





'''simple docstring'''




import logging

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

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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							np.ndarray		)		->  np.ndarray:
   return input_array.reshape((input_array.size, 1)		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							np.ndarray      ,    __lowerCAmelCase       :							np.ndarray      ,    __lowerCAmelCase       :							int		)		->  np.ndarray:
   snake_case 						=   np.nan
   for i in range(__lowerCAmelCase		):
      snake_case 						=   features[:, labels == i]
      snake_case 						=   data.mean(1		)
      # Centralize the data of class i
      snake_case 						=   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)
         snake_case 						=   np.dot(__lowerCAmelCase      ,    centered_data.T		)

   return covariance_sum / features.shape[1]





def 	__lowerCamelCase			(      __lowerCAmelCase       :							np.ndarray      ,    __lowerCAmelCase       :							np.ndarray      ,    __lowerCAmelCase       :							int		)		->  np.ndarray:
   snake_case 						=   features.mean(1		)
   snake_case 						=   np.nan
   for i in range(__lowerCAmelCase		):
      snake_case 						=   features[:, labels == i]
      snake_case 						=   data.shape[1]
      snake_case 						=   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)
         snake_case 						=   device_data * np.dot(
             column_reshape(__lowerCAmelCase		) - column_reshape(__lowerCAmelCase		)      ,    (column_reshape(__lowerCAmelCase		) - column_reshape(__lowerCAmelCase		)).T      ,    )

   return covariance_sum / features.shape[1]





def 	__lowerCamelCase			(      __lowerCAmelCase       :							np.ndarray      ,    __lowerCAmelCase       :							int		)		->  np.ndarray:

   # Check if the features have been loaded
   if features.any():
      snake_case 						=   features.mean(1		)
      # Center the dataset
      snake_case 						=   features - np.reshape(__lowerCAmelCase      ,    (data_mean.size, 1)		)
      snake_case 						=   np.dot(__lowerCAmelCase      ,    centered_data.T		) / features.shape[1]
      snake_case						,    snake_case 						=   np.linalg.eigh(__lowerCAmelCase		)
      # Take all the columns in the reverse order (-1), and then takes only the first
      snake_case 						=   eigenvectors[:, ::-1][:, 0:dimensions]
      # Project the database on the new space
      snake_case 						=   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 	__lowerCamelCase			(      __lowerCAmelCase       :							np.ndarray      ,    __lowerCAmelCase       :							np.ndarray      ,    __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							int		)		->  np.ndarray:
   assert classes > dimensions

   # Check if features have been already loaded
   if features.any:
      snake_case						,    snake_case 						=   eigh(
          covariance_between_classes(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)      ,    covariance_within_classes(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)      ,    )
      snake_case 						=   eigenvectors[:, ::-1][:, :dimensions]
      snake_case						,    snake_case						,    snake_case 						=   np.linalg.svd(__lowerCAmelCase		)
      snake_case 						=   svd_matrix[:, 0:dimensions]
      snake_case 						=   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 	__lowerCamelCase			(      )		->  None:
   # Create dummy dataset with 2 classes and 3 features
   snake_case 						=   np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]		)
   snake_case 						=   np.array([0, 0, 0, 1, 1]		)
   snake_case 						=   2
   snake_case 						=   2

   # Assert that the function raises an AssertionError if dimensions > classes
   with pytest.raises(__lowerCAmelCase		) as error_info:
      snake_case 						=   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 	__lowerCamelCase			(      )		->  None:
   snake_case 						=   np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
   snake_case 						=   2
   snake_case 						=   np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]]		)

   with pytest.raises(__lowerCAmelCase		) as error_info:
      snake_case 						=   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 logging

from transformers.configuration_utils import PretrainedConfig


_SCREAMING_SNAKE_CASE							     =			logging.getLogger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "masked_bert"



     def __init__(   self							:			Any   ,       __snake_case							:			Optional[Any]=3_05_22   ,       __snake_case							:			List[Any]=7_68   ,       __snake_case							:			int=12   ,       __snake_case							:			List[str]=12   ,       __snake_case							:			int=30_72   ,       __snake_case							:			Optional[int]="gelu"   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			List[str]=5_12   ,       __snake_case							:			Tuple=2   ,       __snake_case							:			Tuple=0.02   ,       __snake_case							:			Tuple=1e-12   ,       __snake_case							:			Union[str, Any]=0   ,       __snake_case							:			int="topK"   ,       __snake_case							:			int="constant"   ,       __snake_case							:			List[str]=0.0   ,       **__snake_case							:			Any   ,       )->	Union[str, Any]:
        super().__init__(pad_token_id=__snake_case   ,       **__snake_case						)

        snake_case 						=   vocab_size
        snake_case 						=   hidden_size
        snake_case 						=   num_hidden_layers
        snake_case 						=   num_attention_heads
        snake_case 						=   hidden_act
        snake_case 						=   intermediate_size
        snake_case 						=   hidden_dropout_prob
        snake_case 						=   attention_probs_dropout_prob
        snake_case 						=   max_position_embeddings
        snake_case 						=   type_vocab_size
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   pruning_method
        snake_case 						=   mask_init
        snake_case 						=   mask_scale

