




'''simple docstring'''



import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''MCTCTFeatureExtractor'''
   lowerCamelCase_      =						'''AutoTokenizer'''



   def __init__(   self :     Optional[int]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      super().__init__(__lowerCamelCase  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.feature_extractor
      _SCREAMING_SNAKE_CASE	=				False



   def __call__(   self :     Any  ,				*__lowerCamelCase :     int  ,				**__lowerCamelCase :     Union[str, Any]   ):

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

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

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



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				*__lowerCamelCase :     str  ,				**__lowerCamelCase :     Optional[int]   ):

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



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				*__lowerCamelCase :     Tuple  ,				**__lowerCamelCase :     List[Any]   ):

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

      _SCREAMING_SNAKE_CASE	=				kwargs.pop("input_features"  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				kwargs.pop("labels"  ,				__lowerCamelCase   )
      if len(__lowerCamelCase   ) > 0:
         _SCREAMING_SNAKE_CASE	=				args[0]
         _SCREAMING_SNAKE_CASE	=				args[1:]

      if input_features is not None:
         _SCREAMING_SNAKE_CASE	=				self.feature_extractor.pad(__lowerCamelCase  ,				*__lowerCamelCase  ,				**__lowerCamelCase   )
      if labels is not None:
         _SCREAMING_SNAKE_CASE	=				self.tokenizer.pad(__lowerCamelCase  ,				**__lowerCamelCase   )

      if labels is None:
         return input_features
      elif input_features is None:
         return labels
      else:
         _SCREAMING_SNAKE_CASE	=				labels["input_ids"]
         return input_features



   def 							lowerCAmelCase_      (   self :     List[str]  ,				*__lowerCamelCase :     str  ,				**__lowerCamelCase :     Tuple   ):

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



   @contextmanager
   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """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."   )
      _SCREAMING_SNAKE_CASE	=				True
      _SCREAMING_SNAKE_CASE	=				self.tokenizer
      yield
      _SCREAMING_SNAKE_CASE	=				self.feature_extractor
      _SCREAMING_SNAKE_CASE	=				False







'''simple docstring'''



lowerCamelCase_			=						'Tobias Carryer'

from time import time







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Dict=int(time()   )   ):  # noqa: B008

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				multiplier
      _SCREAMING_SNAKE_CASE	=				increment
      _SCREAMING_SNAKE_CASE	=				modulo
      _SCREAMING_SNAKE_CASE	=				seed



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				(self.multiplier * self.seed + self.increment) % self.modulo
      return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 lowerCamelCase_			=						LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
 while True:
  print(lcg.next_number())

