


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


lowerCamelCase											=       logging.get_logger(__name__)

lowerCamelCase											=       {
    "uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}




class A  (   lowerCAmelCase__     ):
      UpperCamelCase__							:  int      ="""mra"""






      def __init__(					self						:     Optional[Any]			,   lowercase_						:     List[str]=5_0265			,   lowercase_						:     int=768			,   lowercase_						:     Optional[int]=12			,   lowercase_						:     Dict=12			,   lowercase_						:     Tuple=3072			,   lowercase_						:     Union[str, Any]="gelu"			,   lowercase_						:     Union[str, Any]=0.1			,   lowercase_						:     Union[str, Any]=0.1			,   lowercase_						:     int=512			,   lowercase_						:     List[Any]=1			,   lowercase_						:     Union[str, Any]=0.02			,   lowercase_						:     int=1E-5			,   lowercase_						:     Union[str, Any]="absolute"			,   lowercase_						:     Tuple=4			,   lowercase_						:     Union[str, Any]="full"			,   lowercase_						:     str=0			,   lowercase_						:     Dict=0			,   lowercase_						:     int=1			,   lowercase_						:     Any=0			,   lowercase_						:     Dict=2			,   **lowercase_						:     List[str]			,   )					->						Dict:
             """simple docstring"""




             super().__init__(pad_token_id=__a			,   bos_token_id=__a			,   eos_token_id=__a			,   **__a )

             _lowerCamelCase       :		Union[str, Any]		=vocab_size
             _lowerCamelCase       :		Tuple		=max_position_embeddings
             _lowerCamelCase       :		Union[str, Any]		=hidden_size
             _lowerCamelCase       :		Any		=num_hidden_layers
             _lowerCamelCase       :		int		=num_attention_heads
             _lowerCamelCase       :		Tuple		=intermediate_size
             _lowerCamelCase       :		List[str]		=hidden_act
             _lowerCamelCase       :		int		=hidden_dropout_prob
             _lowerCamelCase       :		Optional[int]		=attention_probs_dropout_prob
             _lowerCamelCase       :		int		=initializer_range
             _lowerCamelCase       :		Any		=type_vocab_size
             _lowerCamelCase       :		Tuple		=layer_norm_eps
             _lowerCamelCase       :		List[str]		=position_embedding_type
             _lowerCamelCase       :		Dict		=block_per_row
             _lowerCamelCase       :		Dict		=approx_mode
             _lowerCamelCase       :		str		=initial_prior_first_n_blocks
             _lowerCamelCase       :		Dict		=initial_prior_diagonal_n_blocks






"""simple docstring"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =["""image_processor""", """tokenizer"""]
							__UpperCAmelCase						:    Optional[Any]				      ="""CLIPImageProcessor"""
							__UpperCAmelCase						:    Union[str, Any]				      =("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")

							def __init__(     self    ,   __a=None    ,   __a=None    ,   **__a	):
										__lowerCAmelCase				    =			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    ,   )
													__lowerCAmelCase				    =			kwargs.pop("feature_extractor"	)

										__lowerCAmelCase				    =			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    ,   __a=None    ,   __a=None    ,   __a=None    ,   **__a	):

										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:
													__lowerCAmelCase				    =			self.tokenizer(__a    ,   return_tensors=__a    ,   **__a	)

										if images is not None:
													__lowerCAmelCase				    =			self.image_processor(__a    ,   return_tensors=__a    ,   **__a	)

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

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.batch_decode(*__a    ,   **__a	)

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.decode(*__a    ,   **__a	)



							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer.model_input_names
										__lowerCAmelCase				    =			self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)




