




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(						__a       ):



 '''simple docstring'''


 UpperCamelCase						       =						["""image_processor""", """tokenizer"""]
 UpperCamelCase						       =						"""BridgeTowerImageProcessor"""
 UpperCamelCase						       =						("""RobertaTokenizer""", """RobertaTokenizerFast""")

 def __init__(    self :  Dict						,							A_ :  Optional[int]						,							A_ :  Tuple  )       ->     Tuple:

       """simple docstring"""






       super().__init__(a__						,							a__  )

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

       """simple docstring"""






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

       return encoding

 def     a__			(    self :  Dict						,							*A_ :  Optional[int]						,							**A_ :  Union[str, Any]  )       ->     int:

       """simple docstring"""






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

 def     a__			(    self :  Dict						,							*A_ :  Tuple						,							**A_ :  int  )       ->     Tuple:

       """simple docstring"""






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





 @property
 def     a__			(    self :  int  )       ->     int:

       """simple docstring"""






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









def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	int					):


      '''simple docstring'''



      return [sentence[i : i + ngram_size] for i in range(len(lowercase					) - ngram_size + 1					)]


if __name__ == "__main__":
 from doctest import testmod

 testmod()



