


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			:  Dict     ="""src/transformers"""
_UpperCAmelCase			:  int     ="""docs/source/en/tasks"""



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Dict:
				with open(lowerCAmelCase_ ,   '''r''' ,   encoding='''utf-8''' ,   newline='''\n''' ) as f:
								lowerCAmelCase_ :      Optional[int]    				=				f.readlines()
				# Find the start prompt.
				lowerCAmelCase_ :      List[str]    				=				0
				while not lines[start_index].startswith(lowerCAmelCase_ ):
								start_index += 1
				start_index += 1

				lowerCAmelCase_ :      str    				=				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.
_UpperCAmelCase			:  Dict     =direct_transformers_import(TRANSFORMERS_PATH)

_UpperCAmelCase			:  int     ={
    """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			:  Optional[int]     ={
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}



def 			lowerCAmelCase (		lowerCAmelCase_ )-> Any:
				lowerCAmelCase_ :      Optional[int]    				=				TASK_GUIDE_TO_MODELS[task_guide]
				lowerCAmelCase_ :      Tuple    				=				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCAmelCase_ ,   set() )
				lowerCAmelCase_ :      Dict    				=				{
				    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 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_=False )-> Dict:
				lowerCAmelCase_ :      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-->''' ,   )

				lowerCAmelCase_ :      Optional[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__":
	_UpperCAmelCase			:  Optional[Any]     =argparse.ArgumentParser()
	parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
	_UpperCAmelCase			:  Union[str, Any]     =parser.parse_args()

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



_UpperCAmelCase			:  Dict     =[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]



def 			lowerCAmelCase (		lowerCAmelCase_ )-> int:
				lowerCAmelCase_ :      Any    				=				{'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1_000}
				lowerCAmelCase_ :      Optional[int]    				=				0
				lowerCAmelCase_ :      List[str]    				=				0
				while place < len(lowerCAmelCase_ ):
								if (place + 1 < len(lowerCAmelCase_ )) and (vals[roman[place]] < vals[roman[place + 1]]):
												total += vals[roman[place + 1]] - vals[roman[place]]
												place += 2
								else:
												total += vals[roman[place]]
												place += 1
				return total



def 			lowerCAmelCase (		lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      List[Any]    				=				[]
				for arabic, roman in ROMAN:
								((lowerCAmelCase_)				,  (lowerCAmelCase_)) :      Optional[int]    				=				divmod(lowerCAmelCase_ ,   lowerCAmelCase_ )
								result.append(roman * factor )
								if number == 0:
												break
				return "".join(lowerCAmelCase_ )


if __name__ == "__main__":
						import doctest

						doctest.testmod()