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        SCREAMING_SNAKE_CASE__(		)		->					List[Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						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__ 						=						getattr(transformers.models.auto    ,		_UpperCamelCase    ,		_UpperCamelCase   )
        if module is None:
           continue
        # all mappings in a single auto modeling file
        UpperCamelCase__ 						=						[
            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__ 						=						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        SCREAMING_SNAKE_CASE__(		)		->					List[Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						set(get_all_model_names()   )
  
     with open("tests/utils/tiny_model_summary.json"   ) as fp:
        UpperCamelCase__ 						=						json.load(_UpperCamelCase   )
     UpperCamelCase__ 						=						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__ 						=						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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]   )		->					Tuple:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						COMPOSITE_MODELS.values()
  
     # All tiny model base names on Hub
     UpperCamelCase__ 						=						get_all_model_names()
     UpperCamelCase__ 						=						hf_api.list_models(
         filter=ModelFilter(
             author="hf-internal-testing"    ,		)   )
     UpperCamelCase__ 						=						set()
     for x in models:
        UpperCamelCase__ 						=						x.modelId
        UpperCamelCase__						,		UpperCamelCase__ 						=						model.split("/"   )
        if not model.startswith("tiny-random-"   ):
           continue
        UpperCamelCase__ 						=						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__ 						=						sorted(_models   )
     # All tiny model names on Hub
     UpperCamelCase__ 						=						{}
     for model in models:
        UpperCamelCase__ 						=						F'hf-internal-testing/tiny-random-{model}'
        UpperCamelCase__ 						=						model.split("-"   )[0]
        try:
           UpperCamelCase__ 						=						hf_api.repo_info(_UpperCamelCase   )
           UpperCamelCase__ 						=						{
               "tokenizer_classes": set(),
               "processor_classes": set(),
               "model_classes": set(),
               "sha": repo_info.sha,
           }
        except Exception:
           continue
        try:
           time.sleep(1   )
           UpperCamelCase__ 						=						AutoTokenizer.from_pretrained(_UpperCamelCase   )
           content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__   )
        except Exception:
           pass
        try:
           time.sleep(1   )
           UpperCamelCase__ 						=						AutoTokenizer.from_pretrained(_UpperCamelCase    ,		use_fast=_UpperCamelCase   )
           content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__   )
        except Exception:
           pass
        try:
           time.sleep(1   )
           UpperCamelCase__ 						=						AutoImageProcessor.from_pretrained(_UpperCamelCase   )
           content["processor_classes"].add(img_p.__class__.__name__   )
        except Exception:
           pass
        try:
           time.sleep(1   )
           UpperCamelCase__ 						=						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__ 						=						getattr(_UpperCamelCase    ,		_UpperCamelCase   )
           UpperCamelCase__ 						=						model_class.from_pretrained(_UpperCamelCase   )
           content["model_classes"].add(m.__class__.__name__   )
        except Exception:
           pass
        try:
           time.sleep(1   )
           UpperCamelCase__ 						=						getattr(_UpperCamelCase    ,		F'TF{model}'   )
           UpperCamelCase__ 						=						model_class.from_pretrained(_UpperCamelCase   )
           content["model_classes"].add(m.__class__.__name__   )
        except Exception:
           pass
  
        UpperCamelCase__ 						=						sorted(content["tokenizer_classes"]   )
        UpperCamelCase__ 						=						sorted(content["processor_classes"]   )
        UpperCamelCase__ 						=						sorted(content["model_classes"]   )
  
        UpperCamelCase__ 						=						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:		str							=     argparse.ArgumentParser()
         parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
         __lowercase:		Tuple							=     parser.parse_args()
  
         # This has to be `spawn` to avoid hanging forever!
         multiprocessing.set_start_method("spawn")
  
         __lowercase:		List[str]							=     "tiny_models"
         __lowercase:		Union[str, Any]							=     True
         __lowercase:		str							=     None
         __lowercase:		Union[str, Any]							=     get_tiny_model_names_from_repo()
         __lowercase:		Any							=     True
         __lowercase:		Tuple							=     True
         __lowercase:		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'''
  
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_vision
  from transformers.utils import is_vision_available
  
  
  if is_vision_available():
         from PIL import Image
  
         from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
  
  
  
  
  
  
  
  @require_vision
  class  UpperCAmelCase (       unittest.TestCase):
  
   def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						tempfile.mkdtemp()
  
      UpperCamelCase__ 						=						CLIPImageProcessor()
      UpperCamelCase__ 						=						BertTokenizer.from_pretrained(
          "hf-internal-testing/tiny-random-BertModel",					model_input_names=["input_ids", "attention_mask"]					)
  
      UpperCamelCase__ 						=						GitProcessor(a_,					a_					)
  
      processor.save_pretrained(self.tmpdirname					)
  
   def 		lowercase_						( self :	Union[str, Any],					**a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).tokenizer
  
   def 		lowercase_						( self :	Union[str, Any],					**a_ :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).image_processor
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      shutil.rmtree(self.tmpdirname					)
  
   def 		lowercase_						( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta					)]
  
      UpperCamelCase__ 						=						[Image.fromarray(np.moveaxis(a_,					0,					-1					)					) for x in image_inputs]
  
      return image_inputs
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=self.get_tokenizer(),					image_processor=self.get_image_processor()					)
      processor.save_pretrained(self.tmpdirname					)
  
      UpperCamelCase__ 						=						self.get_tokenizer(bos_token="(BOS)",					eos_token="(EOS)"					)
      UpperCamelCase__ 						=						self.get_image_processor(do_normalize=a_,					padding_value=1.0					)
  
      UpperCamelCase__ 						=						GitProcessor.from_pretrained(
          self.tmpdirname,					bos_token="(BOS)",					eos_token="(EOS)",					do_normalize=a_,					padding_value=1.0					)
  
      self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()					)
      self.assertIsInstance(processor.tokenizer,					a_					)
  
      self.assertEqual(processor.image_processor.to_json_string(),					image_processor_add_kwargs.to_json_string()					)
      self.assertIsInstance(processor.image_processor,					a_					)
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						image_processor(a_,					return_tensors="np"					)
      UpperCamelCase__ 						=						processor(images=a_,					return_tensors="np"					)
  
      for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
  
      UpperCamelCase__ 						=						processor(text=a_					)
  
      UpperCamelCase__ 						=						tokenizer(a_,					return_token_type_ids=a_					)
  
      for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key],					encoded_processor[key]					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						processor(text=a_,					images=a_					)
  
      self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)
  
      # test if it raises when no input is passed
      with pytest.raises(a_					):
         processor()
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
      UpperCamelCase__ 						=						processor.batch_decode(a_					)
      UpperCamelCase__ 						=						tokenizer.batch_decode(a_					)
  
      self.assertListEqual(a_,					a_					)
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						processor(text=a_,					images=a_					)
  
      # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
      self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)