
"""simple docstring"""




from __future__ import annotations

from decimal import Decimal

from numpy import array



def        _lowerCamelCase ( __a    ):
  SCREAMING_SNAKE_CASE_						 = Decimal

  # Check if the provided matrix has 2 rows and 2 columns
  # since this implementation only works for 2x2 matrices
  if len(lowerCAmelCase__    ) == 2 and len(matrix[0]    ) == 2 and len(matrix[1]    ) == 2:
    # Calculate the determinant of the matrix
    SCREAMING_SNAKE_CASE_						 = float(
        d(matrix[0][0]    ) * d(matrix[1][1]    ) - d(matrix[1][0]    ) * d(matrix[0][1]    )    )
    if determinant == 0:
      raise ValueError('''This matrix has no inverse.'''    )

    # Creates a copy of the matrix with swapped positions of the elements
    SCREAMING_SNAKE_CASE_						 = [[0.0, 0.0], [0.0, 0.0]]
    SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = matrix[1][1], matrix[0][0]
    SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = -matrix[1][0], -matrix[0][1]

    # Calculate the inverse of the matrix
    return [
        [(float(d(lowerCAmelCase__    )    ) / determinant) or 0.0 for n in row] for row in swapped_matrix
    ]
  elif (
      len(lowerCAmelCase__    ) == 3
      and len(matrix[0]    ) == 3
      and len(matrix[1]    ) == 3
      and len(matrix[2]    ) == 3
  ):
    # Calculate the determinant of the matrix using Sarrus rule
    SCREAMING_SNAKE_CASE_						 = float(
        (
            (d(matrix[0][0]    ) * d(matrix[1][1]    ) * d(matrix[2][2]    ))
            + (d(matrix[0][1]    ) * d(matrix[1][2]    ) * d(matrix[2][0]    ))
            + (d(matrix[0][2]    ) * d(matrix[1][0]    ) * d(matrix[2][1]    ))
        )
        - (
            (d(matrix[0][2]    ) * d(matrix[1][1]    ) * d(matrix[2][0]    ))
            + (d(matrix[0][1]    ) * d(matrix[1][0]    ) * d(matrix[2][2]    ))
            + (d(matrix[0][0]    ) * d(matrix[1][2]    ) * d(matrix[2][1]    ))
        )    )
    if determinant == 0:
      raise ValueError('''This matrix has no inverse.'''    )

    # Creating cofactor matrix
    SCREAMING_SNAKE_CASE_						 = [
        [d(0.0    ), d(0.0    ), d(0.0    )],
        [d(0.0    ), d(0.0    ), d(0.0    )],
        [d(0.0    ), d(0.0    ), d(0.0    )],
    ]
    SCREAMING_SNAKE_CASE_						 = (d(matrix[1][1]    ) * d(matrix[2][2]    )) - (
        d(matrix[1][2]    ) * d(matrix[2][1]    )
    )
    SCREAMING_SNAKE_CASE_						 = -(
        (d(matrix[1][0]    ) * d(matrix[2][2]    )) - (d(matrix[1][2]    ) * d(matrix[2][0]    ))
    )
    SCREAMING_SNAKE_CASE_						 = (d(matrix[1][0]    ) * d(matrix[2][1]    )) - (
        d(matrix[1][1]    ) * d(matrix[2][0]    )
    )
    SCREAMING_SNAKE_CASE_						 = -(
        (d(matrix[0][1]    ) * d(matrix[2][2]    )) - (d(matrix[0][2]    ) * d(matrix[2][1]    ))
    )
    SCREAMING_SNAKE_CASE_						 = (d(matrix[0][0]    ) * d(matrix[2][2]    )) - (
        d(matrix[0][2]    ) * d(matrix[2][0]    )
    )
    SCREAMING_SNAKE_CASE_						 = -(
        (d(matrix[0][0]    ) * d(matrix[2][1]    )) - (d(matrix[0][1]    ) * d(matrix[2][0]    ))
    )
    SCREAMING_SNAKE_CASE_						 = (d(matrix[0][1]    ) * d(matrix[1][2]    )) - (
        d(matrix[0][2]    ) * d(matrix[1][1]    )
    )
    SCREAMING_SNAKE_CASE_						 = -(
        (d(matrix[0][0]    ) * d(matrix[1][2]    )) - (d(matrix[0][2]    ) * d(matrix[1][0]    ))
    )
    SCREAMING_SNAKE_CASE_						 = (d(matrix[0][0]    ) * d(matrix[1][1]    )) - (
        d(matrix[0][1]    ) * d(matrix[1][0]    )
    )

    # Transpose the cofactor matrix (Adjoint matrix)
    SCREAMING_SNAKE_CASE_						 = array(lowerCAmelCase__    )
    for i in range(3    ):
      for j in range(3    ):
        SCREAMING_SNAKE_CASE_						 = cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
    SCREAMING_SNAKE_CASE_						 = array(lowerCAmelCase__    )
    for i in range(3    ):
      for j in range(3    ):
        inverse_matrix[i][j] /= d(lowerCAmelCase__    )

        # Calculate the inverse of the matrix
    return [[float(d(lowerCAmelCase__    )    ) or 0.0 for n in row] for row in inverse_matrix]
  raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''    )

"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      (DDIMParallelScheduler,)
							UpperCAmelCase__						     =      (('''eta''', 0.0), ('''num_inference_steps''', 50))

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {
									    '''num_train_timesteps''': 10_00,
									    '''beta_start''': 0.00_01,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''clip_sample''': True,
									}

									config.update(**SCREAMING_SNAKE_CASE_    )
									return config

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter

									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).prev_sample

									return sample

							def _lowercase					(self    ):

									"""simple docstring"""







									for timesteps in [1_00, 5_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(steps_offset=1    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )
									scheduler.set_timesteps(5    )
									assert torch.equal(scheduler.timesteps       ,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,       [0.0_02, 0.02, 0.2, 2]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,       beta_end=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_    )
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=SCREAMING_SNAKE_CASE_       ,       prediction_type=SCREAMING_SNAKE_CASE_       ,       sample_max_value=SCREAMING_SNAKE_CASE_       ,       )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t in [1, 10, 49]:
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, num_inference_steps in zip([1, 10, 50]       ,       [10, 50, 5_00]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, eta in zip([1, 10, 49]       ,       [0.0, 0.5, 1.0]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       eta=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_20       ,       4_00    ) - 0.1_47_71    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_80       ,       9_60    ) - 0.3_24_60    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87       ,       4_86    ) - 0.0_09_79    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99       ,       9_98    ) - 0.02    )    ) < 1e-5

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0
									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter + 0.1
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter - 0.1

									SCREAMING_SNAKE_CASE_						 = samplea.shape[0]
									SCREAMING_SNAKE_CASE_						 = torch.stack([samplea, samplea, samplea]       ,       dim=0    )
									SCREAMING_SNAKE_CASE_						 = torch.arange(SCREAMING_SNAKE_CASE_    )[0:3, None].repeat(1       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = model(samples.flatten(0       ,       1    )       ,       timesteps.flatten(0       ,       1    )    )
									SCREAMING_SNAKE_CASE_						 = scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_       ,       timesteps.flatten(0       ,       1    )       ,       samples.flatten(0       ,       1    )       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 11_47.79_04    ) < 1e-2
									assert abs(result_mean.item() - 0.49_82    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop()

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
									assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(prediction_type='''v_prediction'''    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 52.53_02    ) < 1e-2
									assert abs(result_mean.item() - 0.06_84    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
									assert abs(result_mean.item() - 0.19_51    ) < 1e-3







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
									assert abs(result_mean.item() - 0.19_41    ) < 1e-3