

'''simple docstring'''





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	:     Optional[Any]		       = """src/transformers"""
_UpperCAmelCase	:     Any		       = """docs/source/en/tasks"""





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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

							snake_case_           =					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	:     Optional[Any]		       = direct_transformers_import(TRANSFORMERS_PATH)

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





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					TASK_GUIDE_TO_MODELS[task_guide]
							snake_case_           =					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__lowerCAmelCase     ,							set()   )
							snake_case_           =					{
							    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			(				UpperCamelCase__     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							snake_case_           =					_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-->'     ,							)

							snake_case_           =					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	:     Any		       = argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					_UpperCAmelCase	:     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       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							if not nums:  # Makes sure that the list is not empty
														raise ValueError('List is empty'   )

							snake_case_           =					sum(UpperCamelCase__   ) / len(UpperCamelCase__   )  # Calculate the average
							return sum(abs(x - average   ) for x in nums   ) / len(UpperCamelCase__   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




