



'''simple docstring'''




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							:Any				    =					logging.get_logger(__name__)



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Tuple	=     set()
			A_	:						str	=     []

			def parse_line(lowerCamelCase__    ):
						for line in fp:
									if isinstance(lowerCamelCase__						,      lowerCamelCase__    ):
												A_	:						Dict	=     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(lowerCamelCase__    ) > 0:
															A_	:						Optional[Any]	=     """\n""".join(lowerCamelCase__    )
															# Only keep the warnings specified in `targets`
															if any(f': {x}: ' in warning for x in targets    ):
																		selected_warnings.add(lowerCamelCase__    )
															buffer.clear()
												continue
									else:
												A_	:						Optional[int]	=     line.strip()
												buffer.append(lowerCamelCase__    )

			if from_gh:
						for filename in os.listdir(lowerCamelCase__    ):
									A_	:						Tuple	=     os.path.join(lowerCamelCase__						,      lowerCamelCase__    )
									if not os.path.isdir(lowerCamelCase__    ):
												# read the file
												if filename != "warnings.txt":
															continue
												with open(lowerCamelCase__    ) as fp:
															parse_line(lowerCamelCase__    )
			else:
						try:
									with zipfile.ZipFile(lowerCamelCase__    ) as z:
												for filename in z.namelist():
															if not os.path.isdir(lowerCamelCase__    ):
																		# read the file
																		if filename != "warnings.txt":
																					continue
																		with z.open(lowerCamelCase__    ) as fp:
																					parse_line(lowerCamelCase__    )
						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 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Union[str, Any]	=     set()

			A_	:						int	=     [os.path.join(lowerCamelCase__						,      lowerCamelCase__    ) for p in os.listdir(lowerCamelCase__    ) if (p.endswith(""".zip"""    ) or from_gh)]
			for p in paths:
						selected_warnings.update(extract_warnings_from_single_artifact(lowerCamelCase__						,      lowerCamelCase__    )    )

			return selected_warnings


if __name__ == "__main__":



						def 	a			( lowerCamelCase__    ):
									'''simple docstring'''





									return values.split(""","""    )

						lowerCamelCase							:Union[str, Any]				    =					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							:List[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							:Union[str, Any]				    =					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('''=''' * 8_0)
																		download_artifact(name, url, args.output_dir, args.token)
																		# Be gentle to GitHub
																		time.sleep(1)

    # extract warnings from artifacts
						lowerCamelCase							:Optional[Any]				    =					extract_warnings(args.output_dir, args.targets)
						lowerCamelCase							:Any				    =					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)




'''simple docstring'''




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Any					=					['image_processor', 'tokenizer']
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					'BlipImageProcessor'
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					('BertTokenizer', 'BertTokenizerFast')


	def __init__(self       ,      lowercase       ,      lowercase							):
				A_	:						List[Any]	=     False
				super().__init__(lowercase       ,      lowercase							)
				A_	:						Tuple	=     self.image_processor


	def __call__(self       ,      lowercase = None       ,      lowercase = None       ,      lowercase = True       ,      lowercase = False       ,      lowercase = None       ,      lowercase = None       ,      lowercase = 0       ,      lowercase = None       ,      lowercase = None       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = True       ,      lowercase = None       ,      **lowercase       ,      ):
				if images is None and text is None:
							raise ValueError("""You have to specify either images or text."""							)

				# Get only text
				if images is None:
							A_	:						Optional[Any]	=     self.tokenizer
							A_	:						Tuple	=     self.tokenizer(
							    text=lowercase       ,      add_special_tokens=lowercase       ,      padding=lowercase       ,      truncation=lowercase       ,      max_length=lowercase       ,      stride=lowercase       ,      pad_to_multiple_of=lowercase       ,      return_attention_mask=lowercase       ,      return_overflowing_tokens=lowercase       ,      return_special_tokens_mask=lowercase       ,      return_offsets_mapping=lowercase       ,      return_token_type_ids=lowercase       ,      return_length=lowercase       ,      verbose=lowercase       ,      return_tensors=lowercase       ,      **lowercase       ,      )
							return text_encoding

				# add pixel_values
				A_	:						int	=     self.image_processor(lowercase       ,      return_tensors=lowercase							)

				if text is not None:
							A_	:						Optional[Any]	=     self.tokenizer(
							    text=lowercase       ,      add_special_tokens=lowercase       ,      padding=lowercase       ,      truncation=lowercase       ,      max_length=lowercase       ,      stride=lowercase       ,      pad_to_multiple_of=lowercase       ,      return_attention_mask=lowercase       ,      return_overflowing_tokens=lowercase       ,      return_special_tokens_mask=lowercase       ,      return_offsets_mapping=lowercase       ,      return_token_type_ids=lowercase       ,      return_length=lowercase       ,      verbose=lowercase       ,      return_tensors=lowercase       ,      **lowercase       ,      )
				else:
							A_	:						List[str]	=     None

				if text_encoding is not None:
							encoding_image_processor.update(lowercase							)

				return encoding_image_processor


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				return self.tokenizer.batch_decode(*lowercase       ,      **lowercase							)


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				return self.tokenizer.decode(*lowercase       ,      **lowercase							)


	@property
	def     _a	(self							):
				A_	:						int	=     self.tokenizer.model_input_names
				A_	:						Any	=     self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)