from __future__ import annotations

from decimal import Decimal

from numpy import array




def     UpperCamelCase						(	__lowercase						:			list[list[float]]     ):
		'''simple docstring'''




		A_		:    List[str]         =				Decimal

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

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

				# Transpose the cofactor matrix (Adjoint matrix)
				A_		:    Tuple         =				array(SCREAMING_SNAKE_CASE__     )
				for i in range(3     ):
						for j in range(3     ):
								A_		:    List[Any]         =				cofactor_matrix[j][i]

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

								    # Calculate the inverse of the matrix
				return [[float(d(SCREAMING_SNAKE_CASE__     )     ) 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 transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    int         =				UNetaDModel(
						    block_out_channels=(3_2, 6_4)		,     layers_per_block=2		,     sample_size=3_2		,     in_channels=3		,     out_channels=3		,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,     )
						return model
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    Optional[Any]         =				VQModel(
						    block_out_channels=[3_2, 6_4]		,     in_channels=3		,     out_channels=3		,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,     latent_channels=3		,     )
						return model
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    List[Any]         =				CLIPTextConfig(
						    bos_token_id=0		,     eos_token_id=2		,     hidden_size=3_2		,     intermediate_size=3_7		,     layer_norm_eps=1E-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=1_0_0_0		,     )
						return CLIPTextModel(lowercase )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.dummy_uncond_unet
						A_		:    List[Any]         =				DDIMScheduler()
						A_		:    Any         =				self.dummy_vq_model

						A_		:    int         =				LDMPipeline(unet=lowercase		,     vqvae=lowercase		,     scheduler=lowercase )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    Dict         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy' ).images

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy'		,     return_dict=lowercase )[0]

						A_		:    Union[str, Any]         =				image[0, -3:, -3:, -1]
						A_		:    int         =				image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						A_		:    List[str]         =				np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
						A_		:    str         =				1E-2 if torch_device != 'mps' else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance





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


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=5		,     output_type='numpy' ).images

						A_		:    List[Any]         =				image[0, -3:, -3:, -1]

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						A_		:    Tuple         =				np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
						A_		:    Dict         =				1E-2 if torch_device != 'mps' else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance






