




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,
)


_lowerCamelCase     :		str								=       logging.get_logger(__name__)

_lowerCamelCase     :		Tuple								=       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"""),
    ]
)

_lowerCamelCase     :		Optional[Any]								=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Any:



       """simple docstring"""

       for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
              if class_name in extractors:
                     A__							=   model_type_to_module_name(lowercase_						)

                     A__							=   importlib.import_module(f""".{module_name}"""			,    '''transformers.models'''						)
                     try:
                            return getattr(lowercase_			,    lowercase_						)
                     except AttributeError:
                            continue

       for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
              if getattr(lowercase_			,    '''__name__'''			,    lowercase_						) == 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.
       A__							=   importlib.import_module('''transformers'''						)
       if hasattr(lowercase_			,    lowercase_						):
              return getattr(lowercase_			,    lowercase_						)

       return None







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_ = None			,    lowercase_ = False			,    lowercase_ = False			,    lowercase_ = None			,    lowercase_ = None			,    lowercase_ = None			,    lowercase_ = False			,    **lowercase_			,    )       ->	str:



       """simple docstring"""

       A__							=   get_file_from_repo(
           lowercase_			,    lowercase_			,    cache_dir=lowercase_			,    force_download=lowercase_			,    resume_download=lowercase_			,    proxies=lowercase_			,    use_auth_token=lowercase_			,    revision=lowercase_			,    local_files_only=lowercase_			,    )
       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(lowercase_			,    encoding='''utf-8'''						) as reader:
              return json.load(lowercase_						)


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Optional[Any])						->List[str]:





           '''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(UpperCAmelCase__)
    def 	SCREAMING_SNAKE_CASE							(      cls				:     str ,			UpperCAmelCase__				:     int ,			**UpperCAmelCase__				:     List[str])						->Union[str, Any]:





           '''simple docstring'''

           A__							=   kwargs.pop('''config''' ,			UpperCAmelCase__)
           A__							=   kwargs.pop('''trust_remote_code''' ,			UpperCAmelCase__)
           A__							=   True

           A__			,							A__							=   FeatureExtractionMixin.get_feature_extractor_dict(UpperCAmelCase__ ,			**UpperCAmelCase__)
           A__							=   config_dict.get('''feature_extractor_type''' ,			UpperCAmelCase__)
           A__							=   None
           if "AutoFeatureExtractor" in config_dict.get('''auto_map''' ,			{}):
                  A__							=   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(UpperCAmelCase__ ,			UpperCAmelCase__):
                         A__							=   AutoConfig.from_pretrained(UpperCAmelCase__ ,			**UpperCAmelCase__)
                  # It could be in `config.feature_extractor_type``
                  A__							=   getattr(UpperCAmelCase__ ,			'''feature_extractor_type''' ,			UpperCAmelCase__)
                  if hasattr(UpperCAmelCase__ ,			'''auto_map''') and "AutoFeatureExtractor" in config.auto_map:
                         A__							=   config.auto_map['''AutoFeatureExtractor''']

           if feature_extractor_class is not None:
                  A__							=   feature_extractor_class_from_name(UpperCAmelCase__)

           A__							=   feature_extractor_auto_map is not None
           A__							=   feature_extractor_class is not None or type(UpperCAmelCase__) in FEATURE_EXTRACTOR_MAPPING
           A__							=   resolve_trust_remote_code(
               UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)

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

           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 	SCREAMING_SNAKE_CASE							(      UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Any)						->List[str]:





           '''simple docstring'''

           FEATURE_EXTRACTOR_MAPPING.register(UpperCAmelCase__ ,			UpperCAmelCase__)








from __future__ import annotations

from typing import Any







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       create_state_space_tree(lowercase_			,    []			,    0						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       if index == len(lowercase_						):
              print(lowercase_						)
              return

       create_state_space_tree(lowercase_			,    lowercase_			,    index + 1						)
       current_subsequence.append(sequence[index]						)
       create_state_space_tree(lowercase_			,    lowercase_			,    index + 1						)
       current_subsequence.pop()


if __name__ == "__main__":
  _lowerCamelCase     :		list[Any]								=       [3, 1, 2, 4]
  generate_all_subsequences(seq)

  seq.clear()
  seq.extend(["""A""", """B""", """C"""])
  generate_all_subsequences(seq)


