code: 
  
  
  
  
  
  '''simple docstring'''
  
  import warnings
  
  from ...utils import logging
  from .image_processing_mobilenet_va import MobileNetVaImageProcessor
  
  
  _UpperCAmelCase   :      List[Any] 						=  logging.get_logger(__name__)
  
  class 			UpperCAmelCase				(					UpperCAmelCase_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					*_snake_case  ,					**_snake_case     )				->    str:
  				warnings.warn(
  				    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
  				    ''' Please use MobileNetV2ImageProcessor instead.'''  ,					__UpperCamelCase  ,					)
  				super().__init__(*__UpperCamelCase  ,					**__UpperCamelCase     )
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  import argparse
  import copy
  import json
  import multiprocessing
  import os
  import time
  
  from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
  from huggingface_hub import ModelFilter, hf_api
  
  import transformers
  from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
  from transformers.image_processing_utils import BaseImageProcessor
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      List[Any]:
  		_UpperCamelCase       : Union[str, Any]    =      set()
  		# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
  		for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
  				_UpperCamelCase       : List[Any]    =      getattr(transformers.models.auto			,UpperCamelCase			,UpperCamelCase	)
  				if module is None:
  						continue
  				# all mappings in a single auto modeling file
  				_UpperCamelCase       : Optional[Any]    =      [
  				    x
  				    for x in dir(UpperCamelCase	)
  				    if x.endswith('''_MAPPING_NAMES'''	)
  				    and (x.startswith('''MODEL_'''	) or x.startswith('''TF_MODEL_'''	) or x.startswith('''FLAX_MODEL_'''	))
  				]
  				for name in mapping_names:
  						_UpperCamelCase       : List[str]    =      getattr(UpperCamelCase			,UpperCamelCase	)
  						if mapping is not None:
  								for v in mapping.values():
  										if isinstance(UpperCamelCase			,(list, tuple)	):
  												model_names.update(UpperCamelCase	)
  										elif isinstance(UpperCamelCase			,UpperCamelCase	):
  												model_names.add(UpperCamelCase	)
  
  		return sorted(UpperCamelCase	)
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      Any:
  		# All model names defined in auto mappings
  		_UpperCamelCase       : Union[str, Any]    =      set(get_all_model_names()	)
  
  		with open('''tests/utils/tiny_model_summary.json'''	) as fp:
  				_UpperCamelCase       : Optional[Any]    =      json.load(UpperCamelCase	)
  		_UpperCamelCase       : Tuple    =      set()
  		for model_base_name in tiny_model_info:
  				tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']	)
  
  		# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
  		_UpperCamelCase       : List[Any]    =      model_names.difference(UpperCamelCase	)
  		for model_name in copy.copy(UpperCamelCase	):
  				if not model_name.startswith('''TF'''	) and f'''TF{model_name}''' in not_on_hub:
  						tiny_models_names.remove(UpperCamelCase	)
  				elif model_name.startswith('''TF'''	) and model_name[2:] in not_on_hub:
  						tiny_models_names.remove(UpperCamelCase	)
  
  		return sorted(UpperCamelCase	)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      List[str]:
  		_UpperCamelCase       : Optional[Any]    =      COMPOSITE_MODELS.values()
  
  		# All tiny model base names on Hub
  		_UpperCamelCase       : Optional[Any]    =      get_all_model_names()
  		_UpperCamelCase       : Union[str, Any]    =      hf_api.list_models(
  		    filter=ModelFilter(
  		        author='''hf-internal-testing'''			,)	)
  		_UpperCamelCase       : Any    =      set()
  		for x in models:
  				_UpperCamelCase       : Optional[int]    =      x.modelId
  				_UpperCamelCase,							_UpperCamelCase       : str    =      model.split('''/'''	)
  				if not model.startswith('''tiny-random-'''	):
  						continue
  				_UpperCamelCase       : Optional[Any]    =      model.replace('''tiny-random-'''			,''''''	)
  				if not model[0].isupper():
  						continue
  				if model not in model_names and model not in special_models:
  						continue
  				_models.add(UpperCamelCase	)
  
  		_UpperCamelCase       : List[Any]    =      sorted(_models	)
  		# All tiny model names on Hub
  		_UpperCamelCase       : int    =      {}
  		for model in models:
  				_UpperCamelCase       : Optional[Any]    =      f'''hf-internal-testing/tiny-random-{model}'''
  				_UpperCamelCase       : Any    =      model.split('''-'''	)[0]
  				try:
  						_UpperCamelCase       : List[str]    =      hf_api.repo_info(UpperCamelCase	)
  						_UpperCamelCase       : List[str]    =      {
  						    '''tokenizer_classes''': set(),
  						    '''processor_classes''': set(),
  						    '''model_classes''': set(),
  						    '''sha''': repo_info.sha,
  						}
  				except Exception:
  						continue
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : Union[str, Any]    =      AutoTokenizer.from_pretrained(UpperCamelCase	)
  						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : int    =      AutoTokenizer.from_pretrained(UpperCamelCase			,use_fast=UpperCamelCase	)
  						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : int    =      AutoImageProcessor.from_pretrained(UpperCamelCase	)
  						content["processor_classes"].add(img_p.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : List[Any]    =      AutoFeatureExtractor.from_pretrained(UpperCamelCase	)
  						if not isinstance(UpperCamelCase			,UpperCamelCase	):
  								content["processor_classes"].add(feat_p.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : Tuple    =      getattr(UpperCamelCase			,UpperCamelCase	)
  						_UpperCamelCase       : Tuple    =      model_class.from_pretrained(UpperCamelCase	)
  						content["model_classes"].add(m.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : Dict    =      getattr(UpperCamelCase			,f'''TF{model}'''	)
  						_UpperCamelCase       : Optional[Any]    =      model_class.from_pretrained(UpperCamelCase	)
  						content["model_classes"].add(m.__class__.__name__	)
  				except Exception:
  						pass
  
  				_UpperCamelCase       : Dict    =      sorted(content['''tokenizer_classes''']	)
  				_UpperCamelCase       : Tuple    =      sorted(content['''processor_classes''']	)
  				_UpperCamelCase       : Union[str, Any]    =      sorted(content['''model_classes''']	)
  
  				_UpperCamelCase       : List[Any]    =      content
  				with open(os.path.join(UpperCamelCase			,'''hub_tiny_model_summary.json'''	)			,'''w'''	) as fp:
  						json.dump(UpperCamelCase			,UpperCamelCase			,ensure_ascii=UpperCamelCase			,indent=4	)
  
  
  if __name__ == "__main__":
  	_UpperCAmelCase   :      List[str] 						=  argparse.ArgumentParser()
  	parser.add_argument("""--num_workers""", default=1, type=int, help="""The number of workers to run.""")
  	_UpperCAmelCase   :      List[Any] 						=  parser.parse_args()
  
  	# This has to be `spawn` to avoid hanging forever!
  	multiprocessing.set_start_method("""spawn""")
  
  	_UpperCAmelCase   :      Optional[Any] 						=  """tiny_models"""
  	_UpperCAmelCase   :      Optional[Any] 						=  True
  	_UpperCAmelCase   :      str 						=  None
  	_UpperCAmelCase   :      int 						=  get_tiny_model_names_from_repo()
  	_UpperCAmelCase   :      Optional[Any] 						=  True
  	_UpperCAmelCase   :      List[str] 						=  True
  	_UpperCAmelCase   :      str 						=  """hf-internal-testing"""
  
  	create_tiny_models(
  	    output_path,
  	    all,
  	    model_types,
  	    models_to_skip,
  	    no_check,
  	    upload,
  	    organization,
  	    token=os.environ.get("""TOKEN""", None),
  	    num_workers=args.num_workers,
  	)
  
  
  
  
  
  