




import numpy as np

lowerCamelCase							:						Optional[int]					       =  [
    ["a", "b", "c", "d", "e"],
    ["f", "g", "h", "i", "k"],
    ["l", "m", "n", "o", "p"],
    ["q", "r", "s", "t", "u"],
    ["v", "w", "x", "y", "z"],
]







class     A:



 '''simple docstring'''


 def __init__(							self   :    Dict    )  ->      None:

       """simple docstring"""






       lowerCamelCase_							=       np.array(A_    )

 def 							a__					(							self   :    Dict		,				A_   :    str    )  ->      np.ndarray:

       """simple docstring"""






       lowerCamelCase_	,							lowerCamelCase_							=       np.where(letter == self.SQUARE    )
       lowerCamelCase_							=       np.concatenate([indexa + 1, indexa + 1]    )
       return indexes

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

       """simple docstring"""






       lowerCamelCase_							=       self.SQUARE[indexa - 1, indexa - 1]
       return letter

 def 							a__					(							self   :    Optional[Any]		,				A_   :    str    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       message.lower()
       lowerCamelCase_							=       message.replace(' '		,				''    )
       lowerCamelCase_							=       message.replace('j'		,				'i'    )

       lowerCamelCase_							=       np.empty((2, len(A_    ))    )
       for letter_index in range(len(A_    )    ):
             lowerCamelCase_							=       self.letter_to_numbers(message[letter_index]    )

             lowerCamelCase_							=       numbers[0]
             lowerCamelCase_							=       numbers[1]

       lowerCamelCase_							=       first_step.reshape(2 * len(A_    )    )
       lowerCamelCase_							=       ''
       for numbers_index in range(len(A_    )    ):
             lowerCamelCase_							=       int(second_step[numbers_index * 2]    )
             lowerCamelCase_							=       int(second_step[(numbers_index * 2) + 1]    )
             lowerCamelCase_							=       self.numbers_to_letter(A_		,				A_    )
             lowerCamelCase_							=       encoded_message + letter

       return encoded_message





 def 							a__					(							self   :    Optional[int]		,				A_   :    str    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       message.lower()
       message.replace(' '		,				''    )
       lowerCamelCase_							=       np.empty(2 * len(A_    )    )
       for letter_index in range(len(A_    )    ):
             lowerCamelCase_							=       self.letter_to_numbers(message[letter_index]    )
             lowerCamelCase_							=       numbers[0]
             lowerCamelCase_							=       numbers[1]

       lowerCamelCase_							=       first_step.reshape((2, len(A_    ))    )
       lowerCamelCase_							=       ''
       for numbers_index in range(len(A_    )    ):
             lowerCamelCase_							=       int(second_step[0, numbers_index]    )
             lowerCamelCase_							=       int(second_step[1, numbers_index]    )
             lowerCamelCase_							=       self.numbers_to_letter(A_		,				A_    )
             lowerCamelCase_							=       decoded_message + letter

       return decoded_message











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__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   ["image_processor", "tokenizer"]
		__snake_case      :       Tuple     =   "BridgeTowerImageProcessor"
		__snake_case      :       List[str]     =   ("RobertaTokenizer", "RobertaTokenizerFast")







		def __init__(					self       ,A__       ,A__		):
						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__       ,):
						_A	:				List[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
						_A	:				Optional[Any]   =      self.image_processor(
						    A__       ,return_tensors=A__       ,do_normalize=A__       ,do_center_crop=A__       ,**A__		)
						encoding.update(A__		)

						return encoding







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.batch_decode(*A__       ,**A__		)







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.decode(*A__       ,**A__		)







		@property
		def  A__      (					self		):
						_A	:				Union[str, Any]   =      self.tokenizer.model_input_names
						_A	:				str   =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



