
"""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
_snake_case          =						'''src/transformers'''
_snake_case          =						'''docs/source/en/tasks'''






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					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.
_snake_case          =						direct_transformers_import(TRANSFORMERS_PATH)

_snake_case          =						{
    '''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`).
_snake_case          =						{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Tuple						):






							"""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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					int=False						):






							"""simple docstring"""



							_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__":
	_snake_case          =						argparse.ArgumentParser()
	parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
	_snake_case          =						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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					"mobilenet_v1"


				def __init__(					self	:			List[Any]				,     UpperCAmelCase_	:			Optional[int]=3				,     UpperCAmelCase_	:			str=224				,     UpperCAmelCase_	:			Union[str, Any]=1.0				,     UpperCAmelCase_	:			List[str]=8				,     UpperCAmelCase_	:			Union[str, Any]="relu6"				,     UpperCAmelCase_	:			Optional[Any]=True				,     UpperCAmelCase_	:			Optional[int]=0.999				,     UpperCAmelCase_	:			Optional[Any]=0.02				,     UpperCAmelCase_	:			Union[str, Any]=0.001				,     **UpperCAmelCase_	:			List[Any]				,     )		->  Dict:







											"""simple docstring"""




											super().__init__(**UpperCAmelCase_  )

											if depth_multiplier <= 0:
																		raise ValueError('depth_multiplier must be greater than zero.'  )

											_lowerCAmelCase  	=			num_channels
											_lowerCAmelCase  	=			image_size
											_lowerCAmelCase  	=			depth_multiplier
											_lowerCAmelCase  	=			min_depth
											_lowerCAmelCase  	=			hidden_act
											_lowerCAmelCase  	=			tf_padding
											_lowerCAmelCase  	=			classifier_dropout_prob
											_lowerCAmelCase  	=			initializer_range
											_lowerCAmelCase  	=			layer_norm_eps






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					version.parse("1.11"							)


				@property
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Mapping[str, Mapping[int, str]]:







											"""simple docstring"""




											return OrderedDict([('pixel_values', {0: 'batch'})]  )


				@property
				def 				__lowerCamelCase	(					self	:			Dict  )		->  Mapping[str, Mapping[int, str]]:







											"""simple docstring"""




											if self.task == "image-classification":
																		return OrderedDict([('logits', {0: 'batch'})]  )
											else:
																		return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]  )







				@property
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  float:







											"""simple docstring"""




											return 1E-4






