



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
UpperCAmelCase__     = "src/transformers"
UpperCAmelCase__     = "docs/source/en/tasks"





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				int:







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

			_UpperCAmelCase	 =     start_index
			while not lines[end_index].startswith(_UpperCAmelCase					):
						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.
UpperCAmelCase__     = direct_transformers_import(TRANSFORMERS_PATH)

UpperCAmelCase__     = {
    "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`).
UpperCAmelCase__     = {
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 							A			(  _UpperCAmelCase       :     str					)       ->				List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     TASK_GUIDE_TO_MODELS[task_guide]
			_UpperCAmelCase	 =     SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_UpperCAmelCase							,     set()					)
			_UpperCAmelCase	 =     {
			    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			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     List[str]=False					)       ->				Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     _find_text_in_file(
			    filename=os.path.join(_UpperCAmelCase							,     _UpperCAmelCase					)							,     start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'							,     end_prompt='<!--End of the generated tip-->'							,     )

			_UpperCAmelCase	 =     get_model_list_for_task(_UpperCAmelCase					)

			if current_list != new_list:
						if overwrite:
									with open(os.path.join(_UpperCAmelCase							,     _UpperCAmelCase					)							,     '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__":
							UpperCAmelCase__     = argparse.ArgumentParser()
							parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
							UpperCAmelCase__     = parser.parse_args()

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





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     int					)       ->				str:







			'''simple docstring'''
			if a < 0 or b < 0:
						raise ValueError('the value of both inputs must be positive'					)

			_UpperCAmelCase	 =     str(bin(_UpperCAmelCase					)					)[2:]  # remove the leading "0b"
			_UpperCAmelCase	 =     str(bin(_UpperCAmelCase					)					)[2:]  # remove the leading "0b"

			_UpperCAmelCase	 =     max(len(_UpperCAmelCase					)							,     len(_UpperCAmelCase					)					)

			return "0b" + "".join(
			    str(int(char_a != char_b					)					)
			    for char_a, char_b in zip(a_binary.zfill(_UpperCAmelCase					)							,     b_binary.zfill(_UpperCAmelCase					)					)					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
