
"""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 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		['''image_processor''', '''tokenizer''']
       UpperCamelCase_   =		'''BridgeTowerImageProcessor'''
       UpperCamelCase_   =		('''RobertaTokenizer''', '''RobertaTokenizerFast''')
       def __init__(     self ,				A_ ,				A_       )     ->	Tuple:





         """simple docstring"""




         super().__init__(A_ ,				A_       )
       def __call__(     self ,				A_ ,				A_ = None ,				A_ = True ,				A_ = False ,				A_ = None ,				A_ = None ,				A_ = 0 ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = False ,				A_ = False ,				A_ = False ,				A_ = False ,				A_ = True ,				A_ = None ,				**A_ ,				)     ->	BatchEncoding:





         """simple docstring"""




         _lowercase:			Any       =     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
         _lowercase:			List[Any]       =     self.image_processor(
             A_ ,				return_tensors=A_ ,				do_normalize=A_ ,				do_center_crop=A_ ,				**A_       )
         encoding.update(A_       )

         return encoding
       def 							lowercase_							(     self ,				*A_ ,				**A_       )     ->	Any:





         """simple docstring"""




         return self.tokenizer.batch_decode(*A_ ,				**A_       )
       def 							lowercase_							(     self ,				*A_ ,				**A_       )     ->	Optional[int]:





         """simple docstring"""




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



       @property
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Any       =     self.tokenizer.model_input_names
         _lowercase:			List[str]       =     self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )


"""simple docstring"""




from __future__ import annotations

import numpy as np




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase ,				_lowercase:			List[str]       =     np.shape(_UpperCamelCase						)
  if rows != columns:
    _lowercase:			int       =     (
        '''\'table\' has to be of square shaped array but got a '''
        f'''{rows}x{columns} array:\n{table}'''
    )
    raise ValueError(_UpperCamelCase						)

  _lowercase:			Tuple       =     np.zeros((rows, columns)						)
  _lowercase:			Tuple       =     np.zeros((rows, columns)						)
  for i in range(_UpperCamelCase						):
    for j in range(_UpperCamelCase						):
      _lowercase:			List[Any]       =     sum(lower[i][k] * upper[k][j] for k in range(_UpperCamelCase						)						)
      if upper[j][j] == 0:
        raise ArithmeticError('''No LU decomposition exists'''						)
      _lowercase:			Any       =     (table[i][j] - total) / upper[j][j]
    _lowercase:			Any       =     1
    for j in range(_UpperCamelCase ,				_UpperCamelCase						):
      _lowercase:			Optional[int]       =     sum(lower[i][k] * upper[k][j] for k in range(_UpperCamelCase						)						)
      _lowercase:			List[str]       =     table[i][j] - total
  return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()
