


"""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


UpperCAmelCase    :     str   						=		logging.get_logger(__name__)





class      lowerCamelCase__   (     A_				):







					"""simple docstring"""


					__a	       =							["input_features"]






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






												super().__init__(
												    feature_size=snake_case__ ,       sampling_rate=snake_case__ ,       padding_value=snake_case__ ,       return_attention_mask=snake_case__ ,       **snake_case__ ,       )
												__UpperCAmelCase   :						Dict      							=					n_fft
												__UpperCAmelCase   :						List[Any]      							=					hop_length
												__UpperCAmelCase   :						List[Any]      							=					chunk_length
												__UpperCAmelCase   :						List[str]      							=					chunk_length * sampling_rate
												__UpperCAmelCase   :						int      							=					self.n_samples // hop_length
												__UpperCAmelCase   :						List[Any]      							=					sampling_rate
												__UpperCAmelCase   :						List[str]      							=					mel_filter_bank(
												    num_frequency_bins=1 + n_fft // 2 ,       num_mel_filters=snake_case__ ,       min_frequency=0.0 ,       max_frequency=8000.0 ,       sampling_rate=snake_case__ ,       norm="""slaney""" ,       mel_scale="""slaney""" ,       )






					def lowerCamelCase__							(       self    : Union[str, Any] ,       UpperCamelCase    : np.array  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					spectrogram(
												    snake_case__ ,       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""" ,       )
												__UpperCAmelCase   :						str      							=					log_spec[:, :-1]
												__UpperCAmelCase   :						Any      							=					np.maximum(snake_case__ ,       log_spec.max() - 8.0  )
												__UpperCAmelCase   :						List[str]      							=					(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 lowerCamelCase__							(       UpperCamelCase    : List[np.ndarray] ,       UpperCamelCase    : List[np.ndarray] ,       UpperCamelCase    : float = 0.0  ):
												'''simple docstring'''






												if attention_mask is not None:
																			__UpperCAmelCase   :						Any      							=					np.array(snake_case__ ,       np.intaa  )
																			__UpperCAmelCase   :						Dict      							=					[]

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

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

												return normed_input_values






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

												__UpperCAmelCase   :						Optional[int]      							=					isinstance(snake_case__ ,       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}'''  )
												__UpperCAmelCase   :						List[Any]      							=					is_batched_numpy or (
												    isinstance(snake_case__ ,       (list, tuple)  ) and (isinstance(raw_speech[0] ,       (np.ndarray, tuple, list)  ))
												)

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

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

												__UpperCAmelCase   :						Optional[int]      							=					BatchFeature({"""input_features""": raw_speech}  )

												# convert into correct format for padding

												__UpperCAmelCase   :						Optional[Any]      							=					self.pad(
												    snake_case__ ,       padding=snake_case__ ,       max_length=max_length if max_length else self.n_samples ,       truncation=snake_case__ ,       pad_to_multiple_of=snake_case__ ,       return_attention_mask=return_attention_mask or do_normalize ,       )

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

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

												__UpperCAmelCase   :						str      							=					[self._np_extract_fbank_features(snake_case__  ) for waveform in input_features[0]]

												if isinstance(input_features[0] ,       snake_case__  ):
																			__UpperCAmelCase   :						Dict      							=					[np.asarray(snake_case__ ,       dtype=np.floataa  ) for feature in input_features]
												else:
																			__UpperCAmelCase   :						Tuple      							=					input_features

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

												if return_tensors is not None:
																			__UpperCAmelCase   :						int      							=					padded_inputs.convert_to_tensors(snake_case__  )

												return padded_inputs






					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






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


import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="""%(message)s""")


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray    ):
       return input_array.reshape((input_array.size, 1)    )


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : int    ):
       snake_case       :   Any							=      np.nan
       for i in range(__lowerCamelCase    ):
              snake_case       :   List[str]							=      features[:, labels == i]
              snake_case       :   Dict							=      data.mean(1    )
              # Centralize the data of class i
              snake_case       :   Optional[Any]							=      data - column_reshape(__lowerCamelCase    )
              if i > 0:
                     # If covariance_sum is not None
                     covariance_sum += np.dot(__lowerCamelCase				,					centered_data.T    )
              else:
                     # If covariance_sum is np.nan (i.e. first loop)
                     snake_case       :   Optional[Any]							=      np.dot(__lowerCamelCase				,					centered_data.T    )

       return covariance_sum / features.shape[1]


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : int    ):
       snake_case       :   Optional[Any]							=      features.mean(1    )
       snake_case       :   Tuple							=      np.nan
       for i in range(__lowerCamelCase    ):
              snake_case       :   Tuple							=      features[:, labels == i]
              snake_case       :   Tuple							=      data.shape[1]
              snake_case       :   List[str]							=      data.mean(1    )
              if i > 0:
                     # If covariance_sum is not None
                     covariance_sum += device_data * np.dot(
                         column_reshape(__lowerCamelCase    ) - column_reshape(__lowerCamelCase    )				,					(column_reshape(__lowerCamelCase    ) - column_reshape(__lowerCamelCase    )).T				,					)
              else:
                     # If covariance_sum is np.nan (i.e. first loop)
                     snake_case       :   Optional[int]							=      device_data * np.dot(
                         column_reshape(__lowerCamelCase    ) - column_reshape(__lowerCamelCase    )				,					(column_reshape(__lowerCamelCase    ) - column_reshape(__lowerCamelCase    )).T				,					)

       return covariance_sum / features.shape[1]


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : int    ):

       # Check if the features have been loaded
       if features.any():
              snake_case       :   Tuple							=      features.mean(1    )
              # Center the dataset
              snake_case       :   List[str]							=      features - np.reshape(__lowerCamelCase				,					(data_mean.size, 1)    )
              snake_case       :   Optional[Any]							=      np.dot(__lowerCamelCase				,					centered_data.T    ) / features.shape[1]
              snake_case				,					snake_case       :   Dict							=      np.linalg.eigh(__lowerCamelCase    )
              # Take all the columns in the reverse order (-1), and then takes only the first
              snake_case       :   Optional[Any]							=      eigenvectors[:, ::-1][:, 0:dimensions]
              # Project the database on the new space
              snake_case       :   Union[str, Any]							=      np.dot(filtered_eigenvectors.T				,					__lowerCamelCase    )
              logging.info("Principal Component Analysis computed"    )

              return projected_data
       else:
              logging.basicConfig(level=logging.ERROR				,					format="%(message)s"				,					force=__lowerCamelCase    )
              logging.error("Dataset empty"    )
              raise AssertionError


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : int				,					__lowerCamelCase     : int    ):
       assert classes > dimensions

       # Check if features have been already loaded
       if features.any:
              snake_case				,					snake_case       :   str							=      eigh(
                  covariance_between_classes(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )				,					covariance_within_classes(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )				,					)
              snake_case       :   str							=      eigenvectors[:, ::-1][:, :dimensions]
              snake_case				,					snake_case				,					snake_case       :   int							=      np.linalg.svd(__lowerCamelCase    )
              snake_case       :   List[Any]							=      svd_matrix[:, 0:dimensions]
              snake_case       :   Optional[Any]							=      np.dot(filtered_svd_matrix.T				,					__lowerCamelCase    )
              logging.info("Linear Discriminant Analysis computed"    )

              return projected_data
       else:
              logging.basicConfig(level=logging.ERROR				,					format="%(message)s"				,					force=__lowerCamelCase    )
              logging.error("Dataset empty"    )
              raise AssertionError


def 			UpperCamelCase					(    ):
       # Create dummy dataset with 2 classes and 3 features
       snake_case       :   str							=      np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]    )
       snake_case       :   Union[str, Any]							=      np.array([0, 0, 0, 1, 1]    )
       snake_case       :   List[Any]							=      2
       snake_case       :   Any							=      2

       # Assert that the function raises an AssertionError if dimensions > classes
       with pytest.raises(__lowerCamelCase    ) as error_info:
              snake_case       :   str							=      linear_discriminant_analysis(
                  __lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )
              if isinstance(__lowerCamelCase				,					np.ndarray    ):
                     raise AssertionError(
                         "Did not raise AssertionError for dimensions > classes"    )
              assert error_info.type is AssertionError


def 			UpperCamelCase					(    ):
       snake_case       :   List[str]							=      np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]    )
       snake_case       :   List[str]							=      2
       snake_case       :   int							=      np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]]    )

       with pytest.raises(__lowerCamelCase    ) as error_info:
              snake_case       :   Union[str, Any]							=      principal_component_analysis(__lowerCamelCase				,					__lowerCamelCase    )
              if not np.allclose(__lowerCamelCase				,					__lowerCamelCase    ):
                     raise AssertionError
              assert error_info.type is AssertionError


if __name__ == "__main__":
   import doctest

   doctest.testmod()





