





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       int       =	"""MCTCTFeatureExtractor"""
		__a       :       Any       =	"""AutoTokenizer"""






		def __init__(							self,   snake_case__,   snake_case__  )       -> Optional[Any]:






									"""simple docstring"""
									super().__init__(snake_case__,   snake_case__  )
									lowercase_	:   Optional[int]  				=     self.feature_extractor
									lowercase_	:   Tuple  				=     False






		def __call__(							self,   *snake_case__,   **snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									# For backward compatibility
									if self._in_target_context_manager:
																return self.current_processor(*snake_case__,   **snake_case__  )

									if "raw_speech" in kwargs:
																warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""  )
																lowercase_	:   Tuple  				=     kwargs.pop("""raw_speech"""  )
									else:
																lowercase_	:   int  				=     kwargs.pop("""audio""",   snake_case__  )
									lowercase_	:   int  				=     kwargs.pop("""sampling_rate""",   snake_case__  )
									lowercase_	:   Tuple  				=     kwargs.pop("""text""",   snake_case__  )
									if len(snake_case__  ) > 0:
																lowercase_	:   Dict  				=     args[0]
																lowercase_	:   Union[str, Any]  				=     args[1:]

									if audio is None and text is None:
																raise ValueError("""You need to specify either an `audio` or `text` input to process."""  )

									if audio is not None:
																lowercase_	:   Tuple  				=     self.feature_extractor(snake_case__,   *snake_case__,   sampling_rate=snake_case__,   **snake_case__  )
									if text is not None:
																lowercase_	:   Optional[Any]  				=     self.tokenizer(snake_case__,   **snake_case__  )

									if text is None:
																return inputs
									elif audio is None:
																return encodings
									else:
																lowercase_	:   int  				=     encodings["""input_ids"""]
																return inputs






		def 						snake_case__				(							self,   *snake_case__,   **snake_case__  )       -> int:






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






		def 						snake_case__				(							self,   *snake_case__,   **snake_case__  )       -> Tuple:






									"""simple docstring"""
									# For backward compatibility
									if self._in_target_context_manager:
																return self.current_processor.pad(*snake_case__,   **snake_case__  )

									lowercase_	:   Optional[Any]  				=     kwargs.pop("""input_features""",   snake_case__  )
									lowercase_	:   List[Any]  				=     kwargs.pop("""labels""",   snake_case__  )
									if len(snake_case__  ) > 0:
																lowercase_	:   Any  				=     args[0]
																lowercase_	:   Optional[int]  				=     args[1:]

									if input_features is not None:
																lowercase_	:   int  				=     self.feature_extractor.pad(snake_case__,   *snake_case__,   **snake_case__  )
									if labels is not None:
																lowercase_	:   int  				=     self.tokenizer.pad(snake_case__,   **snake_case__  )

									if labels is None:
																return input_features
									elif input_features is None:
																return labels
									else:
																lowercase_	:   Optional[int]  				=     labels["""input_ids"""]
																return input_features






		def 						snake_case__				(							self,   *snake_case__,   **snake_case__  )       -> List[str]:






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






		@contextmanager
		def 						snake_case__				(							self  )       -> Optional[int]:






									"""simple docstring"""
									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your audio inputs, or in a separate call."""  )
									lowercase_	:   Dict  				=     True
									lowercase_	:   Dict  				=     self.tokenizer
									yield
									lowercase_	:   List[str]  				=     self.feature_extractor
									lowercase_	:   List[Any]  				=     False






from string import ascii_uppercase

UpperCAmelCase_							=				{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_							=				dict(enumerate(ascii_uppercase))





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Dict  				=     len(lowercase				)
							lowercase_	:   Tuple  				=     0
							while True:
														if x == i:
																					lowercase_	:   Dict  				=     0
														if len(lowercase				) == len(lowercase				):
																					break
														key += key[i]
														i += 1
							return key





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Any  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in message:
														if letter == " ":
																					cipher_text += " "
														else:
																					lowercase_	:   Any  				=     (dicta[letter] - dicta[key_new[i]]) % 26
																					i += 1
																					cipher_text += dicta[x]
							return cipher_text





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in cipher_text:
														if letter == " ":
																					or_txt += " "
														else:
																					lowercase_	:   Optional[int]  				=     (dicta[letter] + dicta[key_new[i]] + 26) % 26
																					i += 1
																					or_txt += dicta[x]
							return or_txt





def  __magic_name__  (				)				-> None:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     """THE GERMAN ATTACK"""
							lowercase_	:   str  				=     """SECRET"""
							lowercase_	:   int  				=     generate_key(lowercase				,       lowercase				)
							lowercase_	:   List[str]  				=     cipher_text(lowercase				,       lowercase				)
							print(f"""Encrypted Text = {s}"""				)
							print(f"""Original Text = {original_text(lowercase				,       lowercase				)}"""				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()