
'''simple docstring'''

import logging

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

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







def 	A  (   UpperCamelCase_       :  np.ndarray   )       ->							np.ndarray:







			'''simple docstring'''



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







def 	A  (   UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  int   )       ->							np.ndarray:







			'''simple docstring'''



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

			return covariance_sum / features.shape[1]







def 	A  (   UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  int   )       ->							np.ndarray:







			'''simple docstring'''



			lowerCAmelCase__										=				features.mean(1   )
			lowerCAmelCase__										=				np.nan
			for i in range(UpperCamelCase_   ):
						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(UpperCamelCase_   ) - column_reshape(UpperCamelCase_   )    ,						(column_reshape(UpperCamelCase_   ) - column_reshape(UpperCamelCase_   )).T    ,						)
						else:
									# If covariance_sum is np.nan (i.e. first loop)
									lowerCAmelCase__										=				device_data * np.dot(
									    column_reshape(UpperCamelCase_   ) - column_reshape(UpperCamelCase_   )    ,						(column_reshape(UpperCamelCase_   ) - column_reshape(UpperCamelCase_   )).T    ,						)

			return covariance_sum / features.shape[1]







def 	A  (   UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  int   )       ->							np.ndarray:







			'''simple docstring'''



			if features.any():
						lowerCAmelCase__										=				features.mean(1   )
						# Center the dataset
						lowerCAmelCase__										=				features - np.reshape(UpperCamelCase_    ,						(data_mean.size, 1)   )
						lowerCAmelCase__										=				np.dot(UpperCamelCase_    ,						centered_data.T   ) / features.shape[1]
						lowerCAmelCase__						,lowerCAmelCase__										=				np.linalg.eigh(UpperCamelCase_   )
						# 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    ,						UpperCamelCase_   )
						logging.info("Principal Component Analysis computed"   )

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







def 	A  (   UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  int    ,						UpperCamelCase_       :  int   )       ->							np.ndarray:







			'''simple docstring'''



			assert classes > dimensions

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

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







def 	A  (   )       ->							None:







			'''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(UpperCamelCase_   ) as error_info:
						lowerCAmelCase__										=				linear_discriminant_analysis(
						    UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_   )
						if isinstance(UpperCamelCase_    ,						np.ndarray   ):
									raise AssertionError(
									    "Did not raise AssertionError for dimensions > classes"   )
						assert error_info.type is AssertionError







def 	A  (   )       ->							None:







			'''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, 10.39_230_485], [3.0, 3.0, 3.0]]   )

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()









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


UpperCAmelCase   :    str     =		logging.get_logger(__name__)

UpperCAmelCase   :    List[Any]     =		{
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[Any]			       =		"vit_mae"







		def __init__( self		,      A=7_68		,      A=12		,      A=12		,      A=30_72		,      A="gelu"		,      A=0.0		,      A=0.0		,      A=0.02		,      A=1e-1_2		,      A=2_24		,      A=16		,      A=3		,      A=True		,      A=16		,      A=5_12		,      A=8		,      A=20_48		,      A=0.75		,      A=False		,      **A		,      )  -> Dict:







					'''simple docstring'''







					super().__init__(**A	)

					lowerCamelCase    		=  hidden_size
					lowerCamelCase    		=  num_hidden_layers
					lowerCamelCase    		=  num_attention_heads
					lowerCamelCase    		=  intermediate_size
					lowerCamelCase    		=  hidden_act
					lowerCamelCase    		=  hidden_dropout_prob
					lowerCamelCase    		=  attention_probs_dropout_prob
					lowerCamelCase    		=  initializer_range
					lowerCamelCase    		=  layer_norm_eps
					lowerCamelCase    		=  image_size
					lowerCamelCase    		=  patch_size
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  qkv_bias
					lowerCamelCase    		=  decoder_num_attention_heads
					lowerCamelCase    		=  decoder_hidden_size
					lowerCamelCase    		=  decoder_num_hidden_layers
					lowerCamelCase    		=  decoder_intermediate_size
					lowerCamelCase    		=  mask_ratio
					lowerCamelCase    		=  norm_pix_loss

