






import logging

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

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

def 	__lowerCamelCase      (		snake_case__		)							->  np.ndarray:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  np.ndarray:






						"""simple docstring"""





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

						return covariance_sum / features.shape[1]

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  np.ndarray:






						"""simple docstring"""





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

						return covariance_sum / features.shape[1]

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  np.ndarray:






						"""simple docstring"""





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

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

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__		)							->  np.ndarray:






						"""simple docstring"""





						assert classes > dimensions

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

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

def 	__lowerCamelCase      (		)							->  None:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]		)
						_SCREAMING_SNAKE_CASE     				=  np.array([0, 0, 0, 1, 1]		)
						_SCREAMING_SNAKE_CASE     				=  2
						_SCREAMING_SNAKE_CASE     				=  2

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

def 	__lowerCamelCase      (		)							->  None:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
						_SCREAMING_SNAKE_CASE     				=  2
						_SCREAMING_SNAKE_CASE     				=  np.array([[6.92_820_323, 8.66_025_404, 10.39_230_485], [3.0, 3.0, 3.0]]		)

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()









import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				int									=							TransfoXLTokenizer
			__snake_case    :				Tuple									=							False
			__snake_case    :				List[Any]									=							False







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									super().setUp()

									_SCREAMING_SNAKE_CASE     				=  [
									    """<unk>""",
									    """[CLS]""",
									    """[SEP]""",
									    """want""",
									    """unwanted""",
									    """wa""",
									    """un""",
									    """running""",
									    """,""",
									    """low""",
									    """l""",
									]
									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""vocab_file"""]					)
									with open(self.vocab_file				,     """w"""				,     encoding="""utf-8"""					) as vocab_writer:
															vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)







			def 							UpperCamelCase				(     self:  Any				,     **UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  True
									return TransfoXLTokenizer.from_pretrained(self.tmpdirname				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """<unk> UNwanted , running"""
									_SCREAMING_SNAKE_CASE     				=  """<unk> unwanted, running"""
									return input_text, output_text







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(vocab_file=self.vocab_file				,     lower_case=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize("""<unk> UNwanted , running"""					)
									self.assertListEqual(UpperCAmelCase_				,     ["""<unk>""", """unwanted""", """,""", """running"""]					)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     [0, 4, 8, 7]					)







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(lower_case=UpperCAmelCase_					)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """					)				,     ["""hello""", """!""", """how""", """are""", """you""", """?"""]					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(lower_case=UpperCAmelCase_					)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """					)				,     ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(lower_case=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  """Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
									_SCREAMING_SNAKE_CASE     				=  [
									    """Hello""",
									    """(""",
									    """bracket""",
									    """)""",
									    """and""",
									    """side""",
									    """@-@""",
									    """scrolled""",
									    """[""",
									    """and""",
									    """]""",
									    """Henry""",
									    """'s""",
									    """$""",
									    """5""",
									    """@,@""",
									    """000""",
									    """with""",
									    """3""",
									    """@.@""",
									    """34""",
									    """m""",
									    """.""",
									    """What""",
									    """'s""",
									    """up""",
									    """!""",
									    """?""",
									]

									self.assertListEqual(tokenizer.tokenize(UpperCAmelCase_					)				,     UpperCAmelCase_					)

									self.assertEqual(tokenizer.convert_tokens_to_string(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_tokenizer()
									_SCREAMING_SNAKE_CASE     				=  len(UpperCAmelCase_					)

									tokenizer.add_tokens(["""new1""", """new2"""]					)
									tokenizer.move_added_token("""new1"""				,     1					)

									# Check that moved token is not copied (duplicate)
									self.assertEqual(len(UpperCAmelCase_					)				,     original_len + 2					)
									# Check that token is moved to specified id
									self.assertEqual(tokenizer.encode("""new1"""					)				,     [1]					)
									self.assertEqual(tokenizer.decode([1]					)				,     """new1"""					)

