




import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_UpperCAmelCase		:  Any	=   random.Random()

if is_torch_available():
     import torch



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase=1.0,	lowerCamelCase=None,	lowerCamelCase=None      ):


  if rng is None:
    lowercase			:str							=  global_rng

  lowercase			:Dict							=  []
  for batch_idx in range(shape[0]      ):
    values.append([]      )
    for _ in range(shape[1]      ):
      values[-1].append(rng.random() * scale      )

  return values




class       __lowerCAmelCase				(							unittest.TestCase):






       def __init__(      self:    List[Any]				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    List[Any]=7				,    _lowerCAmelCase:    Optional[Any]=4_00				,    _lowerCAmelCase:    Union[str, Any]=20_00				,    _lowerCAmelCase:    Tuple=1				,    _lowerCAmelCase:    Union[str, Any]=0.0				,    _lowerCAmelCase:    List[Any]=1_60_00				,    _lowerCAmelCase:    int=True				,    _lowerCAmelCase:    Optional[Any]=True				,    ):
         lowercase			:Union[str, Any]							=  parent
         lowercase			:Optional[Any]							=  batch_size
         lowercase			:List[Any]							=  min_seq_length
         lowercase			:Tuple							=  max_seq_length
         lowercase			:Tuple							=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         lowercase			:Tuple							=  feature_size
         lowercase			:Any							=  padding_value
         lowercase			:List[Any]							=  sampling_rate
         lowercase			:List[str]							=  return_attention_mask
         lowercase			:List[Any]							=  do_normalize






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         return {
             "feature_size": self.feature_size,
             "padding_value": self.padding_value,
             "sampling_rate": self.sampling_rate,
             "return_attention_mask": self.return_attention_mask,
             "do_normalize": self.do_normalize,
         }






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    Tuple=False				,    _lowerCAmelCase:    Any=False ):




         def _flatten(_lowerCAmelCase:    Dict ):
           return list(itertools.chain(*__SCREAMING_SNAKE_CASE ) )

         if equal_length:
           lowercase			:Any							=  floats_list((self.batch_size, self.max_seq_length) )
         else:
           # make sure that inputs increase in size
           lowercase			:str							=  [
               _flatten(floats_list((x, self.feature_size) ) )
               for x in range(self.min_seq_length				,    self.max_seq_length				,    self.seq_length_diff )
           ]

         if numpify:
           lowercase			:Union[str, Any]							=  [np.asarray(__SCREAMING_SNAKE_CASE ) for x in speech_inputs]

         return speech_inputs







@require_torch
@require_torchaudio
class       __lowerCAmelCase				(							lowerCAmelCase					,       unittest.TestCase):
       _a           =  ASTFeatureExtractor






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:int							=  ASTFeatureExtractionTester(self )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         lowercase			:str							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
         # create three inputs of length 800, 1000, and 1200
         lowercase			:Optional[int]							=  [floats_list((1, x) )[0] for x in range(8_00				,    14_00				,    2_00 )]
         lowercase			:List[Any]							=  [np.asarray(__SCREAMING_SNAKE_CASE ) for speech_input in speech_inputs]

         # Test not batched input
         lowercase			:List[str]							=  feat_extract(speech_inputs[0]				,    return_tensors="np" ).input_values
         lowercase			:List[Any]							=  feat_extract(np_speech_inputs[0]				,    return_tensors="np" ).input_values
         self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    atol=1e-3 ) )

         # Test batched
         lowercase			:str							=  feat_extract(__SCREAMING_SNAKE_CASE				,    padding=__SCREAMING_SNAKE_CASE				,    return_tensors="np" ).input_values
         lowercase			:List[Any]							=  feat_extract(__SCREAMING_SNAKE_CASE				,    padding=__SCREAMING_SNAKE_CASE				,    return_tensors="np" ).input_values
         for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE ):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    atol=1e-3 ) )

         # Test 2-D numpy arrays are batched.
         lowercase			:Optional[int]							=  [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
         lowercase			:List[str]							=  np.asarray(__SCREAMING_SNAKE_CASE )
         lowercase			:List[str]							=  feat_extract(__SCREAMING_SNAKE_CASE				,    return_tensors="np" ).input_values
         lowercase			:Optional[int]							=  feat_extract(__SCREAMING_SNAKE_CASE				,    return_tensors="np" ).input_values
         for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE ):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    atol=1e-3 ) )






       @require_torch
       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         import torch

         lowercase			:str							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
         lowercase			:Optional[int]							=  np.random.rand(1_00 ).astype(np.floataa )
         lowercase			:List[Any]							=  np_speech_inputs.tolist()

         for inputs in [py_speech_inputs, np_speech_inputs]:
           lowercase			:List[str]							=  feature_extractor.pad([{"input_values": inputs}]				,    return_tensors="np" )
           self.assertTrue(np_processed.input_values.dtype == np.floataa )
           lowercase			:List[Any]							=  feature_extractor.pad([{"input_values": inputs}]				,    return_tensors="pt" )
           self.assertTrue(pt_processed.input_values.dtype == torch.floataa )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    Optional[Any] ):
         from datasets import load_dataset

         lowercase			:Tuple							=  load_dataset("hf-internal-testing/librispeech_asr_dummy"				,    "clean"				,    split="validation" )
         # automatic decoding with librispeech
         lowercase			:Union[str, Any]							=  ds.sort("id" ).select(range(__SCREAMING_SNAKE_CASE ) )[:num_samples]["audio"]

         return [x["array"] for x in speech_samples]






       @require_torch
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:str							=  torch.tensor(
             [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
              -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
              -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
              -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69] )
         # fmt: on

         lowercase			:int							=  self._load_datasamples(1 )
         lowercase			:Any							=  ASTFeatureExtractor()
         lowercase			:Union[str, Any]							=  feature_extractor(__SCREAMING_SNAKE_CASE				,    return_tensors="pt" ).input_values
         self.assertEquals(input_values.shape				,    (1, 10_24, 1_28) )
         self.assertTrue(torch.allclose(input_values[0, 0, :30]				,    __SCREAMING_SNAKE_CASE				,    atol=1e-4 ) )






'''simple docstring'''



import numpy as np







def    a__       (  a__      ,    a__      ,    a__ = 1E-1_2      ,    a__ = 1_00      ,    ):




     """simple docstring"""


     assert np.shape(a__						)[0] == np.shape(a__						)[1]
     # Ensure proper dimensionality.
     assert np.shape(a__						)[0] == np.shape(a__						)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(a__						) == np.iscomplexobj(a__						)
     __SCREAMING_SNAKE_CASE     =      np.iscomplexobj(a__						)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(a__      ,    input_matrix.conj().T						)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     __SCREAMING_SNAKE_CASE     =      False
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          __SCREAMING_SNAKE_CASE     =      np.dot(a__      ,    a__						)
          # Normalize the resulting output vector.
          __SCREAMING_SNAKE_CASE     =      w / np.linalg.norm(a__						)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          __SCREAMING_SNAKE_CASE     =      vector.conj().T if is_complex else vector.T
          __SCREAMING_SNAKE_CASE     =      np.dot(a__      ,    np.dot(a__      ,    a__						)						)

          # Check convergence.
          __SCREAMING_SNAKE_CASE     =      np.abs(lambda_ - lambda_previous						) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               __SCREAMING_SNAKE_CASE     =      True

          __SCREAMING_SNAKE_CASE     =      lambda_

     if is_complex:
          __SCREAMING_SNAKE_CASE     =      np.real(lambda_						)

     return lambda_, vector







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]						)
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						)
     __SCREAMING_SNAKE_CASE     =      real_input_matrix.astype(np.complexaaa						)
     __SCREAMING_SNAKE_CASE     =      np.triu(1J * complex_input_matrix      ,    1						)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						).astype(np.complexaaa						)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               __SCREAMING_SNAKE_CASE     =      real_input_matrix
               __SCREAMING_SNAKE_CASE     =      real_vector
          elif problem_type == "complex":
               __SCREAMING_SNAKE_CASE     =      complex_input_matrix
               __SCREAMING_SNAKE_CASE     =      complex_vector

          # Our implementation.
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      power_iteration(a__      ,    a__						)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      np.linalg.eigh(a__						)
          # Last eigenvalue is the maximum one.
          __SCREAMING_SNAKE_CASE     =      eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          __SCREAMING_SNAKE_CASE     =      eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max						) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(a__						) - np.abs(a__						)						) <= 1E-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()






