'''simple docstring'''




import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Vector([1, 2, 3])
					self.assertEqual(x.component(0)       ,    1)
					self.assertEqual(x.component(2)       ,    3)
					lowerCAmelCase   					=  Vector()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Vector([0, 0, 0, 0, 0, 1])
					self.assertEqual(str(__lowerCAmelCase)       ,    """(0,0,0,0,0,1)""")



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Vector([1, 2, 3, 4])
					self.assertEqual(len(__lowerCAmelCase)       ,    4)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Vector([1, 2])
					lowerCAmelCase   					=  Vector([1, 2, 3, 4, 5])
					lowerCAmelCase   					=  Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
					lowerCAmelCase   					=  Vector([1, -1, 1, -1, 2, -3, 4, -5])
					self.assertAlmostEqual(x.euclidean_length()       ,    2.236       ,    3)
					self.assertAlmostEqual(y.euclidean_length()       ,    7.416       ,    3)
					self.assertEqual(z.euclidean_length()       ,    0)
					self.assertAlmostEqual(w.euclidean_length()       ,    7.616       ,    3)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Vector([1, 2, 3])
					lowerCAmelCase   					=  Vector([1, 1, 1])
					self.assertEqual((x + y).component(0)       ,    2)
					self.assertEqual((x + y).component(1)       ,    3)
					self.assertEqual((x + y).component(2)       ,    4)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Vector([1, 2, 3])
					lowerCAmelCase   					=  Vector([1, 1, 1])
					self.assertEqual((x - y).component(0)       ,    0)
					self.assertEqual((x - y).component(1)       ,    1)
					self.assertEqual((x - y).component(2)       ,    2)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Vector([1, 2, 3])
					lowerCAmelCase   					=  Vector([2, -1, 4])  # for test of dot product
					lowerCAmelCase   					=  Vector([1, -2, -1])
					self.assertEqual(str(x * 3.0)       ,    """(3.0,6.0,9.0)""")
					self.assertEqual((a * b)       ,    0)



			def 							a_       (   self):




					"""simple docstring"""


					self.assertEqual(str(zero_vector(10)).count("""0""")       ,    10)



			def 							a_       (   self):




					"""simple docstring"""


					self.assertEqual(str(unit_basis_vector(3       ,    1))       ,    """(0,1,0)""")



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Vector([1, 2, 3])
					lowerCAmelCase   					=  Vector([1, 0, 1])
					self.assertEqual(str(axpy(2       ,    __lowerCAmelCase       ,    __lowerCAmelCase))       ,    """(3,4,7)""")



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Vector([1, 0, 0, 0, 0, 0])
					lowerCAmelCase   					=  x.copy()
					self.assertEqual(str(__lowerCAmelCase)       ,    str(__lowerCAmelCase))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Vector([1, 0, 0])
					x.change_component(0       ,    0)
					x.change_component(1       ,    1)
					self.assertEqual(str(__lowerCAmelCase)       ,    """(0,1,0)""")



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,    3       ,    3)
					self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n"""       ,    str(__lowerCAmelCase))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,    3       ,    3)
					lowerCAmelCase   					=  [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
					for x in range(a.height()):
							for y in range(a.width()):
									self.assertEqual(minors[x][y]       ,    a.minor(__lowerCAmelCase       ,    __lowerCAmelCase))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,    3       ,    3)
					lowerCAmelCase   					=  [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
					for x in range(a.height()):
							for y in range(a.width()):
									self.assertEqual(cofactors[x][y]       ,    a.cofactor(__lowerCAmelCase       ,    __lowerCAmelCase))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,    3       ,    3)
					self.assertEqual(-5       ,    a.determinant())



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]       ,    3       ,    3)
					lowerCAmelCase   					=  Vector([1, 2, 3])
					self.assertEqual("""(14,32,50)"""       ,    str(a * x))
					self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n"""       ,    str(a * 2))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,    3       ,    3)
					a.change_component(0       ,    2       ,    5)
					self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n"""       ,    str(__lowerCAmelCase))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,    3       ,    3)
					self.assertEqual(7       ,    a.component(2       ,    1)       ,    0.01)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,    3       ,    3)
					lowerCAmelCase   					=  Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]       ,    3       ,    3)
					self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n"""       ,    str(a + b))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,    3       ,    3)
					lowerCAmelCase   					=  Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]       ,    3       ,    3)
					self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n"""       ,    str(a - b))







			def 							a_       (   self):




					"""simple docstring"""


					self.assertEqual(
					    """|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n"""       ,    str(square_zero_matrix(5))       ,    )


if __name__ == "__main__":
						unittest.main()





'''simple docstring'''




import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Union[str, Any]      =       ['''input_features''']



			def __init__(   self       ,    __lowerCAmelCase=80       ,    __lowerCAmelCase=16000       ,    __lowerCAmelCase=160       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase=False       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__(
					    feature_size=__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    padding_value=__lowerCAmelCase       ,    return_attention_mask=__lowerCAmelCase       ,    **__lowerCAmelCase       ,    )
					lowerCAmelCase   					=  n_fft
					lowerCAmelCase   					=  hop_length
					lowerCAmelCase   					=  chunk_length
					lowerCAmelCase   					=  chunk_length * sampling_rate
					lowerCAmelCase   					=  self.n_samples // hop_length
					lowerCAmelCase   					=  sampling_rate
					lowerCAmelCase   					=  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2       ,    num_mel_filters=__lowerCAmelCase       ,    min_frequency=0.0       ,    max_frequency=8000.0       ,    sampling_rate=__lowerCAmelCase       ,    norm="""slaney"""       ,    mel_scale="""slaney"""       ,    )



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  spectrogram(
					    __lowerCAmelCase       ,    window_function(self.n_fft       ,    """hann""")       ,    frame_length=self.n_fft       ,    hop_length=self.hop_length       ,    power=2.0       ,    mel_filters=self.mel_filters       ,    log_mel="""log10"""       ,    )
					lowerCAmelCase   					=  log_spec[:, :-1]
					lowerCAmelCase   					=  np.maximum(__lowerCAmelCase       ,    log_spec.max() - 8.0)
					lowerCAmelCase   					=  (log_spec + 4.0) / 4.0
					return log_spec



			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def 							a_       (   __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = 0.0):




					"""simple docstring"""


					if attention_mask is not None:
							lowerCAmelCase   					=  np.array(__lowerCAmelCase       ,    np.intaa)
							lowerCAmelCase   					=  []

							for vector, length in zip(__lowerCAmelCase       ,    attention_mask.sum(-1)):
									lowerCAmelCase   					=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7)
									if length < normed_slice.shape[0]:
											lowerCAmelCase   					=  padding_value

									normed_input_values.append(__lowerCAmelCase)
					else:
							lowerCAmelCase   					=  [(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

					return normed_input_values



			def __call__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = True       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = "max_length"       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
									raise ValueError(
									    f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
									    f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
									    f" was sampled with {self.sampling_rate} and not {sampling_rate}.")
					else:
							logger.warning(
							    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
							    """Failing to do so can result in silent errors that might be hard to debug.""")

					lowerCAmelCase   					=  isinstance(__lowerCAmelCase       ,    np.ndarray) and len(raw_speech.shape) > 1
					if is_batched_numpy and len(raw_speech.shape) > 2:
							raise ValueError(f"Only mono-channel audio is supported for input to {self}")
					lowerCAmelCase   					=  is_batched_numpy or (
					    isinstance(__lowerCAmelCase       ,    (list, tuple)) and (isinstance(raw_speech[0]       ,    (np.ndarray, tuple, list)))
					)

					if is_batched:
							lowerCAmelCase   					=  [np.asarray([speech]       ,    dtype=np.floataa).T for speech in raw_speech]
					elif not is_batched and not isinstance(__lowerCAmelCase       ,    np.ndarray):
							lowerCAmelCase   					=  np.asarray(__lowerCAmelCase       ,    dtype=np.floataa)
					elif isinstance(__lowerCAmelCase       ,    np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
							lowerCAmelCase   					=  raw_speech.astype(np.floataa)

					# always return batch
					if not is_batched:
							lowerCAmelCase   					=  [np.asarray([raw_speech]).T]

					lowerCAmelCase   					=  BatchFeature({"""input_features""": raw_speech})

					# convert into correct format for padding

					lowerCAmelCase   					=  self.pad(
					    __lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    max_length=max_length if max_length else self.n_samples       ,    truncation=__lowerCAmelCase       ,    pad_to_multiple_of=__lowerCAmelCase       ,    return_attention_mask=return_attention_mask or do_normalize       ,    )

					# zero-mean and unit-variance normalization
					if do_normalize:
							lowerCAmelCase   					=  self.zero_mean_unit_var_norm(
							    padded_inputs["""input_features"""]       ,    attention_mask=padded_inputs["""attention_mask"""]       ,    padding_value=self.padding_value       ,    )
							lowerCAmelCase   					=  np.stack(padded_inputs["""input_features"""]       ,    axis=0)

					# make sure list is in array format
					lowerCAmelCase   					=  padded_inputs.get("""input_features""").transpose(2       ,    0       ,    1)

					lowerCAmelCase   					=  [self._np_extract_fbank_features(__lowerCAmelCase) for waveform in input_features[0]]

					if isinstance(input_features[0]       ,    __lowerCAmelCase):
							lowerCAmelCase   					=  [np.asarray(__lowerCAmelCase       ,    dtype=np.floataa) for feature in input_features]
					else:
							lowerCAmelCase   					=  input_features

					if return_attention_mask:
							# rescale from sample (48000) to feature (3000)
							lowerCAmelCase   					=  padded_inputs["""attention_mask"""][:, :: self.hop_length]

					if return_tensors is not None:
							lowerCAmelCase   					=  padded_inputs.convert_to_tensors(__lowerCAmelCase)

					return padded_inputs







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  copy.deepcopy(self.__dict__)
					lowerCAmelCase   					=  self.__class__.__name__
					if "mel_filters" in output:
							del output["mel_filters"]
					return output




