from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__				= logging.get_logger(__name__)

snake_case__				= {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  'autoformer'
     A_										=  {
         'hidden_size': 'd_model',
         'num_attention_heads': 'encoder_attention_heads',
         'num_hidden_layers': 'encoder_layers',
     }



     def __init__(					self				,				A_ = None				,				A_ = None				,				A_ = "student_t"				,				A_ = "nll"				,				A_ = 1				,				A_ = [1, 2, 3, 4, 5, 6, 7]				,				A_ = True				,				A_ = 0				,				A_ = 0				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_ = 64				,				A_ = 2				,				A_ = 2				,				A_ = 2				,				A_ = 2				,				A_ = 32				,				A_ = 32				,				A_ = "gelu"				,				A_ = 0.1				,				A_ = 0.1				,				A_ = 0.1				,				A_ = 0.1				,				A_ = 0.1				,				A_ = 1_00				,				A_ = 0.02				,				A_ = True				,				A_=True				,				A_ = 10				,				A_ = 25				,				A_ = 3				,				**A_				,				) ->				str:






           """simple docstring"""
           # time series specific configuration
           _lowerCamelCase			    =				prediction_length
           _lowerCamelCase			    =				context_length if context_length is not None else prediction_length
           _lowerCamelCase			    =				distribution_output
           _lowerCamelCase			    =				loss
           _lowerCamelCase			    =				input_size
           _lowerCamelCase			    =				num_time_features
           _lowerCamelCase			    =				lags_sequence
           _lowerCamelCase			    =				scaling
           _lowerCamelCase			    =				num_dynamic_real_features
           _lowerCamelCase			    =				num_static_real_features
           _lowerCamelCase			    =				num_static_categorical_features
           if cardinality is not None and num_static_categorical_features > 0:
                 if len(A_						) != num_static_categorical_features:
                       raise ValueError(
                           '''The cardinality should be a list of the same length as `num_static_categorical_features`'''						)
                 _lowerCamelCase			    =				cardinality
           else:
                 _lowerCamelCase			    =				[0]
           if embedding_dimension is not None and num_static_categorical_features > 0:
                 if len(A_						) != num_static_categorical_features:
                       raise ValueError(
                           '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''						)
                 _lowerCamelCase			    =				embedding_dimension
           else:
                 _lowerCamelCase			    =				[min(50				,				(cat + 1) // 2						) for cat in self.cardinality]
           _lowerCamelCase			    =				num_parallel_samples

           # Transformer architecture configuration
           _lowerCamelCase			    =				input_size * len(self.lags_sequence						) + self._number_of_features
           _lowerCamelCase			    =				d_model
           _lowerCamelCase			    =				encoder_attention_heads
           _lowerCamelCase			    =				decoder_attention_heads
           _lowerCamelCase			    =				encoder_ffn_dim
           _lowerCamelCase			    =				decoder_ffn_dim
           _lowerCamelCase			    =				encoder_layers
           _lowerCamelCase			    =				decoder_layers

           _lowerCamelCase			    =				dropout
           _lowerCamelCase			    =				attention_dropout
           _lowerCamelCase			    =				activation_dropout
           _lowerCamelCase			    =				encoder_layerdrop
           _lowerCamelCase			    =				decoder_layerdrop

           _lowerCamelCase			    =				activation_function
           _lowerCamelCase			    =				init_std

           _lowerCamelCase			    =				use_cache

           # Autoformer
           _lowerCamelCase			    =				label_length
           _lowerCamelCase			    =				moving_average
           _lowerCamelCase			    =				autocorrelation_factor

           super().__init__(is_encoder_decoder=A_				,				**A_						)





     @property
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return (
               sum(self.embedding_dimension						)
               + self.num_dynamic_real_features
               + self.num_time_features
               + self.num_static_real_features
               + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
           )
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()