






import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowerCamelCase      :       str    =						[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]





def    __lowerCAmelCase   (    __snake_case							):
     # Test all the extensions added in the setup
     for file in FILES_TO_FIND:
          if not (transformers_path / file).exists():
               return False
     return True


if __name__ == "__main__":
 lowerCamelCase      :       str    =						argparse.ArgumentParser()
 parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
 lowerCamelCase      :       Dict    =						parser.parse_args()
 if args.check_lib:
  lowerCamelCase      :       Optional[int]    =						importlib.import_module('''transformers''')
  lowerCamelCase      :       str    =						Path(transformers_module.__file__).parent
 else:
  lowerCamelCase      :       List[Any]    =						Path.cwd() / '''build/lib/transformers'''
 if not test_custom_files_are_present(transformers_path):
  raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')














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      :       Optional[Any]    =						logging.get_logger(__name__)

lowerCamelCase      :       Any    =						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[int]    =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





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

               __lowerCAmelCase             =       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             =       importlib.import_module("transformers"							)
     if hasattr(__snake_case       ,			__snake_case							):
          return getattr(__snake_case       ,			__snake_case							)

     return None





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case = None       ,			__snake_case = False       ,			__snake_case = False       ,			__snake_case = None       ,			__snake_case = None       ,			__snake_case = None       ,			__snake_case = False       ,			**__snake_case       ,			):
     __lowerCAmelCase             =       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 				_UpperCamelCase      :


      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(__UpperCamelCase						)
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       kwargs.pop("config"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       kwargs.pop("trust_remote_code"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       True

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

           if feature_extractor_class is not None:
                __lowerCAmelCase             =       feature_extractor_class_from_name(__UpperCamelCase						)

           __lowerCAmelCase             =       feature_extractor_auto_map is not None
           __lowerCAmelCase             =       feature_extractor_class is not None or type(__UpperCamelCase						) in FEATURE_EXTRACTOR_MAPPING
           __lowerCAmelCase             =       resolve_trust_remote_code(
               __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)

           if has_remote_code and trust_remote_code:
                __lowerCAmelCase             =       get_class_from_dynamic_module(
                    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						)
                __lowerCAmelCase             =       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:
                __lowerCAmelCase             =       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 			__UpperCAmelCase		(						__UpperCamelCase       ,    __UpperCamelCase						)->						Optional[int]:
           FEATURE_EXTRACTOR_MAPPING.register(__UpperCamelCase       ,    __UpperCamelCase						)






