





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


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

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



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Optional[int]							=     'mra'





      def __init__(				self ,							A_=5_0265 ,							A_=768 ,							A_=12 ,							A_=12 ,							A_=3072 ,							A_="gelu" ,							A_=0.1 ,							A_=0.1 ,							A_=512 ,							A_=1 ,							A_=0.02 ,							A_=1e-5 ,							A_="absolute" ,							A_=4 ,							A_="full" ,							A_=0 ,							A_=0 ,							A_=1 ,							A_=0 ,							A_=2 ,							**A_ ,							):
       '''simple docstring'''




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

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







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Optional[int]							=     ['image_processor', 'tokenizer']
      _UpperCAmelCase			:Tuple							=     'BlipImageProcessor'
      _UpperCAmelCase			:Optional[int]							=     'AutoTokenizer'





      def __init__(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     False
       super().__init__(A_ ,							A_     )
       UpperCamelCase			:      str 			=     self.image_processor





      def __call__(				self ,							A_ = None ,							A_ = None ,							A_ = True ,							A_ = False ,							A_ = None ,							A_ = None ,							A_ = 0 ,							A_ = None ,							A_ = None ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = False ,							A_ = True ,							A_ = None ,							**A_ ,							):
       '''simple docstring'''




       if images is None and text is None:
        raise ValueError("You have to specify either images or text."     )

       # Get only text
       if images is None:
        UpperCamelCase			:      int 			=     self.tokenizer
        UpperCamelCase			:      Optional[int] 			=     self.tokenizer(
            text=A_ ,							add_special_tokens=A_ ,							padding=A_ ,							truncation=A_ ,							max_length=A_ ,							stride=A_ ,							pad_to_multiple_of=A_ ,							return_attention_mask=A_ ,							return_overflowing_tokens=A_ ,							return_special_tokens_mask=A_ ,							return_offsets_mapping=A_ ,							return_token_type_ids=A_ ,							return_length=A_ ,							verbose=A_ ,							return_tensors=A_ ,							**A_ ,							)
        return text_encoding

       # add pixel_values
       UpperCamelCase			:      int 			=     self.image_processor(A_ ,							return_tensors=A_     )

       if text is not None:
        UpperCamelCase			:      Dict 			=     self.tokenizer(
            text=A_ ,							add_special_tokens=A_ ,							padding=A_ ,							truncation=A_ ,							max_length=A_ ,							stride=A_ ,							pad_to_multiple_of=A_ ,							return_attention_mask=A_ ,							return_overflowing_tokens=A_ ,							return_special_tokens_mask=A_ ,							return_offsets_mapping=A_ ,							return_token_type_ids=A_ ,							return_length=A_ ,							verbose=A_ ,							return_tensors=A_ ,							**A_ ,							)
       else:
        UpperCamelCase			:      Dict 			=     None

       if text_encoding is not None:
        encoding_image_processor.update(A_     )

       return encoding_image_processor





      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




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





      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.tokenizer.model_input_names
       UpperCamelCase			:      int 			=     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
