






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





def 	lowerCAmelCase_		(lowercase__						:       Optional[Any]     ,						lowercase__						:       List[str]     ,						lowercase__						:       List[str]       )		->    int:


				'''simple docstring'''




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

				lowerCAmelCase__      			=						start_index
				while not lines[end_index].startswith(lowercase__       ):
								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			:		Tuple   				=    {
    "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			:		str   				=    {
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 	lowerCAmelCase_		(lowercase__						:       int       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						TASK_GUIDE_TO_MODELS[task_guide]
				lowerCAmelCase__      			=						SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowercase__     ,						set()       )
				lowerCAmelCase__      			=						{
				    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_		(lowercase__						:       int     ,						lowercase__						:       Dict=False       )		->    Tuple:


				'''simple docstring'''




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

				lowerCAmelCase__      			=						get_model_list_for_task(lowercase__       )

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(lowercase__     ,						lowercase__       )     ,						'''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[int]   				=    argparse.ArgumentParser()
			parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
			_UpperCAmelCase			:		Optional[Any]   				=    parser.parse_args()

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













from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase			:		Optional[Any]   				=    {
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		str   				=    [
			    "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "MCTCTForCTC",
			    "MCTCTModel",
			    "MCTCTPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
			from .feature_extraction_mctct import MCTCTFeatureExtractor
			from .processing_mctct import MCTCTProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
			import sys

			_UpperCAmelCase			:		List[str]   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





