



'''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      (			lowerCamelCase__   ):
			'''simple docstring'''





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



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





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

			return covariance_sum / features.shape[1]



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





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

			return covariance_sum / features.shape[1]



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





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

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



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			assert classes > dimensions

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

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



def      a      (			):
			'''simple docstring'''





			A_     :						Tuple     =					np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]   )
			A_     :						Union[str, Any]     =					np.array([0, 0, 0, 1, 1]   )
			A_     :						Dict     =					2
			A_     :						str     =					2

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



def      a      (			):
			'''simple docstring'''





			A_     :						Any     =					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]   )
			A_     :						Union[str, Any]     =					2
			A_     :						Optional[int]     =					np.array([[6.92_820_323, 8.66_025_404, 10.39_230_485], [3.0, 3.0, 3.0]]   )

			with pytest.raises(lowerCamelCase__   ) as error_info:
						A_     :						Optional[int]     =					principal_component_analysis(lowerCamelCase__						,				lowerCamelCase__   )
						if not np.allclose(lowerCamelCase__						,				lowerCamelCase__   ):
									raise AssertionError
						assert error_info.type is AssertionError


if __name__ == "__main__":
						import doctest

						doctest.testmod()




'''simple docstring'''




import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self							):
				A_     :						Dict     =					10


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4]
				A_     :						List[str]     =					[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				A_     :						Tuple     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						List[Any]     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
				A_     :						str     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						Any     =					"""It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
				A_,   A_     :						Optional[int]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					""""""
				A_,   A_     :						Union[str, Any]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						List[Any]     =					(
				    """It was the year of Our Lord one thousand seven hundred and """
				    """seventy-five\n\nSpiritual revelations were conceded to England """
				    """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
				)
				A_,   A_     :						int     =					process_story(lowercase							)

				A_     :						List[Any]     =					[
				    """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
				    """Spiritual revelations were conceded to England at that favoured period, as at this.""",
				]
				self.assertEqual(lowercase	,				lowercase							)

				A_     :						Tuple     =					["""It was the best of times."""]
				self.assertEqual(lowercase	,				lowercase							)


	def    _a     (self							):
				A_     :						Dict     =					torch.tensor([1, 2, 3, 4]							)
				A_     :						Union[str, Any]     =					torch.tensor([1, 1, 1, 1]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				0							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					torch.tensor([1, 2, 3, 4, 23, 23, 23]							)
				A_     :						List[str]     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				23							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Optional[Any]     =					torch.tensor([8, 2, 3, 4, 1, 1, 1]							)
				A_     :						Tuple     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				1							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						List[str]     =					101
				A_     :						str     =					torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]							)
				A_     :						int     =					torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]							)

				A_     :						Optional[Any]     =					compute_token_type_ids(lowercase	,				lowercase							)
				np.testing.assert_array_equal(lowercase	,				lowercase							)