


import logging

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

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					np.ndarray				):
     return input_array.reshape((input_array.size, 1)				)

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

     return covariance_sum / features.shape[1]

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

     return covariance_sum / features.shape[1]

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					np.ndarray			,     SCREAMING_SNAKE_CASE__			:					int				):

     # Check if the features have been loaded
     if features.any():
          __UpperCamelCase    							=features.mean(1				)
          # Center the dataset
          __UpperCamelCase    							=features - np.reshape(SCREAMING_SNAKE_CASE__			,     (data_mean.size, 1)				)
          __UpperCamelCase    							=np.dot(SCREAMING_SNAKE_CASE__			,     centered_data.T				) / features.shape[1]
          __UpperCamelCase							,      __UpperCamelCase    							=np.linalg.eigh(SCREAMING_SNAKE_CASE__				)
          # Take all the columns in the reverse order (-1), and then takes only the first
          __UpperCamelCase    							=eigenvectors[:, ::-1][:, 0:dimensions]
          # Project the database on the new space
          __UpperCamelCase    							=np.dot(filtered_eigenvectors.T			,     SCREAMING_SNAKE_CASE__				)
          logging.info('Principal Component Analysis computed'				)

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					np.ndarray			,     SCREAMING_SNAKE_CASE__			:					np.ndarray			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int				):
     assert classes > dimensions

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

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

def 						_UpperCAmelCase				(			):
     # Create dummy dataset with 2 classes and 3 features
     __UpperCamelCase    							=np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]				)
     __UpperCamelCase    							=np.array([0, 0, 0, 1, 1]				)
     __UpperCamelCase    							=2
     __UpperCamelCase    							=2

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

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

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()









'''simple docstring'''

import unittest

from transformers import DonutProcessor


lowerCamelCase							:   Tuple  						=	'naver-clova-ix/donut-base'
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= DonutProcessor.from_pretrained(UpperCamelCase )






      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= {
                 '''name''': '''John Doe''',
                 '''age''': '''99''',
                 '''city''': '''Atlanta''',
                 '''state''': '''GA''',
                 '''zip''': '''30301''',
                 '''phone''': '''123-4567''',
                 '''nicknames''': [{'''nickname''': '''Johnny'''}, {'''nickname''': '''JD'''}],
             }

             lowercase__      			= (
                 '''<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'''
                 '''<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'''
                 '''<s_nicknames><s_nickname>Johnny</s_nickname>'''
                 '''<sep/><s_nickname>JD</s_nickname></s_nicknames>'''
             )
             lowercase__      			= self.processor.tokenajson(UpperCamelCase )

             self.assertDictEqual(UpperCamelCase   ,						UpperCamelCase )




