






"""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__    (   )->						Optional[Any]:






     '''simple docstring'''



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

     return sorted(snake_case							)


def      SCREAMING_SNAKE_CASE__    (   )->						Tuple:






     '''simple docstring'''



     UpperCAmelCase__		:  str				=				set(get_all_model_names()							)

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

     return sorted(snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[Any]							)->						Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  int				=				COMPOSITE_MODELS.values()

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

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

          UpperCAmelCase__		:  Union[str, Any]				=				sorted(content["tokenizer_classes"]							)
          UpperCAmelCase__		:  Union[str, Any]				=				sorted(content["processor_classes"]							)
          UpperCAmelCase__		:  str				=				sorted(content["model_classes"]							)

          UpperCAmelCase__		:  Union[str, Any]				=				content
          with open(os.path.join(snake_case       , "hub_tiny_model_summary.json"							)       , "w"							) as fp:
               json.dump(snake_case       , snake_case       , ensure_ascii=snake_case       , indent=4							)


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

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

 _lowerCAmelCase     :     Optional[int]            =	"""tiny_models"""
 _lowerCAmelCase     :     str            =	True
 _lowerCAmelCase     :     Optional[Any]            =	None
 _lowerCAmelCase     :     List[Any]            =	get_tiny_model_names_from_repo()
 _lowerCAmelCase     :     Union[str, Any]            =	True
 _lowerCAmelCase     :     int            =	True
 _lowerCAmelCase     :     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,
 )










"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						None:






     '''simple docstring'''



     if right <= left:
          return
     UpperCAmelCase__		:  str				=				left
     UpperCAmelCase__		:  List[str]				=				right
     UpperCAmelCase__		:  List[Any]				=				sorting[left]
     while i <= b:
          if sorting[i] < pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  List[str]				=				sorting[i], sorting[a]
               a += 1
               i += 1
          elif sorting[i] > pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  Optional[int]				=				sorting[i], sorting[b]
               b -= 1
          else:
               i += 1
     quick_sort_apartition(snake_case       , snake_case       , a - 1							)
     quick_sort_apartition(snake_case       , b + 1       , snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						None:






     '''simple docstring'''



     if left < right:
          UpperCAmelCase__		:  int				=				lomuto_partition(snake_case       , snake_case       , snake_case							)
          quick_sort_lomuto_partition(snake_case       , snake_case       , pivot_index - 1							)
          quick_sort_lomuto_partition(snake_case       , pivot_index + 1       , snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						int:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				sorting[right]
     UpperCAmelCase__		:  Optional[Any]				=				left
     for i in range(snake_case       , snake_case							):
          if sorting[i] < pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				sorting[i], sorting[store_index]
               store_index += 1
     UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				sorting[store_index], sorting[right]
     return store_index


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list							)->						list:






     '''simple docstring'''



     if len(snake_case							) <= 1:
          return sorting
     return (
         three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
         + [i for i in sorting if i == sorting[0]]
         + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
     )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

 _lowerCAmelCase     :     Optional[int]            =	input("""Enter numbers separated by a comma:\n""").strip()
 _lowerCAmelCase     :     str            =	[int(item) for item in user_input.split(""",""")]
 quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
 print(unsorted)


