





'''simple docstring'''


import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)
class 				a_       (    unittest.TestCase							):





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        lowercase_      :Dict         =			Vector([1, 2, 3]						)
        self.assertEqual(x.component(0						)     ,				1						)
        self.assertEqual(x.component(2						)     ,				3						)
        lowercase_      :int         =			Vector()





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        lowercase_      :Any         =			Vector([0, 0, 0, 0, 0, 1]						)
        self.assertEqual(str(lowercase						)     ,				"(0,0,0,0,0,1)"						)





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			Vector([1, 2, 3, 4]						)
        self.assertEqual(len(lowercase						)     ,				4						)





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			Vector([1, 2]						)
        lowercase_      :Optional[int]         =			Vector([1, 2, 3, 4, 5]						)
        lowercase_      :List[Any]         =			Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]						)
        lowercase_      :List[Any]         =			Vector([1, -1, 1, -1, 2, -3, 4, -5]						)
        self.assertAlmostEqual(x.euclidean_length()     ,				2.2_36     ,				3						)
        self.assertAlmostEqual(y.euclidean_length()     ,				7.4_16     ,				3						)
        self.assertEqual(z.euclidean_length()     ,				0						)
        self.assertAlmostEqual(w.euclidean_length()     ,				7.6_16     ,				3						)





     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        lowercase_      :int         =			Vector([1, 2, 3]						)
        lowercase_      :List[Any]         =			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 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      :List[Any]         =			Vector([1, 2, 3]						)
        lowercase_      :Any         =			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 							lowercase__				(			self		: Dict						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			Vector([1, 2, 3]						)
        lowercase_      :Optional[int]         =			Vector([1, 0, 1]						)
        self.assertEqual(str(axpy(2     ,				lowercase     ,				lowercase						)						)     ,				"(3,4,7)"						)





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        lowercase_      :Dict         =			Vector([1, 0, 0, 0, 0, 0]						)
        lowercase_      :List[Any]         =			x.copy()
        self.assertEqual(str(lowercase						)     ,				str(lowercase						)						)





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :Any         =			Vector([1, 0, 0]						)
        x.change_component(0     ,				0						)
        x.change_component(1     ,				1						)
        self.assertEqual(str(lowercase						)     ,				"(0,1,0)"						)





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :List[Any]         =			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(lowercase						)						)





     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,				3     ,				3						)
        lowercase_      :Union[str, Any]         =			[[-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(lowercase     ,				lowercase						)						)





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :str         =			Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,				3     ,				3						)
        lowercase_      :Tuple         =			[[-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(lowercase     ,				lowercase						)						)





     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      :str         =			Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     ,				3     ,				3						)
        lowercase_      :Optional[Any]         =			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 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      :Tuple         =			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(lowercase						)						)





     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,				3     ,				3						)
        self.assertEqual(7     ,				a.component(2     ,				1						)     ,				0.01						)





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        lowercase_      :Any         =			Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,				3     ,				3						)
        lowercase_      :Union[str, Any]         =			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 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]     ,				3     ,				3						)
        lowercase_      :str         =			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 							lowercase__				(			self		: Tuple						):





        """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


lowerCAmelCase :					Optional[int] 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      ["input_features"]





     def __init__(			self		: Any     ,				lowercase		: Tuple=80     ,				lowercase		: Optional[int]=16_000     ,				lowercase		: Optional[Any]=160     ,				lowercase		: Optional[int]=30     ,				lowercase		: List[Any]=400     ,				lowercase		: Dict=0.0     ,				lowercase		: Tuple=False     ,				**lowercase		: Optional[int]     ,				):





        """simple docstring"""




        super().__init__(
            feature_size=lowercase     ,				sampling_rate=lowercase     ,				padding_value=lowercase     ,				return_attention_mask=lowercase     ,				**lowercase     ,				)
        lowercase_      :Optional[int]         =			n_fft
        lowercase_      :List[Any]         =			hop_length
        lowercase_      :Tuple         =			chunk_length
        lowercase_      :List[str]         =			chunk_length * sampling_rate
        lowercase_      :Optional[Any]         =			self.n_samples // hop_length
        lowercase_      :Any         =			sampling_rate
        lowercase_      :List[Any]         =			mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2     ,				num_mel_filters=lowercase     ,				min_frequency=0.0     ,				max_frequency=80_00.0     ,				sampling_rate=lowercase     ,				norm="slaney"     ,				mel_scale="slaney"     ,				)





     def 							lowercase__				(			self		: str     ,				lowercase		: np.array						):





        """simple docstring"""




        lowercase_      :Any         =			spectrogram(
            lowercase     ,				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"     ,				)
        lowercase_      :Any         =			log_spec[:, :-1]
        lowercase_      :List[Any]         =			np.maximum(lowercase     ,				log_spec.max() - 8.0						)
        lowercase_      :Dict         =			(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 							lowercase__				(			lowercase		: List[np.ndarray]     ,				lowercase		: List[np.ndarray]     ,				lowercase		: float = 0.0						):





        """simple docstring"""




        if attention_mask is not None:
           lowercase_      :Optional[int]         =			np.array(lowercase     ,				np.intaa						)
           lowercase_      :Any         =			[]

           for vector, length in zip(lowercase     ,				attention_mask.sum(-1						)						):
              lowercase_      :Dict         =			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
              if length < normed_slice.shape[0]:
                 lowercase_      :List[Any]         =			padding_value

              normed_input_values.append(lowercase						)
        else:
           lowercase_      :List[Any]         =			[(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

        return normed_input_values





     def __call__(			self		: Tuple     ,				lowercase		: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,				lowercase		: bool = True     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[Union[str, TensorType]] = None     ,				lowercase		: Optional[bool] = None     ,				lowercase		: Optional[str] = "max_length"     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[bool] = None     ,				**lowercase		: Union[str, Any]     ,				):





        """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."						)

        lowercase_      :List[str]         =			isinstance(lowercase     ,				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}'						)
        lowercase_      :Optional[Any]         =			is_batched_numpy or (
            isinstance(lowercase     ,				(list, tuple)						) and (isinstance(raw_speech[0]     ,				(np.ndarray, tuple, list)						))
        )

        if is_batched:
           lowercase_      :Any         =			[np.asarray([speech]     ,				dtype=np.floataa						).T for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase     ,				np.ndarray						):
           lowercase_      :List[Any]         =			np.asarray(lowercase     ,				dtype=np.floataa						)
        elif isinstance(lowercase     ,				np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
           lowercase_      :Union[str, Any]         =			raw_speech.astype(np.floataa						)

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

        lowercase_      :int         =			BatchFeature({"input_features": raw_speech}						)

        # convert into correct format for padding

        lowercase_      :Tuple         =			self.pad(
            lowercase     ,				padding=lowercase     ,				max_length=max_length if max_length else self.n_samples     ,				truncation=lowercase     ,				pad_to_multiple_of=lowercase     ,				return_attention_mask=return_attention_mask or do_normalize     ,				)

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

        # make sure list is in array format
        lowercase_      :Union[str, Any]         =			padded_inputs.get("input_features"						).transpose(2     ,				0     ,				1						)

        lowercase_      :List[str]         =			[self._np_extract_fbank_features(lowercase						) for waveform in input_features[0]]

        if isinstance(input_features[0]     ,				lowercase						):
           lowercase_      :Tuple         =			[np.asarray(lowercase     ,				dtype=np.floataa						) for feature in input_features]
        else:
           lowercase_      :Union[str, Any]         =			input_features

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

        if return_tensors is not None:
           lowercase_      :Tuple         =			padded_inputs.convert_to_tensors(lowercase						)

        return padded_inputs







     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			copy.deepcopy(self.__dict__						)
        lowercase_      :List[str]         =			self.__class__.__name__
        if "mel_filters" in output:
           del output["mel_filters"]
        return output


