

import logging

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

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





def a(lowercase__	):





	'''simple docstring'''
	return input_array.reshape((input_array.size, 1)	)





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= np.nan
	for i in range(SCREAMING_SNAKE_CASE__	):
		snake_case_      		= features[:, labels == i]
		snake_case_      		= data.mean(1	)
		# Centralize the data of class i
		snake_case_      		= 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)
			snake_case_      		= np.dot(SCREAMING_SNAKE_CASE__			, centered_data.T	)

	return covariance_sum / features.shape[1]





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= features.mean(1	)
	snake_case_      		= np.nan
	for i in range(SCREAMING_SNAKE_CASE__	):
		snake_case_      		= features[:, labels == i]
		snake_case_      		= data.shape[1]
		snake_case_      		= 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)
			snake_case_      		= 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 a(lowercase__			, lowercase__	):





	'''simple docstring'''





	if features.any():
		snake_case_      		= features.mean(1	)
		# Center the dataset
		snake_case_      		= features - np.reshape(SCREAMING_SNAKE_CASE__			, (data_mean.size, 1)	)
		snake_case_      		= np.dot(SCREAMING_SNAKE_CASE__			, centered_data.T	) / features.shape[1]
		snake_case_      		= np.linalg.eigh(SCREAMING_SNAKE_CASE__	)
		# Take all the columns in the reverse order (-1), and then takes only the first
		snake_case_      		= eigenvectors[:, ::-1][:, 0:dimensions]
		# Project the database on the new space
		snake_case_      		= 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 a(lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	assert classes > dimensions

	# Check if features have been already loaded
	if features.any:
		snake_case_      		= 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__	)			, )
		snake_case_      		= eigenvectors[:, ::-1][:, :dimensions]
		snake_case_      		= np.linalg.svd(SCREAMING_SNAKE_CASE__	)
		snake_case_      		= svd_matrix[:, 0:dimensions]
		snake_case_      		= 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 a():





	'''simple docstring'''
	snake_case_      		= np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]	)
	snake_case_      		= np.array([0, 0, 0, 1, 1]	)
	snake_case_      		= 2
	snake_case_      		= 2

	# Assert that the function raises an AssertionError if dimensions > classes
	with pytest.raises(SCREAMING_SNAKE_CASE__	) as error_info:
		snake_case_      		= 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 a():





	'''simple docstring'''
	snake_case_      		= np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]	)
	snake_case_      		= 2
	snake_case_      		= np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]]	)

	with pytest.raises(SCREAMING_SNAKE_CASE__	) as error_info:
		snake_case_      		= 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 functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A											=     logging.get_logger(__name__)

A											=     {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  """unispeech-sat"""


			def __init__(    self ,				__UpperCamelCase=32 ,				__UpperCamelCase=7_68 ,				__UpperCamelCase=12 ,				__UpperCamelCase=12 ,				__UpperCamelCase=30_72 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=1E-5 ,				__UpperCamelCase="group" ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,				__UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) ,				__UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) ,				__UpperCamelCase=False ,				__UpperCamelCase=1_28 ,				__UpperCamelCase=16 ,				__UpperCamelCase=False ,				__UpperCamelCase=True ,				__UpperCamelCase=0.05 ,				__UpperCamelCase=10 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=10 ,				__UpperCamelCase=0 ,				__UpperCamelCase=3_20 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=1_00 ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase="mean" ,				__UpperCamelCase=False ,				__UpperCamelCase=False ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00) ,				__UpperCamelCase=(5, 3, 3, 1, 1) ,				__UpperCamelCase=(1, 2, 3, 1, 1) ,				__UpperCamelCase=5_12 ,				__UpperCamelCase=0 ,				__UpperCamelCase=1 ,				__UpperCamelCase=2 ,				__UpperCamelCase=5_04 ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase ,				pad_token_id=__UpperCamelCase ,				bos_token_id=__UpperCamelCase ,				eos_token_id=__UpperCamelCase							)
				snake_case_      		= hidden_size
				snake_case_      		= feat_extract_norm
				snake_case_      		= feat_extract_activation
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= conv_bias
				snake_case_      		= num_conv_pos_embeddings
				snake_case_      		= num_conv_pos_embedding_groups
				snake_case_      		= len(self.conv_dim							)
				snake_case_      		= num_hidden_layers
				snake_case_      		= intermediate_size
				snake_case_      		= hidden_act
				snake_case_      		= num_attention_heads
				snake_case_      		= hidden_dropout
				snake_case_      		= attention_dropout
				snake_case_      		= activation_dropout
				snake_case_      		= feat_proj_dropout
				snake_case_      		= final_dropout
				snake_case_      		= layerdrop
				snake_case_      		= layer_norm_eps
				snake_case_      		= initializer_range
				snake_case_      		= vocab_size
				snake_case_      		= num_clusters
				snake_case_      		= do_stable_layer_norm
				snake_case_      		= use_weighted_layer_sum

				if (
				    (len(self.conv_stride							) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
				    or (len(self.conv_dim							) != self.num_feat_extract_layers)
				):
					raise ValueError(
					    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
					    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
					    f""" {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,"""
					    f""" `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				snake_case_      		= apply_spec_augment
				snake_case_      		= mask_time_prob
				snake_case_      		= mask_time_length
				snake_case_      		= mask_time_min_masks
				snake_case_      		= mask_feature_prob
				snake_case_      		= mask_feature_length
				snake_case_      		= mask_feature_min_masks

				# parameters for pretraining with codevector quantized representations
				snake_case_      		= num_codevectors_per_group
				snake_case_      		= num_codevector_groups
				snake_case_      		= contrastive_logits_temperature
				snake_case_      		= feat_quantizer_dropout
				snake_case_      		= num_negatives
				snake_case_      		= codevector_dim
				snake_case_      		= proj_codevector_dim
				snake_case_      		= diversity_loss_weight

				# ctc loss
				snake_case_      		= ctc_loss_reduction
				snake_case_      		= ctc_zero_infinity

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				snake_case_      		= classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= xvector_output_dim







			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return functools.reduce(operator.mul ,				self.conv_stride ,				1							)
