
"""simple docstring"""






from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml




class 			_SCREAMING_SNAKE_CASE					(			yaml.SafeLoader							):






			def  __lowerCAmelCase (					self       ,   __A       )			->     Optional[int]:
						lowerCAmelCase_    :Dict			     =  [self.constructed_objects[key_node] for key_node, _ in node.value]
						lowerCAmelCase_    :Optional[int]			     =  [tuple(__A       ) if isinstance(__A       ,   __A       ) else key for key in keys]
						lowerCAmelCase_    :List[Any]			     =  Counter(__A       )
						lowerCAmelCase_    :List[str]			     =  [key for key in counter if counter[key] > 1]
						if duplicate_keys:
									raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A=False       )			->     Optional[Any]:
						lowerCAmelCase_    :Union[str, Any]			     =  super().construct_mapping(__A       ,   deep=__A       )
						self._check_no_duplicates_on_constructed_node(__A       )
						return mapping






def 						_snake_case       (	lowercase__		:  str		)  ->				Tuple[Optional[str], str]:


			'''simple docstring'''





			lowerCAmelCase_    :List[Any]			     =  list(readme_content.splitlines()		)
			if full_content and full_content[0] == "---" and "---" in full_content[1:]:
						lowerCAmelCase_    :Optional[Any]			     =  full_content[1:].index("""---"""		) + 1
						lowerCAmelCase_    :List[Any]			     =  """\n""".join(full_content[1:sep_idx]		)
						return yamlblock, "\n".join(full_content[sep_idx + 1 :]		)

			return None, "\n".join(lowercase__		)




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			# class attributes
			UpperCAmelCase_  :Union[str, Any]				 =						{"train_eval_index"}  # train-eval-index in the YAML metadata






			@classmethod
			def  __lowerCAmelCase (					cls       ,   __A       )			->     "DatasetMetadata":
						with open(__A       ,   encoding="""utf-8"""       ) as readme_file:
									lowerCAmelCase_     ,       lowerCAmelCase_    :Tuple			     =  _split_yaml_from_readme(readme_file.read()       )
						if yaml_string is not None:
									return cls.from_yaml_string(__A       )
						else:
									return cls()






			def  __lowerCAmelCase (					self       ,   __A       )			->     List[Any]:
						if path.exists():
									with open(__A       ,   encoding="""utf-8"""       ) as readme_file:
												lowerCAmelCase_    :List[str]			     =  readme_file.read()
						else:
									lowerCAmelCase_    :str			     =  None
						lowerCAmelCase_    :Optional[Any]			     =  self._to_readme(__A       )
						with open(__A       ,   """w"""       ,   encoding="""utf-8"""       ) as readme_file:
									readme_file.write(__A       )






			def  __lowerCAmelCase (					self       ,   __A = None       )			->     str:
						if readme_content is not None:
									lowerCAmelCase_     ,       lowerCAmelCase_    :List[str]			     =  _split_yaml_from_readme(__A       )
									lowerCAmelCase_    :Optional[int]			     =  """---\n""" + self.to_yaml_string() + """---\n""" + content
						else:
									lowerCAmelCase_    :int			     =  """---\n""" + self.to_yaml_string() + """---\n"""
						return full_content






			@classmethod
			def  __lowerCAmelCase (					cls       ,   __A       )			->     "DatasetMetadata":
						lowerCAmelCase_    :int			     =  yaml.load(__A       ,   Loader=_NoDuplicateSafeLoader       ) or {}

						# Convert the YAML keys to DatasetMetadata fields
						lowerCAmelCase_    :Tuple			     =  {
						    (key.replace("""-"""       ,   """_"""       ) if key.replace("""-"""       ,   """_"""       ) in cls._FIELDS_WITH_DASHES else key): value
						    for key, value in metadata_dict.items()
						}
						return cls(**__A       )






			def  __lowerCAmelCase (					self       )			->     str:
						return yaml.safe_dump(
						    {
						        (key.replace("""_"""       ,   """-"""       ) if key in self._FIELDS_WITH_DASHES else key): value
						        for key, value in self.items()
						    }       ,   sort_keys=__A       ,   allow_unicode=__A       ,   encoding="""utf-8"""       ,   ).decode("""utf-8"""       )




__UpperCAmelCase       =  {
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
				from argparse import ArgumentParser

				__UpperCAmelCase       =  ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
				ap.add_argument('readme_filepath')
				__UpperCAmelCase       =  ap.parse_args()

				__UpperCAmelCase       =  Path(args.readme_filepath)
				__UpperCAmelCase       =  DatasetMetadata.from_readme(readme_filepath)
				print(dataset_metadata)
				dataset_metadata.to_readme(readme_filepath)













import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase      :       List[str]									=  """src/transformers"""
lowercase      :       Optional[int]									=  """docs/source/en/tasks"""



def A_	(		A__		,      A__		,      A__				)  ->		Tuple:
       with open(A__		,      'r'		,      encoding='utf-8'		,      newline='\n'				) as f:
              a__						:	Any =				f.readlines()
       # Find the start prompt.
       a__						:	str =				0
       while not lines[start_index].startswith(A__				):
              start_index += 1
       start_index += 1

       a__						:	int =				start_index
       while not lines[end_index].startswith(A__				):
              end_index += 1
       end_index -= 1

       while len(lines[start_index]				) <= 1:
              start_index += 1
       while len(lines[end_index]				) <= 1:
              end_index -= 1
       end_index += 1
       return "".join(lines[start_index:end_index]				), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
lowercase      :       Tuple									=  direct_transformers_import(TRANSFORMERS_PATH)

lowercase      :       Optional[Any]									=  {
    """asr.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    """audio_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    """language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    """image_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    """masked_language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    """multiple_choice.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    """object_detection.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    """question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    """semantic_segmentation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    """sequence_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    """summarization.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    """token_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    """translation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    """video_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    """document_question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    """monocular_depth_estimation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
lowercase      :       Optional[Any]									=  {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}



def A_	(		A__				)  ->		Optional[int]:
       a__						:	Optional[Any] =				TASK_GUIDE_TO_MODELS[task_guide]
       a__						:	int =				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A__		,      set()				)
       a__						:	Optional[Any] =				{
           code: name
           for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
           if (code in model_maping_names or code in special_model_types)
       }
       return ", ".join([F'[{name}](../model_doc/{code})' for code, name in model_names.items()]				) + "\n"



def A_	(		A__		,      A__=False				)  ->		Optional[int]:
       a__ ,							a__ ,							a__ ,							a__						:	Dict =				_find_text_in_file(
           filename=os.path.join(A__		,      A__				)		,      start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'		,      end_prompt='<!--End of the generated tip-->'		,      )

       a__						:	List[Any] =				get_model_list_for_task(A__				)

       if current_list != new_list:
              if overwrite:
                     with open(os.path.join(A__		,      A__				)		,      'w'		,      encoding='utf-8'		,      newline='\n'				) as f:
                            f.writelines(lines[:start_index] + [new_list] + lines[end_index:]				)
              else:
                     raise ValueError(
                         F'The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'
                         ' to fix this.'				)


if __name__ == "__main__":
    lowercase      :       Dict									=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    lowercase      :       str									=  parser.parse_args()

    for task_guide in TASK_GUIDE_TO_MODELS.keys():
        check_model_list_for_task(task_guide, args.fix_and_overwrite)


