import logging

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

logging.basicConfig(level=logging.INFO, format='%(message)s')
def       __magic_name__(  __UpperCAmelCase		)						->    np.ndarray:



						'''simple docstring'''

						return input_array.reshape((input_array.size, 1)		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    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       __magic_name__(  )						->    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       __magic_name__(  )						->    None:



						'''simple docstring'''

						_lowerCamelCase			    =				np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
						_lowerCamelCase			    =				2
						_lowerCamelCase			    =				np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 10.3923_0485], [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()
import argparse
import json

from tqdm import tqdm
def       __magic_name__(  )						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				argparse.ArgumentParser()

      # Required parameters
      parser.add_argument(
          '''--src_path''' ,						type=__UpperCAmelCase ,						default='''biencoder-nq-dev.json''' ,						help='''Path to raw DPR training data''' ,						)
      parser.add_argument(
          '''--evaluation_set''' ,						type=__UpperCAmelCase ,						help='''where to store parsed evaluation_set file''' ,						)
      parser.add_argument(
          '''--gold_data_path''' ,						type=__UpperCAmelCase ,						help='''where to store parsed gold_data_path file''' ,						)
      _lowerCamelCase			    =				parser.parse_args()

      with open(args.src_path ,						'''r'''		) as src_file, open(args.evaluation_set ,						'''w'''		) as eval_file, open(
          args.gold_data_path ,						'''w'''		) as gold_file:
            _lowerCamelCase			    =				json.load(__UpperCAmelCase		)
            for dpr_record in tqdm(__UpperCAmelCase		):
                  _lowerCamelCase			    =				dpr_record['''question''']
                  _lowerCamelCase			    =				[context['''title'''] for context in dpr_record['''positive_ctxs''']]
                  eval_file.write(question + '''\n'''		)
                  gold_file.write('''\t'''.join(__UpperCAmelCase		) + '''\n'''		)


if __name__ == "__main__":
     main()