


'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


UpperCamelCase__          = logging.get_logger(__name__)

UpperCamelCase__          = OrderedDict(
    [
        ('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
        ('''beit''', '''BeitFeatureExtractor'''),
        ('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
        ('''clap''', '''ClapFeatureExtractor'''),
        ('''clip''', '''CLIPFeatureExtractor'''),
        ('''clipseg''', '''ViTFeatureExtractor'''),
        ('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
        ('''convnext''', '''ConvNextFeatureExtractor'''),
        ('''cvt''', '''ConvNextFeatureExtractor'''),
        ('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
        ('''data2vec-vision''', '''BeitFeatureExtractor'''),
        ('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
        ('''deit''', '''DeiTFeatureExtractor'''),
        ('''detr''', '''DetrFeatureExtractor'''),
        ('''dinat''', '''ViTFeatureExtractor'''),
        ('''donut-swin''', '''DonutFeatureExtractor'''),
        ('''dpt''', '''DPTFeatureExtractor'''),
        ('''encodec''', '''EncodecFeatureExtractor'''),
        ('''flava''', '''FlavaFeatureExtractor'''),
        ('''glpn''', '''GLPNFeatureExtractor'''),
        ('''groupvit''', '''CLIPFeatureExtractor'''),
        ('''hubert''', '''Wav2Vec2FeatureExtractor'''),
        ('''imagegpt''', '''ImageGPTFeatureExtractor'''),
        ('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
        ('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
        ('''levit''', '''LevitFeatureExtractor'''),
        ('''maskformer''', '''MaskFormerFeatureExtractor'''),
        ('''mctct''', '''MCTCTFeatureExtractor'''),
        ('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
        ('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
        ('''mobilevit''', '''MobileViTFeatureExtractor'''),
        ('''nat''', '''ViTFeatureExtractor'''),
        ('''owlvit''', '''OwlViTFeatureExtractor'''),
        ('''perceiver''', '''PerceiverFeatureExtractor'''),
        ('''poolformer''', '''PoolFormerFeatureExtractor'''),
        ('''regnet''', '''ConvNextFeatureExtractor'''),
        ('''resnet''', '''ConvNextFeatureExtractor'''),
        ('''segformer''', '''SegformerFeatureExtractor'''),
        ('''sew''', '''Wav2Vec2FeatureExtractor'''),
        ('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
        ('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
        ('''speecht5''', '''SpeechT5FeatureExtractor'''),
        ('''swiftformer''', '''ViTFeatureExtractor'''),
        ('''swin''', '''ViTFeatureExtractor'''),
        ('''swinv2''', '''ViTFeatureExtractor'''),
        ('''table-transformer''', '''DetrFeatureExtractor'''),
        ('''timesformer''', '''VideoMAEFeatureExtractor'''),
        ('''tvlt''', '''TvltFeatureExtractor'''),
        ('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
        ('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
        ('''van''', '''ConvNextFeatureExtractor'''),
        ('''videomae''', '''VideoMAEFeatureExtractor'''),
        ('''vilt''', '''ViltFeatureExtractor'''),
        ('''vit''', '''ViTFeatureExtractor'''),
        ('''vit_mae''', '''ViTFeatureExtractor'''),
        ('''vit_msn''', '''ViTFeatureExtractor'''),
        ('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
        ('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
        ('''whisper''', '''WhisperFeatureExtractor'''),
        ('''xclip''', '''CLIPFeatureExtractor'''),
        ('''yolos''', '''YolosFeatureExtractor'''),
    ]
)

UpperCamelCase__          = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





def    a__			(   lowerCAmelCase__		)       ->      str:


    for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
        if class_name in extractors:
            UpperCAmelCase__     :						Dict						=							model_type_to_module_name(_UpperCAmelCase		)

            UpperCAmelCase__     :						Optional[int]						=							importlib.import_module(F""".{module_name}"""    ,   '''transformers.models'''		)
            try:
                return getattr(_UpperCAmelCase    ,   _UpperCAmelCase		)
            except AttributeError:
                continue

    for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
        if getattr(_UpperCAmelCase    ,   '''__name__'''    ,   _UpperCAmelCase		) == class_name:
            return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
    UpperCAmelCase__     :						Optional[Any]						=							importlib.import_module('''transformers'''		)
    if hasattr(_UpperCAmelCase    ,   _UpperCAmelCase		):
        return getattr(_UpperCAmelCase    ,   _UpperCAmelCase		)

    return None





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__ = None    ,   lowerCAmelCase__ = False    ,   lowerCAmelCase__ = False    ,   lowerCAmelCase__ = None    ,   lowerCAmelCase__ = None    ,   lowerCAmelCase__ = None    ,   lowerCAmelCase__ = False    ,   **lowerCAmelCase__    ,   )       ->      List[Any]:
    UpperCAmelCase__     :						int						=							get_file_from_repo(
        _UpperCAmelCase    ,   _UpperCAmelCase    ,   cache_dir=_UpperCAmelCase    ,   force_download=_UpperCAmelCase    ,   resume_download=_UpperCAmelCase    ,   proxies=_UpperCAmelCase    ,   use_auth_token=_UpperCAmelCase    ,   revision=_UpperCAmelCase    ,   local_files_only=_UpperCAmelCase    ,   )
    if resolved_config_file is None:
        logger.info(
            '''Could not locate the feature extractor configuration file, will try to use the model config instead.'''		)
        return {}

    with open(_UpperCAmelCase    ,   encoding='''utf-8'''		) as reader:
        return json.load(_UpperCAmelCase		)







class      lowerCamelCase_  :






      def __init__(   self							:       Dict					):


          '''simple docstring'''


          raise EnvironmentError(
              '''AutoFeatureExtractor is designed to be instantiated '''
              '''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'''					)






      @classmethod
      @replace_list_option_in_docstrings(__snake_case					)
      def lowercase_				(   cls							:       Union[str, Any]		,		_A							:       Union[str, Any]		,		**_A							:       List[str]					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							kwargs.pop('''config'''		,		__snake_case					)
          UpperCAmelCase__     :						Optional[Any]						=							kwargs.pop('''trust_remote_code'''		,		__snake_case					)
          UpperCAmelCase__     :						Optional[int]						=							True

          UpperCAmelCase__		,		UpperCAmelCase__     :						Optional[int]						=							FeatureExtractionMixin.get_feature_extractor_dict(__snake_case		,		**__snake_case					)
          UpperCAmelCase__     :						Optional[int]						=							config_dict.get('''feature_extractor_type'''		,		__snake_case					)
          UpperCAmelCase__     :						Any						=							None
          if "AutoFeatureExtractor" in config_dict.get('''auto_map'''		,		{}					):
              UpperCAmelCase__     :						str						=							config_dict['''auto_map''']['''AutoFeatureExtractor''']

          # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
          if feature_extractor_class is None and feature_extractor_auto_map is None:
              if not isinstance(__snake_case		,		__snake_case					):
                  UpperCAmelCase__     :						Optional[Any]						=							AutoConfig.from_pretrained(__snake_case		,		**__snake_case					)
              # It could be in `config.feature_extractor_type``
              UpperCAmelCase__     :						List[str]						=							getattr(__snake_case		,		'''feature_extractor_type'''		,		__snake_case					)
              if hasattr(__snake_case		,		'''auto_map'''					) and "AutoFeatureExtractor" in config.auto_map:
                  UpperCAmelCase__     :						List[str]						=							config.auto_map['''AutoFeatureExtractor''']

          if feature_extractor_class is not None:
              UpperCAmelCase__     :						Optional[int]						=							feature_extractor_class_from_name(__snake_case					)

          UpperCAmelCase__     :						Optional[int]						=							feature_extractor_auto_map is not None
          UpperCAmelCase__     :						Optional[int]						=							feature_extractor_class is not None or type(__snake_case					) in FEATURE_EXTRACTOR_MAPPING
          UpperCAmelCase__     :						Tuple						=							resolve_trust_remote_code(
              __snake_case		,		__snake_case		,		__snake_case		,		__snake_case					)

          if has_remote_code and trust_remote_code:
              UpperCAmelCase__     :						List[Any]						=							get_class_from_dynamic_module(
                  __snake_case		,		__snake_case		,		**__snake_case					)
              UpperCAmelCase__     :						Union[str, Any]						=							kwargs.pop('''code_revision'''		,		__snake_case					)
              if os.path.isdir(__snake_case					):
                  feature_extractor_class.register_for_auto_class()
              return feature_extractor_class.from_dict(__snake_case		,		**__snake_case					)
          elif feature_extractor_class is not None:
              return feature_extractor_class.from_dict(__snake_case		,		**__snake_case					)
          # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
          elif type(__snake_case					) in FEATURE_EXTRACTOR_MAPPING:
              UpperCAmelCase__     :						str						=							FEATURE_EXTRACTOR_MAPPING[type(__snake_case					)]
              return feature_extractor_class.from_dict(__snake_case		,		**__snake_case					)

          raise ValueError(
              f"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
              f"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
              f"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()					)}"""					)






      @staticmethod
      def lowercase_				(   _A							:       Dict		,		_A							:       Any					):


          '''simple docstring'''


          FEATURE_EXTRACTOR_MAPPING.register(__snake_case		,		__snake_case					)







def      UpperCAmelCase_    (    _UpperCAmelCase							:list				)	->				list:
     '''simple docstring'''


     if len(_UpperCAmelCase				) < 2:
          return collection

     def circle_sort_util(_UpperCAmelCase							:list  ,						_UpperCAmelCase							:int  ,						_UpperCAmelCase							:int				) -> bool:
          A_          =       False

          if low == high:
               return swapped

          A_          =       low
          A_          =       high

          while left < right:
               if collection[left] > collection[right]:
                    A_   ,    A_          =       (
                        collection[right],
                        collection[left],
                    )
                    A_          =       True

               left += 1
               right -= 1

          if left == right and collection[left] > collection[right + 1]:
               A_   ,    A_          =       (
                   collection[right + 1],
                   collection[left],
               )

               A_          =       True

          A_          =       low + int((high - low) / 2				)
          A_          =       circle_sort_util(_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)
          A_          =       circle_sort_util(_UpperCAmelCase  ,						mid + 1  ,						_UpperCAmelCase				)

          return swapped or left_swap or right_swap

     A_          =       True

     while is_not_sorted is True:
          A_          =       circle_sort_util(_UpperCAmelCase  ,						0  ,						len(_UpperCAmelCase				) - 1				)

     return collection


if __name__ == "__main__":
      a__      :     Optional[Any]         =		input('Enter numbers separated by a comma:\n').strip()
      a__      :     List[str]         =		[int(item) for item in user_input.split(',')]
      print(circle_sort(unsorted))



