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    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  ['image_processor', 'tokenizer']
  lowercase_								=  'BridgeTowerImageProcessor'
  lowercase_								=  ('RobertaTokenizer', 'RobertaTokenizerFast')




  def __init__(   self  :						Dict  ,			a_  :						str  ,			a_  :						Tuple					)->							str:




       """simple docstring"""
       super().__init__(a_  ,			a_					)




  def __call__(   self  :						List[Any]  ,			a_  :						str  ,			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_  :						List[Any]  ,			)->							BatchEncoding:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					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_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
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.image_processor(
           a_  ,			return_tensors=a_  ,			do_normalize=a_  ,			do_center_crop=a_  ,			**a_					)
       encoding.update(a_					)

       return encoding




  def 						__lowercase(   self  :						Dict  ,			*a_  :						Any  ,			**a_  :						int					)->							List[Any]:




       """simple docstring"""
       return self.tokenizer.batch_decode(*a_  ,			**a_					)




  def 						__lowercase(   self  :						str  ,			*a_  :						int  ,			**a_  :						str					)->							Any:




       """simple docstring"""
       return self.tokenizer.decode(*a_  ,			**a_					)




  @property
  def 						__lowercase(   self  :						Optional[int]					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE__					:					str      =		self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)









class 							UpperCamelCase_       :  # Public class to implement a graph

    '''simple docstring'''






    def __init__(     self :      str   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->None:





           '''simple docstring'''

           A__ 					=       row
           A__ 					=       col
           A__ 					=       graph


    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->bool:





           '''simple docstring'''

           return (
               0 <= i < self.ROW
               and 0 <= j < self.COL
               and not visited[i][j]
               and self.graph[i][j]
           )


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      list[list[bool]])		->None:





           '''simple docstring'''

           A__ 					=       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
           A__ 					=       [-1, 0, 1, -1, 1, -1, 0, 1]
           A__ 					=       True  # Make those cells visited
           for k in range(8):
                  if self.is_safe(i + row_nbr[k]   ,					j + col_nbr[k]   ,					UpperCAmelCase__):
                         self.diffs(i + row_nbr[k]   ,					j + col_nbr[k]   ,					UpperCAmelCase__)





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->int:  # And finally, count all islands.





           '''simple docstring'''

           A__ 					=       [[False for j in range(self.COL)] for i in range(self.ROW)]
           A__ 					=       0
           for i in range(self.ROW):
                  for j in range(self.COL):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                                self.diffs(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)
                                count += 1
           return count


