'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


snake_case__				= {
    'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ErnieForCausalLM',
         'ErnieForMaskedLM',
         'ErnieForMultipleChoice',
         'ErnieForNextSentencePrediction',
         'ErnieForPreTraining',
         'ErnieForQuestionAnswering',
         'ErnieForSequenceClassification',
         'ErnieForTokenClassification',
         'ErnieModel',
         'ErniePreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ernie import (
              ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
              ErnieForCausalLM,
              ErnieForMaskedLM,
              ErnieForMultipleChoice,
              ErnieForNextSentencePrediction,
              ErnieForPreTraining,
              ErnieForQuestionAnswering,
              ErnieForSequenceClassification,
              ErnieForTokenClassification,
              ErnieModel,
              ErniePreTrainedModel,
          )

else:
     import sys

     snake_case__				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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, 1_0.3_9_2_3_0_4_8_5], [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()