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       _UpperCamelCase	(					)     ->       List[str]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[int]     =	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"]:
  				snake_case     :					int     =	getattr(transformers.models.auto   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  				if module is None:
  						continue
  				# all mappings in a single auto modeling file
  				snake_case     :					str     =	[
  				    x
  				    for x in dir(SCREAMING_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:
  						snake_case     :					List[Any]     =	getattr(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  						if mapping is not None:
  								for v in mapping.values():
  										if isinstance(SCREAMING_SNAKE_CASE__   ,					(list, tuple) ):
  												model_names.update(SCREAMING_SNAKE_CASE__ )
  										elif isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
  												model_names.add(SCREAMING_SNAKE_CASE__ )
  
  		return sorted(SCREAMING_SNAKE_CASE__ )
  
  
  
  
  def       _UpperCamelCase	(					)     ->       List[Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					str     =	set(get_all_model_names() )
  
  		with open('''tests/utils/tiny_model_summary.json''' ) as fp:
  				snake_case     :					str     =	json.load(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					Dict     =	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.
  		snake_case     :					List[str]     =	model_names.difference(SCREAMING_SNAKE_CASE__ )
  		for model_name in copy.copy(SCREAMING_SNAKE_CASE__ ):
  				if not model_name.startswith('''TF''' ) and F'TF{model_name}' in not_on_hub:
  						tiny_models_names.remove(SCREAMING_SNAKE_CASE__ )
  				elif model_name.startswith('''TF''' ) and model_name[2:] in not_on_hub:
  						tiny_models_names.remove(SCREAMING_SNAKE_CASE__ )
  
  		return sorted(SCREAMING_SNAKE_CASE__ )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Any:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Union[str, Any]     =	COMPOSITE_MODELS.values()
  
  		# All tiny model base names on Hub
  		snake_case     :					Any     =	get_all_model_names()
  		snake_case     :					Any     =	hf_api.list_models(
  		    filter=ModelFilter(
  		        author='''hf-internal-testing'''   ,					) )
  		snake_case     :					Dict     =	set()
  		for x in models:
  				snake_case     :					str     =	x.modelId
  				snake_case ,snake_case     :					Dict     =	model.split('''/''' )
  				if not model.startswith('''tiny-random-''' ):
  						continue
  				snake_case     :					Optional[int]     =	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(SCREAMING_SNAKE_CASE__ )
  
  		snake_case     :					Optional[int]     =	sorted(_models )
  		# All tiny model names on Hub
  		snake_case     :					Union[str, Any]     =	{}
  		for model in models:
  				snake_case     :					List[str]     =	F'hf-internal-testing/tiny-random-{model}'
  				snake_case     :					Optional[int]     =	model.split('''-''' )[0]
  				try:
  						snake_case     :					str     =	hf_api.repo_info(SCREAMING_SNAKE_CASE__ )
  						snake_case     :					Optional[int]     =	{
  						    '''tokenizer_classes''': set(),
  						    '''processor_classes''': set(),
  						    '''model_classes''': set(),
  						    '''sha''': repo_info.sha,
  						}
  				except Exception:
  						continue
  				try:
  						time.sleep(1 )
  						snake_case     :					Dict     =	AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ )
  						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__ )
  				except Exception:
  						pass
  				try:
  						time.sleep(1 )
  						snake_case     :					Dict     =	AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__   ,					use_fast=SCREAMING_SNAKE_CASE__ )
  						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__ )
  				except Exception:
  						pass
  				try:
  						time.sleep(1 )
  						snake_case     :					List[Any]     =	AutoImageProcessor.from_pretrained(SCREAMING_SNAKE_CASE__ )
  						content["processor_classes"].add(img_p.__class__.__name__ )
  				except Exception:
  						pass
  				try:
  						time.sleep(1 )
  						snake_case     :					Optional[int]     =	AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ )
  						if not isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
  								content["processor_classes"].add(feat_p.__class__.__name__ )
  				except Exception:
  						pass
  				try:
  						time.sleep(1 )
  						snake_case     :					Tuple     =	getattr(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  						snake_case     :					List[str]     =	model_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
  						content["model_classes"].add(m.__class__.__name__ )
  				except Exception:
  						pass
  				try:
  						time.sleep(1 )
  						snake_case     :					Tuple     =	getattr(SCREAMING_SNAKE_CASE__   ,					F'TF{model}' )
  						snake_case     :					Optional[Any]     =	model_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
  						content["model_classes"].add(m.__class__.__name__ )
  				except Exception:
  						pass
  
  				snake_case     :					str     =	sorted(content['''tokenizer_classes'''] )
  				snake_case     :					Tuple     =	sorted(content['''processor_classes'''] )
  				snake_case     :					Tuple     =	sorted(content['''model_classes'''] )
  
  				snake_case     :					int     =	content
  				with open(os.path.join(SCREAMING_SNAKE_CASE__   ,					'''hub_tiny_model_summary.json''' )   ,					'''w''' ) as fp:
  						json.dump(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					ensure_ascii=SCREAMING_SNAKE_CASE__   ,					indent=4 )
  
  
  if __name__ == "__main__":
  						lowercase__						=			argparse.ArgumentParser()
  						parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
  						lowercase__						=			parser.parse_args()
  
  						# This has to be `spawn` to avoid hanging forever!
  						multiprocessing.set_start_method("spawn")
  
  						lowercase__						=			"tiny_models"
  						lowercase__						=			True
  						lowercase__						=			None
  						lowercase__						=			get_tiny_model_names_from_repo()
  						lowercase__						=			True
  						lowercase__						=			True
  						lowercase__						=			"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 typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowercase__						=			{
      "configuration_flava": [
          "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "FlavaConfig",
          "FlavaImageCodebookConfig",
          "FlavaImageConfig",
          "FlavaMultimodalConfig",
          "FlavaTextConfig",
      ],
  }
  
  try:
  						if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						lowercase__						=			["FlavaFeatureExtractor"]
  						lowercase__						=			["FlavaImageProcessor"]
  						lowercase__						=			["FlavaProcessor"]
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						lowercase__						=			[
  						    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "FlavaForPreTraining",
  						    "FlavaImageCodebook",
  						    "FlavaImageModel",
  						    "FlavaModel",
  						    "FlavaMultimodalModel",
  						    "FlavaPreTrainedModel",
  						    "FlavaTextModel",
  						]
  
  if TYPE_CHECKING:
  						from .configuration_flava import (
  						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
  						    FlavaConfig,
  						    FlavaImageCodebookConfig,
  						    FlavaImageConfig,
  						    FlavaMultimodalConfig,
  						    FlavaTextConfig,
  						)
  
  						try:
  												if not is_vision_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .feature_extraction_flava import FlavaFeatureExtractor
  												from .image_processing_flava import FlavaImageProcessor
  												from .processing_flava import FlavaProcessor
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_flava import (
  												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    FlavaForPreTraining,
  												    FlavaImageCodebook,
  												    FlavaImageModel,
  												    FlavaModel,
  												    FlavaMultimodalModel,
  												    FlavaPreTrainedModel,
  												    FlavaTextModel,
  												)
  
  else:
  						import sys
  
  						lowercase__						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  