






import random
class     a    :



      """simple docstring"""


      @staticmethod
      def 	UpperCAmelCase      (      __lowercase					:			str					)							->      tuple[list[int], list[int]]:
        __UpperCAmelCase							:	Union[str, Any]           =  [ord(__lowercase					) for i in text]
        __UpperCAmelCase							:	str           =  []
        __UpperCAmelCase							:	Optional[Any]           =  []
        for i in plain:
          __UpperCAmelCase							:	str           =  random.randint(1		,      300					)
          __UpperCAmelCase							:	List[str]           =  (i + k) * k
          cipher.append(__lowercase					)
          key.append(__lowercase					)
        return cipher, key




      @staticmethod
      def 	UpperCAmelCase      (      __lowercase					:			list[int]		,      __lowercase					:			list[int]					)							->      str:
        __UpperCAmelCase							:	List[str]           =  []
        for i in range(len(__lowercase					)					):
          __UpperCAmelCase							:	Tuple           =  int((cipher[i] - (key[i]) ** 2) / key[i]					)
          plain.append(chr(__lowercase					)					)
        return "".join(__lowercase					)


if __name__ == "__main__":
       a     ,a		:       Any			     = Onepad().encrypt("Hello")
       print(c, k)
       print(Onepad().decrypt(c, k))










import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[str]							       =	'MCTCTFeatureExtractor'
      a  : str							       =	'AutoTokenizer'
      def __init__(      self					:			Tuple		,      __lowercase					:			int		,      __lowercase					:			Dict					)							->      Any:
        super().__init__(__lowercase		,      __lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  self.feature_extractor
        __UpperCAmelCase							:	Optional[int]           =  False
      def __call__(      self					:			int		,      *__lowercase					:			Tuple		,      **__lowercase					:			Optional[int]					)							->      Union[str, Any]:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor(*__lowercase		,      **__lowercase					)

        if "raw_speech" in kwargs:
          warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""					)
          __UpperCAmelCase							:	Dict           =  kwargs.pop("""raw_speech"""					)
        else:
          __UpperCAmelCase							:	Dict           =  kwargs.pop("""audio"""		,      __lowercase					)
        __UpperCAmelCase							:	List[str]           =  kwargs.pop("""sampling_rate"""		,      __lowercase					)
        __UpperCAmelCase							:	Tuple           =  kwargs.pop("""text"""		,      __lowercase					)
        if len(__lowercase					) > 0:
          __UpperCAmelCase							:	Tuple           =  args[0]
          __UpperCAmelCase							:	str           =  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:
          __UpperCAmelCase							:	Tuple           =  self.feature_extractor(__lowercase		,      *__lowercase		,      sampling_rate=__lowercase		,      **__lowercase					)
        if text is not None:
          __UpperCAmelCase							:	str           =  self.tokenizer(__lowercase		,      **__lowercase					)

        if text is None:
          return inputs
        elif audio is None:
          return encodings
        else:
          __UpperCAmelCase							:	Dict           =  encodings["""input_ids"""]
          return inputs
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      *__lowercase					:			List[Any]		,      **__lowercase					:			int					)							->      List[Any]:
        return self.tokenizer.batch_decode(*__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      *__lowercase					:			Optional[Any]		,      **__lowercase					:			List[str]					)							->      int:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor.pad(*__lowercase		,      **__lowercase					)

        __UpperCAmelCase							:	Optional[int]           =  kwargs.pop("""input_features"""		,      __lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  kwargs.pop("""labels"""		,      __lowercase					)
        if len(__lowercase					) > 0:
          __UpperCAmelCase							:	Union[str, Any]           =  args[0]
          __UpperCAmelCase							:	str           =  args[1:]

        if input_features is not None:
          __UpperCAmelCase							:	Any           =  self.feature_extractor.pad(__lowercase		,      *__lowercase		,      **__lowercase					)
        if labels is not None:
          __UpperCAmelCase							:	Union[str, Any]           =  self.tokenizer.pad(__lowercase		,      **__lowercase					)

        if labels is None:
          return input_features
        elif input_features is None:
          return labels
        else:
          __UpperCAmelCase							:	Any           =  labels["""input_ids"""]
          return input_features
      def 	UpperCAmelCase      (      self					:			Any		,      *__lowercase					:			Union[str, Any]		,      **__lowercase					:			Dict					)							->      List[Any]:
        return self.tokenizer.decode(*__lowercase		,      **__lowercase					)




      @contextmanager
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Tuple:
        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."""					)
        __UpperCAmelCase							:	Any           =  True
        __UpperCAmelCase							:	Optional[int]           =  self.tokenizer
        yield
        __UpperCAmelCase							:	List[Any]           =  self.feature_extractor
        __UpperCAmelCase							:	int           =  False


