




def       snake_case      (							A		,     A		,     A		,     A		,     A		,     A		,     )							->							tuple[list[list[int]], list[list[str]]]:




	'''simple docstring'''


	lowerCamelCase_							:	Optional[int]							     =							list(SCREAMING_SNAKE_CASE__    )
	lowerCamelCase_							:	List[str]							     =							list(SCREAMING_SNAKE_CASE__    )
	lowerCamelCase_							:	Optional[int]							     =							len(SCREAMING_SNAKE_CASE__    )
	lowerCamelCase_							:	Optional[int]							     =							len(SCREAMING_SNAKE_CASE__    )

	lowerCamelCase_							:	Tuple							     =							[
	    [0 for _ in range(len_destination_seq + 1    )] for _ in range(len_source_seq + 1    )
	]
	lowerCamelCase_							:	Optional[Any]							     =							[
	    ['''0''' for _ in range(len_destination_seq + 1    )] for _ in range(len_source_seq + 1    )
	]

	for i in range(1		,     len_source_seq + 1    ):
		lowerCamelCase_							:	Optional[Any]							     =							i * delete_cost
		lowerCamelCase_							:	Union[str, Any]							     =							f"D{source_seq[i - 1]:c}"

	for i in range(1		,     len_destination_seq + 1    ):
		lowerCamelCase_							:	Any							     =							i * insert_cost
		lowerCamelCase_							:	List[Any]							     =							f"I{destination_seq[i - 1]:c}"

	for i in range(1		,     len_source_seq + 1    ):
		for j in range(1		,     len_destination_seq + 1    ):
			if source_seq[i - 1] == destination_seq[j - 1]:
				lowerCamelCase_							:	Union[str, Any]							     =							costs[i - 1][j - 1] + copy_cost
				lowerCamelCase_							:	Optional[Any]							     =							f"C{source_seq[i - 1]:c}"
			else:
				lowerCamelCase_							:	Dict							     =							costs[i - 1][j - 1] + replace_cost
				lowerCamelCase_							:	str							     =							f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]    )

			if costs[i - 1][j] + delete_cost < costs[i][j]:
				lowerCamelCase_							:	Any							     =							costs[i - 1][j] + delete_cost
				lowerCamelCase_							:	str							     =							f"D{source_seq[i - 1]:c}"

			if costs[i][j - 1] + insert_cost < costs[i][j]:
				lowerCamelCase_							:	List[Any]							     =							costs[i][j - 1] + insert_cost
				lowerCamelCase_							:	Tuple							     =							f"I{destination_seq[j - 1]:c}"

	return costs, ops






def       snake_case      (							A		,     A		,     A    )							->							list[str]:




	'''simple docstring'''


	if i == 0 and j == 0:
		return []
	else:
		if ops[i][j][0] == "C" or ops[i][j][0] == "R":
			lowerCamelCase_							:	Dict							     =							assemble_transformation(SCREAMING_SNAKE_CASE__		,     i - 1		,     j - 1    )
			seq.append(ops[i][j]    )
			return seq
		elif ops[i][j][0] == "D":
			lowerCamelCase_							:	Optional[int]							     =							assemble_transformation(SCREAMING_SNAKE_CASE__		,     i - 1		,     SCREAMING_SNAKE_CASE__    )
			seq.append(ops[i][j]    )
			return seq
		else:
			lowerCamelCase_							:	Tuple							     =							assemble_transformation(SCREAMING_SNAKE_CASE__		,     SCREAMING_SNAKE_CASE__		,     j - 1    )
			seq.append(ops[i][j]    )
			return seq


if __name__ == "__main__":
				UpperCamelCase__ ,				UpperCamelCase__  =		compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

				UpperCamelCase__  =		len(operations)
				UpperCamelCase__  =		len(operations[0])
				UpperCamelCase__  =		assemble_transformation(operations, m - 1, n - 1)

				UpperCamelCase__  =		list('Python')
				UpperCamelCase__  =		0
				UpperCamelCase__  =		0

				with open('min_cost.txt', 'w') as file:
								for op in sequence:
												print(''.join(string))

												if op[0] == "C":
																file.write('%-16s' % 'Copy %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost -= 1
												elif op[0] == "R":
																UpperCamelCase__  =		op[2]

																file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
																file.write('\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 1
												elif op[0] == "D":
																string.pop(i)

																file.write('%-16s' % 'Delete %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2
												else:
																string.insert(i, op[1])

																file.write('%-16s' % 'Insert %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2

												i += 1

								print(''.join(string))
								print('Cost: ', cost)

								file.write('\r\nMinimum cost: ' + str(cost))






# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase     ,  lowerCamelCase     ,  lowerCamelCase     ,  unittest.TestCase						):
					_lowerCamelCase							=				StableDiffusionControlNetInpaintPipeline
					_lowerCamelCase							=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					_lowerCamelCase							=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					_lowerCamelCase							=				frozenset({"""control_image"""}						)  # skip `image` and `mask` for now, only test for control_image
					_lowerCamelCase							=				TEXT_TO_IMAGE_IMAGE_PARAMS

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						torch.manual_seed(0      )
						lowerCamelCase_							:	List[str]							     =							UNetaDConditionModel(
						    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=9				, out_channels=4				, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				, up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				, cross_attention_dim=32				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	str							     =							ControlNetModel(
						    block_out_channels=(32, 64)				, layers_per_block=2				, in_channels=4				, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				, cross_attention_dim=32				, conditioning_embedding_out_channels=(16, 32)				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	List[Any]							     =							DDIMScheduler(
						    beta_start=0.0_00_85				, beta_end=0.0_12				, beta_schedule='scaled_linear'				, clip_sample=__lowercase				, set_alpha_to_one=__lowercase				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	Dict							     =							AutoencoderKL(
						    block_out_channels=[32, 64]				, in_channels=3				, out_channels=3				, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				, up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				, latent_channels=4				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	Dict							     =							CLIPTextConfig(
						    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1_000				, )
						lowerCamelCase_							:	str							     =							CLIPTextModel(__lowercase      )
						lowerCamelCase_							:	Dict							     =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

						lowerCamelCase_							:	int							     =							{
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components

					def 			lowercase_							(       self				, __lowercase				, __lowercase=0      ):







						'''simple docstring'''




						if str(__lowercase      ).startswith('mps'      ):
							lowerCamelCase_							:	Optional[int]							     =							torch.manual_seed(__lowercase      )
						else:
							lowerCamelCase_							:	List[Any]							     =							torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )

						lowerCamelCase_							:	Optional[int]							     =							2
						lowerCamelCase_							:	Union[str, Any]							     =							randn_tensor(
						    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=__lowercase				, device=torch.device(__lowercase      )				, )
						lowerCamelCase_							:	str							     =							floats_tensor((1, 3, 32, 32)				, rng=random.Random(__lowercase      )      ).to(__lowercase      )
						lowerCamelCase_							:	Tuple							     =							init_image.cpu().permute(0				, 2				, 3				, 1      )[0]

						lowerCamelCase_							:	Optional[Any]							     =							Image.fromarray(np.uinta(__lowercase      )      ).convert('RGB'      ).resize((64, 64)      )
						lowerCamelCase_							:	List[str]							     =							Image.fromarray(np.uinta(init_image + 4      )      ).convert('RGB'      ).resize((64, 64)      )

						lowerCamelCase_							:	Any							     =							{
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						    'image': image,
						    'mask_image': mask_image,
						    'control_image': control_image,
						}

						return inputs

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3      )

					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()				, reason='XFormers attention is only available with CUDA and `xformers` installed'				, )
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self._test_inference_batch_single_identical(expected_max_diff=2E-3      )


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				StableDiffusionControlNetInpaintPipeline
					_lowerCamelCase							=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					_lowerCamelCase							=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					_lowerCamelCase							=				frozenset([]						)

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						torch.manual_seed(0      )
						lowerCamelCase_							:	int							     =							UNetaDConditionModel(
						    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=4				, out_channels=4				, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				, up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				, cross_attention_dim=32				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	Union[str, Any]							     =							ControlNetModel(
						    block_out_channels=(32, 64)				, layers_per_block=2				, in_channels=4				, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				, cross_attention_dim=32				, conditioning_embedding_out_channels=(16, 32)				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	Optional[Any]							     =							DDIMScheduler(
						    beta_start=0.0_00_85				, beta_end=0.0_12				, beta_schedule='scaled_linear'				, clip_sample=__lowercase				, set_alpha_to_one=__lowercase				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	List[Any]							     =							AutoencoderKL(
						    block_out_channels=[32, 64]				, in_channels=3				, out_channels=3				, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				, up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				, latent_channels=4				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	str							     =							CLIPTextConfig(
						    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1_000				, )
						lowerCamelCase_							:	str							     =							CLIPTextModel(__lowercase      )
						lowerCamelCase_							:	Dict							     =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

						lowerCamelCase_							:	List[Any]							     =							{
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase     ,  lowerCamelCase     ,  unittest.TestCase						):
					_lowerCamelCase							=				StableDiffusionControlNetInpaintPipeline
					_lowerCamelCase							=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					_lowerCamelCase							=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						torch.manual_seed(0      )
						lowerCamelCase_							:	List[str]							     =							UNetaDConditionModel(
						    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=9				, out_channels=4				, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				, up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				, cross_attention_dim=32				, )
						torch.manual_seed(0      )

						def init_weights(__lowercase      ):
							if isinstance(__lowercase				, torch.nn.Convad      ):
								torch.nn.init.normal(m.weight      )
								m.bias.data.fill_(1.0      )

						lowerCamelCase_							:	Union[str, Any]							     =							ControlNetModel(
						    block_out_channels=(32, 64)				, layers_per_block=2				, in_channels=4				, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				, cross_attention_dim=32				, conditioning_embedding_out_channels=(16, 32)				, )
						controlneta.controlnet_down_blocks.apply(__lowercase      )

						torch.manual_seed(0      )
						lowerCamelCase_							:	Tuple							     =							ControlNetModel(
						    block_out_channels=(32, 64)				, layers_per_block=2				, in_channels=4				, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				, cross_attention_dim=32				, conditioning_embedding_out_channels=(16, 32)				, )
						controlneta.controlnet_down_blocks.apply(__lowercase      )

						torch.manual_seed(0      )
						lowerCamelCase_							:	Optional[Any]							     =							DDIMScheduler(
						    beta_start=0.0_00_85				, beta_end=0.0_12				, beta_schedule='scaled_linear'				, clip_sample=__lowercase				, set_alpha_to_one=__lowercase				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	Union[str, Any]							     =							AutoencoderKL(
						    block_out_channels=[32, 64]				, in_channels=3				, out_channels=3				, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				, up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				, latent_channels=4				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	Tuple							     =							CLIPTextConfig(
						    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1_000				, )
						lowerCamelCase_							:	List[Any]							     =							CLIPTextModel(__lowercase      )
						lowerCamelCase_							:	Tuple							     =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

						lowerCamelCase_							:	Union[str, Any]							     =							MultiControlNetModel([controlneta, controlneta]      )

						lowerCamelCase_							:	Optional[Any]							     =							{
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components

					def 			lowercase_							(       self				, __lowercase				, __lowercase=0      ):







						'''simple docstring'''




						if str(__lowercase      ).startswith('mps'      ):
							lowerCamelCase_							:	List[Any]							     =							torch.manual_seed(__lowercase      )
						else:
							lowerCamelCase_							:	Optional[Any]							     =							torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )

						lowerCamelCase_							:	Optional[int]							     =							2

						lowerCamelCase_							:	Union[str, Any]							     =							[
						    randn_tensor(
						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=__lowercase				, device=torch.device(__lowercase      )				, ),
						    randn_tensor(
						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=__lowercase				, device=torch.device(__lowercase      )				, ),
						]
						lowerCamelCase_							:	Optional[int]							     =							floats_tensor((1, 3, 32, 32)				, rng=random.Random(__lowercase      )      ).to(__lowercase      )
						lowerCamelCase_							:	List[str]							     =							init_image.cpu().permute(0				, 2				, 3				, 1      )[0]

						lowerCamelCase_							:	int							     =							Image.fromarray(np.uinta(__lowercase      )      ).convert('RGB'      ).resize((64, 64)      )
						lowerCamelCase_							:	str							     =							Image.fromarray(np.uinta(init_image + 4      )      ).convert('RGB'      ).resize((64, 64)      )

						lowerCamelCase_							:	Tuple							     =							{
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						    'image': image,
						    'mask_image': mask_image,
						    'control_image': control_image,
						}

						return inputs

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Tuple							     =							self.get_dummy_components()
						lowerCamelCase_							:	Any							     =							self.pipeline_class(**__lowercase      )
						pipe.to(__lowercase      )

						lowerCamelCase_							:	int							     =							10.0
						lowerCamelCase_							:	int							     =							4

						lowerCamelCase_							:	Optional[int]							     =							self.get_dummy_inputs(__lowercase      )
						lowerCamelCase_							:	Tuple							     =							steps
						lowerCamelCase_							:	List[Any]							     =							scale
						lowerCamelCase_							:	Any							     =							pipe(**__lowercase      )[0]

						lowerCamelCase_							:	Optional[int]							     =							self.get_dummy_inputs(__lowercase      )
						lowerCamelCase_							:	Optional[Any]							     =							steps
						lowerCamelCase_							:	Dict							     =							scale
						lowerCamelCase_							:	Dict							     =							pipe(**__lowercase				, control_guidance_start=0.1				, control_guidance_end=0.2      )[0]

						lowerCamelCase_							:	Dict							     =							self.get_dummy_inputs(__lowercase      )
						lowerCamelCase_							:	Optional[int]							     =							steps
						lowerCamelCase_							:	Tuple							     =							scale
						lowerCamelCase_							:	List[Any]							     =							pipe(**__lowercase				, control_guidance_start=[0.1, 0.3]				, control_guidance_end=[0.2, 0.7]      )[0]

						lowerCamelCase_							:	Union[str, Any]							     =							self.get_dummy_inputs(__lowercase      )
						lowerCamelCase_							:	str							     =							steps
						lowerCamelCase_							:	Optional[int]							     =							scale
						lowerCamelCase_							:	str							     =							pipe(**__lowercase				, control_guidance_start=0.4				, control_guidance_end=[0.5, 0.8]      )[0]

						# make sure that all outputs are different
						assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3
						assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3
						assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3      )

					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()				, reason='XFormers attention is only available with CUDA and `xformers` installed'				, )
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self._test_inference_batch_single_identical(expected_max_diff=2E-3      )





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							self.get_dummy_components()
						lowerCamelCase_							:	Optional[int]							     =							self.pipeline_class(**__lowercase      )
						pipe.to(__lowercase      )
						pipe.set_progress_bar_config(disable=__lowercase      )
						with tempfile.TemporaryDirectory() as tmpdir:
							try:
								# save_pretrained is not implemented for Multi-ControlNet
								pipe.save_pretrained(__lowercase      )
							except NotImplementedError:
								pass


@slow
@require_torch_gpu
class        __SCREAMING_SNAKE_CASE							(	unittest.TestCase						):

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[int]							     =							ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'      )

						lowerCamelCase_							:	Union[str, Any]							     =							StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-inpainting'				, safety_checker=__lowercase				, controlnet=__lowercase      )
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=__lowercase      )

						lowerCamelCase_							:	Optional[Any]							     =							torch.Generator(device='cpu'      ).manual_seed(0      )
						lowerCamelCase_							:	Optional[Any]							     =							load_image(
						    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'      ).resize((512, 512)      )

						lowerCamelCase_							:	Tuple							     =							load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
						    '/stable_diffusion_inpaint/input_bench_mask.png'      ).resize((512, 512)      )

						lowerCamelCase_							:	Tuple							     =							'pitch black hole'

						lowerCamelCase_							:	List[str]							     =							load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'      ).resize((512, 512)      )

						lowerCamelCase_							:	str							     =							pipe(
						    __lowercase				, image=__lowercase				, mask_image=__lowercase				, control_image=__lowercase				, generator=__lowercase				, output_type='np'				, num_inference_steps=3				, )

						lowerCamelCase_							:	Optional[Any]							     =							output.images[0]

						assert image.shape == (512, 512, 3)

						lowerCamelCase_							:	Optional[int]							     =							load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'      )

						assert np.abs(expected_image - image      ).max() < 9E-2





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Dict							     =							ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'      )

						lowerCamelCase_							:	Optional[int]							     =							StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-v1-5'				, safety_checker=__lowercase				, controlnet=__lowercase      )
						lowerCamelCase_							:	Union[str, Any]							     =							DDIMScheduler.from_config(pipe.scheduler.config      )
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=__lowercase      )

						lowerCamelCase_							:	Tuple							     =							torch.Generator(device='cpu'      ).manual_seed(33      )

						lowerCamelCase_							:	Dict							     =							load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'      )
						lowerCamelCase_							:	Union[str, Any]							     =							init_image.resize((512, 512)      )

						lowerCamelCase_							:	Optional[int]							     =							load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'      )
						lowerCamelCase_							:	int							     =							mask_image.resize((512, 512)      )

						lowerCamelCase_							:	Tuple							     =							'a handsome man with ray-ban sunglasses'

						def make_inpaint_condition(__lowercase				, __lowercase      ):
							lowerCamelCase_							:	str							     =							np.array(image.convert('RGB'      )      ).astype(np.floataa      ) / 2_55.0
							lowerCamelCase_							:	str							     =							np.array(image_mask.convert('L'      )      ).astype(np.floataa      ) / 2_55.0

							assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
							lowerCamelCase_							:	Dict							     =							-1.0  # set as masked pixel
							lowerCamelCase_							:	Optional[Any]							     =							np.expand_dims(__lowercase				, 0      ).transpose(0				, 3				, 1				, 2      )
							lowerCamelCase_							:	str							     =							torch.from_numpy(__lowercase      )
							return image

						lowerCamelCase_							:	Optional[int]							     =							make_inpaint_condition(__lowercase				, __lowercase      )

						lowerCamelCase_							:	List[str]							     =							pipe(
						    __lowercase				, image=__lowercase				, mask_image=__lowercase				, control_image=__lowercase				, guidance_scale=9.0				, eta=1.0				, generator=__lowercase				, num_inference_steps=20				, output_type='np'				, )
						lowerCamelCase_							:	int							     =							output.images[0]

						assert image.shape == (512, 512, 3)

						lowerCamelCase_							:	Dict							     =							load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'      )

						assert np.abs(expected_image - image      ).max() < 9E-2
