{
// 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'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
snake_case__ = datasets.load_iris()
snake_case__ = np.array(data["""data"""])
snake_case__ = np.array(data["""target"""])
snake_case__ = data["""target_names"""]
snake_case__ , snake_case__ , snake_case__ , snake_case__ = train_test_split(X, y)
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : List[str] ) -> Optional[int]:
return np.linalg.norm(np.array(lowerCamelCase__ ) - np.array(lowerCamelCase__ ) )
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : Dict , lowerCamelCase__ : int , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Optional[int]=5 ) -> Optional[int]:
A_ : Optional[int] = zip(lowerCamelCase__ , lowerCamelCase__ )
# List of distances of all points from the point to be classified
A_ : str = []
for data_point in data:
A_ : int = euclidean_distance(data_point[0] , lowerCamelCase__ )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
A_ : int = [i[1] for i in sorted(lowerCamelCase__ )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
A_ : Union[str, Any] = Counter(lowerCamelCase__ ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 360 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
snake_case__ = get_tests_dir("""fixtures""")
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] ):
"""simple docstring"""
A_ : List[Any] = mock.Mock()
A_ : List[str] = 500
A_ : Tuple = {}
A_ : int = HTTPError
A_ : Optional[Any] = {}
# Download this model to make sure it's in the cache.
A_ : Tuple = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=_lowerCamelCase ) as mock_head:
A_ : List[Any] = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# This check we did call the fake head request
mock_head.assert_called()
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = ViTImageProcessor.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json''' )
def _a ( self : Dict ):
"""simple docstring"""
with self.assertRaises(_lowerCamelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
A_ : Any = AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants''' )
A_ : Tuple = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/stable-diffusion-all-variants''' , subfolder='''feature_extractor''' )
self.assertIsNotNone(_lowerCamelCase )
@is_staging_test
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@classmethod
def _a ( cls : Tuple ):
"""simple docstring"""
A_ : int = TOKEN
HfFolder.save_token(_lowerCamelCase )
@classmethod
def _a ( cls : str ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='''test-image-processor''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-image-processor-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-image-processor''' )
except HTTPError:
pass
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Dict = ViTImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''test-image-processor''' , use_auth_token=self._token )
A_ : Optional[int] = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCamelCase , repo_id='''test-image-processor''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : List[Any] = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : int = ViTImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''valid_org/test-image-processor''' , use_auth_token=self._token )
A_ : List[str] = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCamelCase , repo_id='''valid_org/test-image-processor-org''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : Any = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
CustomImageProcessor.register_for_auto_class()
A_ : Any = CustomImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''test-dynamic-image-processor''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''} , )
A_ : str = AutoImageProcessor.from_pretrained(
f'{USER}/test-dynamic-image-processor' , trust_remote_code=_lowerCamelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , '''CustomImageProcessor''' )
| 4 | 0 |
'''simple docstring'''
from maths.prime_factors import prime_factors
def snake_case__ ( lowerCamelCase__ : int ) -> int:
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
A_ : Dict = f'Input value of [number={number}] must be an integer'
raise TypeError(lowerCamelCase__ )
if number < 1:
raise ValueError('''Input must be a positive integer''' )
return -1 if len(prime_factors(lowerCamelCase__ ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 361 |
'''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.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = (
'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
)
_lowerCAmelCase = 'CIDAS/clipseg-rd64-refined'
_lowerCAmelCase = 'image_segmenter'
_lowerCAmelCase = CLIPSegForImageSegmentation
_lowerCAmelCase = ['image', 'text']
_lowerCAmelCase = ['image']
def __init__( self : Optional[int] , *_lowerCamelCase : Optional[int] , **_lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def _a ( self : List[str] , _lowerCamelCase : "Image" , _lowerCamelCase : str ):
"""simple docstring"""
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def _a ( self : Union[str, Any] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
with torch.no_grad():
A_ : Optional[int] = self.model(**_lowerCamelCase ).logits
return logits
def _a ( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : int = outputs.cpu().detach().numpy()
A_ : Tuple = 0
A_ : List[str] = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 4 | 0 |
'''simple docstring'''
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'naver-clova-ix/donut-base-finetuned-docvqa'
_lowerCAmelCase = (
'This is a tool that answers a question about an document (pdf). It takes an input named `document` which '
'should be the document containing the information, as well as a `question` that is the question about the '
'document. It returns a text that contains the answer to the question.'
)
_lowerCAmelCase = 'document_qa'
_lowerCAmelCase = AutoProcessor
_lowerCAmelCase = VisionEncoderDecoderModel
_lowerCAmelCase = ['image', 'text']
_lowerCAmelCase = ['text']
def __init__( self : Optional[int] , *_lowerCamelCase : Any , **_lowerCamelCase : Dict ):
"""simple docstring"""
if not is_vision_available():
raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.''' )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def _a ( self : str , _lowerCamelCase : "Image" , _lowerCamelCase : str ):
"""simple docstring"""
A_ : Tuple = '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
A_ : List[str] = task_prompt.replace('''{user_input}''' , _lowerCamelCase )
A_ : str = self.pre_processor.tokenizer(
_lowerCamelCase , add_special_tokens=_lowerCamelCase , return_tensors='''pt''' ).input_ids
A_ : Optional[Any] = self.pre_processor(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def _a ( self : Optional[Any] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
return self.model.generate(
inputs['''pixel_values'''].to(self.device ) , decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=_lowerCamelCase , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=_lowerCamelCase , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=_lowerCamelCase , ).sequences
def _a ( self : Optional[Any] , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
A_ : Optional[int] = self.pre_processor.batch_decode(_lowerCamelCase )[0]
A_ : int = sequence.replace(self.pre_processor.tokenizer.eos_token , '''''' )
A_ : Tuple = sequence.replace(self.pre_processor.tokenizer.pad_token , '''''' )
A_ : str = re.sub(R'''<.*?>''' , '''''' , _lowerCamelCase , count=1 ).strip() # remove first task start token
A_ : Any = self.pre_processor.tokenajson(_lowerCamelCase )
return sequence["answer"]
| 362 |
'''simple docstring'''
from collections.abc import Sequence
def snake_case__ ( lowerCamelCase__ : Sequence[float] , lowerCamelCase__ : bool = False ) -> float:
if not arr:
return 0
A_ : Union[str, Any] = 0 if allow_empty_subarrays else float('''-inf''' )
A_ : str = 0.0
for num in arr:
A_ : Any = max(0 if allow_empty_subarrays else num , curr_sum + num )
A_ : Tuple = max(lowerCamelCase__ , lowerCamelCase__ )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
snake_case__ = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F'{max_subarray_sum(nums) = }')
| 4 | 0 |
'''simple docstring'''
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : Tuple , lowerCamelCase__ : Optional[int] ) -> int:
# Initialise PyTorch model
A_ : int = AlbertConfig.from_json_file(lowerCamelCase__ )
print(f'Building PyTorch model from configuration: {config}' )
A_ : List[Any] = AlbertForPreTraining(lowerCamelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--albert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained ALBERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
snake_case__ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 363 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'speech_to_text_2'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Optional[Any] , _lowerCamelCase : Optional[Any]=10000 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : int=2048 , _lowerCamelCase : Dict=4 , _lowerCamelCase : str=0.0 , _lowerCamelCase : int=True , _lowerCamelCase : int="relu" , _lowerCamelCase : Any=256 , _lowerCamelCase : List[Any]=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Union[str, Any]=0.0 , _lowerCamelCase : Optional[Any]=0.02 , _lowerCamelCase : int=2 , _lowerCamelCase : List[str]=True , _lowerCamelCase : str=1 , _lowerCamelCase : List[Any]=0 , _lowerCamelCase : Optional[int]=2 , _lowerCamelCase : Tuple=1024 , **_lowerCamelCase : int , ):
"""simple docstring"""
A_ : Optional[int] = vocab_size
A_ : Tuple = d_model
A_ : List[str] = decoder_ffn_dim
A_ : str = decoder_layers
A_ : Any = decoder_attention_heads
A_ : int = dropout
A_ : str = attention_dropout
A_ : Optional[int] = activation_dropout
A_ : str = activation_function
A_ : List[Any] = init_std
A_ : Union[str, Any] = decoder_layerdrop
A_ : Any = use_cache
A_ : Optional[Any] = decoder_layers
A_ : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
A_ : Optional[Any] = max_target_positions
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
| 4 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
snake_case__ = logging.get_logger(__name__)
snake_case__ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
snake_case__ = [
"""small""",
"""small-base""",
"""medium""",
"""medium-base""",
"""intermediate""",
"""intermediate-base""",
"""large""",
"""large-base""",
"""xlarge""",
"""xlarge-base""",
]
snake_case__ = {
"""vocab_file""": {
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt""",
"""funnel-transformer/small-base""": """https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt""",
"""funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt""",
"""funnel-transformer/medium-base""": (
"""https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"""
),
"""funnel-transformer/intermediate""": (
"""https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"""
),
"""funnel-transformer/intermediate-base""": (
"""https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"""
),
"""funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt""",
"""funnel-transformer/large-base""": """https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt""",
"""funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt""",
"""funnel-transformer/xlarge-base""": (
"""https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json""",
"""funnel-transformer/small-base""": (
"""https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"""
),
"""funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json""",
"""funnel-transformer/medium-base""": (
"""https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"""
),
"""funnel-transformer/intermediate""": (
"""https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"""
),
"""funnel-transformer/intermediate-base""": (
"""https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"""
),
"""funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json""",
"""funnel-transformer/large-base""": (
"""https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"""
),
"""funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json""",
"""funnel-transformer/xlarge-base""": (
"""https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"""
),
},
}
snake_case__ = {F'funnel-transformer/{name}': 5_12 for name in _model_names}
snake_case__ = {F'funnel-transformer/{name}': {"""do_lower_case""": True} for name in _model_names}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = VOCAB_FILES_NAMES
_lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase = PRETRAINED_INIT_CONFIGURATION
_lowerCAmelCase = FunnelTokenizer
_lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase = 2
def __init__( self : Union[str, Any] , _lowerCamelCase : List[Any]=None , _lowerCamelCase : Tuple=None , _lowerCamelCase : Any=True , _lowerCamelCase : Union[str, Any]="<unk>" , _lowerCamelCase : str="<sep>" , _lowerCamelCase : Union[str, Any]="<pad>" , _lowerCamelCase : Optional[Any]="<cls>" , _lowerCamelCase : List[Any]="<mask>" , _lowerCamelCase : Union[str, Any]="<s>" , _lowerCamelCase : List[Any]="</s>" , _lowerCamelCase : List[Any]=True , _lowerCamelCase : int=True , _lowerCamelCase : List[Any]=None , _lowerCamelCase : Union[str, Any]="##" , **_lowerCamelCase : List[str] , ):
"""simple docstring"""
super().__init__(
_lowerCamelCase , tokenizer_file=_lowerCamelCase , do_lower_case=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , pad_token=_lowerCamelCase , cls_token=_lowerCamelCase , mask_token=_lowerCamelCase , bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , clean_text=_lowerCamelCase , tokenize_chinese_chars=_lowerCamelCase , strip_accents=_lowerCamelCase , wordpieces_prefix=_lowerCamelCase , **_lowerCamelCase , )
A_ : Union[str, Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , _lowerCamelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , _lowerCamelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , _lowerCamelCase ) != tokenize_chinese_chars
):
A_ : str = getattr(_lowerCamelCase , normalizer_state.pop('''type''' ) )
A_ : Optional[Any] = do_lower_case
A_ : List[Any] = strip_accents
A_ : Any = tokenize_chinese_chars
A_ : Any = normalizer_class(**_lowerCamelCase )
A_ : Union[str, Any] = do_lower_case
def _a ( self : int , _lowerCamelCase : Any , _lowerCamelCase : Optional[Any]=None ):
"""simple docstring"""
A_ : str = [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 : Union[str, Any] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
A_ : int = [self.sep_token_id]
A_ : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _a ( self : List[str] , _lowerCamelCase : str , _lowerCamelCase : Optional[str] = None ):
"""simple docstring"""
A_ : Optional[int] = self._tokenizer.model.save(_lowerCamelCase , name=_lowerCamelCase )
return tuple(_lowerCamelCase )
| 364 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'table-transformer'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Any , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Dict=None , _lowerCamelCase : int=3 , _lowerCamelCase : Any=100 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : Tuple=2048 , _lowerCamelCase : Any=8 , _lowerCamelCase : Dict=6 , _lowerCamelCase : Tuple=2048 , _lowerCamelCase : int=8 , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : List[Any]=0.0 , _lowerCamelCase : List[Any]=True , _lowerCamelCase : Optional[int]="relu" , _lowerCamelCase : Union[str, Any]=256 , _lowerCamelCase : Any=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : str=0.02 , _lowerCamelCase : Tuple=1.0 , _lowerCamelCase : Dict=False , _lowerCamelCase : str="sine" , _lowerCamelCase : str="resnet50" , _lowerCamelCase : Any=True , _lowerCamelCase : List[str]=False , _lowerCamelCase : Any=1 , _lowerCamelCase : int=5 , _lowerCamelCase : Tuple=2 , _lowerCamelCase : Optional[int]=1 , _lowerCamelCase : Any=1 , _lowerCamelCase : Dict=5 , _lowerCamelCase : str=2 , _lowerCamelCase : Union[str, Any]=0.1 , **_lowerCamelCase : int , ):
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
A_ : int = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : str = backbone_config.get('''model_type''' )
A_ : Optional[int] = CONFIG_MAPPING[backbone_model_type]
A_ : List[str] = config_class.from_dict(_lowerCamelCase )
# set timm attributes to None
A_ ,A_ ,A_ : Union[str, Any] = None, None, None
A_ : Optional[Any] = use_timm_backbone
A_ : Optional[int] = backbone_config
A_ : Optional[Any] = num_channels
A_ : Dict = num_queries
A_ : str = d_model
A_ : List[str] = encoder_ffn_dim
A_ : int = encoder_layers
A_ : Optional[Any] = encoder_attention_heads
A_ : List[str] = decoder_ffn_dim
A_ : Any = decoder_layers
A_ : List[str] = decoder_attention_heads
A_ : Tuple = dropout
A_ : Optional[Any] = attention_dropout
A_ : Any = activation_dropout
A_ : List[Any] = activation_function
A_ : Dict = init_std
A_ : Any = init_xavier_std
A_ : List[Any] = encoder_layerdrop
A_ : int = decoder_layerdrop
A_ : Any = encoder_layers
A_ : List[str] = auxiliary_loss
A_ : List[Any] = position_embedding_type
A_ : Optional[Any] = backbone
A_ : Tuple = use_pretrained_backbone
A_ : List[Any] = dilation
# Hungarian matcher
A_ : List[str] = class_cost
A_ : str = bbox_cost
A_ : Union[str, Any] = giou_cost
# Loss coefficients
A_ : Any = mask_loss_coefficient
A_ : Optional[int] = dice_loss_coefficient
A_ : Dict = bbox_loss_coefficient
A_ : int = giou_loss_coefficient
A_ : int = eos_coefficient
super().__init__(is_encoder_decoder=_lowerCamelCase , **_lowerCamelCase )
@property
def _a ( self : List[Any] ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def _a ( self : Any ):
"""simple docstring"""
return self.d_model
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : Tuple ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def _a ( self : Optional[int] ):
"""simple docstring"""
return 1E-5
@property
def _a ( self : str ):
"""simple docstring"""
return 12
| 4 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def snake_case__ ( lowerCamelCase__ : Namespace ) -> Union[str, Any]:
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
snake_case__ = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class UpperCamelCase_ (a__ ):
"""simple docstring"""
@staticmethod
def _a ( _lowerCamelCase : ArgumentParser ):
"""simple docstring"""
A_ : List[str] = parser.add_parser(
'''convert''' , help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.''' , )
train_parser.add_argument('''--model_type''' , type=_lowerCamelCase , required=_lowerCamelCase , help='''Model\'s type.''' )
train_parser.add_argument(
'''--tf_checkpoint''' , type=_lowerCamelCase , required=_lowerCamelCase , help='''TensorFlow checkpoint path or folder.''' )
train_parser.add_argument(
'''--pytorch_dump_output''' , type=_lowerCamelCase , required=_lowerCamelCase , help='''Path to the PyTorch saved model output.''' )
train_parser.add_argument('''--config''' , type=_lowerCamelCase , default='''''' , help='''Configuration file path or folder.''' )
train_parser.add_argument(
'''--finetuning_task_name''' , type=_lowerCamelCase , default=_lowerCamelCase , help='''Optional fine-tuning task name if the TF model was a finetuned model.''' , )
train_parser.set_defaults(func=_lowerCamelCase )
def __init__( self : Dict , _lowerCamelCase : str , _lowerCamelCase : str , _lowerCamelCase : str , _lowerCamelCase : str , _lowerCamelCase : str , *_lowerCamelCase : Optional[Any] , ):
"""simple docstring"""
A_ : Dict = logging.get_logger('''transformers-cli/converting''' )
self._logger.info(f'Loading model {model_type}' )
A_ : Tuple = model_type
A_ : List[Any] = tf_checkpoint
A_ : Any = pytorch_dump_output
A_ : Union[str, Any] = config
A_ : Dict = finetuning_task_name
def _a ( self : Any ):
"""simple docstring"""
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_lowerCamelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_lowerCamelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_lowerCamelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(_lowerCamelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_lowerCamelCase )
if "ckpt" in self._tf_checkpoint.lower():
A_ : List[Any] = self._tf_checkpoint
A_ : Optional[int] = ''''''
else:
A_ : Union[str, Any] = self._tf_checkpoint
A_ : int = ''''''
convert_transfo_xl_checkpoint_to_pytorch(
_lowerCamelCase , self._config , self._pytorch_dump_output , _lowerCamelCase )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_lowerCamelCase )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_lowerCamelCase )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
'''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]''' )
| 365 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : int , _lowerCamelCase : List[str]=3 , _lowerCamelCase : Any=32 , _lowerCamelCase : Union[str, Any]=3 , _lowerCamelCase : int=10 , _lowerCamelCase : Union[str, Any]=[8, 16, 32, 64] , _lowerCamelCase : Dict=[1, 1, 2, 1] , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Optional[int]=True , _lowerCamelCase : Any="relu" , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Dict=["stage2", "stage3", "stage4"] , _lowerCamelCase : Union[str, Any]=[2, 3, 4] , _lowerCamelCase : Tuple=1 , ):
"""simple docstring"""
A_ : List[str] = parent
A_ : List[str] = batch_size
A_ : Union[str, Any] = image_size
A_ : Tuple = num_channels
A_ : Any = embeddings_size
A_ : int = hidden_sizes
A_ : Optional[Any] = depths
A_ : List[Any] = is_training
A_ : Optional[int] = use_labels
A_ : int = hidden_act
A_ : Tuple = num_labels
A_ : Union[str, Any] = scope
A_ : List[Any] = len(_lowerCamelCase )
A_ : Union[str, Any] = out_features
A_ : List[Any] = out_indices
A_ : Dict = num_groups
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : Union[str, Any] = None
if self.use_labels:
A_ : Any = ids_tensor([self.batch_size] , self.num_labels )
A_ : Any = self.get_config()
return config, pixel_values, labels
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return BitConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def _a ( self : List[Any] , _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
A_ : Any = BitModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase )
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 : Optional[int] , _lowerCamelCase : List[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : Dict = self.num_labels
A_ : Optional[Any] = BitForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : List[Any] = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Any , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : List[Any] ):
"""simple docstring"""
A_ : List[Any] = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
A_ : Optional[Any] = None
A_ : int = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Optional[int] = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = self.prepare_config_and_inputs()
A_ ,A_ ,A_ : Union[str, Any] = config_and_inputs
A_ : str = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase_ (a__, a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
_lowerCAmelCase = (
{'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
if is_torch_available()
else {}
)
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[str] = BitModelTester(self )
A_ : Optional[Any] = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : List[Any] ):
"""simple docstring"""
return
@unittest.skip(reason='''Bit does not output attentions''' )
def _a ( self : str ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def _a ( self : Any ):
"""simple docstring"""
pass
def _a ( self : List[Any] ):
"""simple docstring"""
A_ ,A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Dict = model_class(_lowerCamelCase )
A_ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : int = [*signature.parameters.keys()]
A_ : Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ ,A_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : str = model_class(config=_lowerCamelCase )
for name, module in model.named_modules():
if isinstance(_lowerCamelCase , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
def _a ( self : int ):
"""simple docstring"""
def check_hidden_states_output(_lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : int ):
A_ : Union[str, Any] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
A_ : Union[str, Any] = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
A_ : int = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
A_ : List[Any] = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , expected_num_stages + 1 )
# Bit'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 // 4, self.model_tester.image_size // 4] , )
A_ ,A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Tuple = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
A_ : Tuple = layer_type
A_ : Optional[Any] = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A_ : List[str] = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def _a ( self : Tuple ):
"""simple docstring"""
pass
def _a ( self : str ):
"""simple docstring"""
A_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
@slow
def _a ( self : Union[str, Any] ):
"""simple docstring"""
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : List[Any] = BitModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def snake_case__ ( ) -> Optional[int]:
A_ : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _a ( self : List[Any] ):
"""simple docstring"""
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_lowerCamelCase )
A_ : Union[str, Any] = self.default_image_processor
A_ : Optional[int] = prepare_img()
A_ : int = image_processor(images=_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
A_ : Union[str, Any] = model(**_lowerCamelCase )
# verify the logits
A_ : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
A_ : Tuple = torch.tensor([[-0.65_26, -0.52_63, -1.43_98]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1E-4 ) )
@require_torch
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (BitBackbone,) if is_torch_available() else ()
_lowerCAmelCase = BitConfig
_lowerCAmelCase = False
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Union[str, Any] = BitModelTester(self )
| 4 | 0 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : set ) -> int:
A_ : Optional[Any] = len(lowerCamelCase__ ), len(grid[0] )
if (
min(lowerCamelCase__ , lowerCamelCase__ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
A_ : str = 0
count += depth_first_search(lowerCamelCase__ , row + 1 , lowerCamelCase__ , lowerCamelCase__ )
count += depth_first_search(lowerCamelCase__ , row - 1 , lowerCamelCase__ , lowerCamelCase__ )
count += depth_first_search(lowerCamelCase__ , lowerCamelCase__ , col + 1 , lowerCamelCase__ )
count += depth_first_search(lowerCamelCase__ , lowerCamelCase__ , col - 1 , lowerCamelCase__ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366 |
'''simple docstring'''
import pprint
import requests
snake_case__ = """https://zenquotes.io/api"""
def snake_case__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def snake_case__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if __name__ == "__main__":
snake_case__ = random_quotes()
pprint.pprint(response)
| 4 | 0 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : str ) -> str:
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367 |
'''simple docstring'''
from __future__ import annotations
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[int] , _lowerCamelCase : int ):
"""simple docstring"""
A_ : Union[str, Any] = order
# a_{0} ... a_{k}
A_ : Union[str, Any] = [1.0] + [0.0] * order
# b_{0} ... b_{k}
A_ : int = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
A_ : str = [0.0] * self.order
# y[n-1] ... y[n-k]
A_ : Optional[Any] = [0.0] * self.order
def _a ( self : Dict , _lowerCamelCase : list[float] , _lowerCamelCase : list[float] ):
"""simple docstring"""
if len(_lowerCamelCase ) < self.order:
A_ : Any = [1.0, *a_coeffs]
if len(_lowerCamelCase ) != self.order + 1:
A_ : List[Any] = (
f'Expected a_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(_lowerCamelCase )}'
)
raise ValueError(_lowerCamelCase )
if len(_lowerCamelCase ) != self.order + 1:
A_ : Union[str, Any] = (
f'Expected b_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(_lowerCamelCase )}'
)
raise ValueError(_lowerCamelCase )
A_ : Tuple = a_coeffs
A_ : str = b_coeffs
def _a ( self : Tuple , _lowerCamelCase : float ):
"""simple docstring"""
A_ : Any = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
A_ : str = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
A_ : Optional[Any] = self.input_history[:-1]
A_ : List[str] = self.output_history[:-1]
A_ : Tuple = sample
A_ : Tuple = result
return result
| 4 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : int , _lowerCamelCase : int , _lowerCamelCase : MutableSequence[float] ):
"""simple docstring"""
if len(_lowerCamelCase ) != degree + 1:
raise ValueError(
'''The number of coefficients should be equal to the degree + 1.''' )
A_ : list[float] = list(_lowerCamelCase )
A_ : Optional[Any] = degree
def __add__( self : List[str] , _lowerCamelCase : Polynomial ):
"""simple docstring"""
if self.degree > polynomial_a.degree:
A_ : Union[str, Any] = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , _lowerCamelCase )
else:
A_ : Tuple = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , _lowerCamelCase )
def __sub__( self : str , _lowerCamelCase : Polynomial ):
"""simple docstring"""
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : Any ):
"""simple docstring"""
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : Optional[int] , _lowerCamelCase : Polynomial ):
"""simple docstring"""
A_ : list[float] = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , _lowerCamelCase )
def _a ( self : str , _lowerCamelCase : int | float ):
"""simple docstring"""
A_ : int | float = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : Optional[Any] ):
"""simple docstring"""
A_ : List[Any] = ''''''
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(_lowerCamelCase )
return polynomial
def __repr__( self : Any ):
"""simple docstring"""
return self.__str__()
def _a ( self : Dict ):
"""simple docstring"""
A_ : list[float] = [0] * self.degree
for i in range(self.degree ):
A_ : int = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , _lowerCamelCase )
def _a ( self : Union[str, Any] , _lowerCamelCase : int | float = 0 ):
"""simple docstring"""
A_ : list[float] = [0] * (self.degree + 2)
A_ : str = constant
for i in range(self.degree + 1 ):
A_ : List[str] = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , _lowerCamelCase )
def __eq__( self : List[Any] , _lowerCamelCase : object ):
"""simple docstring"""
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : Dict , _lowerCamelCase : object ):
"""simple docstring"""
return not self.__eq__(_lowerCamelCase )
| 368 |
'''simple docstring'''
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ : Union[str, Any] = val
A_ : Tuple = None
A_ : Any = None
def _a ( self : Tuple , _lowerCamelCase : List[Any] ):
"""simple docstring"""
if self.val:
if val < self.val:
if self.left is None:
A_ : int = Node(_lowerCamelCase )
else:
self.left.insert(_lowerCamelCase )
elif val > self.val:
if self.right is None:
A_ : List[str] = Node(_lowerCamelCase )
else:
self.right.insert(_lowerCamelCase )
else:
A_ : Any = val
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int] ) -> str:
# Recursive traversal
if root:
inorder(root.left , lowerCamelCase__ )
res.append(root.val )
inorder(root.right , lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Optional[int] ) -> Tuple:
# Build BST
if len(lowerCamelCase__ ) == 0:
return arr
A_ : Dict = Node(arr[0] )
for i in range(1 , len(lowerCamelCase__ ) ):
root.insert(arr[i] )
# Traverse BST in order.
A_ : Tuple = []
inorder(lowerCamelCase__ , lowerCamelCase__ )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 4 | 0 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
snake_case__ = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
booktitle = {},
year = {2002},
pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
author = \"Lin, Chin-Yew and
Och, Franz Josef\",
booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
month = \"aug 23{--}aug 27\",
year = \"2004\",
address = \"Geneva, Switzerland\",
publisher = \"COLING\",
url = \"https://www.aclweb.org/anthology/C04-1072\",
pages = \"501--507\",
}
"""
snake_case__ = """\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.
Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].
BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""
snake_case__ = """
Computes BLEU score of translated segments against one or more references.
Args:
predictions: list of translations to score.
Each translation should be tokenized into a list of tokens.
references: list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
'bleu': bleu score,
'precisions': geometric mean of n-gram precisions,
'brevity_penalty': brevity penalty,
'length_ratio': ratio of lengths,
'translation_length': translation_length,
'reference_length': reference_length
Examples:
>>> predictions = [
... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample
... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample
... ]
>>> references = [
... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)
... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)
... ]
>>> bleu = datasets.load_metric(\"bleu\")
>>> results = bleu.compute(predictions=predictions, references=references)
>>> print(results[\"bleu\"])
1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class UpperCamelCase_ (datasets.Metric ):
"""simple docstring"""
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def _a ( self : Union[str, Any] , _lowerCamelCase : Tuple , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[int]=4 , _lowerCamelCase : int=False ):
"""simple docstring"""
A_ : Optional[Any] = compute_bleu(
reference_corpus=_lowerCamelCase , translation_corpus=_lowerCamelCase , max_order=_lowerCamelCase , smooth=_lowerCamelCase )
(A_) : Optional[Any] = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
}
| 369 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list ) -> list:
if len(lowerCamelCase__ ) <= 1:
return [tuple(lowerCamelCase__ )]
A_ : List[str] = []
def generate(lowerCamelCase__ : int , lowerCamelCase__ : list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , lowerCamelCase__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
A_ ,A_ : Optional[int] = arr[k - 1], arr[i]
else: # k is odd
A_ ,A_ : Union[str, Any] = arr[k - 1], arr[0]
generate(k - 1 , lowerCamelCase__ )
generate(len(lowerCamelCase__ ) , lowerCamelCase__ )
return res
if __name__ == "__main__":
snake_case__ = input("""Enter numbers separated by a comma:\n""").strip()
snake_case__ = [int(item) for item in user_input.split(""",""")]
print(heaps(arr))
| 4 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}
class UpperCamelCase_ (a__, a__ ):
"""simple docstring"""
_lowerCAmelCase = 'resnet'
_lowerCAmelCase = ['basic', 'bottleneck']
def __init__( self : Union[str, Any] , _lowerCamelCase : List[str]=3 , _lowerCamelCase : str=64 , _lowerCamelCase : str=[256, 512, 1024, 2048] , _lowerCamelCase : Optional[Any]=[3, 4, 6, 3] , _lowerCamelCase : Any="bottleneck" , _lowerCamelCase : List[Any]="relu" , _lowerCamelCase : List[Any]=False , _lowerCamelCase : str=None , _lowerCamelCase : List[str]=None , **_lowerCamelCase : Tuple , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types )}' )
A_ : int = num_channels
A_ : Dict = embedding_size
A_ : Union[str, Any] = hidden_sizes
A_ : Dict = depths
A_ : str = layer_type
A_ : int = hidden_act
A_ : str = downsample_in_first_stage
A_ : Dict = ['''stem'''] + [f'stage{idx}' for idx in range(1 , len(_lowerCamelCase ) + 1 )]
A_ : Any = get_aligned_output_features_output_indices(
out_features=_lowerCamelCase , out_indices=_lowerCamelCase , stage_names=self.stage_names )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : int ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _a ( self : Optional[int] ):
"""simple docstring"""
return 1E-3
| 370 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : Tuple = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Dict = -1
A_ : List[str] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Any = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : List[str] = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
A_ : List[str] = TextStreamer(_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
A_ : Dict = cs.out[:-1]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : List[str] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Dict = -1
A_ : List[str] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Optional[int] = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : str = tokenizer.decode(greedy_ids[0] )
A_ : int = TextIteratorStreamer(_lowerCamelCase )
A_ : List[Any] = {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
A_ : List[Any] = Thread(target=model.generate , kwargs=_lowerCamelCase )
thread.start()
A_ : List[Any] = ''''''
for new_text in streamer:
streamer_text += new_text
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : List[str] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : List[Any] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : List[str] = -1
A_ : Any = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Tuple = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : Tuple = greedy_ids[:, input_ids.shape[1] :]
A_ : Tuple = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
A_ : Any = TextStreamer(_lowerCamelCase , skip_prompt=_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
A_ : Any = cs.out[:-1]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = AutoTokenizer.from_pretrained('''distilgpt2''' )
A_ : Tuple = AutoModelForCausalLM.from_pretrained('''distilgpt2''' ).to(_lowerCamelCase )
A_ : List[Any] = -1
A_ : Union[str, Any] = torch.ones((1, 5) , device=_lowerCamelCase ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
A_ : List[Any] = TextStreamer(_lowerCamelCase , skip_special_tokens=_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=1 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
A_ : List[str] = cs.out[:-1] # Remove the final "\n"
A_ : List[Any] = tokenizer(_lowerCamelCase , return_tensors='''pt''' )
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1) )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : str = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : str = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Union[str, Any] = -1
A_ : Union[str, Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : List[str] = TextIteratorStreamer(_lowerCamelCase , timeout=0.0_01 )
A_ : str = {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
A_ : List[str] = Thread(target=model.generate , kwargs=_lowerCamelCase )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(_lowerCamelCase ):
A_ : str = ''''''
for new_text in streamer:
streamer_text += new_text
| 4 | 0 |
'''simple docstring'''
import numpy as np
def snake_case__ ( lowerCamelCase__ : np.ndarray , lowerCamelCase__ : float ) -> np.ndarray:
return np.where(vector > 0 , lowerCamelCase__ , (alpha * (np.exp(lowerCamelCase__ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 371 |
'''simple docstring'''
import heapq
def snake_case__ ( lowerCamelCase__ : dict ) -> set[int]:
A_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase__ , [-1 * len(lowerCamelCase__ ), (key, value)] )
# chosen_vertices = set of chosen vertices
A_ : str = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
A_ : Tuple = heapq.heappop(lowerCamelCase__ )[1][0]
chosen_vertices.add(lowerCamelCase__ )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
A_ : List[str] = elem[1][1].index(lowerCamelCase__ )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase__ )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case__ = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')
| 4 | 0 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'mvp'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Dict , _lowerCamelCase : Dict=50267 , _lowerCamelCase : Union[str, Any]=1024 , _lowerCamelCase : Optional[int]=12 , _lowerCamelCase : str=4096 , _lowerCamelCase : str=16 , _lowerCamelCase : int=12 , _lowerCamelCase : Union[str, Any]=4096 , _lowerCamelCase : Any=16 , _lowerCamelCase : List[str]=0.0 , _lowerCamelCase : Union[str, Any]=0.0 , _lowerCamelCase : Optional[Any]="gelu" , _lowerCamelCase : Tuple=1024 , _lowerCamelCase : Optional[Any]=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : List[Any]=0.0 , _lowerCamelCase : Union[str, Any]=0.02 , _lowerCamelCase : int=0.0 , _lowerCamelCase : List[str]=False , _lowerCamelCase : int=True , _lowerCamelCase : Tuple=1 , _lowerCamelCase : Union[str, Any]=0 , _lowerCamelCase : Optional[Any]=2 , _lowerCamelCase : List[Any]=True , _lowerCamelCase : int=2 , _lowerCamelCase : int=2 , _lowerCamelCase : Tuple=False , _lowerCamelCase : Dict=100 , _lowerCamelCase : int=800 , **_lowerCamelCase : int , ):
"""simple docstring"""
A_ : List[Any] = vocab_size
A_ : Dict = max_position_embeddings
A_ : str = d_model
A_ : int = encoder_ffn_dim
A_ : Optional[Any] = encoder_layers
A_ : Dict = encoder_attention_heads
A_ : Optional[Any] = decoder_ffn_dim
A_ : Dict = decoder_layers
A_ : str = decoder_attention_heads
A_ : List[str] = dropout
A_ : str = attention_dropout
A_ : int = activation_dropout
A_ : List[str] = activation_function
A_ : Dict = init_std
A_ : List[Any] = encoder_layerdrop
A_ : int = decoder_layerdrop
A_ : int = classifier_dropout
A_ : int = use_cache
A_ : Any = encoder_layers
A_ : List[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
A_ : List[Any] = use_prompt
A_ : int = prompt_length
A_ : Optional[Any] = prompt_mid_dim
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , is_encoder_decoder=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , forced_eos_token_id=_lowerCamelCase , **_lowerCamelCase , )
if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''' , _lowerCamelCase ):
A_ : List[str] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
'''The config can simply be saved and uploaded again to be fixed.''' )
| 350 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__ = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
F'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
F'decoder.layers.{i}.encoder_attn.out_proj.bias',
)
)
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
]
)
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[Any] ) -> Optional[Any]:
A_ : Tuple = state_dict.pop(lowerCamelCase__ )
A_ : Optional[Any] = val
def snake_case__ ( lowerCamelCase__ : Dict ) -> Any:
A_ : int = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
A_ : int = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
A_ : List[str] = value
else:
A_ : Optional[int] = value
return new_state_dict
def snake_case__ ( lowerCamelCase__ : Union[str, Any] ) -> Optional[Any]:
A_ : Any = ''''''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
A_ : Tuple = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
A_ : Dict = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
A_ : str = in_proj_weight[:2_5_6, :]
A_ : Optional[Any] = in_proj_bias[:2_5_6]
A_ : Dict = in_proj_weight[2_5_6:5_1_2, :]
A_ : Tuple = in_proj_bias[2_5_6:5_1_2]
A_ : Tuple = in_proj_weight[-2_5_6:, :]
A_ : Optional[int] = in_proj_bias[-2_5_6:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
A_ : Union[str, Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
A_ : Dict = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
A_ : List[str] = in_proj_weight[:2_5_6, :]
A_ : int = in_proj_bias[:2_5_6]
A_ : Any = in_proj_weight[2_5_6:5_1_2, :]
A_ : List[str] = in_proj_bias[2_5_6:5_1_2]
A_ : Union[str, Any] = in_proj_weight[-2_5_6:, :]
A_ : Optional[Any] = in_proj_bias[-2_5_6:]
# read in weights + bias of input projection layer of cross-attention
A_ : Tuple = state_dict.pop(
f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
A_ : Optional[Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
A_ : Dict = in_proj_weight_cross_attn[:2_5_6, :]
A_ : Tuple = in_proj_bias_cross_attn[:2_5_6]
A_ : int = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
A_ : List[str] = in_proj_bias_cross_attn[2_5_6:5_1_2]
A_ : Any = in_proj_weight_cross_attn[-2_5_6:, :]
A_ : Any = in_proj_bias_cross_attn[-2_5_6:]
def snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Tuple ) -> Dict:
A_ ,A_ : int = image.size
A_ : Tuple = max(lowerCamelCase__ , lowerCamelCase__ )
A_ : Optional[Any] = 8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0
A_ : Union[str, Any] = target_max_size / current_max_size
A_ : Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def snake_case__ ( lowerCamelCase__ : Tuple ) -> str:
A_ : Any = F.to_tensor(lowerCamelCase__ )
A_ : Optional[Any] = F.normalize(lowerCamelCase__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : int , lowerCamelCase__ : int ) -> str:
logger.info('''Converting model...''' )
# load original state dict
A_ : Tuple = torch.hub.load_state_dict_from_url(lowerCamelCase__ , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
A_ : str = rename_backbone_keys(lowerCamelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCamelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
A_ : List[Any] = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
A_ : List[Any] = state_dict.pop(lowerCamelCase__ )
A_ : str = val
# create HuggingFace model and load state dict
A_ : Union[str, Any] = TableTransformerConfig(
backbone='''resnet18''' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
A_ : Dict = 1_5
A_ : Dict = 2
A_ : int = {0: '''table''', 1: '''table rotated'''}
A_ : List[str] = idalabel
A_ : Optional[int] = {v: k for k, v in idalabel.items()}
else:
A_ : Union[str, Any] = 1_2_5
A_ : Optional[Any] = 6
A_ : Optional[Any] = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
A_ : int = idalabel
A_ : Tuple = {v: k for k, v in idalabel.items()}
A_ : Optional[Any] = DetrImageProcessor(
format='''coco_detection''' , max_size=8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0 )
A_ : int = TableTransformerForObjectDetection(lowerCamelCase__ )
model.load_state_dict(lowerCamelCase__ )
model.eval()
# verify our conversion
A_ : Optional[int] = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
A_ : Union[str, Any] = hf_hub_download(repo_id='''nielsr/example-pdf''' , repo_type='''dataset''' , filename=lowerCamelCase__ )
A_ : Tuple = Image.open(lowerCamelCase__ ).convert('''RGB''' )
A_ : int = normalize(resize(lowerCamelCase__ , lowerCamelCase__ ) ).unsqueeze(0 )
A_ : str = model(lowerCamelCase__ )
if "detection" in checkpoint_url:
A_ : str = (1, 1_5, 3)
A_ : int = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
A_ : Tuple = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
A_ : Optional[int] = (1, 1_2_5, 7)
A_ : Dict = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
A_ : Any = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(lowerCamelCase__ ).mkdir(exist_ok=lowerCamelCase__ )
model.save_pretrained(lowerCamelCase__ )
image_processor.save_pretrained(lowerCamelCase__ )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
A_ : List[Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(lowerCamelCase__ )
image_processor.push_to_hub(lowerCamelCase__ )
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
snake_case__ = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 4 | 0 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
snake_case__ = 16
snake_case__ = 32
def snake_case__ ( lowerCamelCase__ : Accelerator , lowerCamelCase__ : DatasetDict , lowerCamelCase__ : List[int] , lowerCamelCase__ : List[int] , lowerCamelCase__ : int = 1_6 ) -> Tuple:
A_ : str = AutoTokenizer.from_pretrained('''bert-base-cased''' )
A_ : Any = DatasetDict(
{
'''train''': dataset['''train'''].select(lowerCamelCase__ ),
'''validation''': dataset['''train'''].select(lowerCamelCase__ ),
'''test''': dataset['''validation'''],
} )
def tokenize_function(lowerCamelCase__ : Union[str, Any] ):
# max_length=None => use the model max length (it's actually the default)
A_ : Dict = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A_ : Optional[Any] = datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A_ : Optional[Any] = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(lowerCamelCase__ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
A_ : Optional[Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
A_ : int = 1_6
elif accelerator.mixed_precision != "no":
A_ : str = 8
else:
A_ : Tuple = None
return tokenizer.pad(
lowerCamelCase__ , padding='''longest''' , max_length=lowerCamelCase__ , pad_to_multiple_of=lowerCamelCase__ , return_tensors='''pt''' , )
# Instantiate dataloaders.
A_ : Union[str, Any] = DataLoader(
tokenized_datasets['''train'''] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
A_ : Optional[int] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
A_ : Dict = DataLoader(
tokenized_datasets['''test'''] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
return train_dataloader, eval_dataloader, test_dataloader
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : int ) -> Optional[int]:
# New Code #
A_ : List[Any] = []
# Download the dataset
A_ : Tuple = load_dataset('''glue''' , '''mrpc''' )
# Create our splits
A_ : Tuple = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
A_ : int = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A_ : Tuple = config['''lr''']
A_ : int = int(config['''num_epochs'''] )
A_ : Optional[int] = int(config['''seed'''] )
A_ : Tuple = int(config['''batch_size'''] )
A_ : Optional[int] = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
A_ : Any = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
A_ : int = batch_size // MAX_GPU_BATCH_SIZE
A_ : Any = MAX_GPU_BATCH_SIZE
set_seed(lowerCamelCase__ )
# New Code #
# Create our folds:
A_ : List[Any] = kfold.split(np.zeros(datasets['''train'''].num_rows ) , datasets['''train''']['''label'''] )
A_ : List[str] = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(lowerCamelCase__ ):
A_ : str = get_fold_dataloaders(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A_ : int = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=lowerCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
A_ : List[str] = AdamW(params=model.parameters() , lr=lowerCamelCase__ )
# Instantiate scheduler
A_ : Optional[int] = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(lowerCamelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A_ : Optional[int] = accelerator.prepare(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Now we train the model
for epoch in range(lowerCamelCase__ ):
model.train()
for step, batch in enumerate(lowerCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
A_ : Tuple = model(**lowerCamelCase__ )
A_ : Optional[Any] = outputs.loss
A_ : Optional[int] = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A_ : Optional[Any] = model(**lowerCamelCase__ )
A_ : List[Any] = outputs.logits.argmax(dim=-1 )
A_ : Optional[int] = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=lowerCamelCase__ , references=lowerCamelCase__ , )
A_ : Optional[int] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , lowerCamelCase__ )
# New Code #
# We also run predictions on the test set at the very end
A_ : Union[str, Any] = []
for step, batch in enumerate(lowerCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A_ : List[str] = model(**lowerCamelCase__ )
A_ : Any = outputs.logits
A_ : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(lowerCamelCase__ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
A_ : Tuple = torch.cat(lowerCamelCase__ , dim=0 )
A_ : Dict = torch.stack(lowerCamelCase__ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
A_ : str = metric.compute(predictions=lowerCamelCase__ , references=lowerCamelCase__ )
accelerator.print('''Average test metrics from all folds:''' , lowerCamelCase__ )
def snake_case__ ( ) -> int:
A_ : Union[str, Any] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=lowerCamelCase__ , default=lowerCamelCase__ , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
# New Code #
parser.add_argument('''--num_folds''' , type=lowerCamelCase__ , default=3 , help='''The number of splits to perform across the dataset''' )
A_ : int = parser.parse_args()
A_ : Dict = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
training_function(lowerCamelCase__ , lowerCamelCase__ )
if __name__ == "__main__":
main()
| 351 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
snake_case__ = logging.getLogger(__name__)
@dataclass(frozen=a__ )
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
@dataclass(frozen=a__ )
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 42
def __init__( self : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : PreTrainedTokenizer , _lowerCamelCase : str , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : List[Any]=False , _lowerCamelCase : bool = False , ):
"""simple docstring"""
A_ : Optional[int] = hans_processors[task]()
A_ : int = os.path.join(
_lowerCamelCase , '''cached_{}_{}_{}_{}'''.format(
'''dev''' if evaluate else '''train''' , tokenizer.__class__.__name__ , str(_lowerCamelCase ) , _lowerCamelCase , ) , )
A_ : Dict = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
A_ ,A_ : List[str] = label_list[2], label_list[1]
A_ : Optional[int] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
A_ : str = cached_features_file + '''.lock'''
with FileLock(_lowerCamelCase ):
if os.path.exists(_lowerCamelCase ) and not overwrite_cache:
logger.info(f'Loading features from cached file {cached_features_file}' )
A_ : List[str] = torch.load(_lowerCamelCase )
else:
logger.info(f'Creating features from dataset file at {data_dir}' )
A_ : Optional[int] = (
processor.get_dev_examples(_lowerCamelCase ) if evaluate else processor.get_train_examples(_lowerCamelCase )
)
logger.info('''Training examples: %s''' , len(_lowerCamelCase ) )
A_ : Optional[int] = hans_convert_examples_to_features(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
logger.info('''Saving features into cached file %s''' , _lowerCamelCase )
torch.save(self.features , _lowerCamelCase )
def __len__( self : List[str] ):
"""simple docstring"""
return len(self.features )
def __getitem__( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
return self.features[i]
def _a ( self : str ):
"""simple docstring"""
return self.label_list
if is_tf_available():
import tensorflow as tf
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
def __init__( self : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : PreTrainedTokenizer , _lowerCamelCase : str , _lowerCamelCase : Optional[int] = 128 , _lowerCamelCase : Dict=False , _lowerCamelCase : bool = False , ):
"""simple docstring"""
A_ : Optional[int] = hans_processors[task]()
A_ : Optional[int] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
A_ ,A_ : Union[str, Any] = label_list[2], label_list[1]
A_ : Tuple = label_list
A_ : Optional[int] = processor.get_dev_examples(_lowerCamelCase ) if evaluate else processor.get_train_examples(_lowerCamelCase )
A_ : Tuple = hans_convert_examples_to_features(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='''convert examples to features''' ):
if ex_index % 10000 == 0:
logger.info('''Writing example %d of %d''' % (ex_index, len(_lowerCamelCase )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
A_ : List[Any] = tf.data.Dataset.from_generator(
_lowerCamelCase , (
{
'''example_id''': tf.intaa,
'''input_ids''': tf.intaa,
'''attention_mask''': tf.intaa,
'''token_type_ids''': tf.intaa,
},
tf.intaa,
) , (
{
'''example_id''': tf.TensorShape([] ),
'''input_ids''': tf.TensorShape([None, None] ),
'''attention_mask''': tf.TensorShape([None, None] ),
'''token_type_ids''': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def _a ( self : Any ):
"""simple docstring"""
return self.dataset
def __len__( self : Dict ):
"""simple docstring"""
return len(self.features )
def __getitem__( self : Optional[int] , _lowerCamelCase : List[str] ):
"""simple docstring"""
return self.features[i]
def _a ( self : Tuple ):
"""simple docstring"""
return self.label_list
class UpperCamelCase_ (a__ ):
"""simple docstring"""
def _a ( self : List[str] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(_lowerCamelCase , '''heuristics_train_set.txt''' ) ) , '''train''' )
def _a ( self : List[str] , _lowerCamelCase : Tuple ):
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(_lowerCamelCase , '''heuristics_evaluation_set.txt''' ) ) , '''dev''' )
def _a ( self : Any ):
"""simple docstring"""
return ["contradiction", "entailment", "neutral"]
def _a ( self : Optional[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : Tuple = []
for i, line in enumerate(_lowerCamelCase ):
if i == 0:
continue
A_ : str = '''%s-%s''' % (set_type, line[0])
A_ : Optional[Any] = line[5]
A_ : Union[str, Any] = line[6]
A_ : List[str] = line[7][2:] if line[7].startswith('''ex''' ) else line[7]
A_ : str = line[0]
examples.append(InputExample(guid=_lowerCamelCase , text_a=_lowerCamelCase , text_b=_lowerCamelCase , label=_lowerCamelCase , pairID=_lowerCamelCase ) )
return examples
def snake_case__ ( lowerCamelCase__ : List[InputExample] , lowerCamelCase__ : List[str] , lowerCamelCase__ : int , lowerCamelCase__ : PreTrainedTokenizer , ) -> int:
A_ : Union[str, Any] = {label: i for i, label in enumerate(lowerCamelCase__ )}
A_ : Optional[Any] = []
for ex_index, example in tqdm.tqdm(enumerate(lowerCamelCase__ ) , desc='''convert examples to features''' ):
if ex_index % 1_0_0_0_0 == 0:
logger.info('''Writing example %d''' % (ex_index) )
A_ : Optional[int] = tokenizer(
example.text_a , example.text_b , add_special_tokens=lowerCamelCase__ , max_length=lowerCamelCase__ , padding='''max_length''' , truncation=lowerCamelCase__ , return_overflowing_tokens=lowerCamelCase__ , )
A_ : List[str] = label_map[example.label] if example.label in label_map else 0
A_ : Tuple = int(example.pairID )
features.append(InputFeatures(**lowerCamelCase__ , label=lowerCamelCase__ , pairID=lowerCamelCase__ ) )
for i, example in enumerate(examples[:5] ):
logger.info('''*** Example ***''' )
logger.info(f'guid: {example}' )
logger.info(f'features: {features[i]}' )
return features
snake_case__ = {
"""hans""": 3,
}
snake_case__ = {
"""hans""": HansProcessor,
}
| 4 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/swin-tiny-patch4-window7-224""": (
"""https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class UpperCamelCase_ (a__, a__ ):
"""simple docstring"""
_lowerCAmelCase = 'swin'
_lowerCAmelCase = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Any , _lowerCamelCase : Optional[Any]=224 , _lowerCamelCase : List[str]=4 , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Tuple=96 , _lowerCamelCase : List[Any]=[2, 2, 6, 2] , _lowerCamelCase : List[str]=[3, 6, 12, 24] , _lowerCamelCase : List[Any]=7 , _lowerCamelCase : Optional[int]=4.0 , _lowerCamelCase : List[str]=True , _lowerCamelCase : List[str]=0.0 , _lowerCamelCase : Any=0.0 , _lowerCamelCase : Dict=0.1 , _lowerCamelCase : List[str]="gelu" , _lowerCamelCase : Tuple=False , _lowerCamelCase : Dict=0.02 , _lowerCamelCase : Optional[Any]=1E-5 , _lowerCamelCase : Any=32 , _lowerCamelCase : Tuple=None , _lowerCamelCase : Any=None , **_lowerCamelCase : str , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
A_ : Optional[int] = image_size
A_ : Optional[int] = patch_size
A_ : Optional[int] = num_channels
A_ : Any = embed_dim
A_ : List[Any] = depths
A_ : Any = len(_lowerCamelCase )
A_ : List[Any] = num_heads
A_ : Tuple = window_size
A_ : Tuple = mlp_ratio
A_ : Dict = qkv_bias
A_ : List[str] = hidden_dropout_prob
A_ : List[str] = attention_probs_dropout_prob
A_ : Any = drop_path_rate
A_ : List[Any] = hidden_act
A_ : Tuple = use_absolute_embeddings
A_ : int = layer_norm_eps
A_ : Optional[Any] = initializer_range
A_ : Union[str, Any] = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
A_ : str = int(embed_dim * 2 ** (len(_lowerCamelCase ) - 1) )
A_ : str = ['''stem'''] + [f'stage{idx}' for idx in range(1 , len(_lowerCamelCase ) + 1 )]
A_ : Optional[Any] = get_aligned_output_features_output_indices(
out_features=_lowerCamelCase , out_indices=_lowerCamelCase , stage_names=self.stage_names )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : str ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return 1E-4
| 352 |
'''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
snake_case__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCamelCase_ (datasets.BuilderConfig ):
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = "utf-8"
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = True # deprecated
_lowerCAmelCase = None # deprecated
_lowerCAmelCase = 1_0 << 2_0 # 10MB
_lowerCAmelCase = None
class UpperCamelCase_ (datasets.ArrowBasedBuilder ):
"""simple docstring"""
_lowerCAmelCase = JsonConfig
def _a ( self : int ):
"""simple docstring"""
if self.config.block_size is not None:
logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''' )
A_ : List[Any] = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
'''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''' )
if self.config.newlines_in_values is not None:
raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''' )
return datasets.DatasetInfo(features=self.config.features )
def _a ( self : Any , _lowerCamelCase : List[str] ):
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}' )
A_ : int = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_lowerCamelCase , (str, list, tuple) ):
A_ : Union[str, Any] = data_files
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : List[str] = [files]
A_ : List[Any] = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
A_ : Tuple = []
for split_name, files in data_files.items():
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : int = [files]
A_ : Union[str, Any] = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=_lowerCamelCase , gen_kwargs={'''files''': files} ) )
return splits
def _a ( self : int , _lowerCamelCase : pa.Table ):
"""simple docstring"""
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
A_ : Optional[int] = self.config.features.arrow_schema.field(_lowerCamelCase ).type
A_ : Optional[int] = pa_table.append_column(_lowerCamelCase , pa.array([None] * len(_lowerCamelCase ) , type=_lowerCamelCase ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
A_ : str = table_cast(_lowerCamelCase , self.config.features.arrow_schema )
return pa_table
def _a ( self : List[str] , _lowerCamelCase : int ):
"""simple docstring"""
for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(_lowerCamelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
A_ : int = json.load(_lowerCamelCase )
# We keep only the field we are interested in
A_ : List[str] = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(_lowerCamelCase , (list, tuple) ):
A_ : int = set().union(*[row.keys() for row in dataset] )
A_ : List[str] = {col: [row.get(_lowerCamelCase ) for row in dataset] for col in keys}
else:
A_ : Tuple = dataset
A_ : Dict = pa.Table.from_pydict(_lowerCamelCase )
yield file_idx, self._cast_table(_lowerCamelCase )
# If the file has one json object per line
else:
with open(_lowerCamelCase , '''rb''' ) as f:
A_ : int = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
A_ : int = max(self.config.chunksize // 32 , 16 << 10 )
A_ : int = (
self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
)
while True:
A_ : Any = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(_lowerCamelCase )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
A_ : Optional[Any] = batch.decode(self.config.encoding , errors=_lowerCamelCase ).encode('''utf-8''' )
try:
while True:
try:
A_ : List[Any] = paj.read_json(
io.BytesIO(_lowerCamelCase ) , read_options=paj.ReadOptions(block_size=_lowerCamelCase ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(_lowerCamelCase , pa.ArrowInvalid )
and "straddling" not in str(_lowerCamelCase )
or block_size > len(_lowerCamelCase )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f'Batch of {len(_lowerCamelCase )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.' )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
_lowerCamelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
A_ : Optional[Any] = json.load(_lowerCamelCase )
except json.JSONDecodeError:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(_lowerCamelCase , _lowerCamelCase ): # list is the only sequence type supported in JSON
try:
A_ : Optional[int] = set().union(*[row.keys() for row in dataset] )
A_ : Tuple = {col: [row.get(_lowerCamelCase ) for row in dataset] for col in keys}
A_ : int = pa.Table.from_pydict(_lowerCamelCase )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise ValueError(f'Not able to read records in the JSON file at {file}.' ) from None
yield file_idx, self._cast_table(_lowerCamelCase )
break
else:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise ValueError(
f'Not able to read records in the JSON file at {file}. '
f'You should probably indicate the field of the JSON file containing your records. '
f'This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '
f'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ' ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(_lowerCamelCase )
batch_idx += 1
| 4 | 0 |
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 snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : int , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Tuple ) -> Tuple:
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
A_ : Tuple = np.full((len(lowerCamelCase__ ), sequence_length, 2) , lowerCamelCase__ )
else:
A_ : Optional[Any] = np.full((len(lowerCamelCase__ ), sequence_length) , lowerCamelCase__ )
for i, tensor in enumerate(lowerCamelCase__ ):
if padding_side == "right":
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
A_ : Union[str, Any] = tensor[:sequence_length]
else:
A_ : str = tensor[:sequence_length]
else:
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
A_ : str = tensor[:sequence_length]
else:
A_ : Union[str, Any] = tensor[:sequence_length]
return out_tensor.tolist()
def snake_case__ ( lowerCamelCase__ : List[str] ) -> Any:
A_ : int = ord(lowerCamelCase__ )
if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
return True
A_ : List[Any] = unicodedata.category(lowerCamelCase__ )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 4_2
_lowerCAmelCase = True
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = -1_0_0
_lowerCAmelCase = 'pt'
def _a ( self : Tuple , _lowerCamelCase : List[Any] ):
"""simple docstring"""
import torch
A_ : Any = '''label''' if '''label''' in features[0].keys() else '''labels'''
A_ : int = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
A_ : List[str] = self.tokenizer.pad(
_lowerCamelCase , 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
A_ : Dict = torch.tensor(batch['''entity_ids'''] ).shape[1]
A_ : List[str] = self.tokenizer.padding_side
if padding_side == "right":
A_ : Dict = [
list(_lowerCamelCase ) + [self.label_pad_token_id] * (sequence_length - len(_lowerCamelCase )) for label in labels
]
else:
A_ : str = [
[self.label_pad_token_id] * (sequence_length - len(_lowerCamelCase )) + list(_lowerCamelCase ) for label in labels
]
A_ : List[Any] = [feature['''ner_tags'''] for feature in features]
A_ : Dict = padding_tensor(_lowerCamelCase , -1 , _lowerCamelCase , _lowerCamelCase )
A_ : Any = [feature['''original_entity_spans'''] for feature in features]
A_ : Dict = padding_tensor(_lowerCamelCase , (-1, -1) , _lowerCamelCase , _lowerCamelCase )
A_ : Union[str, Any] = {k: torch.tensor(_lowerCamelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 353 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/swin-tiny-patch4-window7-224""": (
"""https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class UpperCamelCase_ (a__, a__ ):
"""simple docstring"""
_lowerCAmelCase = 'swin'
_lowerCAmelCase = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Any , _lowerCamelCase : Optional[Any]=224 , _lowerCamelCase : List[str]=4 , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Tuple=96 , _lowerCamelCase : List[Any]=[2, 2, 6, 2] , _lowerCamelCase : List[str]=[3, 6, 12, 24] , _lowerCamelCase : List[Any]=7 , _lowerCamelCase : Optional[int]=4.0 , _lowerCamelCase : List[str]=True , _lowerCamelCase : List[str]=0.0 , _lowerCamelCase : Any=0.0 , _lowerCamelCase : Dict=0.1 , _lowerCamelCase : List[str]="gelu" , _lowerCamelCase : Tuple=False , _lowerCamelCase : Dict=0.02 , _lowerCamelCase : Optional[Any]=1E-5 , _lowerCamelCase : Any=32 , _lowerCamelCase : Tuple=None , _lowerCamelCase : Any=None , **_lowerCamelCase : str , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
A_ : Optional[int] = image_size
A_ : Optional[int] = patch_size
A_ : Optional[int] = num_channels
A_ : Any = embed_dim
A_ : List[Any] = depths
A_ : Any = len(_lowerCamelCase )
A_ : List[Any] = num_heads
A_ : Tuple = window_size
A_ : Tuple = mlp_ratio
A_ : Dict = qkv_bias
A_ : List[str] = hidden_dropout_prob
A_ : List[str] = attention_probs_dropout_prob
A_ : Any = drop_path_rate
A_ : List[Any] = hidden_act
A_ : Tuple = use_absolute_embeddings
A_ : int = layer_norm_eps
A_ : Optional[Any] = initializer_range
A_ : Union[str, Any] = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
A_ : str = int(embed_dim * 2 ** (len(_lowerCamelCase ) - 1) )
A_ : str = ['''stem'''] + [f'stage{idx}' for idx in range(1 , len(_lowerCamelCase ) + 1 )]
A_ ,A_ : Optional[Any] = get_aligned_output_features_output_indices(
out_features=_lowerCamelCase , out_indices=_lowerCamelCase , stage_names=self.stage_names )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : str ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return 1E-4
| 4 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
snake_case__ = """0.12""" # assumed parallelism: 8
if is_torch_available():
import torch
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Optional[int]=None ) -> Optional[int]:
if rng is None:
A_ : Any = random.Random()
A_ : Optional[Any] = 1
for dim in shape:
total_dims *= dim
A_ : Tuple = []
for _ in range(lowerCamelCase__ ):
values.append(rng.randint(0 , vocab_size - 1 ) )
A_ : str = np.array(lowerCamelCase__ , dtype=jnp.intaa ).reshape(lowerCamelCase__ )
return output
def snake_case__ ( lowerCamelCase__ : Dict , lowerCamelCase__ : str=None ) -> int:
A_ : Union[str, Any] = ids_tensor(lowerCamelCase__ , vocab_size=2 , rng=lowerCamelCase__ )
# make sure that at least one token is attended to for each batch
A_ : List[Any] = 1
return attn_mask
@require_flax
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase : int = None
_lowerCAmelCase : Optional[Any] = ()
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
A_ : Any = 2
A_ : int = inputs['''input_ids'''].shape[-1] // 2
A_ : Optional[int] = inputs['''input_ids'''][:max_batch_size, :sequence_length]
A_ : int = jnp.ones_like(_lowerCamelCase )
A_ : Dict = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
A_ : Optional[Any] = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
A_ : Tuple = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Union[str, Any] = self._get_input_ids_and_config()
A_ : List[Any] = False
A_ : Union[str, Any] = max_length
A_ : List[Any] = 0
for model_class in self.all_generative_model_classes:
A_ : Tuple = model_class(_lowerCamelCase )
A_ : Any = model_class.__name__[4:] # Skip the "Flax" at the beginning
A_ : Dict = getattr(_lowerCamelCase , _lowerCamelCase )
A_ : str = pt_model_class(_lowerCamelCase ).eval()
A_ : int = load_flax_weights_in_pytorch_model(_lowerCamelCase , flax_model.params )
A_ : Union[str, Any] = flax_model.generate(_lowerCamelCase ).sequences
A_ : Optional[int] = pt_model.generate(torch.tensor(_lowerCamelCase , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
A_ : List[Any] = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def _a ( self : Any ):
"""simple docstring"""
A_ : str = self._get_input_ids_and_config()
A_ : Optional[int] = False
A_ : List[Any] = max_length
for model_class in self.all_generative_model_classes:
A_ : List[Any] = model_class(_lowerCamelCase )
A_ : str = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
A_ : List[Any] = jit(model.generate )
A_ : Optional[Any] = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _a ( self : Any ):
"""simple docstring"""
A_ : List[Any] = self._get_input_ids_and_config()
A_ : str = True
A_ : Union[str, Any] = max_length
for model_class in self.all_generative_model_classes:
A_ : Union[str, Any] = model_class(_lowerCamelCase )
A_ : Optional[int] = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
A_ : str = jit(model.generate )
A_ : List[Any] = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Tuple = self._get_input_ids_and_config()
A_ : int = False
A_ : str = max_length
A_ : Any = 2
for model_class in self.all_generative_model_classes:
A_ : Union[str, Any] = model_class(_lowerCamelCase )
A_ : Dict = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
A_ : int = jit(model.generate )
A_ : Union[str, Any] = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Dict = self._get_input_ids_and_config()
A_ : Optional[int] = False
A_ : List[str] = max_length
A_ : int = 2
A_ : Union[str, Any] = 2
for model_class in self.all_generative_model_classes:
A_ : Any = model_class(_lowerCamelCase )
A_ : str = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def _a ( self : str ):
"""simple docstring"""
A_ : Optional[int] = self._get_input_ids_and_config()
A_ : Union[str, Any] = True
A_ : Optional[Any] = max_length
A_ : Any = 0.8
A_ : str = 10
A_ : Dict = 0.3
A_ : int = 1
A_ : Union[str, Any] = 8
A_ : str = 9
for model_class in self.all_generative_model_classes:
A_ : Union[str, Any] = model_class(_lowerCamelCase )
A_ : Optional[Any] = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
A_ : Any = jit(model.generate )
A_ : int = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : str = self._get_input_ids_and_config()
A_ : Optional[int] = max_length
A_ : Tuple = 1
A_ : List[str] = 8
A_ : Optional[int] = 9
for model_class in self.all_generative_model_classes:
A_ : Union[str, Any] = model_class(_lowerCamelCase )
A_ : str = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
A_ : Optional[int] = jit(model.generate )
A_ : Optional[int] = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _a ( self : int ):
"""simple docstring"""
A_ : List[Any] = self._get_input_ids_and_config()
A_ : Tuple = max_length
A_ : Dict = 2
A_ : Dict = 1
A_ : List[str] = 8
A_ : int = 9
for model_class in self.all_generative_model_classes:
A_ : Tuple = model_class(_lowerCamelCase )
A_ : int = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
A_ : List[str] = jit(model.generate )
A_ : int = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : str = self._get_input_ids_and_config()
# pad attention mask on the left
A_ : str = attention_mask.at[(0, 0)].set(0 )
A_ : Optional[Any] = False
A_ : List[Any] = max_length
for model_class in self.all_generative_model_classes:
A_ : List[str] = model_class(_lowerCamelCase )
A_ : Any = model.generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
A_ : Optional[Any] = jit(model.generate )
A_ : Tuple = jit_generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _a ( self : int ):
"""simple docstring"""
A_ : int = self._get_input_ids_and_config()
# pad attention mask on the left
A_ : Dict = attention_mask.at[(0, 0)].set(0 )
A_ : int = True
A_ : str = max_length
for model_class in self.all_generative_model_classes:
A_ : List[Any] = model_class(_lowerCamelCase )
A_ : Union[str, Any] = model.generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
A_ : Optional[Any] = jit(model.generate )
A_ : List[str] = jit_generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _a ( self : Any ):
"""simple docstring"""
A_ : Tuple = self._get_input_ids_and_config()
# pad attention mask on the left
A_ : str = attention_mask.at[(0, 0)].set(0 )
A_ : Union[str, Any] = 2
A_ : Union[str, Any] = max_length
for model_class in self.all_generative_model_classes:
A_ : List[Any] = model_class(_lowerCamelCase )
A_ : Dict = model.generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
A_ : List[Any] = jit(model.generate )
A_ : Optional[int] = jit_generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Any = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''' )
A_ : Union[str, Any] = FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
A_ : Any = '''Hello world'''
A_ : int = tokenizer(_lowerCamelCase , return_tensors='''np''' ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(_lowerCamelCase , '''do_samples''' ):
model.generate(_lowerCamelCase , do_samples=_lowerCamelCase )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(_lowerCamelCase , '''foo''' ):
A_ : Optional[int] = {'''foo''': '''bar'''}
model.generate(_lowerCamelCase , **_lowerCamelCase )
| 354 |
'''simple docstring'''
from __future__ import annotations
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> list[int]:
A_ : int = 0
A_ : str = len(lowerCamelCase__ ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
A_ : Tuple = i + 1
else:
A_ : List[str] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{two_pointer([2, 7, 11, 15], 9) = }')
| 4 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
snake_case__ = logging.getLogger(__name__)
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : Any ) -> Any:
return (preds == labels).mean()
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}, )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys() )} )
_lowerCAmelCase = field(metadata={'help': 'Should contain the data files for the task.'} )
_lowerCAmelCase = field(
default=1_2_8, metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
}, )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def snake_case__ ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
A_ : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
A_ : Union[str, Any] = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , lowerCamelCase__ )
# Set seed
set_seed(training_args.seed )
try:
A_ : Optional[Any] = processors[data_args.task_name]()
A_ : str = processor.get_labels()
A_ : List[str] = len(lowerCamelCase__ )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
A_ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowerCamelCase__ , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
A_ : int = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
A_ : Optional[Any] = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=lowerCamelCase__ , cache_dir=model_args.cache_dir , )
# Get datasets
A_ : Tuple = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=lowerCamelCase__ , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
A_ : List[Any] = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=lowerCamelCase__ , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(lowerCamelCase__ : EvalPrediction ) -> Dict:
A_ : Optional[Any] = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(lowerCamelCase__ , p.label_ids )}
# Data collator
A_ : str = DataCollatorWithPadding(lowerCamelCase__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
A_ : Dict = Trainer(
model=lowerCamelCase__ , args=lowerCamelCase__ , train_dataset=lowerCamelCase__ , eval_dataset=lowerCamelCase__ , compute_metrics=lowerCamelCase__ , data_collator=lowerCamelCase__ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
A_ : int = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
A_ : str = trainer.evaluate()
A_ : str = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(lowerCamelCase__ , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , lowerCamelCase__ , lowerCamelCase__ )
writer.write('''%s = %s\n''' % (key, value) )
results.update(lowerCamelCase__ )
return results
def snake_case__ ( lowerCamelCase__ : Optional[int] ) -> int:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 355 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCamelCase__ ) )
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
# Base Case
if index == len(lowerCamelCase__ ):
return True
# Recursive Step
for i in range(lowerCamelCase__ ):
if valid_coloring(graph[index] , lowerCamelCase__ , lowerCamelCase__ ):
# Color current vertex
A_ : int = i
# Validate coloring
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , index + 1 ):
return True
# Backtrack
A_ : str = -1
return False
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int ) -> list[int]:
A_ : List[str] = [-1] * len(lowerCamelCase__ )
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , 0 ):
return colored_vertices
return []
| 4 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[str] = inspect.getfile(accelerate.test_utils )
A_ : List[str] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
A_ : Dict = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_distributed_data_loop.py'''] )
A_ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_ops.py'''] )
@require_multi_gpu
def _a ( self : Union[str, Any] ):
"""simple docstring"""
print(f'Found {torch.cuda.device_count()} devices.' )
A_ : List[str] = ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_lowerCamelCase , env=os.environ.copy() )
@require_multi_gpu
def _a ( self : Optional[Any] ):
"""simple docstring"""
print(f'Found {torch.cuda.device_count()} devices.' )
A_ : Optional[Any] = ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
print(f'Command: {cmd}' )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_lowerCamelCase , env=os.environ.copy() )
@require_multi_gpu
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Dict = ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_lowerCamelCase , env=os.environ.copy() )
@require_multi_gpu
def _a ( self : str ):
"""simple docstring"""
print(f'Found {torch.cuda.device_count()} devices, using 2 devices only' )
A_ : List[Any] = ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices='''0,1''' ):
execute_subprocess_async(_lowerCamelCase , env=os.environ.copy() )
if __name__ == "__main__":
snake_case__ = Accelerator()
snake_case__ = (accelerator.state.process_index + 2, 10)
snake_case__ = torch.randint(0, 10, shape).to(accelerator.device)
snake_case__ = """"""
snake_case__ = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
snake_case__ = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
snake_case__ = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 356 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case__ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[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],
]
# Define blinker example
snake_case__ = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def snake_case__ ( lowerCamelCase__ : list[list[int]] ) -> list[list[int]]:
A_ : str = []
for i in range(len(lowerCamelCase__ ) ):
A_ : Optional[Any] = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
A_ : Optional[int] = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(lowerCamelCase__ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(lowerCamelCase__ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(lowerCamelCase__ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
A_ : List[str] = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(lowerCamelCase__ )
return next_generation
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int ) -> list[Image.Image]:
A_ : List[Any] = []
for _ in range(lowerCamelCase__ ):
# Create output image
A_ : Optional[int] = Image.new('''RGB''' , (len(cells[0] ), len(lowerCamelCase__ )) )
A_ : int = img.load()
# Save cells to image
for x in range(len(lowerCamelCase__ ) ):
for y in range(len(cells[0] ) ):
A_ : Optional[Any] = 2_5_5 - cells[y][x] * 2_5_5
A_ : str = (colour, colour, colour)
# Save image
images.append(lowerCamelCase__ )
A_ : Optional[int] = new_generation(lowerCamelCase__ )
return images
if __name__ == "__main__":
snake_case__ = generate_images(GLIDER, 16)
images[0].save("""out.gif""", save_all=True, append_images=images[1:])
| 4 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'speech_to_text_2'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Optional[Any] , _lowerCamelCase : Optional[Any]=10000 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : int=2048 , _lowerCamelCase : Dict=4 , _lowerCamelCase : str=0.0 , _lowerCamelCase : int=True , _lowerCamelCase : int="relu" , _lowerCamelCase : Any=256 , _lowerCamelCase : List[Any]=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Union[str, Any]=0.0 , _lowerCamelCase : Optional[Any]=0.02 , _lowerCamelCase : int=2 , _lowerCamelCase : List[str]=True , _lowerCamelCase : str=1 , _lowerCamelCase : List[Any]=0 , _lowerCamelCase : Optional[int]=2 , _lowerCamelCase : Tuple=1024 , **_lowerCamelCase : int , ):
"""simple docstring"""
A_ : Optional[int] = vocab_size
A_ : Tuple = d_model
A_ : List[str] = decoder_ffn_dim
A_ : str = decoder_layers
A_ : Any = decoder_attention_heads
A_ : int = dropout
A_ : str = attention_dropout
A_ : Optional[int] = activation_dropout
A_ : str = activation_function
A_ : List[Any] = init_std
A_ : Union[str, Any] = decoder_layerdrop
A_ : Any = use_cache
A_ : Optional[Any] = decoder_layers
A_ : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
A_ : Optional[Any] = max_target_positions
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
| 357 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Any = tempfile.mkdtemp()
A_ : List[Any] = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
A_ : 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] ) )
A_ : Tuple = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
A_ : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Dict , **_lowerCamelCase : Tuple ):
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _a ( self : Optional[int] , **_lowerCamelCase : Optional[int] ):
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _a ( self : Optional[Any] , **_lowerCamelCase : Tuple ):
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self : int ):
"""simple docstring"""
A_ : Union[str, Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A_ : Any = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self : int ):
"""simple docstring"""
A_ : Tuple = self.get_tokenizer()
A_ : Tuple = self.get_rust_tokenizer()
A_ : Dict = self.get_image_processor()
A_ : List[Any] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor_slow.save_pretrained(self.tmpdirname )
A_ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_lowerCamelCase )
A_ : Any = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor_fast.save_pretrained(self.tmpdirname )
A_ : List[Any] = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _lowerCamelCase )
self.assertIsInstance(processor_fast.tokenizer , _lowerCamelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _lowerCamelCase )
self.assertIsInstance(processor_fast.image_processor , _lowerCamelCase )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[str] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A_ : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
A_ : Tuple = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
A_ : List[str] = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = self.get_image_processor()
A_ : Any = self.get_tokenizer()
A_ : List[str] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : Any = self.prepare_image_inputs()
A_ : List[Any] = image_processor(_lowerCamelCase , return_tensors='''np''' )
A_ : str = processor(images=_lowerCamelCase , 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 : Dict ):
"""simple docstring"""
A_ : str = self.get_image_processor()
A_ : List[str] = self.get_tokenizer()
A_ : Optional[int] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : int = '''lower newer'''
A_ : str = processor(text=_lowerCamelCase )
A_ : Dict = tokenizer(_lowerCamelCase , padding='''max_length''' , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self : str ):
"""simple docstring"""
A_ : Optional[int] = self.get_image_processor()
A_ : Optional[Any] = self.get_tokenizer()
A_ : List[str] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : List[Any] = '''lower newer'''
A_ : Optional[int] = self.prepare_image_inputs()
A_ : List[Any] = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(_lowerCamelCase ):
processor()
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Optional[Any] = self.get_image_processor()
A_ : Optional[int] = self.get_tokenizer()
A_ : List[Any] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : str = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A_ : str = processor.batch_decode(_lowerCamelCase )
A_ : Union[str, Any] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : str = self.get_image_processor()
A_ : Tuple = self.get_tokenizer()
A_ : Any = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : str = '''lower newer'''
A_ : List[str] = self.prepare_image_inputs()
A_ : Tuple = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 4 | 0 |
'''simple docstring'''
snake_case__ = {str(digit): digit**5 for digit in range(10)}
def snake_case__ ( lowerCamelCase__ : int ) -> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase__ ) )
def snake_case__ ( ) -> int:
return sum(
number
for number in range(1_0_0_0 , 1_0_0_0_0_0_0 )
if number == digits_fifth_powers_sum(lowerCamelCase__ ) )
if __name__ == "__main__":
print(solution())
| 358 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = """▁"""
snake_case__ = {
"""vocab_file""": """vocab.json""",
"""spm_file""": """sentencepiece.bpe.model""",
}
snake_case__ = {
"""vocab_file""": {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
),
},
"""spm_file""": {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
)
},
}
snake_case__ = {
"""facebook/s2t-small-librispeech-asr""": 10_24,
}
snake_case__ = ["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]
snake_case__ = {"""mustc""": MUSTC_LANGS}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = VOCAB_FILES_NAMES
_lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase = MAX_MODEL_INPUT_SIZES
_lowerCAmelCase = ['input_ids', 'attention_mask']
_lowerCAmelCase = []
def __init__( self : Optional[int] , _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : str="<s>" , _lowerCamelCase : Union[str, Any]="</s>" , _lowerCamelCase : Dict="<pad>" , _lowerCamelCase : str="<unk>" , _lowerCamelCase : Union[str, Any]=False , _lowerCamelCase : int=False , _lowerCamelCase : Any=None , _lowerCamelCase : Any=None , _lowerCamelCase : Optional[Dict[str, Any]] = None , **_lowerCamelCase : Optional[int] , ):
"""simple docstring"""
A_ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , do_upper_case=_lowerCamelCase , do_lower_case=_lowerCamelCase , tgt_lang=_lowerCamelCase , lang_codes=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
A_ : Optional[int] = do_upper_case
A_ : Tuple = do_lower_case
A_ : Tuple = load_json(_lowerCamelCase )
A_ : Tuple = {v: k for k, v in self.encoder.items()}
A_ : List[Any] = spm_file
A_ : List[str] = load_spm(_lowerCamelCase , self.sp_model_kwargs )
if lang_codes is not None:
A_ : Any = lang_codes
A_ : Optional[Any] = LANGUAGES[lang_codes]
A_ : Optional[Any] = [f'<lang:{lang}>' for lang in self.langs]
A_ : Union[str, Any] = {lang: self.sp_model.PieceToId(f'<lang:{lang}>' ) for lang in self.langs}
A_ : Optional[int] = self.lang_tokens
A_ : int = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
A_ : Dict = {}
@property
def _a ( self : Tuple ):
"""simple docstring"""
return len(self.encoder )
@property
def _a ( self : int ):
"""simple docstring"""
return self._tgt_lang
@tgt_lang.setter
def _a ( self : List[str] , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : int = new_tgt_lang
self.set_tgt_lang_special_tokens(_lowerCamelCase )
def _a ( self : Tuple , _lowerCamelCase : str ):
"""simple docstring"""
A_ : List[str] = self.lang_code_to_id[tgt_lang]
A_ : Optional[Any] = [lang_code_id]
def _a ( self : Optional[Any] , _lowerCamelCase : str ):
"""simple docstring"""
return self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
def _a ( self : List[Any] , _lowerCamelCase : int ):
"""simple docstring"""
return self.encoder.get(_lowerCamelCase , self.encoder[self.unk_token] )
def _a ( self : int , _lowerCamelCase : int ):
"""simple docstring"""
return self.decoder.get(_lowerCamelCase , self.unk_token )
def _a ( self : int , _lowerCamelCase : List[str] ):
"""simple docstring"""
A_ : List[Any] = []
A_ : Any = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
A_ : Union[str, Any] = self.sp_model.decode(_lowerCamelCase )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
A_ : Optional[Any] = []
else:
current_sub_tokens.append(_lowerCamelCase )
A_ : Tuple = self.sp_model.decode(_lowerCamelCase )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def _a ( self : int , _lowerCamelCase : Dict , _lowerCamelCase : Any=None ):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def _a ( self : List[Any] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None , _lowerCamelCase : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
A_ : Tuple = [1] * len(self.prefix_tokens )
A_ : Tuple = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(_lowerCamelCase )) + suffix_ones
return prefix_ones + ([0] * len(_lowerCamelCase )) + ([0] * len(_lowerCamelCase )) + suffix_ones
def _a ( self : Dict ):
"""simple docstring"""
A_ : Union[str, Any] = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = self.__dict__.copy()
A_ : List[Any] = None
return state
def __setstate__( self : List[str] , _lowerCamelCase : Dict ):
"""simple docstring"""
A_ : Dict = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
A_ : Optional[int] = {}
A_ : int = load_spm(self.spm_file , self.sp_model_kwargs )
def _a ( self : Optional[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[str] = None ):
"""simple docstring"""
A_ : Dict = Path(_lowerCamelCase )
assert save_dir.is_dir(), f'{save_directory} should be a directory'
A_ : Optional[int] = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
A_ : Optional[int] = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder , _lowerCamelCase )
if os.path.abspath(self.spm_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , _lowerCamelCase )
elif not os.path.isfile(self.spm_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
A_ : List[str] = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (str(_lowerCamelCase ), str(_lowerCamelCase ))
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
A_ : Tuple = sentencepiece.SentencePieceProcessor(**lowerCamelCase__ )
spm.Load(str(lowerCamelCase__ ) )
return spm
def snake_case__ ( lowerCamelCase__ : str ) -> Union[Dict, List]:
with open(lowerCamelCase__ , '''r''' ) as f:
return json.load(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : str ) -> None:
with open(lowerCamelCase__ , '''w''' ) as f:
json.dump(lowerCamelCase__ , lowerCamelCase__ , indent=2 )
| 4 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
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 TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : Tuple , _lowerCamelCase : str=13 , _lowerCamelCase : Tuple=30 , _lowerCamelCase : List[Any]=2 , _lowerCamelCase : Any=3 , _lowerCamelCase : int=True , _lowerCamelCase : List[Any]=True , _lowerCamelCase : int=32 , _lowerCamelCase : int=2 , _lowerCamelCase : int=4 , _lowerCamelCase : List[str]=37 , _lowerCamelCase : Optional[Any]="gelu" , _lowerCamelCase : List[str]=0.1 , _lowerCamelCase : Tuple=0.1 , _lowerCamelCase : Tuple=10 , _lowerCamelCase : Optional[Any]=0.02 , _lowerCamelCase : List[str]=3 , _lowerCamelCase : Optional[Any]=0.6 , _lowerCamelCase : Union[str, Any]=None , ):
"""simple docstring"""
A_ : Optional[int] = parent
A_ : Any = batch_size
A_ : Any = image_size
A_ : Union[str, Any] = patch_size
A_ : Tuple = num_channels
A_ : Tuple = is_training
A_ : Union[str, Any] = use_labels
A_ : Optional[int] = hidden_size
A_ : Tuple = num_hidden_layers
A_ : str = num_attention_heads
A_ : Dict = intermediate_size
A_ : List[Any] = hidden_act
A_ : Union[str, Any] = hidden_dropout_prob
A_ : Any = attention_probs_dropout_prob
A_ : Tuple = type_sequence_label_size
A_ : Optional[int] = initializer_range
A_ : List[Any] = mask_ratio
A_ : Dict = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
A_ : List[str] = (image_size // patch_size) ** 2
A_ : str = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def _a ( self : str ):
"""simple docstring"""
A_ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : List[Any] = None
if self.use_labels:
A_ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : Union[str, Any] = self.get_config()
return config, pixel_values, labels
def _a ( self : Optional[int] ):
"""simple docstring"""
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , decoder_hidden_size=self.hidden_size , decoder_num_hidden_layers=self.num_hidden_layers , decoder_num_attention_heads=self.num_attention_heads , decoder_intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_lowerCamelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def _a ( self : Optional[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : List[Any] , _lowerCamelCase : Tuple ):
"""simple docstring"""
A_ : Union[str, Any] = TFViTMAEModel(config=_lowerCamelCase )
A_ : List[Any] = model(_lowerCamelCase , training=_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Optional[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[int] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ : Any = TFViTMAEForPreTraining(_lowerCamelCase )
A_ : List[str] = model(_lowerCamelCase , training=_lowerCamelCase )
# expected sequence length = num_patches
A_ : int = (self.image_size // self.patch_size) ** 2
A_ : int = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
A_ : str = 1
A_ : Dict = TFViTMAEForPreTraining(_lowerCamelCase )
A_ : Tuple = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : Optional[Any] = model(_lowerCamelCase , training=_lowerCamelCase )
A_ : Tuple = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def _a ( self : int ):
"""simple docstring"""
A_ : Tuple = self.prepare_config_and_inputs()
(A_) : List[str] = config_and_inputs
A_ : Tuple = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class UpperCamelCase_ (a__, a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
_lowerCAmelCase = {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def _a ( self : Dict ):
"""simple docstring"""
A_ : List[str] = TFViTMAEModelTester(self )
A_ : Optional[Any] = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase , hidden_size=37 )
def _a ( self : Tuple ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def _a ( self : Any ):
"""simple docstring"""
pass
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Tuple = model_class(_lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
A_ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCamelCase , tf.keras.layers.Layer ) )
def _a ( self : Any ):
"""simple docstring"""
A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : int = model_class(_lowerCamelCase )
A_ : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : List[Any] = [*signature.parameters.keys()]
A_ : int = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def _a ( self : str ):
"""simple docstring"""
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _a ( self : Any ):
"""simple docstring"""
A_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
np.random.seed(2 )
A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Any = int((config.image_size // config.patch_size) ** 2 )
A_ : Any = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
A_ : List[str] = model_class(_lowerCamelCase )
A_ : Tuple = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
A_ : Tuple = model(_lowerCamelCase , noise=_lowerCamelCase )
A_ : List[str] = copy.deepcopy(self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
A_ : Any = model(**_lowerCamelCase , noise=_lowerCamelCase )
A_ : Optional[Any] = outputs_dict[0].numpy()
A_ : Optional[Any] = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ) , 1E-6 )
def _a ( self : List[Any] ):
"""simple docstring"""
np.random.seed(2 )
A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
A_ : str = int((config.image_size // config.patch_size) ** 2 )
A_ : Dict = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(_lowerCamelCase : Optional[Any] ):
A_ : Any = {}
for k, v in inputs_dict.items():
if tf.is_tensor(_lowerCamelCase ):
A_ : Dict = v.numpy()
else:
A_ : Union[str, Any] = np.array(_lowerCamelCase )
return inputs_np_dict
for model_class in self.all_model_classes:
A_ : List[str] = model_class(_lowerCamelCase )
A_ : List[Any] = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
A_ : int = prepare_numpy_arrays(_lowerCamelCase )
A_ : str = model(_lowerCamelCase , noise=_lowerCamelCase )
A_ : Union[str, Any] = model(**_lowerCamelCase , noise=_lowerCamelCase )
self.assert_outputs_same(_lowerCamelCase , _lowerCamelCase )
def _a ( self : List[str] , _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
np.random.seed(2 )
A_ : List[Any] = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
A_ : int = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
A_ : List[Any] = tf.constant(_lowerCamelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
A_ : str = tf_noise
super().check_pt_tf_models(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def _a ( self : Dict ):
"""simple docstring"""
np.random.seed(2 )
A_ : int = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Dict = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(_lowerCamelCase )
if module_member_name.endswith('''MainLayer''' )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len('''MainLayer''' )] == model_class.__name__[: -len('''Model''' )]
for module_member in (getattr(_lowerCamelCase , _lowerCamelCase ),)
if isinstance(_lowerCamelCase , _lowerCamelCase )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(_lowerCamelCase , '''_keras_serializable''' , _lowerCamelCase )
}
A_ : List[Any] = int((config.image_size // config.patch_size) ** 2 )
A_ : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
A_ : Any = tf.convert_to_tensor(_lowerCamelCase )
inputs_dict.update({'''noise''': noise} )
for main_layer_class in tf_main_layer_classes:
A_ : Optional[Any] = main_layer_class(_lowerCamelCase )
A_ : List[Any] = {
name: tf.keras.Input(tensor.shape[1:] , dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
A_ : Tuple = tf.keras.Model(_lowerCamelCase , outputs=main_layer(_lowerCamelCase ) )
A_ : Dict = model(_lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
A_ : List[Any] = os.path.join(_lowerCamelCase , '''keras_model.h5''' )
model.save(_lowerCamelCase )
A_ : Any = tf.keras.models.load_model(
_lowerCamelCase , custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(_lowerCamelCase , tf.keras.Model )
A_ : Optional[int] = model(_lowerCamelCase )
self.assert_outputs_same(_lowerCamelCase , _lowerCamelCase )
@slow
def _a ( self : str ):
"""simple docstring"""
np.random.seed(2 )
A_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Union[str, Any] = int((config.image_size // config.patch_size) ** 2 )
A_ : int = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
A_ : Any = model_class(_lowerCamelCase )
A_ : List[Any] = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[Any] = model(_lowerCamelCase , noise=_lowerCamelCase )
if model_class.__name__ == "TFViTMAEModel":
A_ : Dict = outputs.last_hidden_state.numpy()
A_ : List[Any] = 0
else:
A_ : Any = outputs.logits.numpy()
A_ : List[Any] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCamelCase , saved_model=_lowerCamelCase )
A_ : List[Any] = model_class.from_pretrained(_lowerCamelCase )
A_ : Any = model(_lowerCamelCase , noise=_lowerCamelCase )
if model_class.__name__ == "TFViTMAEModel":
A_ : List[Any] = after_outputs['''last_hidden_state'''].numpy()
A_ : Optional[Any] = 0
else:
A_ : Dict = after_outputs['''logits'''].numpy()
A_ : Dict = 0
A_ : List[Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCamelCase , 1E-5 )
def _a ( self : Optional[Any] ):
"""simple docstring"""
np.random.seed(2 )
A_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : int = int((config.image_size // config.patch_size) ** 2 )
A_ : Dict = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
A_ : List[Any] = model_class(_lowerCamelCase )
A_ : str = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
A_ : str = model(_lowerCamelCase , noise=_lowerCamelCase )
A_ : Optional[Any] = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(_lowerCamelCase )
A_ : Optional[int] = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
A_ : Any = model_class.from_config(model.config )
A_ : int = new_model(_lowerCamelCase ) # Build model
new_model.set_weights(model.get_weights() )
A_ : Dict = new_model(_lowerCamelCase , noise=_lowerCamelCase )
self.assert_outputs_same(_lowerCamelCase , _lowerCamelCase )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def _a ( self : List[str] ):
"""simple docstring"""
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def _a ( self : List[Any] ):
"""simple docstring"""
pass
@slow
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Tuple = TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(_lowerCamelCase )
def snake_case__ ( ) -> List[str]:
A_ : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _a ( self : int ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def _a ( self : Optional[Any] ):
"""simple docstring"""
np.random.seed(2 )
A_ : List[str] = TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' )
A_ : str = self.default_image_processor
A_ : Dict = prepare_img()
A_ : Union[str, Any] = image_processor(images=_lowerCamelCase , return_tensors='''tf''' )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
A_ : Tuple = ViTMAEConfig()
A_ : List[str] = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
A_ : Optional[Any] = np.random.uniform(size=(1, num_patches) )
# forward pass
A_ : Union[str, Any] = model(**_lowerCamelCase , noise=_lowerCamelCase )
# verify the logits
A_ : Dict = tf.convert_to_tensor([1, 196, 768] )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
A_ : Dict = tf.convert_to_tensor(
[[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3] , _lowerCamelCase , atol=1E-4 )
| 359 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
snake_case__ = sys.version_info >= (3, 10)
def snake_case__ ( lowerCamelCase__ : Union[str, Any]=None , lowerCamelCase__ : str=None ) -> List[Any]:
return field(default_factory=lambda: default , metadata=lowerCamelCase__ )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 4_2
_lowerCAmelCase = field(default='toto', metadata={'help': 'help message'} )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = False
_lowerCAmelCase = True
_lowerCAmelCase = None
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'titi'
_lowerCAmelCase = 'toto'
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'titi'
_lowerCAmelCase = 'toto'
_lowerCAmelCase = 4_2
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Optional[int] = BasicEnum(self.foo )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[Any] = MixedTypeEnum(self.foo )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = field(default=a__, metadata={'help': 'help message'} )
_lowerCAmelCase = None
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[] )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[1, 2, 3] )
_lowerCAmelCase = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
_lowerCAmelCase = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = field()
_lowerCAmelCase = field()
_lowerCAmelCase = field()
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = BasicEnum(self.required_enum )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = field()
_lowerCAmelCase = None
_lowerCAmelCase = field(default='toto', metadata={'help': 'help message'} )
_lowerCAmelCase = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
if is_python_no_less_than_3_10:
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = False
_lowerCAmelCase = True
_lowerCAmelCase = None
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = field(default=a__, metadata={'help': 'help message'} )
_lowerCAmelCase = None
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[] )
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] , _lowerCamelCase : argparse.ArgumentParser , _lowerCamelCase : argparse.ArgumentParser ):
"""simple docstring"""
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
A_ : Union[str, Any] = {k: v for k, v in vars(_lowerCamelCase ).items() if k != '''container'''}
A_ : Optional[Any] = {k: v for k, v in vars(_lowerCamelCase ).items() if k != '''container'''}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('''choices''' , _lowerCamelCase ) and yy.get('''choices''' , _lowerCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['''type'''](_lowerCamelCase ) , yy['''type'''](_lowerCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : Optional[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--bar''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--baz''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--flag''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Union[str, Any] = ['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
((A_) ,) : List[str] = parser.parse_args_into_dataclasses(_lowerCamelCase , look_for_args_file=_lowerCamelCase )
self.assertFalse(example.flag )
def _a ( self : Dict ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : int = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=42 , type=_lowerCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=_lowerCamelCase , help='''help message''' )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Any = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
expected.add_argument('''--baz''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('''--no_baz''' , action='''store_false''' , default=_lowerCamelCase , dest='''baz''' )
expected.add_argument('''--opt''' , type=_lowerCamelCase , default=_lowerCamelCase )
A_ : Dict = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_lowerCamelCase )
for dataclass_type in dataclass_types:
A_ : Any = HfArgumentParser(_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = parser.parse_args([] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : Optional[int] = parser.parse_args(['''--foo''', '''--no_baz'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : Union[str, Any] = parser.parse_args(['''--foo''', '''--baz'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : List[str] = parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : List[Any] = parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : str = HfArgumentParser(_lowerCamelCase )
A_ : Optional[int] = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=['''titi''', '''toto''', 42] , type=make_choice_type_function(['''titi''', '''toto''', 42] ) , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : str = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
A_ : List[Any] = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
A_ : int = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
A_ : Dict = parser.parse_args_into_dataclasses(['''--foo''', '''titi'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
A_ : Tuple = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 42 )
A_ : List[str] = parser.parse_args_into_dataclasses(['''--foo''', '''42'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def _a ( self : Optional[int] ):
"""simple docstring"""
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
A_ : List[str] = HfArgumentParser(_lowerCamelCase )
A_ : Tuple = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=('''titi''', '''toto''', 42) , type=make_choice_type_function(['''titi''', '''toto''', 42] ) , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Tuple = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
A_ : List[str] = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
A_ : int = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 42 )
def _a ( self : Dict ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : List[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo_int''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
expected.add_argument('''--bar_int''' , nargs='''+''' , default=[1, 2, 3] , type=_lowerCamelCase )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=_lowerCamelCase )
expected.add_argument('''--foo_float''' , nargs='''+''' , default=[0.1, 0.2, 0.3] , type=_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[int] = parser.parse_args([] )
self.assertEqual(
_lowerCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['''Hallo''', '''Bonjour''', '''Hello'''] , foo_float=[0.1, 0.2, 0.3] ) , )
A_ : str = parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split() )
self.assertEqual(_lowerCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['''a''', '''b''', '''c'''] , foo_float=[0.1, 0.7] ) )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=_lowerCamelCase , type=_lowerCamelCase )
expected.add_argument('''--bar''' , default=_lowerCamelCase , type=_lowerCamelCase , help='''help message''' )
expected.add_argument('''--baz''' , default=_lowerCamelCase , type=_lowerCamelCase )
expected.add_argument('''--ces''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
expected.add_argument('''--des''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
A_ : Tuple = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_lowerCamelCase )
for dataclass_type in dataclass_types:
A_ : int = HfArgumentParser(_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = parser.parse_args([] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , bar=_lowerCamelCase , baz=_lowerCamelCase , ces=[] , des=[] ) )
A_ : Optional[Any] = parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split() )
self.assertEqual(_lowerCamelCase , Namespace(foo=12 , bar=3.14 , baz='''42''' , ces=['''a''', '''b''', '''c'''] , des=[1, 2, 3] ) )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = HfArgumentParser(_lowerCamelCase )
A_ : Dict = argparse.ArgumentParser()
expected.add_argument('''--required_list''' , nargs='''+''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--required_str''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=_lowerCamelCase , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : List[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=_lowerCamelCase , )
expected.add_argument('''--opt''' , type=_lowerCamelCase , default=_lowerCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=_lowerCamelCase , help='''help message''' )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : List[Any] = HfArgumentParser(_lowerCamelCase )
A_ : Union[str, Any] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
A_ : Optional[int] = parser.parse_dict(_lowerCamelCase )[0]
A_ : str = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Any = HfArgumentParser(_lowerCamelCase )
A_ : List[str] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
'''extra''': 42,
}
self.assertRaises(_lowerCamelCase , parser.parse_dict , _lowerCamelCase , allow_extra_keys=_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : List[str] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : Tuple = os.path.join(_lowerCamelCase , '''temp_json''' )
os.mkdir(_lowerCamelCase )
with open(temp_local_path + '''.json''' , '''w+''' ) as f:
json.dump(_lowerCamelCase , _lowerCamelCase )
A_ : List[str] = parser.parse_yaml_file(Path(temp_local_path + '''.json''' ) )[0]
A_ : Optional[Any] = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : Tuple = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : int = os.path.join(_lowerCamelCase , '''temp_yaml''' )
os.mkdir(_lowerCamelCase )
with open(temp_local_path + '''.yaml''' , '''w+''' ) as f:
yaml.dump(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[Any] = parser.parse_yaml_file(Path(temp_local_path + '''.yaml''' ) )[0]
A_ : int = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = HfArgumentParser(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
| 4 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
),
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'van'
def __init__( self : List[str] , _lowerCamelCase : Dict=224 , _lowerCamelCase : str=3 , _lowerCamelCase : List[str]=[7, 3, 3, 3] , _lowerCamelCase : Tuple=[4, 2, 2, 2] , _lowerCamelCase : Union[str, Any]=[64, 128, 320, 512] , _lowerCamelCase : Optional[int]=[3, 3, 12, 3] , _lowerCamelCase : str=[8, 8, 4, 4] , _lowerCamelCase : Optional[int]="gelu" , _lowerCamelCase : Any=0.02 , _lowerCamelCase : str=1E-6 , _lowerCamelCase : Union[str, Any]=1E-2 , _lowerCamelCase : List[Any]=0.0 , _lowerCamelCase : Any=0.0 , **_lowerCamelCase : Optional[Any] , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
A_ : int = image_size
A_ : int = num_channels
A_ : Any = patch_sizes
A_ : List[Any] = strides
A_ : Tuple = hidden_sizes
A_ : Any = depths
A_ : Dict = mlp_ratios
A_ : Optional[int] = hidden_act
A_ : str = initializer_range
A_ : Optional[Any] = layer_norm_eps
A_ : List[str] = layer_scale_init_value
A_ : Optional[int] = drop_path_rate
A_ : Optional[Any] = dropout_rate
| 360 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
snake_case__ = get_tests_dir("""fixtures""")
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] ):
"""simple docstring"""
A_ : List[Any] = mock.Mock()
A_ : List[str] = 500
A_ : Tuple = {}
A_ : int = HTTPError
A_ : Optional[Any] = {}
# Download this model to make sure it's in the cache.
A_ : Tuple = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=_lowerCamelCase ) as mock_head:
A_ : List[Any] = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# This check we did call the fake head request
mock_head.assert_called()
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = ViTImageProcessor.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json''' )
def _a ( self : Dict ):
"""simple docstring"""
with self.assertRaises(_lowerCamelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
A_ : Any = AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants''' )
A_ : Tuple = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/stable-diffusion-all-variants''' , subfolder='''feature_extractor''' )
self.assertIsNotNone(_lowerCamelCase )
@is_staging_test
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@classmethod
def _a ( cls : Tuple ):
"""simple docstring"""
A_ : int = TOKEN
HfFolder.save_token(_lowerCamelCase )
@classmethod
def _a ( cls : str ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='''test-image-processor''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-image-processor-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-image-processor''' )
except HTTPError:
pass
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Dict = ViTImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''test-image-processor''' , use_auth_token=self._token )
A_ : Optional[int] = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCamelCase , repo_id='''test-image-processor''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : List[Any] = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : int = ViTImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''valid_org/test-image-processor''' , use_auth_token=self._token )
A_ : List[str] = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCamelCase , repo_id='''valid_org/test-image-processor-org''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : Any = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
CustomImageProcessor.register_for_auto_class()
A_ : Any = CustomImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''test-dynamic-image-processor''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''} , )
A_ : str = AutoImageProcessor.from_pretrained(
f'{USER}/test-dynamic-image-processor' , trust_remote_code=_lowerCamelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , '''CustomImageProcessor''' )
| 4 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
snake_case__ = {
"""vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
"""merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
"""tokenizer_config_file""": {
"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
},
}
snake_case__ = {"""facebook/blenderbot-3B""": 1_28}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = VOCAB_FILES_NAMES
_lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase = ['input_ids', 'attention_mask']
_lowerCAmelCase = BlenderbotTokenizer
def __init__( self : Dict , _lowerCamelCase : List[str]=None , _lowerCamelCase : Tuple=None , _lowerCamelCase : Dict=None , _lowerCamelCase : List[str]="replace" , _lowerCamelCase : str="<s>" , _lowerCamelCase : List[str]="</s>" , _lowerCamelCase : Optional[int]="</s>" , _lowerCamelCase : List[str]="<s>" , _lowerCamelCase : Optional[Any]="<unk>" , _lowerCamelCase : str="<pad>" , _lowerCamelCase : Optional[int]="<mask>" , _lowerCamelCase : Dict=False , _lowerCamelCase : Union[str, Any]=True , **_lowerCamelCase : List[Any] , ):
"""simple docstring"""
super().__init__(
_lowerCamelCase , _lowerCamelCase , tokenizer_file=_lowerCamelCase , errors=_lowerCamelCase , bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , sep_token=_lowerCamelCase , cls_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token=_lowerCamelCase , add_prefix_space=_lowerCamelCase , trim_offsets=_lowerCamelCase , **_lowerCamelCase , )
A_ : int = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , _lowerCamelCase ) != add_prefix_space:
A_ : str = getattr(_lowerCamelCase , pre_tok_state.pop('''type''' ) )
A_ : Any = add_prefix_space
A_ : Optional[Any] = pre_tok_class(**_lowerCamelCase )
A_ : Tuple = add_prefix_space
A_ : int = '''post_processor'''
A_ : Union[str, Any] = getattr(self.backend_tokenizer , _lowerCamelCase , _lowerCamelCase )
if tokenizer_component_instance:
A_ : Optional[int] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
A_ : Dict = tuple(state['''sep'''] )
if "cls" in state:
A_ : Dict = tuple(state['''cls'''] )
A_ : Optional[int] = False
if state.get('''add_prefix_space''' , _lowerCamelCase ) != add_prefix_space:
A_ : Any = add_prefix_space
A_ : Union[str, Any] = True
if state.get('''trim_offsets''' , _lowerCamelCase ) != trim_offsets:
A_ : Dict = trim_offsets
A_ : List[Any] = True
if changes_to_apply:
A_ : int = getattr(_lowerCamelCase , state.pop('''type''' ) )
A_ : List[Any] = component_class(**_lowerCamelCase )
setattr(self.backend_tokenizer , _lowerCamelCase , _lowerCamelCase )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def _a ( self : Union[str, Any] ):
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def _a ( self : Optional[Any] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : Any = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else value
A_ : str = value
def _a ( self : int , *_lowerCamelCase : str , **_lowerCamelCase : Any ):
"""simple docstring"""
A_ : List[str] = kwargs.get('''is_split_into_words''' , _lowerCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*_lowerCamelCase , **_lowerCamelCase )
def _a ( self : Optional[Any] , *_lowerCamelCase : Union[str, Any] , **_lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : Any = kwargs.get('''is_split_into_words''' , _lowerCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._encode_plus(*_lowerCamelCase , **_lowerCamelCase )
def _a ( self : List[str] , _lowerCamelCase : str , _lowerCamelCase : Optional[str] = None ):
"""simple docstring"""
A_ : Tuple = self._tokenizer.model.save(_lowerCamelCase , name=_lowerCamelCase )
return tuple(_lowerCamelCase )
def _a ( self : Dict , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
A_ : int = [self.sep_token_id]
A_ : 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 + sep + token_ids_a + sep ) * [0]
def _a ( self : List[Any] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
return token_ids_a + [self.eos_token_id]
def _a ( self : List[Any] , _lowerCamelCase : "Conversation" ):
"""simple docstring"""
A_ : Optional[int] = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(_lowerCamelCase )
A_ : Optional[int] = ''' '''.join(_lowerCamelCase )
A_ : List[str] = self.encode(_lowerCamelCase )
if len(_lowerCamelCase ) > self.model_max_length:
A_ : int = input_ids[-self.model_max_length :]
logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.' )
return input_ids
| 361 |
'''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.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = (
'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
)
_lowerCAmelCase = 'CIDAS/clipseg-rd64-refined'
_lowerCAmelCase = 'image_segmenter'
_lowerCAmelCase = CLIPSegForImageSegmentation
_lowerCAmelCase = ['image', 'text']
_lowerCAmelCase = ['image']
def __init__( self : Optional[int] , *_lowerCamelCase : Optional[int] , **_lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def _a ( self : List[str] , _lowerCamelCase : "Image" , _lowerCamelCase : str ):
"""simple docstring"""
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def _a ( self : Union[str, Any] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
with torch.no_grad():
A_ : Optional[int] = self.model(**_lowerCamelCase ).logits
return logits
def _a ( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : int = outputs.cpu().detach().numpy()
A_ : Tuple = 0
A_ : List[str] = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 4 | 0 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def snake_case__ ( lowerCamelCase__ : List[str]=None , lowerCamelCase__ : int=None ) -> Any:
return field(default_factory=lambda: default , metadata=lowerCamelCase__ )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = field(
metadata={'help': 'The csv file to plot.'}, )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}, )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}, )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Disable logarithmic scale when plotting'}, )
_lowerCAmelCase = field(
default=a__, metadata={
'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
}, )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}, )
_lowerCAmelCase = list_field(
default=a__, metadata={'help': 'List of model names that are used instead of the ones in the csv file.'} )
def snake_case__ ( lowerCamelCase__ : Tuple ) -> Optional[int]:
try:
int(lowerCamelCase__ )
return True
except ValueError:
return False
def snake_case__ ( lowerCamelCase__ : List[Any] ) -> Union[str, Any]:
try:
float(lowerCamelCase__ )
return True
except ValueError:
return False
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : int , _lowerCamelCase : Tuple ):
"""simple docstring"""
A_ : Optional[int] = args
A_ : Optional[int] = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )
with open(self.args.csv_file , newline='''''' ) as csv_file:
A_ : Dict = csv.DictReader(_lowerCamelCase )
for row in reader:
A_ : Union[str, Any] = row['''model''']
self.result_dict[model_name]["bsz"].append(int(row['''batch_size'''] ) )
self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length'''] ) )
if can_convert_to_int(row['''result'''] ):
# value is not None
A_ : Dict = int(row['''result'''] )
elif can_convert_to_float(row['''result'''] ):
# value is not None
A_ : Optional[Any] = float(row['''result'''] )
def _a ( self : Any ):
"""simple docstring"""
A_ : int = plt.subplots()
A_ : Optional[int] = '''Time usage''' if self.args.is_time else '''Memory usage'''
A_ : Optional[int] = title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale('''log''' )
ax.set_yscale('''log''' )
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter() )
for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
A_ : int = sorted(set(self.result_dict[model_name]['''bsz'''] ) )
A_ : List[str] = sorted(set(self.result_dict[model_name]['''seq_len'''] ) )
A_ : Union[str, Any] = self.result_dict[model_name]['''result''']
(A_) : List[Any] = (
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
A_ : Union[str, Any] = (
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
A_ : Optional[Any] = np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] , dtype=_lowerCamelCase , )
else:
A_ : List[str] = np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] , dtype=np.floataa , )
(A_) : Optional[int] = (
('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
)
A_ : Union[str, Any] = np.asarray(_lowerCamelCase , _lowerCamelCase )[: len(_lowerCamelCase )]
plt.scatter(
_lowerCamelCase , _lowerCamelCase , label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}' )
plt.plot(_lowerCamelCase , _lowerCamelCase , '''--''' )
title_str += f' {label_model_name} vs.'
A_ : List[Any] = title_str[:-4]
A_ : Optional[Any] = '''Time in s''' if self.args.is_time else '''Memory in MB'''
# plot
plt.title(_lowerCamelCase )
plt.xlabel(_lowerCamelCase )
plt.ylabel(_lowerCamelCase )
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file )
else:
plt.show()
def snake_case__ ( ) -> Optional[int]:
A_ : str = HfArgumentParser(lowerCamelCase__ )
A_ : Tuple = parser.parse_args_into_dataclasses()[0]
A_ : Any = Plot(args=lowerCamelCase__ )
plot.plot()
if __name__ == "__main__":
main()
| 362 |
'''simple docstring'''
from collections.abc import Sequence
def snake_case__ ( lowerCamelCase__ : Sequence[float] , lowerCamelCase__ : bool = False ) -> float:
if not arr:
return 0
A_ : Union[str, Any] = 0 if allow_empty_subarrays else float('''-inf''' )
A_ : str = 0.0
for num in arr:
A_ : Any = max(0 if allow_empty_subarrays else num , curr_sum + num )
A_ : Tuple = max(lowerCamelCase__ , lowerCamelCase__ )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
snake_case__ = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F'{max_subarray_sum(nums) = }')
| 4 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'biogpt'
def __init__( self : Tuple , _lowerCamelCase : List[Any]=42384 , _lowerCamelCase : Dict=1024 , _lowerCamelCase : List[str]=24 , _lowerCamelCase : Union[str, Any]=16 , _lowerCamelCase : Tuple=4096 , _lowerCamelCase : Dict="gelu" , _lowerCamelCase : Optional[int]=0.1 , _lowerCamelCase : Any=0.1 , _lowerCamelCase : int=1024 , _lowerCamelCase : List[str]=0.02 , _lowerCamelCase : int=1E-12 , _lowerCamelCase : Dict=True , _lowerCamelCase : Tuple=True , _lowerCamelCase : str=0.0 , _lowerCamelCase : Dict=0.0 , _lowerCamelCase : List[str]=1 , _lowerCamelCase : Union[str, Any]=0 , _lowerCamelCase : int=2 , **_lowerCamelCase : Tuple , ):
"""simple docstring"""
A_ : List[str] = vocab_size
A_ : Optional[Any] = max_position_embeddings
A_ : int = hidden_size
A_ : Union[str, Any] = num_hidden_layers
A_ : Optional[int] = num_attention_heads
A_ : int = intermediate_size
A_ : Optional[Any] = hidden_act
A_ : List[str] = hidden_dropout_prob
A_ : List[str] = attention_probs_dropout_prob
A_ : Optional[Any] = initializer_range
A_ : Union[str, Any] = layer_norm_eps
A_ : Any = scale_embedding
A_ : str = use_cache
A_ : Optional[Any] = layerdrop
A_ : List[str] = activation_dropout
super().__init__(pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase )
| 363 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'speech_to_text_2'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Optional[Any] , _lowerCamelCase : Optional[Any]=10000 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : int=2048 , _lowerCamelCase : Dict=4 , _lowerCamelCase : str=0.0 , _lowerCamelCase : int=True , _lowerCamelCase : int="relu" , _lowerCamelCase : Any=256 , _lowerCamelCase : List[Any]=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Union[str, Any]=0.0 , _lowerCamelCase : Optional[Any]=0.02 , _lowerCamelCase : int=2 , _lowerCamelCase : List[str]=True , _lowerCamelCase : str=1 , _lowerCamelCase : List[Any]=0 , _lowerCamelCase : Optional[int]=2 , _lowerCamelCase : Tuple=1024 , **_lowerCamelCase : int , ):
"""simple docstring"""
A_ : Optional[int] = vocab_size
A_ : Tuple = d_model
A_ : List[str] = decoder_ffn_dim
A_ : str = decoder_layers
A_ : Any = decoder_attention_heads
A_ : int = dropout
A_ : str = attention_dropout
A_ : Optional[int] = activation_dropout
A_ : str = activation_function
A_ : List[Any] = init_std
A_ : Union[str, Any] = decoder_layerdrop
A_ : Any = use_cache
A_ : Optional[Any] = decoder_layers
A_ : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
A_ : Optional[Any] = max_target_positions
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
| 4 | 0 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
snake_case__ = {
"""facebook/maskformer-swin-base-ade""": (
"""https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
snake_case__ = logging.get_logger(__name__)
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'maskformer'
_lowerCAmelCase = {'hidden_size': 'mask_feature_size'}
_lowerCAmelCase = ['resnet', 'swin']
_lowerCAmelCase = ['detr']
def __init__( self : Optional[int] , _lowerCamelCase : int = 256 , _lowerCamelCase : int = 256 , _lowerCamelCase : float = 0.1 , _lowerCamelCase : bool = False , _lowerCamelCase : Optional[Dict] = None , _lowerCamelCase : Optional[Dict] = None , _lowerCamelCase : float = 0.02 , _lowerCamelCase : float = 1.0 , _lowerCamelCase : float = 1.0 , _lowerCamelCase : float = 1.0 , _lowerCamelCase : float = 20.0 , _lowerCamelCase : Optional[bool] = None , **_lowerCamelCase : Optional[int] , ):
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
A_ : List[Any] = SwinConfig(
image_size=384 , in_channels=3 , patch_size=4 , embed_dim=128 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] , )
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : Union[str, Any] = backbone_config.pop('''model_type''' )
A_ : Dict = CONFIG_MAPPING[backbone_model_type]
A_ : Any = config_class.from_dict(_lowerCamelCase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
f'Supported model types: {",".join(self.backbones_supported )}' )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
A_ : List[Any] = DetrConfig()
else:
# verify that the decoder is supported
A_ : Optional[int] = (
decoder_config.pop('''model_type''' ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f'Transformer Decoder {decoder_type} not supported, please use one of'
f' {",".join(self.decoders_supported )}' )
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : Any = CONFIG_MAPPING[decoder_type]
A_ : str = config_class.from_dict(_lowerCamelCase )
A_ : Any = backbone_config
A_ : List[str] = decoder_config
# main feature dimension for the model
A_ : Union[str, Any] = fpn_feature_size
A_ : Dict = mask_feature_size
# initializer
A_ : str = init_std
A_ : List[str] = init_xavier_std
# Hungarian matcher && loss
A_ : Union[str, Any] = cross_entropy_weight
A_ : Dict = dice_weight
A_ : Union[str, Any] = mask_weight
A_ : Optional[Any] = use_auxiliary_loss
A_ : Optional[int] = no_object_weight
A_ : Any = output_auxiliary_logits
A_ : Optional[int] = self.decoder_config.encoder_attention_heads
A_ : Any = self.decoder_config.num_hidden_layers
super().__init__(**_lowerCamelCase )
@classmethod
def _a ( cls : Any , _lowerCamelCase : PretrainedConfig , _lowerCamelCase : PretrainedConfig , **_lowerCamelCase : Optional[Any] ):
"""simple docstring"""
return cls(
backbone_config=_lowerCamelCase , decoder_config=_lowerCamelCase , **_lowerCamelCase , )
def _a ( self : int ):
"""simple docstring"""
A_ : Union[str, Any] = copy.deepcopy(self.__dict__ )
A_ : int = self.backbone_config.to_dict()
A_ : Optional[Any] = self.decoder_config.to_dict()
A_ : List[str] = self.__class__.model_type
return output
| 364 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'table-transformer'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Any , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Dict=None , _lowerCamelCase : int=3 , _lowerCamelCase : Any=100 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : Tuple=2048 , _lowerCamelCase : Any=8 , _lowerCamelCase : Dict=6 , _lowerCamelCase : Tuple=2048 , _lowerCamelCase : int=8 , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : List[Any]=0.0 , _lowerCamelCase : List[Any]=True , _lowerCamelCase : Optional[int]="relu" , _lowerCamelCase : Union[str, Any]=256 , _lowerCamelCase : Any=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : str=0.02 , _lowerCamelCase : Tuple=1.0 , _lowerCamelCase : Dict=False , _lowerCamelCase : str="sine" , _lowerCamelCase : str="resnet50" , _lowerCamelCase : Any=True , _lowerCamelCase : List[str]=False , _lowerCamelCase : Any=1 , _lowerCamelCase : int=5 , _lowerCamelCase : Tuple=2 , _lowerCamelCase : Optional[int]=1 , _lowerCamelCase : Any=1 , _lowerCamelCase : Dict=5 , _lowerCamelCase : str=2 , _lowerCamelCase : Union[str, Any]=0.1 , **_lowerCamelCase : int , ):
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
A_ : int = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : str = backbone_config.get('''model_type''' )
A_ : Optional[int] = CONFIG_MAPPING[backbone_model_type]
A_ : List[str] = config_class.from_dict(_lowerCamelCase )
# set timm attributes to None
A_ ,A_ ,A_ : Union[str, Any] = None, None, None
A_ : Optional[Any] = use_timm_backbone
A_ : Optional[int] = backbone_config
A_ : Optional[Any] = num_channels
A_ : Dict = num_queries
A_ : str = d_model
A_ : List[str] = encoder_ffn_dim
A_ : int = encoder_layers
A_ : Optional[Any] = encoder_attention_heads
A_ : List[str] = decoder_ffn_dim
A_ : Any = decoder_layers
A_ : List[str] = decoder_attention_heads
A_ : Tuple = dropout
A_ : Optional[Any] = attention_dropout
A_ : Any = activation_dropout
A_ : List[Any] = activation_function
A_ : Dict = init_std
A_ : Any = init_xavier_std
A_ : List[Any] = encoder_layerdrop
A_ : int = decoder_layerdrop
A_ : Any = encoder_layers
A_ : List[str] = auxiliary_loss
A_ : List[Any] = position_embedding_type
A_ : Optional[Any] = backbone
A_ : Tuple = use_pretrained_backbone
A_ : List[Any] = dilation
# Hungarian matcher
A_ : List[str] = class_cost
A_ : str = bbox_cost
A_ : Union[str, Any] = giou_cost
# Loss coefficients
A_ : Any = mask_loss_coefficient
A_ : Optional[int] = dice_loss_coefficient
A_ : Dict = bbox_loss_coefficient
A_ : int = giou_loss_coefficient
A_ : int = eos_coefficient
super().__init__(is_encoder_decoder=_lowerCamelCase , **_lowerCamelCase )
@property
def _a ( self : List[Any] ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def _a ( self : Any ):
"""simple docstring"""
return self.d_model
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : Tuple ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def _a ( self : Optional[int] ):
"""simple docstring"""
return 1E-5
@property
def _a ( self : str ):
"""simple docstring"""
return 12
| 4 | 0 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class UpperCamelCase_ (ctypes.Structure ):
"""simple docstring"""
_lowerCAmelCase = [('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def snake_case__ ( ) -> Tuple:
if os.name == "nt":
A_ : str = CursorInfo()
A_ : int = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase__ , ctypes.byref(lowerCamelCase__ ) )
A_ : Any = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase__ , ctypes.byref(lowerCamelCase__ ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def snake_case__ ( ) -> int:
if os.name == "nt":
A_ : Tuple = CursorInfo()
A_ : List[str] = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase__ , ctypes.byref(lowerCamelCase__ ) )
A_ : int = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase__ , ctypes.byref(lowerCamelCase__ ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def snake_case__ ( ) -> Dict:
try:
hide_cursor()
yield
finally:
show_cursor()
| 365 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : int , _lowerCamelCase : List[str]=3 , _lowerCamelCase : Any=32 , _lowerCamelCase : Union[str, Any]=3 , _lowerCamelCase : int=10 , _lowerCamelCase : Union[str, Any]=[8, 16, 32, 64] , _lowerCamelCase : Dict=[1, 1, 2, 1] , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Optional[int]=True , _lowerCamelCase : Any="relu" , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Dict=["stage2", "stage3", "stage4"] , _lowerCamelCase : Union[str, Any]=[2, 3, 4] , _lowerCamelCase : Tuple=1 , ):
"""simple docstring"""
A_ : List[str] = parent
A_ : List[str] = batch_size
A_ : Union[str, Any] = image_size
A_ : Tuple = num_channels
A_ : Any = embeddings_size
A_ : int = hidden_sizes
A_ : Optional[Any] = depths
A_ : List[Any] = is_training
A_ : Optional[int] = use_labels
A_ : int = hidden_act
A_ : Tuple = num_labels
A_ : Union[str, Any] = scope
A_ : List[Any] = len(_lowerCamelCase )
A_ : Union[str, Any] = out_features
A_ : List[Any] = out_indices
A_ : Dict = num_groups
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : Union[str, Any] = None
if self.use_labels:
A_ : Any = ids_tensor([self.batch_size] , self.num_labels )
A_ : Any = self.get_config()
return config, pixel_values, labels
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return BitConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def _a ( self : List[Any] , _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
A_ : Any = BitModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase )
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 : Optional[int] , _lowerCamelCase : List[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : Dict = self.num_labels
A_ : Optional[Any] = BitForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : List[Any] = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Any , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : List[Any] ):
"""simple docstring"""
A_ : List[Any] = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
A_ : Optional[Any] = None
A_ : int = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Optional[int] = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = self.prepare_config_and_inputs()
A_ ,A_ ,A_ : Union[str, Any] = config_and_inputs
A_ : str = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase_ (a__, a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
_lowerCAmelCase = (
{'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
if is_torch_available()
else {}
)
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[str] = BitModelTester(self )
A_ : Optional[Any] = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : List[Any] ):
"""simple docstring"""
return
@unittest.skip(reason='''Bit does not output attentions''' )
def _a ( self : str ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def _a ( self : Any ):
"""simple docstring"""
pass
def _a ( self : List[Any] ):
"""simple docstring"""
A_ ,A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Dict = model_class(_lowerCamelCase )
A_ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : int = [*signature.parameters.keys()]
A_ : Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ ,A_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : str = model_class(config=_lowerCamelCase )
for name, module in model.named_modules():
if isinstance(_lowerCamelCase , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
def _a ( self : int ):
"""simple docstring"""
def check_hidden_states_output(_lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : int ):
A_ : Union[str, Any] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
A_ : Union[str, Any] = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
A_ : int = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
A_ : List[Any] = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , expected_num_stages + 1 )
# Bit'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 // 4, self.model_tester.image_size // 4] , )
A_ ,A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Tuple = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
A_ : Tuple = layer_type
A_ : Optional[Any] = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A_ : List[str] = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def _a ( self : Tuple ):
"""simple docstring"""
pass
def _a ( self : str ):
"""simple docstring"""
A_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
@slow
def _a ( self : Union[str, Any] ):
"""simple docstring"""
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : List[Any] = BitModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def snake_case__ ( ) -> Optional[int]:
A_ : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _a ( self : List[Any] ):
"""simple docstring"""
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_lowerCamelCase )
A_ : Union[str, Any] = self.default_image_processor
A_ : Optional[int] = prepare_img()
A_ : int = image_processor(images=_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
A_ : Union[str, Any] = model(**_lowerCamelCase )
# verify the logits
A_ : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
A_ : Tuple = torch.tensor([[-0.65_26, -0.52_63, -1.43_98]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1E-4 ) )
@require_torch
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (BitBackbone,) if is_torch_available() else ()
_lowerCAmelCase = BitConfig
_lowerCAmelCase = False
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Union[str, Any] = BitModelTester(self )
| 4 | 0 |
'''simple docstring'''
from ... import PretrainedConfig
snake_case__ = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
_lowerCAmelCase = 'nezha'
def __init__( self : Tuple , _lowerCamelCase : int=21128 , _lowerCamelCase : Optional[Any]=768 , _lowerCamelCase : Dict=12 , _lowerCamelCase : List[str]=12 , _lowerCamelCase : int=3072 , _lowerCamelCase : List[str]="gelu" , _lowerCamelCase : Optional[int]=0.1 , _lowerCamelCase : Any=0.1 , _lowerCamelCase : List[Any]=512 , _lowerCamelCase : Optional[Any]=64 , _lowerCamelCase : Any=2 , _lowerCamelCase : int=0.02 , _lowerCamelCase : List[Any]=1E-12 , _lowerCamelCase : Optional[Any]=0.1 , _lowerCamelCase : Tuple=0 , _lowerCamelCase : Dict=2 , _lowerCamelCase : List[str]=3 , _lowerCamelCase : Union[str, Any]=True , **_lowerCamelCase : Optional[Any] , ):
"""simple docstring"""
super().__init__(pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase )
A_ : Dict = vocab_size
A_ : int = hidden_size
A_ : str = num_hidden_layers
A_ : Optional[int] = num_attention_heads
A_ : List[Any] = hidden_act
A_ : Optional[Any] = intermediate_size
A_ : int = hidden_dropout_prob
A_ : Union[str, Any] = attention_probs_dropout_prob
A_ : Optional[int] = max_position_embeddings
A_ : List[str] = max_relative_position
A_ : Union[str, Any] = type_vocab_size
A_ : Dict = initializer_range
A_ : Tuple = layer_norm_eps
A_ : Optional[Any] = classifier_dropout
A_ : int = use_cache
| 366 |
'''simple docstring'''
import pprint
import requests
snake_case__ = """https://zenquotes.io/api"""
def snake_case__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def snake_case__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if __name__ == "__main__":
snake_case__ = random_quotes()
pprint.pprint(response)
| 4 | 0 |
'''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 snake_case__ ( ) -> int:
A_ : Any = 1_0
A_ : Dict = 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''' ),
} )
A_ : Dict = datasets.Dataset.from_dict(
{
'''tokens''': [['''foo'''] * 5] * n,
'''labels''': [[1] * 5] * n,
'''answers''': [{'''answer_start''': [9_7], '''text''': ['''1976''']}] * 1_0,
'''id''': list(range(lowerCamelCase__ ) ),
} , features=lowerCamelCase__ , )
return dataset
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : List[str] ) -> Union[str, Any]:
A_ : Dict = str(tmp_path_factory.mktemp('''data''' ) / '''file.arrow''' )
dataset.map(cache_file_name=lowerCamelCase__ )
return filename
# FILE_CONTENT + files
snake_case__ = """\
Text data.
Second line of data."""
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Dict ) -> List[str]:
A_ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''file.txt'''
A_ : int = FILE_CONTENT
with open(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ )
return filename
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : List[Any] ) -> Dict:
import bza
A_ : int = tmp_path_factory.mktemp('''data''' ) / '''file.txt.bz2'''
A_ : Union[str, Any] = bytes(lowerCamelCase__ , '''utf-8''' )
with bza.open(lowerCamelCase__ , '''wb''' ) as f:
f.write(lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : List[str] ) -> List[Any]:
import gzip
A_ : List[Any] = str(tmp_path_factory.mktemp('''data''' ) / '''file.txt.gz''' )
A_ : int = bytes(lowerCamelCase__ , '''utf-8''' )
with gzip.open(lowerCamelCase__ , '''wb''' ) as f:
f.write(lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : List[str] ) -> Optional[Any]:
if datasets.config.LZ4_AVAILABLE:
import lza.frame
A_ : List[str] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.lz4'''
A_ : Optional[int] = bytes(lowerCamelCase__ , '''utf-8''' )
with lza.frame.open(lowerCamelCase__ , '''wb''' ) as f:
f.write(lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : Any ) -> List[str]:
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
A_ : int = tmp_path_factory.mktemp('''data''' ) / '''file.txt.7z'''
with pyazr.SevenZipFile(lowerCamelCase__ , '''w''' ) as archive:
archive.write(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : str ) -> List[Any]:
import tarfile
A_ : Optional[Any] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.tar'''
with tarfile.TarFile(lowerCamelCase__ , '''w''' ) as f:
f.add(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : str ) -> str:
import lzma
A_ : List[str] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.xz'''
A_ : Any = bytes(lowerCamelCase__ , '''utf-8''' )
with lzma.open(lowerCamelCase__ , '''wb''' ) as f:
f.write(lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Dict ) -> Union[str, Any]:
import zipfile
A_ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.zip'''
with zipfile.ZipFile(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : List[str] ) -> int:
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
A_ : Any = tmp_path_factory.mktemp('''data''' ) / '''file.txt.zst'''
A_ : List[str] = bytes(lowerCamelCase__ , '''utf-8''' )
with zstd.open(lowerCamelCase__ , '''wb''' ) as f:
f.write(lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Tuple ) -> Tuple:
A_ : List[Any] = tmp_path_factory.mktemp('''data''' ) / '''file.xml'''
A_ : Optional[int] = textwrap.dedent(
'''\
<?xml version="1.0" encoding="UTF-8" ?>
<tmx version="1.4">
<header segtype="sentence" srclang="ca" />
<body>
<tu>
<tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>
<tuv xml:lang="en"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>
<tuv xml:lang="en"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>
<tuv xml:lang="en"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>
<tuv xml:lang="en"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>
<tuv xml:lang="en"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>''' )
with open(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ )
return filename
snake_case__ = [
{"""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},
]
snake_case__ = [
{"""col_1""": """4""", """col_2""": 4, """col_3""": 4.0},
{"""col_1""": """5""", """col_2""": 5, """col_3""": 5.0},
]
snake_case__ = {
"""col_1""": ["""0""", """1""", """2""", """3"""],
"""col_2""": [0, 1, 2, 3],
"""col_3""": [0.0, 1.0, 2.0, 3.0],
}
snake_case__ = [
{"""col_3""": 0.0, """col_1""": """0""", """col_2""": 0},
{"""col_3""": 1.0, """col_1""": """1""", """col_2""": 1},
]
snake_case__ = [
{"""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 snake_case__ ( ) -> List[str]:
return DATA_DICT_OF_LISTS
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : List[Any] ) -> Any:
A_ : Optional[Any] = datasets.Dataset.from_dict(lowerCamelCase__ )
A_ : Union[str, Any] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.arrow''' )
dataset.map(cache_file_name=lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : List[str] ) -> List[Any]:
A_ : Tuple = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.sqlite''' )
with contextlib.closing(sqlitea.connect(lowerCamelCase__ ) ) as con:
A_ : 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 snake_case__ ( lowerCamelCase__ : int ) -> Optional[int]:
A_ : str = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.csv''' )
with open(lowerCamelCase__ , '''w''' , newline='''''' ) as f:
A_ : Any = csv.DictWriter(lowerCamelCase__ , fieldnames=['''col_1''', '''col_2''', '''col_3'''] )
writer.writeheader()
for item in DATA:
writer.writerow(lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : List[str] ) -> Optional[int]:
A_ : str = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.csv''' )
with open(lowerCamelCase__ , '''w''' , newline='''''' ) as f:
A_ : str = csv.DictWriter(lowerCamelCase__ , fieldnames=['''col_1''', '''col_2''', '''col_3'''] )
writer.writeheader()
for item in DATA:
writer.writerow(lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Optional[int] , lowerCamelCase__ : List[Any] ) -> int:
import bza
A_ : List[str] = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.bz2'''
with open(lowerCamelCase__ , '''rb''' ) as f:
A_ : Union[str, Any] = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(lowerCamelCase__ , '''wb''' ) as f:
f.write(lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[Any] ) -> List[Any]:
A_ : Tuple = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.zip'''
with zipfile.ZipFile(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ) )
f.write(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Dict , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Optional[Any] ) -> Optional[int]:
A_ : Dict = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.zip'''
with zipfile.ZipFile(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ , arcname=os.path.basename(csv_path.replace('''.csv''' , '''.CSV''' ) ) )
f.write(lowerCamelCase__ , arcname=os.path.basename(csva_path.replace('''.csv''' , '''.CSV''' ) ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Any ) -> List[Any]:
A_ : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.csv.zip'''
with zipfile.ZipFile(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ , arcname=os.path.join('''main_dir''' , os.path.basename(lowerCamelCase__ ) ) )
f.write(lowerCamelCase__ , arcname=os.path.join('''main_dir''' , os.path.basename(lowerCamelCase__ ) ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Optional[Any] ) -> List[str]:
A_ : Optional[Any] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.parquet''' )
A_ : Optional[int] = pa.schema(
{
'''col_1''': pa.string(),
'''col_2''': pa.intaa(),
'''col_3''': pa.floataa(),
} )
with open(lowerCamelCase__ , '''wb''' ) as f:
A_ : List[str] = pq.ParquetWriter(lowerCamelCase__ , schema=lowerCamelCase__ )
A_ : Union[str, Any] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(lowerCamelCase__ ) )] for k in DATA[0]} , schema=lowerCamelCase__ )
writer.write_table(lowerCamelCase__ )
writer.close()
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : int ) -> List[Any]:
A_ : Tuple = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.json''' )
A_ : List[Any] = {'''data''': DATA}
with open(lowerCamelCase__ , '''w''' ) as f:
json.dump(lowerCamelCase__ , lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : str ) -> Dict:
A_ : Tuple = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.json''' )
A_ : Optional[Any] = {'''data''': DATA_DICT_OF_LISTS}
with open(lowerCamelCase__ , '''w''' ) as f:
json.dump(lowerCamelCase__ , lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : List[Any] ) -> Optional[int]:
A_ : Union[str, Any] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl''' )
with open(lowerCamelCase__ , '''w''' ) as f:
for item in DATA:
f.write(json.dumps(lowerCamelCase__ ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Union[str, Any] ) -> List[str]:
A_ : Dict = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.jsonl''' )
with open(lowerCamelCase__ , '''w''' ) as f:
for item in DATA:
f.write(json.dumps(lowerCamelCase__ ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : int ) -> List[Any]:
A_ : Any = str(tmp_path_factory.mktemp('''data''' ) / '''dataset_312.jsonl''' )
with open(lowerCamelCase__ , '''w''' ) as f:
for item in DATA_312:
f.write(json.dumps(lowerCamelCase__ ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Optional[Any] ) -> Optional[Any]:
A_ : List[str] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset-str.jsonl''' )
with open(lowerCamelCase__ , '''w''' ) as f:
for item in DATA_STR:
f.write(json.dumps(lowerCamelCase__ ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Tuple , lowerCamelCase__ : str ) -> List[Any]:
import gzip
A_ : Optional[int] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.txt.gz''' )
with open(lowerCamelCase__ , '''rb''' ) as orig_file:
with gzip.open(lowerCamelCase__ , '''wb''' ) as zipped_file:
zipped_file.writelines(lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Tuple , lowerCamelCase__ : Union[str, Any] ) -> List[str]:
import gzip
A_ : str = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.gz''' )
with open(lowerCamelCase__ , '''rb''' ) as orig_file:
with gzip.open(lowerCamelCase__ , '''wb''' ) as zipped_file:
zipped_file.writelines(lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : List[str] , lowerCamelCase__ : List[Any] ) -> str:
A_ : Any = tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.zip'''
with zipfile.ZipFile(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ) )
f.write(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : int , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Optional[int] ) -> Union[str, Any]:
A_ : str = tmp_path_factory.mktemp('''data''' ) / '''dataset_nested.jsonl.zip'''
with zipfile.ZipFile(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ , arcname=os.path.join('''nested''' , os.path.basename(lowerCamelCase__ ) ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[Any] ) -> List[str]:
A_ : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.jsonl.zip'''
with zipfile.ZipFile(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ , arcname=os.path.join('''main_dir''' , os.path.basename(lowerCamelCase__ ) ) )
f.write(lowerCamelCase__ , arcname=os.path.join('''main_dir''' , os.path.basename(lowerCamelCase__ ) ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Dict , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : int ) -> Tuple:
A_ : List[Any] = tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.tar'''
with tarfile.TarFile(lowerCamelCase__ , '''w''' ) as f:
f.add(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ) )
f.add(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Tuple , lowerCamelCase__ : List[Any] ) -> Any:
A_ : int = tmp_path_factory.mktemp('''data''' ) / '''dataset_nested.jsonl.tar'''
with tarfile.TarFile(lowerCamelCase__ , '''w''' ) as f:
f.add(lowerCamelCase__ , arcname=os.path.join('''nested''' , os.path.basename(lowerCamelCase__ ) ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Tuple ) -> Tuple:
A_ : Any = ['''0''', '''1''', '''2''', '''3''']
A_ : Optional[int] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.txt''' )
with open(lowerCamelCase__ , '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Dict ) -> Any:
A_ : Union[str, Any] = ['''0''', '''1''', '''2''', '''3''']
A_ : int = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.txt''' )
with open(lowerCamelCase__ , '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : int ) -> Union[str, Any]:
A_ : Union[str, Any] = ['''0''', '''1''', '''2''', '''3''']
A_ : str = tmp_path_factory.mktemp('''data''' ) / '''dataset.abc'''
with open(lowerCamelCase__ , '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Any , lowerCamelCase__ : Union[str, Any] ) -> Optional[int]:
A_ : Dict = tmp_path_factory.mktemp('''data''' ) / '''dataset.text.zip'''
with zipfile.ZipFile(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ) )
f.write(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : Any ) -> List[Any]:
A_ : int = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.text.zip'''
with zipfile.ZipFile(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ , arcname=os.path.join('''main_dir''' , os.path.basename(lowerCamelCase__ ) ) )
f.write(lowerCamelCase__ , arcname=os.path.join('''main_dir''' , os.path.basename(lowerCamelCase__ ) ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[Any] ) -> str:
A_ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''dataset.ext.zip'''
with zipfile.ZipFile(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ , arcname=os.path.basename('''unsupported.ext''' ) )
f.write(lowerCamelCase__ , arcname=os.path.basename('''unsupported_2.ext''' ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : int ) -> List[Any]:
A_ : Union[str, Any] = '''\n'''.join(['''First''', '''Second\u2029with Unicode new line''', '''Third'''] )
A_ : Any = str(tmp_path_factory.mktemp('''data''' ) / '''dataset_with_unicode_new_lines.txt''' )
with open(lowerCamelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(lowerCamelCase__ )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( ) -> List[Any]:
return os.path.join('''tests''' , '''features''' , '''data''' , '''test_image_rgb.jpg''' )
@pytest.fixture(scope='''session''' )
def snake_case__ ( ) -> Optional[int]:
return os.path.join('''tests''' , '''features''' , '''data''' , '''test_audio_44100.wav''' )
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : int ) -> List[str]:
A_ : Tuple = tmp_path_factory.mktemp('''data''' ) / '''dataset.img.zip'''
with zipfile.ZipFile(lowerCamelCase__ , '''w''' ) as f:
f.write(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ) )
f.write(lowerCamelCase__ , arcname=os.path.basename(lowerCamelCase__ ).replace('''.jpg''' , '''2.jpg''' ) )
return path
@pytest.fixture(scope='''session''' )
def snake_case__ ( lowerCamelCase__ : Any ) -> Tuple:
A_ : Union[str, Any] = tmp_path_factory.mktemp('''data_dir''' )
(data_dir / "subdir").mkdir()
with open(data_dir / '''subdir''' / '''train.txt''' , '''w''' ) as f:
f.write('''foo\n''' * 1_0 )
with open(data_dir / '''subdir''' / '''test.txt''' , '''w''' ) as f:
f.write('''bar\n''' * 1_0 )
# hidden file
with open(data_dir / '''subdir''' / '''.test.txt''' , '''w''' ) as f:
f.write('''bar\n''' * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / '''.subdir''' / '''train.txt''' , '''w''' ) as f:
f.write('''foo\n''' * 1_0 )
with open(data_dir / '''.subdir''' / '''test.txt''' , '''w''' ) as f:
f.write('''bar\n''' * 1_0 )
return data_dir
| 367 |
'''simple docstring'''
from __future__ import annotations
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[int] , _lowerCamelCase : int ):
"""simple docstring"""
A_ : Union[str, Any] = order
# a_{0} ... a_{k}
A_ : Union[str, Any] = [1.0] + [0.0] * order
# b_{0} ... b_{k}
A_ : int = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
A_ : str = [0.0] * self.order
# y[n-1] ... y[n-k]
A_ : Optional[Any] = [0.0] * self.order
def _a ( self : Dict , _lowerCamelCase : list[float] , _lowerCamelCase : list[float] ):
"""simple docstring"""
if len(_lowerCamelCase ) < self.order:
A_ : Any = [1.0, *a_coeffs]
if len(_lowerCamelCase ) != self.order + 1:
A_ : List[Any] = (
f'Expected a_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(_lowerCamelCase )}'
)
raise ValueError(_lowerCamelCase )
if len(_lowerCamelCase ) != self.order + 1:
A_ : Union[str, Any] = (
f'Expected b_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(_lowerCamelCase )}'
)
raise ValueError(_lowerCamelCase )
A_ : Tuple = a_coeffs
A_ : str = b_coeffs
def _a ( self : Tuple , _lowerCamelCase : float ):
"""simple docstring"""
A_ : Any = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
A_ : str = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
A_ : Optional[Any] = self.input_history[:-1]
A_ : List[str] = self.output_history[:-1]
A_ : Tuple = sample
A_ : Tuple = result
return result
| 4 | 0 |
'''simple docstring'''
def snake_case__ ( ) -> int:
return 1
def snake_case__ ( lowerCamelCase__ : int ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def snake_case__ ( lowerCamelCase__ : int ) -> int:
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : int ) -> int:
return 0 if x < 0 else ten_pence(x - 1_0 ) + five_pence(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : int ) -> int:
return 0 if x < 0 else twenty_pence(x - 2_0 ) + ten_pence(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : int ) -> int:
return 0 if x < 0 else fifty_pence(x - 5_0 ) + twenty_pence(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : int ) -> int:
return 0 if x < 0 else one_pound(x - 1_0_0 ) + fifty_pence(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : int ) -> int:
return 0 if x < 0 else two_pound(x - 2_0_0 ) + one_pound(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : int = 2_0_0 ) -> int:
return two_pound(lowerCamelCase__ )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 368 |
'''simple docstring'''
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ : Union[str, Any] = val
A_ : Tuple = None
A_ : Any = None
def _a ( self : Tuple , _lowerCamelCase : List[Any] ):
"""simple docstring"""
if self.val:
if val < self.val:
if self.left is None:
A_ : int = Node(_lowerCamelCase )
else:
self.left.insert(_lowerCamelCase )
elif val > self.val:
if self.right is None:
A_ : List[str] = Node(_lowerCamelCase )
else:
self.right.insert(_lowerCamelCase )
else:
A_ : Any = val
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int] ) -> str:
# Recursive traversal
if root:
inorder(root.left , lowerCamelCase__ )
res.append(root.val )
inorder(root.right , lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Optional[int] ) -> Tuple:
# Build BST
if len(lowerCamelCase__ ) == 0:
return arr
A_ : Dict = Node(arr[0] )
for i in range(1 , len(lowerCamelCase__ ) ):
root.insert(arr[i] )
# Traverse BST in order.
A_ : Tuple = []
inorder(lowerCamelCase__ , lowerCamelCase__ )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 4 | 0 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : str , _lowerCamelCase : int=2 , _lowerCamelCase : Any=8 , _lowerCamelCase : Tuple=True , _lowerCamelCase : List[Any]=True , _lowerCamelCase : int=True , _lowerCamelCase : Tuple=True , _lowerCamelCase : Tuple=99 , _lowerCamelCase : List[str]=16 , _lowerCamelCase : Dict=5 , _lowerCamelCase : str=2 , _lowerCamelCase : Dict=36 , _lowerCamelCase : Union[str, Any]="gelu" , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : Union[str, Any]=0.0 , _lowerCamelCase : Dict=512 , _lowerCamelCase : str=16 , _lowerCamelCase : Optional[Any]=2 , _lowerCamelCase : Union[str, Any]=0.02 , _lowerCamelCase : Tuple=3 , _lowerCamelCase : Optional[int]=4 , _lowerCamelCase : List[Any]=None , ):
"""simple docstring"""
A_ : Optional[Any] = parent
A_ : Dict = batch_size
A_ : str = seq_length
A_ : Any = is_training
A_ : List[Any] = use_input_mask
A_ : Union[str, Any] = use_token_type_ids
A_ : Optional[int] = use_labels
A_ : Any = vocab_size
A_ : List[str] = hidden_size
A_ : Union[str, Any] = num_hidden_layers
A_ : Any = num_attention_heads
A_ : List[str] = intermediate_size
A_ : List[str] = hidden_act
A_ : Optional[Any] = hidden_dropout_prob
A_ : Union[str, Any] = attention_probs_dropout_prob
A_ : Optional[Any] = max_position_embeddings
A_ : int = type_vocab_size
A_ : List[str] = type_sequence_label_size
A_ : Tuple = initializer_range
A_ : int = num_labels
A_ : Optional[Any] = num_choices
A_ : Union[str, Any] = scope
def _a ( self : str ):
"""simple docstring"""
A_ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ : Any = None
if self.use_input_mask:
A_ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
A_ : List[Any] = None
if self.use_token_type_ids:
A_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A_ : List[Any] = None
A_ : Union[str, Any] = None
A_ : Tuple = None
if self.use_labels:
A_ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ : Dict = ids_tensor([self.batch_size] , self.num_choices )
A_ : int = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : str ):
"""simple docstring"""
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_lowerCamelCase , initializer_range=self.initializer_range , )
def _a ( self : Any ):
"""simple docstring"""
A_ : List[Any] = self.get_config()
A_ : str = 300
return config
def _a ( self : Dict ):
"""simple docstring"""
(
A_
) : List[Any] = self.prepare_config_and_inputs()
A_ : List[str] = True
A_ : Dict = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
A_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def _a ( self : str , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : int , _lowerCamelCase : Dict , _lowerCamelCase : Any , _lowerCamelCase : Any , _lowerCamelCase : Dict ):
"""simple docstring"""
A_ : List[str] = MraModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : List[str] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase )
A_ : Any = model(_lowerCamelCase , token_type_ids=_lowerCamelCase )
A_ : Optional[Any] = model(_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Dict , _lowerCamelCase : Tuple , _lowerCamelCase : Any , _lowerCamelCase : Dict , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : List[Any] , _lowerCamelCase : List[str] , _lowerCamelCase : List[Any] , _lowerCamelCase : List[str] , ):
"""simple docstring"""
A_ : Dict = True
A_ : str = MraModel(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Union[str, Any] = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , encoder_attention_mask=_lowerCamelCase , )
A_ : str = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , )
A_ : List[str] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : int , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : int ):
"""simple docstring"""
A_ : List[Any] = MraForMaskedLM(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Optional[Any] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : Dict , _lowerCamelCase : int , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Dict , _lowerCamelCase : str , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
A_ : Optional[int] = MraForQuestionAnswering(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : List[str] = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , start_positions=_lowerCamelCase , end_positions=_lowerCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _a ( self : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Dict , _lowerCamelCase : List[str] , _lowerCamelCase : Tuple , _lowerCamelCase : Tuple , _lowerCamelCase : List[str] , _lowerCamelCase : int ):
"""simple docstring"""
A_ : Optional[Any] = self.num_labels
A_ : Any = MraForSequenceClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : List[str] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Tuple , _lowerCamelCase : str , _lowerCamelCase : Any , _lowerCamelCase : Dict , _lowerCamelCase : Any , _lowerCamelCase : Any , _lowerCamelCase : Any , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
A_ : int = self.num_labels
A_ : Optional[int] = MraForTokenClassification(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Any = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _a ( self : int , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : List[str] , _lowerCamelCase : int , _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : Union[str, Any] = self.num_choices
A_ : str = MraForMultipleChoice(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : List[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A_ : Optional[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A_ : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A_ : Any = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Tuple = self.prepare_config_and_inputs()
(
A_
) : Optional[Any] = config_and_inputs
A_ : Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = ()
def _a ( self : str ):
"""simple docstring"""
A_ : List[str] = MraModelTester(self )
A_ : Optional[int] = ConfigTester(self , config_class=_lowerCamelCase , hidden_size=37 )
def _a ( self : int ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : str ):
"""simple docstring"""
A_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _a ( self : Any ):
"""simple docstring"""
A_ : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
A_ : List[Any] = type
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase )
@slow
def _a ( self : int ):
"""simple docstring"""
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Optional[Any] = MraModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
@unittest.skip(reason='''MRA does not output attentions''' )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return
@require_torch
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@slow
def _a ( self : Dict ):
"""simple docstring"""
A_ : str = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' )
A_ : int = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
A_ : List[str] = model(_lowerCamelCase )[0]
A_ : List[str] = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , _lowerCamelCase )
A_ : Any = torch.tensor(
[[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowerCamelCase , atol=1E-4 ) )
@slow
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Dict = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' )
A_ : int = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
A_ : Tuple = model(_lowerCamelCase )[0]
A_ : List[str] = 50265
A_ : Union[str, Any] = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , _lowerCamelCase )
A_ : Optional[int] = torch.tensor(
[[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowerCamelCase , atol=1E-4 ) )
@slow
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : int = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' )
A_ : Optional[Any] = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
A_ : str = model(_lowerCamelCase )[0]
A_ : Dict = 50265
A_ : Any = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , _lowerCamelCase )
A_ : List[Any] = torch.tensor(
[[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowerCamelCase , atol=1E-4 ) )
| 369 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list ) -> list:
if len(lowerCamelCase__ ) <= 1:
return [tuple(lowerCamelCase__ )]
A_ : List[str] = []
def generate(lowerCamelCase__ : int , lowerCamelCase__ : list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , lowerCamelCase__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
A_ ,A_ : Optional[int] = arr[k - 1], arr[i]
else: # k is odd
A_ ,A_ : Union[str, Any] = arr[k - 1], arr[0]
generate(k - 1 , lowerCamelCase__ )
generate(len(lowerCamelCase__ ) , lowerCamelCase__ )
return res
if __name__ == "__main__":
snake_case__ = input("""Enter numbers separated by a comma:\n""").strip()
snake_case__ = [int(item) for item in user_input.split(""",""")]
print(heaps(arr))
| 4 | 0 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def snake_case__ ( ) -> Optional[Any]:
A_ : Optional[int] = 9
A_ : str = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 1_4],
[3, 4, 9],
[5, 4, 1_0],
[1, 7, 1_1],
]
A_ : List[Any] = kruskal(lowerCamelCase__ , lowerCamelCase__ )
A_ : Optional[int] = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(lowerCamelCase__ ) == sorted(lowerCamelCase__ )
| 370 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : Tuple = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Dict = -1
A_ : List[str] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Any = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : List[str] = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
A_ : List[str] = TextStreamer(_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
A_ : Dict = cs.out[:-1]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : List[str] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Dict = -1
A_ : List[str] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Optional[int] = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : str = tokenizer.decode(greedy_ids[0] )
A_ : int = TextIteratorStreamer(_lowerCamelCase )
A_ : List[Any] = {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
A_ : List[Any] = Thread(target=model.generate , kwargs=_lowerCamelCase )
thread.start()
A_ : List[Any] = ''''''
for new_text in streamer:
streamer_text += new_text
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : List[str] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : List[Any] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : List[str] = -1
A_ : Any = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Tuple = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : Tuple = greedy_ids[:, input_ids.shape[1] :]
A_ : Tuple = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
A_ : Any = TextStreamer(_lowerCamelCase , skip_prompt=_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
A_ : Any = cs.out[:-1]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = AutoTokenizer.from_pretrained('''distilgpt2''' )
A_ : Tuple = AutoModelForCausalLM.from_pretrained('''distilgpt2''' ).to(_lowerCamelCase )
A_ : List[Any] = -1
A_ : Union[str, Any] = torch.ones((1, 5) , device=_lowerCamelCase ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
A_ : List[Any] = TextStreamer(_lowerCamelCase , skip_special_tokens=_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=1 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
A_ : List[str] = cs.out[:-1] # Remove the final "\n"
A_ : List[Any] = tokenizer(_lowerCamelCase , return_tensors='''pt''' )
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1) )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : str = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : str = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Union[str, Any] = -1
A_ : Union[str, Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : List[str] = TextIteratorStreamer(_lowerCamelCase , timeout=0.0_01 )
A_ : str = {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
A_ : List[str] = Thread(target=model.generate , kwargs=_lowerCamelCase )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(_lowerCamelCase ):
A_ : str = ''''''
for new_text in streamer:
streamer_text += new_text
| 4 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
snake_case__ = logging.get_logger(__name__)
class UpperCamelCase_ (a__ ):
"""simple docstring"""
def __init__( self : Tuple , *_lowerCamelCase : List[str] , **_lowerCamelCase : Tuple ):
"""simple docstring"""
warnings.warn(
'''The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use FlavaImageProcessor instead.''' , _lowerCamelCase , )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
| 371 |
'''simple docstring'''
import heapq
def snake_case__ ( lowerCamelCase__ : dict ) -> set[int]:
A_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase__ , [-1 * len(lowerCamelCase__ ), (key, value)] )
# chosen_vertices = set of chosen vertices
A_ : str = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
A_ : Tuple = heapq.heappop(lowerCamelCase__ )[1][0]
chosen_vertices.add(lowerCamelCase__ )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
A_ : List[str] = elem[1][1].index(lowerCamelCase__ )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase__ )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case__ = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')
| 4 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'levit'
def __init__( self : int , _lowerCamelCase : List[Any]=224 , _lowerCamelCase : Union[str, Any]=3 , _lowerCamelCase : int=3 , _lowerCamelCase : Tuple=2 , _lowerCamelCase : Optional[int]=1 , _lowerCamelCase : List[str]=16 , _lowerCamelCase : Tuple=[128, 256, 384] , _lowerCamelCase : List[str]=[4, 8, 12] , _lowerCamelCase : Optional[int]=[4, 4, 4] , _lowerCamelCase : Union[str, Any]=[16, 16, 16] , _lowerCamelCase : int=0 , _lowerCamelCase : Union[str, Any]=[2, 2, 2] , _lowerCamelCase : Optional[Any]=[2, 2, 2] , _lowerCamelCase : Optional[Any]=0.02 , **_lowerCamelCase : List[Any] , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
A_ : List[Any] = image_size
A_ : List[str] = num_channels
A_ : Tuple = kernel_size
A_ : Optional[int] = stride
A_ : Dict = padding
A_ : Tuple = hidden_sizes
A_ : Tuple = num_attention_heads
A_ : int = depths
A_ : Any = key_dim
A_ : Any = drop_path_rate
A_ : Tuple = patch_size
A_ : Union[str, Any] = attention_ratio
A_ : str = mlp_ratio
A_ : Optional[Any] = initializer_range
A_ : Union[str, Any] = [
['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : Optional[int] ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _a ( self : int ):
"""simple docstring"""
return 1E-4
| 350 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__ = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
F'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
F'decoder.layers.{i}.encoder_attn.out_proj.bias',
)
)
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
]
)
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[Any] ) -> Optional[Any]:
A_ : Tuple = state_dict.pop(lowerCamelCase__ )
A_ : Optional[Any] = val
def snake_case__ ( lowerCamelCase__ : Dict ) -> Any:
A_ : int = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
A_ : int = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
A_ : List[str] = value
else:
A_ : Optional[int] = value
return new_state_dict
def snake_case__ ( lowerCamelCase__ : Union[str, Any] ) -> Optional[Any]:
A_ : Any = ''''''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
A_ : Tuple = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
A_ : Dict = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
A_ : str = in_proj_weight[:2_5_6, :]
A_ : Optional[Any] = in_proj_bias[:2_5_6]
A_ : Dict = in_proj_weight[2_5_6:5_1_2, :]
A_ : Tuple = in_proj_bias[2_5_6:5_1_2]
A_ : Tuple = in_proj_weight[-2_5_6:, :]
A_ : Optional[int] = in_proj_bias[-2_5_6:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
A_ : Union[str, Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
A_ : Dict = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
A_ : List[str] = in_proj_weight[:2_5_6, :]
A_ : int = in_proj_bias[:2_5_6]
A_ : Any = in_proj_weight[2_5_6:5_1_2, :]
A_ : List[str] = in_proj_bias[2_5_6:5_1_2]
A_ : Union[str, Any] = in_proj_weight[-2_5_6:, :]
A_ : Optional[Any] = in_proj_bias[-2_5_6:]
# read in weights + bias of input projection layer of cross-attention
A_ : Tuple = state_dict.pop(
f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
A_ : Optional[Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
A_ : Dict = in_proj_weight_cross_attn[:2_5_6, :]
A_ : Tuple = in_proj_bias_cross_attn[:2_5_6]
A_ : int = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
A_ : List[str] = in_proj_bias_cross_attn[2_5_6:5_1_2]
A_ : Any = in_proj_weight_cross_attn[-2_5_6:, :]
A_ : Any = in_proj_bias_cross_attn[-2_5_6:]
def snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Tuple ) -> Dict:
A_ ,A_ : int = image.size
A_ : Tuple = max(lowerCamelCase__ , lowerCamelCase__ )
A_ : Optional[Any] = 8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0
A_ : Union[str, Any] = target_max_size / current_max_size
A_ : Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def snake_case__ ( lowerCamelCase__ : Tuple ) -> str:
A_ : Any = F.to_tensor(lowerCamelCase__ )
A_ : Optional[Any] = F.normalize(lowerCamelCase__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : int , lowerCamelCase__ : int ) -> str:
logger.info('''Converting model...''' )
# load original state dict
A_ : Tuple = torch.hub.load_state_dict_from_url(lowerCamelCase__ , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
A_ : str = rename_backbone_keys(lowerCamelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCamelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
A_ : List[Any] = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
A_ : List[Any] = state_dict.pop(lowerCamelCase__ )
A_ : str = val
# create HuggingFace model and load state dict
A_ : Union[str, Any] = TableTransformerConfig(
backbone='''resnet18''' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
A_ : Dict = 1_5
A_ : Dict = 2
A_ : int = {0: '''table''', 1: '''table rotated'''}
A_ : List[str] = idalabel
A_ : Optional[int] = {v: k for k, v in idalabel.items()}
else:
A_ : Union[str, Any] = 1_2_5
A_ : Optional[Any] = 6
A_ : Optional[Any] = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
A_ : int = idalabel
A_ : Tuple = {v: k for k, v in idalabel.items()}
A_ : Optional[Any] = DetrImageProcessor(
format='''coco_detection''' , max_size=8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0 )
A_ : int = TableTransformerForObjectDetection(lowerCamelCase__ )
model.load_state_dict(lowerCamelCase__ )
model.eval()
# verify our conversion
A_ : Optional[int] = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
A_ : Union[str, Any] = hf_hub_download(repo_id='''nielsr/example-pdf''' , repo_type='''dataset''' , filename=lowerCamelCase__ )
A_ : Tuple = Image.open(lowerCamelCase__ ).convert('''RGB''' )
A_ : int = normalize(resize(lowerCamelCase__ , lowerCamelCase__ ) ).unsqueeze(0 )
A_ : str = model(lowerCamelCase__ )
if "detection" in checkpoint_url:
A_ : str = (1, 1_5, 3)
A_ : int = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
A_ : Tuple = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
A_ : Optional[int] = (1, 1_2_5, 7)
A_ : Dict = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
A_ : Any = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(lowerCamelCase__ ).mkdir(exist_ok=lowerCamelCase__ )
model.save_pretrained(lowerCamelCase__ )
image_processor.save_pretrained(lowerCamelCase__ )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
A_ : List[Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(lowerCamelCase__ )
image_processor.push_to_hub(lowerCamelCase__ )
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
snake_case__ = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 4 | 0 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> list[int]:
A_ : Any = [0] * no_of_processes
A_ : Optional[int] = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(lowerCamelCase__ ):
A_ : Optional[Any] = burst_time[i]
A_ : list[int] = []
A_ : Any = 0
A_ : Optional[Any] = 0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
A_ : List[Any] = []
A_ : Optional[Any] = -1
for i in range(lowerCamelCase__ ):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(lowerCamelCase__ )
if len(lowerCamelCase__ ) > 0:
A_ : str = ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
A_ : Tuple = i
total_time += burst_time[target_process]
completed += 1
A_ : List[Any] = 0
A_ : Any = (
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : int , lowerCamelCase__ : list[int] ) -> list[int]:
A_ : Any = [0] * no_of_processes
for i in range(lowerCamelCase__ ):
A_ : Optional[Any] = burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print("""[TEST CASE 01]""")
snake_case__ = 4
snake_case__ = [2, 5, 3, 7]
snake_case__ = [0, 0, 0, 0]
snake_case__ = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
snake_case__ = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
for i, process_id in enumerate(list(range(1, 5))):
print(
F'{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'
F'{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'
)
print(F'\nAverage waiting time = {mean(waiting_time):.5f}')
print(F'Average turnaround time = {mean(turn_around_time):.5f}')
| 351 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
snake_case__ = logging.getLogger(__name__)
@dataclass(frozen=a__ )
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
@dataclass(frozen=a__ )
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 42
def __init__( self : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : PreTrainedTokenizer , _lowerCamelCase : str , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : List[Any]=False , _lowerCamelCase : bool = False , ):
"""simple docstring"""
A_ : Optional[int] = hans_processors[task]()
A_ : int = os.path.join(
_lowerCamelCase , '''cached_{}_{}_{}_{}'''.format(
'''dev''' if evaluate else '''train''' , tokenizer.__class__.__name__ , str(_lowerCamelCase ) , _lowerCamelCase , ) , )
A_ : Dict = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
A_ ,A_ : List[str] = label_list[2], label_list[1]
A_ : Optional[int] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
A_ : str = cached_features_file + '''.lock'''
with FileLock(_lowerCamelCase ):
if os.path.exists(_lowerCamelCase ) and not overwrite_cache:
logger.info(f'Loading features from cached file {cached_features_file}' )
A_ : List[str] = torch.load(_lowerCamelCase )
else:
logger.info(f'Creating features from dataset file at {data_dir}' )
A_ : Optional[int] = (
processor.get_dev_examples(_lowerCamelCase ) if evaluate else processor.get_train_examples(_lowerCamelCase )
)
logger.info('''Training examples: %s''' , len(_lowerCamelCase ) )
A_ : Optional[int] = hans_convert_examples_to_features(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
logger.info('''Saving features into cached file %s''' , _lowerCamelCase )
torch.save(self.features , _lowerCamelCase )
def __len__( self : List[str] ):
"""simple docstring"""
return len(self.features )
def __getitem__( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
return self.features[i]
def _a ( self : str ):
"""simple docstring"""
return self.label_list
if is_tf_available():
import tensorflow as tf
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
def __init__( self : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : PreTrainedTokenizer , _lowerCamelCase : str , _lowerCamelCase : Optional[int] = 128 , _lowerCamelCase : Dict=False , _lowerCamelCase : bool = False , ):
"""simple docstring"""
A_ : Optional[int] = hans_processors[task]()
A_ : Optional[int] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
A_ ,A_ : Union[str, Any] = label_list[2], label_list[1]
A_ : Tuple = label_list
A_ : Optional[int] = processor.get_dev_examples(_lowerCamelCase ) if evaluate else processor.get_train_examples(_lowerCamelCase )
A_ : Tuple = hans_convert_examples_to_features(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='''convert examples to features''' ):
if ex_index % 10000 == 0:
logger.info('''Writing example %d of %d''' % (ex_index, len(_lowerCamelCase )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
A_ : List[Any] = tf.data.Dataset.from_generator(
_lowerCamelCase , (
{
'''example_id''': tf.intaa,
'''input_ids''': tf.intaa,
'''attention_mask''': tf.intaa,
'''token_type_ids''': tf.intaa,
},
tf.intaa,
) , (
{
'''example_id''': tf.TensorShape([] ),
'''input_ids''': tf.TensorShape([None, None] ),
'''attention_mask''': tf.TensorShape([None, None] ),
'''token_type_ids''': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def _a ( self : Any ):
"""simple docstring"""
return self.dataset
def __len__( self : Dict ):
"""simple docstring"""
return len(self.features )
def __getitem__( self : Optional[int] , _lowerCamelCase : List[str] ):
"""simple docstring"""
return self.features[i]
def _a ( self : Tuple ):
"""simple docstring"""
return self.label_list
class UpperCamelCase_ (a__ ):
"""simple docstring"""
def _a ( self : List[str] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(_lowerCamelCase , '''heuristics_train_set.txt''' ) ) , '''train''' )
def _a ( self : List[str] , _lowerCamelCase : Tuple ):
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(_lowerCamelCase , '''heuristics_evaluation_set.txt''' ) ) , '''dev''' )
def _a ( self : Any ):
"""simple docstring"""
return ["contradiction", "entailment", "neutral"]
def _a ( self : Optional[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : Tuple = []
for i, line in enumerate(_lowerCamelCase ):
if i == 0:
continue
A_ : str = '''%s-%s''' % (set_type, line[0])
A_ : Optional[Any] = line[5]
A_ : Union[str, Any] = line[6]
A_ : List[str] = line[7][2:] if line[7].startswith('''ex''' ) else line[7]
A_ : str = line[0]
examples.append(InputExample(guid=_lowerCamelCase , text_a=_lowerCamelCase , text_b=_lowerCamelCase , label=_lowerCamelCase , pairID=_lowerCamelCase ) )
return examples
def snake_case__ ( lowerCamelCase__ : List[InputExample] , lowerCamelCase__ : List[str] , lowerCamelCase__ : int , lowerCamelCase__ : PreTrainedTokenizer , ) -> int:
A_ : Union[str, Any] = {label: i for i, label in enumerate(lowerCamelCase__ )}
A_ : Optional[Any] = []
for ex_index, example in tqdm.tqdm(enumerate(lowerCamelCase__ ) , desc='''convert examples to features''' ):
if ex_index % 1_0_0_0_0 == 0:
logger.info('''Writing example %d''' % (ex_index) )
A_ : Optional[int] = tokenizer(
example.text_a , example.text_b , add_special_tokens=lowerCamelCase__ , max_length=lowerCamelCase__ , padding='''max_length''' , truncation=lowerCamelCase__ , return_overflowing_tokens=lowerCamelCase__ , )
A_ : List[str] = label_map[example.label] if example.label in label_map else 0
A_ : Tuple = int(example.pairID )
features.append(InputFeatures(**lowerCamelCase__ , label=lowerCamelCase__ , pairID=lowerCamelCase__ ) )
for i, example in enumerate(examples[:5] ):
logger.info('''*** Example ***''' )
logger.info(f'guid: {example}' )
logger.info(f'features: {features[i]}' )
return features
snake_case__ = {
"""hans""": 3,
}
snake_case__ = {
"""hans""": HansProcessor,
}
| 4 | 0 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : int ):
"""simple docstring"""
A_ : List[str] = torch.nn.Linear(10 , 10 )
A_ : List[Any] = torch.optim.SGD(model.parameters() , 0.1 )
A_ : List[Any] = Accelerator()
A_ : int = accelerator.prepare(_lowerCamelCase )
try:
pickle.loads(pickle.dumps(_lowerCamelCase ) )
except Exception as e:
self.fail(f'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 352 |
'''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
snake_case__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCamelCase_ (datasets.BuilderConfig ):
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = "utf-8"
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = True # deprecated
_lowerCAmelCase = None # deprecated
_lowerCAmelCase = 1_0 << 2_0 # 10MB
_lowerCAmelCase = None
class UpperCamelCase_ (datasets.ArrowBasedBuilder ):
"""simple docstring"""
_lowerCAmelCase = JsonConfig
def _a ( self : int ):
"""simple docstring"""
if self.config.block_size is not None:
logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''' )
A_ : List[Any] = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
'''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''' )
if self.config.newlines_in_values is not None:
raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''' )
return datasets.DatasetInfo(features=self.config.features )
def _a ( self : Any , _lowerCamelCase : List[str] ):
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}' )
A_ : int = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_lowerCamelCase , (str, list, tuple) ):
A_ : Union[str, Any] = data_files
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : List[str] = [files]
A_ : List[Any] = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
A_ : Tuple = []
for split_name, files in data_files.items():
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : int = [files]
A_ : Union[str, Any] = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=_lowerCamelCase , gen_kwargs={'''files''': files} ) )
return splits
def _a ( self : int , _lowerCamelCase : pa.Table ):
"""simple docstring"""
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
A_ : Optional[int] = self.config.features.arrow_schema.field(_lowerCamelCase ).type
A_ : Optional[int] = pa_table.append_column(_lowerCamelCase , pa.array([None] * len(_lowerCamelCase ) , type=_lowerCamelCase ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
A_ : str = table_cast(_lowerCamelCase , self.config.features.arrow_schema )
return pa_table
def _a ( self : List[str] , _lowerCamelCase : int ):
"""simple docstring"""
for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(_lowerCamelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
A_ : int = json.load(_lowerCamelCase )
# We keep only the field we are interested in
A_ : List[str] = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(_lowerCamelCase , (list, tuple) ):
A_ : int = set().union(*[row.keys() for row in dataset] )
A_ : List[str] = {col: [row.get(_lowerCamelCase ) for row in dataset] for col in keys}
else:
A_ : Tuple = dataset
A_ : Dict = pa.Table.from_pydict(_lowerCamelCase )
yield file_idx, self._cast_table(_lowerCamelCase )
# If the file has one json object per line
else:
with open(_lowerCamelCase , '''rb''' ) as f:
A_ : int = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
A_ : int = max(self.config.chunksize // 32 , 16 << 10 )
A_ : int = (
self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
)
while True:
A_ : Any = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(_lowerCamelCase )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
A_ : Optional[Any] = batch.decode(self.config.encoding , errors=_lowerCamelCase ).encode('''utf-8''' )
try:
while True:
try:
A_ : List[Any] = paj.read_json(
io.BytesIO(_lowerCamelCase ) , read_options=paj.ReadOptions(block_size=_lowerCamelCase ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(_lowerCamelCase , pa.ArrowInvalid )
and "straddling" not in str(_lowerCamelCase )
or block_size > len(_lowerCamelCase )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f'Batch of {len(_lowerCamelCase )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.' )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
_lowerCamelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
A_ : Optional[Any] = json.load(_lowerCamelCase )
except json.JSONDecodeError:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(_lowerCamelCase , _lowerCamelCase ): # list is the only sequence type supported in JSON
try:
A_ : Optional[int] = set().union(*[row.keys() for row in dataset] )
A_ : Tuple = {col: [row.get(_lowerCamelCase ) for row in dataset] for col in keys}
A_ : int = pa.Table.from_pydict(_lowerCamelCase )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise ValueError(f'Not able to read records in the JSON file at {file}.' ) from None
yield file_idx, self._cast_table(_lowerCamelCase )
break
else:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise ValueError(
f'Not able to read records in the JSON file at {file}. '
f'You should probably indicate the field of the JSON file containing your records. '
f'This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '
f'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ' ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(_lowerCamelCase )
batch_idx += 1
| 4 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
"""RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
"""RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
"""RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
"""RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'rwkv'
_lowerCAmelCase = {'max_position_embeddings': 'context_length'}
def __init__( self : Tuple , _lowerCamelCase : str=50277 , _lowerCamelCase : Tuple=1024 , _lowerCamelCase : Optional[int]=4096 , _lowerCamelCase : Tuple=32 , _lowerCamelCase : List[str]=None , _lowerCamelCase : str=None , _lowerCamelCase : Tuple=1E-5 , _lowerCamelCase : Optional[Any]=0 , _lowerCamelCase : Dict=0 , _lowerCamelCase : Dict=6 , _lowerCamelCase : List[Any]=False , _lowerCamelCase : List[Any]=True , **_lowerCamelCase : List[str] , ):
"""simple docstring"""
A_ : Optional[int] = vocab_size
A_ : List[Any] = context_length
A_ : Optional[int] = hidden_size
A_ : Union[str, Any] = num_hidden_layers
A_ : Optional[int] = attention_hidden_size if attention_hidden_size is not None else hidden_size
A_ : Union[str, Any] = intermediate_size if intermediate_size is not None else 4 * hidden_size
A_ : str = layer_norm_epsilon
A_ : Dict = rescale_every
A_ : Union[str, Any] = use_cache
A_ : Dict = bos_token_id
A_ : Dict = eos_token_id
super().__init__(
tie_word_embeddings=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase )
| 353 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/swin-tiny-patch4-window7-224""": (
"""https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class UpperCamelCase_ (a__, a__ ):
"""simple docstring"""
_lowerCAmelCase = 'swin'
_lowerCAmelCase = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Any , _lowerCamelCase : Optional[Any]=224 , _lowerCamelCase : List[str]=4 , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Tuple=96 , _lowerCamelCase : List[Any]=[2, 2, 6, 2] , _lowerCamelCase : List[str]=[3, 6, 12, 24] , _lowerCamelCase : List[Any]=7 , _lowerCamelCase : Optional[int]=4.0 , _lowerCamelCase : List[str]=True , _lowerCamelCase : List[str]=0.0 , _lowerCamelCase : Any=0.0 , _lowerCamelCase : Dict=0.1 , _lowerCamelCase : List[str]="gelu" , _lowerCamelCase : Tuple=False , _lowerCamelCase : Dict=0.02 , _lowerCamelCase : Optional[Any]=1E-5 , _lowerCamelCase : Any=32 , _lowerCamelCase : Tuple=None , _lowerCamelCase : Any=None , **_lowerCamelCase : str , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
A_ : Optional[int] = image_size
A_ : Optional[int] = patch_size
A_ : Optional[int] = num_channels
A_ : Any = embed_dim
A_ : List[Any] = depths
A_ : Any = len(_lowerCamelCase )
A_ : List[Any] = num_heads
A_ : Tuple = window_size
A_ : Tuple = mlp_ratio
A_ : Dict = qkv_bias
A_ : List[str] = hidden_dropout_prob
A_ : List[str] = attention_probs_dropout_prob
A_ : Any = drop_path_rate
A_ : List[Any] = hidden_act
A_ : Tuple = use_absolute_embeddings
A_ : int = layer_norm_eps
A_ : Optional[Any] = initializer_range
A_ : Union[str, Any] = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
A_ : str = int(embed_dim * 2 ** (len(_lowerCamelCase ) - 1) )
A_ : str = ['''stem'''] + [f'stage{idx}' for idx in range(1 , len(_lowerCamelCase ) + 1 )]
A_ ,A_ : Optional[Any] = get_aligned_output_features_output_indices(
out_features=_lowerCamelCase , out_indices=_lowerCamelCase , stage_names=self.stage_names )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : str ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return 1E-4
| 4 | 0 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
snake_case__ = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class UpperCamelCase_ (a__ ):
"""simple docstring"""
def __init__( self : List[Any] , *_lowerCamelCase : int , _lowerCamelCase : Tuple=None , _lowerCamelCase : List[str]=None , _lowerCamelCase : Dict=None , **_lowerCamelCase : int ):
"""simple docstring"""
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
A_ : Optional[int] = eval_examples
A_ : Any = post_process_function
A_ : int = quant_trainer_args
A_ : str = 128 # default number of calibration samples
def _a ( self : int , _lowerCamelCase : str=None ):
"""simple docstring"""
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
A_ : List[str] = calib_dataset if calib_dataset is not None else self.calib_dataset
A_ : Optional[int] = self._remove_unused_columns(_lowerCamelCase , description='''Calibration''' )
return DataLoader(
_lowerCamelCase , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=_lowerCamelCase , )
def _a ( self : List[Any] , _lowerCamelCase : Union[str, Any]=None ):
"""simple docstring"""
A_ : Dict = self.train_dataset if calib_dataset is None else calib_dataset
A_ : Optional[Any] = self.get_calib_dataloader(_lowerCamelCase )
A_ : str = self.model
quant_trainer.configure_model(_lowerCamelCase , self.quant_trainer_args , calib=_lowerCamelCase )
model.eval()
quant_trainer.enable_calibration(_lowerCamelCase )
logger.info('''***** Running calibration *****''' )
logger.info(f' Num examples = {self.calib_num}' )
logger.info(f' Batch size = {calib_dataloader.batch_size}' )
for step, inputs in enumerate(_lowerCamelCase ):
# Prediction step
A_ : Union[str, Any] = self.prediction_step(_lowerCamelCase , _lowerCamelCase , prediction_loss_only=_lowerCamelCase )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(_lowerCamelCase , self.quant_trainer_args )
A_ : int = model
def _a ( self : Any , _lowerCamelCase : List[Any]=None , _lowerCamelCase : Tuple=None , _lowerCamelCase : Union[str, Any]=None , _lowerCamelCase : str = "eval" ):
"""simple docstring"""
A_ : List[Any] = self.eval_dataset if eval_dataset is None else eval_dataset
A_ : Optional[int] = self.get_eval_dataloader(_lowerCamelCase )
A_ : List[Any] = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
A_ : Tuple = self.compute_metrics
A_ : Optional[Any] = None
A_ : str = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
A_ : List[Any] = eval_loop(
_lowerCamelCase , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_lowerCamelCase , )
finally:
A_ : List[str] = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
A_ : Dict = self.post_process_function(_lowerCamelCase , _lowerCamelCase , output.predictions )
A_ : Tuple = self.compute_metrics(_lowerCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f'{metric_key_prefix}_' ):
A_ : str = metrics.pop(_lowerCamelCase )
self.log(_lowerCamelCase )
else:
A_ : Tuple = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
A_ : Optional[Any] = self.callback_handler.on_evaluate(self.args , self.state , self.control , _lowerCamelCase )
return metrics
def _a ( self : List[Any] , _lowerCamelCase : int , _lowerCamelCase : Dict , _lowerCamelCase : Optional[int]=None , _lowerCamelCase : str = "test" ):
"""simple docstring"""
A_ : int = self.get_test_dataloader(_lowerCamelCase )
# Temporarily disable metric computation, we will do it in the loop here.
A_ : Optional[int] = self.compute_metrics
A_ : int = None
A_ : Union[str, Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
A_ : Any = eval_loop(
_lowerCamelCase , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_lowerCamelCase , )
finally:
A_ : Optional[Any] = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
A_ : List[Any] = self.post_process_function(_lowerCamelCase , _lowerCamelCase , output.predictions , '''predict''' )
A_ : List[str] = self.compute_metrics(_lowerCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f'{metric_key_prefix}_' ):
A_ : Any = metrics.pop(_lowerCamelCase )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_lowerCamelCase )
def _a ( self : List[Any] , _lowerCamelCase : Optional[int]="./" ):
"""simple docstring"""
A_ : Optional[int] = self.eval_dataset
A_ : Any = self.get_eval_dataloader(_lowerCamelCase )
A_ : Any = next(iter(_lowerCamelCase ) )
# saving device - to make it consistent
A_ : Optional[int] = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
A_ : List[Any] = tuple(v.to(_lowerCamelCase ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
A_ : int = True
A_ : List[Any] = self.model.to(_lowerCamelCase )
model.eval()
model.float()
A_ : Dict = model.module if hasattr(_lowerCamelCase , '''module''' ) else model
quant_trainer.configure_model(_lowerCamelCase , self.quant_trainer_args )
A_ : Any = os.path.join(_lowerCamelCase , '''model.onnx''' )
logger.info(f'exporting model to {output_model_file}' )
A_ : str = {0: '''batch_size''', 1: '''seq_len'''}
torch.onnx.export(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , export_params=_lowerCamelCase , opset_version=13 , do_constant_folding=_lowerCamelCase , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=_lowerCamelCase , )
logger.info('''onnx export finished''' )
| 354 |
'''simple docstring'''
from __future__ import annotations
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> list[int]:
A_ : int = 0
A_ : str = len(lowerCamelCase__ ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
A_ : Tuple = i + 1
else:
A_ : List[str] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{two_pointer([2, 7, 11, 15], 9) = }')
| 4 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = LEDConfig
_lowerCAmelCase = {}
_lowerCAmelCase = 'gelu'
def __init__( self : List[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : List[Any]=13 , _lowerCamelCase : Dict=7 , _lowerCamelCase : Dict=True , _lowerCamelCase : int=False , _lowerCamelCase : Optional[Any]=99 , _lowerCamelCase : List[Any]=32 , _lowerCamelCase : List[Any]=2 , _lowerCamelCase : Optional[int]=4 , _lowerCamelCase : Optional[int]=37 , _lowerCamelCase : str=0.1 , _lowerCamelCase : Union[str, Any]=0.1 , _lowerCamelCase : List[str]=20 , _lowerCamelCase : int=2 , _lowerCamelCase : Optional[Any]=1 , _lowerCamelCase : Tuple=0 , _lowerCamelCase : Any=4 , ):
"""simple docstring"""
A_ : List[Any] = parent
A_ : Tuple = batch_size
A_ : List[Any] = seq_length
A_ : List[Any] = is_training
A_ : str = use_labels
A_ : List[str] = vocab_size
A_ : Union[str, Any] = hidden_size
A_ : List[str] = num_hidden_layers
A_ : List[Any] = num_attention_heads
A_ : Optional[Any] = intermediate_size
A_ : str = hidden_dropout_prob
A_ : Any = attention_probs_dropout_prob
A_ : Optional[Any] = max_position_embeddings
A_ : List[Any] = eos_token_id
A_ : str = pad_token_id
A_ : Tuple = bos_token_id
A_ : List[Any] = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
A_ : Optional[Any] = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
A_ : Optional[int] = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[str] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
A_ : Any = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
A_ : Union[str, Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
A_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ : 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 , attention_window=self.attention_window , **self.config_updates , )
A_ : Dict = prepare_led_inputs_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
A_ : Optional[Any] = tf.concat(
[tf.zeros_like(_lowerCamelCase )[:, :-1], tf.ones_like(_lowerCamelCase )[:, -1:]] , axis=-1 , )
A_ : Optional[int] = global_attention_mask
return config, inputs_dict
def _a ( self : Optional[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : Optional[int] = TFLEDModel(config=_lowerCamelCase ).get_decoder()
A_ : str = inputs_dict['''input_ids''']
A_ : Optional[Any] = input_ids[:1, :]
A_ : Optional[Any] = inputs_dict['''attention_mask'''][:1, :]
A_ : List[Any] = 1
# first forward pass
A_ : Optional[int] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , use_cache=_lowerCamelCase )
A_ : List[str] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
A_ : List[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
A_ : Any = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
A_ : List[Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
A_ : Tuple = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
A_ : Optional[int] = model(_lowerCamelCase , attention_mask=_lowerCamelCase )[0]
A_ : Dict = model(_lowerCamelCase , attention_mask=_lowerCamelCase , past_key_values=_lowerCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
A_ : List[str] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
A_ : List[str] = output_from_no_past[:, -3:, random_slice_idx]
A_ : List[str] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_lowerCamelCase , _lowerCamelCase , rtol=1E-3 )
def snake_case__ ( lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Dict , lowerCamelCase__ : Tuple , lowerCamelCase__ : int=None , lowerCamelCase__ : int=None , lowerCamelCase__ : List[Any]=None , lowerCamelCase__ : Dict=None , ) -> Dict:
if attention_mask is None:
A_ : List[str] = tf.cast(tf.math.not_equal(lowerCamelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
A_ : List[str] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
A_ : Union[str, Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
A_ : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class UpperCamelCase_ (a__, a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
_lowerCAmelCase = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
_lowerCAmelCase = (
{
'conversational': TFLEDForConditionalGeneration,
'feature-extraction': TFLEDModel,
'summarization': TFLEDForConditionalGeneration,
'text2text-generation': TFLEDForConditionalGeneration,
'translation': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
_lowerCAmelCase = True
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Tuple = TFLEDModelTester(self )
A_ : Tuple = ConfigTester(self , config_class=_lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : Any ):
"""simple docstring"""
A_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : str = tf.zeros_like(inputs_dict['''attention_mask'''] )
A_ : str = 2
A_ : Dict = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['''global_attention_mask'''] , )
A_ : str = True
A_ : Dict = self.model_tester.seq_length
A_ : Dict = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_lowerCamelCase : Union[str, Any] ):
A_ : str = outputs.decoder_attentions
self.assertEqual(len(_lowerCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(_lowerCamelCase : Union[str, Any] ):
A_ : Optional[int] = [t.numpy() for t in outputs.encoder_attentions]
A_ : Any = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_lowerCamelCase ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(_lowerCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
A_ : Optional[int] = True
A_ : str = False
A_ : Optional[Any] = False
A_ : List[Any] = model_class(_lowerCamelCase )
A_ : str = model(self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
A_ : int = len(_lowerCamelCase )
self.assertEqual(config.output_hidden_states , _lowerCamelCase )
check_encoder_attentions_output(_lowerCamelCase )
if self.is_encoder_decoder:
A_ : str = model_class(_lowerCamelCase )
A_ : Dict = model(self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
self.assertEqual(config.output_hidden_states , _lowerCamelCase )
check_decoder_attentions_output(_lowerCamelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
A_ : List[Any] = True
A_ : Optional[int] = model_class(_lowerCamelCase )
A_ : Dict = model(self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
self.assertEqual(config.output_hidden_states , _lowerCamelCase )
check_encoder_attentions_output(_lowerCamelCase )
# Check attention is always last and order is fine
A_ : Tuple = True
A_ : Dict = True
A_ : Union[str, Any] = model_class(_lowerCamelCase )
A_ : List[str] = model(self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_lowerCamelCase ) )
self.assertEqual(model.config.output_hidden_states , _lowerCamelCase )
check_encoder_attentions_output(_lowerCamelCase )
@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''' )
def _a ( self : int ):
"""simple docstring"""
pass
def _a ( self : Optional[int] ):
"""simple docstring"""
pass
def snake_case__ ( lowerCamelCase__ : List[str] ) -> Dict:
return tf.constant(lowerCamelCase__ , dtype=tf.intaa )
snake_case__ = 1e-4
@slow
@require_tf
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : List[Any] = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ).led
# change to intended input here
A_ : Any = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
A_ : List[Any] = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
A_ : List[Any] = prepare_led_inputs_dict(model.config , _lowerCamelCase , _lowerCamelCase )
A_ : Tuple = model(**_lowerCamelCase )[0]
A_ : Dict = (1, 1024, 768)
self.assertEqual(output.shape , _lowerCamelCase )
# change to expected output here
A_ : Optional[Any] = tf.convert_to_tensor(
[[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]] , )
tf.debugging.assert_near(output[:, :3, :3] , _lowerCamelCase , atol=1E-3 )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' )
# change to intended input here
A_ : Optional[Any] = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
A_ : List[Any] = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
A_ : Union[str, Any] = prepare_led_inputs_dict(model.config , _lowerCamelCase , _lowerCamelCase )
A_ : Any = model(**_lowerCamelCase )[0]
A_ : Optional[int] = (1, 1024, model.config.vocab_size)
self.assertEqual(output.shape , _lowerCamelCase )
# change to expected output here
A_ : int = tf.convert_to_tensor(
[[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]] , )
tf.debugging.assert_near(output[:, :3, :3] , _lowerCamelCase , atol=1E-3 , rtol=1E-3 )
| 355 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCamelCase__ ) )
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
# Base Case
if index == len(lowerCamelCase__ ):
return True
# Recursive Step
for i in range(lowerCamelCase__ ):
if valid_coloring(graph[index] , lowerCamelCase__ , lowerCamelCase__ ):
# Color current vertex
A_ : int = i
# Validate coloring
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , index + 1 ):
return True
# Backtrack
A_ : str = -1
return False
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int ) -> list[int]:
A_ : List[str] = [-1] * len(lowerCamelCase__ )
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , 0 ):
return colored_vertices
return []
| 4 | 0 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : int = 1_0_0_0 ) -> int:
A_ : int = 1, 1
A_ : Dict = 2
while True:
A_ : List[Any] = 0
A_ : str = fa + fa
A_ : str = fa, f
index += 1
for _ in str(lowerCamelCase__ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 356 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case__ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[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],
]
# Define blinker example
snake_case__ = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def snake_case__ ( lowerCamelCase__ : list[list[int]] ) -> list[list[int]]:
A_ : str = []
for i in range(len(lowerCamelCase__ ) ):
A_ : Optional[Any] = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
A_ : Optional[int] = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(lowerCamelCase__ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(lowerCamelCase__ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(lowerCamelCase__ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
A_ : List[str] = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(lowerCamelCase__ )
return next_generation
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int ) -> list[Image.Image]:
A_ : List[Any] = []
for _ in range(lowerCamelCase__ ):
# Create output image
A_ : Optional[int] = Image.new('''RGB''' , (len(cells[0] ), len(lowerCamelCase__ )) )
A_ : int = img.load()
# Save cells to image
for x in range(len(lowerCamelCase__ ) ):
for y in range(len(cells[0] ) ):
A_ : Optional[Any] = 2_5_5 - cells[y][x] * 2_5_5
A_ : str = (colour, colour, colour)
# Save image
images.append(lowerCamelCase__ )
A_ : Optional[int] = new_generation(lowerCamelCase__ )
return images
if __name__ == "__main__":
snake_case__ = generate_images(GLIDER, 16)
images[0].save("""out.gif""", save_all=True, append_images=images[1:])
| 4 | 0 |
'''simple docstring'''
from typing import List
import numpy as np
def snake_case__ ( lowerCamelCase__ : dict ) -> int:
A_ : Tuple = {key: len(lowerCamelCase__ ) for key, value in gen_kwargs.items() if isinstance(lowerCamelCase__ , lowerCamelCase__ )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
'''Sharding is ambiguous for this dataset: '''
+ '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
+ '''\n'''.join(f'\t- key {key} has length {length}' for key, length in lists_lengths.items() )
+ '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
+ '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
) )
A_ : Optional[Any] = max(lists_lengths.values() , default=0 )
return max(1 , lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : int ) -> List[range]:
A_ : str = []
for group_idx in range(lowerCamelCase__ ):
A_ : List[Any] = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
A_ : Union[str, Any] = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
A_ : int = range(lowerCamelCase__ , start + num_shards_to_add )
shards_indices_per_group.append(lowerCamelCase__ )
return shards_indices_per_group
def snake_case__ ( lowerCamelCase__ : dict , lowerCamelCase__ : int ) -> List[dict]:
A_ : Optional[int] = _number_of_shards_in_gen_kwargs(lowerCamelCase__ )
if num_shards == 1:
return [dict(lowerCamelCase__ )]
else:
A_ : List[Any] = _distribute_shards(num_shards=lowerCamelCase__ , max_num_jobs=lowerCamelCase__ )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(lowerCamelCase__ , lowerCamelCase__ )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(lowerCamelCase__ ) )
]
def snake_case__ ( lowerCamelCase__ : List[dict] ) -> dict:
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , lowerCamelCase__ )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def snake_case__ ( lowerCamelCase__ : np.random.Generator , lowerCamelCase__ : dict ) -> dict:
A_ : Union[str, Any] = {len(lowerCamelCase__ ) for value in gen_kwargs.values() if isinstance(lowerCamelCase__ , lowerCamelCase__ )}
A_ : Tuple = {}
for size in list_sizes:
A_ : str = list(range(lowerCamelCase__ ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
A_ : Optional[Any] = dict(lowerCamelCase__ )
for key, value in shuffled_kwargs.items():
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
A_ : int = [value[i] for i in indices_per_size[len(lowerCamelCase__ )]]
return shuffled_kwargs
| 357 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Any = tempfile.mkdtemp()
A_ : List[Any] = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
A_ : 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] ) )
A_ : Tuple = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
A_ : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Dict , **_lowerCamelCase : Tuple ):
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _a ( self : Optional[int] , **_lowerCamelCase : Optional[int] ):
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _a ( self : Optional[Any] , **_lowerCamelCase : Tuple ):
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self : int ):
"""simple docstring"""
A_ : Union[str, Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A_ : Any = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self : int ):
"""simple docstring"""
A_ : Tuple = self.get_tokenizer()
A_ : Tuple = self.get_rust_tokenizer()
A_ : Dict = self.get_image_processor()
A_ : List[Any] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor_slow.save_pretrained(self.tmpdirname )
A_ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_lowerCamelCase )
A_ : Any = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor_fast.save_pretrained(self.tmpdirname )
A_ : List[Any] = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _lowerCamelCase )
self.assertIsInstance(processor_fast.tokenizer , _lowerCamelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _lowerCamelCase )
self.assertIsInstance(processor_fast.image_processor , _lowerCamelCase )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[str] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A_ : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
A_ : Tuple = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
A_ : List[str] = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = self.get_image_processor()
A_ : Any = self.get_tokenizer()
A_ : List[str] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : Any = self.prepare_image_inputs()
A_ : List[Any] = image_processor(_lowerCamelCase , return_tensors='''np''' )
A_ : str = processor(images=_lowerCamelCase , 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 : Dict ):
"""simple docstring"""
A_ : str = self.get_image_processor()
A_ : List[str] = self.get_tokenizer()
A_ : Optional[int] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : int = '''lower newer'''
A_ : str = processor(text=_lowerCamelCase )
A_ : Dict = tokenizer(_lowerCamelCase , padding='''max_length''' , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self : str ):
"""simple docstring"""
A_ : Optional[int] = self.get_image_processor()
A_ : Optional[Any] = self.get_tokenizer()
A_ : List[str] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : List[Any] = '''lower newer'''
A_ : Optional[int] = self.prepare_image_inputs()
A_ : List[Any] = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(_lowerCamelCase ):
processor()
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Optional[Any] = self.get_image_processor()
A_ : Optional[int] = self.get_tokenizer()
A_ : List[Any] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : str = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A_ : str = processor.batch_decode(_lowerCamelCase )
A_ : Union[str, Any] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : str = self.get_image_processor()
A_ : Tuple = self.get_tokenizer()
A_ : Any = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : str = '''lower newer'''
A_ : List[str] = self.prepare_image_inputs()
A_ : Tuple = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 4 | 0 |
'''simple docstring'''
from __future__ import annotations
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[int] , _lowerCamelCase : int ):
"""simple docstring"""
A_ : Union[str, Any] = order
# a_{0} ... a_{k}
A_ : Union[str, Any] = [1.0] + [0.0] * order
# b_{0} ... b_{k}
A_ : int = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
A_ : str = [0.0] * self.order
# y[n-1] ... y[n-k]
A_ : Optional[Any] = [0.0] * self.order
def _a ( self : Dict , _lowerCamelCase : list[float] , _lowerCamelCase : list[float] ):
"""simple docstring"""
if len(_lowerCamelCase ) < self.order:
A_ : Any = [1.0, *a_coeffs]
if len(_lowerCamelCase ) != self.order + 1:
A_ : List[Any] = (
f'Expected a_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(_lowerCamelCase )}'
)
raise ValueError(_lowerCamelCase )
if len(_lowerCamelCase ) != self.order + 1:
A_ : Union[str, Any] = (
f'Expected b_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(_lowerCamelCase )}'
)
raise ValueError(_lowerCamelCase )
A_ : Tuple = a_coeffs
A_ : str = b_coeffs
def _a ( self : Tuple , _lowerCamelCase : float ):
"""simple docstring"""
A_ : Any = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
A_ : str = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
A_ : Optional[Any] = self.input_history[:-1]
A_ : List[str] = self.output_history[:-1]
A_ : Tuple = sample
A_ : Tuple = result
return result
| 358 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = """▁"""
snake_case__ = {
"""vocab_file""": """vocab.json""",
"""spm_file""": """sentencepiece.bpe.model""",
}
snake_case__ = {
"""vocab_file""": {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
),
},
"""spm_file""": {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
)
},
}
snake_case__ = {
"""facebook/s2t-small-librispeech-asr""": 10_24,
}
snake_case__ = ["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]
snake_case__ = {"""mustc""": MUSTC_LANGS}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = VOCAB_FILES_NAMES
_lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase = MAX_MODEL_INPUT_SIZES
_lowerCAmelCase = ['input_ids', 'attention_mask']
_lowerCAmelCase = []
def __init__( self : Optional[int] , _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : str="<s>" , _lowerCamelCase : Union[str, Any]="</s>" , _lowerCamelCase : Dict="<pad>" , _lowerCamelCase : str="<unk>" , _lowerCamelCase : Union[str, Any]=False , _lowerCamelCase : int=False , _lowerCamelCase : Any=None , _lowerCamelCase : Any=None , _lowerCamelCase : Optional[Dict[str, Any]] = None , **_lowerCamelCase : Optional[int] , ):
"""simple docstring"""
A_ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , do_upper_case=_lowerCamelCase , do_lower_case=_lowerCamelCase , tgt_lang=_lowerCamelCase , lang_codes=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
A_ : Optional[int] = do_upper_case
A_ : Tuple = do_lower_case
A_ : Tuple = load_json(_lowerCamelCase )
A_ : Tuple = {v: k for k, v in self.encoder.items()}
A_ : List[Any] = spm_file
A_ : List[str] = load_spm(_lowerCamelCase , self.sp_model_kwargs )
if lang_codes is not None:
A_ : Any = lang_codes
A_ : Optional[Any] = LANGUAGES[lang_codes]
A_ : Optional[Any] = [f'<lang:{lang}>' for lang in self.langs]
A_ : Union[str, Any] = {lang: self.sp_model.PieceToId(f'<lang:{lang}>' ) for lang in self.langs}
A_ : Optional[int] = self.lang_tokens
A_ : int = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
A_ : Dict = {}
@property
def _a ( self : Tuple ):
"""simple docstring"""
return len(self.encoder )
@property
def _a ( self : int ):
"""simple docstring"""
return self._tgt_lang
@tgt_lang.setter
def _a ( self : List[str] , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : int = new_tgt_lang
self.set_tgt_lang_special_tokens(_lowerCamelCase )
def _a ( self : Tuple , _lowerCamelCase : str ):
"""simple docstring"""
A_ : List[str] = self.lang_code_to_id[tgt_lang]
A_ : Optional[Any] = [lang_code_id]
def _a ( self : Optional[Any] , _lowerCamelCase : str ):
"""simple docstring"""
return self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
def _a ( self : List[Any] , _lowerCamelCase : int ):
"""simple docstring"""
return self.encoder.get(_lowerCamelCase , self.encoder[self.unk_token] )
def _a ( self : int , _lowerCamelCase : int ):
"""simple docstring"""
return self.decoder.get(_lowerCamelCase , self.unk_token )
def _a ( self : int , _lowerCamelCase : List[str] ):
"""simple docstring"""
A_ : List[Any] = []
A_ : Any = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
A_ : Union[str, Any] = self.sp_model.decode(_lowerCamelCase )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
A_ : Optional[Any] = []
else:
current_sub_tokens.append(_lowerCamelCase )
A_ : Tuple = self.sp_model.decode(_lowerCamelCase )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def _a ( self : int , _lowerCamelCase : Dict , _lowerCamelCase : Any=None ):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def _a ( self : List[Any] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None , _lowerCamelCase : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
A_ : Tuple = [1] * len(self.prefix_tokens )
A_ : Tuple = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(_lowerCamelCase )) + suffix_ones
return prefix_ones + ([0] * len(_lowerCamelCase )) + ([0] * len(_lowerCamelCase )) + suffix_ones
def _a ( self : Dict ):
"""simple docstring"""
A_ : Union[str, Any] = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = self.__dict__.copy()
A_ : List[Any] = None
return state
def __setstate__( self : List[str] , _lowerCamelCase : Dict ):
"""simple docstring"""
A_ : Dict = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
A_ : Optional[int] = {}
A_ : int = load_spm(self.spm_file , self.sp_model_kwargs )
def _a ( self : Optional[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[str] = None ):
"""simple docstring"""
A_ : Dict = Path(_lowerCamelCase )
assert save_dir.is_dir(), f'{save_directory} should be a directory'
A_ : Optional[int] = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
A_ : Optional[int] = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder , _lowerCamelCase )
if os.path.abspath(self.spm_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , _lowerCamelCase )
elif not os.path.isfile(self.spm_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
A_ : List[str] = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (str(_lowerCamelCase ), str(_lowerCamelCase ))
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
A_ : Tuple = sentencepiece.SentencePieceProcessor(**lowerCamelCase__ )
spm.Load(str(lowerCamelCase__ ) )
return spm
def snake_case__ ( lowerCamelCase__ : str ) -> Union[Dict, List]:
with open(lowerCamelCase__ , '''r''' ) as f:
return json.load(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : str ) -> None:
with open(lowerCamelCase__ , '''w''' ) as f:
json.dump(lowerCamelCase__ , lowerCamelCase__ , indent=2 )
| 4 | 0 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list ) -> list:
A_ : str = len(lowerCamelCase__ )
for i in range(1 , lowerCamelCase__ ):
A_ : List[Any] = collection[i]
A_ : int = 0
A_ : str = i - 1
while low <= high:
A_ : Optional[int] = (low + high) // 2
if val < collection[mid]:
A_ : Any = mid - 1
else:
A_ : List[Any] = mid + 1
for j in range(lowerCamelCase__ , lowerCamelCase__ , -1 ):
A_ : Optional[int] = collection[j - 1]
A_ : Any = val
return collection
if __name__ == "__main__":
snake_case__ = input("""Enter numbers separated by a comma:\n""").strip()
snake_case__ = [int(item) for item in user_input.split(""",""")]
print(binary_insertion_sort(unsorted))
| 359 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
snake_case__ = sys.version_info >= (3, 10)
def snake_case__ ( lowerCamelCase__ : Union[str, Any]=None , lowerCamelCase__ : str=None ) -> List[Any]:
return field(default_factory=lambda: default , metadata=lowerCamelCase__ )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 4_2
_lowerCAmelCase = field(default='toto', metadata={'help': 'help message'} )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = False
_lowerCAmelCase = True
_lowerCAmelCase = None
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'titi'
_lowerCAmelCase = 'toto'
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'titi'
_lowerCAmelCase = 'toto'
_lowerCAmelCase = 4_2
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Optional[int] = BasicEnum(self.foo )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[Any] = MixedTypeEnum(self.foo )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = field(default=a__, metadata={'help': 'help message'} )
_lowerCAmelCase = None
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[] )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[1, 2, 3] )
_lowerCAmelCase = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
_lowerCAmelCase = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = field()
_lowerCAmelCase = field()
_lowerCAmelCase = field()
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = BasicEnum(self.required_enum )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = field()
_lowerCAmelCase = None
_lowerCAmelCase = field(default='toto', metadata={'help': 'help message'} )
_lowerCAmelCase = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
if is_python_no_less_than_3_10:
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = False
_lowerCAmelCase = True
_lowerCAmelCase = None
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = field(default=a__, metadata={'help': 'help message'} )
_lowerCAmelCase = None
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[] )
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] , _lowerCamelCase : argparse.ArgumentParser , _lowerCamelCase : argparse.ArgumentParser ):
"""simple docstring"""
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
A_ : Union[str, Any] = {k: v for k, v in vars(_lowerCamelCase ).items() if k != '''container'''}
A_ : Optional[Any] = {k: v for k, v in vars(_lowerCamelCase ).items() if k != '''container'''}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('''choices''' , _lowerCamelCase ) and yy.get('''choices''' , _lowerCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['''type'''](_lowerCamelCase ) , yy['''type'''](_lowerCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : Optional[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--bar''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--baz''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--flag''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Union[str, Any] = ['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
((A_) ,) : List[str] = parser.parse_args_into_dataclasses(_lowerCamelCase , look_for_args_file=_lowerCamelCase )
self.assertFalse(example.flag )
def _a ( self : Dict ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : int = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=42 , type=_lowerCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=_lowerCamelCase , help='''help message''' )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Any = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
expected.add_argument('''--baz''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('''--no_baz''' , action='''store_false''' , default=_lowerCamelCase , dest='''baz''' )
expected.add_argument('''--opt''' , type=_lowerCamelCase , default=_lowerCamelCase )
A_ : Dict = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_lowerCamelCase )
for dataclass_type in dataclass_types:
A_ : Any = HfArgumentParser(_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = parser.parse_args([] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : Optional[int] = parser.parse_args(['''--foo''', '''--no_baz'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : Union[str, Any] = parser.parse_args(['''--foo''', '''--baz'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : List[str] = parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : List[Any] = parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : str = HfArgumentParser(_lowerCamelCase )
A_ : Optional[int] = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=['''titi''', '''toto''', 42] , type=make_choice_type_function(['''titi''', '''toto''', 42] ) , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : str = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
A_ : List[Any] = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
A_ : int = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
A_ : Dict = parser.parse_args_into_dataclasses(['''--foo''', '''titi'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
A_ : Tuple = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 42 )
A_ : List[str] = parser.parse_args_into_dataclasses(['''--foo''', '''42'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def _a ( self : Optional[int] ):
"""simple docstring"""
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
A_ : List[str] = HfArgumentParser(_lowerCamelCase )
A_ : Tuple = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=('''titi''', '''toto''', 42) , type=make_choice_type_function(['''titi''', '''toto''', 42] ) , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Tuple = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
A_ : List[str] = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
A_ : int = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 42 )
def _a ( self : Dict ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : List[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo_int''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
expected.add_argument('''--bar_int''' , nargs='''+''' , default=[1, 2, 3] , type=_lowerCamelCase )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=_lowerCamelCase )
expected.add_argument('''--foo_float''' , nargs='''+''' , default=[0.1, 0.2, 0.3] , type=_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[int] = parser.parse_args([] )
self.assertEqual(
_lowerCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['''Hallo''', '''Bonjour''', '''Hello'''] , foo_float=[0.1, 0.2, 0.3] ) , )
A_ : str = parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split() )
self.assertEqual(_lowerCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['''a''', '''b''', '''c'''] , foo_float=[0.1, 0.7] ) )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=_lowerCamelCase , type=_lowerCamelCase )
expected.add_argument('''--bar''' , default=_lowerCamelCase , type=_lowerCamelCase , help='''help message''' )
expected.add_argument('''--baz''' , default=_lowerCamelCase , type=_lowerCamelCase )
expected.add_argument('''--ces''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
expected.add_argument('''--des''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
A_ : Tuple = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_lowerCamelCase )
for dataclass_type in dataclass_types:
A_ : int = HfArgumentParser(_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = parser.parse_args([] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , bar=_lowerCamelCase , baz=_lowerCamelCase , ces=[] , des=[] ) )
A_ : Optional[Any] = parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split() )
self.assertEqual(_lowerCamelCase , Namespace(foo=12 , bar=3.14 , baz='''42''' , ces=['''a''', '''b''', '''c'''] , des=[1, 2, 3] ) )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = HfArgumentParser(_lowerCamelCase )
A_ : Dict = argparse.ArgumentParser()
expected.add_argument('''--required_list''' , nargs='''+''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--required_str''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=_lowerCamelCase , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : List[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=_lowerCamelCase , )
expected.add_argument('''--opt''' , type=_lowerCamelCase , default=_lowerCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=_lowerCamelCase , help='''help message''' )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : List[Any] = HfArgumentParser(_lowerCamelCase )
A_ : Union[str, Any] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
A_ : Optional[int] = parser.parse_dict(_lowerCamelCase )[0]
A_ : str = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Any = HfArgumentParser(_lowerCamelCase )
A_ : List[str] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
'''extra''': 42,
}
self.assertRaises(_lowerCamelCase , parser.parse_dict , _lowerCamelCase , allow_extra_keys=_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : List[str] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : Tuple = os.path.join(_lowerCamelCase , '''temp_json''' )
os.mkdir(_lowerCamelCase )
with open(temp_local_path + '''.json''' , '''w+''' ) as f:
json.dump(_lowerCamelCase , _lowerCamelCase )
A_ : List[str] = parser.parse_yaml_file(Path(temp_local_path + '''.json''' ) )[0]
A_ : Optional[Any] = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : Tuple = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : int = os.path.join(_lowerCamelCase , '''temp_yaml''' )
os.mkdir(_lowerCamelCase )
with open(temp_local_path + '''.yaml''' , '''w+''' ) as f:
yaml.dump(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[Any] = parser.parse_yaml_file(Path(temp_local_path + '''.yaml''' ) )[0]
A_ : int = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = HfArgumentParser(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
| 4 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
snake_case__ = logging.getLogger(__name__)
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}, )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}, )
_lowerCAmelCase = field(
default='main', metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}, )
_lowerCAmelCase = field(
default=a__, metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
}, )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = field(default=a__, metadata={'help': 'The input training data file (a text file).'} )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}, )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Overwrite the cached training and evaluation sets'} )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'The number of processes to use for the preprocessing.'}, )
_lowerCAmelCase = field(
default=a__, metadata={
'help': (
'The maximum total input sequence length after tokenization. If passed, sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
}, )
_lowerCAmelCase = field(
default=a__, metadata={
'help': (
'Whether to pad all samples to the maximum sentence length. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch. More '
'efficient on GPU but very bad for TPU.'
)
}, )
_lowerCAmelCase = field(
default=a__, metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
}, )
_lowerCAmelCase = field(
default=a__, metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
}, )
def _a ( self : str ):
"""simple docstring"""
if self.train_file is not None:
A_ : Any = self.train_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
A_ : str = self.validation_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 4_2
_lowerCAmelCase = True
_lowerCAmelCase = None
_lowerCAmelCase = None
def __call__( self : int , _lowerCamelCase : List[str] ):
"""simple docstring"""
A_ : int = '''label''' if '''label''' in features[0].keys() else '''labels'''
A_ : Any = [feature.pop(_lowerCamelCase ) for feature in features]
A_ : Dict = len(_lowerCamelCase )
A_ : Dict = len(features[0]['''input_ids'''] )
A_ : List[Any] = [
[{k: v[i] for k, v in feature.items()} for i in range(_lowerCamelCase )] for feature in features
]
A_ : Optional[int] = list(chain(*_lowerCamelCase ) )
A_ : Optional[int] = self.tokenizer.pad(
_lowerCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
# Un-flatten
A_ : str = {k: v.view(_lowerCamelCase , _lowerCamelCase , -1 ) for k, v in batch.items()}
# Add back labels
A_ : Optional[Any] = torch.tensor(_lowerCamelCase , dtype=torch.intaa )
return batch
def snake_case__ ( ) -> List[str]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
A_ : Tuple = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
A_ : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
A_ : str = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_swag''' , lowerCamelCase__ , lowerCamelCase__ )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
A_ : Optional[Any] = training_args.get_process_log_level()
logger.setLevel(lowerCamelCase__ )
datasets.utils.logging.set_verbosity(lowerCamelCase__ )
transformers.utils.logging.set_verbosity(lowerCamelCase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
A_ : List[str] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
A_ : Any = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
A_ : Optional[Any] = {}
if data_args.train_file is not None:
A_ : Any = data_args.train_file
if data_args.validation_file is not None:
A_ : Tuple = data_args.validation_file
A_ : Optional[int] = data_args.train_file.split('''.''' )[-1]
A_ : str = load_dataset(
lowerCamelCase__ , data_files=lowerCamelCase__ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
A_ : List[Any] = load_dataset(
'''swag''' , '''regular''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
A_ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
A_ : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
A_ : Optional[Any] = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=lowerCamelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
A_ : str = [f'ending{i}' for i in range(4 )]
A_ : Union[str, Any] = '''sent1'''
A_ : List[Any] = '''sent2'''
if data_args.max_seq_length is None:
A_ : int = tokenizer.model_max_length
if max_seq_length > 1_0_2_4:
logger.warning(
'''The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'''
''' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'''
''' override this default with `--block_size xxx`.''' )
A_ : Tuple = 1_0_2_4
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.' )
A_ : List[Any] = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(lowerCamelCase__ : str ):
A_ : Tuple = [[context] * 4 for context in examples[context_name]]
A_ : Union[str, Any] = examples[question_header_name]
A_ : Optional[Any] = [
[f'{header} {examples[end][i]}' for end in ending_names] for i, header in enumerate(lowerCamelCase__ )
]
# Flatten out
A_ : int = list(chain(*lowerCamelCase__ ) )
A_ : int = list(chain(*lowerCamelCase__ ) )
# Tokenize
A_ : Tuple = tokenizer(
lowerCamelCase__ , lowerCamelCase__ , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , padding='''max_length''' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(lowerCamelCase__ ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('''--do_train requires a train dataset''' )
A_ : Tuple = raw_datasets['''train''']
if data_args.max_train_samples is not None:
A_ : Union[str, Any] = min(len(lowerCamelCase__ ) , data_args.max_train_samples )
A_ : Any = train_dataset.select(range(lowerCamelCase__ ) )
with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
A_ : Optional[Any] = train_dataset.map(
lowerCamelCase__ , batched=lowerCamelCase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('''--do_eval requires a validation dataset''' )
A_ : int = raw_datasets['''validation''']
if data_args.max_eval_samples is not None:
A_ : Optional[Any] = min(len(lowerCamelCase__ ) , data_args.max_eval_samples )
A_ : Any = eval_dataset.select(range(lowerCamelCase__ ) )
with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
A_ : List[str] = eval_dataset.map(
lowerCamelCase__ , batched=lowerCamelCase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
A_ : Optional[Any] = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=lowerCamelCase__ , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(lowerCamelCase__ : List[str] ):
A_ : Any = eval_predictions
A_ : str = np.argmax(lowerCamelCase__ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
A_ : Any = Trainer(
model=lowerCamelCase__ , args=lowerCamelCase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=lowerCamelCase__ , data_collator=lowerCamelCase__ , compute_metrics=lowerCamelCase__ , )
# Training
if training_args.do_train:
A_ : Tuple = None
if training_args.resume_from_checkpoint is not None:
A_ : Any = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
A_ : Any = last_checkpoint
A_ : List[str] = trainer.train(resume_from_checkpoint=lowerCamelCase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
A_ : str = train_result.metrics
A_ : List[str] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase__ )
)
A_ : Dict = min(lowerCamelCase__ , len(lowerCamelCase__ ) )
trainer.log_metrics('''train''' , lowerCamelCase__ )
trainer.save_metrics('''train''' , lowerCamelCase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
A_ : Union[str, Any] = trainer.evaluate()
A_ : Union[str, Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase__ )
A_ : Union[str, Any] = min(lowerCamelCase__ , len(lowerCamelCase__ ) )
trainer.log_metrics('''eval''' , lowerCamelCase__ )
trainer.save_metrics('''eval''' , lowerCamelCase__ )
A_ : Optional[int] = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''multiple-choice''',
'''dataset_tags''': '''swag''',
'''dataset_args''': '''regular''',
'''dataset''': '''SWAG''',
'''language''': '''en''',
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCamelCase__ )
else:
trainer.create_model_card(**lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Dict ) -> Tuple:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 360 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
snake_case__ = get_tests_dir("""fixtures""")
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] ):
"""simple docstring"""
A_ : List[Any] = mock.Mock()
A_ : List[str] = 500
A_ : Tuple = {}
A_ : int = HTTPError
A_ : Optional[Any] = {}
# Download this model to make sure it's in the cache.
A_ : Tuple = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=_lowerCamelCase ) as mock_head:
A_ : List[Any] = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# This check we did call the fake head request
mock_head.assert_called()
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = ViTImageProcessor.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json''' )
def _a ( self : Dict ):
"""simple docstring"""
with self.assertRaises(_lowerCamelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
A_ : Any = AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants''' )
A_ : Tuple = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/stable-diffusion-all-variants''' , subfolder='''feature_extractor''' )
self.assertIsNotNone(_lowerCamelCase )
@is_staging_test
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@classmethod
def _a ( cls : Tuple ):
"""simple docstring"""
A_ : int = TOKEN
HfFolder.save_token(_lowerCamelCase )
@classmethod
def _a ( cls : str ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='''test-image-processor''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-image-processor-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-image-processor''' )
except HTTPError:
pass
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Dict = ViTImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''test-image-processor''' , use_auth_token=self._token )
A_ : Optional[int] = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCamelCase , repo_id='''test-image-processor''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : List[Any] = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : int = ViTImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''valid_org/test-image-processor''' , use_auth_token=self._token )
A_ : List[str] = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCamelCase , repo_id='''valid_org/test-image-processor-org''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : Any = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
CustomImageProcessor.register_for_auto_class()
A_ : Any = CustomImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''test-dynamic-image-processor''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''} , )
A_ : str = AutoImageProcessor.from_pretrained(
f'{USER}/test-dynamic-image-processor' , trust_remote_code=_lowerCamelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , '''CustomImageProcessor''' )
| 4 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase_ (metaclass=a__ ):
"""simple docstring"""
_lowerCAmelCase = ['keras_nlp']
def __init__( self : Optional[Any] , *_lowerCamelCase : Dict , **_lowerCamelCase : List[str] ):
"""simple docstring"""
requires_backends(self , ['''keras_nlp'''] )
| 361 |
'''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.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = (
'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
)
_lowerCAmelCase = 'CIDAS/clipseg-rd64-refined'
_lowerCAmelCase = 'image_segmenter'
_lowerCAmelCase = CLIPSegForImageSegmentation
_lowerCAmelCase = ['image', 'text']
_lowerCAmelCase = ['image']
def __init__( self : Optional[int] , *_lowerCamelCase : Optional[int] , **_lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def _a ( self : List[str] , _lowerCamelCase : "Image" , _lowerCamelCase : str ):
"""simple docstring"""
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def _a ( self : Union[str, Any] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
with torch.no_grad():
A_ : Optional[int] = self.model(**_lowerCamelCase ).logits
return logits
def _a ( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : int = outputs.cpu().detach().numpy()
A_ : Tuple = 0
A_ : List[str] = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 4 | 0 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
snake_case__ = logging.getLogger(__name__)
snake_case__ = """Hello world! cécé herlolip"""
snake_case__ = namedtuple(
"""BertAbsConfig""",
[
"""temp_dir""",
"""large""",
"""use_bert_emb""",
"""finetune_bert""",
"""encoder""",
"""share_emb""",
"""max_pos""",
"""enc_layers""",
"""enc_hidden_size""",
"""enc_heads""",
"""enc_ff_size""",
"""enc_dropout""",
"""dec_layers""",
"""dec_hidden_size""",
"""dec_heads""",
"""dec_ff_size""",
"""dec_dropout""",
],
)
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int] ) -> Tuple:
A_ : str = BertAbsConfig(
temp_dir='''.''' , finetune_bert=lowerCamelCase__ , large=lowerCamelCase__ , share_emb=lowerCamelCase__ , use_bert_emb=lowerCamelCase__ , encoder='''bert''' , max_pos=5_1_2 , enc_layers=6 , enc_hidden_size=5_1_2 , enc_heads=8 , enc_ff_size=5_1_2 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_6_8 , dec_heads=8 , dec_ff_size=2_0_4_8 , dec_dropout=0.2 , )
A_ : List[Any] = torch.load(lowerCamelCase__ , lambda lowerCamelCase__ , lowerCamelCase__ : storage )
A_ : int = AbsSummarizer(lowerCamelCase__ , torch.device('''cpu''' ) , lowerCamelCase__ )
original.eval()
A_ : Tuple = BertAbsSummarizer(lowerCamelCase__ , torch.device('''cpu''' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('''convert the model''' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('''Make sure that the models\' outputs are identical''' )
A_ : int = BertTokenizer.from_pretrained('''bert-base-uncased''' )
# prepare the model inputs
A_ : Optional[int] = tokenizer.encode('''This is sample éàalj\'-.''' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(lowerCamelCase__ )) )
A_ : List[str] = torch.tensor(lowerCamelCase__ ).unsqueeze(0 )
A_ : Dict = tokenizer.encode('''This is sample 3 éàalj\'-.''' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(lowerCamelCase__ )) )
A_ : List[Any] = torch.tensor(lowerCamelCase__ ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
A_ : str = encoder_input_ids
A_ : Dict = decoder_input_ids
A_ : Any = None
A_ : Optional[int] = None
A_ : int = None
A_ : Optional[Any] = None
A_ : Optional[Any] = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
A_ : Union[str, Any] = original(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )[0]
A_ : Optional[int] = original.generator(lowerCamelCase__ )
A_ : Union[str, Any] = new_model(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )[0]
A_ : str = new_model.generator(lowerCamelCase__ )
A_ : Optional[int] = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(lowerCamelCase__ ) )
A_ : Tuple = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(lowerCamelCase__ ) )
A_ : Any = torch.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1e-3 )
if are_identical:
logging.info('''all weights are equal up to 1e-3''' )
else:
raise ValueError('''the weights are different. The new model is likely different from the original one.''' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('''saving the model\'s state dictionary''' )
torch.save(
new_model.state_dict() , '''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin''' )
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
parser.add_argument(
"""--bertabs_checkpoint_path""",
default=None,
type=str,
required=True,
help="""Path the official PyTorch dump.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the output PyTorch model.""",
)
snake_case__ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 362 |
'''simple docstring'''
from collections.abc import Sequence
def snake_case__ ( lowerCamelCase__ : Sequence[float] , lowerCamelCase__ : bool = False ) -> float:
if not arr:
return 0
A_ : Union[str, Any] = 0 if allow_empty_subarrays else float('''-inf''' )
A_ : str = 0.0
for num in arr:
A_ : Any = max(0 if allow_empty_subarrays else num , curr_sum + num )
A_ : Tuple = max(lowerCamelCase__ , lowerCamelCase__ )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
snake_case__ = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F'{max_subarray_sum(nums) = }')
| 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ = {"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = ["""PLBartTokenizer"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = [
"""PLBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""PLBartForCausalLM""",
"""PLBartForConditionalGeneration""",
"""PLBartForSequenceClassification""",
"""PLBartModel""",
"""PLBartPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
snake_case__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 363 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'speech_to_text_2'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Optional[Any] , _lowerCamelCase : Optional[Any]=10000 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : int=2048 , _lowerCamelCase : Dict=4 , _lowerCamelCase : str=0.0 , _lowerCamelCase : int=True , _lowerCamelCase : int="relu" , _lowerCamelCase : Any=256 , _lowerCamelCase : List[Any]=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Union[str, Any]=0.0 , _lowerCamelCase : Optional[Any]=0.02 , _lowerCamelCase : int=2 , _lowerCamelCase : List[str]=True , _lowerCamelCase : str=1 , _lowerCamelCase : List[Any]=0 , _lowerCamelCase : Optional[int]=2 , _lowerCamelCase : Tuple=1024 , **_lowerCamelCase : int , ):
"""simple docstring"""
A_ : Optional[int] = vocab_size
A_ : Tuple = d_model
A_ : List[str] = decoder_ffn_dim
A_ : str = decoder_layers
A_ : Any = decoder_attention_heads
A_ : int = dropout
A_ : str = attention_dropout
A_ : Optional[int] = activation_dropout
A_ : str = activation_function
A_ : List[Any] = init_std
A_ : Union[str, Any] = decoder_layerdrop
A_ : Any = use_cache
A_ : Optional[Any] = decoder_layers
A_ : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
A_ : Optional[Any] = max_target_positions
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
| 4 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
snake_case__ = None
snake_case__ = logging.get_logger(__name__)
snake_case__ = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
snake_case__ = {
"""vocab_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
),
},
}
snake_case__ = {
"""facebook/nllb-large-en-ro""": 10_24,
"""facebook/nllb-200-distilled-600M""": 10_24,
}
# fmt: off
snake_case__ = ["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = VOCAB_FILES_NAMES
_lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase = ['input_ids', 'attention_mask']
_lowerCAmelCase = NllbTokenizer
_lowerCAmelCase = []
_lowerCAmelCase = []
def __init__( self : Optional[Any] , _lowerCamelCase : Any=None , _lowerCamelCase : List[str]=None , _lowerCamelCase : str="<s>" , _lowerCamelCase : Optional[Any]="</s>" , _lowerCamelCase : str="</s>" , _lowerCamelCase : Dict="<s>" , _lowerCamelCase : Optional[int]="<unk>" , _lowerCamelCase : Tuple="<pad>" , _lowerCamelCase : List[str]="<mask>" , _lowerCamelCase : Union[str, Any]=None , _lowerCamelCase : List[Any]=None , _lowerCamelCase : Tuple=None , _lowerCamelCase : Union[str, Any]=False , **_lowerCamelCase : Tuple , ):
"""simple docstring"""
A_ : Dict = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else mask_token
A_ : Union[str, Any] = legacy_behaviour
super().__init__(
vocab_file=_lowerCamelCase , tokenizer_file=_lowerCamelCase , bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , sep_token=_lowerCamelCase , cls_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token=_lowerCamelCase , src_lang=_lowerCamelCase , tgt_lang=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , legacy_behaviour=_lowerCamelCase , **_lowerCamelCase , )
A_ : str = vocab_file
A_ : int = False if not self.vocab_file else True
A_ : Any = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
A_ : str = {
lang_code: self.convert_tokens_to_ids(_lowerCamelCase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
A_ : Any = src_lang if src_lang is not None else '''eng_Latn'''
A_ : Tuple = self.convert_tokens_to_ids(self._src_lang )
A_ : Union[str, Any] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _a ( self : List[str] ):
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _a ( self : int , _lowerCamelCase : str ):
"""simple docstring"""
A_ : Dict = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _a ( self : int , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _a ( self : Optional[int] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
A_ : Dict = [self.sep_token_id]
A_ : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self : Optional[Any] , _lowerCamelCase : List[str] , _lowerCamelCase : str , _lowerCamelCase : Optional[str] , _lowerCamelCase : Optional[str] , **_lowerCamelCase : str ):
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
A_ : List[Any] = src_lang
A_ : List[Any] = self(_lowerCamelCase , add_special_tokens=_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
A_ : str = self.convert_tokens_to_ids(_lowerCamelCase )
A_ : Tuple = tgt_lang_id
return inputs
def _a ( self : Any , _lowerCamelCase : List[str] , _lowerCamelCase : str = "eng_Latn" , _lowerCamelCase : Optional[List[str]] = None , _lowerCamelCase : str = "fra_Latn" , **_lowerCamelCase : List[Any] , ):
"""simple docstring"""
A_ : Dict = src_lang
A_ : Any = tgt_lang
return super().prepare_seqaseq_batch(_lowerCamelCase , _lowerCamelCase , **_lowerCamelCase )
def _a ( self : List[str] ):
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _a ( self : Optional[int] ):
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _a ( self : Any , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
A_ : Optional[int] = self.convert_tokens_to_ids(_lowerCamelCase )
if self.legacy_behaviour:
A_ : Tuple = []
A_ : int = [self.eos_token_id, self.cur_lang_code]
else:
A_ : Any = [self.cur_lang_code]
A_ : str = [self.eos_token_id]
A_ : Dict = self.convert_ids_to_tokens(self.prefix_tokens )
A_ : str = self.convert_ids_to_tokens(self.suffix_tokens )
A_ : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self : Union[str, Any] , _lowerCamelCase : str ):
"""simple docstring"""
A_ : str = self.convert_tokens_to_ids(_lowerCamelCase )
if self.legacy_behaviour:
A_ : Tuple = []
A_ : Optional[Any] = [self.eos_token_id, self.cur_lang_code]
else:
A_ : Dict = [self.cur_lang_code]
A_ : Tuple = [self.eos_token_id]
A_ : List[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
A_ : int = self.convert_ids_to_tokens(self.suffix_tokens )
A_ : Union[str, Any] = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self : Tuple , _lowerCamelCase : str , _lowerCamelCase : Optional[str] = None ):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory.' )
return
A_ : Any = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ):
copyfile(self.vocab_file , _lowerCamelCase )
return (out_vocab_file,)
| 364 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'table-transformer'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Any , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Dict=None , _lowerCamelCase : int=3 , _lowerCamelCase : Any=100 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : Tuple=2048 , _lowerCamelCase : Any=8 , _lowerCamelCase : Dict=6 , _lowerCamelCase : Tuple=2048 , _lowerCamelCase : int=8 , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : List[Any]=0.0 , _lowerCamelCase : List[Any]=True , _lowerCamelCase : Optional[int]="relu" , _lowerCamelCase : Union[str, Any]=256 , _lowerCamelCase : Any=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : str=0.02 , _lowerCamelCase : Tuple=1.0 , _lowerCamelCase : Dict=False , _lowerCamelCase : str="sine" , _lowerCamelCase : str="resnet50" , _lowerCamelCase : Any=True , _lowerCamelCase : List[str]=False , _lowerCamelCase : Any=1 , _lowerCamelCase : int=5 , _lowerCamelCase : Tuple=2 , _lowerCamelCase : Optional[int]=1 , _lowerCamelCase : Any=1 , _lowerCamelCase : Dict=5 , _lowerCamelCase : str=2 , _lowerCamelCase : Union[str, Any]=0.1 , **_lowerCamelCase : int , ):
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
A_ : int = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : str = backbone_config.get('''model_type''' )
A_ : Optional[int] = CONFIG_MAPPING[backbone_model_type]
A_ : List[str] = config_class.from_dict(_lowerCamelCase )
# set timm attributes to None
A_ ,A_ ,A_ : Union[str, Any] = None, None, None
A_ : Optional[Any] = use_timm_backbone
A_ : Optional[int] = backbone_config
A_ : Optional[Any] = num_channels
A_ : Dict = num_queries
A_ : str = d_model
A_ : List[str] = encoder_ffn_dim
A_ : int = encoder_layers
A_ : Optional[Any] = encoder_attention_heads
A_ : List[str] = decoder_ffn_dim
A_ : Any = decoder_layers
A_ : List[str] = decoder_attention_heads
A_ : Tuple = dropout
A_ : Optional[Any] = attention_dropout
A_ : Any = activation_dropout
A_ : List[Any] = activation_function
A_ : Dict = init_std
A_ : Any = init_xavier_std
A_ : List[Any] = encoder_layerdrop
A_ : int = decoder_layerdrop
A_ : Any = encoder_layers
A_ : List[str] = auxiliary_loss
A_ : List[Any] = position_embedding_type
A_ : Optional[Any] = backbone
A_ : Tuple = use_pretrained_backbone
A_ : List[Any] = dilation
# Hungarian matcher
A_ : List[str] = class_cost
A_ : str = bbox_cost
A_ : Union[str, Any] = giou_cost
# Loss coefficients
A_ : Any = mask_loss_coefficient
A_ : Optional[int] = dice_loss_coefficient
A_ : Dict = bbox_loss_coefficient
A_ : int = giou_loss_coefficient
A_ : int = eos_coefficient
super().__init__(is_encoder_decoder=_lowerCamelCase , **_lowerCamelCase )
@property
def _a ( self : List[Any] ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def _a ( self : Any ):
"""simple docstring"""
return self.d_model
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : Tuple ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def _a ( self : Optional[int] ):
"""simple docstring"""
return 1E-5
@property
def _a ( self : str ):
"""simple docstring"""
return 12
| 4 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__ = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
F'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
F'decoder.layers.{i}.encoder_attn.out_proj.bias',
)
)
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
]
)
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[Any] ) -> Optional[Any]:
A_ : Tuple = state_dict.pop(lowerCamelCase__ )
A_ : Optional[Any] = val
def snake_case__ ( lowerCamelCase__ : Dict ) -> Any:
A_ : int = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
A_ : int = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
A_ : List[str] = value
else:
A_ : Optional[int] = value
return new_state_dict
def snake_case__ ( lowerCamelCase__ : Union[str, Any] ) -> Optional[Any]:
A_ : Any = ''''''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
A_ : Tuple = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
A_ : Dict = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
A_ : str = in_proj_weight[:2_5_6, :]
A_ : Optional[Any] = in_proj_bias[:2_5_6]
A_ : Dict = in_proj_weight[2_5_6:5_1_2, :]
A_ : Tuple = in_proj_bias[2_5_6:5_1_2]
A_ : Tuple = in_proj_weight[-2_5_6:, :]
A_ : Optional[int] = in_proj_bias[-2_5_6:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
A_ : Union[str, Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
A_ : Dict = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
A_ : List[str] = in_proj_weight[:2_5_6, :]
A_ : int = in_proj_bias[:2_5_6]
A_ : Any = in_proj_weight[2_5_6:5_1_2, :]
A_ : List[str] = in_proj_bias[2_5_6:5_1_2]
A_ : Union[str, Any] = in_proj_weight[-2_5_6:, :]
A_ : Optional[Any] = in_proj_bias[-2_5_6:]
# read in weights + bias of input projection layer of cross-attention
A_ : Tuple = state_dict.pop(
f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
A_ : Optional[Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
A_ : Dict = in_proj_weight_cross_attn[:2_5_6, :]
A_ : Tuple = in_proj_bias_cross_attn[:2_5_6]
A_ : int = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
A_ : List[str] = in_proj_bias_cross_attn[2_5_6:5_1_2]
A_ : Any = in_proj_weight_cross_attn[-2_5_6:, :]
A_ : Any = in_proj_bias_cross_attn[-2_5_6:]
def snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Tuple ) -> Dict:
A_ : int = image.size
A_ : Tuple = max(lowerCamelCase__ , lowerCamelCase__ )
A_ : Optional[Any] = 8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0
A_ : Union[str, Any] = target_max_size / current_max_size
A_ : Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def snake_case__ ( lowerCamelCase__ : Tuple ) -> str:
A_ : Any = F.to_tensor(lowerCamelCase__ )
A_ : Optional[Any] = F.normalize(lowerCamelCase__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : int , lowerCamelCase__ : int ) -> str:
logger.info('''Converting model...''' )
# load original state dict
A_ : Tuple = torch.hub.load_state_dict_from_url(lowerCamelCase__ , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
A_ : str = rename_backbone_keys(lowerCamelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCamelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
A_ : List[Any] = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
A_ : List[Any] = state_dict.pop(lowerCamelCase__ )
A_ : str = val
# create HuggingFace model and load state dict
A_ : Union[str, Any] = TableTransformerConfig(
backbone='''resnet18''' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
A_ : Dict = 1_5
A_ : Dict = 2
A_ : int = {0: '''table''', 1: '''table rotated'''}
A_ : List[str] = idalabel
A_ : Optional[int] = {v: k for k, v in idalabel.items()}
else:
A_ : Union[str, Any] = 1_2_5
A_ : Optional[Any] = 6
A_ : Optional[Any] = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
A_ : int = idalabel
A_ : Tuple = {v: k for k, v in idalabel.items()}
A_ : Optional[Any] = DetrImageProcessor(
format='''coco_detection''' , max_size=8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0 )
A_ : int = TableTransformerForObjectDetection(lowerCamelCase__ )
model.load_state_dict(lowerCamelCase__ )
model.eval()
# verify our conversion
A_ : Optional[int] = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
A_ : Union[str, Any] = hf_hub_download(repo_id='''nielsr/example-pdf''' , repo_type='''dataset''' , filename=lowerCamelCase__ )
A_ : Tuple = Image.open(lowerCamelCase__ ).convert('''RGB''' )
A_ : int = normalize(resize(lowerCamelCase__ , lowerCamelCase__ ) ).unsqueeze(0 )
A_ : str = model(lowerCamelCase__ )
if "detection" in checkpoint_url:
A_ : str = (1, 1_5, 3)
A_ : int = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
A_ : Tuple = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
A_ : Optional[int] = (1, 1_2_5, 7)
A_ : Dict = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
A_ : Any = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(lowerCamelCase__ ).mkdir(exist_ok=lowerCamelCase__ )
model.save_pretrained(lowerCamelCase__ )
image_processor.save_pretrained(lowerCamelCase__ )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
A_ : List[Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(lowerCamelCase__ )
image_processor.push_to_hub(lowerCamelCase__ )
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
snake_case__ = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 365 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : int , _lowerCamelCase : List[str]=3 , _lowerCamelCase : Any=32 , _lowerCamelCase : Union[str, Any]=3 , _lowerCamelCase : int=10 , _lowerCamelCase : Union[str, Any]=[8, 16, 32, 64] , _lowerCamelCase : Dict=[1, 1, 2, 1] , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Optional[int]=True , _lowerCamelCase : Any="relu" , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Dict=["stage2", "stage3", "stage4"] , _lowerCamelCase : Union[str, Any]=[2, 3, 4] , _lowerCamelCase : Tuple=1 , ):
"""simple docstring"""
A_ : List[str] = parent
A_ : List[str] = batch_size
A_ : Union[str, Any] = image_size
A_ : Tuple = num_channels
A_ : Any = embeddings_size
A_ : int = hidden_sizes
A_ : Optional[Any] = depths
A_ : List[Any] = is_training
A_ : Optional[int] = use_labels
A_ : int = hidden_act
A_ : Tuple = num_labels
A_ : Union[str, Any] = scope
A_ : List[Any] = len(_lowerCamelCase )
A_ : Union[str, Any] = out_features
A_ : List[Any] = out_indices
A_ : Dict = num_groups
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : Union[str, Any] = None
if self.use_labels:
A_ : Any = ids_tensor([self.batch_size] , self.num_labels )
A_ : Any = self.get_config()
return config, pixel_values, labels
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return BitConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def _a ( self : List[Any] , _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
A_ : Any = BitModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase )
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 : Optional[int] , _lowerCamelCase : List[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : Dict = self.num_labels
A_ : Optional[Any] = BitForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : List[Any] = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Any , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : List[Any] ):
"""simple docstring"""
A_ : List[Any] = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
A_ : Optional[Any] = None
A_ : int = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Optional[int] = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = self.prepare_config_and_inputs()
A_ ,A_ ,A_ : Union[str, Any] = config_and_inputs
A_ : str = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase_ (a__, a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
_lowerCAmelCase = (
{'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
if is_torch_available()
else {}
)
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[str] = BitModelTester(self )
A_ : Optional[Any] = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : List[Any] ):
"""simple docstring"""
return
@unittest.skip(reason='''Bit does not output attentions''' )
def _a ( self : str ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def _a ( self : Any ):
"""simple docstring"""
pass
def _a ( self : List[Any] ):
"""simple docstring"""
A_ ,A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Dict = model_class(_lowerCamelCase )
A_ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : int = [*signature.parameters.keys()]
A_ : Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ ,A_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : str = model_class(config=_lowerCamelCase )
for name, module in model.named_modules():
if isinstance(_lowerCamelCase , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
def _a ( self : int ):
"""simple docstring"""
def check_hidden_states_output(_lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : int ):
A_ : Union[str, Any] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
A_ : Union[str, Any] = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
A_ : int = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
A_ : List[Any] = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , expected_num_stages + 1 )
# Bit'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 // 4, self.model_tester.image_size // 4] , )
A_ ,A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Tuple = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
A_ : Tuple = layer_type
A_ : Optional[Any] = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A_ : List[str] = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def _a ( self : Tuple ):
"""simple docstring"""
pass
def _a ( self : str ):
"""simple docstring"""
A_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
@slow
def _a ( self : Union[str, Any] ):
"""simple docstring"""
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : List[Any] = BitModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def snake_case__ ( ) -> Optional[int]:
A_ : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _a ( self : List[Any] ):
"""simple docstring"""
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_lowerCamelCase )
A_ : Union[str, Any] = self.default_image_processor
A_ : Optional[int] = prepare_img()
A_ : int = image_processor(images=_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
A_ : Union[str, Any] = model(**_lowerCamelCase )
# verify the logits
A_ : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
A_ : Tuple = torch.tensor([[-0.65_26, -0.52_63, -1.43_98]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1E-4 ) )
@require_torch
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (BitBackbone,) if is_torch_available() else ()
_lowerCAmelCase = BitConfig
_lowerCAmelCase = False
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Union[str, Any] = BitModelTester(self )
| 4 | 0 |
'''simple docstring'''
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def snake_case__ ( lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : str , lowerCamelCase__ : Optional[int]=0 ) -> str:
# Format the message.
if name is None:
A_ : Any = None
else:
A_ : int = '''.''' * max(0 , spaces - 2 ) + '''# {:''' + str(5_0 - spaces ) + '''s}'''
A_ : Tuple = fmt.format(lowerCamelCase__ )
# Print and recurse (if needed).
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
if msg is not None:
print(lowerCamelCase__ )
for k in val.keys():
recursive_print(lowerCamelCase__ , val[k] , spaces + 2 )
elif isinstance(lowerCamelCase__ , torch.Tensor ):
print(lowerCamelCase__ , ''':''' , val.size() )
else:
print(lowerCamelCase__ , ''':''' , lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Tuple , lowerCamelCase__ : str , lowerCamelCase__ : str , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Optional[Any] ) -> Tuple:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
A_ : Union[str, Any] = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
A_ : Optional[Any] = (num_heads, hidden_size, num_splits) + input_shape[1:]
A_ : Dict = param.view(*lowerCamelCase__ )
A_ : str = param.transpose(0 , 2 )
A_ : Any = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
A_ : Union[str, Any] = (num_heads, num_splits, hidden_size) + input_shape[1:]
A_ : str = param.view(*lowerCamelCase__ )
A_ : Any = param.transpose(0 , 1 ).contiguous()
A_ : Any = param.view(*lowerCamelCase__ )
return param
def snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : int , lowerCamelCase__ : List[Any] ) -> Optional[Any]:
# The converted output model.
A_ : List[Any] = {}
# old versions did not store training args
A_ : str = input_state_dict.get('''args''' , lowerCamelCase__ )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
A_ : List[Any] = ds_args.padded_vocab_size
A_ : Dict = ds_args.max_position_embeddings
A_ : List[Any] = ds_args.hidden_size
A_ : str = ds_args.num_layers
A_ : Any = ds_args.num_attention_heads
A_ : Any = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
A_ : int = config.n_head
# The hidden_size per head.
A_ : str = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
A_ : Union[str, Any] = input_state_dict['''checkpoint_version''']
else:
A_ : Union[str, Any] = 0.0
# The model.
A_ : Optional[int] = input_state_dict['''model''']
# The language model.
A_ : str = model['''language_model''']
# The embeddings.
A_ : str = lm['''embedding''']
# The word embeddings.
A_ : Any = embeddings['''word_embeddings''']['''weight''']
# Truncate the embedding table to vocab_size rows.
A_ : Union[str, Any] = word_embeddings[: config.vocab_size, :]
A_ : Dict = word_embeddings
# The position embeddings.
A_ : List[Any] = embeddings['''position_embeddings''']['''weight''']
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
A_ : Tuple = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
f'pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match' )
# Store the position embeddings.
A_ : str = pos_embeddings
# The transformer.
A_ : List[Any] = lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']
# The regex to extract layer names.
A_ : Optional[Any] = re.compile(R'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)''' )
# The simple map of names for "automated" rules.
A_ : Any = {
'''attention.dense''': '''.attn.c_proj.''',
'''self_attention.dense''': '''.attn.c_proj.''',
'''mlp.dense_h_to_4h''': '''.mlp.c_fc.''',
'''mlp.dense_4h_to_h''': '''.mlp.c_proj.''',
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
A_ : List[Any] = layer_re.match(lowerCamelCase__ )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
A_ : Dict = int(m.group(1 ) )
# The name of the operation.
A_ : Dict = m.group(2 )
# Is it a weight or a bias?
A_ : Dict = m.group(3 )
# The name of the layer.
A_ : Tuple = f'transformer.h.{layer_idx}'
# For layernorm(s), simply store the layer norm.
if op_name.endswith('''layernorm''' ):
A_ : Optional[int] = '''ln_1''' if op_name.startswith('''input''' ) else '''ln_2'''
A_ : Union[str, Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
A_ : Optional[Any] = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , lowerCamelCase__ , lowerCamelCase__ )
A_ : Union[str, Any] = causal_mask
# Insert a "dummy" tensor for masked_bias.
A_ : Optional[int] = torch.tensor(-1e4 , dtype=torch.floataa )
A_ : int = masked_bias
A_ : Dict = fix_query_key_value_ordering(lowerCamelCase__ , lowerCamelCase__ , 3 , lowerCamelCase__ , lowerCamelCase__ )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
A_ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
A_ : int = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
A_ : Optional[int] = fix_query_key_value_ordering(lowerCamelCase__ , lowerCamelCase__ , 3 , lowerCamelCase__ , lowerCamelCase__ )
# Store. No change of shape.
A_ : List[Any] = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
A_ : List[str] = megatron_to_transformers[op_name]
A_ : List[Any] = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
A_ : List[str] = megatron_to_transformers[op_name]
A_ : Tuple = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
A_ : Tuple = transformer['''final_layernorm.weight''']
A_ : Union[str, Any] = transformer['''final_layernorm.bias''']
# For LM head, transformers' wants the matrix to weight embeddings.
A_ : Dict = word_embeddings
# It should be done!
return output_state_dict
def snake_case__ ( ) -> Any:
# Create the argument parser.
A_ : Tuple = argparse.ArgumentParser()
parser.add_argument('''--print-checkpoint-structure''' , action='''store_true''' )
parser.add_argument(
'''path_to_checkpoint''' , type=lowerCamelCase__ , help='''Path to the checkpoint file (.zip archive or direct .pt file)''' , )
parser.add_argument(
'''--config_file''' , default='''''' , type=lowerCamelCase__ , help='''An optional config json file describing the pre-trained model.''' , )
A_ : Union[str, Any] = parser.parse_args()
# Extract the basename.
A_ : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(f'Extracting PyTorch state dictionary from {args.path_to_checkpoint}' )
if args.path_to_checkpoint.endswith('''.zip''' ):
with zipfile.ZipFile(args.path_to_checkpoint , '''r''' ) as checkpoint:
with checkpoint.open('''release/mp_rank_00/model_optim_rng.pt''' ) as pytorch_dict:
A_ : Optional[int] = torch.load(lowerCamelCase__ , map_location='''cpu''' )
else:
A_ : Optional[Any] = torch.load(args.path_to_checkpoint , map_location='''cpu''' )
A_ : Optional[Any] = input_state_dict.get('''args''' , lowerCamelCase__ )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
A_ : Union[str, Any] = '''gelu_fast'''
elif ds_args.openai_gelu:
A_ : Optional[int] = '''gelu_new'''
else:
A_ : Any = '''gelu'''
else:
# in the very early days this used to be "gelu_new"
A_ : Tuple = '''gelu_new'''
# Spell out all parameters in case the defaults change.
A_ : List[Any] = GPTaConfig(
vocab_size=5_0_2_5_7 , n_positions=1_0_2_4 , n_embd=1_0_2_4 , n_layer=2_4 , n_head=1_6 , n_inner=4_0_9_6 , activation_function=lowerCamelCase__ , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1e-5 , initializer_range=0.02 , summary_type='''cls_index''' , summary_use_proj=lowerCamelCase__ , summary_activation=lowerCamelCase__ , summary_proj_to_labels=lowerCamelCase__ , summary_first_dropout=0.1 , scale_attn_weights=lowerCamelCase__ , use_cache=lowerCamelCase__ , bos_token_id=5_0_2_5_6 , eos_token_id=5_0_2_5_6 , )
else:
A_ : str = GPTaConfig.from_json_file(args.config_file )
A_ : Optional[int] = ['''GPT2LMHeadModel''']
# Convert.
print('''Converting''' )
A_ : Union[str, Any] = convert_megatron_checkpoint(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(lowerCamelCase__ , lowerCamelCase__ )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
A_ : List[Any] = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
A_ : List[Any] = '''gpt2'''
elif tokenizer_type == "PretrainedFromHF":
A_ : Union[str, Any] = ds_args.tokenizer_name_or_path
else:
raise ValueError(f'Unrecognized tokenizer_type {tokenizer_type}' )
else:
A_ : int = '''gpt2'''
A_ : Any = AutoTokenizer.from_pretrained(lowerCamelCase__ )
A_ : Any = type(lowerCamelCase__ ).__name__
A_ : Optional[Any] = tokenizer_class
# Store the config to file.
print('''Saving config''' )
config.save_pretrained(lowerCamelCase__ )
# Save tokenizer based on args
print(f'Adding {tokenizer_class} tokenizer files' )
tokenizer.save_pretrained(lowerCamelCase__ )
# Store the state_dict to file.
A_ : int = os.path.join(lowerCamelCase__ , '''pytorch_model.bin''' )
print(f'Saving checkpoint to "{output_checkpoint_file}"' )
torch.save(lowerCamelCase__ , lowerCamelCase__ )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 366 |
'''simple docstring'''
import pprint
import requests
snake_case__ = """https://zenquotes.io/api"""
def snake_case__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def snake_case__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if __name__ == "__main__":
snake_case__ = random_quotes()
pprint.pprint(response)
| 4 | 0 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
snake_case__ = [
"""cross_validation.py""",
"""gradient_accumulation.py""",
"""local_sgd.py""",
"""multi_process_metrics.py""",
"""memory.py""",
"""automatic_gradient_accumulation.py""",
"""fsdp_with_peak_mem_tracking.py""",
"""deepspeed_with_config_support.py""",
"""megatron_lm_gpt_pretraining.py""",
]
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : str , _lowerCamelCase : str , _lowerCamelCase : bool , _lowerCamelCase : str = None , _lowerCamelCase : list = None ):
"""simple docstring"""
A_ : Union[str, Any] = None
A_ : Tuple = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
A_ : int = os.path.abspath('''examples''' )
for item in os.listdir(_lowerCamelCase ):
if item not in EXCLUDE_EXAMPLES:
A_ : Optional[Any] = os.path.join(_lowerCamelCase , _lowerCamelCase )
if os.path.isfile(_lowerCamelCase ) and ".py" in item_path:
with self.subTest(
tested_script=_lowerCamelCase , feature_script=_lowerCamelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
A_ : Tuple = compare_against_test(
os.path.join(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
A_ : Tuple = '''\n'''.join(_lowerCamelCase )
if special_strings is not None:
for string in special_strings:
A_ : Optional[int] = diff.replace(_lowerCamelCase , '''''' )
self.assertEqual(_lowerCamelCase , '''''' )
def _a ( self : Tuple ):
"""simple docstring"""
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Tuple = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
A_ : Optional[int] = [
''' ''' * 16 + '''{\n\n''',
''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 20 + '''"epoch": epoch,\n\n''',
''' ''' * 16 + '''},\n\n''',
''' ''' * 16 + '''step=epoch,\n''',
''' ''' * 12,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@mock.patch.dict(os.environ, {'TESTING_MOCKED_DATALOADERS': '1'} )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = False
@classmethod
def _a ( cls : int ):
"""simple docstring"""
super().setUpClass()
A_ : Optional[Any] = tempfile.mkdtemp()
A_ : Any = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
A_ : List[str] = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def _a ( cls : Optional[Any] ):
"""simple docstring"""
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def _a ( self : str ):
"""simple docstring"""
A_ : int = f'\n examples/by_feature/checkpointing.py\n --checkpointing_steps epoch\n --output_dir {self.tmpdir}\n '.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : List[Any] = f'\n examples/by_feature/checkpointing.py\n --checkpointing_steps 1\n --output_dir {self.tmpdir}\n '.split()
A_ : List[str] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : List[Any] = f'\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , "epoch_0" )}\n '.split()
A_ : List[str] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Tuple = f'\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , "step_2" )}\n '.split()
A_ : int = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
if torch.cuda.is_available():
A_ : str = torch.cuda.device_count()
else:
A_ : Tuple = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
else:
self.assertIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
@slow
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[Any] = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
A_ : Optional[Any] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
A_ : int = re.findall('''({.+})''' , _lowerCamelCase )
A_ : List[Any] = [r for r in results if '''accuracy''' in r][-1]
A_ : int = ast.literal_eval(_lowerCamelCase )
self.assertGreaterEqual(results['''accuracy'''] , 0.75 )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def _a ( self : List[Any] ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdir:
A_ : Union[str, Any] = f'\n examples/by_feature/tracking.py\n --with_tracking\n --project_dir {tmpdir}\n '.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase , '''tracking''' ) ) )
def _a ( self : Dict ):
"""simple docstring"""
A_ : List[Any] = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Tuple = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 367 |
'''simple docstring'''
from __future__ import annotations
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[int] , _lowerCamelCase : int ):
"""simple docstring"""
A_ : Union[str, Any] = order
# a_{0} ... a_{k}
A_ : Union[str, Any] = [1.0] + [0.0] * order
# b_{0} ... b_{k}
A_ : int = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
A_ : str = [0.0] * self.order
# y[n-1] ... y[n-k]
A_ : Optional[Any] = [0.0] * self.order
def _a ( self : Dict , _lowerCamelCase : list[float] , _lowerCamelCase : list[float] ):
"""simple docstring"""
if len(_lowerCamelCase ) < self.order:
A_ : Any = [1.0, *a_coeffs]
if len(_lowerCamelCase ) != self.order + 1:
A_ : List[Any] = (
f'Expected a_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(_lowerCamelCase )}'
)
raise ValueError(_lowerCamelCase )
if len(_lowerCamelCase ) != self.order + 1:
A_ : Union[str, Any] = (
f'Expected b_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(_lowerCamelCase )}'
)
raise ValueError(_lowerCamelCase )
A_ : Tuple = a_coeffs
A_ : str = b_coeffs
def _a ( self : Tuple , _lowerCamelCase : float ):
"""simple docstring"""
A_ : Any = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
A_ : str = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
A_ : Optional[Any] = self.input_history[:-1]
A_ : List[str] = self.output_history[:-1]
A_ : Tuple = sample
A_ : Tuple = result
return result
| 4 | 0 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
snake_case__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCamelCase_ (datasets.BuilderConfig ):
"""simple docstring"""
_lowerCAmelCase = 1_0_0_0_0
_lowerCAmelCase = None
_lowerCAmelCase = None
class UpperCamelCase_ (datasets.ArrowBasedBuilder ):
"""simple docstring"""
_lowerCAmelCase = ParquetConfig
def _a ( self : Any ):
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def _a ( self : str , _lowerCamelCase : Dict ):
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}' )
A_ : List[Any] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_lowerCamelCase , (str, list, tuple) ):
A_ : Union[str, Any] = data_files
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : Optional[int] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
A_ : Optional[int] = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
A_ : str = []
for split_name, files in data_files.items():
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : Union[str, Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
A_ : Optional[int] = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(_lowerCamelCase ):
with open(_lowerCamelCase , '''rb''' ) as f:
A_ : Dict = datasets.Features.from_arrow_schema(pq.read_schema(_lowerCamelCase ) )
break
splits.append(datasets.SplitGenerator(name=_lowerCamelCase , gen_kwargs={'''files''': files} ) )
return splits
def _a ( self : Tuple , _lowerCamelCase : pa.Table ):
"""simple docstring"""
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
A_ : Optional[Any] = table_cast(_lowerCamelCase , self.info.features.arrow_schema )
return pa_table
def _a ( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : List[str] = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
f'Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'' )
for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase ) ):
with open(_lowerCamelCase , '''rb''' ) as f:
A_ : List[str] = pq.ParquetFile(_lowerCamelCase )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
A_ : Any = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield f'{file_idx}_{batch_idx}', self._cast_table(_lowerCamelCase )
except ValueError as e:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise
| 368 |
'''simple docstring'''
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ : Union[str, Any] = val
A_ : Tuple = None
A_ : Any = None
def _a ( self : Tuple , _lowerCamelCase : List[Any] ):
"""simple docstring"""
if self.val:
if val < self.val:
if self.left is None:
A_ : int = Node(_lowerCamelCase )
else:
self.left.insert(_lowerCamelCase )
elif val > self.val:
if self.right is None:
A_ : List[str] = Node(_lowerCamelCase )
else:
self.right.insert(_lowerCamelCase )
else:
A_ : Any = val
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int] ) -> str:
# Recursive traversal
if root:
inorder(root.left , lowerCamelCase__ )
res.append(root.val )
inorder(root.right , lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Optional[int] ) -> Tuple:
# Build BST
if len(lowerCamelCase__ ) == 0:
return arr
A_ : Dict = Node(arr[0] )
for i in range(1 , len(lowerCamelCase__ ) ):
root.insert(arr[i] )
# Traverse BST in order.
A_ : Tuple = []
inorder(lowerCamelCase__ , lowerCamelCase__ )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 4 | 0 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
snake_case__ = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = AlbertTokenizer
_lowerCAmelCase = AlbertTokenizerFast
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = True
def _a ( self : int ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
A_ : str = AlbertTokenizer(_lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self : int , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : Any = '''this is a test'''
A_ : Dict = '''this is a test'''
return input_text, output_text
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Union[str, Any] = '''<pad>'''
A_ : Optional[int] = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase ) , _lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''▁eloquent''' )
self.assertEqual(len(_lowerCamelCase ) , 30000 )
def _a ( self : Any ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 30000 )
def _a ( self : str ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
A_ : Any = self.get_tokenizer()
A_ : List[str] = self.get_rust_tokenizer()
A_ : List[str] = '''I was born in 92000, and this is falsé.'''
A_ : List[Any] = tokenizer.tokenize(_lowerCamelCase )
A_ : List[Any] = rust_tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
A_ : Any = rust_tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
A_ : str = self.get_rust_tokenizer()
A_ : List[Any] = tokenizer.encode(_lowerCamelCase )
A_ : List[str] = rust_tokenizer.encode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Union[str, Any] = AlbertTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
A_ : Optional[Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_lowerCamelCase , ['''▁this''', '''▁is''', '''▁a''', '''▁test'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [48, 25, 21, 1289] )
A_ : Dict = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_lowerCamelCase , ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.'''] )
A_ : Tuple = tokenizer.convert_tokens_to_ids(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9] )
A_ : Optional[int] = tokenizer.convert_ids_to_tokens(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase , ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.'''] , )
def _a ( self : Any ):
"""simple docstring"""
A_ : Dict = AlbertTokenizer(_lowerCamelCase )
A_ : List[Any] = tokenizer.encode('''sequence builders''' )
A_ : Union[str, Any] = tokenizer.encode('''multi-sequence build''' )
A_ : Optional[int] = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
A_ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase , _lowerCamelCase )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
@slow
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Tuple = {'''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]], '''input_ids''': [[2, 21970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 12051, 18, 17, 7103, 2153, 673, 8, 3515, 18684, 8, 4461, 6, 1927, 297, 8, 12060, 2607, 18, 13, 5, 4461, 15, 10538, 38, 8, 135, 15, 822, 58, 15, 993, 10363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 10641, 6, 29, 84, 2512, 2430, 782, 18684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 11712, 15, 7103, 2153, 673, 17, 24883, 9990, 9, 3], [2, 11502, 25, 1006, 20, 782, 8, 11809, 855, 1732, 19393, 18667, 37, 367, 21018, 69, 1854, 34, 11860, 19124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 17659, 84, 14, 16792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_lowerCamelCase , model_name='''albert-base-v2''' , revision='''6b6560eaf5ff2e250b00c50f380c5389a9c2d82e''' , )
| 369 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list ) -> list:
if len(lowerCamelCase__ ) <= 1:
return [tuple(lowerCamelCase__ )]
A_ : List[str] = []
def generate(lowerCamelCase__ : int , lowerCamelCase__ : list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , lowerCamelCase__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
A_ ,A_ : Optional[int] = arr[k - 1], arr[i]
else: # k is odd
A_ ,A_ : Union[str, Any] = arr[k - 1], arr[0]
generate(k - 1 , lowerCamelCase__ )
generate(len(lowerCamelCase__ ) , lowerCamelCase__ )
return res
if __name__ == "__main__":
snake_case__ = input("""Enter numbers separated by a comma:\n""").strip()
snake_case__ = [int(item) for item in user_input.split(""",""")]
print(heaps(arr))
| 4 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Union[str, Any] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Dict ):
"""simple docstring"""
A_ : Any = StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
A_ : List[Any] = sd_pipe.to(_lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
sd_pipe.set_scheduler('''sample_euler''' )
A_ : Any = '''A painting of a squirrel eating a burger'''
A_ : Dict = torch.manual_seed(0 )
A_ : Optional[int] = sd_pipe([prompt] , generator=_lowerCamelCase , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''' )
A_ : Optional[int] = output.images
A_ : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : List[str] = np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : int = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
A_ : int = sd_pipe.to(_lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
sd_pipe.set_scheduler('''sample_euler''' )
A_ : List[Any] = '''A painting of a squirrel eating a burger'''
A_ : Optional[int] = torch.manual_seed(0 )
A_ : Optional[int] = sd_pipe([prompt] , generator=_lowerCamelCase , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''' )
A_ : Dict = output.images
A_ : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : Tuple = np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Any = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
A_ : Union[str, Any] = sd_pipe.to(_lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
sd_pipe.set_scheduler('''sample_dpmpp_2m''' )
A_ : str = '''A painting of a squirrel eating a burger'''
A_ : Union[str, Any] = torch.manual_seed(0 )
A_ : Tuple = sd_pipe(
[prompt] , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=15 , output_type='''np''' , use_karras_sigmas=_lowerCamelCase , )
A_ : List[str] = output.images
A_ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : Any = np.array(
[0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 370 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : Tuple = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Dict = -1
A_ : List[str] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Any = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : List[str] = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
A_ : List[str] = TextStreamer(_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
A_ : Dict = cs.out[:-1]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : List[str] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Dict = -1
A_ : List[str] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Optional[int] = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : str = tokenizer.decode(greedy_ids[0] )
A_ : int = TextIteratorStreamer(_lowerCamelCase )
A_ : List[Any] = {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
A_ : List[Any] = Thread(target=model.generate , kwargs=_lowerCamelCase )
thread.start()
A_ : List[Any] = ''''''
for new_text in streamer:
streamer_text += new_text
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : List[str] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : List[Any] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : List[str] = -1
A_ : Any = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Tuple = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : Tuple = greedy_ids[:, input_ids.shape[1] :]
A_ : Tuple = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
A_ : Any = TextStreamer(_lowerCamelCase , skip_prompt=_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
A_ : Any = cs.out[:-1]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = AutoTokenizer.from_pretrained('''distilgpt2''' )
A_ : Tuple = AutoModelForCausalLM.from_pretrained('''distilgpt2''' ).to(_lowerCamelCase )
A_ : List[Any] = -1
A_ : Union[str, Any] = torch.ones((1, 5) , device=_lowerCamelCase ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
A_ : List[Any] = TextStreamer(_lowerCamelCase , skip_special_tokens=_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=1 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
A_ : List[str] = cs.out[:-1] # Remove the final "\n"
A_ : List[Any] = tokenizer(_lowerCamelCase , return_tensors='''pt''' )
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1) )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : str = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : str = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Union[str, Any] = -1
A_ : Union[str, Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : List[str] = TextIteratorStreamer(_lowerCamelCase , timeout=0.0_01 )
A_ : str = {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
A_ : List[str] = Thread(target=model.generate , kwargs=_lowerCamelCase )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(_lowerCamelCase ):
A_ : str = ''''''
for new_text in streamer:
streamer_text += new_text
| 4 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = KandinskyImgaImgPipeline
_lowerCAmelCase = ['prompt', 'image_embeds', 'negative_image_embeds', 'image']
_lowerCAmelCase = [
'prompt',
'negative_prompt',
'image_embeds',
'negative_image_embeds',
'image',
]
_lowerCAmelCase = [
'generator',
'height',
'width',
'strength',
'guidance_scale',
'negative_prompt',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
_lowerCAmelCase = False
@property
def _a ( self : List[str] ):
"""simple docstring"""
return 32
@property
def _a ( self : List[str] ):
"""simple docstring"""
return 32
@property
def _a ( self : List[Any] ):
"""simple docstring"""
return self.time_input_dim
@property
def _a ( self : Tuple ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def _a ( self : Optional[Any] ):
"""simple docstring"""
return 100
@property
def _a ( self : int ):
"""simple docstring"""
A_ : Dict = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def _a ( self : Union[str, Any] ):
"""simple docstring"""
torch.manual_seed(0 )
A_ : Tuple = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
A_ : int = MultilingualCLIP(_lowerCamelCase )
A_ : Dict = text_encoder.eval()
return text_encoder
@property
def _a ( self : Any ):
"""simple docstring"""
torch.manual_seed(0 )
A_ : Optional[Any] = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
A_ : List[Any] = UNetaDConditionModel(**_lowerCamelCase )
return model
@property
def _a ( self : List[str] ):
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _a ( self : Optional[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
A_ : Optional[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def _a ( self : str ):
"""simple docstring"""
A_ : Tuple = self.dummy_text_encoder
A_ : Optional[int] = self.dummy_tokenizer
A_ : Dict = self.dummy_unet
A_ : Dict = self.dummy_movq
A_ : Any = {
'''num_train_timesteps''': 1000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.0_00_85,
'''beta_end''': 0.0_12,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
A_ : int = DDIMScheduler(**_lowerCamelCase )
A_ : str = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def _a ( self : Optional[int] , _lowerCamelCase : Dict , _lowerCamelCase : List[str]=0 ):
"""simple docstring"""
A_ : List[str] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
A_ : Dict = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_lowerCamelCase )
# create init_image
A_ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
A_ : str = image.cpu().permute(0 , 2 , 3 , 1 )[0]
A_ : Optional[Any] = Image.fromarray(np.uinta(_lowerCamelCase ) ).convert('''RGB''' ).resize((256, 256) )
if str(_lowerCamelCase ).startswith('''mps''' ):
A_ : Optional[int] = torch.manual_seed(_lowerCamelCase )
else:
A_ : Tuple = torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
A_ : Optional[Any] = {
'''prompt''': '''horse''',
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def _a ( self : Any ):
"""simple docstring"""
A_ : Any = '''cpu'''
A_ : Optional[Any] = self.get_dummy_components()
A_ : Optional[int] = self.pipeline_class(**_lowerCamelCase )
A_ : Dict = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
A_ : Optional[int] = pipe(**self.get_dummy_inputs(_lowerCamelCase ) )
A_ : Optional[Any] = output.images
A_ : Dict = pipe(
**self.get_dummy_inputs(_lowerCamelCase ) , return_dict=_lowerCamelCase , )[0]
A_ : List[str] = image[0, -3:, -3:, -1]
A_ : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A_ : Any = np.array(
[0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : str ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : List[str] ):
"""simple docstring"""
A_ : List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_img2img_frog.npy''' )
A_ : str = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
A_ : Union[str, Any] = '''A red cartoon frog, 4k'''
A_ : List[str] = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_lowerCamelCase )
A_ : Tuple = KandinskyImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1''' , torch_dtype=torch.floataa )
A_ : Any = pipeline.to(_lowerCamelCase )
pipeline.set_progress_bar_config(disable=_lowerCamelCase )
A_ : List[str] = torch.Generator(device='''cpu''' ).manual_seed(0 )
A_ : Optional[int] = pipe_prior(
_lowerCamelCase , generator=_lowerCamelCase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
A_ : Optional[Any] = pipeline(
_lowerCamelCase , image=_lowerCamelCase , image_embeds=_lowerCamelCase , negative_image_embeds=_lowerCamelCase , generator=_lowerCamelCase , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='''np''' , )
A_ : Union[str, Any] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_lowerCamelCase , _lowerCamelCase )
| 371 |
'''simple docstring'''
import heapq
def snake_case__ ( lowerCamelCase__ : dict ) -> set[int]:
A_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase__ , [-1 * len(lowerCamelCase__ ), (key, value)] )
# chosen_vertices = set of chosen vertices
A_ : str = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
A_ : Tuple = heapq.heappop(lowerCamelCase__ )[1][0]
chosen_vertices.add(lowerCamelCase__ )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
A_ : List[str] = elem[1][1].index(lowerCamelCase__ )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase__ )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case__ = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')
| 4 | 0 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = ''
_lowerCAmelCase = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self : Optional[int] , _lowerCamelCase : Optional[DatasetInfo] = None , _lowerCamelCase : Optional[str] = None , **_lowerCamelCase : Dict , ):
"""simple docstring"""
super().__init__(self , **_lowerCamelCase )
A_ : Any = repo_info
A_ : Optional[int] = token
A_ : List[Any] = None
def _a ( self : List[str] ):
"""simple docstring"""
if self.dir_cache is None:
A_ : Tuple = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
A_ : int = {
'''name''': hf_file.rfilename,
'''size''': None,
'''type''': '''file''',
}
self.dir_cache.update(
{
str(_lowerCamelCase ): {'''name''': str(_lowerCamelCase ), '''size''': None, '''type''': '''directory'''}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def _a ( self : Union[str, Any] , _lowerCamelCase : str , _lowerCamelCase : str = "rb" , **_lowerCamelCase : Dict , ):
"""simple docstring"""
if not isinstance(self.repo_info , _lowerCamelCase ):
raise NotImplementedError(f'Open is only implemented for dataset repositories, but got {self.repo_info}' )
A_ : Tuple = hf_hub_url(self.repo_info.id , _lowerCamelCase , revision=self.repo_info.sha )
return fsspec.open(
_lowerCamelCase , mode=_lowerCamelCase , headers=get_authentication_headers_for_url(_lowerCamelCase , use_auth_token=self.token ) , client_kwargs={'''trust_env''': True} , ).open()
def _a ( self : Optional[int] , _lowerCamelCase : Union[str, Any] , **_lowerCamelCase : int ):
"""simple docstring"""
self._get_dirs()
A_ : int = self._strip_protocol(_lowerCamelCase )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(_lowerCamelCase )
def _a ( self : Tuple , _lowerCamelCase : List[Any] , _lowerCamelCase : Optional[Any]=False , **_lowerCamelCase : List[str] ):
"""simple docstring"""
self._get_dirs()
A_ : str = PurePosixPath(path.strip('''/''' ) )
A_ : Any = {}
for p, f in self.dir_cache.items():
A_ : Union[str, Any] = PurePosixPath(p.strip('''/''' ) )
A_ : Optional[Any] = p.parent
if root == path:
A_ : Union[str, Any] = f
A_ : str = list(paths.values() )
if detail:
return out
else:
return sorted(f['''name'''] for f in out )
| 350 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__ = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
F'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
F'decoder.layers.{i}.encoder_attn.out_proj.bias',
)
)
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
]
)
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[Any] ) -> Optional[Any]:
A_ : Tuple = state_dict.pop(lowerCamelCase__ )
A_ : Optional[Any] = val
def snake_case__ ( lowerCamelCase__ : Dict ) -> Any:
A_ : int = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
A_ : int = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
A_ : List[str] = value
else:
A_ : Optional[int] = value
return new_state_dict
def snake_case__ ( lowerCamelCase__ : Union[str, Any] ) -> Optional[Any]:
A_ : Any = ''''''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
A_ : Tuple = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
A_ : Dict = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
A_ : str = in_proj_weight[:2_5_6, :]
A_ : Optional[Any] = in_proj_bias[:2_5_6]
A_ : Dict = in_proj_weight[2_5_6:5_1_2, :]
A_ : Tuple = in_proj_bias[2_5_6:5_1_2]
A_ : Tuple = in_proj_weight[-2_5_6:, :]
A_ : Optional[int] = in_proj_bias[-2_5_6:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
A_ : Union[str, Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
A_ : Dict = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
A_ : List[str] = in_proj_weight[:2_5_6, :]
A_ : int = in_proj_bias[:2_5_6]
A_ : Any = in_proj_weight[2_5_6:5_1_2, :]
A_ : List[str] = in_proj_bias[2_5_6:5_1_2]
A_ : Union[str, Any] = in_proj_weight[-2_5_6:, :]
A_ : Optional[Any] = in_proj_bias[-2_5_6:]
# read in weights + bias of input projection layer of cross-attention
A_ : Tuple = state_dict.pop(
f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
A_ : Optional[Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
A_ : Dict = in_proj_weight_cross_attn[:2_5_6, :]
A_ : Tuple = in_proj_bias_cross_attn[:2_5_6]
A_ : int = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
A_ : List[str] = in_proj_bias_cross_attn[2_5_6:5_1_2]
A_ : Any = in_proj_weight_cross_attn[-2_5_6:, :]
A_ : Any = in_proj_bias_cross_attn[-2_5_6:]
def snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Tuple ) -> Dict:
A_ ,A_ : int = image.size
A_ : Tuple = max(lowerCamelCase__ , lowerCamelCase__ )
A_ : Optional[Any] = 8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0
A_ : Union[str, Any] = target_max_size / current_max_size
A_ : Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def snake_case__ ( lowerCamelCase__ : Tuple ) -> str:
A_ : Any = F.to_tensor(lowerCamelCase__ )
A_ : Optional[Any] = F.normalize(lowerCamelCase__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : int , lowerCamelCase__ : int ) -> str:
logger.info('''Converting model...''' )
# load original state dict
A_ : Tuple = torch.hub.load_state_dict_from_url(lowerCamelCase__ , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
A_ : str = rename_backbone_keys(lowerCamelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCamelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
A_ : List[Any] = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
A_ : List[Any] = state_dict.pop(lowerCamelCase__ )
A_ : str = val
# create HuggingFace model and load state dict
A_ : Union[str, Any] = TableTransformerConfig(
backbone='''resnet18''' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
A_ : Dict = 1_5
A_ : Dict = 2
A_ : int = {0: '''table''', 1: '''table rotated'''}
A_ : List[str] = idalabel
A_ : Optional[int] = {v: k for k, v in idalabel.items()}
else:
A_ : Union[str, Any] = 1_2_5
A_ : Optional[Any] = 6
A_ : Optional[Any] = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
A_ : int = idalabel
A_ : Tuple = {v: k for k, v in idalabel.items()}
A_ : Optional[Any] = DetrImageProcessor(
format='''coco_detection''' , max_size=8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0 )
A_ : int = TableTransformerForObjectDetection(lowerCamelCase__ )
model.load_state_dict(lowerCamelCase__ )
model.eval()
# verify our conversion
A_ : Optional[int] = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
A_ : Union[str, Any] = hf_hub_download(repo_id='''nielsr/example-pdf''' , repo_type='''dataset''' , filename=lowerCamelCase__ )
A_ : Tuple = Image.open(lowerCamelCase__ ).convert('''RGB''' )
A_ : int = normalize(resize(lowerCamelCase__ , lowerCamelCase__ ) ).unsqueeze(0 )
A_ : str = model(lowerCamelCase__ )
if "detection" in checkpoint_url:
A_ : str = (1, 1_5, 3)
A_ : int = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
A_ : Tuple = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
A_ : Optional[int] = (1, 1_2_5, 7)
A_ : Dict = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
A_ : Any = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(lowerCamelCase__ ).mkdir(exist_ok=lowerCamelCase__ )
model.save_pretrained(lowerCamelCase__ )
image_processor.save_pretrained(lowerCamelCase__ )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
A_ : List[Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(lowerCamelCase__ )
image_processor.push_to_hub(lowerCamelCase__ )
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
snake_case__ = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 4 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, 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 (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
)
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : int=3 , _lowerCamelCase : Dict=7 , _lowerCamelCase : str=True , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Tuple=False , _lowerCamelCase : str=True , _lowerCamelCase : Tuple=99 , _lowerCamelCase : str=32 , _lowerCamelCase : Dict=5 , _lowerCamelCase : Union[str, Any]=4 , _lowerCamelCase : Dict=37 , _lowerCamelCase : Any="gelu" , _lowerCamelCase : int=0.1 , _lowerCamelCase : Tuple=0.1 , _lowerCamelCase : Union[str, Any]=512 , _lowerCamelCase : Optional[Any]=16 , _lowerCamelCase : Union[str, Any]=2 , _lowerCamelCase : List[Any]=0.02 , _lowerCamelCase : Any=3 , _lowerCamelCase : Optional[int]=4 , _lowerCamelCase : Any=None , ):
"""simple docstring"""
A_ : List[Any] = parent
A_ : Optional[int] = batch_size
A_ : Optional[int] = seq_length
A_ : Dict = is_training
A_ : Optional[int] = use_input_mask
A_ : Any = use_token_type_ids
A_ : Any = use_labels
A_ : Optional[int] = vocab_size
A_ : Optional[Any] = hidden_size
A_ : Union[str, Any] = num_hidden_layers
A_ : str = num_attention_heads
A_ : List[Any] = intermediate_size
A_ : str = hidden_act
A_ : Any = hidden_dropout_prob
A_ : int = attention_probs_dropout_prob
A_ : Union[str, Any] = max_position_embeddings
A_ : Optional[Any] = type_vocab_size
A_ : Optional[int] = type_sequence_label_size
A_ : Dict = initializer_range
A_ : Any = num_labels
A_ : int = num_choices
A_ : Optional[int] = scope
def _a ( self : List[str] ):
"""simple docstring"""
A_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ : List[str] = None
if self.use_input_mask:
A_ : Any = random_attention_mask([self.batch_size, self.seq_length] )
A_ : int = None
A_ : str = None
A_ : Union[str, Any] = None
A_ : Dict = None
if self.use_labels:
A_ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ : str = ids_tensor([self.batch_size] , self.num_choices )
A_ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : List[Any] ):
"""simple docstring"""
return FalconConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_lowerCamelCase , initializer_range=self.initializer_range , pad_token_id=1 , new_decoder_architecture=_lowerCamelCase , )
def _a ( self : Dict , _lowerCamelCase : Tuple , _lowerCamelCase : Optional[Any] , _lowerCamelCase : List[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Dict ):
"""simple docstring"""
A_ : Dict = FalconModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Dict = model(_lowerCamelCase , attention_mask=_lowerCamelCase )
A_ : Any = model(_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : int , _lowerCamelCase : Dict , _lowerCamelCase : Any , _lowerCamelCase : Dict , _lowerCamelCase : str , _lowerCamelCase : Any , _lowerCamelCase : Any , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : List[str] , ):
"""simple docstring"""
A_ : Union[str, Any] = True
A_ : Any = FalconModel(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Optional[int] = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , encoder_attention_mask=_lowerCamelCase , )
A_ : Optional[int] = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , )
A_ : Tuple = model(_lowerCamelCase , attention_mask=_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : Any , _lowerCamelCase : str , _lowerCamelCase : Tuple , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : int , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : List[Any] , ):
"""simple docstring"""
A_ : str = FalconForCausalLM(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : str = model(_lowerCamelCase , attention_mask=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : List[str] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : int , _lowerCamelCase : List[str] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : str , _lowerCamelCase : str , _lowerCamelCase : Any , ):
"""simple docstring"""
A_ : List[Any] = True
A_ : Optional[int] = True
A_ : List[str] = FalconForCausalLM(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
# first forward pass
A_ : List[str] = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , encoder_attention_mask=_lowerCamelCase , use_cache=_lowerCamelCase , )
A_ : Optional[int] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
A_ : Any = ids_tensor((self.batch_size, 3) , config.vocab_size )
A_ : List[str] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
A_ : List[str] = torch.cat([input_ids, next_tokens] , dim=-1 )
A_ : List[str] = torch.cat([input_mask, next_mask] , dim=-1 )
A_ : str = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , encoder_attention_mask=_lowerCamelCase , output_hidden_states=_lowerCamelCase , )['''hidden_states'''][0]
A_ : Union[str, Any] = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , encoder_attention_mask=_lowerCamelCase , past_key_values=_lowerCamelCase , output_hidden_states=_lowerCamelCase , )['''hidden_states'''][0]
# select random slice
A_ : Union[str, Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
A_ : Optional[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
A_ : List[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_lowerCamelCase , _lowerCamelCase , atol=1E-3 ) )
def _a ( self : int ):
"""simple docstring"""
A_ : Dict = self.prepare_config_and_inputs()
(
A_
) : str = config_and_inputs
A_ : Union[str, Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase_ (a__, a__, a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (
(
FalconModel,
FalconForCausalLM,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconForQuestionAnswering,
)
if is_torch_available()
else ()
)
_lowerCAmelCase = (FalconForCausalLM,) if is_torch_available() else ()
_lowerCAmelCase = (
{
'feature-extraction': FalconModel,
'text-classification': FalconForSequenceClassification,
'text-generation': FalconForCausalLM,
'question-answering': FalconForQuestionAnswering,
'token-classification': FalconForTokenClassification,
'zero-shot': FalconForSequenceClassification,
}
if is_torch_available()
else {}
)
_lowerCAmelCase = False
_lowerCAmelCase = False
def _a ( self : List[str] ):
"""simple docstring"""
A_ : str = FalconModelTester(self )
A_ : List[Any] = ConfigTester(self , config_class=_lowerCamelCase , hidden_size=37 )
def _a ( self : Any ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : int ):
"""simple docstring"""
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : str = self.model_tester.prepare_config_and_inputs()
for alibi in [True, False]:
A_ : Dict = alibi
self.model_tester.create_and_check_model(_lowerCamelCase , *_lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : int = self.model_tester.prepare_config_and_inputs_for_common()
A_ : List[Any] = 3
A_ : Any = input_dict['''input_ids''']
A_ : Tuple = input_ids.ne(1 ).to(_lowerCamelCase )
A_ : List[str] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
A_ : Dict = FalconForSequenceClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Optional[int] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , labels=_lowerCamelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Dict = 3
A_ : int = '''single_label_classification'''
A_ : Dict = input_dict['''input_ids''']
A_ : Dict = input_ids.ne(1 ).to(_lowerCamelCase )
A_ : Dict = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
A_ : List[str] = FalconForSequenceClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : List[Any] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , labels=_lowerCamelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Dict = input_dict['''input_ids''']
A_ : List[Any] = FalconForCausalLM(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Union[str, Any] = model(_lowerCamelCase , use_cache=_lowerCamelCase )
A_ : Union[str, Any] = input_ids.shape[0]
A_ : Tuple = model._convert_to_rw_cache(result.past_key_values )
A_ : List[str] = model._convert_cache_to_standard_format(_lowerCamelCase , _lowerCamelCase )
for layer in range(len(_lowerCamelCase ) ):
for tensor_idx in range(2 ):
self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3 )
self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4 )
self.assertTrue(
torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx] ) )
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : int = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Tuple = 3
A_ : str = '''multi_label_classification'''
A_ : Union[str, Any] = input_dict['''input_ids''']
A_ : List[Any] = input_ids.ne(1 ).to(_lowerCamelCase )
A_ : Tuple = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
A_ : List[str] = FalconForSequenceClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Optional[int] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , labels=_lowerCamelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
for model_class in self.all_generative_model_classes:
A_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(_lowerCamelCase , '''use_cache''' ):
return
A_ : Tuple = model_class(_lowerCamelCase ).to(_lowerCamelCase )
if "use_cache" not in inputs:
A_ : str = True
A_ : Tuple = model(**_lowerCamelCase )
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
return
A_ : List[Any] = (
getattr(_lowerCamelCase , '''decoder_layers''' , _lowerCamelCase )
or getattr(_lowerCamelCase , '''num_decoder_layers''' , _lowerCamelCase )
or config.num_hidden_layers
)
A_ : List[Any] = getattr(_lowerCamelCase , '''num_kv_heads''' , config.num_attention_heads )
A_ : Dict = getattr(_lowerCamelCase , '''d_model''' , config.hidden_size )
A_ : Optional[int] = embed_dim // num_attention_heads
A_ : List[Any] = outputs['''past_key_values''']
self.assertEqual(len(_lowerCamelCase ) , _lowerCamelCase )
A_ : int = inputs['''input_ids'''].shape
for i in range(_lowerCamelCase ):
if config.new_decoder_architecture:
A_ : int = config.num_attention_heads
elif config.multi_query:
A_ : List[Any] = 1
self.assertEqual(len(past_kv[0] ) , 2 ) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
self.assertEqual(
past_kv[i][1].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
@require_torch
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@slow
def _a ( self : int ):
"""simple docstring"""
A_ : List[Any] = AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b''' )
A_ : Tuple = FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b''' )
model.eval()
model.to(_lowerCamelCase )
A_ : List[Any] = tokenizer('''My favorite food is''' , return_tensors='''pt''' ).to(_lowerCamelCase )
A_ : Optional[Any] = (
'''My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'''
)
A_ : Tuple = model.generate(**_lowerCamelCase , do_sample=_lowerCamelCase , max_new_tokens=19 )
A_ : int = tokenizer.batch_decode(_lowerCamelCase )[0]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
@slow
def _a ( self : Union[str, Any] ):
"""simple docstring"""
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
A_ : str = AutoTokenizer.from_pretrained(_lowerCamelCase )
A_ : str = FalconForCausalLM.from_pretrained(_lowerCamelCase )
model.eval()
model.to(_lowerCamelCase )
A_ : int = tokenizer('''My favorite food is''' , return_tensors='''pt''' ).to(_lowerCamelCase )
# We just test that these run without errors - the models are randomly initialized
# and so the actual text outputs will be garbage
model.generate(**_lowerCamelCase , do_sample=_lowerCamelCase , max_new_tokens=4 )
model.generate(**_lowerCamelCase , do_sample=_lowerCamelCase , max_new_tokens=4 )
model.generate(**_lowerCamelCase , num_beams=2 , max_new_tokens=4 )
@slow
def _a ( self : Optional[int] ):
"""simple docstring"""
with torch.no_grad():
for repo in [
"Rocketknight1/falcon-rw-1b",
"Rocketknight1/tiny-random-falcon-7b",
"Rocketknight1/tiny-random-falcon-40b",
]:
A_ : Optional[Any] = AutoTokenizer.from_pretrained(_lowerCamelCase )
A_ : Optional[Any] = FalconForCausalLM.from_pretrained(_lowerCamelCase )
model.eval()
model.to(device=_lowerCamelCase )
A_ : Tuple = tokenizer('''My favorite food is''' , return_tensors='''pt''' ).to(_lowerCamelCase )
# Test results are the same with and without cache
A_ : str = model.generate(**_lowerCamelCase , do_sample=_lowerCamelCase , max_new_tokens=20 , use_cache=_lowerCamelCase )
A_ : int = model.generate(**_lowerCamelCase , do_sample=_lowerCamelCase , max_new_tokens=20 , use_cache=_lowerCamelCase )
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0 )
| 351 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
snake_case__ = logging.getLogger(__name__)
@dataclass(frozen=a__ )
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
@dataclass(frozen=a__ )
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 42
def __init__( self : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : PreTrainedTokenizer , _lowerCamelCase : str , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : List[Any]=False , _lowerCamelCase : bool = False , ):
"""simple docstring"""
A_ : Optional[int] = hans_processors[task]()
A_ : int = os.path.join(
_lowerCamelCase , '''cached_{}_{}_{}_{}'''.format(
'''dev''' if evaluate else '''train''' , tokenizer.__class__.__name__ , str(_lowerCamelCase ) , _lowerCamelCase , ) , )
A_ : Dict = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
A_ ,A_ : List[str] = label_list[2], label_list[1]
A_ : Optional[int] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
A_ : str = cached_features_file + '''.lock'''
with FileLock(_lowerCamelCase ):
if os.path.exists(_lowerCamelCase ) and not overwrite_cache:
logger.info(f'Loading features from cached file {cached_features_file}' )
A_ : List[str] = torch.load(_lowerCamelCase )
else:
logger.info(f'Creating features from dataset file at {data_dir}' )
A_ : Optional[int] = (
processor.get_dev_examples(_lowerCamelCase ) if evaluate else processor.get_train_examples(_lowerCamelCase )
)
logger.info('''Training examples: %s''' , len(_lowerCamelCase ) )
A_ : Optional[int] = hans_convert_examples_to_features(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
logger.info('''Saving features into cached file %s''' , _lowerCamelCase )
torch.save(self.features , _lowerCamelCase )
def __len__( self : List[str] ):
"""simple docstring"""
return len(self.features )
def __getitem__( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
return self.features[i]
def _a ( self : str ):
"""simple docstring"""
return self.label_list
if is_tf_available():
import tensorflow as tf
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
def __init__( self : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : PreTrainedTokenizer , _lowerCamelCase : str , _lowerCamelCase : Optional[int] = 128 , _lowerCamelCase : Dict=False , _lowerCamelCase : bool = False , ):
"""simple docstring"""
A_ : Optional[int] = hans_processors[task]()
A_ : Optional[int] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
A_ ,A_ : Union[str, Any] = label_list[2], label_list[1]
A_ : Tuple = label_list
A_ : Optional[int] = processor.get_dev_examples(_lowerCamelCase ) if evaluate else processor.get_train_examples(_lowerCamelCase )
A_ : Tuple = hans_convert_examples_to_features(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='''convert examples to features''' ):
if ex_index % 10000 == 0:
logger.info('''Writing example %d of %d''' % (ex_index, len(_lowerCamelCase )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
A_ : List[Any] = tf.data.Dataset.from_generator(
_lowerCamelCase , (
{
'''example_id''': tf.intaa,
'''input_ids''': tf.intaa,
'''attention_mask''': tf.intaa,
'''token_type_ids''': tf.intaa,
},
tf.intaa,
) , (
{
'''example_id''': tf.TensorShape([] ),
'''input_ids''': tf.TensorShape([None, None] ),
'''attention_mask''': tf.TensorShape([None, None] ),
'''token_type_ids''': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def _a ( self : Any ):
"""simple docstring"""
return self.dataset
def __len__( self : Dict ):
"""simple docstring"""
return len(self.features )
def __getitem__( self : Optional[int] , _lowerCamelCase : List[str] ):
"""simple docstring"""
return self.features[i]
def _a ( self : Tuple ):
"""simple docstring"""
return self.label_list
class UpperCamelCase_ (a__ ):
"""simple docstring"""
def _a ( self : List[str] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(_lowerCamelCase , '''heuristics_train_set.txt''' ) ) , '''train''' )
def _a ( self : List[str] , _lowerCamelCase : Tuple ):
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(_lowerCamelCase , '''heuristics_evaluation_set.txt''' ) ) , '''dev''' )
def _a ( self : Any ):
"""simple docstring"""
return ["contradiction", "entailment", "neutral"]
def _a ( self : Optional[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : Tuple = []
for i, line in enumerate(_lowerCamelCase ):
if i == 0:
continue
A_ : str = '''%s-%s''' % (set_type, line[0])
A_ : Optional[Any] = line[5]
A_ : Union[str, Any] = line[6]
A_ : List[str] = line[7][2:] if line[7].startswith('''ex''' ) else line[7]
A_ : str = line[0]
examples.append(InputExample(guid=_lowerCamelCase , text_a=_lowerCamelCase , text_b=_lowerCamelCase , label=_lowerCamelCase , pairID=_lowerCamelCase ) )
return examples
def snake_case__ ( lowerCamelCase__ : List[InputExample] , lowerCamelCase__ : List[str] , lowerCamelCase__ : int , lowerCamelCase__ : PreTrainedTokenizer , ) -> int:
A_ : Union[str, Any] = {label: i for i, label in enumerate(lowerCamelCase__ )}
A_ : Optional[Any] = []
for ex_index, example in tqdm.tqdm(enumerate(lowerCamelCase__ ) , desc='''convert examples to features''' ):
if ex_index % 1_0_0_0_0 == 0:
logger.info('''Writing example %d''' % (ex_index) )
A_ : Optional[int] = tokenizer(
example.text_a , example.text_b , add_special_tokens=lowerCamelCase__ , max_length=lowerCamelCase__ , padding='''max_length''' , truncation=lowerCamelCase__ , return_overflowing_tokens=lowerCamelCase__ , )
A_ : List[str] = label_map[example.label] if example.label in label_map else 0
A_ : Tuple = int(example.pairID )
features.append(InputFeatures(**lowerCamelCase__ , label=lowerCamelCase__ , pairID=lowerCamelCase__ ) )
for i, example in enumerate(examples[:5] ):
logger.info('''*** Example ***''' )
logger.info(f'guid: {example}' )
logger.info(f'features: {features[i]}' )
return features
snake_case__ = {
"""hans""": 3,
}
snake_case__ = {
"""hans""": HansProcessor,
}
| 4 | 0 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class UpperCamelCase_ (a__ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : Any=0.01 , _lowerCamelCase : List[str]=1000 ):
"""simple docstring"""
A_ : Optional[Any] = p_stop
A_ : Dict = max_length
def __iter__( self : Optional[Any] ):
"""simple docstring"""
A_ : Optional[int] = 0
A_ : Optional[int] = False
while not stop and count < self.max_length:
yield count
count += 1
A_ : Tuple = random.random() < self.p_stop
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : str=False , _lowerCamelCase : List[Any]=True ):
"""simple docstring"""
A_ : Dict = [
BatchSamplerShard(_lowerCamelCase , 2 , _lowerCamelCase , split_batches=_lowerCamelCase , even_batches=_lowerCamelCase )
for i in range(2 )
]
A_ : int = [list(_lowerCamelCase ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(_lowerCamelCase ) for shard in batch_sampler_shards] , [len(_lowerCamelCase ) for e in expected] )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Tuple = BatchSampler(range(24 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=_lowerCamelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
A_ : Optional[int] = BatchSampler(range(21 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase )
A_ : str = BatchSampler(range(21 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Tuple = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
A_ : Dict = BatchSampler(range(22 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Dict = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase )
A_ : Union[str, Any] = BatchSampler(range(22 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
A_ : Any = BatchSampler(range(20 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Dict = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase )
# Check the shards when the dataset is very small.
A_ : str = BatchSampler(range(2 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Union[str, Any] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase )
A_ : int = BatchSampler(range(2 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Union[str, Any] = [[], []]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase )
def _a ( self : str ):
"""simple docstring"""
A_ : Dict = BatchSampler(range(24 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : List[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase )
A_ : List[str] = BatchSampler(range(24 ) , batch_size=4 , drop_last=_lowerCamelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase )
# Check the shards when the dataset is not a round multiple of batch size.
A_ : List[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : Any = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase )
A_ : Any = BatchSampler(range(22 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
A_ : Dict = BatchSampler(range(21 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : Tuple = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase )
A_ : Optional[Any] = BatchSampler(range(21 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : Optional[int] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase )
# Check the shards when the dataset is very small.
A_ : Optional[Any] = BatchSampler(range(2 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : Tuple = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase )
A_ : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : Any = [[], []]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase )
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Any = BatchSampler(range(24 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , even_batches=_lowerCamelCase )
A_ : Tuple = BatchSampler(range(24 ) , batch_size=3 , drop_last=_lowerCamelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , even_batches=_lowerCamelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
A_ : Tuple = BatchSampler(range(21 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , even_batches=_lowerCamelCase )
A_ : List[str] = BatchSampler(range(21 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Dict = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , even_batches=_lowerCamelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
A_ : Optional[int] = BatchSampler(range(22 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Optional[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , even_batches=_lowerCamelCase )
A_ : List[str] = BatchSampler(range(22 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , even_batches=_lowerCamelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
A_ : Tuple = BatchSampler(range(20 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : str = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , even_batches=_lowerCamelCase )
A_ : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Dict = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , even_batches=_lowerCamelCase )
# Check the shards when the dataset is very small.
A_ : str = BatchSampler(range(2 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : Optional[Any] = [[[0, 1]], []]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , even_batches=_lowerCamelCase )
A_ : List[str] = BatchSampler(range(2 ) , batch_size=3 , drop_last=_lowerCamelCase )
A_ : int = [[], []]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , even_batches=_lowerCamelCase )
def _a ( self : Dict ):
"""simple docstring"""
A_ : List[str] = BatchSampler(range(24 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : Optional[int] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase , even_batches=_lowerCamelCase )
A_ : Union[str, Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=_lowerCamelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase , even_batches=_lowerCamelCase )
# Check the shards when the dataset is not a round multiple of batch size.
A_ : Optional[int] = BatchSampler(range(22 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : int = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase , even_batches=_lowerCamelCase )
A_ : int = BatchSampler(range(22 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase , even_batches=_lowerCamelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
A_ : Optional[Any] = BatchSampler(range(21 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase , even_batches=_lowerCamelCase )
A_ : List[str] = BatchSampler(range(21 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : Optional[int] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase , even_batches=_lowerCamelCase )
# Check the shards when the dataset is very small.
A_ : Union[str, Any] = BatchSampler(range(2 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : str = [[[0, 1]], []]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase , even_batches=_lowerCamelCase )
A_ : Any = BatchSampler(range(2 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : Union[str, Any] = [[], []]
self.check_batch_sampler_shards(_lowerCamelCase , _lowerCamelCase , split_batches=_lowerCamelCase , even_batches=_lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
A_ : Union[str, Any] = [BatchSamplerShard(_lowerCamelCase , 2 , _lowerCamelCase , even_batches=_lowerCamelCase ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def _a ( self : List[str] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Any=False , _lowerCamelCase : int=2 , _lowerCamelCase : Union[str, Any]=False ):
"""simple docstring"""
random.seed(_lowerCamelCase )
A_ : Dict = list(_lowerCamelCase )
A_ : Dict = [
IterableDatasetShard(
_lowerCamelCase , batch_size=_lowerCamelCase , drop_last=_lowerCamelCase , num_processes=_lowerCamelCase , process_index=_lowerCamelCase , split_batches=_lowerCamelCase , )
for i in range(_lowerCamelCase )
]
A_ : List[Any] = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(_lowerCamelCase )
iterable_dataset_lists.append(list(_lowerCamelCase ) )
A_ : Any = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
A_ : int = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) )
self.assertTrue(len(_lowerCamelCase ) % shard_batch_size == 0 )
A_ : Union[str, Any] = []
for idx in range(0 , len(_lowerCamelCase ) , _lowerCamelCase ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(_lowerCamelCase ) < len(_lowerCamelCase ):
reference += reference
self.assertListEqual(_lowerCamelCase , reference[: len(_lowerCamelCase )] )
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Dict = 42
A_ : Optional[int] = RandomIterableDataset()
self.check_iterable_dataset_shards(_lowerCamelCase , _lowerCamelCase , batch_size=4 , drop_last=_lowerCamelCase , split_batches=_lowerCamelCase )
self.check_iterable_dataset_shards(_lowerCamelCase , _lowerCamelCase , batch_size=4 , drop_last=_lowerCamelCase , split_batches=_lowerCamelCase )
self.check_iterable_dataset_shards(_lowerCamelCase , _lowerCamelCase , batch_size=4 , drop_last=_lowerCamelCase , split_batches=_lowerCamelCase )
self.check_iterable_dataset_shards(_lowerCamelCase , _lowerCamelCase , batch_size=4 , drop_last=_lowerCamelCase , split_batches=_lowerCamelCase )
# Edge case with a very small dataset
A_ : List[Any] = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(_lowerCamelCase , _lowerCamelCase , batch_size=4 , drop_last=_lowerCamelCase , split_batches=_lowerCamelCase )
self.check_iterable_dataset_shards(_lowerCamelCase , _lowerCamelCase , batch_size=4 , drop_last=_lowerCamelCase , split_batches=_lowerCamelCase )
self.check_iterable_dataset_shards(_lowerCamelCase , _lowerCamelCase , batch_size=4 , drop_last=_lowerCamelCase , split_batches=_lowerCamelCase )
self.check_iterable_dataset_shards(_lowerCamelCase , _lowerCamelCase , batch_size=4 , drop_last=_lowerCamelCase , split_batches=_lowerCamelCase )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Union[str, Any] = BatchSampler(range(16 ) , batch_size=4 , drop_last=_lowerCamelCase )
A_ : int = SkipBatchSampler(_lowerCamelCase , 2 )
self.assertListEqual(list(_lowerCamelCase ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : str = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : int = DataLoader(list(range(16 ) ) , batch_size=4 )
A_ : Tuple = skip_first_batches(_lowerCamelCase , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Optional[Any] = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(_lowerCamelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(_lowerCamelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def _a ( self : Tuple ):
"""simple docstring"""
Accelerator()
A_ : int = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(_lowerCamelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(_lowerCamelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 352 |
'''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
snake_case__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCamelCase_ (datasets.BuilderConfig ):
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = "utf-8"
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = True # deprecated
_lowerCAmelCase = None # deprecated
_lowerCAmelCase = 1_0 << 2_0 # 10MB
_lowerCAmelCase = None
class UpperCamelCase_ (datasets.ArrowBasedBuilder ):
"""simple docstring"""
_lowerCAmelCase = JsonConfig
def _a ( self : int ):
"""simple docstring"""
if self.config.block_size is not None:
logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''' )
A_ : List[Any] = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
'''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''' )
if self.config.newlines_in_values is not None:
raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''' )
return datasets.DatasetInfo(features=self.config.features )
def _a ( self : Any , _lowerCamelCase : List[str] ):
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}' )
A_ : int = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_lowerCamelCase , (str, list, tuple) ):
A_ : Union[str, Any] = data_files
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : List[str] = [files]
A_ : List[Any] = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
A_ : Tuple = []
for split_name, files in data_files.items():
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : int = [files]
A_ : Union[str, Any] = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=_lowerCamelCase , gen_kwargs={'''files''': files} ) )
return splits
def _a ( self : int , _lowerCamelCase : pa.Table ):
"""simple docstring"""
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
A_ : Optional[int] = self.config.features.arrow_schema.field(_lowerCamelCase ).type
A_ : Optional[int] = pa_table.append_column(_lowerCamelCase , pa.array([None] * len(_lowerCamelCase ) , type=_lowerCamelCase ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
A_ : str = table_cast(_lowerCamelCase , self.config.features.arrow_schema )
return pa_table
def _a ( self : List[str] , _lowerCamelCase : int ):
"""simple docstring"""
for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(_lowerCamelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
A_ : int = json.load(_lowerCamelCase )
# We keep only the field we are interested in
A_ : List[str] = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(_lowerCamelCase , (list, tuple) ):
A_ : int = set().union(*[row.keys() for row in dataset] )
A_ : List[str] = {col: [row.get(_lowerCamelCase ) for row in dataset] for col in keys}
else:
A_ : Tuple = dataset
A_ : Dict = pa.Table.from_pydict(_lowerCamelCase )
yield file_idx, self._cast_table(_lowerCamelCase )
# If the file has one json object per line
else:
with open(_lowerCamelCase , '''rb''' ) as f:
A_ : int = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
A_ : int = max(self.config.chunksize // 32 , 16 << 10 )
A_ : int = (
self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
)
while True:
A_ : Any = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(_lowerCamelCase )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
A_ : Optional[Any] = batch.decode(self.config.encoding , errors=_lowerCamelCase ).encode('''utf-8''' )
try:
while True:
try:
A_ : List[Any] = paj.read_json(
io.BytesIO(_lowerCamelCase ) , read_options=paj.ReadOptions(block_size=_lowerCamelCase ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(_lowerCamelCase , pa.ArrowInvalid )
and "straddling" not in str(_lowerCamelCase )
or block_size > len(_lowerCamelCase )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f'Batch of {len(_lowerCamelCase )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.' )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
_lowerCamelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
A_ : Optional[Any] = json.load(_lowerCamelCase )
except json.JSONDecodeError:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(_lowerCamelCase , _lowerCamelCase ): # list is the only sequence type supported in JSON
try:
A_ : Optional[int] = set().union(*[row.keys() for row in dataset] )
A_ : Tuple = {col: [row.get(_lowerCamelCase ) for row in dataset] for col in keys}
A_ : int = pa.Table.from_pydict(_lowerCamelCase )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise ValueError(f'Not able to read records in the JSON file at {file}.' ) from None
yield file_idx, self._cast_table(_lowerCamelCase )
break
else:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise ValueError(
f'Not able to read records in the JSON file at {file}. '
f'You should probably indicate the field of the JSON file containing your records. '
f'This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '
f'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ' ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(_lowerCamelCase )
batch_idx += 1
| 4 | 0 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = CpmAntTokenizer
_lowerCAmelCase = False
def _a ( self : Union[str, Any] ):
"""simple docstring"""
super().setUp()
A_ : List[str] = [
'''<d>''',
'''</d>''',
'''<s>''',
'''</s>''',
'''</_>''',
'''<unk>''',
'''<pad>''',
'''</n>''',
'''我''',
'''是''',
'''C''',
'''P''',
'''M''',
'''A''',
'''n''',
'''t''',
]
A_ : List[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] ) )
@tooslow
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Union[str, Any] = CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b''' )
A_ : Optional[Any] = '''今天天气真好！'''
A_ : str = ['''今天''', '''天气''', '''真''', '''好''', '''！''']
A_ : int = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
A_ : List[str] = '''今天天气真好！'''
A_ : List[Any] = [tokenizer.bos_token] + tokens
A_ : Any = [6, 9802, 14962, 2082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , _lowerCamelCase )
A_ : List[Any] = tokenizer.decode(_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
| 353 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/swin-tiny-patch4-window7-224""": (
"""https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class UpperCamelCase_ (a__, a__ ):
"""simple docstring"""
_lowerCAmelCase = 'swin'
_lowerCAmelCase = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Any , _lowerCamelCase : Optional[Any]=224 , _lowerCamelCase : List[str]=4 , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Tuple=96 , _lowerCamelCase : List[Any]=[2, 2, 6, 2] , _lowerCamelCase : List[str]=[3, 6, 12, 24] , _lowerCamelCase : List[Any]=7 , _lowerCamelCase : Optional[int]=4.0 , _lowerCamelCase : List[str]=True , _lowerCamelCase : List[str]=0.0 , _lowerCamelCase : Any=0.0 , _lowerCamelCase : Dict=0.1 , _lowerCamelCase : List[str]="gelu" , _lowerCamelCase : Tuple=False , _lowerCamelCase : Dict=0.02 , _lowerCamelCase : Optional[Any]=1E-5 , _lowerCamelCase : Any=32 , _lowerCamelCase : Tuple=None , _lowerCamelCase : Any=None , **_lowerCamelCase : str , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
A_ : Optional[int] = image_size
A_ : Optional[int] = patch_size
A_ : Optional[int] = num_channels
A_ : Any = embed_dim
A_ : List[Any] = depths
A_ : Any = len(_lowerCamelCase )
A_ : List[Any] = num_heads
A_ : Tuple = window_size
A_ : Tuple = mlp_ratio
A_ : Dict = qkv_bias
A_ : List[str] = hidden_dropout_prob
A_ : List[str] = attention_probs_dropout_prob
A_ : Any = drop_path_rate
A_ : List[Any] = hidden_act
A_ : Tuple = use_absolute_embeddings
A_ : int = layer_norm_eps
A_ : Optional[Any] = initializer_range
A_ : Union[str, Any] = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
A_ : str = int(embed_dim * 2 ** (len(_lowerCamelCase ) - 1) )
A_ : str = ['''stem'''] + [f'stage{idx}' for idx in range(1 , len(_lowerCamelCase ) + 1 )]
A_ ,A_ : Optional[Any] = get_aligned_output_features_output_indices(
out_features=_lowerCamelCase , out_indices=_lowerCamelCase , stage_names=self.stage_names )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : str ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return 1E-4
| 4 | 0 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
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.models.esm.modeling_esmfold import EsmForProteinFolding
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Tuple=13 , _lowerCamelCase : Optional[Any]=7 , _lowerCamelCase : List[str]=False , _lowerCamelCase : Dict=True , _lowerCamelCase : List[str]=False , _lowerCamelCase : List[Any]=False , _lowerCamelCase : str=19 , _lowerCamelCase : Dict=32 , _lowerCamelCase : str=5 , _lowerCamelCase : Optional[int]=4 , _lowerCamelCase : List[Any]=37 , _lowerCamelCase : List[str]="gelu" , _lowerCamelCase : Optional[int]=0.1 , _lowerCamelCase : List[Any]=0.1 , _lowerCamelCase : List[Any]=512 , _lowerCamelCase : List[str]=16 , _lowerCamelCase : Union[str, Any]=2 , _lowerCamelCase : Dict=0.02 , _lowerCamelCase : str=3 , _lowerCamelCase : Optional[int]=4 , _lowerCamelCase : str=None , ):
"""simple docstring"""
A_ : List[str] = parent
A_ : Tuple = batch_size
A_ : Dict = seq_length
A_ : List[Any] = is_training
A_ : Optional[int] = use_input_mask
A_ : int = use_token_type_ids
A_ : Dict = use_labels
A_ : List[str] = vocab_size
A_ : Optional[Any] = hidden_size
A_ : Optional[Any] = num_hidden_layers
A_ : Optional[Any] = num_attention_heads
A_ : Optional[Any] = intermediate_size
A_ : int = hidden_act
A_ : int = hidden_dropout_prob
A_ : List[Any] = attention_probs_dropout_prob
A_ : Any = max_position_embeddings
A_ : Optional[Any] = type_vocab_size
A_ : Optional[Any] = type_sequence_label_size
A_ : Tuple = initializer_range
A_ : Any = num_labels
A_ : Optional[Any] = num_choices
A_ : int = scope
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ : int = None
if self.use_input_mask:
A_ : Dict = random_attention_mask([self.batch_size, self.seq_length] )
A_ : Any = None
A_ : Tuple = None
A_ : List[str] = None
if self.use_labels:
A_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
A_ : List[Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[Any] = EsmConfig(
vocab_size=33 , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , is_folding_model=_lowerCamelCase , esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False} , )
return config
def _a ( self : Optional[Any] , _lowerCamelCase : List[Any] , _lowerCamelCase : Dict , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ : List[str] = EsmForProteinFolding(config=_lowerCamelCase ).float()
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase , attention_mask=_lowerCamelCase )
A_ : Dict = model(_lowerCamelCase )
A_ : str = model(_lowerCamelCase )
self.parent.assertEqual(result.positions.shape , (8, self.batch_size, self.seq_length, 14, 3) )
self.parent.assertEqual(result.angles.shape , (8, self.batch_size, self.seq_length, 7, 2) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[str] = self.prepare_config_and_inputs()
(
A_
) : str = config_and_inputs
A_ : Any = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase_ (a__, a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase : Optional[int] = False
_lowerCAmelCase : Optional[int] = (EsmForProteinFolding,) if is_torch_available() else ()
_lowerCAmelCase : List[str] = ()
_lowerCAmelCase : Tuple = {} if is_torch_available() else {}
_lowerCAmelCase : str = False
def _a ( self : Tuple ):
"""simple docstring"""
A_ : List[str] = EsmFoldModelTester(self )
A_ : Union[str, Any] = ConfigTester(self , config_class=_lowerCamelCase , hidden_size=37 )
def _a ( self : Optional[int] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : int ):
"""simple docstring"""
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
@unittest.skip('''Does not support attention outputs''' )
def _a ( self : Tuple ):
"""simple docstring"""
pass
@unittest.skip
def _a ( self : Dict ):
"""simple docstring"""
pass
@unittest.skip('''Esm does not support embedding resizing''' )
def _a ( self : int ):
"""simple docstring"""
pass
@unittest.skip('''Esm does not support embedding resizing''' )
def _a ( self : Any ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold does not support passing input embeds!''' )
def _a ( self : str ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def _a ( self : Tuple ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def _a ( self : List[str] ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def _a ( self : Optional[Any] ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold does not support head pruning.''' )
def _a ( self : int ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold does not output hidden states in the normal way.''' )
def _a ( self : Tuple ):
"""simple docstring"""
pass
@unittest.skip('''ESMfold does not output hidden states in the normal way.''' )
def _a ( self : List[Any] ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold only has one output format.''' )
def _a ( self : Dict ):
"""simple docstring"""
pass
@unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality''' )
def _a ( self : Any ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold does not support input chunking.''' )
def _a ( self : Any ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.''' )
def _a ( self : List[Any] ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''' )
def _a ( self : Any ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''' )
def _a ( self : Optional[Any] ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold doesn\'t support torchscript compilation.''' )
def _a ( self : List[str] ):
"""simple docstring"""
pass
@unittest.skip('''ESMFold doesn\'t support data parallel.''' )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def _a ( self : Optional[int] ):
"""simple docstring"""
pass
@require_torch
class UpperCamelCase_ (a__ ):
"""simple docstring"""
@slow
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Tuple = EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1''' ).float()
model.eval()
A_ : List[Any] = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
A_ : str = model(_lowerCamelCase )['''positions''']
A_ : Any = torch.tensor([2.58_28, 0.79_93, -10.93_34] , dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] , _lowerCamelCase , atol=1E-4 ) )
| 354 |
'''simple docstring'''
from __future__ import annotations
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> list[int]:
A_ : int = 0
A_ : str = len(lowerCamelCase__ ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
A_ : Tuple = i + 1
else:
A_ : List[str] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{two_pointer([2, 7, 11, 15], 9) = }')
| 4 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : float = 0 ):
"""simple docstring"""
A_ : Tuple = row, column
A_ : Union[str, Any] = [[default_value for c in range(_lowerCamelCase )] for r in range(_lowerCamelCase )]
def __str__( self : List[Any] ):
"""simple docstring"""
A_ : List[str] = f'Matrix consist of {self.row} rows and {self.column} columns\n'
# Make string identifier
A_ : Any = 0
for row_vector in self.array:
for obj in row_vector:
A_ : int = max(_lowerCamelCase , len(str(_lowerCamelCase ) ) )
A_ : str = f'%{max_element_length}s'
# Make string and return
def single_line(_lowerCamelCase : list[float] ) -> str:
nonlocal string_format_identifier
A_ : Optional[int] = '''['''
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
line += "]"
return line
s += "\n".join(single_line(_lowerCamelCase ) for row_vector in self.array )
return s
def __repr__( self : List[Any] ):
"""simple docstring"""
return str(self )
def _a ( self : Optional[Any] , _lowerCamelCase : tuple[int, int] ):
"""simple docstring"""
if not (isinstance(_lowerCamelCase , (list, tuple) ) and len(_lowerCamelCase ) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__( self : Optional[Any] , _lowerCamelCase : tuple[int, int] ):
"""simple docstring"""
assert self.validate_indicies(_lowerCamelCase )
return self.array[loc[0]][loc[1]]
def __setitem__( self : Dict , _lowerCamelCase : tuple[int, int] , _lowerCamelCase : float ):
"""simple docstring"""
assert self.validate_indicies(_lowerCamelCase )
A_ : List[str] = value
def __add__( self : Tuple , _lowerCamelCase : Matrix ):
"""simple docstring"""
assert isinstance(_lowerCamelCase , _lowerCamelCase )
assert self.row == another.row and self.column == another.column
# Add
A_ : int = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
A_ : Dict = self[r, c] + another[r, c]
return result
def __neg__( self : List[str] ):
"""simple docstring"""
A_ : List[str] = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
A_ : str = -self[r, c]
return result
def __sub__( self : Union[str, Any] , _lowerCamelCase : Matrix ):
"""simple docstring"""
return self + (-another)
def __mul__( self : str , _lowerCamelCase : int | float | Matrix ):
"""simple docstring"""
if isinstance(_lowerCamelCase , (int, float) ): # Scalar multiplication
A_ : int = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
A_ : Union[str, Any] = self[r, c] * another
return result
elif isinstance(_lowerCamelCase , _lowerCamelCase ): # Matrix multiplication
assert self.column == another.row
A_ : int = Matrix(self.row , another.column )
for r in range(self.row ):
for c in range(another.column ):
for i in range(self.column ):
result[r, c] += self[r, i] * another[i, c]
return result
else:
A_ : List[Any] = f'Unsupported type given for another ({type(_lowerCamelCase )})'
raise TypeError(_lowerCamelCase )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[Any] = Matrix(self.column , self.row )
for r in range(self.row ):
for c in range(self.column ):
A_ : Union[str, Any] = self[r, c]
return result
def _a ( self : str , _lowerCamelCase : Matrix , _lowerCamelCase : Matrix ):
"""simple docstring"""
assert isinstance(_lowerCamelCase , _lowerCamelCase ) and isinstance(_lowerCamelCase , _lowerCamelCase )
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
A_ : Tuple = v.transpose()
A_ : Dict = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def snake_case__ ( ) -> None:
# a^(-1)
A_ : Union[str, Any] = Matrix(3 , 3 , 0 )
for i in range(3 ):
A_ : Optional[Any] = 1
print(f'a^(-1) is {ainv}' )
# u, v
A_ : str = Matrix(3 , 1 , 0 )
A_ : int = 1, 2, -3
A_ : str = Matrix(3 , 1 , 0 )
A_ : Optional[Any] = 4, -2, 5
print(f'u is {u}' )
print(f'v is {v}' )
print(f'uv^T is {u * v.transpose()}' )
# Sherman Morrison
print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(lowerCamelCase__ , lowerCamelCase__ )}' )
def snake_case__ ( ) -> None:
import doctest
doctest.testmod()
testa()
| 355 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCamelCase__ ) )
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
# Base Case
if index == len(lowerCamelCase__ ):
return True
# Recursive Step
for i in range(lowerCamelCase__ ):
if valid_coloring(graph[index] , lowerCamelCase__ , lowerCamelCase__ ):
# Color current vertex
A_ : int = i
# Validate coloring
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , index + 1 ):
return True
# Backtrack
A_ : str = -1
return False
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int ) -> list[int]:
A_ : List[str] = [-1] * len(lowerCamelCase__ )
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , 0 ):
return colored_vertices
return []
| 4 | 0 |
'''simple docstring'''
snake_case__ = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def snake_case__ ( lowerCamelCase__ : dict , lowerCamelCase__ : int , lowerCamelCase__ : Optional[Any] ) -> list[str]:
A_ : List[str] = set()
# keep track of all the paths to be checked
A_ : List[Any] = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
A_ : Optional[Any] = queue.pop(0 )
# get the last node from the path
A_ : Optional[int] = path[-1]
if node not in explored:
A_ : Union[str, Any] = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
A_ : Dict = list(lowerCamelCase__ )
new_path.append(lowerCamelCase__ )
queue.append(lowerCamelCase__ )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(lowerCamelCase__ )
# in case there's no path between the 2 nodes
return []
def snake_case__ ( lowerCamelCase__ : dict , lowerCamelCase__ : Tuple , lowerCamelCase__ : Any ) -> int:
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
A_ : str = [start]
A_ : Dict = set(lowerCamelCase__ )
# Keep tab on distances from `start` node.
A_ : Optional[Any] = {start: 0, target: -1}
while queue:
A_ : List[str] = queue.pop(0 )
if node == target:
A_ : Tuple = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(lowerCamelCase__ )
queue.append(lowerCamelCase__ )
A_ : List[str] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, """G""", """D""")) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, """G""", """D""")) # returns 4
| 356 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case__ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[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],
]
# Define blinker example
snake_case__ = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def snake_case__ ( lowerCamelCase__ : list[list[int]] ) -> list[list[int]]:
A_ : str = []
for i in range(len(lowerCamelCase__ ) ):
A_ : Optional[Any] = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
A_ : Optional[int] = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(lowerCamelCase__ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(lowerCamelCase__ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(lowerCamelCase__ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
A_ : List[str] = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(lowerCamelCase__ )
return next_generation
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int ) -> list[Image.Image]:
A_ : List[Any] = []
for _ in range(lowerCamelCase__ ):
# Create output image
A_ : Optional[int] = Image.new('''RGB''' , (len(cells[0] ), len(lowerCamelCase__ )) )
A_ : int = img.load()
# Save cells to image
for x in range(len(lowerCamelCase__ ) ):
for y in range(len(cells[0] ) ):
A_ : Optional[Any] = 2_5_5 - cells[y][x] * 2_5_5
A_ : str = (colour, colour, colour)
# Save image
images.append(lowerCamelCase__ )
A_ : Optional[int] = new_generation(lowerCamelCase__ )
return images
if __name__ == "__main__":
snake_case__ = generate_images(GLIDER, 16)
images[0].save("""out.gif""", save_all=True, append_images=images[1:])
| 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ = {
"""configuration_blip_2""": [
"""BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Blip2Config""",
"""Blip2QFormerConfig""",
"""Blip2VisionConfig""",
],
"""processing_blip_2""": ["""Blip2Processor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = [
"""BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Blip2Model""",
"""Blip2QFormerModel""",
"""Blip2PreTrainedModel""",
"""Blip2ForConditionalGeneration""",
"""Blip2VisionModel""",
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
snake_case__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 357 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Any = tempfile.mkdtemp()
A_ : List[Any] = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
A_ : 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] ) )
A_ : Tuple = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
A_ : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Dict , **_lowerCamelCase : Tuple ):
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _a ( self : Optional[int] , **_lowerCamelCase : Optional[int] ):
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _a ( self : Optional[Any] , **_lowerCamelCase : Tuple ):
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self : int ):
"""simple docstring"""
A_ : Union[str, Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A_ : Any = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self : int ):
"""simple docstring"""
A_ : Tuple = self.get_tokenizer()
A_ : Tuple = self.get_rust_tokenizer()
A_ : Dict = self.get_image_processor()
A_ : List[Any] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor_slow.save_pretrained(self.tmpdirname )
A_ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_lowerCamelCase )
A_ : Any = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor_fast.save_pretrained(self.tmpdirname )
A_ : List[Any] = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _lowerCamelCase )
self.assertIsInstance(processor_fast.tokenizer , _lowerCamelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _lowerCamelCase )
self.assertIsInstance(processor_fast.image_processor , _lowerCamelCase )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[str] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A_ : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
A_ : Tuple = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
A_ : List[str] = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = self.get_image_processor()
A_ : Any = self.get_tokenizer()
A_ : List[str] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : Any = self.prepare_image_inputs()
A_ : List[Any] = image_processor(_lowerCamelCase , return_tensors='''np''' )
A_ : str = processor(images=_lowerCamelCase , 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 : Dict ):
"""simple docstring"""
A_ : str = self.get_image_processor()
A_ : List[str] = self.get_tokenizer()
A_ : Optional[int] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : int = '''lower newer'''
A_ : str = processor(text=_lowerCamelCase )
A_ : Dict = tokenizer(_lowerCamelCase , padding='''max_length''' , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self : str ):
"""simple docstring"""
A_ : Optional[int] = self.get_image_processor()
A_ : Optional[Any] = self.get_tokenizer()
A_ : List[str] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : List[Any] = '''lower newer'''
A_ : Optional[int] = self.prepare_image_inputs()
A_ : List[Any] = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(_lowerCamelCase ):
processor()
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Optional[Any] = self.get_image_processor()
A_ : Optional[int] = self.get_tokenizer()
A_ : List[Any] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : str = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A_ : str = processor.batch_decode(_lowerCamelCase )
A_ : Union[str, Any] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : str = self.get_image_processor()
A_ : Tuple = self.get_tokenizer()
A_ : Any = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : str = '''lower newer'''
A_ : List[str] = self.prepare_image_inputs()
A_ : Tuple = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case__ = {
"""configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = [
"""RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ResNetForImageClassification""",
"""ResNetModel""",
"""ResNetPreTrainedModel""",
"""ResNetBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = [
"""TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFResNetForImageClassification""",
"""TFResNetModel""",
"""TFResNetPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = [
"""FlaxResNetForImageClassification""",
"""FlaxResNetModel""",
"""FlaxResNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
snake_case__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 358 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = """▁"""
snake_case__ = {
"""vocab_file""": """vocab.json""",
"""spm_file""": """sentencepiece.bpe.model""",
}
snake_case__ = {
"""vocab_file""": {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
),
},
"""spm_file""": {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
)
},
}
snake_case__ = {
"""facebook/s2t-small-librispeech-asr""": 10_24,
}
snake_case__ = ["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]
snake_case__ = {"""mustc""": MUSTC_LANGS}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = VOCAB_FILES_NAMES
_lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase = MAX_MODEL_INPUT_SIZES
_lowerCAmelCase = ['input_ids', 'attention_mask']
_lowerCAmelCase = []
def __init__( self : Optional[int] , _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : str="<s>" , _lowerCamelCase : Union[str, Any]="</s>" , _lowerCamelCase : Dict="<pad>" , _lowerCamelCase : str="<unk>" , _lowerCamelCase : Union[str, Any]=False , _lowerCamelCase : int=False , _lowerCamelCase : Any=None , _lowerCamelCase : Any=None , _lowerCamelCase : Optional[Dict[str, Any]] = None , **_lowerCamelCase : Optional[int] , ):
"""simple docstring"""
A_ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , do_upper_case=_lowerCamelCase , do_lower_case=_lowerCamelCase , tgt_lang=_lowerCamelCase , lang_codes=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
A_ : Optional[int] = do_upper_case
A_ : Tuple = do_lower_case
A_ : Tuple = load_json(_lowerCamelCase )
A_ : Tuple = {v: k for k, v in self.encoder.items()}
A_ : List[Any] = spm_file
A_ : List[str] = load_spm(_lowerCamelCase , self.sp_model_kwargs )
if lang_codes is not None:
A_ : Any = lang_codes
A_ : Optional[Any] = LANGUAGES[lang_codes]
A_ : Optional[Any] = [f'<lang:{lang}>' for lang in self.langs]
A_ : Union[str, Any] = {lang: self.sp_model.PieceToId(f'<lang:{lang}>' ) for lang in self.langs}
A_ : Optional[int] = self.lang_tokens
A_ : int = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
A_ : Dict = {}
@property
def _a ( self : Tuple ):
"""simple docstring"""
return len(self.encoder )
@property
def _a ( self : int ):
"""simple docstring"""
return self._tgt_lang
@tgt_lang.setter
def _a ( self : List[str] , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : int = new_tgt_lang
self.set_tgt_lang_special_tokens(_lowerCamelCase )
def _a ( self : Tuple , _lowerCamelCase : str ):
"""simple docstring"""
A_ : List[str] = self.lang_code_to_id[tgt_lang]
A_ : Optional[Any] = [lang_code_id]
def _a ( self : Optional[Any] , _lowerCamelCase : str ):
"""simple docstring"""
return self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
def _a ( self : List[Any] , _lowerCamelCase : int ):
"""simple docstring"""
return self.encoder.get(_lowerCamelCase , self.encoder[self.unk_token] )
def _a ( self : int , _lowerCamelCase : int ):
"""simple docstring"""
return self.decoder.get(_lowerCamelCase , self.unk_token )
def _a ( self : int , _lowerCamelCase : List[str] ):
"""simple docstring"""
A_ : List[Any] = []
A_ : Any = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
A_ : Union[str, Any] = self.sp_model.decode(_lowerCamelCase )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
A_ : Optional[Any] = []
else:
current_sub_tokens.append(_lowerCamelCase )
A_ : Tuple = self.sp_model.decode(_lowerCamelCase )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def _a ( self : int , _lowerCamelCase : Dict , _lowerCamelCase : Any=None ):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def _a ( self : List[Any] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None , _lowerCamelCase : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
A_ : Tuple = [1] * len(self.prefix_tokens )
A_ : Tuple = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(_lowerCamelCase )) + suffix_ones
return prefix_ones + ([0] * len(_lowerCamelCase )) + ([0] * len(_lowerCamelCase )) + suffix_ones
def _a ( self : Dict ):
"""simple docstring"""
A_ : Union[str, Any] = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = self.__dict__.copy()
A_ : List[Any] = None
return state
def __setstate__( self : List[str] , _lowerCamelCase : Dict ):
"""simple docstring"""
A_ : Dict = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
A_ : Optional[int] = {}
A_ : int = load_spm(self.spm_file , self.sp_model_kwargs )
def _a ( self : Optional[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[str] = None ):
"""simple docstring"""
A_ : Dict = Path(_lowerCamelCase )
assert save_dir.is_dir(), f'{save_directory} should be a directory'
A_ : Optional[int] = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
A_ : Optional[int] = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder , _lowerCamelCase )
if os.path.abspath(self.spm_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , _lowerCamelCase )
elif not os.path.isfile(self.spm_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
A_ : List[str] = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (str(_lowerCamelCase ), str(_lowerCamelCase ))
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
A_ : Tuple = sentencepiece.SentencePieceProcessor(**lowerCamelCase__ )
spm.Load(str(lowerCamelCase__ ) )
return spm
def snake_case__ ( lowerCamelCase__ : str ) -> Union[Dict, List]:
with open(lowerCamelCase__ , '''r''' ) as f:
return json.load(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : str ) -> None:
with open(lowerCamelCase__ , '''w''' ) as f:
json.dump(lowerCamelCase__ , lowerCamelCase__ , indent=2 )
| 4 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Dict , _lowerCamelCase : Tuple , _lowerCamelCase : Tuple=13 , _lowerCamelCase : Optional[Any]=30 , _lowerCamelCase : str=2 , _lowerCamelCase : Any=3 , _lowerCamelCase : int=True , _lowerCamelCase : List[str]=True , _lowerCamelCase : str=32 , _lowerCamelCase : Any=5 , _lowerCamelCase : int=4 , _lowerCamelCase : str=37 , _lowerCamelCase : Optional[int]="gelu" , _lowerCamelCase : Optional[Any]=0.1 , _lowerCamelCase : Union[str, Any]=0.1 , _lowerCamelCase : Optional[int]=10 , _lowerCamelCase : str=0.02 , _lowerCamelCase : List[str]=None , _lowerCamelCase : Any=2 , ):
"""simple docstring"""
A_ : Dict = parent
A_ : int = batch_size
A_ : int = image_size
A_ : Optional[Any] = patch_size
A_ : Optional[int] = num_channels
A_ : List[Any] = is_training
A_ : Dict = use_labels
A_ : Optional[Any] = hidden_size
A_ : List[str] = num_hidden_layers
A_ : List[Any] = num_attention_heads
A_ : Optional[Any] = intermediate_size
A_ : Any = hidden_act
A_ : Union[str, Any] = hidden_dropout_prob
A_ : Tuple = attention_probs_dropout_prob
A_ : List[Any] = type_sequence_label_size
A_ : Optional[Any] = initializer_range
A_ : List[str] = scope
A_ : int = encoder_stride
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
A_ : Union[str, Any] = (image_size // patch_size) ** 2
A_ : Any = num_patches + 1
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : Optional[Any] = None
if self.use_labels:
A_ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : Optional[int] = self.get_config()
return config, pixel_values, labels
def _a ( self : Dict ):
"""simple docstring"""
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_lowerCamelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _a ( self : Optional[Any] , _lowerCamelCase : Tuple , _lowerCamelCase : Tuple , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = ViTModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Optional[Any] = model(_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : int , _lowerCamelCase : Dict , _lowerCamelCase : Dict , _lowerCamelCase : Tuple ):
"""simple docstring"""
A_ : Tuple = ViTForMaskedImageModeling(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Any = model(_lowerCamelCase )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A_ : List[Any] = 1
A_ : int = ViTForMaskedImageModeling(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : List[str] = model(_lowerCamelCase )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _a ( self : List[str] , _lowerCamelCase : Any , _lowerCamelCase : Dict , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ : Optional[int] = self.type_sequence_label_size
A_ : Optional[Any] = ViTForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : List[Any] = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A_ : str = 1
A_ : Optional[Any] = ViTForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : List[Any] = model(_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Any = self.prepare_config_and_inputs()
(
A_
) : Dict = config_and_inputs
A_ : List[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase_ (a__, a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
_lowerCAmelCase = (
{'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
if is_torch_available()
else {}
)
_lowerCAmelCase = True
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Union[str, Any] = ViTModelTester(self )
A_ : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase , hidden_size=37 )
def _a ( self : Optional[Any] ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def _a ( self : str ):
"""simple docstring"""
pass
def _a ( self : Tuple ):
"""simple docstring"""
A_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Any = model_class(_lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A_ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCamelCase , nn.Linear ) )
def _a ( self : int ):
"""simple docstring"""
A_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : List[Any] = model_class(_lowerCamelCase )
A_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : Optional[int] = [*signature.parameters.keys()]
A_ : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCamelCase )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
@slow
def _a ( self : Dict ):
"""simple docstring"""
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Optional[int] = ViTModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def snake_case__ ( ) -> Optional[int]:
A_ : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _a ( self : Optional[int] ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224''' ) if is_vision_available() else None
@slow
def _a ( self : List[str] ):
"""simple docstring"""
A_ : List[str] = ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224''' ).to(_lowerCamelCase )
A_ : int = self.default_image_processor
A_ : Optional[Any] = prepare_img()
A_ : Optional[Any] = image_processor(images=_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
A_ : List[Any] = model(**_lowerCamelCase )
# verify the logits
A_ : Optional[int] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
A_ : Any = torch.tensor([-0.27_44, 0.82_15, -0.08_36] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1E-4 ) )
@slow
def _a ( self : int ):
"""simple docstring"""
A_ : str = ViTModel.from_pretrained('''facebook/dino-vits8''' ).to(_lowerCamelCase )
A_ : List[Any] = ViTImageProcessor.from_pretrained('''facebook/dino-vits8''' , size=480 )
A_ : Optional[Any] = prepare_img()
A_ : List[Any] = image_processor(images=_lowerCamelCase , return_tensors='''pt''' )
A_ : Optional[int] = inputs.pixel_values.to(_lowerCamelCase )
# forward pass
with torch.no_grad():
A_ : Union[str, Any] = model(_lowerCamelCase , interpolate_pos_encoding=_lowerCamelCase )
# verify the logits
A_ : str = torch.Size((1, 3601, 384) )
self.assertEqual(outputs.last_hidden_state.shape , _lowerCamelCase )
A_ : List[Any] = torch.tensor(
[[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=1E-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = ViTModel.from_pretrained('''facebook/dino-vits8''' , torch_dtype=torch.floataa , device_map='''auto''' )
A_ : Dict = self.default_image_processor
A_ : str = prepare_img()
A_ : Any = image_processor(images=_lowerCamelCase , return_tensors='''pt''' )
A_ : int = inputs.pixel_values.to(_lowerCamelCase )
# forward pass to make sure inference works in fp16
with torch.no_grad():
A_ : List[str] = model(_lowerCamelCase )
| 359 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
snake_case__ = sys.version_info >= (3, 10)
def snake_case__ ( lowerCamelCase__ : Union[str, Any]=None , lowerCamelCase__ : str=None ) -> List[Any]:
return field(default_factory=lambda: default , metadata=lowerCamelCase__ )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 4_2
_lowerCAmelCase = field(default='toto', metadata={'help': 'help message'} )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = False
_lowerCAmelCase = True
_lowerCAmelCase = None
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'titi'
_lowerCAmelCase = 'toto'
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'titi'
_lowerCAmelCase = 'toto'
_lowerCAmelCase = 4_2
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Optional[int] = BasicEnum(self.foo )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[Any] = MixedTypeEnum(self.foo )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = field(default=a__, metadata={'help': 'help message'} )
_lowerCAmelCase = None
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[] )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[1, 2, 3] )
_lowerCAmelCase = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
_lowerCAmelCase = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = field()
_lowerCAmelCase = field()
_lowerCAmelCase = field()
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = BasicEnum(self.required_enum )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = field()
_lowerCAmelCase = None
_lowerCAmelCase = field(default='toto', metadata={'help': 'help message'} )
_lowerCAmelCase = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
if is_python_no_less_than_3_10:
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = False
_lowerCAmelCase = True
_lowerCAmelCase = None
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = field(default=a__, metadata={'help': 'help message'} )
_lowerCAmelCase = None
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[] )
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] , _lowerCamelCase : argparse.ArgumentParser , _lowerCamelCase : argparse.ArgumentParser ):
"""simple docstring"""
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
A_ : Union[str, Any] = {k: v for k, v in vars(_lowerCamelCase ).items() if k != '''container'''}
A_ : Optional[Any] = {k: v for k, v in vars(_lowerCamelCase ).items() if k != '''container'''}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('''choices''' , _lowerCamelCase ) and yy.get('''choices''' , _lowerCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['''type'''](_lowerCamelCase ) , yy['''type'''](_lowerCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : Optional[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--bar''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--baz''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--flag''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Union[str, Any] = ['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
((A_) ,) : List[str] = parser.parse_args_into_dataclasses(_lowerCamelCase , look_for_args_file=_lowerCamelCase )
self.assertFalse(example.flag )
def _a ( self : Dict ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : int = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=42 , type=_lowerCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=_lowerCamelCase , help='''help message''' )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Any = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
expected.add_argument('''--baz''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('''--no_baz''' , action='''store_false''' , default=_lowerCamelCase , dest='''baz''' )
expected.add_argument('''--opt''' , type=_lowerCamelCase , default=_lowerCamelCase )
A_ : Dict = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_lowerCamelCase )
for dataclass_type in dataclass_types:
A_ : Any = HfArgumentParser(_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = parser.parse_args([] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : Optional[int] = parser.parse_args(['''--foo''', '''--no_baz'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : Union[str, Any] = parser.parse_args(['''--foo''', '''--baz'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : List[str] = parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : List[Any] = parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : str = HfArgumentParser(_lowerCamelCase )
A_ : Optional[int] = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=['''titi''', '''toto''', 42] , type=make_choice_type_function(['''titi''', '''toto''', 42] ) , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : str = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
A_ : List[Any] = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
A_ : int = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
A_ : Dict = parser.parse_args_into_dataclasses(['''--foo''', '''titi'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
A_ : Tuple = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 42 )
A_ : List[str] = parser.parse_args_into_dataclasses(['''--foo''', '''42'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def _a ( self : Optional[int] ):
"""simple docstring"""
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
A_ : List[str] = HfArgumentParser(_lowerCamelCase )
A_ : Tuple = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=('''titi''', '''toto''', 42) , type=make_choice_type_function(['''titi''', '''toto''', 42] ) , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Tuple = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
A_ : List[str] = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
A_ : int = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 42 )
def _a ( self : Dict ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : List[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo_int''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
expected.add_argument('''--bar_int''' , nargs='''+''' , default=[1, 2, 3] , type=_lowerCamelCase )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=_lowerCamelCase )
expected.add_argument('''--foo_float''' , nargs='''+''' , default=[0.1, 0.2, 0.3] , type=_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[int] = parser.parse_args([] )
self.assertEqual(
_lowerCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['''Hallo''', '''Bonjour''', '''Hello'''] , foo_float=[0.1, 0.2, 0.3] ) , )
A_ : str = parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split() )
self.assertEqual(_lowerCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['''a''', '''b''', '''c'''] , foo_float=[0.1, 0.7] ) )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=_lowerCamelCase , type=_lowerCamelCase )
expected.add_argument('''--bar''' , default=_lowerCamelCase , type=_lowerCamelCase , help='''help message''' )
expected.add_argument('''--baz''' , default=_lowerCamelCase , type=_lowerCamelCase )
expected.add_argument('''--ces''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
expected.add_argument('''--des''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
A_ : Tuple = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_lowerCamelCase )
for dataclass_type in dataclass_types:
A_ : int = HfArgumentParser(_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = parser.parse_args([] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , bar=_lowerCamelCase , baz=_lowerCamelCase , ces=[] , des=[] ) )
A_ : Optional[Any] = parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split() )
self.assertEqual(_lowerCamelCase , Namespace(foo=12 , bar=3.14 , baz='''42''' , ces=['''a''', '''b''', '''c'''] , des=[1, 2, 3] ) )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = HfArgumentParser(_lowerCamelCase )
A_ : Dict = argparse.ArgumentParser()
expected.add_argument('''--required_list''' , nargs='''+''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--required_str''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=_lowerCamelCase , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : List[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=_lowerCamelCase , )
expected.add_argument('''--opt''' , type=_lowerCamelCase , default=_lowerCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=_lowerCamelCase , help='''help message''' )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : List[Any] = HfArgumentParser(_lowerCamelCase )
A_ : Union[str, Any] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
A_ : Optional[int] = parser.parse_dict(_lowerCamelCase )[0]
A_ : str = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Any = HfArgumentParser(_lowerCamelCase )
A_ : List[str] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
'''extra''': 42,
}
self.assertRaises(_lowerCamelCase , parser.parse_dict , _lowerCamelCase , allow_extra_keys=_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : List[str] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : Tuple = os.path.join(_lowerCamelCase , '''temp_json''' )
os.mkdir(_lowerCamelCase )
with open(temp_local_path + '''.json''' , '''w+''' ) as f:
json.dump(_lowerCamelCase , _lowerCamelCase )
A_ : List[str] = parser.parse_yaml_file(Path(temp_local_path + '''.json''' ) )[0]
A_ : Optional[Any] = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : Tuple = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : int = os.path.join(_lowerCamelCase , '''temp_yaml''' )
os.mkdir(_lowerCamelCase )
with open(temp_local_path + '''.yaml''' , '''w+''' ) as f:
yaml.dump(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[Any] = parser.parse_yaml_file(Path(temp_local_path + '''.yaml''' ) )[0]
A_ : int = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = HfArgumentParser(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
| 4 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""Salesforce/blip-vqa-base""": """https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json""",
"""Salesforce/blip-vqa-capfit-large""": (
"""https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"""
),
"""Salesforce/blip-image-captioning-base""": (
"""https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"""
),
"""Salesforce/blip-image-captioning-large""": (
"""https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"""
),
"""Salesforce/blip-itm-base-coco""": """https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json""",
"""Salesforce/blip-itm-large-coco""": """https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json""",
"""Salesforce/blip-itm-base-flikr""": """https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json""",
"""Salesforce/blip-itm-large-flikr""": (
"""https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"""
),
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'blip_text_model'
def __init__( self : Optional[int] , _lowerCamelCase : List[str]=30524 , _lowerCamelCase : Optional[int]=768 , _lowerCamelCase : Union[str, Any]=768 , _lowerCamelCase : List[Any]=3072 , _lowerCamelCase : Optional[Any]=768 , _lowerCamelCase : Union[str, Any]=12 , _lowerCamelCase : Tuple=8 , _lowerCamelCase : Optional[Any]=512 , _lowerCamelCase : List[str]="gelu" , _lowerCamelCase : Optional[Any]=1E-12 , _lowerCamelCase : int=0.0 , _lowerCamelCase : str=0.0 , _lowerCamelCase : Dict=0.02 , _lowerCamelCase : Optional[Any]=30522 , _lowerCamelCase : Tuple=2 , _lowerCamelCase : int=0 , _lowerCamelCase : int=102 , _lowerCamelCase : List[Any]=True , _lowerCamelCase : List[str]=True , **_lowerCamelCase : Any , ):
"""simple docstring"""
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , sep_token_id=_lowerCamelCase , **_lowerCamelCase , )
A_ : List[Any] = vocab_size
A_ : Tuple = hidden_size
A_ : List[Any] = encoder_hidden_size
A_ : Union[str, Any] = intermediate_size
A_ : Tuple = projection_dim
A_ : Union[str, Any] = hidden_dropout_prob
A_ : str = num_hidden_layers
A_ : List[Any] = num_attention_heads
A_ : str = max_position_embeddings
A_ : List[str] = layer_norm_eps
A_ : str = hidden_act
A_ : List[str] = initializer_range
A_ : Union[str, Any] = attention_probs_dropout_prob
A_ : List[Any] = is_decoder
A_ : Union[str, Any] = use_cache
@classmethod
def _a ( cls : Optional[Any] , _lowerCamelCase : Union[str, os.PathLike] , **_lowerCamelCase : str ):
"""simple docstring"""
cls._set_token_in_kwargs(_lowerCamelCase )
A_ : Dict = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
# get the text config dict if we are loading from BlipConfig
if config_dict.get('''model_type''' ) == "blip":
A_ : str = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_lowerCamelCase , **_lowerCamelCase )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'blip_vision_model'
def __init__( self : List[str] , _lowerCamelCase : str=768 , _lowerCamelCase : List[str]=3072 , _lowerCamelCase : Any=512 , _lowerCamelCase : Dict=12 , _lowerCamelCase : Any=12 , _lowerCamelCase : Optional[Any]=384 , _lowerCamelCase : List[Any]=16 , _lowerCamelCase : Dict="gelu" , _lowerCamelCase : int=1E-5 , _lowerCamelCase : Union[str, Any]=0.0 , _lowerCamelCase : Any=1E-10 , **_lowerCamelCase : str , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
A_ : Union[str, Any] = hidden_size
A_ : int = intermediate_size
A_ : List[Any] = projection_dim
A_ : Dict = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : Dict = patch_size
A_ : Dict = image_size
A_ : List[Any] = initializer_range
A_ : int = attention_dropout
A_ : Optional[Any] = layer_norm_eps
A_ : Optional[int] = hidden_act
@classmethod
def _a ( cls : Dict , _lowerCamelCase : Union[str, os.PathLike] , **_lowerCamelCase : Tuple ):
"""simple docstring"""
cls._set_token_in_kwargs(_lowerCamelCase )
A_ : Dict = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
# get the vision config dict if we are loading from BlipConfig
if config_dict.get('''model_type''' ) == "blip":
A_ : Union[str, Any] = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_lowerCamelCase , **_lowerCamelCase )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'blip'
_lowerCAmelCase = True
def __init__( self : Union[str, Any] , _lowerCamelCase : Optional[int]=None , _lowerCamelCase : Tuple=None , _lowerCamelCase : Union[str, Any]=512 , _lowerCamelCase : Union[str, Any]=2.65_92 , _lowerCamelCase : int=256 , **_lowerCamelCase : int , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
if text_config is None:
A_ : Union[str, Any] = {}
logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.''' )
if vision_config is None:
A_ : Optional[int] = {}
logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.''' )
A_ : Union[str, Any] = BlipTextConfig(**_lowerCamelCase )
A_ : Optional[int] = BlipVisionConfig(**_lowerCamelCase )
A_ : List[str] = self.vision_config.hidden_size
A_ : List[str] = projection_dim
A_ : Union[str, Any] = logit_scale_init_value
A_ : Tuple = 1.0
A_ : Optional[Any] = 0.02
A_ : Union[str, Any] = image_text_hidden_size
@classmethod
def _a ( cls : str , _lowerCamelCase : BlipTextConfig , _lowerCamelCase : BlipVisionConfig , **_lowerCamelCase : Tuple ):
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_lowerCamelCase )
def _a ( self : str ):
"""simple docstring"""
A_ : List[Any] = copy.deepcopy(self.__dict__ )
A_ : Optional[int] = self.text_config.to_dict()
A_ : str = self.vision_config.to_dict()
A_ : Optional[Any] = self.__class__.model_type
return output
| 360 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
snake_case__ = get_tests_dir("""fixtures""")
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] ):
"""simple docstring"""
A_ : List[Any] = mock.Mock()
A_ : List[str] = 500
A_ : Tuple = {}
A_ : int = HTTPError
A_ : Optional[Any] = {}
# Download this model to make sure it's in the cache.
A_ : Tuple = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=_lowerCamelCase ) as mock_head:
A_ : List[Any] = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# This check we did call the fake head request
mock_head.assert_called()
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = ViTImageProcessor.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json''' )
def _a ( self : Dict ):
"""simple docstring"""
with self.assertRaises(_lowerCamelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
A_ : Any = AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants''' )
A_ : Tuple = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/stable-diffusion-all-variants''' , subfolder='''feature_extractor''' )
self.assertIsNotNone(_lowerCamelCase )
@is_staging_test
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@classmethod
def _a ( cls : Tuple ):
"""simple docstring"""
A_ : int = TOKEN
HfFolder.save_token(_lowerCamelCase )
@classmethod
def _a ( cls : str ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='''test-image-processor''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-image-processor-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-image-processor''' )
except HTTPError:
pass
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Dict = ViTImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''test-image-processor''' , use_auth_token=self._token )
A_ : Optional[int] = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCamelCase , repo_id='''test-image-processor''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : List[Any] = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : int = ViTImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''valid_org/test-image-processor''' , use_auth_token=self._token )
A_ : List[str] = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCamelCase , repo_id='''valid_org/test-image-processor-org''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : Any = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
CustomImageProcessor.register_for_auto_class()
A_ : Any = CustomImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''test-dynamic-image-processor''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''} , )
A_ : str = AutoImageProcessor.from_pretrained(
f'{USER}/test-dynamic-image-processor' , trust_remote_code=_lowerCamelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , '''CustomImageProcessor''' )
| 4 | 0 |
'''simple docstring'''
import argparse
import struct
import unittest
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Any , _lowerCamelCase : bytes ):
"""simple docstring"""
A_ : Dict = data
# Initialize hash values
A_ : Optional[Any] = [
0x6a09e667,
0xbb67ae85,
0x3c6ef372,
0xa54ff53a,
0x510e527f,
0x9b05688c,
0x1f83d9ab,
0x5be0cd19,
]
# Initialize round constants
A_ : Tuple = [
0x428a2f98,
0x71374491,
0xb5c0fbcf,
0xe9b5dba5,
0x3956c25b,
0x59f111f1,
0x923f82a4,
0xab1c5ed5,
0xd807aa98,
0x12835b01,
0x243185be,
0x550c7dc3,
0x72be5d74,
0x80deb1fe,
0x9bdc06a7,
0xc19bf174,
0xe49b69c1,
0xefbe4786,
0x0fc19dc6,
0x240ca1cc,
0x2de92c6f,
0x4a7484aa,
0x5cb0a9dc,
0x76f988da,
0x983e5152,
0xa831c66d,
0xb00327c8,
0xbf597fc7,
0xc6e00bf3,
0xd5a79147,
0x06ca6351,
0x14292967,
0x27b70a85,
0x2e1b2138,
0x4d2c6dfc,
0x53380d13,
0x650a7354,
0x766a0abb,
0x81c2c92e,
0x92722c85,
0xa2bfe8a1,
0xa81a664b,
0xc24b8b70,
0xc76c51a3,
0xd192e819,
0xd6990624,
0xf40e3585,
0x106aa070,
0x19a4c116,
0x1e376c08,
0x2748774c,
0x34b0bcb5,
0x391c0cb3,
0x4ed8aa4a,
0x5b9cca4f,
0x682e6ff3,
0x748f82ee,
0x78a5636f,
0x84c87814,
0x8cc70208,
0x90befffa,
0xa4506ceb,
0xbef9a3f7,
0xc67178f2,
]
A_ : Union[str, Any] = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def _a ( _lowerCamelCase : bytes ):
"""simple docstring"""
A_ : Union[str, Any] = B'''\x80''' + (B'''\x00''' * (63 - (len(_lowerCamelCase ) + 8) % 64))
A_ : Optional[int] = struct.pack('''>Q''' , (len(_lowerCamelCase ) * 8) )
return data + padding + big_endian_integer
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Optional[int] = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
A_ : List[str] = list(struct.unpack('''>16L''' , _lowerCamelCase ) )
# add 48 0-ed integers
words += [0] * 48
A_ : str = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
A_ : int = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
A_ : Union[str, Any] = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
A_ : List[Any] = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x100000000
# Compression
A_ : Any = self.ror(_lowerCamelCase , 6 ) ^ self.ror(_lowerCamelCase , 11 ) ^ self.ror(_lowerCamelCase , 25 )
A_ : List[Any] = (e & f) ^ ((~e & 0xffffffff) & g)
A_ : List[Any] = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x100000000
A_ : str = self.ror(_lowerCamelCase , 2 ) ^ self.ror(_lowerCamelCase , 13 ) ^ self.ror(_lowerCamelCase , 22 )
A_ : Optional[Any] = (a & b) ^ (a & c) ^ (b & c)
A_ : Dict = (sa + maj) % 0x100000000
A_ : str = (
g,
f,
e,
((d + tempa) % 0x100000000),
c,
b,
a,
((tempa + tempa) % 0x100000000),
)
A_ : Optional[Any] = [a, b, c, d, e, f, g, h]
# Modify final values
A_ : Union[str, Any] = [
((element + mutated_hash_values[index]) % 0x100000000)
for index, element in enumerate(self.hashes )
]
A_ : Optional[int] = ''''''.join([hex(_lowerCamelCase )[2:].zfill(8 ) for value in self.hashes] )
def _a ( self : int , _lowerCamelCase : int , _lowerCamelCase : int ):
"""simple docstring"""
return 0xffffffff & (value << (32 - rotations)) | (value >> rotations)
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Tuple ):
"""simple docstring"""
import hashlib
A_ : str = bytes('''Test String''' , '''utf-8''' )
self.assertEqual(SHAaaa(_lowerCamelCase ).hash , hashlib.shaaaa(_lowerCamelCase ).hexdigest() )
def snake_case__ ( ) -> None:
import doctest
doctest.testmod()
A_ : str = argparse.ArgumentParser()
parser.add_argument(
'''-s''' , '''--string''' , dest='''input_string''' , default='''Hello World!! Welcome to Cryptography''' , help='''Hash the string''' , )
parser.add_argument(
'''-f''' , '''--file''' , dest='''input_file''' , help='''Hash contents of a file''' )
A_ : Optional[int] = parser.parse_args()
A_ : Optional[int] = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , '''rb''' ) as f:
A_ : Optional[Any] = f.read()
else:
A_ : Optional[Any] = bytes(lowerCamelCase__ , '''utf-8''' )
print(SHAaaa(lowerCamelCase__ ).hash )
if __name__ == "__main__":
main()
| 361 |
'''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.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = (
'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
)
_lowerCAmelCase = 'CIDAS/clipseg-rd64-refined'
_lowerCAmelCase = 'image_segmenter'
_lowerCAmelCase = CLIPSegForImageSegmentation
_lowerCAmelCase = ['image', 'text']
_lowerCAmelCase = ['image']
def __init__( self : Optional[int] , *_lowerCamelCase : Optional[int] , **_lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def _a ( self : List[str] , _lowerCamelCase : "Image" , _lowerCamelCase : str ):
"""simple docstring"""
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def _a ( self : Union[str, Any] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
with torch.no_grad():
A_ : Optional[int] = self.model(**_lowerCamelCase ).logits
return logits
def _a ( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : int = outputs.cpu().detach().numpy()
A_ : Tuple = 0
A_ : List[str] = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 4 | 0 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
A_ : Dict = tau * frequency / samplerate
A_ : Union[str, Any] = sin(lowerCamelCase__ )
A_ : str = cos(lowerCamelCase__ )
A_ : Optional[int] = _sin / (2 * q_factor)
A_ : Dict = (1 - _cos) / 2
A_ : Optional[int] = 1 - _cos
A_ : List[Any] = 1 + alpha
A_ : str = -2 * _cos
A_ : Optional[int] = 1 - alpha
A_ : Union[str, Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
A_ : Union[str, Any] = tau * frequency / samplerate
A_ : str = sin(lowerCamelCase__ )
A_ : str = cos(lowerCamelCase__ )
A_ : Union[str, Any] = _sin / (2 * q_factor)
A_ : str = (1 + _cos) / 2
A_ : Optional[int] = -1 - _cos
A_ : Optional[Any] = 1 + alpha
A_ : int = -2 * _cos
A_ : Tuple = 1 - alpha
A_ : Optional[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
A_ : Optional[Any] = tau * frequency / samplerate
A_ : List[str] = sin(lowerCamelCase__ )
A_ : str = cos(lowerCamelCase__ )
A_ : Optional[Any] = _sin / (2 * q_factor)
A_ : List[str] = _sin / 2
A_ : Optional[int] = 0
A_ : List[str] = -ba
A_ : Optional[Any] = 1 + alpha
A_ : Optional[int] = -2 * _cos
A_ : Tuple = 1 - alpha
A_ : Dict = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
A_ : Dict = tau * frequency / samplerate
A_ : List[Any] = sin(lowerCamelCase__ )
A_ : str = cos(lowerCamelCase__ )
A_ : Optional[int] = _sin / (2 * q_factor)
A_ : Optional[Any] = 1 - alpha
A_ : List[str] = -2 * _cos
A_ : Tuple = 1 + alpha
A_ : Any = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : float , lowerCamelCase__ : float = 1 / sqrt(2 ) , ) -> IIRFilter:
A_ : str = tau * frequency / samplerate
A_ : str = sin(lowerCamelCase__ )
A_ : Union[str, Any] = cos(lowerCamelCase__ )
A_ : Any = _sin / (2 * q_factor)
A_ : Tuple = 1_0 ** (gain_db / 4_0)
A_ : List[str] = 1 + alpha * big_a
A_ : str = -2 * _cos
A_ : Optional[int] = 1 - alpha * big_a
A_ : List[Any] = 1 + alpha / big_a
A_ : Dict = -2 * _cos
A_ : Optional[Any] = 1 - alpha / big_a
A_ : List[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : float , lowerCamelCase__ : float = 1 / sqrt(2 ) , ) -> IIRFilter:
A_ : Any = tau * frequency / samplerate
A_ : Any = sin(lowerCamelCase__ )
A_ : Any = cos(lowerCamelCase__ )
A_ : str = _sin / (2 * q_factor)
A_ : Tuple = 1_0 ** (gain_db / 4_0)
A_ : List[Any] = (big_a + 1) - (big_a - 1) * _cos
A_ : int = (big_a + 1) + (big_a - 1) * _cos
A_ : Dict = (big_a - 1) - (big_a + 1) * _cos
A_ : str = (big_a - 1) + (big_a + 1) * _cos
A_ : List[str] = 2 * sqrt(lowerCamelCase__ ) * alpha
A_ : Optional[Any] = big_a * (pmc + aaa)
A_ : Any = 2 * big_a * mpc
A_ : int = big_a * (pmc - aaa)
A_ : Optional[int] = ppmc + aaa
A_ : int = -2 * pmpc
A_ : Tuple = ppmc - aaa
A_ : List[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : float , lowerCamelCase__ : float = 1 / sqrt(2 ) , ) -> IIRFilter:
A_ : Any = tau * frequency / samplerate
A_ : int = sin(lowerCamelCase__ )
A_ : str = cos(lowerCamelCase__ )
A_ : Tuple = _sin / (2 * q_factor)
A_ : Any = 1_0 ** (gain_db / 4_0)
A_ : List[str] = (big_a + 1) - (big_a - 1) * _cos
A_ : Any = (big_a + 1) + (big_a - 1) * _cos
A_ : Tuple = (big_a - 1) - (big_a + 1) * _cos
A_ : Optional[int] = (big_a - 1) + (big_a + 1) * _cos
A_ : Any = 2 * sqrt(lowerCamelCase__ ) * alpha
A_ : Optional[int] = big_a * (ppmc + aaa)
A_ : Optional[Any] = -2 * big_a * pmpc
A_ : Union[str, Any] = big_a * (ppmc - aaa)
A_ : Tuple = pmc + aaa
A_ : List[str] = 2 * mpc
A_ : Optional[Any] = pmc - aaa
A_ : Optional[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 362 |
'''simple docstring'''
from collections.abc import Sequence
def snake_case__ ( lowerCamelCase__ : Sequence[float] , lowerCamelCase__ : bool = False ) -> float:
if not arr:
return 0
A_ : Union[str, Any] = 0 if allow_empty_subarrays else float('''-inf''' )
A_ : str = 0.0
for num in arr:
A_ : Any = max(0 if allow_empty_subarrays else num , curr_sum + num )
A_ : Tuple = max(lowerCamelCase__ , lowerCamelCase__ )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
snake_case__ = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F'{max_subarray_sum(nums) = }')
| 4 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def __init__( self : int , _lowerCamelCase : Tuple , _lowerCamelCase : Any=7 , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Optional[Any]=30 , _lowerCamelCase : Dict=400 , _lowerCamelCase : Dict=True , _lowerCamelCase : str=None , _lowerCamelCase : List[Any]=True , _lowerCamelCase : List[str]=1 / 255 , _lowerCamelCase : Any=True , _lowerCamelCase : str=[0.5, 0.5, 0.5] , _lowerCamelCase : List[str]=[0.5, 0.5, 0.5] , _lowerCamelCase : Any=True , ):
"""simple docstring"""
A_ : Any = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
A_ : Any = parent
A_ : int = batch_size
A_ : str = num_channels
A_ : str = min_resolution
A_ : Dict = max_resolution
A_ : List[Any] = do_resize
A_ : str = size
A_ : str = do_rescale
A_ : List[Any] = rescale_factor
A_ : List[str] = do_normalize
A_ : Dict = image_mean
A_ : int = image_std
A_ : int = do_pad
def _a ( self : Optional[Any] ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def _a ( self : Optional[int] , _lowerCamelCase : Any , _lowerCamelCase : Union[str, Any]=False ):
"""simple docstring"""
if not batched:
A_ : str = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
A_ : Optional[int] = image.size
else:
A_ : int = image.shape[1], image.shape[2]
if w < h:
A_ : Optional[int] = int(self.size['''shortest_edge'''] * h / w )
A_ : Any = self.size['''shortest_edge''']
elif w > h:
A_ : Dict = self.size['''shortest_edge''']
A_ : List[Any] = int(self.size['''shortest_edge'''] * w / h )
else:
A_ : int = self.size['''shortest_edge''']
A_ : List[Any] = self.size['''shortest_edge''']
else:
A_ : List[Any] = []
for image in image_inputs:
A_ : List[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
A_ : Any = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
A_ : Any = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = DetrImageProcessor if is_vision_available() else None
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Any = DetrImageProcessingTester(self )
@property
def _a ( self : Tuple ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_rescale''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''rescale_factor''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_pad''' ) )
def _a ( self : int ):
"""simple docstring"""
A_ : List[str] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
A_ : Any = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def _a ( self : List[Any] ):
"""simple docstring"""
pass
def _a ( self : Dict ):
"""simple docstring"""
A_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
A_ : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
A_ : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A_ : int = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
A_ : Tuple = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A_ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
A_ : int = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
A_ : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A_ : Optional[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
A_ : Optional[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
A_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
A_ : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A_ : Dict = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
A_ : List[str] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
A_ : Union[str, Any] = json.loads(f.read() )
A_ : Any = {'''image_id''': 39769, '''annotations''': target}
# encode them
A_ : Union[str, Any] = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50''' )
A_ : List[str] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
A_ : Optional[Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
A_ : int = torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1E-4 ) )
# verify area
A_ : Dict = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
A_ : int = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
A_ : Dict = torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1E-3 ) )
# verify image_id
A_ : Union[str, Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
A_ : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
A_ : Tuple = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
A_ : List[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
A_ : Optional[int] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def _a ( self : Any ):
"""simple docstring"""
A_ : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
A_ : Tuple = json.loads(f.read() )
A_ : Union[str, Any] = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
A_ : Optional[Any] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
A_ : str = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic''' )
A_ : Tuple = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
A_ : Dict = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
A_ : Optional[Any] = torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1E-4 ) )
# verify area
A_ : Dict = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
A_ : Optional[Any] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
A_ : Any = torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1E-3 ) )
# verify image_id
A_ : Tuple = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
A_ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
A_ : Union[str, Any] = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
A_ : int = 822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
A_ : Dict = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
A_ : Any = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 363 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'speech_to_text_2'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Optional[Any] , _lowerCamelCase : Optional[Any]=10000 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : int=2048 , _lowerCamelCase : Dict=4 , _lowerCamelCase : str=0.0 , _lowerCamelCase : int=True , _lowerCamelCase : int="relu" , _lowerCamelCase : Any=256 , _lowerCamelCase : List[Any]=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Union[str, Any]=0.0 , _lowerCamelCase : Optional[Any]=0.02 , _lowerCamelCase : int=2 , _lowerCamelCase : List[str]=True , _lowerCamelCase : str=1 , _lowerCamelCase : List[Any]=0 , _lowerCamelCase : Optional[int]=2 , _lowerCamelCase : Tuple=1024 , **_lowerCamelCase : int , ):
"""simple docstring"""
A_ : Optional[int] = vocab_size
A_ : Tuple = d_model
A_ : List[str] = decoder_ffn_dim
A_ : str = decoder_layers
A_ : Any = decoder_attention_heads
A_ : int = dropout
A_ : str = attention_dropout
A_ : Optional[int] = activation_dropout
A_ : str = activation_function
A_ : List[Any] = init_std
A_ : Union[str, Any] = decoder_layerdrop
A_ : Any = use_cache
A_ : Optional[Any] = decoder_layers
A_ : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
A_ : Optional[Any] = max_target_positions
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
| 4 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument(
"""--original_config_file""",
type=str,
required=True,
help="""The YAML config file corresponding to the original architecture.""",
)
parser.add_argument(
"""--num_in_channels""",
default=None,
type=int,
help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
)
parser.add_argument(
"""--image_size""",
default=5_12,
type=int,
help=(
"""The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
""" Base. Use 768 for Stable Diffusion v2."""
),
)
parser.add_argument(
"""--extract_ema""",
action="""store_true""",
help=(
"""Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
""" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
""" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
),
)
parser.add_argument(
"""--upcast_attention""",
action="""store_true""",
help=(
"""Whether the attention computation should always be upcasted. This is necessary when running stable"""
""" diffusion 2.1."""
),
)
parser.add_argument(
"""--from_safetensors""",
action="""store_true""",
help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
)
parser.add_argument(
"""--to_safetensors""",
action="""store_true""",
help="""Whether to store pipeline in safetensors format or not.""",
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
def snake_case__ ( lowerCamelCase__ : Optional[int] ) -> List[Any]:
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(f'could not parse string as bool {string}' )
parser.add_argument(
"""--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
)
parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)
snake_case__ = parser.parse_args()
snake_case__ = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 364 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'table-transformer'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Any , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Dict=None , _lowerCamelCase : int=3 , _lowerCamelCase : Any=100 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : Tuple=2048 , _lowerCamelCase : Any=8 , _lowerCamelCase : Dict=6 , _lowerCamelCase : Tuple=2048 , _lowerCamelCase : int=8 , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : List[Any]=0.0 , _lowerCamelCase : List[Any]=True , _lowerCamelCase : Optional[int]="relu" , _lowerCamelCase : Union[str, Any]=256 , _lowerCamelCase : Any=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : str=0.02 , _lowerCamelCase : Tuple=1.0 , _lowerCamelCase : Dict=False , _lowerCamelCase : str="sine" , _lowerCamelCase : str="resnet50" , _lowerCamelCase : Any=True , _lowerCamelCase : List[str]=False , _lowerCamelCase : Any=1 , _lowerCamelCase : int=5 , _lowerCamelCase : Tuple=2 , _lowerCamelCase : Optional[int]=1 , _lowerCamelCase : Any=1 , _lowerCamelCase : Dict=5 , _lowerCamelCase : str=2 , _lowerCamelCase : Union[str, Any]=0.1 , **_lowerCamelCase : int , ):
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
A_ : int = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : str = backbone_config.get('''model_type''' )
A_ : Optional[int] = CONFIG_MAPPING[backbone_model_type]
A_ : List[str] = config_class.from_dict(_lowerCamelCase )
# set timm attributes to None
A_ ,A_ ,A_ : Union[str, Any] = None, None, None
A_ : Optional[Any] = use_timm_backbone
A_ : Optional[int] = backbone_config
A_ : Optional[Any] = num_channels
A_ : Dict = num_queries
A_ : str = d_model
A_ : List[str] = encoder_ffn_dim
A_ : int = encoder_layers
A_ : Optional[Any] = encoder_attention_heads
A_ : List[str] = decoder_ffn_dim
A_ : Any = decoder_layers
A_ : List[str] = decoder_attention_heads
A_ : Tuple = dropout
A_ : Optional[Any] = attention_dropout
A_ : Any = activation_dropout
A_ : List[Any] = activation_function
A_ : Dict = init_std
A_ : Any = init_xavier_std
A_ : List[Any] = encoder_layerdrop
A_ : int = decoder_layerdrop
A_ : Any = encoder_layers
A_ : List[str] = auxiliary_loss
A_ : List[Any] = position_embedding_type
A_ : Optional[Any] = backbone
A_ : Tuple = use_pretrained_backbone
A_ : List[Any] = dilation
# Hungarian matcher
A_ : List[str] = class_cost
A_ : str = bbox_cost
A_ : Union[str, Any] = giou_cost
# Loss coefficients
A_ : Any = mask_loss_coefficient
A_ : Optional[int] = dice_loss_coefficient
A_ : Dict = bbox_loss_coefficient
A_ : int = giou_loss_coefficient
A_ : int = eos_coefficient
super().__init__(is_encoder_decoder=_lowerCamelCase , **_lowerCamelCase )
@property
def _a ( self : List[Any] ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def _a ( self : Any ):
"""simple docstring"""
return self.d_model
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : Tuple ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def _a ( self : Optional[int] ):
"""simple docstring"""
return 1E-5
@property
def _a ( self : str ):
"""simple docstring"""
return 12
| 4 | 0 |
'''simple docstring'''
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ = logging.get_logger(__name__)
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : str ) -> str:
A_ : List[str] = RobertaPreLayerNormConfig.from_pretrained(
lowerCamelCase__ , architectures=['''RobertaPreLayerNormForMaskedLM'''] )
# convert state_dict
A_ : Union[str, Any] = torch.load(hf_hub_download(repo_id=lowerCamelCase__ , filename='''pytorch_model.bin''' ) )
A_ : Dict = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith('''roberta.''' ):
A_ : Tuple = '''roberta_prelayernorm.''' + tensor_key[len('''roberta.''' ) :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith('''.self.LayerNorm.weight''' ) or tensor_key.endswith('''.self.LayerNorm.bias''' ):
continue
A_ : str = tensor_value
A_ : str = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=lowerCamelCase__ , config=lowerCamelCase__ , state_dict=lowerCamelCase__ )
model.save_pretrained(lowerCamelCase__ )
# convert tokenizer
A_ : Optional[Any] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
tokenizer.save_pretrained(lowerCamelCase__ )
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint-repo""",
default=None,
type=str,
required=True,
help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
snake_case__ = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
| 365 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : int , _lowerCamelCase : List[str]=3 , _lowerCamelCase : Any=32 , _lowerCamelCase : Union[str, Any]=3 , _lowerCamelCase : int=10 , _lowerCamelCase : Union[str, Any]=[8, 16, 32, 64] , _lowerCamelCase : Dict=[1, 1, 2, 1] , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Optional[int]=True , _lowerCamelCase : Any="relu" , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Dict=["stage2", "stage3", "stage4"] , _lowerCamelCase : Union[str, Any]=[2, 3, 4] , _lowerCamelCase : Tuple=1 , ):
"""simple docstring"""
A_ : List[str] = parent
A_ : List[str] = batch_size
A_ : Union[str, Any] = image_size
A_ : Tuple = num_channels
A_ : Any = embeddings_size
A_ : int = hidden_sizes
A_ : Optional[Any] = depths
A_ : List[Any] = is_training
A_ : Optional[int] = use_labels
A_ : int = hidden_act
A_ : Tuple = num_labels
A_ : Union[str, Any] = scope
A_ : List[Any] = len(_lowerCamelCase )
A_ : Union[str, Any] = out_features
A_ : List[Any] = out_indices
A_ : Dict = num_groups
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : Union[str, Any] = None
if self.use_labels:
A_ : Any = ids_tensor([self.batch_size] , self.num_labels )
A_ : Any = self.get_config()
return config, pixel_values, labels
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return BitConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def _a ( self : List[Any] , _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
A_ : Any = BitModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase )
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 : Optional[int] , _lowerCamelCase : List[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : Dict = self.num_labels
A_ : Optional[Any] = BitForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : List[Any] = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Any , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : List[Any] ):
"""simple docstring"""
A_ : List[Any] = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
A_ : Optional[Any] = None
A_ : int = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Optional[int] = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = self.prepare_config_and_inputs()
A_ ,A_ ,A_ : Union[str, Any] = config_and_inputs
A_ : str = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase_ (a__, a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
_lowerCAmelCase = (
{'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
if is_torch_available()
else {}
)
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[str] = BitModelTester(self )
A_ : Optional[Any] = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : List[Any] ):
"""simple docstring"""
return
@unittest.skip(reason='''Bit does not output attentions''' )
def _a ( self : str ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def _a ( self : Any ):
"""simple docstring"""
pass
def _a ( self : List[Any] ):
"""simple docstring"""
A_ ,A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Dict = model_class(_lowerCamelCase )
A_ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : int = [*signature.parameters.keys()]
A_ : Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ ,A_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : str = model_class(config=_lowerCamelCase )
for name, module in model.named_modules():
if isinstance(_lowerCamelCase , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
def _a ( self : int ):
"""simple docstring"""
def check_hidden_states_output(_lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : int ):
A_ : Union[str, Any] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
A_ : Union[str, Any] = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
A_ : int = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
A_ : List[Any] = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , expected_num_stages + 1 )
# Bit'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 // 4, self.model_tester.image_size // 4] , )
A_ ,A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Tuple = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
A_ : Tuple = layer_type
A_ : Optional[Any] = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A_ : List[str] = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def _a ( self : Tuple ):
"""simple docstring"""
pass
def _a ( self : str ):
"""simple docstring"""
A_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
@slow
def _a ( self : Union[str, Any] ):
"""simple docstring"""
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : List[Any] = BitModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def snake_case__ ( ) -> Optional[int]:
A_ : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _a ( self : List[Any] ):
"""simple docstring"""
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_lowerCamelCase )
A_ : Union[str, Any] = self.default_image_processor
A_ : Optional[int] = prepare_img()
A_ : int = image_processor(images=_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
A_ : Union[str, Any] = model(**_lowerCamelCase )
# verify the logits
A_ : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
A_ : Tuple = torch.tensor([[-0.65_26, -0.52_63, -1.43_98]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1E-4 ) )
@require_torch
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (BitBackbone,) if is_torch_available() else ()
_lowerCAmelCase = BitConfig
_lowerCAmelCase = False
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Union[str, Any] = BitModelTester(self )
| 4 | 0 |
'''simple docstring'''
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
snake_case__ = logging.get_logger(__name__)
class UpperCamelCase_ (enum.Enum ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = 1
@add_end_docstrings(a__ )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'generated'
def __init__( self : Optional[Any] , *_lowerCamelCase : Dict , **_lowerCamelCase : List[str] ):
"""simple docstring"""
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
self.check_model_type(
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )
def _a ( self : Dict , _lowerCamelCase : Optional[int]=None , _lowerCamelCase : Union[str, Any]=None , _lowerCamelCase : List[Any]=None , _lowerCamelCase : Dict=None , _lowerCamelCase : Optional[int]=None , _lowerCamelCase : Optional[int]=None , **_lowerCamelCase : int , ):
"""simple docstring"""
A_ : Union[str, Any] = {}
if truncation is not None:
A_ : str = truncation
A_ : Dict = generate_kwargs
A_ : Any = {}
if return_tensors is not None and return_type is None:
A_ : str = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
if return_type is not None:
A_ : str = return_type
if clean_up_tokenization_spaces is not None:
A_ : Tuple = clean_up_tokenization_spaces
if stop_sequence is not None:
A_ : Optional[int] = self.tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
if len(_lowerCamelCase ) > 1:
warnings.warn(
'''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
''' the stop sequence will be used as the stop sequence string in the interim.''' )
A_ : Any = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def _a ( self : str , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int ):
"""simple docstring"""
return True
def _a ( self : int , *_lowerCamelCase : int , _lowerCamelCase : int ):
"""simple docstring"""
A_ : Union[str, Any] = self.model.config.prefix if self.model.config.prefix is not None else ''''''
if isinstance(args[0] , _lowerCamelCase ):
if self.tokenizer.pad_token_id is None:
raise ValueError('''Please make sure that the tokenizer has a pad_token_id when using a batch input''' )
A_ : Optional[int] = ([prefix + arg for arg in args[0]],)
A_ : List[str] = True
elif isinstance(args[0] , _lowerCamelCase ):
A_ : str = (prefix + args[0],)
A_ : str = False
else:
raise ValueError(
f' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`' )
A_ : Dict = self.tokenizer(*_lowerCamelCase , padding=_lowerCamelCase , truncation=_lowerCamelCase , return_tensors=self.framework )
# This is produced by tokenizers but is an invalid generate kwargs
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
return inputs
def __call__( self : str , *_lowerCamelCase : int , **_lowerCamelCase : List[Any] ):
"""simple docstring"""
A_ : str = super().__call__(*_lowerCamelCase , **_lowerCamelCase )
if (
isinstance(args[0] , _lowerCamelCase )
and all(isinstance(_lowerCamelCase , _lowerCamelCase ) for el in args[0] )
and all(len(_lowerCamelCase ) == 1 for res in result )
):
return [res[0] for res in result]
return result
def _a ( self : str , _lowerCamelCase : Optional[int] , _lowerCamelCase : List[Any]=TruncationStrategy.DO_NOT_TRUNCATE , **_lowerCamelCase : List[str] ):
"""simple docstring"""
A_ : str = self._parse_and_tokenize(_lowerCamelCase , truncation=_lowerCamelCase , **_lowerCamelCase )
return inputs
def _a ( self : Any , _lowerCamelCase : Tuple , **_lowerCamelCase : Optional[Any] ):
"""simple docstring"""
if self.framework == "pt":
A_ : Dict = model_inputs['''input_ids'''].shape
elif self.framework == "tf":
A_ : Any = tf.shape(model_inputs['''input_ids'''] ).numpy()
A_ : Optional[int] = generate_kwargs.get('''min_length''' , self.model.config.min_length )
A_ : int = generate_kwargs.get('''max_length''' , self.model.config.max_length )
self.check_inputs(_lowerCamelCase , generate_kwargs['''min_length'''] , generate_kwargs['''max_length'''] )
A_ : int = self.model.generate(**_lowerCamelCase , **_lowerCamelCase )
A_ : Optional[int] = output_ids.shape[0]
if self.framework == "pt":
A_ : Optional[Any] = output_ids.reshape(_lowerCamelCase , out_b // in_b , *output_ids.shape[1:] )
elif self.framework == "tf":
A_ : Any = tf.reshape(_lowerCamelCase , (in_b, out_b // in_b, *output_ids.shape[1:]) )
return {"output_ids": output_ids}
def _a ( self : Optional[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : int=ReturnType.TEXT , _lowerCamelCase : Any=False ):
"""simple docstring"""
A_ : List[str] = []
for output_ids in model_outputs["output_ids"][0]:
if return_type == ReturnType.TENSORS:
A_ : Union[str, Any] = {f'{self.return_name}_token_ids': output_ids}
elif return_type == ReturnType.TEXT:
A_ : Optional[int] = {
f'{self.return_name}_text': self.tokenizer.decode(
_lowerCamelCase , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase , )
}
records.append(_lowerCamelCase )
return records
@add_end_docstrings(a__ )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'summary'
def __call__( self : List[str] , *_lowerCamelCase : Tuple , **_lowerCamelCase : str ):
"""simple docstring"""
return super().__call__(*_lowerCamelCase , **_lowerCamelCase )
def _a ( self : Optional[Any] , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int ):
"""simple docstring"""
if max_length < min_length:
logger.warning(f'Your min_length={min_length} must be inferior than your max_length={max_length}.' )
if input_length < max_length:
logger.warning(
f'Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '
'''a summarization task, where outputs shorter than the input are typically wanted, you might '''
f'consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})' )
@add_end_docstrings(a__ )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'translation'
def _a ( self : Optional[Any] , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int ):
"""simple docstring"""
if input_length > 0.9 * max_length:
logger.warning(
f'Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '
'''increasing your max_length manually, e.g. translator(\'...\', max_length=400)''' )
return True
def _a ( self : int , *_lowerCamelCase : List[Any] , _lowerCamelCase : Tuple=TruncationStrategy.DO_NOT_TRUNCATE , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Union[str, Any]=None ):
"""simple docstring"""
if getattr(self.tokenizer , '''_build_translation_inputs''' , _lowerCamelCase ):
return self.tokenizer._build_translation_inputs(
*_lowerCamelCase , return_tensors=self.framework , truncation=_lowerCamelCase , src_lang=_lowerCamelCase , tgt_lang=_lowerCamelCase )
else:
return super()._parse_and_tokenize(*_lowerCamelCase , truncation=_lowerCamelCase )
def _a ( self : int , _lowerCamelCase : Tuple=None , _lowerCamelCase : Optional[int]=None , **_lowerCamelCase : Any ):
"""simple docstring"""
A_ : Optional[Any] = super()._sanitize_parameters(**_lowerCamelCase )
if src_lang is not None:
A_ : Dict = src_lang
if tgt_lang is not None:
A_ : Dict = tgt_lang
if src_lang is None and tgt_lang is None:
# Backward compatibility, direct arguments use is preferred.
A_ : str = kwargs.get('''task''' , self.task )
A_ : Optional[int] = task.split('''_''' )
if task and len(_lowerCamelCase ) == 4:
# translation, XX, to YY
A_ : List[Any] = items[1]
A_ : Any = items[3]
return preprocess_params, forward_params, postprocess_params
def __call__( self : Tuple , *_lowerCamelCase : Dict , **_lowerCamelCase : Optional[int] ):
"""simple docstring"""
return super().__call__(*_lowerCamelCase , **_lowerCamelCase )
| 366 |
'''simple docstring'''
import pprint
import requests
snake_case__ = """https://zenquotes.io/api"""
def snake_case__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def snake_case__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if __name__ == "__main__":
snake_case__ = random_quotes()
pprint.pprint(response)
| 4 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
"""roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
"""roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
"""distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
"""roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
"""roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'roberta'
def __init__( self : Any , _lowerCamelCase : Tuple=50265 , _lowerCamelCase : int=768 , _lowerCamelCase : Optional[Any]=12 , _lowerCamelCase : Tuple=12 , _lowerCamelCase : List[str]=3072 , _lowerCamelCase : Union[str, Any]="gelu" , _lowerCamelCase : Dict=0.1 , _lowerCamelCase : Tuple=0.1 , _lowerCamelCase : Tuple=512 , _lowerCamelCase : Optional[int]=2 , _lowerCamelCase : List[Any]=0.02 , _lowerCamelCase : Any=1E-12 , _lowerCamelCase : Optional[Any]=1 , _lowerCamelCase : Union[str, Any]=0 , _lowerCamelCase : Optional[Any]=2 , _lowerCamelCase : Optional[Any]="absolute" , _lowerCamelCase : str=True , _lowerCamelCase : Optional[Any]=None , **_lowerCamelCase : Any , ):
"""simple docstring"""
super().__init__(pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase )
A_ : Dict = vocab_size
A_ : List[Any] = hidden_size
A_ : str = num_hidden_layers
A_ : int = num_attention_heads
A_ : Dict = hidden_act
A_ : List[str] = intermediate_size
A_ : Optional[int] = hidden_dropout_prob
A_ : Union[str, Any] = attention_probs_dropout_prob
A_ : Union[str, Any] = max_position_embeddings
A_ : Dict = type_vocab_size
A_ : int = initializer_range
A_ : int = layer_norm_eps
A_ : Union[str, Any] = position_embedding_type
A_ : Any = use_cache
A_ : int = classifier_dropout
class UpperCamelCase_ (a__ ):
"""simple docstring"""
@property
def _a ( self : Tuple ):
"""simple docstring"""
if self.task == "multiple-choice":
A_ : List[str] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
A_ : Optional[Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 367 |
'''simple docstring'''
from __future__ import annotations
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[int] , _lowerCamelCase : int ):
"""simple docstring"""
A_ : Union[str, Any] = order
# a_{0} ... a_{k}
A_ : Union[str, Any] = [1.0] + [0.0] * order
# b_{0} ... b_{k}
A_ : int = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
A_ : str = [0.0] * self.order
# y[n-1] ... y[n-k]
A_ : Optional[Any] = [0.0] * self.order
def _a ( self : Dict , _lowerCamelCase : list[float] , _lowerCamelCase : list[float] ):
"""simple docstring"""
if len(_lowerCamelCase ) < self.order:
A_ : Any = [1.0, *a_coeffs]
if len(_lowerCamelCase ) != self.order + 1:
A_ : List[Any] = (
f'Expected a_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(_lowerCamelCase )}'
)
raise ValueError(_lowerCamelCase )
if len(_lowerCamelCase ) != self.order + 1:
A_ : Union[str, Any] = (
f'Expected b_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(_lowerCamelCase )}'
)
raise ValueError(_lowerCamelCase )
A_ : Tuple = a_coeffs
A_ : str = b_coeffs
def _a ( self : Tuple , _lowerCamelCase : float ):
"""simple docstring"""
A_ : Any = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
A_ : str = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
A_ : Optional[Any] = self.input_history[:-1]
A_ : List[str] = self.output_history[:-1]
A_ : Tuple = sample
A_ : Tuple = result
return result
| 4 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase_ (metaclass=a__ ):
"""simple docstring"""
_lowerCAmelCase = ['onnx']
def __init__( self : List[Any] , *_lowerCamelCase : List[Any] , **_lowerCamelCase : List[str] ):
"""simple docstring"""
requires_backends(self , ['''onnx'''] )
@classmethod
def _a ( cls : str , *_lowerCamelCase : Tuple , **_lowerCamelCase : Tuple ):
"""simple docstring"""
requires_backends(cls , ['''onnx'''] )
@classmethod
def _a ( cls : Optional[Any] , *_lowerCamelCase : str , **_lowerCamelCase : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ['''onnx'''] )
| 368 |
'''simple docstring'''
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ : Union[str, Any] = val
A_ : Tuple = None
A_ : Any = None
def _a ( self : Tuple , _lowerCamelCase : List[Any] ):
"""simple docstring"""
if self.val:
if val < self.val:
if self.left is None:
A_ : int = Node(_lowerCamelCase )
else:
self.left.insert(_lowerCamelCase )
elif val > self.val:
if self.right is None:
A_ : List[str] = Node(_lowerCamelCase )
else:
self.right.insert(_lowerCamelCase )
else:
A_ : Any = val
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int] ) -> str:
# Recursive traversal
if root:
inorder(root.left , lowerCamelCase__ )
res.append(root.val )
inorder(root.right , lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Optional[int] ) -> Tuple:
# Build BST
if len(lowerCamelCase__ ) == 0:
return arr
A_ : Dict = Node(arr[0] )
for i in range(1 , len(lowerCamelCase__ ) ):
root.insert(arr[i] )
# Traverse BST in order.
A_ : Tuple = []
inorder(lowerCamelCase__ , lowerCamelCase__ )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 4 | 0 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : int ) -> float:
A_ : int = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def snake_case__ ( ) -> Optional[Any]:
print(sum_of_series(1 , 1 , 1_0 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 369 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list ) -> list:
if len(lowerCamelCase__ ) <= 1:
return [tuple(lowerCamelCase__ )]
A_ : List[str] = []
def generate(lowerCamelCase__ : int , lowerCamelCase__ : list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , lowerCamelCase__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
A_ ,A_ : Optional[int] = arr[k - 1], arr[i]
else: # k is odd
A_ ,A_ : Union[str, Any] = arr[k - 1], arr[0]
generate(k - 1 , lowerCamelCase__ )
generate(len(lowerCamelCase__ ) , lowerCamelCase__ )
return res
if __name__ == "__main__":
snake_case__ = input("""Enter numbers separated by a comma:\n""").strip()
snake_case__ = [int(item) for item in user_input.split(""",""")]
print(heaps(arr))
| 4 | 0 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list ) -> list:
if len(lowerCamelCase__ ) <= 1:
return lst
A_ : Any = 1
while i < len(lowerCamelCase__ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
A_ : Optional[int] = lst[i], lst[i - 1]
i -= 1
if i == 0:
A_ : List[Any] = 1
return lst
if __name__ == "__main__":
snake_case__ = input("""Enter numbers separated by a comma:\n""").strip()
snake_case__ = [int(item) for item in user_input.split(""",""")]
print(gnome_sort(unsorted))
| 370 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : Tuple = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Dict = -1
A_ : List[str] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Any = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : List[str] = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
A_ : List[str] = TextStreamer(_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
A_ : Dict = cs.out[:-1]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : List[str] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Dict = -1
A_ : List[str] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Optional[int] = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : str = tokenizer.decode(greedy_ids[0] )
A_ : int = TextIteratorStreamer(_lowerCamelCase )
A_ : List[Any] = {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
A_ : List[Any] = Thread(target=model.generate , kwargs=_lowerCamelCase )
thread.start()
A_ : List[Any] = ''''''
for new_text in streamer:
streamer_text += new_text
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : List[str] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : List[Any] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : List[str] = -1
A_ : Any = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Tuple = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : Tuple = greedy_ids[:, input_ids.shape[1] :]
A_ : Tuple = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
A_ : Any = TextStreamer(_lowerCamelCase , skip_prompt=_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
A_ : Any = cs.out[:-1]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = AutoTokenizer.from_pretrained('''distilgpt2''' )
A_ : Tuple = AutoModelForCausalLM.from_pretrained('''distilgpt2''' ).to(_lowerCamelCase )
A_ : List[Any] = -1
A_ : Union[str, Any] = torch.ones((1, 5) , device=_lowerCamelCase ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
A_ : List[Any] = TextStreamer(_lowerCamelCase , skip_special_tokens=_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=1 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
A_ : List[str] = cs.out[:-1] # Remove the final "\n"
A_ : List[Any] = tokenizer(_lowerCamelCase , return_tensors='''pt''' )
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1) )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : str = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : str = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Union[str, Any] = -1
A_ : Union[str, Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : List[str] = TextIteratorStreamer(_lowerCamelCase , timeout=0.0_01 )
A_ : str = {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
A_ : List[str] = Thread(target=model.generate , kwargs=_lowerCamelCase )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(_lowerCamelCase ):
A_ : str = ''''''
for new_text in streamer:
streamer_text += new_text
| 4 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@slow
def _a ( self : int ):
"""simple docstring"""
A_ : Optional[Any] = XLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
A_ : Any = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
A_ : List[str] = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
A_ : str = torch.tensor(
[[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
A_ : Union[str, Any] = model(_lowerCamelCase )['''last_hidden_state'''].detach()
self.assertEqual(output.shape , _lowerCamelCase )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _lowerCamelCase , atol=1E-3 ) )
@slow
def _a ( self : str ):
"""simple docstring"""
A_ : Optional[Any] = XLMRobertaModel.from_pretrained('''xlm-roberta-large''' )
A_ : Union[str, Any] = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
A_ : Tuple = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim
A_ : Union[str, Any] = torch.tensor(
[[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
A_ : Tuple = model(_lowerCamelCase )['''last_hidden_state'''].detach()
self.assertEqual(output.shape , _lowerCamelCase )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _lowerCamelCase , atol=1E-3 ) )
| 371 |
'''simple docstring'''
import heapq
def snake_case__ ( lowerCamelCase__ : dict ) -> set[int]:
A_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase__ , [-1 * len(lowerCamelCase__ ), (key, value)] )
# chosen_vertices = set of chosen vertices
A_ : str = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
A_ : Tuple = heapq.heappop(lowerCamelCase__ )[1][0]
chosen_vertices.add(lowerCamelCase__ )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
A_ : List[str] = elem[1][1].index(lowerCamelCase__ )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase__ )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case__ = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')
| 4 | 0 |
'''simple docstring'''
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = None
@property
def _a ( self : int ):
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def _a ( self : Any ):
"""simple docstring"""
A_ : str = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''feature_size''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''sampling_rate''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''padding_value''' ) )
def _a ( self : Dict ):
"""simple docstring"""
A_ : str = self.feat_extract_tester.prepare_inputs_for_common()
A_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
A_ : Optional[int] = feat_extract.model_input_names[0]
A_ : List[str] = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_lowerCamelCase ) == len(_lowerCamelCase ) for x, y in zip(_lowerCamelCase , processed_features[input_name] ) ) )
A_ : Dict = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase )
A_ : Union[str, Any] = BatchFeature({input_name: speech_inputs} , tensor_type='''np''' )
A_ : int = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
A_ : str = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _a ( self : Any ):
"""simple docstring"""
A_ : List[str] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase )
A_ : Any = self.feature_extraction_class(**self.feat_extract_dict )
A_ : int = feat_extract.model_input_names[0]
A_ : List[Any] = BatchFeature({input_name: speech_inputs} , tensor_type='''pt''' )
A_ : List[Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
A_ : str = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Any = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase )
A_ : int = self.feature_extraction_class(**self.feat_extract_dict )
A_ : List[Any] = feat_extract.model_input_names[0]
A_ : Dict = BatchFeature({input_name: speech_inputs} , tensor_type='''tf''' )
A_ : str = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
A_ : Union[str, Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _a ( self : Union[str, Any] , _lowerCamelCase : Dict=False ):
"""simple docstring"""
def _inputs_have_equal_length(_lowerCamelCase : str ):
A_ : Tuple = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCamelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCamelCase : str , _lowerCamelCase : Tuple ):
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCamelCase , _lowerCamelCase ):
if not np.allclose(np.asarray(_lowerCamelCase ) , np.asarray(_lowerCamelCase ) , atol=1E-3 ):
return False
return True
A_ : Tuple = self.feature_extraction_class(**self.feat_extract_dict )
A_ : Any = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase )
A_ : Optional[Any] = feat_extract.model_input_names[0]
A_ : Dict = BatchFeature({input_name: speech_inputs} )
A_ : Optional[int] = self.feat_extract_tester.seq_length_diff
A_ : Optional[int] = self.feat_extract_tester.max_seq_length + pad_diff
A_ : List[str] = self.feat_extract_tester.min_seq_length
A_ : Tuple = self.feat_extract_tester.batch_size
A_ : Any = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
A_ : List[str] = feat_extract.pad(_lowerCamelCase , padding=_lowerCamelCase )
A_ : str = input_a[input_name]
A_ : Union[str, Any] = feat_extract.pad(_lowerCamelCase , padding='''longest''' )
A_ : Optional[Any] = input_a[input_name]
A_ : Optional[int] = feat_extract.pad(_lowerCamelCase , padding='''max_length''' , max_length=len(speech_inputs[-1] ) )
A_ : Union[str, Any] = input_a[input_name]
A_ : Optional[int] = feat_extract.pad(_lowerCamelCase , padding='''longest''' , return_tensors='''np''' )
A_ : Union[str, Any] = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_lowerCamelCase ):
feat_extract.pad(_lowerCamelCase , padding='''max_length''' )[input_name]
A_ : List[str] = feat_extract.pad(
_lowerCamelCase , padding='''max_length''' , max_length=_lowerCamelCase , return_tensors='''np''' )
A_ : List[Any] = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCamelCase , _lowerCamelCase ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
A_ : List[str] = feat_extract.pad(_lowerCamelCase , pad_to_multiple_of=10 )
A_ : int = input_a[input_name]
A_ : Optional[Any] = feat_extract.pad(_lowerCamelCase , padding='''longest''' , pad_to_multiple_of=10 )
A_ : Optional[Any] = input_a[input_name]
A_ : Any = feat_extract.pad(
_lowerCamelCase , padding='''max_length''' , pad_to_multiple_of=10 , max_length=_lowerCamelCase )
A_ : Tuple = input_a[input_name]
A_ : Any = feat_extract.pad(
_lowerCamelCase , padding='''max_length''' , pad_to_multiple_of=10 , max_length=_lowerCamelCase , return_tensors='''np''' , )
A_ : List[str] = input_a[input_name]
self.assertTrue(all(len(_lowerCamelCase ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_lowerCamelCase , _lowerCamelCase ) )
A_ : Union[str, Any] = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_lowerCamelCase ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
A_ : Tuple = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1E-3 )
def _a ( self : Any , _lowerCamelCase : str=False ):
"""simple docstring"""
def _inputs_have_equal_length(_lowerCamelCase : str ):
A_ : Dict = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCamelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCamelCase : Tuple , _lowerCamelCase : int ):
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCamelCase , _lowerCamelCase ):
if not np.allclose(np.asarray(_lowerCamelCase ) , np.asarray(_lowerCamelCase ) , atol=1E-3 ):
return False
return True
A_ : int = self.feature_extraction_class(**self.feat_extract_dict )
A_ : int = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase )
A_ : List[str] = feat_extract.model_input_names[0]
A_ : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
A_ : List[str] = feat_extract.pad(
_lowerCamelCase , padding='''max_length''' , max_length=len(speech_inputs[0] ) , truncation=_lowerCamelCase )
A_ : Any = input_a[input_name]
A_ : Optional[Any] = feat_extract.pad(_lowerCamelCase , padding='''max_length''' , max_length=len(speech_inputs[0] ) )
A_ : List[str] = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )
# truncate to smallest with np
A_ : Optional[Any] = feat_extract.pad(
_lowerCamelCase , padding='''max_length''' , max_length=len(speech_inputs[0] ) , return_tensors='''np''' , truncation=_lowerCamelCase , )
A_ : str = input_a[input_name]
A_ : str = feat_extract.pad(
_lowerCamelCase , padding='''max_length''' , max_length=len(speech_inputs[0] ) , return_tensors='''np''' )
A_ : Optional[Any] = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )
# truncate to middle
A_ : Optional[Any] = feat_extract.pad(
_lowerCamelCase , padding='''max_length''' , max_length=len(speech_inputs[1] ) , truncation=_lowerCamelCase , return_tensors='''np''' , )
A_ : str = input_a[input_name]
A_ : Optional[int] = feat_extract.pad(
_lowerCamelCase , padding='''max_length''' , max_length=len(speech_inputs[1] ) , truncation=_lowerCamelCase )
A_ : Union[str, Any] = input_a[input_name]
A_ : Optional[int] = feat_extract.pad(
_lowerCamelCase , padding='''max_length''' , max_length=len(speech_inputs[1] ) , return_tensors='''np''' )
A_ : Union[str, Any] = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCamelCase , _lowerCamelCase ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCamelCase ):
feat_extract.pad(_lowerCamelCase , truncation=_lowerCamelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCamelCase ):
feat_extract.pad(_lowerCamelCase , padding='''longest''' , truncation=_lowerCamelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCamelCase ):
feat_extract.pad(_lowerCamelCase , padding='''longest''' , truncation=_lowerCamelCase )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_lowerCamelCase ):
feat_extract.pad(_lowerCamelCase , padding='''max_length''' , truncation=_lowerCamelCase )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
A_ : str = 12
A_ : str = feat_extract.pad(
_lowerCamelCase , padding='''max_length''' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCamelCase , truncation=_lowerCamelCase , )
A_ : Tuple = input_a[input_name]
A_ : str = feat_extract.pad(
_lowerCamelCase , padding='''max_length''' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCamelCase , )
A_ : Any = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
A_ : Tuple = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
A_ : Optional[int] = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_lowerCamelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCamelCase ) )
def _a ( self : Optional[int] ):
"""simple docstring"""
self._check_padding(numpify=_lowerCamelCase )
def _a ( self : str ):
"""simple docstring"""
self._check_padding(numpify=_lowerCamelCase )
def _a ( self : str ):
"""simple docstring"""
self._check_truncation(numpify=_lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
self._check_truncation(numpify=_lowerCamelCase )
@require_torch
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Tuple = self.feature_extraction_class(**self.feat_extract_dict )
A_ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_common()
A_ : Union[str, Any] = feat_extract.model_input_names[0]
A_ : Optional[int] = BatchFeature({input_name: speech_inputs} )
A_ : str = feat_extract.pad(_lowerCamelCase , padding='''longest''' , return_tensors='''np''' )[input_name]
A_ : Any = feat_extract.pad(_lowerCamelCase , padding='''longest''' , return_tensors='''pt''' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )
@require_tf
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : int = self.feature_extraction_class(**self.feat_extract_dict )
A_ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_common()
A_ : Union[str, Any] = feat_extract.model_input_names[0]
A_ : Optional[Any] = BatchFeature({input_name: speech_inputs} )
A_ : Tuple = feat_extract.pad(_lowerCamelCase , padding='''longest''' , return_tensors='''np''' )[input_name]
A_ : Optional[int] = feat_extract.pad(_lowerCamelCase , padding='''longest''' , return_tensors='''tf''' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )
def _a ( self : int ):
"""simple docstring"""
A_ : Optional[Any] = self.feat_extract_dict
A_ : Tuple = True
A_ : Optional[Any] = self.feature_extraction_class(**_lowerCamelCase )
A_ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_common()
A_ : List[str] = [len(_lowerCamelCase ) for x in speech_inputs]
A_ : int = feat_extract.model_input_names[0]
A_ : Tuple = BatchFeature({input_name: speech_inputs} )
A_ : Any = feat_extract.pad(_lowerCamelCase , padding='''longest''' , return_tensors='''np''' )
self.assertIn('''attention_mask''' , _lowerCamelCase )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _lowerCamelCase )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Any = self.feat_extract_dict
A_ : Dict = True
A_ : List[str] = self.feature_extraction_class(**_lowerCamelCase )
A_ : str = self.feat_extract_tester.prepare_inputs_for_common()
A_ : Dict = [len(_lowerCamelCase ) for x in speech_inputs]
A_ : Union[str, Any] = feat_extract.model_input_names[0]
A_ : str = BatchFeature({input_name: speech_inputs} )
A_ : Optional[Any] = min(_lowerCamelCase )
A_ : int = feat_extract.pad(
_lowerCamelCase , padding='''max_length''' , max_length=_lowerCamelCase , truncation=_lowerCamelCase , return_tensors='''np''' )
self.assertIn('''attention_mask''' , _lowerCamelCase )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 350 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__ = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
F'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
F'decoder.layers.{i}.encoder_attn.out_proj.bias',
)
)
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
]
)
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[Any] ) -> Optional[Any]:
A_ : Tuple = state_dict.pop(lowerCamelCase__ )
A_ : Optional[Any] = val
def snake_case__ ( lowerCamelCase__ : Dict ) -> Any:
A_ : int = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
A_ : int = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
A_ : List[str] = value
else:
A_ : Optional[int] = value
return new_state_dict
def snake_case__ ( lowerCamelCase__ : Union[str, Any] ) -> Optional[Any]:
A_ : Any = ''''''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
A_ : Tuple = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
A_ : Dict = state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
A_ : str = in_proj_weight[:2_5_6, :]
A_ : Optional[Any] = in_proj_bias[:2_5_6]
A_ : Dict = in_proj_weight[2_5_6:5_1_2, :]
A_ : Tuple = in_proj_bias[2_5_6:5_1_2]
A_ : Tuple = in_proj_weight[-2_5_6:, :]
A_ : Optional[int] = in_proj_bias[-2_5_6:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
A_ : Union[str, Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
A_ : Dict = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
A_ : List[str] = in_proj_weight[:2_5_6, :]
A_ : int = in_proj_bias[:2_5_6]
A_ : Any = in_proj_weight[2_5_6:5_1_2, :]
A_ : List[str] = in_proj_bias[2_5_6:5_1_2]
A_ : Union[str, Any] = in_proj_weight[-2_5_6:, :]
A_ : Optional[Any] = in_proj_bias[-2_5_6:]
# read in weights + bias of input projection layer of cross-attention
A_ : Tuple = state_dict.pop(
f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
A_ : Optional[Any] = state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
A_ : Dict = in_proj_weight_cross_attn[:2_5_6, :]
A_ : Tuple = in_proj_bias_cross_attn[:2_5_6]
A_ : int = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
A_ : List[str] = in_proj_bias_cross_attn[2_5_6:5_1_2]
A_ : Any = in_proj_weight_cross_attn[-2_5_6:, :]
A_ : Any = in_proj_bias_cross_attn[-2_5_6:]
def snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Tuple ) -> Dict:
A_ ,A_ : int = image.size
A_ : Tuple = max(lowerCamelCase__ , lowerCamelCase__ )
A_ : Optional[Any] = 8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0
A_ : Union[str, Any] = target_max_size / current_max_size
A_ : Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def snake_case__ ( lowerCamelCase__ : Tuple ) -> str:
A_ : Any = F.to_tensor(lowerCamelCase__ )
A_ : Optional[Any] = F.normalize(lowerCamelCase__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def snake_case__ ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : int , lowerCamelCase__ : int ) -> str:
logger.info('''Converting model...''' )
# load original state dict
A_ : Tuple = torch.hub.load_state_dict_from_url(lowerCamelCase__ , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
A_ : str = rename_backbone_keys(lowerCamelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCamelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
A_ : List[Any] = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
A_ : List[Any] = state_dict.pop(lowerCamelCase__ )
A_ : str = val
# create HuggingFace model and load state dict
A_ : Union[str, Any] = TableTransformerConfig(
backbone='''resnet18''' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
A_ : Dict = 1_5
A_ : Dict = 2
A_ : int = {0: '''table''', 1: '''table rotated'''}
A_ : List[str] = idalabel
A_ : Optional[int] = {v: k for k, v in idalabel.items()}
else:
A_ : Union[str, Any] = 1_2_5
A_ : Optional[Any] = 6
A_ : Optional[Any] = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
A_ : int = idalabel
A_ : Tuple = {v: k for k, v in idalabel.items()}
A_ : Optional[Any] = DetrImageProcessor(
format='''coco_detection''' , max_size=8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0 )
A_ : int = TableTransformerForObjectDetection(lowerCamelCase__ )
model.load_state_dict(lowerCamelCase__ )
model.eval()
# verify our conversion
A_ : Optional[int] = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
A_ : Union[str, Any] = hf_hub_download(repo_id='''nielsr/example-pdf''' , repo_type='''dataset''' , filename=lowerCamelCase__ )
A_ : Tuple = Image.open(lowerCamelCase__ ).convert('''RGB''' )
A_ : int = normalize(resize(lowerCamelCase__ , lowerCamelCase__ ) ).unsqueeze(0 )
A_ : str = model(lowerCamelCase__ )
if "detection" in checkpoint_url:
A_ : str = (1, 1_5, 3)
A_ : int = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
A_ : Tuple = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
A_ : Optional[int] = (1, 1_2_5, 7)
A_ : Dict = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
A_ : Any = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(lowerCamelCase__ ).mkdir(exist_ok=lowerCamelCase__ )
model.save_pretrained(lowerCamelCase__ )
image_processor.save_pretrained(lowerCamelCase__ )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
A_ : List[Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(lowerCamelCase__ )
image_processor.push_to_hub(lowerCamelCase__ )
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
snake_case__ = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 4 | 0 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
snake_case__ = True
from torch.cuda.amp import autocast
snake_case__ = logging.getLogger(__name__)
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}, )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Whether to log verbose messages or not.'}, )
_lowerCAmelCase = field(
default=2.0, metadata={'help': 'Maximum temperature for gumbel softmax.'} )
_lowerCAmelCase = field(
default=0.5, metadata={'help': 'Minimum temperature for gumbel softmax.'} )
_lowerCAmelCase = field(
default=0.99_99_95, metadata={'help': 'Decay of gumbel temperature during training.'} )
def snake_case__ ( lowerCamelCase__ : ModelArguments , lowerCamelCase__ : TrainingArguments ) -> str:
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
A_ : Any = logging.WARNING
if model_args.verbose_logging:
A_ : Optional[int] = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
A_ : List[Any] = logging.INFO
logger.setLevel(lowerCamelCase__ )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = field(
default=a__, metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
_lowerCAmelCase = field(
default='train', metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
}, )
_lowerCAmelCase = field(
default='validation', metadata={
'help': (
'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
)
}, )
_lowerCAmelCase = field(
default='file', metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''}, )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
_lowerCAmelCase = field(
default=1, metadata={
'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
}, )
_lowerCAmelCase = field(
default=a__, metadata={'help': 'The number of processes to use for the preprocessing.'}, )
_lowerCAmelCase = field(
default=20.0, metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'} )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 4_2
_lowerCAmelCase = 4_2
_lowerCAmelCase = 'longest'
_lowerCAmelCase = None
_lowerCAmelCase = None
def __call__( self : Tuple , _lowerCamelCase : List[Dict[str, Union[List[int], torch.Tensor]]] ):
"""simple docstring"""
A_ : str = self.feature_extractor.pad(
_lowerCamelCase , max_length=self.max_length , padding=self.padding , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
A_ : Dict = self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1] )
A_ : Optional[Any] = batch['''input_values'''].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
A_ : int = self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1 ) ).to(
torch.long )
A_ : int = torch.zeros(
(batch_size, mask_indices_seq_length) , dtype=torch.long , device=batch['''input_values'''].device )
# these two operations makes sure that all values
# before the output lengths indices are attended to
A_ : Dict = 1
A_ : int = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
A_ : List[str] = _compute_mask_indices(
(batch_size, mask_indices_seq_length) , self.model.config.mask_time_prob , self.model.config.mask_time_length , attention_mask=_lowerCamelCase , min_masks=2 , )
return batch
class UpperCamelCase_ (a__ ):
"""simple docstring"""
def __init__( self : Tuple , *_lowerCamelCase : Any , _lowerCamelCase : List[Any]=1 , _lowerCamelCase : Tuple=0 , _lowerCamelCase : Optional[int]=1.0 , **_lowerCamelCase : Any ):
"""simple docstring"""
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
A_ : Optional[Any] = 0
A_ : Tuple = max_gumbel_temp
A_ : Union[str, Any] = min_gumbel_temp
A_ : Any = gumbel_temp_decay
def _a ( self : Tuple , _lowerCamelCase : nn.Module , _lowerCamelCase : Dict[str, Union[torch.Tensor, Any]] ):
"""simple docstring"""
model.train()
A_ : int = self._prepare_inputs(_lowerCamelCase )
if self.use_amp:
with autocast():
A_ : Optional[int] = self.compute_loss(_lowerCamelCase , _lowerCamelCase )
else:
A_ : Tuple = self.compute_loss(_lowerCamelCase , _lowerCamelCase )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
A_ : Optional[int] = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
A_ : Union[str, Any] = loss.sum() / (inputs['''mask_time_indices''']).sum()
else:
raise ValueError(f'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']' )
if self.args.gradient_accumulation_steps > 1:
A_ : Optional[int] = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(_lowerCamelCase ).backward()
elif self.use_apex:
with amp.scale_loss(_lowerCamelCase , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(_lowerCamelCase )
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp ) )
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp ) )
return loss.detach()
def snake_case__ ( ) -> Dict:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
A_ : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
A_ : Optional[Any] = parser.parse_args_into_dataclasses()
configure_logger(lowerCamelCase__ , lowerCamelCase__ )
# Downloading and loading a dataset from the hub.
A_ : int = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
A_ : Dict = DatasetDict()
A_ : Tuple = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'{data_args.train_split_name}[:{data_args.validation_split_percentage}%]' , cache_dir=model_args.cache_dir , )
A_ : str = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'{data_args.train_split_name}[{data_args.validation_split_percentage}%:]' , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
A_ : List[Any] = DatasetDict()
A_ : Dict = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split='''validation''' , cache_dir=model_args.cache_dir , )
A_ : Optional[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'{data_args.train_split_name}' , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
A_ : Union[str, Any] = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=lowerCamelCase__ )
def prepare_dataset(lowerCamelCase__ : List[str] ):
# check that all files have the correct sampling rate
A_ : str = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
A_ : Union[str, Any] = datasets.map(
lowerCamelCase__ , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets['''train'''].column_names )
# filter audio files that are too long
A_ : List[str] = vectorized_datasets.filter(
lambda lowerCamelCase__ : len(data['''speech'''] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(lowerCamelCase__ : Optional[int] ):
return feature_extractor(batch['''speech'''] , sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
A_ : Optional[int] = vectorized_datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets['''train'''].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
A_ : int = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
'''PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'''
''' ``config.feat_extract_norm=\'layer\'''' )
A_ : Optional[int] = WavaVecaForPreTraining(lowerCamelCase__ )
A_ : str = DataCollatorForWavaVecaPretraining(model=lowerCamelCase__ , feature_extractor=lowerCamelCase__ )
A_ : Optional[int] = WavaVecaPreTrainer(
model=lowerCamelCase__ , data_collator=lowerCamelCase__ , args=lowerCamelCase__ , train_dataset=vectorized_datasets['''train'''] , eval_dataset=vectorized_datasets['''validation'''] , tokenizer=lowerCamelCase__ , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main()
| 351 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
snake_case__ = logging.getLogger(__name__)
@dataclass(frozen=a__ )
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
@dataclass(frozen=a__ )
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 42
def __init__( self : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : PreTrainedTokenizer , _lowerCamelCase : str , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : List[Any]=False , _lowerCamelCase : bool = False , ):
"""simple docstring"""
A_ : Optional[int] = hans_processors[task]()
A_ : int = os.path.join(
_lowerCamelCase , '''cached_{}_{}_{}_{}'''.format(
'''dev''' if evaluate else '''train''' , tokenizer.__class__.__name__ , str(_lowerCamelCase ) , _lowerCamelCase , ) , )
A_ : Dict = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
A_ ,A_ : List[str] = label_list[2], label_list[1]
A_ : Optional[int] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
A_ : str = cached_features_file + '''.lock'''
with FileLock(_lowerCamelCase ):
if os.path.exists(_lowerCamelCase ) and not overwrite_cache:
logger.info(f'Loading features from cached file {cached_features_file}' )
A_ : List[str] = torch.load(_lowerCamelCase )
else:
logger.info(f'Creating features from dataset file at {data_dir}' )
A_ : Optional[int] = (
processor.get_dev_examples(_lowerCamelCase ) if evaluate else processor.get_train_examples(_lowerCamelCase )
)
logger.info('''Training examples: %s''' , len(_lowerCamelCase ) )
A_ : Optional[int] = hans_convert_examples_to_features(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
logger.info('''Saving features into cached file %s''' , _lowerCamelCase )
torch.save(self.features , _lowerCamelCase )
def __len__( self : List[str] ):
"""simple docstring"""
return len(self.features )
def __getitem__( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
return self.features[i]
def _a ( self : str ):
"""simple docstring"""
return self.label_list
if is_tf_available():
import tensorflow as tf
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
def __init__( self : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : PreTrainedTokenizer , _lowerCamelCase : str , _lowerCamelCase : Optional[int] = 128 , _lowerCamelCase : Dict=False , _lowerCamelCase : bool = False , ):
"""simple docstring"""
A_ : Optional[int] = hans_processors[task]()
A_ : Optional[int] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
A_ ,A_ : Union[str, Any] = label_list[2], label_list[1]
A_ : Tuple = label_list
A_ : Optional[int] = processor.get_dev_examples(_lowerCamelCase ) if evaluate else processor.get_train_examples(_lowerCamelCase )
A_ : Tuple = hans_convert_examples_to_features(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='''convert examples to features''' ):
if ex_index % 10000 == 0:
logger.info('''Writing example %d of %d''' % (ex_index, len(_lowerCamelCase )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
A_ : List[Any] = tf.data.Dataset.from_generator(
_lowerCamelCase , (
{
'''example_id''': tf.intaa,
'''input_ids''': tf.intaa,
'''attention_mask''': tf.intaa,
'''token_type_ids''': tf.intaa,
},
tf.intaa,
) , (
{
'''example_id''': tf.TensorShape([] ),
'''input_ids''': tf.TensorShape([None, None] ),
'''attention_mask''': tf.TensorShape([None, None] ),
'''token_type_ids''': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def _a ( self : Any ):
"""simple docstring"""
return self.dataset
def __len__( self : Dict ):
"""simple docstring"""
return len(self.features )
def __getitem__( self : Optional[int] , _lowerCamelCase : List[str] ):
"""simple docstring"""
return self.features[i]
def _a ( self : Tuple ):
"""simple docstring"""
return self.label_list
class UpperCamelCase_ (a__ ):
"""simple docstring"""
def _a ( self : List[str] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(_lowerCamelCase , '''heuristics_train_set.txt''' ) ) , '''train''' )
def _a ( self : List[str] , _lowerCamelCase : Tuple ):
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(_lowerCamelCase , '''heuristics_evaluation_set.txt''' ) ) , '''dev''' )
def _a ( self : Any ):
"""simple docstring"""
return ["contradiction", "entailment", "neutral"]
def _a ( self : Optional[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : Tuple = []
for i, line in enumerate(_lowerCamelCase ):
if i == 0:
continue
A_ : str = '''%s-%s''' % (set_type, line[0])
A_ : Optional[Any] = line[5]
A_ : Union[str, Any] = line[6]
A_ : List[str] = line[7][2:] if line[7].startswith('''ex''' ) else line[7]
A_ : str = line[0]
examples.append(InputExample(guid=_lowerCamelCase , text_a=_lowerCamelCase , text_b=_lowerCamelCase , label=_lowerCamelCase , pairID=_lowerCamelCase ) )
return examples
def snake_case__ ( lowerCamelCase__ : List[InputExample] , lowerCamelCase__ : List[str] , lowerCamelCase__ : int , lowerCamelCase__ : PreTrainedTokenizer , ) -> int:
A_ : Union[str, Any] = {label: i for i, label in enumerate(lowerCamelCase__ )}
A_ : Optional[Any] = []
for ex_index, example in tqdm.tqdm(enumerate(lowerCamelCase__ ) , desc='''convert examples to features''' ):
if ex_index % 1_0_0_0_0 == 0:
logger.info('''Writing example %d''' % (ex_index) )
A_ : Optional[int] = tokenizer(
example.text_a , example.text_b , add_special_tokens=lowerCamelCase__ , max_length=lowerCamelCase__ , padding='''max_length''' , truncation=lowerCamelCase__ , return_overflowing_tokens=lowerCamelCase__ , )
A_ : List[str] = label_map[example.label] if example.label in label_map else 0
A_ : Tuple = int(example.pairID )
features.append(InputFeatures(**lowerCamelCase__ , label=lowerCamelCase__ , pairID=lowerCamelCase__ ) )
for i, example in enumerate(examples[:5] ):
logger.info('''*** Example ***''' )
logger.info(f'guid: {example}' )
logger.info(f'features: {features[i]}' )
return features
snake_case__ = {
"""hans""": 3,
}
snake_case__ = {
"""hans""": HansProcessor,
}
| 4 | 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""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 UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 't5'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
def __init__( self : List[str] , _lowerCamelCase : Optional[int]=32128 , _lowerCamelCase : Union[str, Any]=512 , _lowerCamelCase : Optional[int]=64 , _lowerCamelCase : List[str]=2048 , _lowerCamelCase : Optional[int]=6 , _lowerCamelCase : List[Any]=None , _lowerCamelCase : List[Any]=8 , _lowerCamelCase : Dict=32 , _lowerCamelCase : int=128 , _lowerCamelCase : Optional[int]=0.1 , _lowerCamelCase : List[Any]=1E-6 , _lowerCamelCase : int=1.0 , _lowerCamelCase : int="relu" , _lowerCamelCase : Tuple=True , _lowerCamelCase : Optional[Any]=True , _lowerCamelCase : str=0 , _lowerCamelCase : str=1 , **_lowerCamelCase : List[Any] , ):
"""simple docstring"""
A_ : Dict = vocab_size
A_ : Any = d_model
A_ : List[Any] = d_kv
A_ : str = d_ff
A_ : Tuple = num_layers
A_ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
A_ : Optional[Any] = num_heads
A_ : List[str] = relative_attention_num_buckets
A_ : int = relative_attention_max_distance
A_ : str = dropout_rate
A_ : Optional[Any] = layer_norm_epsilon
A_ : str = initializer_factor
A_ : Tuple = feed_forward_proj
A_ : int = use_cache
A_ : List[Any] = self.feed_forward_proj.split('''-''' )
A_ : int = act_info[-1]
A_ : Tuple = act_info[0] == '''gated'''
if len(_lowerCamelCase ) > 1 and act_info[0] != "gated" or len(_lowerCamelCase ) > 2:
raise ValueError(
f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
A_ : Any = '''gelu_new'''
super().__init__(
pad_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , is_encoder_decoder=_lowerCamelCase , **_lowerCamelCase , )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
@property
def _a ( self : str ):
"""simple docstring"""
A_ : int = {
'''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
'''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
}
if self.use_past:
A_ : Optional[int] = '''past_encoder_sequence + sequence'''
A_ : Optional[Any] = {0: '''batch'''}
A_ : Any = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
A_ : Dict = {0: '''batch''', 1: '''decoder_sequence'''}
A_ : List[Any] = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(_lowerCamelCase , direction='''inputs''' )
return common_inputs
@property
def _a ( self : Optional[Any] ):
"""simple docstring"""
return 13
| 352 |
'''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
snake_case__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCamelCase_ (datasets.BuilderConfig ):
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = "utf-8"
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = True # deprecated
_lowerCAmelCase = None # deprecated
_lowerCAmelCase = 1_0 << 2_0 # 10MB
_lowerCAmelCase = None
class UpperCamelCase_ (datasets.ArrowBasedBuilder ):
"""simple docstring"""
_lowerCAmelCase = JsonConfig
def _a ( self : int ):
"""simple docstring"""
if self.config.block_size is not None:
logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''' )
A_ : List[Any] = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
'''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''' )
if self.config.newlines_in_values is not None:
raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''' )
return datasets.DatasetInfo(features=self.config.features )
def _a ( self : Any , _lowerCamelCase : List[str] ):
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}' )
A_ : int = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_lowerCamelCase , (str, list, tuple) ):
A_ : Union[str, Any] = data_files
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : List[str] = [files]
A_ : List[Any] = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
A_ : Tuple = []
for split_name, files in data_files.items():
if isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : int = [files]
A_ : Union[str, Any] = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=_lowerCamelCase , gen_kwargs={'''files''': files} ) )
return splits
def _a ( self : int , _lowerCamelCase : pa.Table ):
"""simple docstring"""
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
A_ : Optional[int] = self.config.features.arrow_schema.field(_lowerCamelCase ).type
A_ : Optional[int] = pa_table.append_column(_lowerCamelCase , pa.array([None] * len(_lowerCamelCase ) , type=_lowerCamelCase ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
A_ : str = table_cast(_lowerCamelCase , self.config.features.arrow_schema )
return pa_table
def _a ( self : List[str] , _lowerCamelCase : int ):
"""simple docstring"""
for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(_lowerCamelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
A_ : int = json.load(_lowerCamelCase )
# We keep only the field we are interested in
A_ : List[str] = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(_lowerCamelCase , (list, tuple) ):
A_ : int = set().union(*[row.keys() for row in dataset] )
A_ : List[str] = {col: [row.get(_lowerCamelCase ) for row in dataset] for col in keys}
else:
A_ : Tuple = dataset
A_ : Dict = pa.Table.from_pydict(_lowerCamelCase )
yield file_idx, self._cast_table(_lowerCamelCase )
# If the file has one json object per line
else:
with open(_lowerCamelCase , '''rb''' ) as f:
A_ : int = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
A_ : int = max(self.config.chunksize // 32 , 16 << 10 )
A_ : int = (
self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
)
while True:
A_ : Any = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(_lowerCamelCase )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
A_ : Optional[Any] = batch.decode(self.config.encoding , errors=_lowerCamelCase ).encode('''utf-8''' )
try:
while True:
try:
A_ : List[Any] = paj.read_json(
io.BytesIO(_lowerCamelCase ) , read_options=paj.ReadOptions(block_size=_lowerCamelCase ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(_lowerCamelCase , pa.ArrowInvalid )
and "straddling" not in str(_lowerCamelCase )
or block_size > len(_lowerCamelCase )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f'Batch of {len(_lowerCamelCase )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.' )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
_lowerCamelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
A_ : Optional[Any] = json.load(_lowerCamelCase )
except json.JSONDecodeError:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(_lowerCamelCase , _lowerCamelCase ): # list is the only sequence type supported in JSON
try:
A_ : Optional[int] = set().union(*[row.keys() for row in dataset] )
A_ : Tuple = {col: [row.get(_lowerCamelCase ) for row in dataset] for col in keys}
A_ : int = pa.Table.from_pydict(_lowerCamelCase )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise ValueError(f'Not able to read records in the JSON file at {file}.' ) from None
yield file_idx, self._cast_table(_lowerCamelCase )
break
else:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}' )
raise ValueError(
f'Not able to read records in the JSON file at {file}. '
f'You should probably indicate the field of the JSON file containing your records. '
f'This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '
f'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ' ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(_lowerCamelCase )
batch_idx += 1
| 4 | 0 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def snake_case__ ( lowerCamelCase__ : int = 8 ) -> str:
A_ : List[Any] = ascii_letters + digits + punctuation
return "".join(secrets.choice(lowerCamelCase__ ) for _ in range(lowerCamelCase__ ) )
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : int ) -> str:
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(lowerCamelCase__ )
A_ : Tuple = i // 3
A_ : Tuple = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
A_ : List[str] = (
chars_incl
+ random(lowerCamelCase__ , quotient + remainder )
+ random(lowerCamelCase__ , lowerCamelCase__ )
+ random(lowerCamelCase__ , lowerCamelCase__ )
)
A_ : int = list(lowerCamelCase__ )
shuffle(lowerCamelCase__ )
return "".join(lowerCamelCase__ )
# random is a generalised function for letters, characters and numbers
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : int ) -> str:
return "".join(secrets.choice(lowerCamelCase__ ) for _ in range(lowerCamelCase__ ) )
def snake_case__ ( lowerCamelCase__ : Tuple , lowerCamelCase__ : List[str] ) -> List[Any]:
pass # Put your code here...
def snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Optional[int] ) -> int:
pass # Put your code here...
def snake_case__ ( lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Optional[int] ) -> str:
pass # Put your code here...
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : int = 8 ) -> bool:
if len(lowerCamelCase__ ) < min_length:
# Your Password must be at least 8 characters long
return False
A_ : List[Any] = any(char in ascii_uppercase for char in password )
A_ : Tuple = any(char in ascii_lowercase for char in password )
A_ : Union[str, Any] = any(char in digits for char in password )
A_ : Dict = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def snake_case__ ( ) -> Optional[Any]:
A_ : Optional[int] = int(input('''Please indicate the max length of your password: ''' ).strip() )
A_ : List[str] = input(
'''Please indicate the characters that must be in your password: ''' ).strip()
print('''Password generated:''' , password_generator(lowerCamelCase__ ) )
print(
'''Alternative Password generated:''' , alternative_password_generator(lowerCamelCase__ , lowerCamelCase__ ) , )
print('''[If you are thinking of using this passsword, You better save it.]''' )
if __name__ == "__main__":
main()
| 353 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/swin-tiny-patch4-window7-224""": (
"""https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class UpperCamelCase_ (a__, a__ ):
"""simple docstring"""
_lowerCAmelCase = 'swin'
_lowerCAmelCase = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Any , _lowerCamelCase : Optional[Any]=224 , _lowerCamelCase : List[str]=4 , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Tuple=96 , _lowerCamelCase : List[Any]=[2, 2, 6, 2] , _lowerCamelCase : List[str]=[3, 6, 12, 24] , _lowerCamelCase : List[Any]=7 , _lowerCamelCase : Optional[int]=4.0 , _lowerCamelCase : List[str]=True , _lowerCamelCase : List[str]=0.0 , _lowerCamelCase : Any=0.0 , _lowerCamelCase : Dict=0.1 , _lowerCamelCase : List[str]="gelu" , _lowerCamelCase : Tuple=False , _lowerCamelCase : Dict=0.02 , _lowerCamelCase : Optional[Any]=1E-5 , _lowerCamelCase : Any=32 , _lowerCamelCase : Tuple=None , _lowerCamelCase : Any=None , **_lowerCamelCase : str , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
A_ : Optional[int] = image_size
A_ : Optional[int] = patch_size
A_ : Optional[int] = num_channels
A_ : Any = embed_dim
A_ : List[Any] = depths
A_ : Any = len(_lowerCamelCase )
A_ : List[Any] = num_heads
A_ : Tuple = window_size
A_ : Tuple = mlp_ratio
A_ : Dict = qkv_bias
A_ : List[str] = hidden_dropout_prob
A_ : List[str] = attention_probs_dropout_prob
A_ : Any = drop_path_rate
A_ : List[Any] = hidden_act
A_ : Tuple = use_absolute_embeddings
A_ : int = layer_norm_eps
A_ : Optional[Any] = initializer_range
A_ : Union[str, Any] = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
A_ : str = int(embed_dim * 2 ** (len(_lowerCamelCase ) - 1) )
A_ : str = ['''stem'''] + [f'stage{idx}' for idx in range(1 , len(_lowerCamelCase ) + 1 )]
A_ ,A_ : Optional[Any] = get_aligned_output_features_output_indices(
out_features=_lowerCamelCase , out_indices=_lowerCamelCase , stage_names=self.stage_names )
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : str ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return 1E-4
| 4 | 0 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def snake_case__ ( lowerCamelCase__ : Callable , lowerCamelCase__ : float , lowerCamelCase__ : float , lowerCamelCase__ : float , lowerCamelCase__ : float ) -> np.array:
A_ : str = int(np.ceil((x_end - xa) / step_size ) )
A_ : List[str] = np.zeros((n + 1,) )
A_ : List[str] = ya
A_ : Union[str, Any] = xa
for k in range(lowerCamelCase__ ):
A_ : Union[str, Any] = y[k] + step_size * ode_func(lowerCamelCase__ , y[k] )
A_ : Optional[Any] = y[k] + (
(step_size / 2) * (ode_func(lowerCamelCase__ , y[k] ) + ode_func(x + step_size , lowerCamelCase__ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354 |
'''simple docstring'''
from __future__ import annotations
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> list[int]:
A_ : int = 0
A_ : str = len(lowerCamelCase__ ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
A_ : Tuple = i + 1
else:
A_ : List[str] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{two_pointer([2, 7, 11, 15], 9) = }')
| 4 | 0 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Dict=False ) -> List[str]:
try:
A_ : Union[str, Any] = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
A_ : Optional[int] = default
else:
# KEY is set, convert it to True or False.
try:
A_ : List[str] = strtobool(lowerCamelCase__ )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f'If set, {key} must be yes or no.' )
return _value
snake_case__ = parse_flag_from_env("""RUN_SLOW""", default=False)
def snake_case__ ( lowerCamelCase__ : Optional[int] ) -> Dict:
return unittest.skip('''Test was skipped''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Dict ) -> str:
return unittest.skipUnless(_run_slow_tests , '''test is slow''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Any ) -> Optional[int]:
return unittest.skipUnless(not torch.cuda.is_available() , '''test requires only a CPU''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : int ) -> Optional[int]:
return unittest.skipUnless(torch.cuda.is_available() , '''test requires a GPU''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Optional[Any] ) -> int:
return unittest.skipUnless(is_xpu_available() , '''test requires a XPU''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : int ) -> Dict:
return unittest.skipUnless(is_mps_available() , '''test requires a `mps` backend support in `torch`''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Union[str, Any] ) -> str:
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , '''test requires the Hugging Face suite''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Dict ) -> Optional[int]:
return unittest.skipUnless(is_bnb_available() , '''test requires the bitsandbytes library''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Any ) -> List[Any]:
return unittest.skipUnless(is_tpu_available() , '''test requires TPU''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Optional[Any] ) -> Optional[int]:
return unittest.skipUnless(torch.cuda.device_count() == 1 , '''test requires a GPU''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : int ) -> Optional[int]:
return unittest.skipUnless(torch.xpu.device_count() == 1 , '''test requires a XPU''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Any ) -> str:
return unittest.skipUnless(torch.cuda.device_count() > 1 , '''test requires multiple GPUs''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Optional[int] ) -> Optional[int]:
return unittest.skipUnless(torch.xpu.device_count() > 1 , '''test requires multiple XPUs''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Dict ) -> Tuple:
return unittest.skipUnless(is_safetensors_available() , '''test requires safetensors''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : List[str] ) -> List[str]:
return unittest.skipUnless(is_deepspeed_available() , '''test requires DeepSpeed''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Union[str, Any] ) -> int:
return unittest.skipUnless(is_torch_version('''>=''' , '''1.12.0''' ) , '''test requires torch version >= 1.12.0''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : List[Any]=None , lowerCamelCase__ : int=None ) -> Optional[Any]:
if test_case is None:
return partial(lowerCamelCase__ , version=lowerCamelCase__ )
return unittest.skipUnless(is_torch_version('''>=''' , lowerCamelCase__ ) , f'test requires torch version >= {version}' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : List[str] ) -> Optional[int]:
return unittest.skipUnless(is_tensorboard_available() , '''test requires Tensorboard''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Optional[Any] ) -> Tuple:
return unittest.skipUnless(is_wandb_available() , '''test requires wandb''' )(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Optional[int] ) -> List[str]:
return unittest.skipUnless(is_comet_ml_available() , '''test requires comet_ml''' )(lowerCamelCase__ )
snake_case__ = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def snake_case__ ( lowerCamelCase__ : List[Any] ) -> Union[str, Any]:
return unittest.skipUnless(
_atleast_one_tracker_available , '''test requires at least one tracker to be available and for `comet_ml` to not be installed''' , )(lowerCamelCase__ )
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = True
@classmethod
def _a ( cls : Dict ):
"""simple docstring"""
A_ : int = tempfile.mkdtemp()
@classmethod
def _a ( cls : Dict ):
"""simple docstring"""
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def _a ( self : str ):
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob('''**/*''' ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(_lowerCamelCase )
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Dict ):
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Any , _lowerCamelCase : Union[mock.Mock, List[mock.Mock]] ):
"""simple docstring"""
A_ : List[Any] = mocks if isinstance(_lowerCamelCase , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def snake_case__ ( lowerCamelCase__ : Optional[Any] ) -> Dict:
A_ : Optional[int] = AcceleratorState()
A_ : Tuple = tensor[None].clone().to(state.device )
A_ : Tuple = gather(lowerCamelCase__ ).cpu()
A_ : int = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , lowerCamelCase__ ):
return False
return True
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Dict , _lowerCamelCase : List[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : int ):
"""simple docstring"""
A_ : Any = returncode
A_ : str = stdout
A_ : Union[str, Any] = stderr
async def snake_case__ ( lowerCamelCase__ : Dict , lowerCamelCase__ : List[Any] ) -> Optional[Any]:
while True:
A_ : List[Any] = await stream.readline()
if line:
callback(lowerCamelCase__ )
else:
break
async def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : Union[str, Any]=None , lowerCamelCase__ : Optional[int]=None , lowerCamelCase__ : Optional[Any]=None , lowerCamelCase__ : int=False , lowerCamelCase__ : str=False ) -> _RunOutput:
if echo:
print('''\nRunning: ''' , ''' '''.join(lowerCamelCase__ ) )
A_ : Union[str, Any] = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=lowerCamelCase__ , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=lowerCamelCase__ , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
A_ : Union[str, Any] = []
A_ : str = []
def tee(lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : int , lowerCamelCase__ : Union[str, Any]="" ):
A_ : int = line.decode('''utf-8''' ).rstrip()
sink.append(lowerCamelCase__ )
if not quiet:
print(lowerCamelCase__ , lowerCamelCase__ , file=lowerCamelCase__ )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda lowerCamelCase__ : tee(lowerCamelCase__ , lowerCamelCase__ , sys.stdout , label='''stdout:''' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda lowerCamelCase__ : tee(lowerCamelCase__ , lowerCamelCase__ , sys.stderr , label='''stderr:''' ) ) ),
] , timeout=lowerCamelCase__ , )
return _RunOutput(await p.wait() , lowerCamelCase__ , lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Dict , lowerCamelCase__ : List[str]=None , lowerCamelCase__ : Optional[int]=None , lowerCamelCase__ : str=1_8_0 , lowerCamelCase__ : Union[str, Any]=False , lowerCamelCase__ : List[str]=True ) -> _RunOutput:
A_ : str = asyncio.get_event_loop()
A_ : Dict = loop.run_until_complete(
_stream_subprocess(lowerCamelCase__ , env=lowerCamelCase__ , stdin=lowerCamelCase__ , timeout=lowerCamelCase__ , quiet=lowerCamelCase__ , echo=lowerCamelCase__ ) )
A_ : Optional[int] = ''' '''.join(lowerCamelCase__ )
if result.returncode > 0:
A_ : Tuple = '''\n'''.join(result.stderr )
raise RuntimeError(
f'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
f'The combined stderr from workers follows:\n{stderr}' )
return result
class UpperCamelCase_ (a__ ):
"""simple docstring"""
pass
def snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Optional[Any]=False ) -> int:
try:
A_ : List[Any] = subprocess.check_output(lowerCamelCase__ , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(lowerCamelCase__ , '''decode''' ):
A_ : Any = output.decode('''utf-8''' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f'Command `{" ".join(lowerCamelCase__ )}` failed with the following error:\n\n{e.output.decode()}' ) from e
| 355 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCamelCase__ ) )
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
# Base Case
if index == len(lowerCamelCase__ ):
return True
# Recursive Step
for i in range(lowerCamelCase__ ):
if valid_coloring(graph[index] , lowerCamelCase__ , lowerCamelCase__ ):
# Color current vertex
A_ : int = i
# Validate coloring
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , index + 1 ):
return True
# Backtrack
A_ : str = -1
return False
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int ) -> list[int]:
A_ : List[str] = [-1] * len(lowerCamelCase__ )
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , 0 ):
return colored_vertices
return []
| 4 | 0 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowerCamelCase__ ) )
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
# Base Case
if index == len(lowerCamelCase__ ):
return True
# Recursive Step
for i in range(lowerCamelCase__ ):
if valid_coloring(graph[index] , lowerCamelCase__ , lowerCamelCase__ ):
# Color current vertex
A_ : int = i
# Validate coloring
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , index + 1 ):
return True
# Backtrack
A_ : str = -1
return False
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int ) -> list[int]:
A_ : List[str] = [-1] * len(lowerCamelCase__ )
if util_color(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , 0 ):
return colored_vertices
return []
| 356 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case__ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[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],
]
# Define blinker example
snake_case__ = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def snake_case__ ( lowerCamelCase__ : list[list[int]] ) -> list[list[int]]:
A_ : str = []
for i in range(len(lowerCamelCase__ ) ):
A_ : Optional[Any] = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
A_ : Optional[int] = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(lowerCamelCase__ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(lowerCamelCase__ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(lowerCamelCase__ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
A_ : List[str] = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(lowerCamelCase__ )
return next_generation
def snake_case__ ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int ) -> list[Image.Image]:
A_ : List[Any] = []
for _ in range(lowerCamelCase__ ):
# Create output image
A_ : Optional[int] = Image.new('''RGB''' , (len(cells[0] ), len(lowerCamelCase__ )) )
A_ : int = img.load()
# Save cells to image
for x in range(len(lowerCamelCase__ ) ):
for y in range(len(cells[0] ) ):
A_ : Optional[Any] = 2_5_5 - cells[y][x] * 2_5_5
A_ : str = (colour, colour, colour)
# Save image
images.append(lowerCamelCase__ )
A_ : Optional[int] = new_generation(lowerCamelCase__ )
return images
if __name__ == "__main__":
snake_case__ = generate_images(GLIDER, 16)
images[0].save("""out.gif""", save_all=True, append_images=images[1:])
| 4 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
snake_case__ = False
snake_case__ = True
snake_case__ = False
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
parser.add_argument(
"""--repo_path""",
default=None,
type=str,
required=True,
help="""The config json file corresponding to the architecture.""",
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
snake_case__ = parser.parse_args()
snake_case__ = {
"""image_size""": """sample_size""",
"""num_res_blocks""": """layers_per_block""",
"""block_channels""": """block_out_channels""",
"""down_blocks""": """down_block_types""",
"""up_blocks""": """up_block_types""",
"""downscale_freq_shift""": """freq_shift""",
"""resnet_num_groups""": """norm_num_groups""",
"""resnet_act_fn""": """act_fn""",
"""resnet_eps""": """norm_eps""",
"""num_head_channels""": """attention_head_dim""",
}
snake_case__ = {
"""time_steps""": """time_proj""",
"""mid""": """mid_block""",
"""downsample_blocks""": """down_blocks""",
"""upsample_blocks""": """up_blocks""",
}
snake_case__ = """""" if has_file(args.repo_path, """config.json""") else """unet"""
with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
snake_case__ = reader.read()
snake_case__ = json.loads(text)
if do_only_config:
for key in config_parameters_to_change.keys():
config.pop(key, None)
if has_file(args.repo_path, """config.json"""):
snake_case__ = UNetaDModel(**config)
else:
snake_case__ = UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
snake_case__ = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
snake_case__ = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
snake_case__ = config[key]
del config[key]
snake_case__ = [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
snake_case__ = [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]
if do_only_weights:
snake_case__ = torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))
snake_case__ = {}
for param_key, param_value in state_dict.items():
if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
continue
snake_case__ = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split(""".""")[0] == key:
snake_case__ = param_value
snake_case__ = True
if not has_changed:
snake_case__ = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| 357 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Any = tempfile.mkdtemp()
A_ : List[Any] = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
A_ : 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] ) )
A_ : Tuple = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
A_ : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Dict , **_lowerCamelCase : Tuple ):
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _a ( self : Optional[int] , **_lowerCamelCase : Optional[int] ):
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _a ( self : Optional[Any] , **_lowerCamelCase : Tuple ):
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self : int ):
"""simple docstring"""
A_ : Union[str, Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A_ : Any = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self : int ):
"""simple docstring"""
A_ : Tuple = self.get_tokenizer()
A_ : Tuple = self.get_rust_tokenizer()
A_ : Dict = self.get_image_processor()
A_ : List[Any] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor_slow.save_pretrained(self.tmpdirname )
A_ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_lowerCamelCase )
A_ : Any = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor_fast.save_pretrained(self.tmpdirname )
A_ : List[Any] = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _lowerCamelCase )
self.assertIsInstance(processor_fast.tokenizer , _lowerCamelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _lowerCamelCase )
self.assertIsInstance(processor_fast.image_processor , _lowerCamelCase )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[str] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A_ : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
A_ : Tuple = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
A_ : List[str] = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = self.get_image_processor()
A_ : Any = self.get_tokenizer()
A_ : List[str] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : Any = self.prepare_image_inputs()
A_ : List[Any] = image_processor(_lowerCamelCase , return_tensors='''np''' )
A_ : str = processor(images=_lowerCamelCase , 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 : Dict ):
"""simple docstring"""
A_ : str = self.get_image_processor()
A_ : List[str] = self.get_tokenizer()
A_ : Optional[int] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : int = '''lower newer'''
A_ : str = processor(text=_lowerCamelCase )
A_ : Dict = tokenizer(_lowerCamelCase , padding='''max_length''' , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self : str ):
"""simple docstring"""
A_ : Optional[int] = self.get_image_processor()
A_ : Optional[Any] = self.get_tokenizer()
A_ : List[str] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : List[Any] = '''lower newer'''
A_ : Optional[int] = self.prepare_image_inputs()
A_ : List[Any] = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(_lowerCamelCase ):
processor()
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Optional[Any] = self.get_image_processor()
A_ : Optional[int] = self.get_tokenizer()
A_ : List[Any] = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : str = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A_ : str = processor.batch_decode(_lowerCamelCase )
A_ : Union[str, Any] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : str = self.get_image_processor()
A_ : Tuple = self.get_tokenizer()
A_ : Any = AlignProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
A_ : str = '''lower newer'''
A_ : List[str] = self.prepare_image_inputs()
A_ : Tuple = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 4 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'dpt'
def __init__( self : int , _lowerCamelCase : Union[str, Any]=768 , _lowerCamelCase : Optional[Any]=12 , _lowerCamelCase : Optional[int]=12 , _lowerCamelCase : Tuple=3072 , _lowerCamelCase : Optional[Any]="gelu" , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Union[str, Any]=0.0 , _lowerCamelCase : Any=0.02 , _lowerCamelCase : str=1E-12 , _lowerCamelCase : Dict=384 , _lowerCamelCase : int=16 , _lowerCamelCase : str=3 , _lowerCamelCase : Optional[int]=False , _lowerCamelCase : List[Any]=True , _lowerCamelCase : Any=[2, 5, 8, 11] , _lowerCamelCase : Dict="project" , _lowerCamelCase : Dict=[4, 2, 1, 0.5] , _lowerCamelCase : Tuple=[96, 192, 384, 768] , _lowerCamelCase : Dict=256 , _lowerCamelCase : Tuple=-1 , _lowerCamelCase : Union[str, Any]=False , _lowerCamelCase : Optional[int]=True , _lowerCamelCase : Tuple=0.4 , _lowerCamelCase : str=255 , _lowerCamelCase : int=0.1 , _lowerCamelCase : Dict=[1, 1024, 24, 24] , _lowerCamelCase : List[Any]=[0, 1] , _lowerCamelCase : str=None , **_lowerCamelCase : Optional[int] , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
A_ : Union[str, Any] = hidden_size
A_ : Tuple = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info('''Initializing the config with a `BiT` backbone.''' )
A_ : Optional[Any] = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
}
A_ : List[str] = BitConfig(**_lowerCamelCase )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
logger.info('''Initializing the config with a `BiT` backbone.''' )
A_ : Optional[Any] = BitConfig(**_lowerCamelCase )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : List[str] = backbone_config
else:
raise ValueError(
f'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.' )
A_ : Any = backbone_featmap_shape
A_ : int = neck_ignore_stages
if readout_type != "project":
raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.''' )
else:
A_ : Tuple = None
A_ : Dict = None
A_ : Optional[Any] = []
A_ : Optional[int] = num_hidden_layers
A_ : Optional[int] = num_attention_heads
A_ : Any = intermediate_size
A_ : Tuple = hidden_act
A_ : Union[str, Any] = hidden_dropout_prob
A_ : str = attention_probs_dropout_prob
A_ : Union[str, Any] = initializer_range
A_ : int = layer_norm_eps
A_ : Tuple = image_size
A_ : Optional[Any] = patch_size
A_ : Dict = num_channels
A_ : str = qkv_bias
A_ : Tuple = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']''' )
A_ : Any = readout_type
A_ : Any = reassemble_factors
A_ : Any = neck_hidden_sizes
A_ : str = fusion_hidden_size
A_ : List[str] = head_in_index
A_ : Optional[int] = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
A_ : int = use_auxiliary_head
A_ : Optional[int] = auxiliary_loss_weight
A_ : Tuple = semantic_loss_ignore_index
A_ : List[Any] = semantic_classifier_dropout
def _a ( self : str ):
"""simple docstring"""
A_ : int = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
A_ : Any = self.backbone_config.to_dict()
A_ : Any = self.__class__.model_type
return output
| 358 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = """▁"""
snake_case__ = {
"""vocab_file""": """vocab.json""",
"""spm_file""": """sentencepiece.bpe.model""",
}
snake_case__ = {
"""vocab_file""": {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
),
},
"""spm_file""": {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
)
},
}
snake_case__ = {
"""facebook/s2t-small-librispeech-asr""": 10_24,
}
snake_case__ = ["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]
snake_case__ = {"""mustc""": MUSTC_LANGS}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = VOCAB_FILES_NAMES
_lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase = MAX_MODEL_INPUT_SIZES
_lowerCAmelCase = ['input_ids', 'attention_mask']
_lowerCAmelCase = []
def __init__( self : Optional[int] , _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : str="<s>" , _lowerCamelCase : Union[str, Any]="</s>" , _lowerCamelCase : Dict="<pad>" , _lowerCamelCase : str="<unk>" , _lowerCamelCase : Union[str, Any]=False , _lowerCamelCase : int=False , _lowerCamelCase : Any=None , _lowerCamelCase : Any=None , _lowerCamelCase : Optional[Dict[str, Any]] = None , **_lowerCamelCase : Optional[int] , ):
"""simple docstring"""
A_ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , do_upper_case=_lowerCamelCase , do_lower_case=_lowerCamelCase , tgt_lang=_lowerCamelCase , lang_codes=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
A_ : Optional[int] = do_upper_case
A_ : Tuple = do_lower_case
A_ : Tuple = load_json(_lowerCamelCase )
A_ : Tuple = {v: k for k, v in self.encoder.items()}
A_ : List[Any] = spm_file
A_ : List[str] = load_spm(_lowerCamelCase , self.sp_model_kwargs )
if lang_codes is not None:
A_ : Any = lang_codes
A_ : Optional[Any] = LANGUAGES[lang_codes]
A_ : Optional[Any] = [f'<lang:{lang}>' for lang in self.langs]
A_ : Union[str, Any] = {lang: self.sp_model.PieceToId(f'<lang:{lang}>' ) for lang in self.langs}
A_ : Optional[int] = self.lang_tokens
A_ : int = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
A_ : Dict = {}
@property
def _a ( self : Tuple ):
"""simple docstring"""
return len(self.encoder )
@property
def _a ( self : int ):
"""simple docstring"""
return self._tgt_lang
@tgt_lang.setter
def _a ( self : List[str] , _lowerCamelCase : Any ):
"""simple docstring"""
A_ : int = new_tgt_lang
self.set_tgt_lang_special_tokens(_lowerCamelCase )
def _a ( self : Tuple , _lowerCamelCase : str ):
"""simple docstring"""
A_ : List[str] = self.lang_code_to_id[tgt_lang]
A_ : Optional[Any] = [lang_code_id]
def _a ( self : Optional[Any] , _lowerCamelCase : str ):
"""simple docstring"""
return self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
def _a ( self : List[Any] , _lowerCamelCase : int ):
"""simple docstring"""
return self.encoder.get(_lowerCamelCase , self.encoder[self.unk_token] )
def _a ( self : int , _lowerCamelCase : int ):
"""simple docstring"""
return self.decoder.get(_lowerCamelCase , self.unk_token )
def _a ( self : int , _lowerCamelCase : List[str] ):
"""simple docstring"""
A_ : List[Any] = []
A_ : Any = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
A_ : Union[str, Any] = self.sp_model.decode(_lowerCamelCase )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
A_ : Optional[Any] = []
else:
current_sub_tokens.append(_lowerCamelCase )
A_ : Tuple = self.sp_model.decode(_lowerCamelCase )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def _a ( self : int , _lowerCamelCase : Dict , _lowerCamelCase : Any=None ):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def _a ( self : List[Any] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None , _lowerCamelCase : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
A_ : Tuple = [1] * len(self.prefix_tokens )
A_ : Tuple = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(_lowerCamelCase )) + suffix_ones
return prefix_ones + ([0] * len(_lowerCamelCase )) + ([0] * len(_lowerCamelCase )) + suffix_ones
def _a ( self : Dict ):
"""simple docstring"""
A_ : Union[str, Any] = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = self.__dict__.copy()
A_ : List[Any] = None
return state
def __setstate__( self : List[str] , _lowerCamelCase : Dict ):
"""simple docstring"""
A_ : Dict = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
A_ : Optional[int] = {}
A_ : int = load_spm(self.spm_file , self.sp_model_kwargs )
def _a ( self : Optional[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[str] = None ):
"""simple docstring"""
A_ : Dict = Path(_lowerCamelCase )
assert save_dir.is_dir(), f'{save_directory} should be a directory'
A_ : Optional[int] = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
A_ : Optional[int] = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder , _lowerCamelCase )
if os.path.abspath(self.spm_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , _lowerCamelCase )
elif not os.path.isfile(self.spm_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
A_ : List[str] = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (str(_lowerCamelCase ), str(_lowerCamelCase ))
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
A_ : Tuple = sentencepiece.SentencePieceProcessor(**lowerCamelCase__ )
spm.Load(str(lowerCamelCase__ ) )
return spm
def snake_case__ ( lowerCamelCase__ : str ) -> Union[Dict, List]:
with open(lowerCamelCase__ , '''r''' ) as f:
return json.load(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : str ) -> None:
with open(lowerCamelCase__ , '''w''' ) as f:
json.dump(lowerCamelCase__ , lowerCamelCase__ , indent=2 )
| 4 | 0 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
snake_case__ = 3
def snake_case__ ( lowerCamelCase__ : int ) -> int:
print('''Generating primitive root of p''' )
while True:
A_ : List[Any] = random.randrange(3 , lowerCamelCase__ )
if pow(lowerCamelCase__ , 2 , lowerCamelCase__ ) == 1:
continue
if pow(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) == 1:
continue
return g
def snake_case__ ( lowerCamelCase__ : int ) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
print('''Generating prime p...''' )
A_ : List[str] = rabin_miller.generate_large_prime(lowerCamelCase__ ) # select large prime number.
A_ : str = primitive_root(lowerCamelCase__ ) # one primitive root on modulo p.
A_ : List[str] = random.randrange(3 , lowerCamelCase__ ) # private_key -> have to be greater than 2 for safety.
A_ : Tuple = cryptomath.find_mod_inverse(pow(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) , lowerCamelCase__ )
A_ : str = (key_size, e_a, e_a, p)
A_ : Dict = (key_size, d)
return public_key, private_key
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : int ) -> None:
if os.path.exists(f'{name}_pubkey.txt' ) or os.path.exists(f'{name}_privkey.txt' ):
print('''\nWARNING:''' )
print(
f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
'''Use a different name or delete these files and re-run this program.''' )
sys.exit()
A_ : Optional[int] = generate_key(lowerCamelCase__ )
print(f'\nWriting public key to file {name}_pubkey.txt...' )
with open(f'{name}_pubkey.txt' , '''w''' ) as fo:
fo.write(f'{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}' )
print(f'Writing private key to file {name}_privkey.txt...' )
with open(f'{name}_privkey.txt' , '''w''' ) as fo:
fo.write(f'{private_key[0]},{private_key[1]}' )
def snake_case__ ( ) -> None:
print('''Making key files...''' )
make_key_files('''elgamal''' , 2_0_4_8 )
print('''Key files generation successful''' )
if __name__ == "__main__":
main()
| 359 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
snake_case__ = sys.version_info >= (3, 10)
def snake_case__ ( lowerCamelCase__ : Union[str, Any]=None , lowerCamelCase__ : str=None ) -> List[Any]:
return field(default_factory=lambda: default , metadata=lowerCamelCase__ )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 4_2
_lowerCAmelCase = field(default='toto', metadata={'help': 'help message'} )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = False
_lowerCAmelCase = True
_lowerCAmelCase = None
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'titi'
_lowerCAmelCase = 'toto'
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'titi'
_lowerCAmelCase = 'toto'
_lowerCAmelCase = 4_2
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Optional[int] = BasicEnum(self.foo )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[Any] = MixedTypeEnum(self.foo )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = field(default=a__, metadata={'help': 'help message'} )
_lowerCAmelCase = None
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[] )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[1, 2, 3] )
_lowerCAmelCase = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
_lowerCAmelCase = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = field()
_lowerCAmelCase = field()
_lowerCAmelCase = field()
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = BasicEnum(self.required_enum )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = field()
_lowerCAmelCase = None
_lowerCAmelCase = field(default='toto', metadata={'help': 'help message'} )
_lowerCAmelCase = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
if is_python_no_less_than_3_10:
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = False
_lowerCAmelCase = True
_lowerCAmelCase = None
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = field(default=a__, metadata={'help': 'help message'} )
_lowerCAmelCase = None
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[] )
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] , _lowerCamelCase : argparse.ArgumentParser , _lowerCamelCase : argparse.ArgumentParser ):
"""simple docstring"""
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
A_ : Union[str, Any] = {k: v for k, v in vars(_lowerCamelCase ).items() if k != '''container'''}
A_ : Optional[Any] = {k: v for k, v in vars(_lowerCamelCase ).items() if k != '''container'''}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('''choices''' , _lowerCamelCase ) and yy.get('''choices''' , _lowerCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['''type'''](_lowerCamelCase ) , yy['''type'''](_lowerCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : Optional[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--bar''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--baz''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--flag''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Union[str, Any] = ['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
((A_) ,) : List[str] = parser.parse_args_into_dataclasses(_lowerCamelCase , look_for_args_file=_lowerCamelCase )
self.assertFalse(example.flag )
def _a ( self : Dict ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : int = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=42 , type=_lowerCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=_lowerCamelCase , help='''help message''' )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Any = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
expected.add_argument('''--baz''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('''--no_baz''' , action='''store_false''' , default=_lowerCamelCase , dest='''baz''' )
expected.add_argument('''--opt''' , type=_lowerCamelCase , default=_lowerCamelCase )
A_ : Dict = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_lowerCamelCase )
for dataclass_type in dataclass_types:
A_ : Any = HfArgumentParser(_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = parser.parse_args([] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : Optional[int] = parser.parse_args(['''--foo''', '''--no_baz'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : Union[str, Any] = parser.parse_args(['''--foo''', '''--baz'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : List[str] = parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : List[Any] = parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : str = HfArgumentParser(_lowerCamelCase )
A_ : Optional[int] = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=['''titi''', '''toto''', 42] , type=make_choice_type_function(['''titi''', '''toto''', 42] ) , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : str = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
A_ : List[Any] = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
A_ : int = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
A_ : Dict = parser.parse_args_into_dataclasses(['''--foo''', '''titi'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
A_ : Tuple = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 42 )
A_ : List[str] = parser.parse_args_into_dataclasses(['''--foo''', '''42'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def _a ( self : Optional[int] ):
"""simple docstring"""
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
A_ : List[str] = HfArgumentParser(_lowerCamelCase )
A_ : Tuple = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=('''titi''', '''toto''', 42) , type=make_choice_type_function(['''titi''', '''toto''', 42] ) , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Tuple = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
A_ : List[str] = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
A_ : int = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 42 )
def _a ( self : Dict ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : List[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo_int''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
expected.add_argument('''--bar_int''' , nargs='''+''' , default=[1, 2, 3] , type=_lowerCamelCase )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=_lowerCamelCase )
expected.add_argument('''--foo_float''' , nargs='''+''' , default=[0.1, 0.2, 0.3] , type=_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[int] = parser.parse_args([] )
self.assertEqual(
_lowerCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['''Hallo''', '''Bonjour''', '''Hello'''] , foo_float=[0.1, 0.2, 0.3] ) , )
A_ : str = parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split() )
self.assertEqual(_lowerCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['''a''', '''b''', '''c'''] , foo_float=[0.1, 0.7] ) )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=_lowerCamelCase , type=_lowerCamelCase )
expected.add_argument('''--bar''' , default=_lowerCamelCase , type=_lowerCamelCase , help='''help message''' )
expected.add_argument('''--baz''' , default=_lowerCamelCase , type=_lowerCamelCase )
expected.add_argument('''--ces''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
expected.add_argument('''--des''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
A_ : Tuple = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_lowerCamelCase )
for dataclass_type in dataclass_types:
A_ : int = HfArgumentParser(_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = parser.parse_args([] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , bar=_lowerCamelCase , baz=_lowerCamelCase , ces=[] , des=[] ) )
A_ : Optional[Any] = parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split() )
self.assertEqual(_lowerCamelCase , Namespace(foo=12 , bar=3.14 , baz='''42''' , ces=['''a''', '''b''', '''c'''] , des=[1, 2, 3] ) )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = HfArgumentParser(_lowerCamelCase )
A_ : Dict = argparse.ArgumentParser()
expected.add_argument('''--required_list''' , nargs='''+''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--required_str''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=_lowerCamelCase , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : List[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=_lowerCamelCase , )
expected.add_argument('''--opt''' , type=_lowerCamelCase , default=_lowerCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=_lowerCamelCase , help='''help message''' )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : List[Any] = HfArgumentParser(_lowerCamelCase )
A_ : Union[str, Any] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
A_ : Optional[int] = parser.parse_dict(_lowerCamelCase )[0]
A_ : str = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Any = HfArgumentParser(_lowerCamelCase )
A_ : List[str] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
'''extra''': 42,
}
self.assertRaises(_lowerCamelCase , parser.parse_dict , _lowerCamelCase , allow_extra_keys=_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : List[str] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : Tuple = os.path.join(_lowerCamelCase , '''temp_json''' )
os.mkdir(_lowerCamelCase )
with open(temp_local_path + '''.json''' , '''w+''' ) as f:
json.dump(_lowerCamelCase , _lowerCamelCase )
A_ : List[str] = parser.parse_yaml_file(Path(temp_local_path + '''.json''' ) )[0]
A_ : Optional[Any] = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : Tuple = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : int = os.path.join(_lowerCamelCase , '''temp_yaml''' )
os.mkdir(_lowerCamelCase )
with open(temp_local_path + '''.yaml''' , '''w+''' ) as f:
yaml.dump(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[Any] = parser.parse_yaml_file(Path(temp_local_path + '''.yaml''' ) )[0]
A_ : int = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = HfArgumentParser(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
| 4 | 0 |
'''simple docstring'''
import re
from ..utils import cached_file
# docstyle-ignore
snake_case__ = """
Human: <<task>>
Assistant: """
snake_case__ = """huggingface-tools/default-prompts"""
snake_case__ = {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}
def snake_case__ ( lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Tuple , lowerCamelCase__ : Dict="run" ) -> Union[str, Any]:
if prompt_or_repo_id is None:
A_ : Optional[int] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search('''\\s''' , lowerCamelCase__ ) is not None:
return prompt_or_repo_id
A_ : Optional[Any] = cached_file(
lowerCamelCase__ , PROMPT_FILES[mode] , repo_type='''dataset''' , user_agent={'''agent''': agent_name} )
with open(lowerCamelCase__ , '''r''' , encoding='''utf-8''' ) as f:
return f.read()
| 360 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
snake_case__ = get_tests_dir("""fixtures""")
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] ):
"""simple docstring"""
A_ : List[Any] = mock.Mock()
A_ : List[str] = 500
A_ : Tuple = {}
A_ : int = HTTPError
A_ : Optional[Any] = {}
# Download this model to make sure it's in the cache.
A_ : Tuple = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=_lowerCamelCase ) as mock_head:
A_ : List[Any] = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# This check we did call the fake head request
mock_head.assert_called()
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = ViTImageProcessor.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json''' )
def _a ( self : Dict ):
"""simple docstring"""
with self.assertRaises(_lowerCamelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
A_ : Any = AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants''' )
A_ : Tuple = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/stable-diffusion-all-variants''' , subfolder='''feature_extractor''' )
self.assertIsNotNone(_lowerCamelCase )
@is_staging_test
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@classmethod
def _a ( cls : Tuple ):
"""simple docstring"""
A_ : int = TOKEN
HfFolder.save_token(_lowerCamelCase )
@classmethod
def _a ( cls : str ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='''test-image-processor''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-image-processor-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-image-processor''' )
except HTTPError:
pass
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Dict = ViTImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''test-image-processor''' , use_auth_token=self._token )
A_ : Optional[int] = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCamelCase , repo_id='''test-image-processor''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : List[Any] = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : int = ViTImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''valid_org/test-image-processor''' , use_auth_token=self._token )
A_ : List[str] = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCamelCase , repo_id='''valid_org/test-image-processor-org''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
A_ : Any = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCamelCase , getattr(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : Optional[Any] ):
"""simple docstring"""
CustomImageProcessor.register_for_auto_class()
A_ : Any = CustomImageProcessor.from_pretrained(_lowerCamelCase )
image_processor.push_to_hub('''test-dynamic-image-processor''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''} , )
A_ : str = AutoImageProcessor.from_pretrained(
f'{USER}/test-dynamic-image-processor' , trust_remote_code=_lowerCamelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , '''CustomImageProcessor''' )
| 4 | 0 |
'''simple docstring'''
snake_case__ = {
"""joule""": 1.0,
"""kilojoule""": 10_00,
"""megajoule""": 1_00_00_00,
"""gigajoule""": 10_00_00_00_00,
"""wattsecond""": 1.0,
"""watthour""": 36_00,
"""kilowatthour""": 3_60_00_00,
"""newtonmeter""": 1.0,
"""calorie_nutr""": 41_86.8,
"""kilocalorie_nutr""": 4_18_68_00.00,
"""electronvolt""": 1.6_0217_6634e-19,
"""britishthermalunit_it""": 10_55.0_55_85,
"""footpound""": 1.3_5_5_8_1_8,
}
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : str , lowerCamelCase__ : float ) -> float:
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
A_ : Dict = (
f'Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'
f'Valid values are: {", ".join(lowerCamelCase__ )}'
)
raise ValueError(lowerCamelCase__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 361 |
'''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.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = (
'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
)
_lowerCAmelCase = 'CIDAS/clipseg-rd64-refined'
_lowerCAmelCase = 'image_segmenter'
_lowerCAmelCase = CLIPSegForImageSegmentation
_lowerCAmelCase = ['image', 'text']
_lowerCAmelCase = ['image']
def __init__( self : Optional[int] , *_lowerCamelCase : Optional[int] , **_lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def _a ( self : List[str] , _lowerCamelCase : "Image" , _lowerCamelCase : str ):
"""simple docstring"""
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def _a ( self : Union[str, Any] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
with torch.no_grad():
A_ : Optional[int] = self.model(**_lowerCamelCase ).logits
return logits
def _a ( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : int = outputs.cpu().detach().numpy()
A_ : Tuple = 0
A_ : List[str] = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 4 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] , _lowerCamelCase : Any ):
"""simple docstring"""
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['''bs'''] , model_result['''ss'''] ):
A_ : Optional[int] = model_result['''result'''][batch_size][sequence_length]
self.assertIsNotNone(_lowerCamelCase )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Dict = '''sshleifer/tiny-gpt2'''
A_ : Optional[int] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowerCamelCase , )
A_ : Union[str, Any] = PyTorchBenchmark(_lowerCamelCase )
A_ : int = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = '''sgugger/tiny-distilbert-classification'''
A_ : int = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowerCamelCase , only_pretrain_model=_lowerCamelCase , )
A_ : List[str] = PyTorchBenchmark(_lowerCamelCase )
A_ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = '''sshleifer/tiny-gpt2'''
A_ : Any = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , torchscript=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowerCamelCase , )
A_ : List[Any] = PyTorchBenchmark(_lowerCamelCase )
A_ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == '''cpu''' , '''Cant do half precision''' )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : List[str] = '''sshleifer/tiny-gpt2'''
A_ : Dict = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , fpaa=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowerCamelCase , )
A_ : int = PyTorchBenchmark(_lowerCamelCase )
A_ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : int = '''sshleifer/tiny-gpt2'''
A_ : Dict = AutoConfig.from_pretrained(_lowerCamelCase )
# set architectures equal to `None`
A_ : List[str] = None
A_ : str = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowerCamelCase , )
A_ : Optional[int] = PyTorchBenchmark(_lowerCamelCase , configs=[config] )
A_ : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Union[str, Any] = '''sshleifer/tiny-gpt2'''
A_ : List[Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowerCamelCase , )
A_ : List[Any] = PyTorchBenchmark(_lowerCamelCase )
A_ : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == '''cpu''' , '''Can\'t do half precision''' )
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Optional[int] = '''sshleifer/tiny-gpt2'''
A_ : Union[str, Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , fpaa=_lowerCamelCase , multi_process=_lowerCamelCase , )
A_ : int = PyTorchBenchmark(_lowerCamelCase )
A_ : str = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[Any] = '''sshleifer/tiny-gpt2'''
A_ : int = AutoConfig.from_pretrained(_lowerCamelCase )
A_ : List[str] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowerCamelCase , )
A_ : str = PyTorchBenchmark(_lowerCamelCase , configs=[config] )
A_ : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : int = '''sshleifer/tinier_bart'''
A_ : str = AutoConfig.from_pretrained(_lowerCamelCase )
A_ : Union[str, Any] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowerCamelCase , )
A_ : Any = PyTorchBenchmark(_lowerCamelCase , configs=[config] )
A_ : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Union[str, Any] = '''sshleifer/tiny-gpt2'''
A_ : Optional[int] = AutoConfig.from_pretrained(_lowerCamelCase )
A_ : Any = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowerCamelCase , )
A_ : List[str] = PyTorchBenchmark(_lowerCamelCase , configs=[config] )
A_ : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[Any] = '''sshleifer/tinier_bart'''
A_ : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
A_ : str = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowerCamelCase , )
A_ : List[Any] = PyTorchBenchmark(_lowerCamelCase , configs=[config] )
A_ : int = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _a ( self : int ):
"""simple docstring"""
A_ : str = '''sshleifer/tiny-gpt2'''
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : Tuple = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , save_to_csv=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(_lowerCamelCase , '''inf_time.csv''' ) , train_memory_csv_file=os.path.join(_lowerCamelCase , '''train_mem.csv''' ) , inference_memory_csv_file=os.path.join(_lowerCamelCase , '''inf_mem.csv''' ) , train_time_csv_file=os.path.join(_lowerCamelCase , '''train_time.csv''' ) , env_info_csv_file=os.path.join(_lowerCamelCase , '''env.csv''' ) , multi_process=_lowerCamelCase , )
A_ : int = PyTorchBenchmark(_lowerCamelCase )
benchmark.run()
self.assertTrue(Path(os.path.join(_lowerCamelCase , '''inf_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(_lowerCamelCase , '''train_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(_lowerCamelCase , '''inf_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(_lowerCamelCase , '''train_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(_lowerCamelCase , '''env.csv''' ) ).exists() )
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Any = '''sshleifer/tiny-gpt2'''
def _check_summary_is_not_empty(_lowerCamelCase : Dict ):
self.assertTrue(hasattr(_lowerCamelCase , '''sequential''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''cumulative''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''current''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''total''' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : List[str] = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowerCamelCase , inference=_lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(_lowerCamelCase , '''log.txt''' ) , log_print=_lowerCamelCase , trace_memory_line_by_line=_lowerCamelCase , multi_process=_lowerCamelCase , )
A_ : str = PyTorchBenchmark(_lowerCamelCase )
A_ : Any = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(_lowerCamelCase , '''log.txt''' ) ).exists() )
| 362 |
'''simple docstring'''
from collections.abc import Sequence
def snake_case__ ( lowerCamelCase__ : Sequence[float] , lowerCamelCase__ : bool = False ) -> float:
if not arr:
return 0
A_ : Union[str, Any] = 0 if allow_empty_subarrays else float('''-inf''' )
A_ : str = 0.0
for num in arr:
A_ : Any = max(0 if allow_empty_subarrays else num , curr_sum + num )
A_ : Tuple = max(lowerCamelCase__ , lowerCamelCase__ )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
snake_case__ = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F'{max_subarray_sum(nums) = }')
| 4 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
snake_case__ = 16
snake_case__ = 32
def snake_case__ ( lowerCamelCase__ : Accelerator , lowerCamelCase__ : int = 1_6 ) -> Any:
A_ : str = AutoTokenizer.from_pretrained('''bert-base-cased''' )
A_ : Optional[Any] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(lowerCamelCase__ : Dict ):
# max_length=None => use the model max length (it's actually the default)
A_ : List[str] = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A_ : Tuple = datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A_ : str = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(lowerCamelCase__ : Optional[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
A_ : Dict = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
A_ : int = 1_6
elif accelerator.mixed_precision != "no":
A_ : Tuple = 8
else:
A_ : List[Any] = None
return tokenizer.pad(
lowerCamelCase__ , padding='''longest''' , max_length=lowerCamelCase__ , pad_to_multiple_of=lowerCamelCase__ , return_tensors='''pt''' , )
# Instantiate dataloaders.
A_ : Dict = DataLoader(
tokenized_datasets['''train'''] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
A_ : Optional[Any] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
snake_case__ = mocked_dataloaders # noqa: F811
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : List[Any] ) -> List[str]:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , lowerCamelCase__ ) == "1":
A_ : str = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
A_ : Any = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='''all''' , project_dir=args.project_dir )
else:
A_ : int = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A_ : Optional[Any] = config['''lr''']
A_ : List[Any] = int(config['''num_epochs'''] )
A_ : Union[str, Any] = int(config['''seed'''] )
A_ : int = int(config['''batch_size'''] )
set_seed(lowerCamelCase__ )
A_ : str = get_dataloaders(lowerCamelCase__ , lowerCamelCase__ )
A_ : int = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
A_ : Optional[int] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
A_ : Dict = batch_size // MAX_GPU_BATCH_SIZE
A_ : Optional[Any] = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A_ : Optional[int] = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=lowerCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A_ : Tuple = model.to(accelerator.device )
# Instantiate optimizer
A_ : List[str] = AdamW(params=model.parameters() , lr=lowerCamelCase__ )
# Instantiate scheduler
A_ : Dict = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(lowerCamelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A_ : Dict = accelerator.prepare(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
A_ : Dict = os.path.split(lowerCamelCase__ )[-1].split('''.''' )[0]
accelerator.init_trackers(lowerCamelCase__ , lowerCamelCase__ )
# Now we train the model
for epoch in range(lowerCamelCase__ ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
A_ : Tuple = 0
for step, batch in enumerate(lowerCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
A_ : Dict = model(**lowerCamelCase__ )
A_ : Tuple = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
A_ : str = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
A_ : str = model(**lowerCamelCase__ )
A_ : Union[str, Any] = outputs.logits.argmax(dim=-1 )
A_ : List[str] = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=lowerCamelCase__ , references=lowerCamelCase__ , )
A_ : Union[str, Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , lowerCamelCase__ )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
'''accuracy''': eval_metric['''accuracy'''],
'''f1''': eval_metric['''f1'''],
'''train_loss''': total_loss.item() / len(lowerCamelCase__ ),
'''epoch''': epoch,
} , step=lowerCamelCase__ , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def snake_case__ ( ) -> Optional[Any]:
A_ : List[Any] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=lowerCamelCase__ , default=lowerCamelCase__ , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
parser.add_argument(
'''--with_tracking''' , action='''store_true''' , help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' , )
parser.add_argument(
'''--project_dir''' , type=lowerCamelCase__ , default='''logs''' , help='''Location on where to store experiment tracking logs` and relevent project information''' , )
A_ : Union[str, Any] = parser.parse_args()
A_ : Any = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
training_function(lowerCamelCase__ , lowerCamelCase__ )
if __name__ == "__main__":
main()
| 363 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'speech_to_text_2'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Optional[Any] , _lowerCamelCase : Optional[Any]=10000 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : int=2048 , _lowerCamelCase : Dict=4 , _lowerCamelCase : str=0.0 , _lowerCamelCase : int=True , _lowerCamelCase : int="relu" , _lowerCamelCase : Any=256 , _lowerCamelCase : List[Any]=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Union[str, Any]=0.0 , _lowerCamelCase : Optional[Any]=0.02 , _lowerCamelCase : int=2 , _lowerCamelCase : List[str]=True , _lowerCamelCase : str=1 , _lowerCamelCase : List[Any]=0 , _lowerCamelCase : Optional[int]=2 , _lowerCamelCase : Tuple=1024 , **_lowerCamelCase : int , ):
"""simple docstring"""
A_ : Optional[int] = vocab_size
A_ : Tuple = d_model
A_ : List[str] = decoder_ffn_dim
A_ : str = decoder_layers
A_ : Any = decoder_attention_heads
A_ : int = dropout
A_ : str = attention_dropout
A_ : Optional[int] = activation_dropout
A_ : str = activation_function
A_ : List[Any] = init_std
A_ : Union[str, Any] = decoder_layerdrop
A_ : Any = use_cache
A_ : Optional[Any] = decoder_layers
A_ : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
A_ : Optional[Any] = max_target_positions
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
| 4 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ = logging.get_logger(__name__)
def snake_case__ ( lowerCamelCase__ : Optional[Any] ) -> Any:
A_ : List[str] = OrderedDict()
for key, value in state_dict.items():
if key.startswith('''module.encoder''' ):
A_ : Dict = key.replace('''module.encoder''' , '''glpn.encoder''' )
if key.startswith('''module.decoder''' ):
A_ : Dict = key.replace('''module.decoder''' , '''decoder.stages''' )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
A_ : Dict = key[key.find('''patch_embed''' ) + len('''patch_embed''' )]
A_ : Tuple = key.replace(f'patch_embed{idx}' , f'patch_embeddings.{int(lowerCamelCase__ )-1}' )
if "norm" in key:
A_ : Dict = key.replace('''norm''' , '''layer_norm''' )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
A_ : Union[str, Any] = key[key.find('''glpn.encoder.layer_norm''' ) + len('''glpn.encoder.layer_norm''' )]
A_ : Any = key.replace(f'layer_norm{idx}' , f'layer_norm.{int(lowerCamelCase__ )-1}' )
if "layer_norm1" in key:
A_ : Union[str, Any] = key.replace('''layer_norm1''' , '''layer_norm_1''' )
if "layer_norm2" in key:
A_ : int = key.replace('''layer_norm2''' , '''layer_norm_2''' )
if "block" in key:
# replace for example block1 by block.0
A_ : Any = key[key.find('''block''' ) + len('''block''' )]
A_ : Optional[int] = key.replace(f'block{idx}' , f'block.{int(lowerCamelCase__ )-1}' )
if "attn.q" in key:
A_ : Any = key.replace('''attn.q''' , '''attention.self.query''' )
if "attn.proj" in key:
A_ : Optional[Any] = key.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in key:
A_ : List[str] = key.replace('''attn''' , '''attention.self''' )
if "fc1" in key:
A_ : Tuple = key.replace('''fc1''' , '''dense1''' )
if "fc2" in key:
A_ : str = key.replace('''fc2''' , '''dense2''' )
if "linear_pred" in key:
A_ : List[Any] = key.replace('''linear_pred''' , '''classifier''' )
if "linear_fuse" in key:
A_ : List[str] = key.replace('''linear_fuse.conv''' , '''linear_fuse''' )
A_ : str = key.replace('''linear_fuse.bn''' , '''batch_norm''' )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
A_ : Union[str, Any] = key[key.find('''linear_c''' ) + len('''linear_c''' )]
A_ : int = key.replace(f'linear_c{idx}' , f'linear_c.{int(lowerCamelCase__ )-1}' )
if "bot_conv" in key:
A_ : str = key.replace('''bot_conv''' , '''0.convolution''' )
if "skip_conv1" in key:
A_ : Dict = key.replace('''skip_conv1''' , '''1.convolution''' )
if "skip_conv2" in key:
A_ : Dict = key.replace('''skip_conv2''' , '''2.convolution''' )
if "fusion1" in key:
A_ : Tuple = key.replace('''fusion1''' , '''1.fusion''' )
if "fusion2" in key:
A_ : Dict = key.replace('''fusion2''' , '''2.fusion''' )
if "fusion3" in key:
A_ : int = key.replace('''fusion3''' , '''3.fusion''' )
if "fusion" in key and "conv" in key:
A_ : List[str] = key.replace('''conv''' , '''convolutional_layer''' )
if key.startswith('''module.last_layer_depth''' ):
A_ : List[str] = key.replace('''module.last_layer_depth''' , '''head.head''' )
A_ : Tuple = value
return new_state_dict
def snake_case__ ( lowerCamelCase__ : Optional[int] , lowerCamelCase__ : int ) -> Tuple:
# 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)
A_ : List[Any] = state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.weight' )
A_ : List[str] = state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.bias' )
# next, add keys and values (in that order) to the state dict
A_ : str = kv_weight[
: config.hidden_sizes[i], :
]
A_ : Any = kv_bias[: config.hidden_sizes[i]]
A_ : Any = kv_weight[
config.hidden_sizes[i] :, :
]
A_ : List[str] = kv_bias[config.hidden_sizes[i] :]
def snake_case__ ( ) -> Tuple:
A_ : List[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
A_ : List[Any] = Image.open(requests.get(lowerCamelCase__ , stream=lowerCamelCase__ ).raw )
return image
@torch.no_grad()
def snake_case__ ( lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Any , lowerCamelCase__ : int=False , lowerCamelCase__ : Union[str, Any]=None ) -> str:
A_ : List[str] = GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2] , decoder_hidden_size=6_4 , depths=[3, 8, 2_7, 3] )
# load image processor (only resize + rescale)
A_ : List[Any] = GLPNImageProcessor()
# prepare image
A_ : Optional[int] = prepare_img()
A_ : List[Any] = image_processor(images=lowerCamelCase__ , return_tensors='''pt''' ).pixel_values
logger.info('''Converting model...''' )
# load original state dict
A_ : List[str] = torch.load(lowerCamelCase__ , map_location=torch.device('''cpu''' ) )
# rename keys
A_ : Optional[int] = rename_keys(lowerCamelCase__ )
# key and value matrices need special treatment
read_in_k_v(lowerCamelCase__ , lowerCamelCase__ )
# create HuggingFace model and load state dict
A_ : Tuple = GLPNForDepthEstimation(lowerCamelCase__ )
model.load_state_dict(lowerCamelCase__ )
model.eval()
# forward pass
A_ : Union[str, Any] = model(lowerCamelCase__ )
A_ : Optional[int] = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
A_ : Tuple = torch.tensor(
[[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
elif "kitti" in model_name:
A_ : Union[str, Any] = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
else:
raise ValueError(f'Unknown model name: {model_name}' )
A_ : List[Any] = torch.Size([1, 4_8_0, 6_4_0] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
print('''Looks ok!''' )
# finally, push to hub if required
if push_to_hub:
logger.info('''Pushing model and image processor to the hub...''' )
model.push_to_hub(
repo_path_or_name=Path(lowerCamelCase__ , lowerCamelCase__ ) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=lowerCamelCase__ , )
image_processor.push_to_hub(
repo_path_or_name=Path(lowerCamelCase__ , lowerCamelCase__ ) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=lowerCamelCase__ , )
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
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."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
)
parser.add_argument(
"""--model_name""",
default="""glpn-kitti""",
type=str,
help="""Name of the model in case you're pushing to the hub.""",
)
snake_case__ = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 364 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'table-transformer'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Any , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Dict=None , _lowerCamelCase : int=3 , _lowerCamelCase : Any=100 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : Tuple=2048 , _lowerCamelCase : Any=8 , _lowerCamelCase : Dict=6 , _lowerCamelCase : Tuple=2048 , _lowerCamelCase : int=8 , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : List[Any]=0.0 , _lowerCamelCase : List[Any]=True , _lowerCamelCase : Optional[int]="relu" , _lowerCamelCase : Union[str, Any]=256 , _lowerCamelCase : Any=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : str=0.02 , _lowerCamelCase : Tuple=1.0 , _lowerCamelCase : Dict=False , _lowerCamelCase : str="sine" , _lowerCamelCase : str="resnet50" , _lowerCamelCase : Any=True , _lowerCamelCase : List[str]=False , _lowerCamelCase : Any=1 , _lowerCamelCase : int=5 , _lowerCamelCase : Tuple=2 , _lowerCamelCase : Optional[int]=1 , _lowerCamelCase : Any=1 , _lowerCamelCase : Dict=5 , _lowerCamelCase : str=2 , _lowerCamelCase : Union[str, Any]=0.1 , **_lowerCamelCase : int , ):
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
A_ : int = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : str = backbone_config.get('''model_type''' )
A_ : Optional[int] = CONFIG_MAPPING[backbone_model_type]
A_ : List[str] = config_class.from_dict(_lowerCamelCase )
# set timm attributes to None
A_ ,A_ ,A_ : Union[str, Any] = None, None, None
A_ : Optional[Any] = use_timm_backbone
A_ : Optional[int] = backbone_config
A_ : Optional[Any] = num_channels
A_ : Dict = num_queries
A_ : str = d_model
A_ : List[str] = encoder_ffn_dim
A_ : int = encoder_layers
A_ : Optional[Any] = encoder_attention_heads
A_ : List[str] = decoder_ffn_dim
A_ : Any = decoder_layers
A_ : List[str] = decoder_attention_heads
A_ : Tuple = dropout
A_ : Optional[Any] = attention_dropout
A_ : Any = activation_dropout
A_ : List[Any] = activation_function
A_ : Dict = init_std
A_ : Any = init_xavier_std
A_ : List[Any] = encoder_layerdrop
A_ : int = decoder_layerdrop
A_ : Any = encoder_layers
A_ : List[str] = auxiliary_loss
A_ : List[Any] = position_embedding_type
A_ : Optional[Any] = backbone
A_ : Tuple = use_pretrained_backbone
A_ : List[Any] = dilation
# Hungarian matcher
A_ : List[str] = class_cost
A_ : str = bbox_cost
A_ : Union[str, Any] = giou_cost
# Loss coefficients
A_ : Any = mask_loss_coefficient
A_ : Optional[int] = dice_loss_coefficient
A_ : Dict = bbox_loss_coefficient
A_ : int = giou_loss_coefficient
A_ : int = eos_coefficient
super().__init__(is_encoder_decoder=_lowerCamelCase , **_lowerCamelCase )
@property
def _a ( self : List[Any] ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def _a ( self : Any ):
"""simple docstring"""
return self.d_model
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : Tuple ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def _a ( self : Optional[int] ):
"""simple docstring"""
return 1E-5
@property
def _a ( self : str ):
"""simple docstring"""
return 12
| 4 | 0 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case__ ( lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Dict , lowerCamelCase__ : Dict=None ) -> Optional[Any]:
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, f'{torch_layer} layer.weight does not match'
A_ : Optional[Any] = nn.Parameter(lowerCamelCase__ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, f'{torch_layer} layer.bias does not match'
A_ : str = nn.Parameter(lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : int ) -> Dict:
# set torch weights for 1-to-1 comparison
A_ : Dict = np.asarray(weights[0] )
A_ : Optional[int] = np.asarray(weights[1] )
A_ : Union[str, Any] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase__ ).view(-1 , lowerCamelCase__ ).contiguous().transpose(0 , 1 ) , )
def snake_case__ ( lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : List[str] , lowerCamelCase__ : str ) -> Dict:
# set torch weights for 1-to-1 comparison
A_ : Optional[int] = np.asarray(weights[0] )
A_ : Union[str, Any] = np.asarray(weights[1] )
A_ : Optional[Any] = np.asarray(weights[2] )
A_ : List[str] = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase__ ).view(-1 , lowerCamelCase__ ).contiguous().transpose(0 , 1 ) , )
def snake_case__ ( lowerCamelCase__ : Tuple , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : str ) -> int:
# layernorm 1
A_ : Optional[Any] = weights[0][0][0]
A_ : Optional[int] = np.asarray(layer_norm_a[0] )
A_ : int = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# lsh weights + output
A_ : Dict = weights[0][1]
if len(lowerCamelCase__ ) < 4:
set_layer_weights_in_torch_lsh(lowerCamelCase__ , torch_block.attention , lowerCamelCase__ )
else:
set_layer_weights_in_torch_local(lowerCamelCase__ , torch_block.attention , lowerCamelCase__ )
# intermediate weighs
A_ : Any = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowerCamelCase__ ) == 4:
A_ : List[str] = intermediate_weights[2]
# layernorm 2
A_ : Tuple = np.asarray(intermediate_weights[0][0] )
A_ : Tuple = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# intermediate dense
A_ : Optional[int] = np.asarray(intermediate_weights[1][0] )
A_ : int = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
# intermediate out
A_ : Tuple = np.asarray(intermediate_weights[4][0] )
A_ : Optional[int] = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : str ) -> str:
# reformer model
A_ : List[Any] = torch_model.reformer
# word embeds
A_ : List[str] = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowerCamelCase__ ) , )
if isinstance(weights[3] , lowerCamelCase__ ):
A_ : int = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
A_ : Tuple = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), f'{position_embeddings[emb_idx]} emb does not match'
A_ : Optional[int] = nn.Parameter(torch.tensor(lowerCamelCase__ ) )
A_ : List[str] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowerCamelCase__ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
A_ : List[Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# output layer norm
A_ : Optional[Any] = np.asarray(weights[7][0] )
A_ : Tuple = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# output embeddings
A_ : Optional[int] = np.asarray(weights[9][0] )
A_ : Optional[int] = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
def snake_case__ ( lowerCamelCase__ : str , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Optional[int] ) -> Tuple:
# Initialise PyTorch model
A_ : Union[str, Any] = ReformerConfig.from_json_file(lowerCamelCase__ )
print(f'Building PyTorch model from configuration: {config}' )
A_ : Tuple = ReformerModelWithLMHead(lowerCamelCase__ )
with open(lowerCamelCase__ , '''rb''' ) as f:
A_ : Tuple = pickle.load(lowerCamelCase__ )['''weights''']
set_model_weights_in_torch(lowerCamelCase__ , lowerCamelCase__ , config.hidden_size )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained Reformer model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
snake_case__ = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 365 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : int , _lowerCamelCase : List[str]=3 , _lowerCamelCase : Any=32 , _lowerCamelCase : Union[str, Any]=3 , _lowerCamelCase : int=10 , _lowerCamelCase : Union[str, Any]=[8, 16, 32, 64] , _lowerCamelCase : Dict=[1, 1, 2, 1] , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Optional[int]=True , _lowerCamelCase : Any="relu" , _lowerCamelCase : Optional[Any]=3 , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Dict=["stage2", "stage3", "stage4"] , _lowerCamelCase : Union[str, Any]=[2, 3, 4] , _lowerCamelCase : Tuple=1 , ):
"""simple docstring"""
A_ : List[str] = parent
A_ : List[str] = batch_size
A_ : Union[str, Any] = image_size
A_ : Tuple = num_channels
A_ : Any = embeddings_size
A_ : int = hidden_sizes
A_ : Optional[Any] = depths
A_ : List[Any] = is_training
A_ : Optional[int] = use_labels
A_ : int = hidden_act
A_ : Tuple = num_labels
A_ : Union[str, Any] = scope
A_ : List[Any] = len(_lowerCamelCase )
A_ : Union[str, Any] = out_features
A_ : List[Any] = out_indices
A_ : Dict = num_groups
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : Union[str, Any] = None
if self.use_labels:
A_ : Any = ids_tensor([self.batch_size] , self.num_labels )
A_ : Any = self.get_config()
return config, pixel_values, labels
def _a ( self : Union[str, Any] ):
"""simple docstring"""
return BitConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def _a ( self : List[Any] , _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any] ):
"""simple docstring"""
A_ : Any = BitModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase )
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 : Optional[int] , _lowerCamelCase : List[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : Dict = self.num_labels
A_ : Optional[Any] = BitForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : List[Any] = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Any , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : List[Any] ):
"""simple docstring"""
A_ : List[Any] = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
A_ : Optional[Any] = None
A_ : int = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Optional[int] = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = self.prepare_config_and_inputs()
A_ ,A_ ,A_ : Union[str, Any] = config_and_inputs
A_ : str = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase_ (a__, a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
_lowerCAmelCase = (
{'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
if is_torch_available()
else {}
)
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[str] = BitModelTester(self )
A_ : Optional[Any] = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : List[Any] ):
"""simple docstring"""
return
@unittest.skip(reason='''Bit does not output attentions''' )
def _a ( self : str ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def _a ( self : Any ):
"""simple docstring"""
pass
def _a ( self : List[Any] ):
"""simple docstring"""
A_ ,A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Dict = model_class(_lowerCamelCase )
A_ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : int = [*signature.parameters.keys()]
A_ : Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ ,A_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : str = model_class(config=_lowerCamelCase )
for name, module in model.named_modules():
if isinstance(_lowerCamelCase , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
def _a ( self : int ):
"""simple docstring"""
def check_hidden_states_output(_lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : int ):
A_ : Union[str, Any] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
A_ : Union[str, Any] = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
A_ : int = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
A_ : List[Any] = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , expected_num_stages + 1 )
# Bit'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 // 4, self.model_tester.image_size // 4] , )
A_ ,A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Tuple = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
A_ : Tuple = layer_type
A_ : Optional[Any] = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A_ : List[str] = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def _a ( self : Tuple ):
"""simple docstring"""
pass
def _a ( self : str ):
"""simple docstring"""
A_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
@slow
def _a ( self : Union[str, Any] ):
"""simple docstring"""
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : List[Any] = BitModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def snake_case__ ( ) -> Optional[int]:
A_ : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _a ( self : List[Any] ):
"""simple docstring"""
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_lowerCamelCase )
A_ : Union[str, Any] = self.default_image_processor
A_ : Optional[int] = prepare_img()
A_ : int = image_processor(images=_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
A_ : Union[str, Any] = model(**_lowerCamelCase )
# verify the logits
A_ : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
A_ : Tuple = torch.tensor([[-0.65_26, -0.52_63, -1.43_98]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1E-4 ) )
@require_torch
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (BitBackbone,) if is_torch_available() else ()
_lowerCAmelCase = BitConfig
_lowerCAmelCase = False
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Union[str, Any] = BitModelTester(self )
| 4 | 0 |
'''simple docstring'''
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def snake_case__ ( lowerCamelCase__ : int ) -> List[str]:
A_ : Tuple = {}
A_ : Optional[Any] = tokenizer(example['''content'''] , truncation=lowerCamelCase__ )['''input_ids''']
A_ : Optional[Any] = len(example['''content'''] ) / len(output['''input_ids'''] )
return output
snake_case__ = HfArgumentParser(PretokenizationArguments)
snake_case__ = parser.parse_args()
if args.num_workers is None:
snake_case__ = multiprocessing.cpu_count()
snake_case__ = AutoTokenizer.from_pretrained(args.tokenizer_dir)
snake_case__ = time.time()
snake_case__ = load_dataset(args.dataset_name, split="""train""")
print(F'Dataset loaded in {time.time()-t_start:.2f}s')
snake_case__ = time.time()
snake_case__ = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
"""repo_name""",
"""path""",
"""copies""",
"""size""",
"""content""",
"""license""",
"""hash""",
"""line_mean""",
"""line_max""",
"""alpha_frac""",
"""autogenerated""",
],
)
print(F'Dataset tokenized in {time.time()-t_start:.2f}s')
snake_case__ = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F'Data pushed to the hub in {time.time()-t_start:.2f}s')
| 366 |
'''simple docstring'''
import pprint
import requests
snake_case__ = """https://zenquotes.io/api"""
def snake_case__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def snake_case__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if __name__ == "__main__":
snake_case__ = random_quotes()
pprint.pprint(response)
| 4 | 0 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def snake_case__ ( lowerCamelCase__ : Dict , lowerCamelCase__ : Tuple ) -> Dict:
A_ : Optional[int] = k_size // 2
A_ : Optional[int] = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
A_ : int = 1 / (2 * pi * sigma) * exp(-(square(lowerCamelCase__ ) + square(lowerCamelCase__ )) / (2 * square(lowerCamelCase__ )) )
return g
def snake_case__ ( lowerCamelCase__ : int , lowerCamelCase__ : List[Any] , lowerCamelCase__ : Union[str, Any] ) -> Dict:
A_ : Optional[Any] = image.shape[0], image.shape[1]
# dst image height and width
A_ : int = height - k_size + 1
A_ : int = width - k_size + 1
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
A_ : Dict = zeros((dst_height * dst_width, k_size * k_size) )
A_ : List[Any] = 0
for i, j in product(range(lowerCamelCase__ ) , range(lowerCamelCase__ ) ):
A_ : Any = ravel(image[i : i + k_size, j : j + k_size] )
A_ : List[str] = window
row += 1
# turn the kernel into shape(k*k, 1)
A_ : str = gen_gaussian_kernel(lowerCamelCase__ , lowerCamelCase__ )
A_ : List[Any] = ravel(lowerCamelCase__ )
# reshape and get the dst image
A_ : List[str] = dot(lowerCamelCase__ , lowerCamelCase__ ).reshape(lowerCamelCase__ , lowerCamelCase__ ).astype(lowerCamelCase__ )
return dst
if __name__ == "__main__":
# read original image
snake_case__ = imread(R"""../image_data/lena.jpg""")
# turn image in gray scale value
snake_case__ = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
snake_case__ = gaussian_filter(gray, 3, sigma=1)
snake_case__ = gaussian_filter(gray, 5, sigma=0.8)
# show result images
imshow("""gaussian filter with 3x3 mask""", gaussianaxa)
imshow("""gaussian filter with 5x5 mask""", gaussianaxa)
waitKey()
| 367 |
'''simple docstring'''
from __future__ import annotations
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[int] , _lowerCamelCase : int ):
"""simple docstring"""
A_ : Union[str, Any] = order
# a_{0} ... a_{k}
A_ : Union[str, Any] = [1.0] + [0.0] * order
# b_{0} ... b_{k}
A_ : int = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
A_ : str = [0.0] * self.order
# y[n-1] ... y[n-k]
A_ : Optional[Any] = [0.0] * self.order
def _a ( self : Dict , _lowerCamelCase : list[float] , _lowerCamelCase : list[float] ):
"""simple docstring"""
if len(_lowerCamelCase ) < self.order:
A_ : Any = [1.0, *a_coeffs]
if len(_lowerCamelCase ) != self.order + 1:
A_ : List[Any] = (
f'Expected a_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(_lowerCamelCase )}'
)
raise ValueError(_lowerCamelCase )
if len(_lowerCamelCase ) != self.order + 1:
A_ : Union[str, Any] = (
f'Expected b_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(_lowerCamelCase )}'
)
raise ValueError(_lowerCamelCase )
A_ : Tuple = a_coeffs
A_ : str = b_coeffs
def _a ( self : Tuple , _lowerCamelCase : float ):
"""simple docstring"""
A_ : Any = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
A_ : str = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
A_ : Optional[Any] = self.input_history[:-1]
A_ : List[str] = self.output_history[:-1]
A_ : Tuple = sample
A_ : Tuple = result
return result
| 4 | 0 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
snake_case__ = tuple[int, int]
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Tuple , _lowerCamelCase : set[int] , _lowerCamelCase : Mapping[EdgeT, int] ):
"""simple docstring"""
A_ : set[int] = vertices
A_ : dict[EdgeT, int] = {
(min(_lowerCamelCase ), max(_lowerCamelCase )): weight for edge, weight in edges.items()
}
def _a ( self : Any , _lowerCamelCase : EdgeT , _lowerCamelCase : int ):
"""simple docstring"""
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
A_ : List[Any] = weight
def _a ( self : Dict ):
"""simple docstring"""
A_ : Graph = Graph({min(self.vertices )} , {} )
A_ : EdgeT
A_ : int
A_ : EdgeT
A_ : int
while len(subgraph.vertices ) < len(self.vertices ):
A_ : Any = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
A_ : Optional[Any] = edge
A_ : Union[str, Any] = weight
subgraph.add_edge(_lowerCamelCase , _lowerCamelCase )
return subgraph
def snake_case__ ( lowerCamelCase__ : str = "p107_network.txt" ) -> int:
A_ : str = os.path.abspath(os.path.dirname(lowerCamelCase__ ) )
A_ : str = os.path.join(lowerCamelCase__ , lowerCamelCase__ )
A_ : dict[EdgeT, int] = {}
A_ : list[str]
A_ : int
A_ : int
with open(lowerCamelCase__ ) as f:
A_ : Any = f.read().strip().split('''\n''' )
A_ : Tuple = [line.split(''',''' ) for line in data]
for edgea in range(1 , len(lowerCamelCase__ ) ):
for edgea in range(lowerCamelCase__ ):
if adjaceny_matrix[edgea][edgea] != "-":
A_ : Union[str, Any] = int(adjaceny_matrix[edgea][edgea] )
A_ : Graph = Graph(set(range(len(lowerCamelCase__ ) ) ) , lowerCamelCase__ )
A_ : Graph = graph.prims_algorithm()
A_ : int = sum(graph.edges.values() )
A_ : int = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F'{solution() = }')
| 368 |
'''simple docstring'''
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , _lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ : Union[str, Any] = val
A_ : Tuple = None
A_ : Any = None
def _a ( self : Tuple , _lowerCamelCase : List[Any] ):
"""simple docstring"""
if self.val:
if val < self.val:
if self.left is None:
A_ : int = Node(_lowerCamelCase )
else:
self.left.insert(_lowerCamelCase )
elif val > self.val:
if self.right is None:
A_ : List[str] = Node(_lowerCamelCase )
else:
self.right.insert(_lowerCamelCase )
else:
A_ : Any = val
def snake_case__ ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int] ) -> str:
# Recursive traversal
if root:
inorder(root.left , lowerCamelCase__ )
res.append(root.val )
inorder(root.right , lowerCamelCase__ )
def snake_case__ ( lowerCamelCase__ : Optional[int] ) -> Tuple:
# Build BST
if len(lowerCamelCase__ ) == 0:
return arr
A_ : Dict = Node(arr[0] )
for i in range(1 , len(lowerCamelCase__ ) ):
root.insert(arr[i] )
# Traverse BST in order.
A_ : Tuple = []
inorder(lowerCamelCase__ , lowerCamelCase__ )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 4 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 4_2
class UpperCamelCase_ (a__, a__ ):
"""simple docstring"""
@register_to_config
def __init__( self : Any , _lowerCamelCase : int = 3 , _lowerCamelCase : int = 3 , _lowerCamelCase : Tuple[str] = ("DownEncoderBlock2D",) , _lowerCamelCase : Tuple[str] = ("UpDecoderBlock2D",) , _lowerCamelCase : Tuple[int] = (64,) , _lowerCamelCase : int = 1 , _lowerCamelCase : str = "silu" , _lowerCamelCase : int = 3 , _lowerCamelCase : int = 32 , _lowerCamelCase : int = 256 , _lowerCamelCase : int = 32 , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : float = 0.1_82_15 , _lowerCamelCase : str = "group" , ):
"""simple docstring"""
super().__init__()
# pass init params to Encoder
A_ : Optional[Any] = Encoder(
in_channels=_lowerCamelCase , out_channels=_lowerCamelCase , down_block_types=_lowerCamelCase , block_out_channels=_lowerCamelCase , layers_per_block=_lowerCamelCase , act_fn=_lowerCamelCase , norm_num_groups=_lowerCamelCase , double_z=_lowerCamelCase , )
A_ : int = vq_embed_dim if vq_embed_dim is not None else latent_channels
A_ : Tuple = nn.Convad(_lowerCamelCase , _lowerCamelCase , 1 )
A_ : Dict = VectorQuantizer(_lowerCamelCase , _lowerCamelCase , beta=0.25 , remap=_lowerCamelCase , sane_index_shape=_lowerCamelCase )
A_ : str = nn.Convad(_lowerCamelCase , _lowerCamelCase , 1 )
# pass init params to Decoder
A_ : List[Any] = Decoder(
in_channels=_lowerCamelCase , out_channels=_lowerCamelCase , up_block_types=_lowerCamelCase , block_out_channels=_lowerCamelCase , layers_per_block=_lowerCamelCase , act_fn=_lowerCamelCase , norm_num_groups=_lowerCamelCase , norm_type=_lowerCamelCase , )
@apply_forward_hook
def _a ( self : List[Any] , _lowerCamelCase : torch.FloatTensor , _lowerCamelCase : bool = True ):
"""simple docstring"""
A_ : Any = self.encoder(_lowerCamelCase )
A_ : str = self.quant_conv(_lowerCamelCase )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=_lowerCamelCase )
@apply_forward_hook
def _a ( self : int , _lowerCamelCase : torch.FloatTensor , _lowerCamelCase : bool = False , _lowerCamelCase : bool = True ):
"""simple docstring"""
if not force_not_quantize:
A_ : Union[str, Any] = self.quantize(_lowerCamelCase )
else:
A_ : Tuple = h
A_ : int = self.post_quant_conv(_lowerCamelCase )
A_ : List[Any] = self.decoder(_lowerCamelCase , quant if self.config.norm_type == '''spatial''' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_lowerCamelCase )
def _a ( self : Optional[Any] , _lowerCamelCase : torch.FloatTensor , _lowerCamelCase : bool = True ):
"""simple docstring"""
A_ : List[str] = sample
A_ : Dict = self.encode(_lowerCamelCase ).latents
A_ : Optional[Any] = self.decode(_lowerCamelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_lowerCamelCase )
| 369 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list ) -> list:
if len(lowerCamelCase__ ) <= 1:
return [tuple(lowerCamelCase__ )]
A_ : List[str] = []
def generate(lowerCamelCase__ : int , lowerCamelCase__ : list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , lowerCamelCase__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
A_ ,A_ : Optional[int] = arr[k - 1], arr[i]
else: # k is odd
A_ ,A_ : Union[str, Any] = arr[k - 1], arr[0]
generate(k - 1 , lowerCamelCase__ )
generate(len(lowerCamelCase__ ) , lowerCamelCase__ )
return res
if __name__ == "__main__":
snake_case__ = input("""Enter numbers separated by a comma:\n""").strip()
snake_case__ = [int(item) for item in user_input.split(""",""")]
print(heaps(arr))
| 4 | 0 |
'''simple docstring'''
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
snake_case__ = """▁"""
snake_case__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class UpperCamelCase_ (a__, unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = BertGenerationTokenizer
_lowerCAmelCase = False
_lowerCAmelCase = True
def _a ( self : Dict ):
"""simple docstring"""
super().setUp()
A_ : Optional[int] = BertGenerationTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : str = '''<s>'''
A_ : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase ) , _lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : List[str] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(_lowerCamelCase ) , 1002 )
def _a ( self : Dict ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : int = BertGenerationTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
A_ : List[Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_lowerCamelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [285, 46, 10, 170, 382] , )
A_ : Any = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_lowerCamelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
A_ : List[Any] = tokenizer.convert_tokens_to_ids(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
A_ : List[Any] = tokenizer.convert_ids_to_tokens(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def _a ( self : List[str] ):
"""simple docstring"""
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def _a ( self : str ):
"""simple docstring"""
A_ : List[str] = '''Hello World!'''
A_ : Any = [18536, 2260, 101]
self.assertListEqual(_lowerCamelCase , self.big_tokenizer.encode(_lowerCamelCase ) )
@slow
def _a ( self : int ):
"""simple docstring"""
A_ : Any = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
A_ : List[str] = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
34324,
497,
391,
408,
11342,
1244,
385,
100,
938,
985,
456,
574,
362,
12597,
3200,
3129,
1172,
]
self.assertListEqual(_lowerCamelCase , self.big_tokenizer.encode(_lowerCamelCase ) )
@require_torch
@slow
def _a ( self : Optional[int] ):
"""simple docstring"""
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
A_ : Union[str, Any] = list(self.big_tokenizer.get_vocab().keys() )[:10]
A_ : Dict = ''' '''.join(_lowerCamelCase )
A_ : List[Any] = self.big_tokenizer.encode_plus(_lowerCamelCase , return_tensors='''pt''' , return_token_type_ids=_lowerCamelCase )
A_ : Optional[int] = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=_lowerCamelCase )
A_ : List[Any] = BertGenerationConfig()
A_ : List[Any] = BertGenerationEncoder(_lowerCamelCase )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_lowerCamelCase )
model(**_lowerCamelCase )
@slow
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[str] = {'''input_ids''': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_lowerCamelCase , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 370 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : Tuple = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Dict = -1
A_ : List[str] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Any = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : List[str] = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
A_ : List[str] = TextStreamer(_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
A_ : Dict = cs.out[:-1]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : List[str] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Dict = -1
A_ : List[str] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Optional[int] = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : str = tokenizer.decode(greedy_ids[0] )
A_ : int = TextIteratorStreamer(_lowerCamelCase )
A_ : List[Any] = {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
A_ : List[Any] = Thread(target=model.generate , kwargs=_lowerCamelCase )
thread.start()
A_ : List[Any] = ''''''
for new_text in streamer:
streamer_text += new_text
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : List[str] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : List[Any] = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : List[str] = -1
A_ : Any = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : Tuple = model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase )
A_ : Tuple = greedy_ids[:, input_ids.shape[1] :]
A_ : Tuple = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
A_ : Any = TextStreamer(_lowerCamelCase , skip_prompt=_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=10 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
A_ : Any = cs.out[:-1]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = AutoTokenizer.from_pretrained('''distilgpt2''' )
A_ : Tuple = AutoModelForCausalLM.from_pretrained('''distilgpt2''' ).to(_lowerCamelCase )
A_ : List[Any] = -1
A_ : Union[str, Any] = torch.ones((1, 5) , device=_lowerCamelCase ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
A_ : List[Any] = TextStreamer(_lowerCamelCase , skip_special_tokens=_lowerCamelCase )
model.generate(_lowerCamelCase , max_new_tokens=1 , do_sample=_lowerCamelCase , streamer=_lowerCamelCase )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
A_ : List[str] = cs.out[:-1] # Remove the final "\n"
A_ : List[Any] = tokenizer(_lowerCamelCase , return_tensors='''pt''' )
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1) )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : str = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
A_ : str = AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(_lowerCamelCase )
A_ : Union[str, Any] = -1
A_ : Union[str, Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCamelCase )
A_ : List[str] = TextIteratorStreamer(_lowerCamelCase , timeout=0.0_01 )
A_ : str = {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
A_ : List[str] = Thread(target=model.generate , kwargs=_lowerCamelCase )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(_lowerCamelCase ):
A_ : str = ''''''
for new_text in streamer:
streamer_text += new_text
| 4 | 0 |
'''simple docstring'''
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class UpperCamelCase_ (nn.Module ):
"""simple docstring"""
def __init__( self : List[str] ):
"""simple docstring"""
super().__init__()
A_ : Any = nn.Linear(3 , 4 )
A_ : Optional[Any] = nn.BatchNormad(4 )
A_ : str = nn.Linear(4 , 5 )
def _a ( self : Dict , _lowerCamelCase : Dict ):
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(_lowerCamelCase ) ) )
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Optional[Any] = ModelForTest()
with TemporaryDirectory() as tmp_dir:
offload_state_dict(_lowerCamelCase , model.state_dict() )
A_ : Tuple = os.path.join(_lowerCamelCase , '''index.json''' )
self.assertTrue(os.path.isfile(_lowerCamelCase ) )
# TODO: add tests on what is inside the index
for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
A_ : List[Any] = os.path.join(_lowerCamelCase , f'{key}.dat' )
self.assertTrue(os.path.isfile(_lowerCamelCase ) )
# TODO: add tests on the fact weights are properly loaded
def _a ( self : Tuple ):
"""simple docstring"""
A_ : List[Any] = [torch.floataa, torch.floataa, torch.bfloataa]
for dtype in dtypes:
A_ : List[str] = torch.randn(2 , 3 , dtype=_lowerCamelCase )
with TemporaryDirectory() as tmp_dir:
A_ : Dict = offload_weight(_lowerCamelCase , '''weight''' , _lowerCamelCase , {} )
A_ : Optional[Any] = os.path.join(_lowerCamelCase , '''weight.dat''' )
self.assertTrue(os.path.isfile(_lowerCamelCase ) )
self.assertDictEqual(_lowerCamelCase , {'''weight''': {'''shape''': [2, 3], '''dtype''': str(_lowerCamelCase ).split('''.''' )[1]}} )
A_ : Any = load_offloaded_weight(_lowerCamelCase , index['''weight'''] )
self.assertTrue(torch.equal(_lowerCamelCase , _lowerCamelCase ) )
def _a ( self : str ):
"""simple docstring"""
A_ : Any = ModelForTest()
A_ : Any = model.state_dict()
A_ : Optional[Any] = {k: v for k, v in state_dict.items() if '''linear2''' not in k}
A_ : str = {k: v for k, v in state_dict.items() if '''linear2''' in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(_lowerCamelCase , _lowerCamelCase )
A_ : Any = OffloadedWeightsLoader(state_dict=_lowerCamelCase , save_folder=_lowerCamelCase )
# Every key is there with the right value
self.assertEqual(sorted(_lowerCamelCase ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(_lowerCamelCase , weight_map[key] ) )
A_ : List[str] = {k: v for k, v in state_dict.items() if '''weight''' in k}
A_ : Optional[Any] = {k: v for k, v in state_dict.items() if '''weight''' not in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(_lowerCamelCase , _lowerCamelCase )
A_ : List[str] = OffloadedWeightsLoader(state_dict=_lowerCamelCase , save_folder=_lowerCamelCase )
# Every key is there with the right value
self.assertEqual(sorted(_lowerCamelCase ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(_lowerCamelCase , weight_map[key] ) )
with TemporaryDirectory() as tmp_dir:
offload_state_dict(_lowerCamelCase , _lowerCamelCase )
# Duplicates are removed
A_ : Tuple = OffloadedWeightsLoader(state_dict=_lowerCamelCase , save_folder=_lowerCamelCase )
# Every key is there with the right value
self.assertEqual(sorted(_lowerCamelCase ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(_lowerCamelCase , weight_map[key] ) )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[int] = {'''a.1''': 0, '''a.10''': 1, '''a.2''': 2}
A_ : Tuple = extract_submodules_state_dict(_lowerCamelCase , ['''a.1''', '''a.2'''] )
self.assertDictEqual(_lowerCamelCase , {'''a.1''': 0, '''a.2''': 2} )
A_ : Optional[int] = {'''a.1.a''': 0, '''a.10.a''': 1, '''a.2.a''': 2}
A_ : Optional[int] = extract_submodules_state_dict(_lowerCamelCase , ['''a.1''', '''a.2'''] )
self.assertDictEqual(_lowerCamelCase , {'''a.1.a''': 0, '''a.2.a''': 2} )
| 371 |
'''simple docstring'''
import heapq
def snake_case__ ( lowerCamelCase__ : dict ) -> set[int]:
A_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase__ , [-1 * len(lowerCamelCase__ ), (key, value)] )
# chosen_vertices = set of chosen vertices
A_ : str = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
A_ : Tuple = heapq.heappop(lowerCamelCase__ )[1][0]
chosen_vertices.add(lowerCamelCase__ )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
A_ : List[str] = elem[1][1].index(lowerCamelCase__ )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase__ )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case__ = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')
| 4 | 0 |
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