__snake_case : List[Any] = 'こんにちは、世界。\nこんばんは、世界。'
__snake_case : Any = tokenizer.tokenize(__a )
self.assertListEqual(__a , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
__snake_case : Optional[Any] = os.path.join(self.tmpdirname , 'tokenizer.bin' )
with open(__a , 'wb' ) as handle:
pickle.dump(__a , __a )
with open(__a , 'rb' ) as handle:
__snake_case : List[str] = pickle.load(__a )
__snake_case : Any = tokenizer_new.tokenize(__a )
self.assertListEqual(__a , __a )
@require_jumanpp
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Any = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] , )
@require_jumanpp
def A_ ( self : Optional[int] ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = JumanppTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iphone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] , )
@require_jumanpp
def A_ ( self : str ) -> Dict:
'''simple docstring'''
__snake_case : Dict = JumanppTokenizer(normalize_text=__a )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['ｱ', 'ｯ', 'ﾌ', 'ﾟ', 'ﾙ', 'ストア', 'で', 'iPhone', '８', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] , )
@require_jumanpp
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : List[Any] = JumanppTokenizer(trim_whitespace=__a )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れた', '。'] , )
@require_jumanpp
def A_ ( self : str ) -> str:
'''simple docstring'''
__snake_case : Optional[Any] = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize('ありがとうございますm(_ _)ｍ見つけるのが大変です。' ) , ['ありがとう', 'ございます', 'm(_ _)m', '見つける', 'の', 'が', '大変です', '。'] , )
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = ['[UNK]', '[CLS]', '[SEP]', 'こんにちは', 'こん', 'にちは', 'ばんは', '##こん', '##にちは', '##ばんは']
__snake_case : Any = {}
for i, token in enumerate(__a ):
__snake_case : str = i
__snake_case : List[Any] = WordpieceTokenizer(vocab=__a , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('こんにちは' ) , ['こんにちは'] )
self.assertListEqual(tokenizer.tokenize('こんばんは' ) , ['こん', '##ばんは'] )
self.assertListEqual(tokenizer.tokenize('こんばんは こんばんにちは こんにちは' ) , ['こん', '##ばんは', '[UNK]', 'こんにちは'] )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = BertJapaneseTokenizer.from_pretrained('nlp-waseda/roberta-base-japanese-with-auto-jumanpp' )
__snake_case : Dict = tokenizer.subword_tokenizer
__snake_case : List[str] = subword_tokenizer.tokenize('国境 の 長い トンネル を 抜ける と 雪国 であった 。' )
self.assertListEqual(__a , ['▁国境', '▁の', '▁長い', '▁トンネル', '▁を', '▁抜ける', '▁と', '▁雪', '国', '▁であった', '▁。'] )
__snake_case : List[Any] = subword_tokenizer.tokenize('こんばんは こんばん にち は こんにちは' )
self.assertListEqual(__a , ['▁こん', 'ばん', 'は', '▁こん', 'ばん', '▁に', 'ち', '▁は', '▁こんにちは'] )
def A_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
__snake_case : List[Any] = self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese' )
__snake_case : List[Any] = tokenizer.encode('ありがとう。' , add_special_tokens=__a )
__snake_case : str = tokenizer.encode('どういたしまして。' , add_special_tokens=__a )
__snake_case : Optional[int] = tokenizer.build_inputs_with_special_tokens(__a )
__snake_case : Dict = tokenizer.build_inputs_with_special_tokens(__a , __a )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = BertJapaneseTokenizer
A__ = False
def A_ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
__snake_case : Optional[Any] = ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']
__snake_case : 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 A_ ( self : Tuple , **__a : Dict ) -> Union[str, Any]:
'''simple docstring'''
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type='character' , **__a )
def A_ ( self : Any , __a : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Dict = 'こんにちは、世界。 \nこんばんは、世界。'
__snake_case : str = 'こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。'
return input_text, output_text
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
pass # TODO add if relevant
def A_ ( self : int ) -> Optional[int]:
'''simple docstring'''
pass # TODO add if relevant
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
pass # TODO add if relevant
def A_ ( self : List[Any] ) -> Any:
'''simple docstring'''
__snake_case : Any = self.tokenizer_class(self.vocab_file , subword_tokenizer_type='character' )
__snake_case : Any = tokenizer.tokenize('こんにちは、世界。 \nこんばんは、世界。' )
self.assertListEqual(
__a , ['こ', 'ん', 'に', 'ち', 'は', '、', '世', '界', '。', 'こ', 'ん', 'ば', 'ん', 'は', '、', '世', '界', '。'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__a ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def A_ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Tuple = ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']
__snake_case : Union[str, Any] = {}
for i, token in enumerate(__a ):
__snake_case : Union[str, Any] = i
__snake_case : int = CharacterTokenizer(vocab=__a , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('こんにちは' ) , ['こ', 'ん', 'に', 'ち', 'は'] )
self.assertListEqual(tokenizer.tokenize('こんにちほ' ) , ['こ', 'ん', 'に', 'ち', '[UNK]'] )
def A_ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[Any] = self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese-char' )
__snake_case : List[str] = tokenizer.encode('ありがとう。' , add_special_tokens=__a )
__snake_case : Union[str, Any] = tokenizer.encode('どういたしまして。' , add_special_tokens=__a )
__snake_case : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(__a )
__snake_case : int = tokenizer.build_inputs_with_special_tokens(__a , __a )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class snake_case__ ( unittest.TestCase ):
def A_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = 'cl-tohoku/bert-base-japanese'
__snake_case : List[str] = AutoTokenizer.from_pretrained(__a )
self.assertIsInstance(__a , __a )
class snake_case__ ( unittest.TestCase ):
def A_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[int] = 'cl-tohoku/bert-base-japanese'
with self.assertLogs('transformers' , level='WARNING' ) as cm:
BertTokenizer.from_pretrained(__a )
self.assertTrue(
cm.records[0].message.startswith(
'The tokenizer class you load from this checkpoint is not the same type as the class this function'
' is called from.' ) )
__snake_case : str = 'bert-base-cased'
with self.assertLogs('transformers' , level='WARNING' ) as cm:
BertJapaneseTokenizer.from_pretrained(__a )
self.assertTrue(
cm.records[0].message.startswith(
'The tokenizer class you load from this checkpoint is not the same type as the class this function'
' is called from.' ) )
| 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> bool:
__snake_case : Union[str, Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 0 | 1 |
'''simple docstring'''
A__ : Tuple = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def a_ ( _UpperCAmelCase : int ) -> int:
__snake_case : Dict = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
number //= 10_00_00
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
A__ : list[bool | None] = [None] * 1_0_0_0_0_0_0_0
A__ : Optional[Any] = True
A__ : int = False
def a_ ( _UpperCAmelCase : int ) -> bool:
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
__snake_case : List[str] = chain(next_number(_UpperCAmelCase ) )
__snake_case : Optional[Any] = number_chain
while number < 10_00_00_00:
__snake_case : Tuple = number_chain
number *= 10
return number_chain
def a_ ( _UpperCAmelCase : int = 10_00_00_00 ) -> int:
for i in range(1 ,_UpperCAmelCase ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"""{solution() = }""")
| 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
A__ : Tuple = pytest.mark.integration
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Dict = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(__a ) for x in np.arange(30 ).tolist()]} )
return dset
def A_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
__snake_case : Dict = dset.map(
lambda __a , __a : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=__a , keep_in_memory=__a )
__snake_case : List[Any] = dset.add_faiss_index('vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
__snake_case , __snake_case : str = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(__a , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
__snake_case : Dataset = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : Any = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
__snake_case : Union[str, Any] = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=__a )
__snake_case , __snake_case : str = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : str ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
__snake_case : Dict = np.zeros(5 , dtype=np.floataa )
__snake_case : List[str] = 1
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertRaises(__a , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
__snake_case : List[str] = np.eye(5 , dtype=np.floataa )[::-1]
__snake_case , __snake_case : Dict = index.search_batch(__a )
self.assertRaises(__a , index.search_batch , queries[0] )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : List[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , __a )
def A_ ( self : int ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
__snake_case : List[str] = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(__a ):
__snake_case : Dict = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def A_ ( self : str ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Tuple = faiss.IndexFlat(5 )
__snake_case : List[Any] = FaissIndex(custom_index=__a )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
import faiss
__snake_case : Optional[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
index.save(tmp_file.name )
__snake_case : List[Any] = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
__snake_case : List[Any] = np.zeros(5 , dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : int = index.search(__a )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def a_ ( _UpperCAmelCase : str ) -> Optional[int]:
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
__snake_case : Dict = 'index.faiss'
__snake_case : Any = f'''mock://{index_name}'''
index.save(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = FaissIndex.load(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = np.zeros(5 ,dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : Tuple = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : int = Elasticsearch()
__snake_case : Dict = {'acknowledged': True}
__snake_case : List[Any] = ElasticSearchIndex(es_client=__a )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
__snake_case : Optional[Any] = 'foo'
__snake_case : int = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
__snake_case : Dict = 'foo'
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : Optional[Any] = index.search(__a , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
__snake_case : List[Any] = ['foo', 'bar', 'foobar']
__snake_case : str = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : Any = index.search_batch(__a )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
# batched queries with timeout
__snake_case : Tuple = ['foo', 'bar', 'foobar']
__snake_case : List[Any] = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : int = index.search_batch(__a , request_timeout=30 )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
| 0 | 1 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def a_ ( *_UpperCAmelCase : Tuple ) -> List[str]:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : str = list(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
__snake_case : Tuple = None
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
return objects
def a_ ( _UpperCAmelCase : Exception ) -> bool:
__snake_case : Optional[Any] = [
'CUDA out of memory.', # CUDA OOM
'cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.', # CUDNN SNAFU
'DefaultCPUAllocator: can\'t allocate memory', # CPU OOM
]
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def a_ ( _UpperCAmelCase : callable = None ,_UpperCAmelCase : int = 1_28 ) -> List[str]:
if function is None:
return functools.partial(_UpperCAmelCase ,starting_batch_size=_UpperCAmelCase )
__snake_case : Optional[Any] = starting_batch_size
def decorator(*_UpperCAmelCase : Optional[Any] ,**_UpperCAmelCase : int ):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
__snake_case : str = list(inspect.signature(_UpperCAmelCase ).parameters.keys() )
# Guard against user error
if len(_UpperCAmelCase ) < (len(_UpperCAmelCase ) + 1):
__snake_case : Dict = ', '.join([f'''{arg}={value}''' for arg, value in zip(params[1:] ,args[1:] )] )
raise TypeError(
f'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
f'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`''' )
while True:
if batch_size == 0:
raise RuntimeError('No executable batch size found, reached zero.' )
try:
return function(_UpperCAmelCase ,*_UpperCAmelCase ,**_UpperCAmelCase )
except Exception as e:
if should_reduce_batch_size(_UpperCAmelCase ):
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
batch_size //= 2
else:
raise
return decorator
| 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A__ : List[Any] = logging.get_logger(__name__)
A__ : Tuple = {
'''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 snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''t5'''
A__ = ['''past_key_values''']
A__ = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : str , __a : Dict=32128 , __a : Dict=512 , __a : Union[str, Any]=64 , __a : str=2048 , __a : Union[str, Any]=6 , __a : Any=None , __a : Any=8 , __a : List[Any]=32 , __a : Any=128 , __a : Tuple=0.1 , __a : str=1e-6 , __a : Dict=1.0 , __a : Tuple="relu" , __a : Dict=True , __a : Union[str, Any]=True , __a : Any=0 , __a : Dict=1 , **__a : Union[str, Any] , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = vocab_size
__snake_case : str = d_model
__snake_case : str = d_kv
__snake_case : List[Any] = d_ff
__snake_case : List[str] = num_layers
__snake_case : Tuple = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__snake_case : Union[str, Any] = num_heads
__snake_case : Tuple = relative_attention_num_buckets
__snake_case : Optional[int] = relative_attention_max_distance
__snake_case : Optional[Any] = dropout_rate
__snake_case : str = layer_norm_epsilon
__snake_case : List[str] = initializer_factor
__snake_case : int = feed_forward_proj
__snake_case : Optional[Any] = use_cache
__snake_case : Optional[Any] = self.feed_forward_proj.split('-' )
__snake_case : Dict = act_info[-1]
__snake_case : List[str] = act_info[0] == 'gated'
if len(__a ) > 1 and act_info[0] != "gated" or len(__a ) > 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":
__snake_case : Dict = 'gelu_new'
super().__init__(
pad_token_id=__a , eos_token_id=__a , is_encoder_decoder=__a , **__a , )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@property
def A_ ( self : str ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__snake_case : Union[str, Any] = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
__snake_case : Tuple = 'past_encoder_sequence + sequence'
__snake_case : Dict = {0: 'batch'}
__snake_case : Dict = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
__snake_case : Tuple = {0: 'batch', 1: 'decoder_sequence'}
__snake_case : int = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(__a , direction='inputs' )
return common_inputs
@property
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
return 13
| 0 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def a_ ( _UpperCAmelCase : Optional[int] ) -> List[str]:
__snake_case : Optional[int] = 3_84
__snake_case : str = 7
if "tiny" in model_name:
__snake_case : str = 96
__snake_case : Tuple = (2, 2, 6, 2)
__snake_case : List[Any] = (3, 6, 12, 24)
elif "small" in model_name:
__snake_case : List[Any] = 96
__snake_case : Union[str, Any] = (2, 2, 18, 2)
__snake_case : int = (3, 6, 12, 24)
elif "base" in model_name:
__snake_case : Union[str, Any] = 1_28
__snake_case : int = (2, 2, 18, 2)
__snake_case : Dict = (4, 8, 16, 32)
__snake_case : List[Any] = 12
__snake_case : Optional[int] = 5_12
elif "large" in model_name:
__snake_case : Optional[Any] = 1_92
__snake_case : Dict = (2, 2, 18, 2)
__snake_case : List[Any] = (6, 12, 24, 48)
__snake_case : Union[str, Any] = 12
__snake_case : List[Any] = 7_68
# set label information
__snake_case : Optional[int] = 1_50
__snake_case : str = 'huggingface/label-files'
__snake_case : List[Any] = 'ade20k-id2label.json'
__snake_case : Any = json.load(open(hf_hub_download(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset' ) ,'r' ) )
__snake_case : int = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__snake_case : Optional[int] = {v: k for k, v in idalabel.items()}
__snake_case : List[Any] = SwinConfig(
embed_dim=_UpperCAmelCase ,depths=_UpperCAmelCase ,num_heads=_UpperCAmelCase ,window_size=_UpperCAmelCase ,out_features=['stage1', 'stage2', 'stage3', 'stage4'] ,)
__snake_case : str = UperNetConfig(
backbone_config=_UpperCAmelCase ,auxiliary_in_channels=_UpperCAmelCase ,num_labels=_UpperCAmelCase ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase ,)
return config
def a_ ( _UpperCAmelCase : Optional[Any] ) -> List[Any]:
__snake_case : str = []
# fmt: off
# stem
rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight') )
rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias') )
rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight') )
rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''') )
rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''') )
if i < 3:
rename_keys.append((f'''backbone.stages.{i}.downsample.reduction.weight''', f'''backbone.encoder.layers.{i}.downsample.reduction.weight''') )
rename_keys.append((f'''backbone.stages.{i}.downsample.norm.weight''', f'''backbone.encoder.layers.{i}.downsample.norm.weight''') )
rename_keys.append((f'''backbone.stages.{i}.downsample.norm.bias''', f'''backbone.encoder.layers.{i}.downsample.norm.bias''') )
rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''') )
rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''') )
# decode head
rename_keys.extend(
[
('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
] )
# fmt: on
return rename_keys
def a_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : str ) -> Tuple:
__snake_case : List[Any] = dct.pop(_UpperCAmelCase )
__snake_case : str = val
def a_ ( _UpperCAmelCase : Any ,_UpperCAmelCase : Optional[int] ) -> str:
__snake_case : str = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__snake_case : str = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
__snake_case : Optional[Any] = state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight''' )
__snake_case : Tuple = state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
__snake_case : Any = in_proj_weight[:dim, :]
__snake_case : int = in_proj_bias[: dim]
__snake_case : str = in_proj_weight[
dim : dim * 2, :
]
__snake_case : str = in_proj_bias[
dim : dim * 2
]
__snake_case : Optional[Any] = in_proj_weight[
-dim :, :
]
__snake_case : Any = in_proj_bias[-dim :]
# fmt: on
def a_ ( _UpperCAmelCase : Any ) -> Optional[Any]:
__snake_case , __snake_case : Union[str, Any] = x.shape
__snake_case : Any = x.reshape(_UpperCAmelCase ,4 ,in_channel // 4 )
__snake_case : str = x[:, [0, 2, 1, 3], :].transpose(1 ,2 ).reshape(_UpperCAmelCase ,_UpperCAmelCase )
return x
def a_ ( _UpperCAmelCase : str ) -> List[Any]:
__snake_case , __snake_case : Union[str, Any] = x.shape
__snake_case : Tuple = x.reshape(_UpperCAmelCase ,in_channel // 4 ,4 )
__snake_case : Any = x[:, :, [0, 2, 1, 3]].transpose(1 ,2 ).reshape(_UpperCAmelCase ,_UpperCAmelCase )
return x
def a_ ( _UpperCAmelCase : List[str] ) -> List[str]:
__snake_case : int = x.shape[0]
__snake_case : Optional[Any] = x.reshape(4 ,in_channel // 4 )
__snake_case : int = x[[0, 2, 1, 3], :].transpose(0 ,1 ).reshape(_UpperCAmelCase )
return x
def a_ ( _UpperCAmelCase : Optional[int] ) -> Any:
__snake_case : Tuple = x.shape[0]
__snake_case : Tuple = x.reshape(in_channel // 4 ,4 )
__snake_case : Dict = x[:, [0, 2, 1, 3]].transpose(0 ,1 ).reshape(_UpperCAmelCase )
return x
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : Dict ) -> str:
__snake_case : Optional[Any] = {
'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
}
__snake_case : List[str] = model_name_to_url[model_name]
__snake_case : str = torch.hub.load_state_dict_from_url(_UpperCAmelCase ,map_location='cpu' ,file_name=_UpperCAmelCase )[
'state_dict'
]
for name, param in state_dict.items():
print(_UpperCAmelCase ,param.shape )
__snake_case : List[str] = get_upernet_config(_UpperCAmelCase )
__snake_case : List[Any] = UperNetForSemanticSegmentation(_UpperCAmelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
__snake_case : int = state_dict.pop(_UpperCAmelCase )
if "bn" in key:
__snake_case : Tuple = key.replace('bn' ,'batch_norm' )
__snake_case : Union[str, Any] = val
# rename keys
__snake_case : Any = create_rename_keys(_UpperCAmelCase )
for src, dest in rename_keys:
rename_key(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
read_in_q_k_v(_UpperCAmelCase ,config.backbone_config )
# fix downsample parameters
for key, value in state_dict.items():
if "downsample" in key:
if "reduction" in key:
__snake_case : str = reverse_correct_unfold_reduction_order(_UpperCAmelCase )
if "norm" in key:
__snake_case : List[str] = reverse_correct_unfold_norm_order(_UpperCAmelCase )
model.load_state_dict(_UpperCAmelCase )
# verify on image
__snake_case : List[str] = 'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
__snake_case : Union[str, Any] = Image.open(requests.get(_UpperCAmelCase ,stream=_UpperCAmelCase ).raw ).convert('RGB' )
__snake_case : Tuple = SegformerImageProcessor()
__snake_case : str = processor(_UpperCAmelCase ,return_tensors='pt' ).pixel_values
with torch.no_grad():
__snake_case : Any = model(_UpperCAmelCase )
__snake_case : Dict = outputs.logits
print(logits.shape )
print('First values of logits:' ,logits[0, 0, :3, :3] )
# assert values
if model_name == "upernet-swin-tiny":
__snake_case : int = torch.tensor(
[[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]] )
elif model_name == "upernet-swin-small":
__snake_case : Any = torch.tensor(
[[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]] )
elif model_name == "upernet-swin-base":
__snake_case : Dict = torch.tensor(
[[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]] )
elif model_name == "upernet-swin-large":
__snake_case : List[str] = torch.tensor(
[[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]] )
print('Logits:' ,outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] ,_UpperCAmelCase ,atol=1E-4 )
print('Looks ok!' )
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 processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
print(f'''Pushing model and processor for {model_name} to hub''' )
model.push_to_hub(f'''openmmlab/{model_name}''' )
processor.push_to_hub(f'''openmmlab/{model_name}''' )
if __name__ == "__main__":
A__ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''upernet-swin-tiny''',
type=str,
choices=[F"""upernet-swin-{size}""" for size in ['''tiny''', '''small''', '''base''', '''large''']],
help='''Name of the Swin + UperNet model 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 or not to push the converted model to the 🤗 hub.'''
)
A__ : str = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : Optional[int] = {}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''llama'''
A__ = ['''past_key_values''']
def __init__( self : Any , __a : List[str]=32000 , __a : Union[str, Any]=4096 , __a : Optional[Any]=11008 , __a : Any=32 , __a : str=32 , __a : Optional[int]=None , __a : Dict="silu" , __a : Dict=2048 , __a : List[str]=0.0_2 , __a : Union[str, Any]=1e-6 , __a : Dict=True , __a : List[str]=0 , __a : Tuple=1 , __a : Tuple=2 , __a : Optional[Any]=1 , __a : Any=False , __a : Tuple=None , **__a : List[Any] , ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = vocab_size
__snake_case : List[str] = max_position_embeddings
__snake_case : List[Any] = hidden_size
__snake_case : Union[str, Any] = intermediate_size
__snake_case : Optional[int] = num_hidden_layers
__snake_case : List[Any] = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
__snake_case : Optional[int] = num_attention_heads
__snake_case : Optional[Any] = num_key_value_heads
__snake_case : int = hidden_act
__snake_case : Any = initializer_range
__snake_case : Any = rms_norm_eps
__snake_case : Union[str, Any] = pretraining_tp
__snake_case : Optional[int] = use_cache
__snake_case : Any = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , tie_word_embeddings=__a , **__a , )
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __a ) 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}''' )
__snake_case : Optional[Any] = self.rope_scaling.get('type' , __a )
__snake_case : Tuple = self.rope_scaling.get('factor' , __a )
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(__a , __a ) or rope_scaling_factor <= 1.0:
raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )
| 0 | 1 |
'''simple docstring'''
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : List[Any] , __a : UNetaDModel , __a : UNetaDModel , __a : DDPMScheduler , __a : List[Any] , ) -> int:
'''simple docstring'''
super().__init__()
__snake_case : Tuple = value_function
__snake_case : Any = unet
__snake_case : Optional[Any] = scheduler
__snake_case : str = env
__snake_case : str = env.get_dataset()
__snake_case : Dict = {}
for key in self.data.keys():
try:
__snake_case : int = self.data[key].mean()
except: # noqa: E722
pass
__snake_case : Tuple = {}
for key in self.data.keys():
try:
__snake_case : List[str] = self.data[key].std()
except: # noqa: E722
pass
__snake_case : Dict = env.observation_space.shape[0]
__snake_case : Optional[int] = env.action_space.shape[0]
def A_ ( self : List[Any] , __a : Optional[Any] , __a : Optional[int] ) -> Dict:
'''simple docstring'''
return (x_in - self.means[key]) / self.stds[key]
def A_ ( self : Optional[Any] , __a : str , __a : List[str] ) -> List[Any]:
'''simple docstring'''
return x_in * self.stds[key] + self.means[key]
def A_ ( self : int , __a : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
if type(__a ) is dict:
return {k: self.to_torch(__a ) for k, v in x_in.items()}
elif torch.is_tensor(__a ):
return x_in.to(self.unet.device )
return torch.tensor(__a , device=self.unet.device )
def A_ ( self : Any , __a : Union[str, Any] , __a : List[str] , __a : Optional[Any] ) -> str:
'''simple docstring'''
for key, val in cond.items():
__snake_case : Dict = val.clone()
return x_in
def A_ ( self : List[Any] , __a : Dict , __a : Any , __a : int , __a : Any ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : str = x.shape[0]
__snake_case : Any = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
__snake_case : Any = torch.full((batch_size,) , __a , device=self.unet.device , dtype=torch.long )
for _ in range(__a ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
__snake_case : Dict = self.value_function(x.permute(0 , 2 , 1 ) , __a ).sample
__snake_case : str = torch.autograd.grad([y.sum()] , [x] )[0]
__snake_case : Tuple = self.scheduler._get_variance(__a )
__snake_case : List[str] = torch.exp(0.5 * posterior_variance )
__snake_case : Any = model_std * grad
__snake_case : str = 0
__snake_case : List[str] = x.detach()
__snake_case : Tuple = x + scale * grad
__snake_case : Optional[Any] = self.reset_xa(__a , __a , self.action_dim )
__snake_case : Union[str, Any] = self.unet(x.permute(0 , 2 , 1 ) , __a ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
__snake_case : str = self.scheduler.step(__a , __a , __a , predict_epsilon=__a )['prev_sample']
# apply conditions to the trajectory (set the initial state)
__snake_case : Optional[int] = self.reset_xa(__a , __a , self.action_dim )
__snake_case : List[Any] = self.to_torch(__a )
return x, y
def __call__( self : Tuple , __a : Any , __a : Optional[Any]=64 , __a : List[Any]=32 , __a : Union[str, Any]=2 , __a : Dict=0.1 ) -> List[str]:
'''simple docstring'''
# normalize the observations and create batch dimension
__snake_case : Optional[Any] = self.normalize(__a , 'observations' )
__snake_case : Optional[int] = obs[None].repeat(__a , axis=0 )
__snake_case : Any = {0: self.to_torch(__a )}
__snake_case : Dict = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
__snake_case : str = randn_tensor(__a , device=self.unet.device )
__snake_case : Any = self.reset_xa(__a , __a , self.action_dim )
__snake_case : List[str] = self.to_torch(__a )
# run the diffusion process
__snake_case , __snake_case : Optional[Any] = self.run_diffusion(__a , __a , __a , __a )
# sort output trajectories by value
__snake_case : List[str] = y.argsort(0 , descending=__a ).squeeze()
__snake_case : List[Any] = x[sorted_idx]
__snake_case : Optional[Any] = sorted_values[:, :, : self.action_dim]
__snake_case : str = actions.detach().cpu().numpy()
__snake_case : List[str] = self.de_normalize(__a , key='actions' )
# select the action with the highest value
if y is not None:
__snake_case : Any = 0
else:
# if we didn't run value guiding, select a random action
__snake_case : Tuple = np.random.randint(0 , __a )
__snake_case : Any = denorm_actions[selected_index, 0]
return denorm_actions
| 0 |
'''simple docstring'''
from __future__ import annotations
A__ : str = '''Muhammad Umer Farooq'''
A__ : int = '''MIT'''
A__ : Optional[int] = '''1.0.0'''
A__ : List[Any] = '''Muhammad Umer Farooq'''
A__ : Optional[Any] = '''[email protected]'''
A__ : Optional[Any] = '''Alpha'''
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Union[str, Any] , __a : str ) -> None:
'''simple docstring'''
super().__init__()
__snake_case : list[str] = []
__snake_case : Dict = domain
def A_ ( self : Dict , __a : str , __a : list[tuple[str, str | None]] ) -> None:
'''simple docstring'''
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__snake_case : Optional[Any] = parse.urljoin(self.domain , __a )
self.urls.append(__a )
def a_ ( _UpperCAmelCase : str ) -> str:
return ".".join(get_sub_domain_name(_UpperCAmelCase ).split('.' )[-2:] )
def a_ ( _UpperCAmelCase : str ) -> str:
return parse.urlparse(_UpperCAmelCase ).netloc
def a_ ( _UpperCAmelCase : str = "https://github.com" ) -> list[str]:
__snake_case : List[Any] = get_domain_name(_UpperCAmelCase )
# Initialize the parser
__snake_case : Tuple = Parser(_UpperCAmelCase )
try:
# Open URL
__snake_case : Any = requests.get(_UpperCAmelCase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__snake_case : Dict = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__snake_case : List[Any] = requests.get(_UpperCAmelCase )
# Get the valid email.
__snake_case : Optional[Any] = re.findall('[a-zA-Z0-9]+@' + domain ,read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_UpperCAmelCase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Tuple = emails_from_url('''https://github.com''')
print(F"""{len(emails)} emails found:""")
print('''\n'''.join(sorted(emails)))
| 0 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
A__ : Union[str, Any] = logging.get_logger(__name__)
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Dict ,_UpperCAmelCase : Any ) -> int:
__snake_case : List[Any] = UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase ,config=_UpperCAmelCase )
__snake_case : List[str] = downstream_dict['projector.weight']
__snake_case : int = downstream_dict['projector.bias']
__snake_case : str = downstream_dict['model.post_net.linear.weight']
__snake_case : Tuple = downstream_dict['model.post_net.linear.bias']
return model
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : List[str] ) -> str:
__snake_case : List[Any] = UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase ,config=_UpperCAmelCase )
__snake_case : Optional[Any] = downstream_dict['model.linear.weight']
__snake_case : Union[str, Any] = downstream_dict['model.linear.bias']
return model
def a_ ( _UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Optional[int] ) -> Tuple:
__snake_case : Dict = UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase ,config=_UpperCAmelCase )
__snake_case : List[Any] = downstream_dict['connector.weight']
__snake_case : int = downstream_dict['connector.bias']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
__snake_case : List[str] = downstream_dict[
f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
__snake_case : int = downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
__snake_case : Union[str, Any] = downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
__snake_case : Any = downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
__snake_case : Union[str, Any] = downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
__snake_case : Tuple = downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
__snake_case : int = downstream_dict['objective.W']
return model
@torch.no_grad()
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
__snake_case : int = torch.load(_UpperCAmelCase ,map_location='cpu' )
__snake_case : Union[str, Any] = checkpoint['Downstream']
__snake_case : List[Any] = UniSpeechSatConfig.from_pretrained(_UpperCAmelCase )
__snake_case : List[Any] = WavaVecaFeatureExtractor.from_pretrained(
_UpperCAmelCase ,return_attention_mask=_UpperCAmelCase ,do_normalize=_UpperCAmelCase )
__snake_case : Any = hf_config.architectures[0]
if arch.endswith('ForSequenceClassification' ):
__snake_case : Optional[Any] = convert_classification(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
elif arch.endswith('ForAudioFrameClassification' ):
__snake_case : Optional[int] = convert_diarization(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
elif arch.endswith('ForXVector' ):
__snake_case : Tuple = convert_xvector(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
else:
raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
__snake_case : Optional[Any] = checkpoint['Featurizer']['weights']
hf_feature_extractor.save_pretrained(_UpperCAmelCase )
hf_model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Tuple = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
)
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
A__ : str = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
A__ : Dict = logging.getLogger()
def a_ ( ) -> Tuple:
__snake_case : List[Any] = argparse.ArgumentParser()
parser.add_argument('-f' )
__snake_case : Any = parser.parse_args()
return args.f
def a_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
__snake_case : Tuple = {}
__snake_case : Union[str, Any] = os.path.join(_UpperCAmelCase ,'all_results.json' )
if os.path.exists(_UpperCAmelCase ):
with open(_UpperCAmelCase ,'r' ) as f:
__snake_case : List[str] = json.load(_UpperCAmelCase )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
def a_ ( ) -> Union[str, Any]:
__snake_case : Union[str, Any] = torch.cuda.is_available() and torch_device == 'cuda'
return is_using_cuda and is_apex_available()
A__ : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@classmethod
def A_ ( cls : Any ) -> List[str]:
'''simple docstring'''
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__snake_case : Optional[int] = tempfile.mkdtemp()
__snake_case : Dict = os.path.join(cls.tmpdir , 'default_config.yml' )
write_basic_config(save_location=cls.configPath )
__snake_case : List[Any] = ['accelerate', 'launch', '--config_file', cls.configPath]
@classmethod
def A_ ( cls : List[str] ) -> List[str]:
'''simple docstring'''
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : List[Any] = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'glue_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
'''.split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertLess(result['perplexity'] , 100 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'clm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : int = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertLess(result['perplexity'] , 42 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'mlm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__snake_case : Any = 7 if get_gpu_count() > 1 else 2
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertLess(result['train_loss'] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'ner_no_trainer' ) ) )
@unittest.skip(reason='Fix me @muellerzr' )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : Tuple = f'''
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['eval_f1'] , 28 )
self.assertGreaterEqual(result['eval_exact'] , 28 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'qa_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : str = self.get_auto_remove_tmp_dir()
__snake_case : Any = f'''
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__a , 'swag_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : int = get_results(__a )
self.assertGreaterEqual(result['eval_rouge1'] , 10 )
self.assertGreaterEqual(result['eval_rouge2'] , 2 )
self.assertGreaterEqual(result['eval_rougeL'] , 7 )
self.assertGreaterEqual(result['eval_rougeLsum'] , 7 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'summarization_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_bleu'] , 30 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'translation_no_trainer' ) ) )
@slow
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = logging.StreamHandler(sys.stdout )
logger.addHandler(__a )
__snake_case : List[str] = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertGreaterEqual(result['eval_overall_accuracy'] , 0.1_0 )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Dict = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : Optional[int] = get_results(__a )
# The base model scores a 25%
self.assertGreaterEqual(result['eval_accuracy'] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__a , 'step_1' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'image_classification_no_trainer' ) ) )
| 0 | 1 |
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