
"""simple docstring"""




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   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							['image_processor', 'tokenizer']
     _A								=							'BlipImageProcessor'
     _A								=							'AutoTokenizer'



     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					List[str]:
          UpperCAmelCase__							: List[Any] 							=						False
          super().__init__(__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: str 							=						self.image_processor



     def __call__(      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			__UpperCamelCase = False ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = 0 ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = False ,			__UpperCamelCase = False ,			__UpperCamelCase = False ,			__UpperCamelCase = False ,			__UpperCamelCase = False ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					BatchEncoding:
          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__							: Optional[Any] 							=						self.tokenizer
               UpperCAmelCase__							: str 							=						self.tokenizer(
                   text=__UpperCamelCase ,			add_special_tokens=__UpperCamelCase ,			padding=__UpperCamelCase ,			truncation=__UpperCamelCase ,			max_length=__UpperCamelCase ,			stride=__UpperCamelCase ,			pad_to_multiple_of=__UpperCamelCase ,			return_attention_mask=__UpperCamelCase ,			return_overflowing_tokens=__UpperCamelCase ,			return_special_tokens_mask=__UpperCamelCase ,			return_offsets_mapping=__UpperCamelCase ,			return_token_type_ids=__UpperCamelCase ,			return_length=__UpperCamelCase ,			verbose=__UpperCamelCase ,			return_tensors=__UpperCamelCase ,			**__UpperCamelCase ,			)
               return text_encoding

          # add pixel_values
          UpperCAmelCase__							: List[str] 							=						self.image_processor(__UpperCamelCase ,			return_tensors=__UpperCamelCase  )

          if text is not None:
               UpperCAmelCase__							: Any 							=						self.tokenizer(
                   text=__UpperCamelCase ,			add_special_tokens=__UpperCamelCase ,			padding=__UpperCamelCase ,			truncation=__UpperCamelCase ,			max_length=__UpperCamelCase ,			stride=__UpperCamelCase ,			pad_to_multiple_of=__UpperCamelCase ,			return_attention_mask=__UpperCamelCase ,			return_overflowing_tokens=__UpperCamelCase ,			return_special_tokens_mask=__UpperCamelCase ,			return_offsets_mapping=__UpperCamelCase ,			return_token_type_ids=__UpperCamelCase ,			return_length=__UpperCamelCase ,			verbose=__UpperCamelCase ,			return_tensors=__UpperCamelCase ,			**__UpperCamelCase ,			)
          else:
               UpperCAmelCase__							: Optional[Any] 							=						None

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

          return encoding_image_processor



     def  lowerCAmelCase__       (      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					str:
          return self.tokenizer.batch_decode(*__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					Optional[Any]:
          return self.tokenizer.decode(*__UpperCamelCase ,			**__UpperCamelCase  )



     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: int 							=						self.tokenizer.model_input_names
          UpperCAmelCase__							: Union[str, Any] 							=						self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )





"""simple docstring"""




from typing import TYPE_CHECKING

from ....utils import _LazyModule


A__      :	List[str]          =    {"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_tapex import TapexTokenizer


else:
    import sys

    A__      :	Any          =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)



