





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      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  List[Any]:






   """simple docstring"""







   for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
      if class_name in extractors:
         _UpperCAmelCase           =   model_type_to_module_name(SCREAMING_SNAKE_CASE_						)

         _UpperCAmelCase           =   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.
   _UpperCAmelCase           =   importlib.import_module('''transformers'''						)
   if hasattr(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						):
      return getattr(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)

   return None
def      A__       (    SCREAMING_SNAKE_CASE_   :      Union[str, os.PathLike]				,	SCREAMING_SNAKE_CASE_   :      Optional[Union[str, os.PathLike]] = None				,	SCREAMING_SNAKE_CASE_   :      bool = False				,	SCREAMING_SNAKE_CASE_   :      bool = False				,	SCREAMING_SNAKE_CASE_   :      Optional[Dict[str, str]] = None				,	SCREAMING_SNAKE_CASE_   :      Optional[Union[bool, str]] = None				,	SCREAMING_SNAKE_CASE_   :      Optional[str] = None				,	SCREAMING_SNAKE_CASE_   :      bool = False				,	**SCREAMING_SNAKE_CASE_   :      Optional[int]				,	)  ->  Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase           =   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 			__UpperCamelCase    :





     def __init__(     self					):
        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					):
        _UpperCAmelCase           =   kwargs.pop('''config'''				,							_UpperCamelCase					)
        _UpperCAmelCase           =   kwargs.pop('''trust_remote_code'''				,							_UpperCamelCase					)
        _UpperCAmelCase           =   True

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

        if feature_extractor_class is not None:
           _UpperCAmelCase           =   feature_extractor_class_from_name(_UpperCamelCase					)

        _UpperCAmelCase           =   feature_extractor_auto_map is not None
        _UpperCAmelCase           =   feature_extractor_class is not None or type(_UpperCamelCase					) in FEATURE_EXTRACTOR_MAPPING
        _UpperCAmelCase           =   resolve_trust_remote_code(
            _UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					)

        if has_remote_code and trust_remote_code:
           _UpperCAmelCase           =   get_class_from_dynamic_module(
               _UpperCamelCase				,							_UpperCamelCase				,							**_UpperCamelCase					)
           _UpperCAmelCase           =   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:
           _UpperCAmelCase           =   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					):
        FEATURE_EXTRACTOR_MAPPING.register(_UpperCamelCase				,							_UpperCamelCase					)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase_           =	{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_           =	[
       "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "XLMForMultipleChoice",
       "XLMForQuestionAnswering",
       "XLMForQuestionAnsweringSimple",
       "XLMForSequenceClassification",
       "XLMForTokenClassification",
       "XLMModel",
       "XLMPreTrainedModel",
       "XLMWithLMHeadModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_           =	[
       "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFXLMForMultipleChoice",
       "TFXLMForQuestionAnsweringSimple",
       "TFXLMForSequenceClassification",
       "TFXLMForTokenClassification",
       "TFXLMMainLayer",
       "TFXLMModel",
       "TFXLMPreTrainedModel",
       "TFXLMWithLMHeadModel",
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

   UpperCAmelCase_           =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)