
import logging

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

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






def      lowerCamelCase__       (       __lowerCAmelCase						:						Any       ):




			"""simple docstring"""


			return input_array.reshape((input_array.size, 1)       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			np.nan
			for i in range(_SCREAMING_SNAKE_CASE       ):
						lowerCAmelCase_      			=			features[:, labels == i]
						lowerCAmelCase_      			=			data.mean(1       )
						# Centralize the data of class i
						lowerCAmelCase_      			=			data - column_reshape(_SCREAMING_SNAKE_CASE       )
						if i > 0:
									# If covariance_sum is not None
									covariance_sum += np.dot(_SCREAMING_SNAKE_CASE					,							centered_data.T       )
						else:
									# If covariance_sum is np.nan (i.e. first loop)
									lowerCAmelCase_      			=			np.dot(_SCREAMING_SNAKE_CASE					,							centered_data.T       )

			return covariance_sum / features.shape[1]






def      lowerCamelCase__       (       __lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			features.mean(1       )
			lowerCAmelCase_      			=			np.nan
			for i in range(_SCREAMING_SNAKE_CASE       ):
						lowerCAmelCase_      			=			features[:, labels == i]
						lowerCAmelCase_      			=			data.shape[1]
						lowerCAmelCase_      			=			data.mean(1       )
						if i > 0:
									# If covariance_sum is not None
									covariance_sum += device_data * np.dot(
									    column_reshape(_SCREAMING_SNAKE_CASE       ) - column_reshape(_SCREAMING_SNAKE_CASE       )					,							(column_reshape(_SCREAMING_SNAKE_CASE       ) - column_reshape(_SCREAMING_SNAKE_CASE       )).T					,							)
						else:
									# If covariance_sum is np.nan (i.e. first loop)
									lowerCAmelCase_      			=			device_data * np.dot(
									    column_reshape(_SCREAMING_SNAKE_CASE       ) - column_reshape(_SCREAMING_SNAKE_CASE       )					,							(column_reshape(_SCREAMING_SNAKE_CASE       ) - column_reshape(_SCREAMING_SNAKE_CASE       )).T					,							)

			return covariance_sum / features.shape[1]






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			if features.any():
						lowerCAmelCase_      			=			features.mean(1       )
						# Center the dataset
						lowerCAmelCase_      			=			features - np.reshape(_SCREAMING_SNAKE_CASE					,							(data_mean.size, 1)       )
						lowerCAmelCase_      			=			np.dot(_SCREAMING_SNAKE_CASE					,							centered_data.T       ) / features.shape[1]
						lowerCAmelCase_			,						lowerCAmelCase_      			=			np.linalg.eigh(_SCREAMING_SNAKE_CASE       )
						# Take all the columns in the reverse order (-1), and then takes only the first
						lowerCAmelCase_      			=			eigenvectors[:, ::-1][:, 0:dimensions]
						# Project the database on the new space
						lowerCAmelCase_      			=			np.dot(filtered_eigenvectors.T					,							_SCREAMING_SNAKE_CASE       )
						logging.info("Principal Component Analysis computed"       )

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






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Optional[Any]					,							__lowerCAmelCase						:						List[Any]       ):




			"""simple docstring"""


			assert classes > dimensions

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

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






def      lowerCamelCase__       (       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]       )
			lowerCAmelCase_      			=			np.array([0, 0, 0, 1, 1]       )
			lowerCAmelCase_      			=			2
			lowerCAmelCase_      			=			2

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






def      lowerCamelCase__       (       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]       )
			lowerCAmelCase_      			=			2
			lowerCAmelCase_      			=			np.array([[6.92_820_323, 8.66_025_404, 10.39230485], [3.0, 3.0, 3.0]]       )

			with pytest.raises(_SCREAMING_SNAKE_CASE       ) as error_info:
						lowerCAmelCase_      			=			principal_component_analysis(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       )
						if not np.allclose(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE       ):
									raise AssertionError
						assert error_info.type is AssertionError


if __name__ == "__main__":
			import doctest

			doctest.testmod()




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A     =  {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Wav2Vec2ForAudioFrameClassification",
			    "Wav2Vec2ForCTC",
			    "Wav2Vec2ForMaskedLM",
			    "Wav2Vec2ForPreTraining",
			    "Wav2Vec2ForSequenceClassification",
			    "Wav2Vec2ForXVector",
			    "Wav2Vec2Model",
			    "Wav2Vec2PreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFWav2Vec2ForCTC",
			    "TFWav2Vec2Model",
			    "TFWav2Vec2PreTrainedModel",
			    "TFWav2Vec2ForSequenceClassification",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "FlaxWav2Vec2ForCTC",
			    "FlaxWav2Vec2ForPreTraining",
			    "FlaxWav2Vec2Model",
			    "FlaxWav2Vec2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
			from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
			from .processing_wavaveca import WavaVecaProcessor
			from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavaveca import (
						    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavaVecaForAudioFrameClassification,
						    WavaVecaForCTC,
						    WavaVecaForMaskedLM,
						    WavaVecaForPreTraining,
						    WavaVecaForSequenceClassification,
						    WavaVecaForXVector,
						    WavaVecaModel,
						    WavaVecaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFWavaVecaForCTC,
						    TFWavaVecaForSequenceClassification,
						    TFWavaVecaModel,
						    TFWavaVecaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    FlaxWavaVecaForCTC,
						    FlaxWavaVecaForPreTraining,
						    FlaxWavaVecaModel,
						    FlaxWavaVecaPreTrainedModel,
						)


else:
			import sys

			_A     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



