code: 
  
  
  
  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     UpperCAmelCase__ (							)   -> List[Any]:
  		_A           =    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"]:
  				_A           =    getattr(transformers.models.auto						,				__snake_case						,				__snake_case							)
  				if module is None:
  						continue
  				# all mappings in a single auto modeling file
  				_A           =    [
  				    x
  				    for x in dir(__snake_case							)
  				    if x.endswith('''_MAPPING_NAMES'''							)
  				    and (x.startswith('''MODEL_'''							) or x.startswith('''TF_MODEL_'''							) or x.startswith('''FLAX_MODEL_'''							))
  				]
  				for name in mapping_names:
  						_A           =    getattr(__snake_case						,				__snake_case							)
  						if mapping is not None:
  								for v in mapping.values():
  										if isinstance(__snake_case						,				(list, tuple)							):
  												model_names.update(__snake_case							)
  										elif isinstance(__snake_case						,				__snake_case							):
  												model_names.add(__snake_case							)
  
  		return sorted(__snake_case							)
  
  
  
  
  def     UpperCAmelCase__ (							)   -> Any:
  		# All model names defined in auto mappings
  		_A           =    set(get_all_model_names()							)
  
  		with open('''tests/utils/tiny_model_summary.json'''							) as fp:
  				_A           =    json.load(__snake_case							)
  		_A           =    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.
  		_A           =    model_names.difference(__snake_case							)
  		for model_name in copy.copy(__snake_case							):
  				if not model_name.startswith('''TF'''							) and F'''TF{model_name}''' in not_on_hub:
  						tiny_models_names.remove(__snake_case							)
  				elif model_name.startswith('''TF'''							) and model_name[2:] in not_on_hub:
  						tiny_models_names.remove(__snake_case							)
  
  		return sorted(__snake_case							)
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> List[str]:
  		_A           =    COMPOSITE_MODELS.values()
  
  		# All tiny model base names on Hub
  		_A           =    get_all_model_names()
  		_A           =    hf_api.list_models(
  		    filter=ModelFilter(
  		        author='''hf-internal-testing'''						,				)							)
  		_A           =    set()
  		for x in models:
  				_A           =    x.modelId
  				_A           =    model.split('''/'''							)
  				if not model.startswith('''tiny-random-'''							):
  						continue
  				_A           =    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(__snake_case							)
  
  		_A           =    sorted(_models							)
  		# All tiny model names on Hub
  		_A           =    {}
  		for model in models:
  				_A           =    F'''hf-internal-testing/tiny-random-{model}'''
  				_A           =    model.split('''-'''							)[0]
  				try:
  						_A           =    hf_api.repo_info(__snake_case							)
  						_A           =    {
  						    '''tokenizer_classes''': set(),
  						    '''processor_classes''': set(),
  						    '''model_classes''': set(),
  						    '''sha''': repo_info.sha,
  						}
  				except Exception:
  						continue
  				try:
  						time.sleep(1							)
  						_A           =    AutoTokenizer.from_pretrained(__snake_case							)
  						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__							)
  				except Exception:
  						pass
  				try:
  						time.sleep(1							)
  						_A           =    AutoTokenizer.from_pretrained(__snake_case						,				use_fast=__snake_case							)
  						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__							)
  				except Exception:
  						pass
  				try:
  						time.sleep(1							)
  						_A           =    AutoImageProcessor.from_pretrained(__snake_case							)
  						content["processor_classes"].add(img_p.__class__.__name__							)
  				except Exception:
  						pass
  				try:
  						time.sleep(1							)
  						_A           =    AutoFeatureExtractor.from_pretrained(__snake_case							)
  						if not isinstance(__snake_case						,				__snake_case							):
  								content["processor_classes"].add(feat_p.__class__.__name__							)
  				except Exception:
  						pass
  				try:
  						time.sleep(1							)
  						_A           =    getattr(__snake_case						,				__snake_case							)
  						_A           =    model_class.from_pretrained(__snake_case							)
  						content["model_classes"].add(m.__class__.__name__							)
  				except Exception:
  						pass
  				try:
  						time.sleep(1							)
  						_A           =    getattr(__snake_case						,				F'''TF{model}'''							)
  						_A           =    model_class.from_pretrained(__snake_case							)
  						content["model_classes"].add(m.__class__.__name__							)
  				except Exception:
  						pass
  
  				_A           =    sorted(content['''tokenizer_classes''']							)
  				_A           =    sorted(content['''processor_classes''']							)
  				_A           =    sorted(content['''model_classes''']							)
  
  				_A           =    content
  				with open(os.path.join(__snake_case						,				'''hub_tiny_model_summary.json'''							)						,				'''w'''							) as fp:
  						json.dump(__snake_case						,				__snake_case						,				ensure_ascii=__snake_case						,				indent=4							)
  
  
  if __name__ == "__main__":
  				__lowerCamelCase  				=					argparse.ArgumentParser()
  				parser.add_argument("""--num_workers""", default=1, type=int, help="""The number of workers to run.""")
  				__lowerCamelCase  				=					parser.parse_args()
  
  				# This has to be `spawn` to avoid hanging forever!
  				multiprocessing.set_start_method("""spawn""")
  
  				__lowerCamelCase  				=					"""tiny_models"""
  				__lowerCamelCase  				=					True
  				__lowerCamelCase  				=					None
  				__lowerCamelCase  				=					get_tiny_model_names_from_repo()
  				__lowerCamelCase  				=					True
  				__lowerCamelCase  				=					True
  				__lowerCamelCase  				=					"""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: 
  
  
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  __lowerCamelCase  				=					{}
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt_decoder'''
   def __init__(						self	,    a=0	,    a=0					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__()
     _A           =    vocab_size
     _A           =    bos_token_id
  
  
  
  
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt'''
   lowerCamelCase_          =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
   def __init__(						self	,    a=["en", "de"]	,    a=4_2_0_2_4	,    a=4_2_0_2_4	,    a="relu"	,    a=1_0_2_4	,    a=2_0_0	,    a=1_0_2_4	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=0.0	,    a=0.1	,    a=0.0	,    a=0.02	,    a=2	,    a=True	,    a=True	,    a=False	,    a=5	,    a=1.0	,    a=False	,    a=True	,    a=1	,    a=0	,    a=2	,    a=2	,    **a	,    )	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    langs
     _A           =    src_vocab_size
     _A           =    tgt_vocab_size
     _A           =    d_model  # encoder_embed_dim and decoder_embed_dim
  
     _A           =    encoder_ffn_dim
     _A           =    _A           =    encoder_layers
     _A           =    encoder_attention_heads
     _A           =    encoder_layerdrop
     _A           =    decoder_layerdrop
     _A           =    decoder_ffn_dim
     _A           =    decoder_layers
     _A           =    decoder_attention_heads
     _A           =    max_position_embeddings
     _A           =    init_std  # Normal(0, this parameter)
     _A           =    activation_function
  
     _A           =    DecoderConfig(vocab_size=a	,    bos_token_id=a					)
     if "decoder" in common_kwargs:
       del common_kwargs["decoder"]
  
     _A           =    scale_embedding  # scale factor will be sqrt(d_model) if True
  
     # 3 Types of Dropout
     _A           =    attention_dropout
     _A           =    activation_dropout
     _A           =    dropout
  
     _A           =    use_cache
     super().__init__(
         pad_token_id=a	,    bos_token_id=a	,    eos_token_id=a	,    decoder_start_token_id=a	,    is_encoder_decoder=a	,    tie_word_embeddings=a	,    forced_eos_token_id=a	,    max_length=a	,    num_beams=a	,    length_penalty=a	,    early_stopping=a	,    **a	,    )
  
  
  
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    copy.deepcopy(self.__dict__					)
     _A           =    self.decoder.to_dict()
     _A           =    self.__class__.model_type
     return output