





import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
    import torch







class 						_lowerCAmelCase(						unittest.TestCase		,   _a):

  """simple docstring"""







  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     __A				 =       load_tool('''speech-to-text'''     )
     self.tool.setup()

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       self.tool(torch.ones(30_00     )     )
     self.assertEqual(UpperCAmelCase  ,    ''' you'''     )





  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A				 =       self.tool(audio=torch.ones(30_00     )     )
     self.assertEqual(UpperCAmelCase  ,    ''' you'''     )








import numpy as np
def 				__UpperCamelCase  (		snake_case     )  ->						Any:


   '''simple docstring'''







   __A ,						__A				 =       a.shape
   __A				 =       min(snake_case    ,	snake_case     )
   __A				 =       np.eye(snake_case     )
   __A				 =       a.copy()

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

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

      __A				 =       q @ q_k.T
      __A				 =       q_k @ r

   return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()

