



'''simple docstring'''





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        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      List[Any]				=      ['''image_processor''', '''tokenizer''']
  __lowercase							:      List[Any]				=      '''BridgeTowerImageProcessor'''
  __lowercase							:      Dict				=      ('''RobertaTokenizer''', '''RobertaTokenizerFast''')




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		List[Any]:
      super().__init__(__UpperCAmelCase  ,__UpperCAmelCase      )




  def __call__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = True  ,__UpperCAmelCase = False  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = 0  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = False  ,__UpperCAmelCase = False  ,__UpperCAmelCase = False  ,__UpperCAmelCase = False  ,__UpperCAmelCase = True  ,__UpperCAmelCase = None  ,**__UpperCAmelCase  ,)    ->		BatchEncoding:
      lowerCAmelCase__ :  int							      =					self.tokenizer(
          text=__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase  ,padding=__UpperCAmelCase  ,truncation=__UpperCAmelCase  ,max_length=__UpperCAmelCase  ,stride=__UpperCAmelCase  ,pad_to_multiple_of=__UpperCAmelCase  ,return_token_type_ids=__UpperCAmelCase  ,return_attention_mask=__UpperCAmelCase  ,return_overflowing_tokens=__UpperCAmelCase  ,return_special_tokens_mask=__UpperCAmelCase  ,return_offsets_mapping=__UpperCAmelCase  ,return_length=__UpperCAmelCase  ,verbose=__UpperCAmelCase  ,return_tensors=__UpperCAmelCase  ,**__UpperCAmelCase  ,)
      # add pixel_values + pixel_mask
      lowerCAmelCase__ :  Optional[int]							      =					self.image_processor(
          __UpperCAmelCase  ,return_tensors=__UpperCAmelCase  ,do_normalize=__UpperCAmelCase  ,do_center_crop=__UpperCAmelCase  ,**__UpperCAmelCase      )
      encoding.update(__UpperCAmelCase      )

      return encoding




  def 							UpperCAmelCase_	(    self  ,*__UpperCAmelCase  ,**__UpperCAmelCase      )    ->		str:
      return self.tokenizer.batch_decode(*__UpperCAmelCase  ,**__UpperCAmelCase      )




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






  @property
  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  List[str]							      =					self.tokenizer.model_input_names
      lowerCAmelCase__ :  Tuple							      =					self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )









from __future__ import annotations




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list[int]		,   __UpperCAmelCase:			int      )      ->	list[int]:
    UpperCamelCase__							:				Optional[int]          =      0
    UpperCamelCase__							:				Tuple          =      len(__UpperCAmelCase      ) - 1

    while i < j:
        if nums[i] + nums[j] == target:
            return [i, j]
        elif nums[i] + nums[j] < target:
            UpperCamelCase__							:				Tuple          =      i + 1
        else:
            UpperCamelCase__							:				str          =      j - 1

    return []


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(F'''{two_pointer([2, 7, 11, 15], 9) = }''')

