
import numpy as np







def       UpperCAmelCase__							(    __magic_name__  :       Tuple					):


					'''simple docstring'''

					lowerCAmelCase		,							lowerCAmelCase						:   Optional[int]							=       a.shape
					lowerCAmelCase						:   Dict							=       min(snake_case__							,					snake_case__					)
					lowerCAmelCase						:   Any							=       np.eye(snake_case__					)
					lowerCAmelCase						:   Tuple							=       a.copy()

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

										# construct the Householder matrix
										lowerCAmelCase						:   Optional[int]							=       np.eye(m - k					) - 2.0 * v @ v.T
										# pad with ones and zeros as necessary
										lowerCAmelCase						:   Dict							=       np.block([[np.eye(snake_case__					), np.zeros((k, m - k)					)], [np.zeros((m - k, k)					), q_k]]					)

										lowerCAmelCase						:   Tuple							=       q @ q_k.T
										lowerCAmelCase						:   Optional[int]							=       q_k @ r

					return q, r


if __name__ == "__main__":
		import doctest

		doctest.testmod()


import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 						__magic_name__				(  snake_case,			unittest.TestCase      ):
		_lowerCAmelCase						=		RePaintPipeline
		_lowerCAmelCase						=		IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
		_lowerCAmelCase						=		PipelineTesterMixin.required_optional_params - {
		    "latents",
		    "num_images_per_prompt",
		    "callback",
		    "callback_steps",
		}
		_lowerCAmelCase						=		IMAGE_INPAINTING_BATCH_PARAMS
		_lowerCAmelCase						=		False





		def  _A							(							self      :		Optional[int]			):
							torch.manual_seed(0			)
							torch.manual_seed(0			)
							lowerCAmelCase						:   Dict							=       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''')  ,					)
							lowerCAmelCase						:   List[Any]							=       RePaintScheduler()
							lowerCAmelCase						:   Tuple							=       {'''unet''': unet, '''scheduler''': scheduler}
							return components





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		int=0			):
							if str(lowerCamelCase__			).startswith('''mps'''			):
												lowerCAmelCase						:   Union[str, Any]							=       torch.manual_seed(lowerCamelCase__			)
							else:
												lowerCAmelCase						:   Optional[Any]							=       torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
							lowerCAmelCase						:   List[str]							=       np.random.RandomState(lowerCamelCase__			).standard_normal((1, 3, 3_2, 3_2)			)
							lowerCAmelCase						:   Tuple							=       torch.from_numpy(lowerCamelCase__			).to(device=lowerCamelCase__  ,					dtype=torch.floataa			)
							lowerCAmelCase						:   Tuple							=       (image > 0).to(device=lowerCamelCase__  ,					dtype=torch.floataa			)
							lowerCAmelCase						:   List[Any]							=       {
							    '''image''': image,
							    '''mask_image''': mask,
							    '''generator''': generator,
							    '''num_inference_steps''': 5,
							    '''eta''': 0.0,
							    '''jump_length''': 2,
							    '''jump_n_sample''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   List[str]							=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase						:   Dict							=       self.get_dummy_components()
							lowerCAmelCase						:   str							=       RePaintPipeline(**lowerCamelCase__			)
							lowerCAmelCase						:   Union[str, Any]							=       sd_pipe.to(lowerCamelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							lowerCAmelCase						:   List[Any]							=       self.get_dummy_inputs(lowerCamelCase__			)
							lowerCAmelCase						:   int							=       sd_pipe(**lowerCamelCase__			).images
							lowerCAmelCase						:   Dict							=       image[0, -3:, -3:, -1]

							assert image.shape == (1, 3_2, 3_2, 3)
							lowerCAmelCase						:   List[str]							=       np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3





		@skip_mps
		def  _A							(							self      :		Optional[Any]			):
							return super().test_save_load_local()





		@unittest.skip('''non-deterministic pipeline'''			)
		def  _A							(							self      :		Any			):
							return super().test_inference_batch_single_identical()





		@skip_mps
		def  _A							(							self      :		List[Any]			):
							return super().test_dict_tuple_outputs_equivalent()





		@skip_mps
		def  _A							(							self      :		Dict			):
							return super().test_save_load_optional_components()





		@skip_mps
		def  _A							(							self      :		Tuple			):
							return super().test_attention_slicing_forward_pass()



@nightly
@require_torch_gpu
class 						__magic_name__				(  unittest.TestCase      ):





		def  _A							(							self      :		Optional[Any]			):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   str							=       load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
							    '''repaint/celeba_hq_256.png'''			)
							lowerCAmelCase						:   Tuple							=       load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''			)
							lowerCAmelCase						:   int							=       load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
							    '''repaint/celeba_hq_256_result.npy'''			)

							lowerCAmelCase						:   List[str]							=       '''google/ddpm-ema-celebahq-256'''
							lowerCAmelCase						:   List[str]							=       UNetaDModel.from_pretrained(lowerCamelCase__			)
							lowerCAmelCase						:   str							=       RePaintScheduler.from_pretrained(lowerCamelCase__			)

							lowerCAmelCase						:   Optional[int]							=       RePaintPipeline(unet=lowerCamelCase__  ,					scheduler=lowerCamelCase__			).to(lowerCamelCase__			)
							repaint.set_progress_bar_config(disable=lowerCamelCase__			)
							repaint.enable_attention_slicing()

							lowerCAmelCase						:   str							=       torch.manual_seed(0			)
							lowerCAmelCase						:   Dict							=       repaint(
							    lowerCamelCase__  ,					lowerCamelCase__  ,					num_inference_steps=2_5_0  ,					eta=0.0  ,					jump_length=1_0  ,					jump_n_sample=1_0  ,					generator=lowerCamelCase__  ,					output_type='''np'''  ,					)
							lowerCAmelCase						:   str							=       output.images[0]

							assert image.shape == (2_5_6, 2_5_6, 3)
							assert np.abs(expected_image - image			).mean() < 1E-2
