


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

			UpperCamelCase__					=       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.
__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        UpperCamelCase_(				_A   :Dict  )->		str:
			UpperCamelCase__					=       TASK_GUIDE_TO_MODELS[task_guide]
			UpperCamelCase__					=       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_A				,     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        UpperCamelCase_(				_A   :int				,     _A   :Any=False  )->		Dict:
			UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__					=       _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-->"				,     )

			UpperCamelCase__					=       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__":
						__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        UpperCamelCase_(				_A   :int				,     _A   :int  )->		str:
			if number < 0 or shift_amount < 0:
						raise ValueError("both inputs must be positive integers"  )

			UpperCamelCase__					=       str(bin(_A  )  )
			binary_number += "0" * shift_amount
			return binary_number
def        UpperCamelCase_(				_A   :int				,     _A   :int  )->		str:
			if number < 0 or shift_amount < 0:
						raise ValueError("both inputs must be positive integers"  )

			UpperCamelCase__					=       str(bin(_A  )  )[2:]
			if shift_amount >= len(_A  ):
						return "0b0"
			UpperCamelCase__					=       binary_number[: len(_A  ) - shift_amount]
			return "0b" + shifted_binary_number
def        UpperCamelCase_(				_A   :int				,     _A   :int  )->		str:
			if number >= 0:  # Get binary representation of positive number
						UpperCamelCase__					=       "0" + str(bin(_A  )  ).strip("-"  )[2:]
			else:  # Get binary (2's complement) representation of negative number
						UpperCamelCase__					=       len(bin(_A  )[3:]  )  # Find 2's complement of number
						UpperCamelCase__					=       bin(abs(_A  ) - (1 << binary_number_length)  )[3:]
						UpperCamelCase__					=       (
						    "1" + "0" * (binary_number_length - len(_A  )) + binary_number
						)

			if shift_amount >= len(_A  ):
						return "0b" + binary_number[0] * len(_A  )
			return (
			    "0b"
			    + binary_number[0] * shift_amount
			    + binary_number[: len(_A  ) - shift_amount]
			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()


