
"""simple docstring"""

import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_A						=			random.Random()
def 		UpperCAmelCase (		a_,						a_=1.0,						a_=None,						a_=None      ):





  '''simple docstring'''


  if rng is None:
    lowerCamelCase					:      Tuple          =   global_rng

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

  return values






class 	_lowercase       (    unittest.TestCase		):




 def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_=7		,			UpperCAmelCase_=400		,			UpperCAmelCase_=2000		,			UpperCAmelCase_=1		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=16000		,			UpperCAmelCase_=True		,			UpperCAmelCase_=True		,			)		->							List[str]:
   lowerCamelCase					:      Dict          =   parent
   lowerCamelCase					:      Any          =   batch_size
   lowerCamelCase					:      Tuple          =   min_seq_length
   lowerCamelCase					:      Tuple          =   max_seq_length
   lowerCamelCase					:      int          =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   lowerCamelCase					:      Union[str, Any]          =   feature_size
   lowerCamelCase					:      Optional[int]          =   padding_value
   lowerCamelCase					:      str          =   sampling_rate
   lowerCamelCase					:      Union[str, Any]          =   return_attention_mask
   lowerCamelCase					:      Union[str, Any]          =   do_normalize




 def       _UpperCamelCase   (			self )		->							List[str]:
   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       _UpperCamelCase   (			self		,			UpperCAmelCase_=False		,			UpperCAmelCase_=False )		->							Optional[Any]:
   def _flatten(UpperCAmelCase_ ):
     return list(itertools.chain(*UpperCAmelCase_ ) )

   if equal_length:
     lowerCamelCase					:      Optional[Any]          =   floats_list((self.batch_size, self.max_seq_length) )
   else:
     # make sure that inputs increase in size
     lowerCamelCase					:      List[Any]          =   [
         _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:
     lowerCamelCase					:      Optional[Any]          =   [np.asarray(UpperCAmelCase_ ) for x in speech_inputs]

   return speech_inputs






class 	_lowercase       (    __UpperCAmelCase    ,	unittest.TestCase		):
 lowercase_				=     WavaVecaFeatureExtractor




 def       _UpperCamelCase   (			self )		->							Optional[Any]:
   lowerCamelCase					:      Optional[int]          =   WavaVecaFeatureExtractionTester(self )




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Dict:
   self.assertTrue(np.all(np.mean(UpperCAmelCase_		,			axis=0 ) < 1E-3 ) )
   self.assertTrue(np.all(np.abs(np.var(UpperCAmelCase_		,			axis=0 ) - 1 ) < 1E-3 ) )




 def       _UpperCamelCase   (			self )		->							List[Any]:
   # Tests that all call wrap to encode_plus and batch_encode_plus
   lowerCamelCase					:      Dict          =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
   # create three inputs of length 800, 1000, and 1200
   lowerCamelCase					:      List[Any]          =   [floats_list((1, x) )[0] for x in range(800		,			1400		,			200 )]
   lowerCamelCase					:      str          =   [np.asarray(UpperCAmelCase_ ) for speech_input in speech_inputs]

   # Test not batched input
   lowerCamelCase					:      List[str]          =   feat_extract(speech_inputs[0]		,			return_tensors='np' ).input_values
   lowerCamelCase					:      Optional[int]          =   feat_extract(np_speech_inputs[0]		,			return_tensors='np' ).input_values
   self.assertTrue(np.allclose(UpperCAmelCase_		,			UpperCAmelCase_		,			atol=1E-3 ) )

   # Test batched
   lowerCamelCase					:      str          =   feat_extract(UpperCAmelCase_		,			return_tensors='np' ).input_values
   lowerCamelCase					:      Any          =   feat_extract(UpperCAmelCase_		,			return_tensors='np' ).input_values
   for enc_seq_a, enc_seq_a in zip(UpperCAmelCase_		,			UpperCAmelCase_ ):
     self.assertTrue(np.allclose(UpperCAmelCase_		,			UpperCAmelCase_		,			atol=1E-3 ) )

   # Test 2-D numpy arrays are batched.
   lowerCamelCase					:      Union[str, Any]          =   [floats_list((1, x) )[0] for x in (800, 800, 800)]
   lowerCamelCase					:      Optional[int]          =   np.asarray(UpperCAmelCase_ )
   lowerCamelCase					:      Dict          =   feat_extract(UpperCAmelCase_		,			return_tensors='np' ).input_values
   lowerCamelCase					:      Tuple          =   feat_extract(UpperCAmelCase_		,			return_tensors='np' ).input_values
   for enc_seq_a, enc_seq_a in zip(UpperCAmelCase_		,			UpperCAmelCase_ ):
     self.assertTrue(np.allclose(UpperCAmelCase_		,			UpperCAmelCase_		,			atol=1E-3 ) )




 def       _UpperCamelCase   (			self )		->							str:
   lowerCamelCase					:      List[Any]          =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
   lowerCamelCase					:      Optional[Any]          =   [floats_list((1, x) )[0] for x in range(800		,			1400		,			200 )]

   lowerCamelCase					:      Any          =   ['longest', 'max_length', 'do_not_pad']
   lowerCamelCase					:      Tuple          =   [None, 1600, None]
   for max_length, padding in zip(UpperCAmelCase_		,			UpperCAmelCase_ ):
     lowerCamelCase					:      Dict          =   feat_extract(UpperCAmelCase_		,			padding=UpperCAmelCase_		,			max_length=UpperCAmelCase_		,			return_tensors='np' )
     lowerCamelCase					:      Union[str, Any]          =   processed.input_values

     self._check_zero_mean_unit_variance(input_values[0][:800] )
     self.assertTrue(input_values[0][800:].sum() < 1E-6 )
     self._check_zero_mean_unit_variance(input_values[1][:1000] )
     self.assertTrue(input_values[0][1000:].sum() < 1E-6 )
     self._check_zero_mean_unit_variance(input_values[2][:1200] )




 def       _UpperCamelCase   (			self )		->							Optional[int]:
   lowerCamelCase					:      List[Any]          =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
   lowerCamelCase					:      int          =   range(800		,			1400		,			200 )
   lowerCamelCase					:      Tuple          =   [floats_list((1, x) )[0] for x in lengths]

   lowerCamelCase					:      List[str]          =   ['longest', 'max_length', 'do_not_pad']
   lowerCamelCase					:      Dict          =   [None, 1600, None]

   for max_length, padding in zip(UpperCAmelCase_		,			UpperCAmelCase_ ):
     lowerCamelCase					:      Optional[Any]          =   feat_extract(UpperCAmelCase_		,			max_length=UpperCAmelCase_		,			padding=UpperCAmelCase_ )
     lowerCamelCase					:      Any          =   processed.input_values

     self._check_zero_mean_unit_variance(input_values[0][:800] )
     self._check_zero_mean_unit_variance(input_values[1][:1000] )
     self._check_zero_mean_unit_variance(input_values[2][:1200] )




 def       _UpperCamelCase   (			self )		->							Optional[int]:
   lowerCamelCase					:      Optional[int]          =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
   lowerCamelCase					:      Optional[Any]          =   [floats_list((1, x) )[0] for x in range(800		,			1400		,			200 )]
   lowerCamelCase					:      Any          =   feat_extract(
       UpperCAmelCase_		,			truncation=UpperCAmelCase_		,			max_length=1000		,			padding='max_length'		,			return_tensors='np' )
   lowerCamelCase					:      Optional[Any]          =   processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :800] )
   self._check_zero_mean_unit_variance(input_values[1] )
   self._check_zero_mean_unit_variance(input_values[2] )




 def       _UpperCamelCase   (			self )		->							List[str]:
   lowerCamelCase					:      Any          =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
   lowerCamelCase					:      List[Any]          =   [floats_list((1, x) )[0] for x in range(800		,			1400		,			200 )]
   lowerCamelCase					:      Any          =   feat_extract(
       UpperCAmelCase_		,			truncation=UpperCAmelCase_		,			max_length=1000		,			padding='longest'		,			return_tensors='np' )
   lowerCamelCase					:      Dict          =   processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :800] )
   self._check_zero_mean_unit_variance(input_values[1, :1000] )
   self._check_zero_mean_unit_variance(input_values[2] )

   # make sure that if max_length < longest -> then pad to max_length
   self.assertTrue(input_values.shape == (3, 1000) )

   lowerCamelCase					:      Optional[Any]          =   [floats_list((1, x) )[0] for x in range(800		,			1400		,			200 )]
   lowerCamelCase					:      Tuple          =   feat_extract(
       UpperCAmelCase_		,			truncation=UpperCAmelCase_		,			max_length=2000		,			padding='longest'		,			return_tensors='np' )
   lowerCamelCase					:      int          =   processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :800] )
   self._check_zero_mean_unit_variance(input_values[1, :1000] )
   self._check_zero_mean_unit_variance(input_values[2] )

   # make sure that if max_length > longest -> then pad to longest
   self.assertTrue(input_values.shape == (3, 1200) )




 @require_torch
 def       _UpperCamelCase   (			self )		->							Any:
   import torch

   lowerCamelCase					:      Dict          =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
   lowerCamelCase					:      Optional[int]          =   np.random.rand(100 ).astype(np.floataa )
   lowerCamelCase					:      Tuple          =   np_speech_inputs.tolist()

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




 @slow
 @require_torch
 def       _UpperCamelCase   (			self )		->							Optional[Any]:
   # this test makes sure that models that are using
   # group norm don't have their feature extractor return the
   # attention_mask
   for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
     lowerCamelCase					:      int          =   WavaVecaConfig.from_pretrained(UpperCAmelCase_ )
     lowerCamelCase					:      Union[str, Any]          =   WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase_ )

     # only "layer" feature extraction norm should make use of
     # attention_mask
     self.assertEqual(feat_extract.return_attention_mask		,			config.feat_extract_norm == 'layer' )








"""simple docstring"""

import numpy as np
def 		UpperCAmelCase (		a_,						a_,						a_ = 1E-12,						a_ = 100,						):





		'''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_      )
		lowerCamelCase					:      Optional[int]          =   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.

		lowerCamelCase					:      Union[str, Any]          =   False
		lowerCamelCase					:      List[str]          =   0
		lowerCamelCase					:      Any          =   0
		lowerCamelCase					:      Dict          =   1E12

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

				# Check convergence.
				lowerCamelCase					:      Optional[int]          =   np.abs(lambda_ - lambda_previous      ) / lambda_
				iterations += 1

				if error <= error_tol or iterations >= max_iterations:
						lowerCamelCase					:      int          =   True

				lowerCamelCase					:      Optional[Any]          =   lambda_

		if is_complex:
				lowerCamelCase					:      Any          =   np.real(lambda_      )

		return lambda_, vector
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      str          =   np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
		lowerCamelCase					:      str          =   np.array([41, 4, 20]      )
		lowerCamelCase					:      Optional[Any]          =   real_input_matrix.astype(np.complexaaa      )
		lowerCamelCase					:      Dict          =   np.triu(1j * complex_input_matrix,						1      )
		complex_input_matrix += imag_matrix
		complex_input_matrix += -1 * imag_matrix.T
		lowerCamelCase					:      List[Any]          =   np.array([41, 4, 20]      ).astype(np.complexaaa      )

		for problem_type in ["real", "complex"]:
				if problem_type == "real":
						lowerCamelCase					:      str          =   real_input_matrix
						lowerCamelCase					:      Any          =   real_vector
				elif problem_type == "complex":
						lowerCamelCase					:      str          =   complex_input_matrix
						lowerCamelCase					:      Dict          =   complex_vector

				# Our implementation.
				lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   power_iteration(a_,						a_      )

				# Numpy implementation.

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






