


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
SCREAMING_SNAKE_CASE			: int				   =					"src/transformers"
SCREAMING_SNAKE_CASE			: Optional[int]				   =					"docs/source/en/tasks"



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

    _lowercase			:				Union[str, Any] 						=						start_index
    while not lines[end_index].startswith(lowerCamelCase_					):
        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.
SCREAMING_SNAKE_CASE			: Dict				   =					direct_transformers_import(TRANSFORMERS_PATH)

SCREAMING_SNAKE_CASE			: List[str]				   =					{
    "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`).
SCREAMING_SNAKE_CASE			: Optional[Any]				   =					{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}



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

    _lowercase			:				int 						=						get_model_list_for_task(lowerCamelCase_					)

    if current_list != new_list:
        if overwrite:
            with open(os.path.join(lowerCamelCase_				,					lowerCamelCase_					)				,					'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__":
 SCREAMING_SNAKE_CASE			: List[str]				   =					argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					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_( lowerCamelCase_					)       ->		int:

    if not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        raise ValueError('multiplicative_persistence() only accepts integral values'					)
    if num < 0:
        raise ValueError('multiplicative_persistence() does not accept negative values'					)

    _lowercase			:				List[str] 						=						0
    _lowercase			:				Optional[int] 						=						str(lowerCamelCase_					)

    while len(lowerCamelCase_					) != 1:
        _lowercase			:				Any 						=						[int(lowerCamelCase_					) for i in num_string]

        _lowercase			:				List[Any] 						=						1
        for i in range(0				,					len(lowerCamelCase_					)					):
            total *= numbers[i]

        _lowercase			:				Optional[Any] 						=						str(lowerCamelCase_					)

        steps += 1
    return steps



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:

    if not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        raise ValueError('additive_persistence() only accepts integral values'					)
    if num < 0:
        raise ValueError('additive_persistence() does not accept negative values'					)

    _lowercase			:				Optional[int] 						=						0
    _lowercase			:				str 						=						str(lowerCamelCase_					)

    while len(lowerCamelCase_					) != 1:
        _lowercase			:				Dict 						=						[int(lowerCamelCase_					) for i in num_string]

        _lowercase			:				Any 						=						0
        for i in range(0				,					len(lowerCamelCase_					)					):
            total += numbers[i]

        _lowercase			:				Dict 						=						str(lowerCamelCase_					)

        steps += 1
    return steps


if __name__ == "__main__":
 import doctest

 doctest.testmod()


