

from typing import List, Optional, Union

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


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							['image_processor', 'tokenizer']
    lowerCamelCase			=							'BridgeTowerImageProcessor'
    lowerCamelCase			=							('RobertaTokenizer', 'RobertaTokenizerFast')


    def __init__(      self :       Dict,lowercase_ :       int,lowercase_ :       Dict		)->    Optional[Any]:





        '''simple docstring'''







        super().__init__(lowercase_,lowercase_		)


    def __call__(      self :       str,lowercase_ :       Dict,lowercase_ :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,lowercase_ :       bool = True,lowercase_ :       Union[bool, str, PaddingStrategy] = False,lowercase_ :       Union[bool, str, TruncationStrategy] = None,lowercase_ :       Optional[int] = None,lowercase_ :       int = 0,lowercase_ :       Optional[int] = None,lowercase_ :       Optional[bool] = None,lowercase_ :       Optional[bool] = None,lowercase_ :       bool = False,lowercase_ :       bool = False,lowercase_ :       bool = False,lowercase_ :       bool = False,lowercase_ :       bool = True,lowercase_ :       Optional[Union[str, TensorType]] = None,**lowercase_ :       Dict,)->    BatchEncoding:





        '''simple docstring'''







        A__          =   self.tokenizer(
            text=lowercase_,add_special_tokens=lowercase_,padding=lowercase_,truncation=lowercase_,max_length=lowercase_,stride=lowercase_,pad_to_multiple_of=lowercase_,return_token_type_ids=lowercase_,return_attention_mask=lowercase_,return_overflowing_tokens=lowercase_,return_special_tokens_mask=lowercase_,return_offsets_mapping=lowercase_,return_length=lowercase_,verbose=lowercase_,return_tensors=lowercase_,**lowercase_,)
        # add pixel_values + pixel_mask
        A__          =   self.image_processor(
            lowercase_,return_tensors=lowercase_,do_normalize=lowercase_,do_center_crop=lowercase_,**lowercase_		)
        encoding.update(lowercase_		)

        return encoding


    def 						snake_case__     (      self :       Tuple,*lowercase_ :       str,**lowercase_ :       str		)->    List[Any]:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       str,*lowercase_ :       int,**lowercase_ :       List[Any]		)->    Tuple:





        '''simple docstring'''







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


    @property
    def 						snake_case__     (      self :       Union[str, Any]		)->    Optional[Any]:





        '''simple docstring'''







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







from typing import TYPE_CHECKING

from ....utils import _LazyModule


_UpperCAmelCase		:  Dict	=   {"tokenization_tapex": ["TapexTokenizer"]}


if TYPE_CHECKING:
     from .tokenization_tapex import TapexTokenizer


else:
     import sys

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



