import logging

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

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







def      _a					(   lowercase__	:      np.ndarray			):







     '''simple docstring'''


     return input_array.reshape((input_array.size, 1)			)







def      _a					(   lowercase__	:      np.ndarray  ,	lowercase__	:      np.ndarray  ,	lowercase__	:      int			):







     '''simple docstring'''


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

     return covariance_sum / features.shape[1]







def      _a					(   lowercase__	:      np.ndarray  ,	lowercase__	:      np.ndarray  ,	lowercase__	:      int			):







     '''simple docstring'''


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

     return covariance_sum / features.shape[1]







def      _a					(   lowercase__	:      np.ndarray  ,	lowercase__	:      int			):







     '''simple docstring'''


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

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







def      _a					(   lowercase__	:      np.ndarray  ,	lowercase__	:      np.ndarray  ,	lowercase__	:      int  ,	lowercase__	:      int			):







     '''simple docstring'''


     assert classes > dimensions

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

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







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]			)
     SCREAMING_SNAKE_CASE__					:					Tuple      =		np.array([0, 0, 0, 1, 1]			)
     SCREAMING_SNAKE_CASE__					:					str      =		2
     SCREAMING_SNAKE_CASE__					:					Dict      =		2

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







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					str      =		np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]			)
     SCREAMING_SNAKE_CASE__					:					List[str]      =		2
     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]]			)

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()








import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     OmegaConf.load(__UpperCamelCase       )
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )['model']
			A__	  =     list(state_dict.keys()       )

			# extract state_dict for VQVAE
			A__	  =     {}
			A__	  =     'first_stage_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

    # extract state_dict for UNetLDM
			A__	  =     {}
			A__	  =     'model.diffusion_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

			A__	  =     config.model.params.first_stage_config.params
			A__	  =     config.model.params.unet_config.params

			A__	  =     VQModel(**__UpperCamelCase       ).eval()
			vqvae.load_state_dict(__UpperCamelCase       )

			A__	  =     UNetLDMModel(**__UpperCamelCase       ).eval()
			unet.load_state_dict(__UpperCamelCase       )

			A__	  =     DDIMScheduler(
			    timesteps=config.model.params.timesteps			,						beta_schedule='scaled_linear'			,						beta_start=config.model.params.linear_start			,						beta_end=config.model.params.linear_end			,						clip_sample=__UpperCamelCase			,						)

			A__	  =     LDMPipeline(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			pipeline.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--checkpoint_path''', type=str, required=True)
							parser.add_argument('''--config_path''', type=str, required=True)
							parser.add_argument('''--output_path''', type=str, required=True)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)




