




import logging

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

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




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				return input_array.reshape((input_array.size, 1)   )




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Tuple								=np.nan
				for i in range(_SCREAMING_SNAKE_CASE   ):
								__magic_name__	:	List[str]								=features[:, labels == i]
								__magic_name__	:	Any								=data.mean(1   )
								# Centralize the data of class i
								__magic_name__	:	str								=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)
												__magic_name__	:	Optional[Any]								=np.dot(_SCREAMING_SNAKE_CASE	,     centered_data.T   )

				return covariance_sum / features.shape[1]




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Optional[int]								=features.mean(1   )
				__magic_name__	:	Any								=np.nan
				for i in range(_SCREAMING_SNAKE_CASE   ):
								__magic_name__	:	Dict								=features[:, labels == i]
								__magic_name__	:	Optional[Any]								=data.shape[1]
								__magic_name__	:	int								=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)
												__magic_name__	:	Any								=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	,     lowerCamelCase   ):

				# Check if the features have been loaded
				if features.any():
								__magic_name__	:	int								=features.mean(1   )
								# Center the dataset
								__magic_name__	:	str								=features - np.reshape(_SCREAMING_SNAKE_CASE	,     (data_mean.size, 1)   )
								__magic_name__	:	Union[str, Any]								=np.dot(_SCREAMING_SNAKE_CASE	,     centered_data.T   ) / features.shape[1]
								__magic_name__				, __magic_name__	:	Tuple								=np.linalg.eigh(_SCREAMING_SNAKE_CASE   )
								# Take all the columns in the reverse order (-1), and then takes only the first
								__magic_name__	:	List[Any]								=eigenvectors[:, ::-1][:, 0:dimensions]
								# Project the database on the new space
								__magic_name__	:	List[str]								=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	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				assert classes > dimensions

				# Check if features have been already loaded
				if features.any:
								__magic_name__				, __magic_name__	:	Optional[Any]								=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   )	,     )
								__magic_name__	:	Optional[Any]								=eigenvectors[:, ::-1][:, :dimensions]
								__magic_name__				, __magic_name__				, __magic_name__	:	str								=np.linalg.svd(_SCREAMING_SNAKE_CASE   )
								__magic_name__	:	Dict								=svd_matrix[:, 0:dimensions]
								__magic_name__	:	Optional[int]								=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_   (	):
				# Create dummy dataset with 2 classes and 3 features
				__magic_name__	:	str								=np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]   )
				__magic_name__	:	int								=np.array([0, 0, 0, 1, 1]   )
				__magic_name__	:	Union[str, Any]								=2
				__magic_name__	:	Dict								=2

				# Assert that the function raises an AssertionError if dimensions > classes
				with pytest.raises(_SCREAMING_SNAKE_CASE   ) as error_info:
								__magic_name__	:	Optional[Any]								=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_   (	):
				__magic_name__	:	Any								=np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]   )
				__magic_name__	:	List[str]								=2
				__magic_name__	:	List[Any]								=np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 10.39230485], [3.0, 3.0, 3.0]]   )

				with pytest.raises(_SCREAMING_SNAKE_CASE   ) as error_info:
								__magic_name__	:	Optional[int]								=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()










import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""Speech2TextFeatureExtractor"""
						UpperCamelCase     						=					"""Speech2TextTokenizer"""

						def __init__(		self   :Any    , __snake_case   :int    , __snake_case   :Any ):
										'''simple docstring'''

										super().__init__(__snake_case    , __snake_case )
										__magic_name__	:	Optional[int]								=self.feature_extractor
										__magic_name__	:	Any								=False

						def __call__(		self   :Tuple    , *__snake_case   :List[str]    , **__snake_case   :List[str] ):
										'''simple docstring'''

										if self._in_target_context_manager:
														return self.current_processor(*__snake_case    , **__snake_case )

										if "raw_speech" in kwargs:
														warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
														__magic_name__	:	Tuple								=kwargs.pop("""raw_speech""" )
										else:
														__magic_name__	:	List[Any]								=kwargs.pop("""audio"""    , __snake_case )
										__magic_name__	:	Union[str, Any]								=kwargs.pop("""sampling_rate"""    , __snake_case )
										__magic_name__	:	Dict								=kwargs.pop("""text"""    , __snake_case )
										if len(__snake_case ) > 0:
														__magic_name__	:	Union[str, Any]								=args[0]
														__magic_name__	:	int								=args[1:]

										if audio is None and text is None:
														raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )

										if audio is not None:
														__magic_name__	:	List[str]								=self.feature_extractor(__snake_case    , *__snake_case    , sampling_rate=__snake_case    , **__snake_case )
										if text is not None:
														__magic_name__	:	Any								=self.tokenizer(__snake_case    , **__snake_case )

										if text is None:
														return inputs
										elif audio is None:
														return encodings
										else:
														__magic_name__	:	List[str]								=encodings["""input_ids"""]
														return inputs

						def     A__						(		self   :List[Any]    , *__snake_case   :str    , **__snake_case   :List[str] ):
										'''simple docstring'''

										return self.tokenizer.batch_decode(*__snake_case    , **__snake_case )

						def     A__						(		self   :int    , *__snake_case   :int    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										return self.tokenizer.decode(*__snake_case    , **__snake_case )


						@contextmanager
						def     A__						(		self   :int ):
										'''simple docstring'''

										warnings.warn(
										    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
										    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
										    """your audio inputs, or in a separate call.""" )
										__magic_name__	:	int								=True
										__magic_name__	:	List[Any]								=self.tokenizer
										yield
										__magic_name__	:	List[Any]								=self.feature_extractor
										__magic_name__	:	str								=False




