



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class 		UpperCAmelCase    (    snake_case_					):
   SCREAMING_SNAKE_CASE__					      =  '''AutoFeatureExtractor'''
   SCREAMING_SNAKE_CASE__					      =  '''Speech2Text2Tokenizer'''





   def __init__(						self							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         super().__init__(_lowerCAmelCase							,  _lowerCAmelCase      )
         _lowerCAmelCase			      =	self.feature_extractor
         _lowerCAmelCase			      =	False





   def __call__(						self							,  *_lowerCAmelCase							,  **_lowerCAmelCase      ):
         # 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.'''      )
               _lowerCAmelCase			      =	kwargs.pop('''raw_speech'''      )
         else:
               _lowerCAmelCase			      =	kwargs.pop('''audio'''							,  _lowerCAmelCase      )
         _lowerCAmelCase			      =	kwargs.pop('''sampling_rate'''							,  _lowerCAmelCase      )
         _lowerCAmelCase			      =	kwargs.pop('''text'''							,  _lowerCAmelCase      )
         if len(_lowerCAmelCase      ) > 0:
               _lowerCAmelCase			      =	args[0]
               _lowerCAmelCase			      =	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:
               _lowerCAmelCase			      =	self.feature_extractor(_lowerCAmelCase							,  *_lowerCAmelCase							,  sampling_rate=_lowerCAmelCase							,  **_lowerCAmelCase      )
         if text is not None:
               _lowerCAmelCase			      =	self.tokenizer(_lowerCAmelCase							,  **_lowerCAmelCase      )

         if text is None:
               return inputs
         elif audio is None:
               return encodings
         else:
               _lowerCAmelCase			      =	encodings['''input_ids''']
               return inputs





   def 						__lowerCAmelCase				(						self							,  *_lowerCAmelCase							,  **_lowerCAmelCase      ):
         return self.tokenizer.batch_decode(*_lowerCAmelCase							,  **_lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self							,  *_lowerCAmelCase							,  **_lowerCAmelCase      ):
         return self.tokenizer.decode(*_lowerCAmelCase							,  **_lowerCAmelCase      )





   @contextmanager
   def 						__lowerCAmelCase				(						self      ):
         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.'''      )
         _lowerCAmelCase			      =	True
         _lowerCAmelCase			      =	self.tokenizer
         yield
         _lowerCAmelCase			      =	self.feature_extractor
         _lowerCAmelCase			      =	False




import numpy as np
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Tuple					)->Union[str, Any]:
      _lowerCAmelCase      , _lowerCAmelCase			      =	a.shape
      _lowerCAmelCase			      =	min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	np.eye(_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	a.copy()

      for k in range(t - 1					):
            # select a column of modified matrix A':
            _lowerCAmelCase			      =	r[k:, [k]]
            # construct first basis vector
            _lowerCAmelCase			      =	np.zeros_like(_SCREAMING_SNAKE_CASE					)
            _lowerCAmelCase			      =	1.0
            # determine scaling factor
            _lowerCAmelCase			      =	np.linalg.norm(_SCREAMING_SNAKE_CASE					)
            # construct vector v for Householder reflection
            _lowerCAmelCase			      =	x + np.sign(x[0]					) * alpha * ea
            v /= np.linalg.norm(_SCREAMING_SNAKE_CASE					)

            # construct the Householder matrix
            _lowerCAmelCase			      =	np.eye(m - k					) - 2.0 * v @ v.T
            # pad with ones and zeros as necessary
            _lowerCAmelCase			      =	np.block([[np.eye(_SCREAMING_SNAKE_CASE					), np.zeros((k, m - k)					)], [np.zeros((m - k, k)					), q_k]]					)

            _lowerCAmelCase			      =	q @ q_k.T
            _lowerCAmelCase			      =	q_k @ r

      return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()