



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

      return sorted(_SCREAMING_SNAKE_CASE					)
def 				UpperCAmelCase__		(					)->Union[str, Any]:
      # All model names defined in auto mappings
      _lowerCAmelCase			      =	set(get_all_model_names()					)

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

      return sorted(_SCREAMING_SNAKE_CASE					)
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						str					)->Tuple:
      _lowerCAmelCase			      =	COMPOSITE_MODELS.values()

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

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

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

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


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

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

    UpperCAmelCase_          =				"tiny_models"
    UpperCAmelCase_          =				True
    UpperCAmelCase_          =				None
    UpperCAmelCase_          =				get_tiny_model_names_from_repo()
    UpperCAmelCase_          =				True
    UpperCAmelCase_          =				True
    UpperCAmelCase_          =				"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

from random import randrange
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						list					)->list:
      if len(_SCREAMING_SNAKE_CASE					) < 2:
            return collection
      _lowerCAmelCase			      =	randrange(len(_SCREAMING_SNAKE_CASE					)					)  # Use random element as pivot
      _lowerCAmelCase			      =	collection[pivot_index]
      _lowerCAmelCase			      =	[]  # All elements greater than pivot
      _lowerCAmelCase			      =	[]  # All elements less than or equal to pivot

      for element in collection[:pivot_index]:
            (greater if element > pivot else lesser).append(_SCREAMING_SNAKE_CASE					)

      for element in collection[pivot_index + 1 :]:
            (greater if element > pivot else lesser).append(_SCREAMING_SNAKE_CASE					)

      return [*quick_sort(_SCREAMING_SNAKE_CASE					), pivot, *quick_sort(_SCREAMING_SNAKE_CASE					)]


if __name__ == "__main__":
    UpperCAmelCase_          =				input("Enter numbers separated by a comma:\n").strip()
    UpperCAmelCase_          =				[int(item) for item in user_input.split(",")]
    print(quick_sort(unsorted))