






import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


lowerCAmelCase_     :   List[Any]           = logging.get_logger(__name__)







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       Union[str, Any]   ):
				A	 =      set()
				A	 =      []

				def parse_line(UpperCAmelCase	:       str   ):
								for line in fp:
												if isinstance(UpperCAmelCase,						UpperCAmelCase   ):
																A	 =      line.decode('UTF-8'   )
												if "warnings summary (final)" in line:
																continue
												# This means we are outside the body of a warning
												elif not line.startswith(' '   ):
																# process a single warning and move it to `selected_warnings`.
																if len(UpperCAmelCase   ) > 0:
																				A	 =      '\n'.join(UpperCAmelCase   )
																				# Only keep the warnings specified in `targets`
																				if any(f': {x}: ' in warning for x in targets   ):
																								selected_warnings.add(UpperCAmelCase   )
																				buffer.clear()
																continue
												else:
																A	 =      line.strip()
																buffer.append(UpperCAmelCase   )

				if from_gh:
								for filename in os.listdir(UpperCAmelCase   ):
												A	 =      os.path.join(UpperCAmelCase,						UpperCAmelCase   )
												if not os.path.isdir(UpperCAmelCase   ):
																# read the file
																if filename != "warnings.txt":
																				continue
																with open(UpperCAmelCase   ) as fp:
																				parse_line(UpperCAmelCase   )
				else:
								try:
												with zipfile.ZipFile(UpperCAmelCase   ) as z:
																for filename in z.namelist():
																				if not os.path.isdir(UpperCAmelCase   ):
																								# read the file
																								if filename != "warnings.txt":
																												continue
																								with z.open(UpperCAmelCase   ) as fp:
																												parse_line(UpperCAmelCase   )
								except Exception:
												logger.warning(
												    f'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'   )

				return selected_warnings







def      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Any   ):
				A	 =      set()

				A	 =      [os.path.join(UpperCAmelCase,						UpperCAmelCase   ) for p in os.listdir(UpperCAmelCase   ) if (p.endswith('.zip'   ) or from_gh)]
				for p in paths:
								selected_warnings.update(extract_warnings_from_single_artifact(UpperCAmelCase,						UpperCAmelCase   )   )

				return selected_warnings


if __name__ == "__main__":







					def      snake_case		(							UpperCAmelCase	:       Dict   ):
									return values.split(','   )

					lowerCAmelCase_     :   List[str]           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
					parser.add_argument(
					    '--output_dir',
					    type=str,
					    required=True,
					    help='Where to store the downloaded artifacts and other result files.',
					)
					parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
					# optional parameters
					parser.add_argument(
					    '--targets',
					    default='DeprecationWarning,UserWarning,FutureWarning',
					    type=list_str,
					    help='Comma-separated list of target warning(s) which we want to extract.',
					)
					parser.add_argument(
					    '--from_gh',
					    action='store_true',
					    help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
					)

					lowerCAmelCase_     :   List[str]           = parser.parse_args()

					lowerCAmelCase_     :   Union[str, Any]           = args.from_gh
					if from_gh:
										# The artifacts have to be downloaded using `actions/download-artifact@v3`
										pass
					else:
										os.makedirs(args.output_dir, exist_ok=True)

										# get download links
										lowerCAmelCase_     :   str           = get_artifacts_links(args.workflow_run_id, token=args.token)
										with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
															json.dump(artifacts, fp, ensure_ascii=False, indent=4)

										# download artifacts
										for idx, (name, url) in enumerate(artifacts.items()):
															print(name)
															print(url)
															print('=' * 80)
															download_artifact(name, url, args.output_dir, args.token)
															# Be gentle to GitHub
															time.sleep(1)

    # extract warnings from artifacts
					lowerCAmelCase_     :   Any           = extract_warnings(args.output_dir, args.targets)
					lowerCAmelCase_     :   Tuple           = sorted(selected_warnings)
					with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
										json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)













import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   ["image_processor", "tokenizer"]
		snake_case    =   "LayoutLMv2ImageProcessor"
		snake_case    =   ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
		def __init__(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       Optional[int]=None						,**_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   List[str]:


						'''simple docstring'''
						if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'						,_SCREAMING_SNAKE_CASE						,)
										A	 =      kwargs.pop('feature_extractor'		)

						A	 =      image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
										raise ValueError('You need to specify an `image_processor`.'		)
						if tokenizer is None:
										raise ValueError('You need to specify a `tokenizer`.'		)

						super().__init__(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
		def __call__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,_SCREAMING_SNAKE_CASE		:       Union[List[List[int]], List[List[List[int]]]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Union[List[int], List[List[int]]]] = None						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Union[bool, str, PaddingStrategy] = False						,_SCREAMING_SNAKE_CASE		:       Union[bool, str, TruncationStrategy] = None						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       int = 0						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       Optional[bool] = None						,_SCREAMING_SNAKE_CASE		:       Optional[bool] = None						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, TensorType]] = None						,**_SCREAMING_SNAKE_CASE		:       Tuple						,)    ->   BatchEncoding:


						'''simple docstring'''
						# verify input
						if self.image_processor.apply_ocr and (boxes is not None):
										raise ValueError(
										    'You cannot provide bounding boxes '
										    'if you initialized the image processor with apply_ocr set to True.'		)

						if self.image_processor.apply_ocr and (word_labels is not None):
										raise ValueError(
										    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'		)

						if return_overflowing_tokens is True and return_offsets_mapping is False:
										raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'		)

						# first, apply the image processor
						A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=_SCREAMING_SNAKE_CASE		)

						# second, apply the tokenizer
						if text is not None and self.image_processor.apply_ocr and text_pair is None:
										if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
														A	 =      [text]  # add batch dimension (as the image processor always adds a batch dimension)
										A	 =      features['words']

						A	 =      self.tokenizer(
						    text=text if text is not None else features['words']						,text_pair=text_pair if text_pair is not None else None						,boxes=boxes if boxes is not None else features['boxes']						,word_labels=_SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE						,padding=_SCREAMING_SNAKE_CASE						,truncation=_SCREAMING_SNAKE_CASE						,max_length=_SCREAMING_SNAKE_CASE						,stride=_SCREAMING_SNAKE_CASE						,pad_to_multiple_of=_SCREAMING_SNAKE_CASE						,return_token_type_ids=_SCREAMING_SNAKE_CASE						,return_attention_mask=_SCREAMING_SNAKE_CASE						,return_overflowing_tokens=_SCREAMING_SNAKE_CASE						,return_special_tokens_mask=_SCREAMING_SNAKE_CASE						,return_offsets_mapping=_SCREAMING_SNAKE_CASE						,return_length=_SCREAMING_SNAKE_CASE						,verbose=_SCREAMING_SNAKE_CASE						,return_tensors=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)

						# add pixel values
						A	 =      features.pop('pixel_values'		)
						if return_overflowing_tokens is True:
										A	 =      self.get_overflowing_images(_SCREAMING_SNAKE_CASE						,encoded_inputs['overflow_to_sample_mapping']		)
						A	 =      images

						return encoded_inputs
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   Dict:


						'''simple docstring'''
						# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
						A	 =      []
						for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]		)

						if len(_SCREAMING_SNAKE_CASE		) != len(_SCREAMING_SNAKE_CASE		):
										raise ValueError(
										    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
										    f' {len(_SCREAMING_SNAKE_CASE		)} and {len(_SCREAMING_SNAKE_CASE		)}'		)

						return images_with_overflow
		def       A(  self		:       List[str]						,*_SCREAMING_SNAKE_CASE		:       str						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Tuple:


						'''simple docstring'''
						return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Dict						,*_SCREAMING_SNAKE_CASE		:       Tuple						,**_SCREAMING_SNAKE_CASE		:       int		)    ->   int:


						'''simple docstring'''
						return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		@property
		def       A(  self		:       Any		)    ->   Union[str, Any]:


						'''simple docstring'''
						return ["input_ids", "bbox", "attention_mask", "image"]
		@property
		def       A(  self		:       Dict		)    ->   int:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,_SCREAMING_SNAKE_CASE						,)
						return self.image_processor_class







		@property
		def       A(  self		:       Optional[int]		)    ->   Union[str, Any]:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,_SCREAMING_SNAKE_CASE						,)
						return self.image_processor





