






'''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[int]							=  "src/transformers"
__UpperCAmelCase							:   Tuple							=  "docs/source/en/tasks"





def 					lowercase_ (    __snake_case     :							str	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							int      )     -> str:





				'''simple docstring'''


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

				snake_case__      :Optional[int]         =   start_index
				while not lines[end_index].startswith(__snake_case      ):
								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							:   List[str]							=  direct_transformers_import(TRANSFORMERS_PATH)

__UpperCAmelCase							:   Dict							=  {
    "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 					lowercase_ (    __snake_case     :							Tuple      )     -> Optional[Any]:





				'''simple docstring'''


				snake_case__      :Union[str, Any]         =   TASK_GUIDE_TO_MODELS[task_guide]
				snake_case__      :Union[str, Any]         =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__snake_case	,	set()      )
				snake_case__      :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 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							List[Any]=False      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Dict         =   _find_text_in_file(
				    filename=os.path.join(__snake_case	,	__snake_case      )	,	start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"	,	end_prompt="<!--End of the generated tip-->"	,	)

				snake_case__      :Union[str, Any]         =   get_model_list_for_task(__snake_case      )

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(__snake_case	,	__snake_case      )	,	"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							:   Dict							=  argparse.ArgumentParser()
	parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
	__UpperCAmelCase							:   Tuple							=  parser.parse_args()

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







import os

try:
	from .build_directory_md import good_file_paths
except ImportError:
	from build_directory_md import good_file_paths  # type: ignore

__UpperCAmelCase							:   List[Any]							=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCAmelCase							:   int							=  [file for file in filepaths if file != file.lower()]
if upper_files:
	print(F'''{len(upper_files)} files contain uppercase characters:''')
	print("\n".join(upper_files) + "\n")

__UpperCAmelCase							:   Any							=  [file for file in filepaths if " " in file]
if space_files:
	print(F'''{len(space_files)} files contain space characters:''')
	print("\n".join(space_files) + "\n")

__UpperCAmelCase							:   str							=  [file for file in filepaths if "-" in file]
if hyphen_files:
	print(F'''{len(hyphen_files)} files contain hyphen characters:''')
	print("\n".join(hyphen_files) + "\n")

__UpperCAmelCase							:   Dict							=  [file for file in filepaths if os.sep not in file]
if nodir_files:
	print(F'''{len(nodir_files)} files are not in a directory:''')
	print("\n".join(nodir_files) + "\n")

__UpperCAmelCase							:   int							=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)