





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       :     Any   =     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       :     List[Any]   =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)



def 		A_				(     _lowerCAmelCase     )						-> List[Any]:
 for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
  if class_name in extractors:
   UpperCamelCase	:					List[Any]       	=   model_type_to_module_name(_lowerCAmelCase     )

   UpperCamelCase	:					Union[str, Any]       	=   importlib.import_module(F""".{module_name}"""							,   "transformers.models"     )
   try:
    return getattr(_lowerCAmelCase							,   _lowerCAmelCase     )
   except AttributeError:
    continue

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

 return None



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



class 					A__   :





      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(A_   )
      def        __UpperCamelCase( cls       ,							A_       ,							**A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   kwargs.pop("config"       ,							A_   )
       UpperCamelCase	:					Tuple       	=   kwargs.pop("trust_remote_code"       ,							A_   )
       UpperCamelCase	:					Dict       	=   True

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

       if feature_extractor_class is not None:
        UpperCamelCase	:					Any       	=   feature_extractor_class_from_name(A_   )

       UpperCamelCase	:					Optional[int]       	=   feature_extractor_auto_map is not None
       UpperCamelCase	:					Optional[int]       	=   feature_extractor_class is not None or type(A_   ) in FEATURE_EXTRACTOR_MAPPING
       UpperCamelCase	:					int       	=   resolve_trust_remote_code(
           A_       ,							A_       ,							A_       ,							A_   )

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

       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( A_       ,							A_   ):
       '''simple docstring'''




       FEATURE_EXTRACTOR_MAPPING.register(A_       ,							A_   )







import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline



@pytest.mark.integration
def 		A_				(     )						-> List[Any]:
 with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT     ):
  with pytest.raises(_lowerCAmelCase     ):
   requests.request("GET"							,   "https://huggingface.co"     )
  with pytest.raises(requests.exceptions.ConnectTimeout     ):
   requests.request("GET"							,   "https://huggingface.co"							,   timeout=1.0     )



@pytest.mark.integration
def 		A_				(     )						-> Tuple:
 with offline(OfflineSimulationMode.CONNECTION_FAILS     ):
  with pytest.raises(requests.exceptions.ConnectionError     ):
   requests.request("GET"							,   "https://huggingface.co"     )



def 		A_				(     )						-> Optional[int]:
 with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1     ):
  with pytest.raises(_lowerCAmelCase     ):
   http_head("https://huggingface.co"     )
