



"""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
__a       :	Union[str, Any]             = 'src/transformers'
__a       :	List[str]             = 'docs/source/en/tasks'





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

					a__          =  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.
__a       :	Any             = direct_transformers_import(TRANSFORMERS_PATH)

__a       :	Optional[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`).
__a       :	Optional[Any]             = {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  TASK_GUIDE_TO_MODELS[task_guide]
					a__          =  SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__lowerCAmelCase ,     set())
					a__          =  {
					    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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False):
					a__          =  _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__          =  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__":
	__a       :	Dict             = argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	__a       :	List[str]             = parser.parse_args()

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










"""simple docstring"""







from typing import TYPE_CHECKING

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


__a       :	str             = {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	__a       :	Dict             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


