
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,
)


lowercase  : List[str]						   =							logging.get_logger(__name__)

lowercase  : str						   =							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"""),
    ]
)

lowercase  : Tuple						   =							_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Any:
       for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
              if class_name in extractors:
                     lowercase							:					Dict	     =				model_type_to_module_name(SCREAMING_SNAKE_CASE__    )

                     lowercase							:					Tuple	     =				importlib.import_module(f".{module_name}" ,     """transformers.models"""    )
                     try:
                            return getattr(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
                     except AttributeError:
                            continue

       for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
              if getattr(SCREAMING_SNAKE_CASE__ ,     """__name__""" ,     SCREAMING_SNAKE_CASE__    ) == 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.
       lowercase							:					List[str]	     =				importlib.import_module("""transformers"""    )
       if hasattr(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
              return getattr(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       return None







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = None ,     SCREAMING_SNAKE_CASE__ = False ,     SCREAMING_SNAKE_CASE__ = False ,     SCREAMING_SNAKE_CASE__ = None ,     SCREAMING_SNAKE_CASE__ = None ,     SCREAMING_SNAKE_CASE__ = None ,     SCREAMING_SNAKE_CASE__ = False ,     **SCREAMING_SNAKE_CASE__ ,     )			->   Tuple:
       lowercase							:					Optional[Any]	     =				get_file_from_repo(
           SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     cache_dir=SCREAMING_SNAKE_CASE__ ,     force_download=SCREAMING_SNAKE_CASE__ ,     resume_download=SCREAMING_SNAKE_CASE__ ,     proxies=SCREAMING_SNAKE_CASE__ ,     use_auth_token=SCREAMING_SNAKE_CASE__ ,     revision=SCREAMING_SNAKE_CASE__ ,     local_files_only=SCREAMING_SNAKE_CASE__ ,     )
       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(SCREAMING_SNAKE_CASE__ ,     encoding="""utf-8"""    ) as reader:
              return json.load(SCREAMING_SNAKE_CASE__    )




class __snake_case		:
    def __init__(  self				):



           '''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		,snake_case		,**snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				kwargs.pop("""config"""		,__UpperCAmelCase				)
           lowercase							:					int	     =				kwargs.pop("""trust_remote_code"""		,__UpperCAmelCase				)
           lowercase							:					Dict	     =				True

           lowercase     ,      lowercase							:					int	     =				FeatureExtractionMixin.get_feature_extractor_dict(__UpperCAmelCase		,**__UpperCAmelCase				)
           lowercase							:					Any	     =				config_dict.get("""feature_extractor_type"""		,__UpperCAmelCase				)
           lowercase							:					Optional[int]	     =				None
           if "AutoFeatureExtractor" in config_dict.get("""auto_map"""		,{}				):
                  lowercase							:					Optional[int]	     =				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				):
                         lowercase							:					Tuple	     =				AutoConfig.from_pretrained(__UpperCAmelCase		,**__UpperCAmelCase				)
                  # It could be in `config.feature_extractor_type``
                  lowercase							:					Any	     =				getattr(__UpperCAmelCase		,"""feature_extractor_type"""		,__UpperCAmelCase				)
                  if hasattr(__UpperCAmelCase		,"""auto_map"""				) and "AutoFeatureExtractor" in config.auto_map:
                         lowercase							:					Optional[int]	     =				config.auto_map["""AutoFeatureExtractor"""]

           if feature_extractor_class is not None:
                  lowercase							:					int	     =				feature_extractor_class_from_name(__UpperCAmelCase				)

           lowercase							:					Optional[int]	     =				feature_extractor_auto_map is not None
           lowercase							:					Dict	     =				feature_extractor_class is not None or type(__UpperCAmelCase				) in FEATURE_EXTRACTOR_MAPPING
           lowercase							:					Dict	     =				resolve_trust_remote_code(
               __UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase				)

           if has_remote_code and trust_remote_code:
                  lowercase							:					Any	     =				get_class_from_dynamic_module(
                      __UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase				)
                  lowercase							:					List[Any]	     =				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:
                  lowercase							:					Dict	     =				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	(  snake_case		,snake_case				):



           '''simple docstring'''







           FEATURE_EXTRACTOR_MAPPING.register(__UpperCAmelCase		,__UpperCAmelCase				)




from bisect import bisect
from itertools import accumulate







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       lowercase							:					Dict	     =				sorted(zip(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     key=lambda SCREAMING_SNAKE_CASE__    : x[0] / x[1] ,     reverse=SCREAMING_SNAKE_CASE__    )
       lowercase     ,      lowercase							:					Optional[Any]	     =				[i[0] for i in r], [i[1] for i in r]
       lowercase							:					Any	     =				list(accumulate(SCREAMING_SNAKE_CASE__    )    )
       lowercase							:					int	     =				bisect(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       return (
           0
           if k == 0
           else sum(vl[:k]    ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
           if k != n
           else sum(vl[:k]    )
       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
