code: 
  
  
  '''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 lowerCamelCase    (    ):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Dict  			=  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"]:
  		__magic_name__ :					List[str]  			=  getattr(transformers.models.auto	,	_UpperCamelCase	,	_UpperCamelCase	)
  		if module is None:
  			continue
  		# all mappings in a single auto modeling file
  		__magic_name__ :					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:
  			__magic_name__ :					List[Any]  			=  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 lowerCamelCase    (    ):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Dict  			=  set(get_all_model_names()	)
  
  	with open('tests/utils/tiny_model_summary.json'	) as fp:
  		__magic_name__ :					Any  			=  json.load(_UpperCamelCase	)
  	__magic_name__ :					str  			=  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.
  	__magic_name__ :					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 lowerCamelCase    (    lowerCAmelCase       :			Optional[int]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Union[str, Any]  			=  COMPOSITE_MODELS.values()
  
  	# All tiny model base names on Hub
  	__magic_name__ :					Optional[Any]  			=  get_all_model_names()
  	__magic_name__ :					str  			=  hf_api.list_models(
  	    filter=ModelFilter(
  	        author='hf-internal-testing'	,	)	)
  	__magic_name__ :					List[Any]  			=  set()
  	for x in models:
  		__magic_name__ :					Dict  			=  x.modelId
  		__magic_name__			,       __magic_name__ :					Optional[int]  			=  model.split('/'	)
  		if not model.startswith('tiny-random-'	):
  			continue
  		__magic_name__ :					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	)
  
  	__magic_name__ :					Any  			=  sorted(_models	)
  	# All tiny model names on Hub
  	__magic_name__ :					str  			=  {}
  	for model in models:
  		__magic_name__ :					Union[str, Any]  			=  f'hf-internal-testing/tiny-random-{model}'
  		__magic_name__ :					int  			=  model.split('-'	)[0]
  		try:
  			__magic_name__ :					str  			=  hf_api.repo_info(_UpperCamelCase	)
  			__magic_name__ :					Union[str, Any]  			=  {
  			    'tokenizer_classes': set(),
  			    'processor_classes': set(),
  			    'model_classes': set(),
  			    'sha': repo_info.sha,
  			}
  		except Exception:
  			continue
  		try:
  			time.sleep(1	)
  			__magic_name__ :					Dict  			=  AutoTokenizer.from_pretrained(_UpperCamelCase	)
  			content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__	)
  		except Exception:
  			pass
  		try:
  			time.sleep(1	)
  			__magic_name__ :					Dict  			=  AutoTokenizer.from_pretrained(_UpperCamelCase	,	use_fast=_UpperCamelCase	)
  			content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__	)
  		except Exception:
  			pass
  		try:
  			time.sleep(1	)
  			__magic_name__ :					Optional[Any]  			=  AutoImageProcessor.from_pretrained(_UpperCamelCase	)
  			content["processor_classes"].add(img_p.__class__.__name__	)
  		except Exception:
  			pass
  		try:
  			time.sleep(1	)
  			__magic_name__ :					Dict  			=  AutoFeatureExtractor.from_pretrained(_UpperCamelCase	)
  			if not isinstance(_UpperCamelCase	,	_UpperCamelCase	):
  				content["processor_classes"].add(feat_p.__class__.__name__	)
  		except Exception:
  			pass
  		try:
  			time.sleep(1	)
  			__magic_name__ :					str  			=  getattr(_UpperCamelCase	,	_UpperCamelCase	)
  			__magic_name__ :					str  			=  model_class.from_pretrained(_UpperCamelCase	)
  			content["model_classes"].add(m.__class__.__name__	)
  		except Exception:
  			pass
  		try:
  			time.sleep(1	)
  			__magic_name__ :					Optional[Any]  			=  getattr(_UpperCamelCase	,	f'TF{model}'	)
  			__magic_name__ :					List[str]  			=  model_class.from_pretrained(_UpperCamelCase	)
  			content["model_classes"].add(m.__class__.__name__	)
  		except Exception:
  			pass
  
  		__magic_name__ :					Union[str, Any]  			=  sorted(content['tokenizer_classes']	)
  		__magic_name__ :					str  			=  sorted(content['processor_classes']	)
  		__magic_name__ :					Union[str, Any]  			=  sorted(content['model_classes']	)
  
  		__magic_name__ :					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__":
  							lowerCAmelCase   :str								=    argparse.ArgumentParser()
  							parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
  							lowerCAmelCase   :Tuple								=    parser.parse_args()
  
  							# This has to be `spawn` to avoid hanging forever!
  							multiprocessing.set_start_method('''spawn''')
  
  							lowerCAmelCase   :List[str]								=    "tiny_models"
  							lowerCAmelCase   :Union[str, Any]								=    True
  							lowerCAmelCase   :str								=    None
  							lowerCAmelCase   :Union[str, Any]								=    get_tiny_model_names_from_repo()
  							lowerCAmelCase   :Any								=    True
  							lowerCAmelCase   :Tuple								=    True
  							lowerCAmelCase   :Optional[int]								=    "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,
  							)
style_context: 
  
  
  '''simple docstring'''
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCAmelCase   :List[Any]								=    logging.get_logger(__name__)
  
  lowerCAmelCase   :Dict								=    {
      '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
      # See all YOSO models at https://huggingface.co/models?filter=yoso
  }
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[Any]   					=       """yoso"""
  
  
  
  
  
  
  
  		def __init__(      self	:		Any						,  _A	:		List[Any]=50265						,  _A	:		Any=768						,  _A	:		List[str]=12						,  _A	:		Dict=12						,  _A	:		Union[str, Any]=3072						,  _A	:		List[Any]="gelu"						,  _A	:		Dict=0.1						,  _A	:		Union[str, Any]=0.1						,  _A	:		Union[str, Any]=4096						,  _A	:		Dict=1						,  _A	:		Tuple=0.02						,  _A	:		Any=1E-12						,  _A	:		Optional[int]="absolute"						,  _A	:		Tuple=True						,  _A	:		Any=9						,  _A	:		Tuple=64						,  _A	:		str=None						,  _A	:		Dict=True						,  _A	:		str=True						,  _A	:		int=1						,  _A	:		Optional[int]=0						,  _A	:		Tuple=2						,  **_A	:		Optional[Any]						,  )						->				Any:
  			super().__init__(pad_token_id=_A						,  bos_token_id=_A						,  eos_token_id=_A						,  **_A    )
  
  			__magic_name__ :					List[Any]  			=  vocab_size
  			__magic_name__ :					int  			=  max_position_embeddings
  			__magic_name__ :					int  			=  hidden_size
  			__magic_name__ :					Any  			=  num_hidden_layers
  			__magic_name__ :					List[str]  			=  num_attention_heads
  			__magic_name__ :					str  			=  intermediate_size
  			__magic_name__ :					Any  			=  hidden_act
  			__magic_name__ :					Union[str, Any]  			=  hidden_dropout_prob
  			__magic_name__ :					str  			=  attention_probs_dropout_prob
  			__magic_name__ :					int  			=  initializer_range
  			__magic_name__ :					List[Any]  			=  type_vocab_size
  			__magic_name__ :					Tuple  			=  layer_norm_eps
  			__magic_name__ :					Tuple  			=  position_embedding_type
  			__magic_name__ :					Optional[Any]  			=  use_expectation
  			__magic_name__ :					Union[str, Any]  			=  hash_code_len
  			__magic_name__ :					int  			=  num_hash
  			__magic_name__ :					List[Any]  			=  conv_window
  			__magic_name__ :					Optional[Any]  			=  use_fast_hash
  			__magic_name__ :					int  			=  lsh_backward