

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			:				Dict   =     """src/transformers"""
lowercase			:				Tuple   =     """docs/source/en/tasks"""






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):

							with open(lowercase_     ,				'''r'''     ,				encoding='''utf-8'''     ,				newline='''\n'''		) as f:
														A      :					Optional[int]      		=					f.readlines()
							# Find the start prompt.
							A      :					Optional[Any]      		=					0
							while not lines[start_index].startswith(lowercase_		):
														start_index += 1
							start_index += 1

							A      :					Any      		=					start_index
							while not lines[end_index].startswith(lowercase_		):
														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			:				Union[str, Any]   =     direct_transformers_import(TRANSFORMERS_PATH)

lowercase			:				List[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			:				List[str]   =     {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}






def        snake_case__		(    lowerCamelCase_		):
							A      :					Tuple      		=					TASK_GUIDE_TO_MODELS[task_guide]
							A      :					Union[str, Any]      		=					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowercase_     ,				set()		)
							A      :					Tuple      		=					{
							    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        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_=False		):
							A		,		A		,		A		,		A      :					List[Any]      		=					_find_text_in_file(
							    filename=os.path.join(lowercase_     ,				lowercase_		)     ,				start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''     ,				end_prompt='''<!--End of the generated tip-->'''     ,				)

							A      :					str      		=					get_model_list_for_task(lowercase_		)

							if current_list != new_list:
														if overwrite:
																					with open(os.path.join(lowercase_     ,				lowercase_		)     ,				'''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			:				List[Any]   =     argparse.ArgumentParser()
		parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
		lowercase			:				Optional[Any]   =     parser.parse_args()

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












class 							UpperCamelCase_       :  # Public class to implement a graph

    '''simple docstring'''






    def __init__(     self :      str   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->None:





           '''simple docstring'''

           A__ 					=       row
           A__ 					=       col
           A__ 					=       graph


    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->bool:





           '''simple docstring'''

           return (
               0 <= i < self.ROW
               and 0 <= j < self.COL
               and not visited[i][j]
               and self.graph[i][j]
           )


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->None:





           '''simple docstring'''

           A__ 					=       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
           A__ 					=       [-1, 0, 1, -1, 1, -1, 0, 1]
           A__ 					=       True  # Make those cells visited
           for k in range(8):
                  if self.is_safe(i + row_nbr[k]   ,					j + col_nbr[k]   ,					UpperCAmelCase__):
                         self.diffs(i + row_nbr[k]   ,					j + col_nbr[k]   ,					UpperCAmelCase__)





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->int:  # And finally, count all islands.





           '''simple docstring'''

           A__ 					=       [[False for j in range(self.COL)] for i in range(self.ROW)]
           A__ 					=       0
           for i in range(self.ROW):
                  for j in range(self.COL):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                                self.diffs(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)
                                count += 1
           return count


