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						(	__lowercase						:			Optional[Any]						,__lowercase						:			List[str]						,__lowercase						:			Optional[int]     ):
		'''simple docstring'''




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

		A_		:    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            =		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						(	__lowercase						:			Optional[int]     ):
		'''simple docstring'''




		A_		:    Dict         =				TASK_GUIDE_TO_MODELS[task_guide]
		A_		:    Tuple         =				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__lowerCAmelCase						,set()     )
		A_		:    Union[str, Any]         =				{
		    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						(	__lowercase						:			Tuple						,__lowercase						:			Tuple=False     ):
		'''simple docstring'''




		A_		:    Optional[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-->'						,)

		A_		:    Tuple         =				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            =		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)







import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

_UpperCAmelCase            =		"""python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
	pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
	pkgs_to_check_at_runtime.append("""importlib_metadata""")

for pkg in pkgs_to_check_at_runtime:
	if pkg in deps:
		if pkg == "tokenizers":
			# must be loaded here, or else tqdm check may fail
			from .utils import is_tokenizers_available

			if not is_tokenizers_available():
				continue  # not required, check version only if installed

		require_version_core(deps[pkg])
	else:
		raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			Dict=None     ):
		'''simple docstring'''




		require_version(deps[pkg]						,__lowercase     )






