


'''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__	(       ):






       """simple docstring"""





       snake_case__					:	str          =					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__					:	Optional[Any]          =					getattr(transformers.models.auto    ,       UpperCAmelCase    ,       UpperCAmelCase     )
              if module is None:
                     continue
              # all mappings in a single auto modeling file
              snake_case__					:	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:
                     snake_case__					:	List[Any]          =					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__	(       ):






       """simple docstring"""





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

       with open("""tests/utils/tiny_model_summary.json"""     ) as fp:
              snake_case__					:	str          =					json.load(UpperCAmelCase     )
       snake_case__					:	str          =					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__					:	str          =					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     ):






       """simple docstring"""





       snake_case__					:	Any          =					COMPOSITE_MODELS.values()

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

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

              snake_case__					:	str          =					sorted(content["""tokenizer_classes"""]     )
              snake_case__					:	Optional[Any]          =					sorted(content["""processor_classes"""]     )
              snake_case__					:	int          =					sorted(content["""model_classes"""]     )

              snake_case__					:	Optional[int]          =					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__":
     lowerCAmelCase__  =     argparse.ArgumentParser()
     parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
     lowerCAmelCase__  =     parser.parse_args()

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

     lowerCAmelCase__  =     'tiny_models'
     lowerCAmelCase__  =     True
     lowerCAmelCase__  =     None
     lowerCAmelCase__  =     get_tiny_model_names_from_repo()
     lowerCAmelCase__  =     True
     lowerCAmelCase__  =     True
     lowerCAmelCase__  =     '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'''
import math



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Tuple          =					math.log(2    ,       10     ) * number
       snake_case__					:	int          =					round((log_number - int(UpperCAmelCase     ))    ,       15     )

       return difference



def 				lowerCAmelCase__	(       UpperCAmelCase = 67_8910     ):






       """simple docstring"""





       snake_case__					:	str          =					90
       snake_case__					:	Dict          =					0

       snake_case__					:	List[str]          =					math.log(1.2_3    ,       10     )
       snake_case__					:	Optional[Any]          =					math.log(1.2_4    ,       10     )
       snake_case__					:	Any          =					0

       while position < number:
              snake_case__					:	Tuple          =					log_difference(UpperCAmelCase     )

              if difference >= upper_limit:
                     power_iterator += 93

              elif difference < lower_limit:
                     power_iterator += 196

              else:
                     snake_case__					:	Union[str, Any]          =					power_iterator
                     power_iterator += 196
                     position += 1

       return previous_power


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(f"""{solution() = }""")




