





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 						lowerCamelCase__     (     UpperCamelCase__			:       str						)			-> Union[str, Any]:



     '''simple docstring'''




     for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
          if class_name in extractors:
               _snake_case									=				model_type_to_module_name(UpperCAmelCase__						)

               _snake_case									=				importlib.import_module(F'''.{module_name}'''       ,    'transformers.models'						)
               try:
                    return getattr(UpperCAmelCase__       ,    UpperCAmelCase__						)
               except AttributeError:
                    continue

     for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
          if getattr(UpperCAmelCase__       ,    '__name__'       ,    UpperCAmelCase__						) == 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.
     _snake_case									=				importlib.import_module('transformers'						)
     if hasattr(UpperCAmelCase__       ,    UpperCAmelCase__						):
          return getattr(UpperCAmelCase__       ,    UpperCAmelCase__						)

     return None





def 						lowerCamelCase__     (     UpperCamelCase__			:       Union[str, os.PathLike]       ,    UpperCamelCase__			:       Optional[Union[str, os.PathLike]] = None       ,    UpperCamelCase__			:       bool = False       ,    UpperCamelCase__			:       bool = False       ,    UpperCamelCase__			:       Optional[Dict[str, str]] = None       ,    UpperCamelCase__			:       Optional[Union[bool, str]] = None       ,    UpperCamelCase__			:       Optional[str] = None       ,    UpperCamelCase__			:       bool = False       ,    **UpperCamelCase__			:       List[str]       ,    )			-> str:



     '''simple docstring'''




     _snake_case									=				get_file_from_repo(
         UpperCAmelCase__       ,    UpperCAmelCase__       ,    cache_dir=UpperCAmelCase__       ,    force_download=UpperCAmelCase__       ,    resume_download=UpperCAmelCase__       ,    proxies=UpperCAmelCase__       ,    use_auth_token=UpperCAmelCase__       ,    revision=UpperCAmelCase__       ,    local_files_only=UpperCAmelCase__       ,    )
     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(UpperCAmelCase__       ,    encoding='utf-8'						) as reader:
          return json.load(UpperCAmelCase__						)




class 			UpperCamelCase_		:




     def __init__(		self  )		->							List[str]:
          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(__lowerCAmelCase  )
     def 	lowerCAmelCase			(		cls				,						lowerCAmelCase_				,						**lowerCAmelCase_  )		->							Union[str, Any]:
          _snake_case									=				kwargs.pop('config'				,						__lowerCAmelCase  )
          _snake_case									=				kwargs.pop('trust_remote_code'				,						__lowerCAmelCase  )
          _snake_case									=				True

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

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

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

          if has_remote_code and trust_remote_code:
               _snake_case									=				get_class_from_dynamic_module(
                   __lowerCAmelCase				,						__lowerCAmelCase				,						**__lowerCAmelCase  )
               _snake_case									=				kwargs.pop('code_revision'				,						__lowerCAmelCase  )
               if os.path.isdir(__lowerCAmelCase  ):
                    feature_extractor_class.register_for_auto_class()
               return feature_extractor_class.from_dict(__lowerCAmelCase				,						**__lowerCAmelCase  )
          elif feature_extractor_class is not None:
               return feature_extractor_class.from_dict(__lowerCAmelCase				,						**__lowerCAmelCase  )
          # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
          elif type(__lowerCAmelCase  ) in FEATURE_EXTRACTOR_MAPPING:
               _snake_case									=				FEATURE_EXTRACTOR_MAPPING[type(__lowerCAmelCase  )]
               return feature_extractor_class.from_dict(__lowerCAmelCase				,						**__lowerCAmelCase  )

          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 	lowerCAmelCase			(		lowerCAmelCase_				,						lowerCAmelCase_  )		->							Dict:
          FEATURE_EXTRACTOR_MAPPING.register(__lowerCAmelCase				,						__lowerCAmelCase  )










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      							=     logging.get_logger(__name__)

UpperCAmelCase_      							=     {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	'''vit_msn'''




					def __init__(		self				,						lowerCAmelCase_=768				,						lowerCAmelCase_=12				,						lowerCAmelCase_=12				,						lowerCAmelCase_=3072				,						lowerCAmelCase_="gelu"				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.02				,						lowerCAmelCase_=1E-06				,						lowerCAmelCase_=224				,						lowerCAmelCase_=16				,						lowerCAmelCase_=3				,						lowerCAmelCase_=True				,						**lowerCAmelCase_				,						)		->							str:
										super().__init__(**lowerCAmelCase_  )

										_snake_case									=				hidden_size
										_snake_case									=				num_hidden_layers
										_snake_case									=				num_attention_heads
										_snake_case									=				intermediate_size
										_snake_case									=				hidden_act
										_snake_case									=				hidden_dropout_prob
										_snake_case									=				attention_probs_dropout_prob
										_snake_case									=				initializer_range
										_snake_case									=				layer_norm_eps
										_snake_case									=				image_size
										_snake_case									=				patch_size
										_snake_case									=				num_channels
										_snake_case									=				qkv_bias



