


"""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 				_snake_case      (   __snake_case							:      str   ):




   """simple docstring"""







   for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
      if class_name in extractors:
         _lowerCamelCase	:					str   =		model_type_to_module_name(__snake_case   )

         _lowerCamelCase	:					str   =		importlib.import_module(F'.{module_name}'						, """transformers.models"""   )
         try:
            return getattr(__snake_case						, __snake_case   )
         except AttributeError:
            continue

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

   return None

def 				_snake_case      (   __snake_case							:      Union[str, os.PathLike]						, __snake_case							:      Optional[Union[str, os.PathLike]] = None						, __snake_case							:      bool = False						, __snake_case							:      bool = False						, __snake_case							:      Optional[Dict[str, str]] = None						, __snake_case							:      Optional[Union[bool, str]] = None						, __snake_case							:      Optional[str] = None						, __snake_case							:      bool = False						, **__snake_case							:      Tuple						, ):




   """simple docstring"""







   _lowerCamelCase	:					Optional[Any]   =		get_file_from_repo(
       __snake_case						, __snake_case						, cache_dir=__snake_case						, force_download=__snake_case						, resume_download=__snake_case						, proxies=__snake_case						, use_auth_token=__snake_case						, revision=__snake_case						, local_files_only=__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(__snake_case						, encoding="""utf-8"""   ) as reader:
      return json.load(__snake_case   )





class 	lowercase__				:





   def __init__(							self)  ->  Union[str, Any]:
      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(SCREAMING_SNAKE_CASE)
   def 		UpperCamelCase_  (							cls      ,   SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  Dict:
      _lowerCamelCase	:					List[Any]   =		kwargs.pop("""config"""      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					str   =		kwargs.pop("""trust_remote_code"""      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		True

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

      if feature_extractor_class is not None:
         _lowerCamelCase	:					Dict   =		feature_extractor_class_from_name(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		feature_extractor_auto_map is not None
      _lowerCamelCase	:					Optional[Any]   =		feature_extractor_class is not None or type(SCREAMING_SNAKE_CASE) in FEATURE_EXTRACTOR_MAPPING
      _lowerCamelCase	:					str   =		resolve_trust_remote_code(
          SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

      if has_remote_code and trust_remote_code:
         _lowerCamelCase	:					List[str]   =		get_class_from_dynamic_module(
             SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[str]   =		kwargs.pop("""code_revision"""      ,   SCREAMING_SNAKE_CASE)
         if os.path.isdir(SCREAMING_SNAKE_CASE):
            feature_extractor_class.register_for_auto_class()
         return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)
      elif feature_extractor_class is not None:
         return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)
      # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
      elif type(SCREAMING_SNAKE_CASE) in FEATURE_EXTRACTOR_MAPPING:
         _lowerCamelCase	:					Union[str, Any]   =		FEATURE_EXTRACTOR_MAPPING[type(SCREAMING_SNAKE_CASE)]
         return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE      ,   **SCREAMING_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 		UpperCamelCase_  (							SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      FEATURE_EXTRACTOR_MAPPING.register(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





"""simple docstring"""
def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int						, __snake_case							:      int						, __snake_case							:      list[int]   ):




   """simple docstring"""







   if graph[path[curr_ind - 1]][next_ver] == 0:
      return False

   # 2. Validate that next vertex is not already in path
   return not any(vertex == next_ver for vertex in path   )

def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      list[int]						, __snake_case							:      int   ):




   """simple docstring"""







   if curr_ind == len(__snake_case   ):
      # return whether path exists between current and starting vertices
      return graph[path[curr_ind - 1]][path[0]] == 1

   # Recursive Step
   for next_ver in range(0						, len(__snake_case   )   ):
      if valid_connection(__snake_case						, __snake_case						, __snake_case						, __snake_case   ):
         # Insert current vertex  into path as next transition
         _lowerCamelCase	:					List[str]   =		next_ver
         # Validate created path
         if util_hamilton_cycle(__snake_case						, __snake_case						, curr_ind + 1   ):
            return True
         # Backtrack
         _lowerCamelCase	:					Tuple   =		-1
   return False





def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int = 0   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		[-1] * (len(__snake_case   ) + 1)
   # initialize start and end of path with starting index
   _lowerCamelCase	:					Optional[int]   =		start_index
   # evaluate and if we find answer return path either return empty array
   return path if util_hamilton_cycle(__snake_case						, __snake_case						, 1   ) else []

