




'''simple docstring'''







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 	lowerCamelCase_				(			unittest.TestCase				):




					'''simple docstring'''






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			1
							_snake_case      			=			3
							_snake_case      			=			(32, 32)

							_snake_case      			=			floats_tensor((batch_size, num_channels) + sizes,    rng=random.Random(0					)					).to(__UpperCamelCase					)
							return image






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							torch.manual_seed(0					)
							_snake_case      			=			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 							__SCREAMING_SNAKE_CASE      (       self					):
							torch.manual_seed(0					)
							_snake_case      			=			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 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			"cpu"
							_snake_case      			=			self.dummy_uncond_unet
							_snake_case      			=			DDIMScheduler()
							_snake_case      			=			self.dummy_vq_model

							_snake_case      			=			LDMSuperResolutionPipeline(unet=__UpperCamelCase,    vqvae=__UpperCamelCase,    scheduler=__UpperCamelCase					)
							ldm.to(__UpperCamelCase					)
							ldm.set_progress_bar_config(disable=__UpperCamelCase					)

							_snake_case      			=			self.dummy_image.to(__UpperCamelCase					)

							_snake_case      			=			torch.Generator(device=__UpperCamelCase					).manual_seed(0					)
							_snake_case      			=			ldm(image=__UpperCamelCase,    generator=__UpperCamelCase,    num_inference_steps=2,    output_type="numpy"					).images

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

							assert image.shape == (1, 64, 64, 3)
							_snake_case      			=			np.array([0.86_78, 0.82_45, 0.63_81, 0.68_30, 0.43_85, 0.55_99, 0.46_41, 0.62_01, 0.51_50]					)

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






					@unittest.skipIf(torch_device != "cuda",    "This test requires a GPU"					)
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.dummy_uncond_unet
							_snake_case      			=			DDIMScheduler()
							_snake_case      			=			self.dummy_vq_model

							# put models in fp16
							_snake_case      			=			unet.half()
							_snake_case      			=			vqvae.half()

							_snake_case      			=			LDMSuperResolutionPipeline(unet=__UpperCamelCase,    vqvae=__UpperCamelCase,    scheduler=__UpperCamelCase					)
							ldm.to(__UpperCamelCase					)
							ldm.set_progress_bar_config(disable=__UpperCamelCase					)

							_snake_case      			=			self.dummy_image.to(__UpperCamelCase					)

							_snake_case      			=			ldm(__UpperCamelCase,    num_inference_steps=2,    output_type="numpy"					).images

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





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




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/vq_diffusion/teddy_bear_pool.png"					)
							_snake_case      			=			init_image.resize((64, 64),    resample=PIL_INTERPOLATION["lanczos"]					)

							_snake_case      			=			LDMSuperResolutionPipeline.from_pretrained("duongna/ldm-super-resolution",    device_map="auto"					)
							ldm.set_progress_bar_config(disable=__UpperCamelCase					)

							_snake_case      			=			torch.manual_seed(0					)
							_snake_case      			=			ldm(image=__UpperCamelCase,    generator=__UpperCamelCase,    num_inference_steps=20,    output_type="numpy"					).images

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

							assert image.shape == (1, 256, 256, 3)
							_snake_case      			=			np.array([0.76_44, 0.76_79, 0.76_42, 0.76_33, 0.76_66, 0.75_60, 0.74_25, 0.72_57, 0.69_07]					)

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











'''simple docstring'''







import numpy as np






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		_snake_case			,       _snake_case      			=			a.shape
		_snake_case      			=			min(lowercase_		,				lowercase_    )
		_snake_case      			=			np.eye(lowercase_    )
		_snake_case      			=			a.copy()

		for k in range(t - 1    ):
				# select a column of modified matrix A':
				_snake_case      			=			r[k:, [k]]
				# construct first basis vector
				_snake_case      			=			np.zeros_like(lowercase_    )
				_snake_case      			=			1.0
				# determine scaling factor
				_snake_case      			=			np.linalg.norm(lowercase_    )
				# construct vector v for Householder reflection
				_snake_case      			=			x + np.sign(x[0]    ) * alpha * ea
				v /= np.linalg.norm(lowercase_    )

				# construct the Householder matrix
				_snake_case      			=			np.eye(m - k    ) - 2.0 * v @ v.T
				# pad with ones and zeros as necessary
				_snake_case      			=			np.block([[np.eye(lowercase_    ), np.zeros((k, m - k)    )], [np.zeros((m - k, k)    ), q_k]]    )

				_snake_case      			=			q @ q_k.T
				_snake_case      			=			q_k @ r

		return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()





