






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


__UpperCamelCase    :						List[str]		     =  logging.get_logger(__name__)

__UpperCamelCase    :						Optional[Any]		     =  {
    """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      __SCREAMING_SNAKE_CASE(		_UpperCAmelCase		):
							_UpperCAmelCase       			=				'vit_msn'
							def __init__(   self:					Union[str, Any]				,		UpperCamelCase:					Optional[Any]=7_68				,		UpperCamelCase:					Optional[Any]=12				,		UpperCamelCase:					Optional[int]=12				,		UpperCamelCase:					Optional[int]=30_72				,		UpperCamelCase:					Union[str, Any]="gelu"				,		UpperCamelCase:					str=0.0				,		UpperCamelCase:					int=0.0				,		UpperCamelCase:					Optional[Any]=0.02				,		UpperCamelCase:					List[Any]=1e-06				,		UpperCamelCase:					Optional[int]=2_24				,		UpperCamelCase:					str=16				,		UpperCamelCase:					Optional[Any]=3				,		UpperCamelCase:					int=True				,		**UpperCamelCase:					List[Any]				,		)						->						int:
											super().__init__(**a__  )

											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











import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[str]							=    ['image_processor', 'tokenizer']
      A_						:				Optional[Any]							=    'CLIPImageProcessor'
      A_						:				Any							=    ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')


      def __init__(self       :      int				,			a__       :      int=None				,			a__       :      Dict=None				,			**a__       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''				,			a__				,			)
            __snake_case							       =   kwargs.pop('''feature_extractor'''      )

         __snake_case							       =   image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''      )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''      )

         super().__init__(a__				,			a__      )


      def __call__(self       :      Any				,			a__       :      Dict=None				,			a__       :      List[str]=None				,			a__       :      Dict=None				,			**a__       :      Tuple      ):





         """simple docstring"""



         if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''      )

         if text is not None:
            __snake_case							       =   self.tokenizer(a__				,			return_tensors=a__				,			**a__      )

         if images is not None:
            __snake_case							       =   self.image_processor(a__				,			return_tensors=a__				,			**a__      )

         if text is not None and images is not None:
            __snake_case							       =   image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**a__      )				,			tensor_type=a__      )


      def    a (self       :      Union[str, Any]				,			*a__       :      int				,			**a__       :      List[Any]      ):





         """simple docstring"""



         return self.tokenizer.batch_decode(*a__				,			**a__      )


      def    a (self       :      Any				,			*a__       :      List[Any]				,			**a__       :      List[str]      ):





         """simple docstring"""



         return self.tokenizer.decode(*a__				,			**a__      )


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer.model_input_names
         __snake_case							       =   self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

