

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    snake_case_()							->			int:



  """simple docstring"""






  _snake_case		=							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		=							getattr(transformers.models.auto				, lowercase__				, lowercase__  )
    if module is None:
      continue
    # all mappings in a single auto modeling file
    _snake_case		=							[
        x
        for x in dir(lowercase__  )
        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		=							getattr(lowercase__				, lowercase__  )
      if mapping is not None:
        for v in mapping.values():
          if isinstance(lowercase__				, (list, tuple)  ):
            model_names.update(lowercase__  )
          elif isinstance(lowercase__				, lowercase__  ):
            model_names.add(lowercase__  )

  return sorted(lowercase__  )



def    snake_case_()							->			int:



  """simple docstring"""






  _snake_case		=							set(get_all_model_names()  )

  with open('''tests/utils/tiny_model_summary.json'''  ) as fp:
    _snake_case		=							json.load(lowercase__  )
  _snake_case		=							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		=							model_names.difference(lowercase__  )
  for model_name in copy.copy(lowercase__  ):
    if not model_name.startswith('''TF'''  ) and F"""TF{model_name}""" in not_on_hub:
      tiny_models_names.remove(lowercase__  )
    elif model_name.startswith('''TF'''  ) and model_name[2:] in not_on_hub:
      tiny_models_names.remove(lowercase__  )

  return sorted(lowercase__  )



def    snake_case_(_UpperCamelCase  )							->			Optional[Any]:



  """simple docstring"""






  _snake_case		=							COMPOSITE_MODELS.values()

  # All tiny model base names on Hub
  _snake_case		=							get_all_model_names()
  _snake_case		=							hf_api.list_models(
      filter=ModelFilter(
          author='''hf-internal-testing'''				, )  )
  _snake_case		=							set()
  for x in models:
    _snake_case		=							x.modelId
    _snake_case,  _snake_case		=							model.split('''/'''  )
    if not model.startswith('''tiny-random-'''  ):
      continue
    _snake_case		=							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(lowercase__  )

  _snake_case		=							sorted(_models  )
  # All tiny model names on Hub
  _snake_case		=							{}
  for model in models:
    _snake_case		=							F"""hf-internal-testing/tiny-random-{model}"""
    _snake_case		=							model.split('''-'''  )[0]
    try:
      _snake_case		=							hf_api.repo_info(lowercase__  )
      _snake_case		=							{
          '''tokenizer_classes''': set(),
          '''processor_classes''': set(),
          '''model_classes''': set(),
          '''sha''': repo_info.sha,
      }
    except Exception:
      continue
    try:
      time.sleep(1  )
      _snake_case		=							AutoTokenizer.from_pretrained(lowercase__  )
      content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__  )
    except Exception:
      pass
    try:
      time.sleep(1  )
      _snake_case		=							AutoTokenizer.from_pretrained(lowercase__				, use_fast=lowercase__  )
      content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__  )
    except Exception:
      pass
    try:
      time.sleep(1  )
      _snake_case		=							AutoImageProcessor.from_pretrained(lowercase__  )
      content["processor_classes"].add(img_p.__class__.__name__  )
    except Exception:
      pass
    try:
      time.sleep(1  )
      _snake_case		=							AutoFeatureExtractor.from_pretrained(lowercase__  )
      if not isinstance(lowercase__				, lowercase__  ):
        content["processor_classes"].add(feat_p.__class__.__name__  )
    except Exception:
      pass
    try:
      time.sleep(1  )
      _snake_case		=							getattr(lowercase__				, lowercase__  )
      _snake_case		=							model_class.from_pretrained(lowercase__  )
      content["model_classes"].add(m.__class__.__name__  )
    except Exception:
      pass
    try:
      time.sleep(1  )
      _snake_case		=							getattr(lowercase__				, F"""TF{model}"""  )
      _snake_case		=							model_class.from_pretrained(lowercase__  )
      content["model_classes"].add(m.__class__.__name__  )
    except Exception:
      pass

    _snake_case		=							sorted(content['''tokenizer_classes''']  )
    _snake_case		=							sorted(content['''processor_classes''']  )
    _snake_case		=							sorted(content['''model_classes''']  )

    _snake_case		=							content
    with open(os.path.join(lowercase__				, '''hub_tiny_model_summary.json'''  )				, '''w'''  ) as fp:
      json.dump(lowercase__				, lowercase__				, ensure_ascii=lowercase__				, indent=4  )


if __name__ == "__main__":
 __A    	=			argparse.ArgumentParser()
 parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
 __A    	=			parser.parse_args()

 # This has to be `spawn` to avoid hanging forever!
 multiprocessing.set_start_method('''spawn''')

 __A    	=			'tiny_models'
 __A    	=			True
 __A    	=			None
 __A    	=			get_tiny_model_names_from_repo()
 __A    	=			True
 __A    	=			True
 __A    	=			'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,
 )




from __future__ import annotations



def    snake_case_(_UpperCamelCase  )							->			int | float:



  """simple docstring"""






  if len(_UpperCamelCase  ) == 0:
    raise ValueError('''find_max() arg is an empty sequence'''  )
  _snake_case		=							nums[0]
  for x in nums:
    if x > max_num:
      _snake_case		=							x
  return max_num


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

