

from __future__ import annotations

from decimal import Decimal

from numpy import array





def a(lowercase__	):





	'''simple docstring'''
	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
		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
		snake_case_      		= [[0.0, 0.0], [0.0, 0.0]]
		snake_case_      		= matrix[1][1], matrix[0][0]
		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
		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
		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	)],
		]
		snake_case_      		= (d(matrix[1][1]	) * d(matrix[2][2]	)) - (
		    d(matrix[1][2]	) * d(matrix[2][1]	)
		)
		snake_case_      		= -(
		    (d(matrix[1][0]	) * d(matrix[2][2]	)) - (d(matrix[1][2]	) * d(matrix[2][0]	))
		)
		snake_case_      		= (d(matrix[1][0]	) * d(matrix[2][1]	)) - (
		    d(matrix[1][1]	) * d(matrix[2][0]	)
		)
		snake_case_      		= -(
		    (d(matrix[0][1]	) * d(matrix[2][2]	)) - (d(matrix[0][2]	) * d(matrix[2][1]	))
		)
		snake_case_      		= (d(matrix[0][0]	) * d(matrix[2][2]	)) - (
		    d(matrix[0][2]	) * d(matrix[2][0]	)
		)
		snake_case_      		= -(
		    (d(matrix[0][0]	) * d(matrix[2][1]	)) - (d(matrix[0][1]	) * d(matrix[2][0]	))
		)
		snake_case_      		= (d(matrix[0][1]	) * d(matrix[1][2]	)) - (
		    d(matrix[0][2]	) * d(matrix[1][1]	)
		)
		snake_case_      		= -(
		    (d(matrix[0][0]	) * d(matrix[1][2]	)) - (d(matrix[0][2]	) * d(matrix[1][0]	))
		)
		snake_case_      		= (d(matrix[0][0]	) * d(matrix[1][1]	)) - (
		    d(matrix[0][1]	) * d(matrix[1][0]	)
		)

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

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
		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.'	)



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= 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							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= ScoreSdeVeScheduler()

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase ,				return_dict=__UpperCamelCase							)[
				    0
				]

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

				assert image.shape == (1, 32, 32, 3)
				snake_case_      		= np.array([0.0, 1.0, 0.0, 0.0, 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 SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'google/ncsnpp-church-256'
				snake_case_      		= UNetaDModel.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=10 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

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

				assert image.shape == (1, 2_56, 2_56, 3)

				snake_case_      		= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

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