





'''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  _a     (				):





      """simple docstring"""



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

      return sorted(lowerCAmelCase_						)




def  _a     (				):





      """simple docstring"""



      _snake_case		:						Optional[int]         =  set(get_all_model_names()						)

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

      return sorted(lowerCAmelCase_						)




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Union[str, Any]         =  COMPOSITE_MODELS.values()

      # All tiny model base names on Hub
      _snake_case		:						Union[str, Any]         =  get_all_model_names()
      _snake_case		:						Union[str, Any]         =  hf_api.list_models(
          filter=ModelFilter(
              author='''hf-internal-testing'''      ,   )						)
      _snake_case		:						Tuple         =  set()
      for x in models:
            _snake_case		:						Tuple         =  x.modelId
            _snake_case  ,				_snake_case		:						Optional[int]         =  model.split('''/'''						)
            if not model.startswith('''tiny-random-'''						):
                  continue
            _snake_case		:						Optional[Any]         =  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(lowerCAmelCase_						)

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

            _snake_case		:						Any         =  sorted(content['''tokenizer_classes''']						)
            _snake_case		:						Union[str, Any]         =  sorted(content['''processor_classes''']						)
            _snake_case		:						List[Any]         =  sorted(content['''model_classes''']						)

            _snake_case		:						List[str]         =  content
            with open(os.path.join(lowerCAmelCase_      ,   '''hub_tiny_model_summary.json'''						)      ,   '''w'''						) as fp:
                  json.dump(lowerCAmelCase_      ,   lowerCAmelCase_      ,   ensure_ascii=lowerCAmelCase_      ,   indent=4						)


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

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

   UpperCAmelCase       :		Optional[Any]		=				'tiny_models'
   UpperCAmelCase       :		Dict		=				True
   UpperCAmelCase       :		List[str]		=				None
   UpperCAmelCase       :		List[str]		=				get_tiny_model_names_from_repo()
   UpperCAmelCase       :		Optional[int]		=				True
   UpperCAmelCase       :		Optional[int]		=				True
   UpperCAmelCase       :		Dict		=				'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,
   )









'''simple docstring'''

def  _a     (				lowerCAmelCase_ = 4_000_000						):





      """simple docstring"""



      _snake_case		:						Optional[int]         =  1
      _snake_case		:						Tuple         =  2
      _snake_case		:						Optional[Any]         =  0
      while j <= n:
            if j % 2 == 0:
                  total += j
            _snake_case  ,				_snake_case		:						str         =  j, i + j

      return total


if __name__ == "__main__":
   print(F"""{solution() = }""")


