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			(						)		->					Union[str, Any]:
  
    """simple docstring"""
  
  
  
  
  
  
  
    lowerCAmelCase_     :							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"]:
      lowerCAmelCase_     :							Any             =    getattr(transformers.models.auto   , __UpperCamelCase   , __UpperCamelCase )
      if module is None:
        continue
      # all mappings in a single auto modeling file
      lowerCAmelCase_     :							Dict             =    [
          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:
        lowerCAmelCase_     :							Optional[int]             =    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			(						)		->					Optional[int]:
  
    """simple docstring"""
  
  
  
  
  
  
  
    lowerCAmelCase_     :							Optional[int]             =    set(get_all_model_names() )
  
    with open("tests/utils/tiny_model_summary.json" ) as fp:
      lowerCAmelCase_     :							Dict             =    json.load(__UpperCamelCase )
    lowerCAmelCase_     :							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.
    lowerCAmelCase_     :							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			(						__UpperCamelCase )		->					Tuple:
  
    """simple docstring"""
  
  
  
  
  
  
  
    lowerCAmelCase_     :							List[str]             =    COMPOSITE_MODELS.values()
  
    # All tiny model base names on Hub
    lowerCAmelCase_     :							Dict             =    get_all_model_names()
    lowerCAmelCase_     :							Tuple             =    hf_api.list_models(
        filter=ModelFilter(
            author="hf-internal-testing"   , ) )
    lowerCAmelCase_     :							Union[str, Any]             =    set()
    for x in models:
      lowerCAmelCase_     :							int             =    x.modelId
      lowerCAmelCase_     :							Union[str, Any]             =    model.split("/" )
      if not model.startswith("tiny-random-" ):
        continue
      lowerCAmelCase_     :							Tuple             =    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 )
  
    lowerCAmelCase_     :							Any             =    sorted(_models )
    # All tiny model names on Hub
    lowerCAmelCase_     :							Dict             =    {}
    for model in models:
      lowerCAmelCase_     :							Optional[Any]             =    f'''hf-internal-testing/tiny-random-{model}'''
      lowerCAmelCase_     :							Dict             =    model.split("-" )[0]
      try:
        lowerCAmelCase_     :							str             =    hf_api.repo_info(__UpperCamelCase )
        lowerCAmelCase_     :							Any             =    {
            "tokenizer_classes": set(),
            "processor_classes": set(),
            "model_classes": set(),
            "sha": repo_info.sha,
        }
      except Exception:
        continue
      try:
        time.sleep(1 )
        lowerCAmelCase_     :							List[str]             =    AutoTokenizer.from_pretrained(__UpperCamelCase )
        content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__ )
      except Exception:
        pass
      try:
        time.sleep(1 )
        lowerCAmelCase_     :							Union[str, Any]             =    AutoTokenizer.from_pretrained(__UpperCamelCase   , use_fast=__UpperCamelCase )
        content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__ )
      except Exception:
        pass
      try:
        time.sleep(1 )
        lowerCAmelCase_     :							Any             =    AutoImageProcessor.from_pretrained(__UpperCamelCase )
        content["processor_classes"].add(img_p.__class__.__name__ )
      except Exception:
        pass
      try:
        time.sleep(1 )
        lowerCAmelCase_     :							Optional[int]             =    AutoFeatureExtractor.from_pretrained(__UpperCamelCase )
        if not isinstance(__UpperCamelCase   , __UpperCamelCase ):
          content["processor_classes"].add(feat_p.__class__.__name__ )
      except Exception:
        pass
      try:
        time.sleep(1 )
        lowerCAmelCase_     :							Any             =    getattr(__UpperCamelCase   , __UpperCamelCase )
        lowerCAmelCase_     :							Optional[Any]             =    model_class.from_pretrained(__UpperCamelCase )
        content["model_classes"].add(m.__class__.__name__ )
      except Exception:
        pass
      try:
        time.sleep(1 )
        lowerCAmelCase_     :							Optional[int]             =    getattr(__UpperCamelCase   , f'''TF{model}''' )
        lowerCAmelCase_     :							Optional[int]             =    model_class.from_pretrained(__UpperCamelCase )
        content["model_classes"].add(m.__class__.__name__ )
      except Exception:
        pass
  
      lowerCAmelCase_     :							Dict             =    sorted(content["tokenizer_classes"] )
      lowerCAmelCase_     :							Optional[int]             =    sorted(content["processor_classes"] )
      lowerCAmelCase_     :							Dict             =    sorted(content["model_classes"] )
  
      lowerCAmelCase_     :							str             =    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__":
       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_mobilevitv2""": [
          """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """MobileViTV2Config""",
          """MobileViTV2OnnxConfig""",
      ],
  }
  
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					lowercase__      =				[
  					    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
  					    """MobileViTV2ForImageClassification""",
  					    """MobileViTV2ForSemanticSegmentation""",
  					    """MobileViTV2Model""",
  					    """MobileViTV2PreTrainedModel""",
  					]
  
  if TYPE_CHECKING:
  					from .configuration_mobilevitva import (
  					    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  					    MobileViTVaConfig,
  					    MobileViTVaOnnxConfig,
  					)
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_mobilevitva import (
  										    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    MobileViTVaForImageClassification,
  										    MobileViTVaForSemanticSegmentation,
  										    MobileViTVaModel,
  										    MobileViTVaPreTrainedModel,
  										)
  
  else:
  					import sys
  
  					lowercase__      =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  