
import logging

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

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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    return input_array.reshape((input_array.size, 1)							)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):

    # 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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    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 	SCREAMING_SNAKE_CASE__    (      ):
    # 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 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__						=				np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]							)
    snake_case__						=				2
    snake_case__						=				np.array([[6.92_820_323, 8.66_025_404, 10.39_230_485], [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()





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Any    	=     (KDPMaDiscreteScheduler,)
      _A						:		Dict    	=     10
      def A_			(			self		,	**lowerCamelCase ):
          snake_case__						=				{
              "num_train_timesteps": 11_00,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
          }

          config.update(**lowerCamelCase )
          return config
      def A_			(			self ):
          for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=lowerCamelCase )
      def A_			(			self ):
          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,	[0.0_0_0_2, 0.0_0_2, 0.0_2] ):
              self.check_over_configs(beta_start=lowerCamelCase		,	beta_end=lowerCamelCase )
      def A_			(			self ):
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=lowerCamelCase )
      def A_			(			self ):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config(prediction_type="v_prediction" )
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps )

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__						=				sample.to(lowerCamelCase )

          for i, t in enumerate(scheduler.timesteps ):
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2
              assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2
              assert abs(result_mean.item() - 0.0_0_0_2 ) < 1e-3
      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps )

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__						=				sample.to(lowerCamelCase )

          for i, t in enumerate(scheduler.timesteps ):
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps		,	device=lowerCamelCase )

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter.to(lowerCamelCase ) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if str(lowerCamelCase ).startswith("cpu" ):
              # The following sum varies between 148 and 156 on mps. Why?
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



