
import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class        lowerCAmelCase__      (   unittest.TestCase	):

						@property
						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								torch.manual_seed(0				)
								_UpperCamelCase						    =	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  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self.dummy_uncond_unet
								_UpperCamelCase						    =	KarrasVeScheduler()

								_UpperCamelCase						    =	KarrasVePipeline(unet=a       ,							scheduler=a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(num_inference_steps=2       ,							generator=a       ,							output_type="""numpy"""				).images

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(num_inference_steps=2       ,							generator=a       ,							output_type="""numpy"""       ,							return_dict=a				)[0]

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

								assert image.shape == (1, 32, 32, 3)
								_UpperCamelCase						    =	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        lowerCAmelCase__      (   unittest.TestCase	):

						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	"""google/ncsnpp-celebahq-256"""
								_UpperCamelCase						    =	UNetaDModel.from_pretrained(a				)
								_UpperCamelCase						    =	KarrasVeScheduler()

								_UpperCamelCase						    =	KarrasVePipeline(unet=a       ,							scheduler=a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(num_inference_steps=20       ,							generator=a       ,							output_type="""numpy"""				).images

								_UpperCamelCase						    =	image[0, -3:, -3:, -1]
								assert image.shape == (1, 2_56, 2_56, 3)
								_UpperCamelCase						    =	np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]				)

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





def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				tuple[float, float]:

		"""simple docstring"""






		if not len(lowerCAmelCase ) == len(lowerCAmelCase ) == 3:
				raise ValueError("""Please enter a valid equation.""" )
		if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
				raise ValueError("""Both a & b of two equations can't be zero.""" )

		# Extract the coefficients
		_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	equationa
		_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	equationa

		# Calculate the determinants of the matrices
		_UpperCamelCase						    =	aa * ba - aa * ba
		_UpperCamelCase						    =	ca * ba - ca * ba
		_UpperCamelCase						    =	aa * ca - aa * ca

		# Check if the system of linear equations has a solution (using Cramer's rule)
		if determinant == 0:
				if determinant_x == determinant_y == 0:
						raise ValueError("""Infinite solutions. (Consistent system)""" )
				else:
						raise ValueError("""No solution. (Inconsistent system)""" )
		else:
				if determinant_x == determinant_y == 0:
						# Trivial solution (Inconsistent system)
						return (0.0, 0.0)
				else:
						_UpperCamelCase						    =	determinant_x / determinant
						_UpperCamelCase						    =	determinant_y / determinant
						# Non-Trivial Solution (Consistent system)
						return (x, y)



