


import logging

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

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





def 	lowerCAmelCase_		(lowercase__						:       np.ndarray       )		->    np.ndarray:


				'''simple docstring'''




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





def 	lowerCAmelCase_		(lowercase__						:       np.ndarray     ,					lowercase__						:       np.ndarray     ,					lowercase__						:       int       )		->    np.ndarray:


				'''simple docstring'''




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

				return covariance_sum / features.shape[1]





def 	lowerCAmelCase_		(lowercase__						:       np.ndarray     ,					lowercase__						:       np.ndarray     ,					lowercase__						:       int       )		->    np.ndarray:


				'''simple docstring'''




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

				return covariance_sum / features.shape[1]





def 	lowerCAmelCase_		(lowercase__						:       np.ndarray     ,					lowercase__						:       int       )		->    np.ndarray:


				'''simple docstring'''




				if features.any():
								lowerCAmelCase__      			=						features.mean(1       )
								# Center the dataset
								lowerCAmelCase__      			=						features - np.reshape(lowercase__     ,					(data_mean.size, 1)       )
								lowerCAmelCase__      			=						np.dot(lowercase__     ,					centered_data.T       ) / features.shape[1]
								lowerCAmelCase__			,		lowerCAmelCase__      			=						np.linalg.eigh(lowercase__       )
								# 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     ,					lowercase__       )
								logging.info('''Principal Component Analysis computed'''       )

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





def 	lowerCAmelCase_		(lowercase__						:       np.ndarray     ,					lowercase__						:       np.ndarray     ,					lowercase__						:       int     ,					lowercase__						:       int       )		->    np.ndarray:


				'''simple docstring'''




				assert classes > dimensions

				# Check if features have been already loaded
				if features.any:
								lowerCAmelCase__			,		lowerCAmelCase__      			=						eigh(
								    covariance_between_classes(lowercase__     ,					lowercase__     ,					lowercase__       )     ,					covariance_within_classes(lowercase__     ,					lowercase__     ,					lowercase__       )     ,					)
								lowerCAmelCase__      			=						eigenvectors[:, ::-1][:, :dimensions]
								lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						np.linalg.svd(lowercase__       )
								lowerCAmelCase__      			=						svd_matrix[:, 0:dimensions]
								lowerCAmelCase__      			=						np.dot(filtered_svd_matrix.T     ,					lowercase__       )
								logging.info('''Linear Discriminant Analysis computed'''       )

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





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





def 	lowerCAmelCase_		()		->    None:


				'''simple docstring'''




				lowerCAmelCase__      			=						np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]       )
				lowerCAmelCase__      			=						2
				lowerCAmelCase__      			=						np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]]       )

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


if __name__ == "__main__":
			import doctest

			doctest.testmod()













import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


_UpperCAmelCase			:		List[Any]   				=    logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.'					)
@require_torch
@require_tf
@slow
class  lowerCAmelCase_       (				unittest.TestCase					):




							def 						__snake_case					(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Path ,							SCREAMING_SNAKE_CASE_     :   Union[str, None] = None ,							SCREAMING_SNAKE_CASE_     :   Union[List[str], None] = None ,							SCREAMING_SNAKE_CASE_     :   Union[str, List[str], None] = None ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							):
											lowerCAmelCase__      			=						[file for file in os.listdir(SCREAMING_SNAKE_CASE_	) if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)	)]

											if identifier is not None:
															lowerCAmelCase__      			=						[file for file in files if identifier in file]

											if n_identifier is not None:
															if isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
																			for n_ in n_identifier:
																							lowerCAmelCase__      			=						[file for file in files if n_ not in file]
															else:
																			lowerCAmelCase__      			=						[file for file in files if n_identifier not in file]

											lowerCAmelCase__      			=						ignore_files or []
											ignore_files.append('''__init__.py'''	)
											lowerCAmelCase__      			=						[file for file in files if file not in ignore_files]

											for file in files:
															# Open all files
															print('''Testing''' ,							SCREAMING_SNAKE_CASE_	)

															if only_modules:
																			lowerCAmelCase__      			=						file.split('''.'''	)[0]
																			try:
																							lowerCAmelCase__      			=						getattr(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)
																							lowerCAmelCase__      			=						doctest.DocTestSuite(SCREAMING_SNAKE_CASE_	)
																							lowerCAmelCase__      			=						unittest.TextTestRunner().run(SCREAMING_SNAKE_CASE_	)
																							self.assertIs(len(result.failures	) ,							0	)
																			except AttributeError:
																							logger.info(f'{module_identifier} is not a module.'	)
															else:
																			lowerCAmelCase__      			=						doctest.testfile(str('''..''' / directory / file	) ,							optionflags=doctest.ELLIPSIS	)
																			self.assertIs(result.failed ,							0	)




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						Path('''src/transformers'''	)
											lowerCAmelCase__      			=						'''modeling'''
											lowerCAmelCase__      			=						[
											    '''modeling_ctrl.py''',
											    '''modeling_tf_ctrl.py''',
											]
											self.analyze_directory(SCREAMING_SNAKE_CASE_ ,							identifier=SCREAMING_SNAKE_CASE_ ,							ignore_files=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Dict	):
											lowerCAmelCase__      			=						Path('''src/transformers'''	)
											lowerCAmelCase__      			=						'''tokenization'''
											self.analyze_directory(SCREAMING_SNAKE_CASE_ ,							identifier=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						Path('''src/transformers'''	)
											lowerCAmelCase__      			=						'''configuration'''
											self.analyze_directory(SCREAMING_SNAKE_CASE_ ,							identifier=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						Path('''src/transformers'''	)
											lowerCAmelCase__      			=						['''configuration''', '''modeling''', '''tokenization''']
											self.analyze_directory(SCREAMING_SNAKE_CASE_ ,							n_identifier=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						Path('''docs/source'''	)
											lowerCAmelCase__      			=						['''favicon.ico''']
											self.analyze_directory(SCREAMING_SNAKE_CASE_ ,							ignore_files=SCREAMING_SNAKE_CASE_ ,							only_modules=SCREAMING_SNAKE_CASE_	)





