
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()





class 						lowercase       (  unittest.TestCase					):
					@property
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    str				      =			1
								lowerCAmelCase__ :    Optional[int]				      =			3
								lowerCAmelCase__ :    Optional[Any]				      =			(32, 32)

								lowerCAmelCase__ :    str				      =			floats_tensor((batch_size, num_channels) + sizes ,	rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE__    )
								return image
					@property
					def lowercase_			(		self    ):
								"""simple docstring"""







								torch.manual_seed(0    )
								lowerCAmelCase__ :    Union[str, Any]				      =			UNetaDModel(
								    block_out_channels=(32, 64) ,	layers_per_block=2 ,	sample_size=32 ,	in_channels=6 ,	out_channels=3 ,	down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') ,	up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') ,	)
								return model
					@property
					def lowercase_			(		self    ):
								"""simple docstring"""







								torch.manual_seed(0    )
								lowerCAmelCase__ :    List[str]				      =			VQModel(
								    block_out_channels=[32, 64] ,	in_channels=3 ,	out_channels=3 ,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,	latent_channels=3 ,	)
								return model
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			'''cpu'''
								lowerCAmelCase__ :    Union[str, Any]				      =			self.dummy_uncond_unet
								lowerCAmelCase__ :    Optional[Any]				      =			DDIMScheduler()
								lowerCAmelCase__ :    Dict				      =			self.dummy_vq_model

								lowerCAmelCase__ :    int				      =			LDMSuperResolutionPipeline(unet=SCREAMING_SNAKE_CASE__ ,	vqvae=SCREAMING_SNAKE_CASE__ ,	scheduler=SCREAMING_SNAKE_CASE__    )
								ldm.to(SCREAMING_SNAKE_CASE__    )
								ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Union[str, Any]				      =			self.dummy_image.to(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Optional[Any]				      =			torch.Generator(device=SCREAMING_SNAKE_CASE__    ).manual_seed(0    )
								lowerCAmelCase__ :    Any				      =			ldm(image=SCREAMING_SNAKE_CASE__ ,	generator=SCREAMING_SNAKE_CASE__ ,	num_inference_steps=2 ,	output_type='''numpy'''    ).images

								lowerCAmelCase__ :    Optional[Any]				      =			image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								lowerCAmelCase__ :    str				      =			np.array([0.8_678, 0.8_245, 0.6_381, 0.6_830, 0.4_385, 0.5_599, 0.4_641, 0.6_201, 0.5_150]    )

								assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
					@unittest.skipIf(torch_device != '''cuda''' ,	'''This test requires a GPU'''    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			self.dummy_uncond_unet
								lowerCAmelCase__ :    Dict				      =			DDIMScheduler()
								lowerCAmelCase__ :    List[str]				      =			self.dummy_vq_model

								# put models in fp16
								lowerCAmelCase__ :    int				      =			unet.half()
								lowerCAmelCase__ :    str				      =			vqvae.half()

								lowerCAmelCase__ :    List[Any]				      =			LDMSuperResolutionPipeline(unet=SCREAMING_SNAKE_CASE__ ,	vqvae=SCREAMING_SNAKE_CASE__ ,	scheduler=SCREAMING_SNAKE_CASE__    )
								ldm.to(SCREAMING_SNAKE_CASE__    )
								ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    List[Any]				      =			self.dummy_image.to(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Dict				      =			ldm(SCREAMING_SNAKE_CASE__ ,	num_inference_steps=2 ,	output_type='''numpy'''    ).images

								assert image.shape == (1, 64, 64, 3)





@slow
@require_torch
class 						lowercase       (  unittest.TestCase					):
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/vq_diffusion/teddy_bear_pool.png'''    )
								lowerCAmelCase__ :    Tuple				      =			init_image.resize((64, 64) ,	resample=PIL_INTERPOLATION['''lanczos''']    )

								lowerCAmelCase__ :    List[Any]				      =			LDMSuperResolutionPipeline.from_pretrained('''duongna/ldm-super-resolution''' ,	device_map='''auto'''    )
								ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    List[Any]				      =			torch.manual_seed(0    )
								lowerCAmelCase__ :    Optional[Any]				      =			ldm(image=SCREAMING_SNAKE_CASE__ ,	generator=SCREAMING_SNAKE_CASE__ ,	num_inference_steps=20 ,	output_type='''numpy'''    ).images

								lowerCAmelCase__ :    Dict				      =			image[0, -3:, -3:, -1]

								assert image.shape == (1, 256, 256, 3)
								lowerCAmelCase__ :    List[str]				      =			np.array([0.7_644, 0.7_679, 0.7_642, 0.7_633, 0.7_666, 0.7_560, 0.7_425, 0.7_257, 0.6_907]    )

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





import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    Any				      =			np.shape(__UpperCamelCase    )

			lowerCAmelCase__ :    NDArray[floataa]				      =			np.zeros((rows, 1)	,dtype=__UpperCamelCase    )
			for row in reversed(range(__UpperCamelCase    )    ):
						lowerCAmelCase__ :    List[str]				      =			0
						for col in range(row + 1	,__UpperCamelCase    ):
									total += coefficients[row, col] * x[col]

						lowerCAmelCase__ :    int				      =			(vector[row] - total) / coefficients[row, row]

			return x



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			np.shape(__UpperCamelCase    )
			if rows != columns:
						return np.array(()	,dtype=__UpperCamelCase    )

			# augmented matrix
			lowerCAmelCase__ :    NDArray[floataa]				      =			np.concatenate((coefficients, vector)	,axis=1    )
			lowerCAmelCase__ :    List[str]				      =			augmented_mat.astype('''float64'''    )

			# scale the matrix leaving it triangular
			for row in range(rows - 1    ):
						lowerCAmelCase__ :    str				      =			augmented_mat[row, row]
						for col in range(row + 1	,__UpperCamelCase    ):
									lowerCAmelCase__ :    Optional[int]				      =			augmented_mat[col, row] / pivot
									augmented_mat[col, :] -= factor * augmented_mat[row, :]

			lowerCAmelCase__ :    int				      =			retroactive_resolution(
			    augmented_mat[:, 0:columns]	,augmented_mat[:, columns : columns + 1]    )

			return x


if __name__ == "__main__":
							import doctest

							doctest.testmod()



