






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 	_UpperCAmelCase  (  SCREAMING_SNAKE_CASE__		):




      """simple docstring"""



      A          =    ['''image_processor''', '''tokenizer''']
      A          =    '''BridgeTowerImageProcessor'''
      A          =    ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          super().__init__(snake_case__    ,	snake_case__		)
      def __call__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = True    ,	_lowerCAmelCase = False    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = False    ,	_lowerCAmelCase = False    ,	_lowerCAmelCase = False    ,	_lowerCAmelCase = False    ,	_lowerCAmelCase = True    ,	_lowerCAmelCase = None    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				self.tokenizer(
              text=snake_case__    ,	add_special_tokens=snake_case__    ,	padding=snake_case__    ,	truncation=snake_case__    ,	max_length=snake_case__    ,	stride=snake_case__    ,	pad_to_multiple_of=snake_case__    ,	return_token_type_ids=snake_case__    ,	return_attention_mask=snake_case__    ,	return_overflowing_tokens=snake_case__    ,	return_special_tokens_mask=snake_case__    ,	return_offsets_mapping=snake_case__    ,	return_length=snake_case__    ,	verbose=snake_case__    ,	return_tensors=snake_case__    ,	**snake_case__    ,	)
          # add pixel_values + pixel_mask
          lowerCAmelCase__   :Optional[int]      							=				self.image_processor(
              snake_case__    ,	return_tensors=snake_case__    ,	do_normalize=snake_case__    ,	do_center_crop=snake_case__    ,	**snake_case__		)
          encoding.update(snake_case__		)

          return encoding
      def 			snake_case_      (   self    ,	*_lowerCAmelCase    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          return self.tokenizer.batch_decode(*snake_case__    ,	**snake_case__		)
      def 			snake_case_      (   self    ,	*_lowerCAmelCase    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          return self.tokenizer.decode(*snake_case__    ,	**snake_case__		)



      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				self.tokenizer.model_input_names
          lowerCAmelCase__   :List[Any]      							=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)











from __future__ import annotations

import numpy as np


def 				snake_case__							(							UpperCAmelCase							:  np.ndarray       ):
    lowerCAmelCase__							,lowerCAmelCase__   :List[str]      							=				np.shape(UpperCAmelCase       )
    if rows != columns:
        lowerCAmelCase__   :Tuple      							=				(
            "'table' has to be of square shaped array but got a "
            F'''{rows}x{columns} array:\n{table}'''
        )
        raise ValueError(UpperCAmelCase       )

    lowerCAmelCase__   :Optional[Any]      							=				np.zeros((rows, columns)       )
    lowerCAmelCase__   :Tuple      							=				np.zeros((rows, columns)       )
    for i in range(UpperCAmelCase       ):
        for j in range(UpperCAmelCase       ):
            lowerCAmelCase__   :Any      							=				sum(lower[i][k] * upper[k][j] for k in range(UpperCAmelCase       )       )
            if upper[j][j] == 0:
                raise ArithmeticError("No LU decomposition exists"       )
            lowerCAmelCase__   :Any      							=				(table[i][j] - total) / upper[j][j]
        lowerCAmelCase__   :Tuple      							=				1
        for j in range(UpperCAmelCase    ,			UpperCAmelCase       ):
            lowerCAmelCase__   :Optional[Any]      							=				sum(lower[i][k] * upper[k][j] for k in range(UpperCAmelCase       )       )
            lowerCAmelCase__   :Dict      							=				table[i][j] - total
    return lower, upper


if __name__ == "__main__":
 import doctest

 doctest.testmod()



