

from __future__ import annotations

from decimal import Decimal

from numpy import array






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						list[list[float]]:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  Decimal

				# Check if the provided matrix has 2 rows and 2 columns
				# since this implementation only works for 2x2 matrices
				if len(_UpperCamelCase			) == 2 and len(matrix[0]			) == 2 and len(matrix[1]			) == 2:
								# Calculate the determinant of the matrix
								snake_case_						:   Optional[Any]           =  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
								snake_case_						:   List[Any]           =  [[0.0, 0.0], [0.0, 0.0]]
								snake_case_						:   List[Any]           =  matrix[1][1], matrix[0][0]
								snake_case_						:   Tuple           =  -matrix[1][0], -matrix[0][1]

								# Calculate the inverse of the matrix
								return [
								    [(float(d(_UpperCamelCase			)			) / determinant) or 0.0 for n in row] for row in swapped_matrix
								]
				elif (
				    len(_UpperCamelCase			) == 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
								snake_case_						:   List[str]           =  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
								snake_case_						:   str           =  [
								    [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			)],
								]
								snake_case_						:   int           =  (d(matrix[1][1]			) * d(matrix[2][2]			)) - (
								    d(matrix[1][2]			) * d(matrix[2][1]			)
								)
								snake_case_						:   Any           =  -(
								    (d(matrix[1][0]			) * d(matrix[2][2]			)) - (d(matrix[1][2]			) * d(matrix[2][0]			))
								)
								snake_case_						:   Optional[Any]           =  (d(matrix[1][0]			) * d(matrix[2][1]			)) - (
								    d(matrix[1][1]			) * d(matrix[2][0]			)
								)
								snake_case_						:   Tuple           =  -(
								    (d(matrix[0][1]			) * d(matrix[2][2]			)) - (d(matrix[0][2]			) * d(matrix[2][1]			))
								)
								snake_case_						:   Optional[int]           =  (d(matrix[0][0]			) * d(matrix[2][2]			)) - (
								    d(matrix[0][2]			) * d(matrix[2][0]			)
								)
								snake_case_						:   Dict           =  -(
								    (d(matrix[0][0]			) * d(matrix[2][1]			)) - (d(matrix[0][1]			) * d(matrix[2][0]			))
								)
								snake_case_						:   Any           =  (d(matrix[0][1]			) * d(matrix[1][2]			)) - (
								    d(matrix[0][2]			) * d(matrix[1][1]			)
								)
								snake_case_						:   Dict           =  -(
								    (d(matrix[0][0]			) * d(matrix[1][2]			)) - (d(matrix[0][2]			) * d(matrix[1][0]			))
								)
								snake_case_						:   List[str]           =  (d(matrix[0][0]			) * d(matrix[1][1]			)) - (
								    d(matrix[0][1]			) * d(matrix[1][0]			)
								)

								# Transpose the cofactor matrix (Adjoint matrix)
								snake_case_						:   List[Any]           =  array(_UpperCamelCase			)
								for i in range(3			):
												for j in range(3			):
																snake_case_						:   int           =  cofactor_matrix[j][i]

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

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







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class  __lowerCAmelCase	(      unittest.TestCase					):






			@property
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   int           =  UNetaDModel(
							    block_out_channels=(32, 64) ,							layers_per_block=2 ,							sample_size=32 ,							in_channels=3 ,							out_channels=3 ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') ,							)
							return model






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  self.dummy_uncond_unet
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Optional[Any]           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   Dict           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy'''   ).images

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   str           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy''' ,							return_dict=__magic_name__   )[0]

							snake_case_						:   Any           =  image[0, -3:, -3:, -1]
							snake_case_						:   str           =  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2




@slow
@require_torch
class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''google/ddpm-cifar10-32'''

							snake_case_						:   Tuple           =  UNetaDModel.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Any           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )
							snake_case_						:   int           =  torch.manual_seed(0   )
							snake_case_						:   Tuple           =  pndm(generator=__magic_name__ ,							output_type='''numpy'''   ).images

							snake_case_						:   List[str]           =  image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
