



'''simple docstring'''







# 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 				__lowerCAmelCase(							lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		Any		=      StableDiffusionControlNetInpaintPipeline
			__snake_case  :		Optional[Any]		=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__snake_case  :		Optional[Any]		=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__snake_case  :		Dict		=      frozenset({'control_image'}      )  # skip `image` and `mask` for now, only test for control_image
			__snake_case  :		Dict		=      TEXT_TO_IMAGE_IMAGE_PARAMS






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      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	)
					SCREAMING_SNAKE_CASE_						: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)      ,       )
					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DDIMScheduler(
					    beta_start=0.0_00_85      ,       beta_end=0.0_12      ,       beta_schedule='scaled_linear'      ,       clip_sample=SCREAMING_SNAKE_CASE      ,       set_alpha_to_one=SCREAMING_SNAKE_CASE      ,       )
					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						: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	)
					SCREAMING_SNAKE_CASE_						:Any										=      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      ,       )
					SCREAMING_SNAKE_CASE_						:Tuple										=      CLIPTextModel(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

					SCREAMING_SNAKE_CASE_						:str										=      {
					    '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  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							Dict=0	):




					"""simple docstring"""







					if str(SCREAMING_SNAKE_CASE	).startswith('mps'	):
							SCREAMING_SNAKE_CASE_						:Any										=      torch.manual_seed(SCREAMING_SNAKE_CASE	)
					else:
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)

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

					SCREAMING_SNAKE_CASE_						:Dict										=      Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE	)	).convert('RGB'	).resize((64, 64)	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      Image.fromarray(np.uinta(init_image + 4	)	).convert('RGB'	).resize((64, 64)	)

					SCREAMING_SNAKE_CASE_						:Union[str, 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  :							Optional[Any]	):




					"""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  :							int	):




					"""simple docstring"""







					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					self._test_inference_batch_single_identical(expected_max_diff=2E-3	)




class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		List[Any]		=      StableDiffusionControlNetInpaintPipeline
			__snake_case  :		int		=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__snake_case  :		Optional[Any]		=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__snake_case  :		Tuple		=      frozenset([]      )






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:str										=      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	)
					SCREAMING_SNAKE_CASE_						: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)      ,       )
					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:int										=      DDIMScheduler(
					    beta_start=0.0_00_85      ,       beta_end=0.0_12      ,       beta_schedule='scaled_linear'      ,       clip_sample=SCREAMING_SNAKE_CASE      ,       set_alpha_to_one=SCREAMING_SNAKE_CASE      ,       )
					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						: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	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      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      ,       )
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      CLIPTextModel(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:str										=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

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




class 				__lowerCAmelCase(							lowerCAmelCase__		,			lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		str		=      StableDiffusionControlNetInpaintPipeline
			__snake_case  :		List[str]		=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__snake_case  :		List[Any]		=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      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(SCREAMING_SNAKE_CASE  :							Dict	):
							if isinstance(SCREAMING_SNAKE_CASE      ,       torch.nn.Convad	):
									torch.nn.init.normal(m.weight	)
									m.bias.data.fill_(1.0	)

					SCREAMING_SNAKE_CASE_						: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(SCREAMING_SNAKE_CASE	)

					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						: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(SCREAMING_SNAKE_CASE	)

					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:int										=      DDIMScheduler(
					    beta_start=0.0_00_85      ,       beta_end=0.0_12      ,       beta_schedule='scaled_linear'      ,       clip_sample=SCREAMING_SNAKE_CASE      ,       set_alpha_to_one=SCREAMING_SNAKE_CASE      ,       )
					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						: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	)
					SCREAMING_SNAKE_CASE_						: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      ,       )
					SCREAMING_SNAKE_CASE_						:Dict										=      CLIPTextModel(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Any										=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

					SCREAMING_SNAKE_CASE_						:Dict										=      MultiControlNetModel([controlneta, controlneta]	)

					SCREAMING_SNAKE_CASE_						:Dict										=      {
					    '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  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=0	):




					"""simple docstring"""







					if str(SCREAMING_SNAKE_CASE	).startswith('mps'	):
							SCREAMING_SNAKE_CASE_						:int										=      torch.manual_seed(SCREAMING_SNAKE_CASE	)
					else:
							SCREAMING_SNAKE_CASE_						:Tuple										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Dict										=      2

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

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

					SCREAMING_SNAKE_CASE_						:str										=      {
					    '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  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.get_dummy_components()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.pipeline_class(**SCREAMING_SNAKE_CASE	)
					pipe.to(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      10.0
					SCREAMING_SNAKE_CASE_						:Any										=      4

					SCREAMING_SNAKE_CASE_						:str										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Any										=      steps
					SCREAMING_SNAKE_CASE_						:List[str]										=      scale
					SCREAMING_SNAKE_CASE_						:str										=      pipe(**SCREAMING_SNAKE_CASE	)[0]

					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      steps
					SCREAMING_SNAKE_CASE_						:Tuple										=      scale
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      pipe(**SCREAMING_SNAKE_CASE      ,       control_guidance_start=0.1      ,       control_guidance_end=0.2	)[0]

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

					SCREAMING_SNAKE_CASE_						:int										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      steps
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      scale
					SCREAMING_SNAKE_CASE_						:int										=      pipe(**SCREAMING_SNAKE_CASE      ,       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  :							Tuple	):




					"""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  :							Dict	):




					"""simple docstring"""







					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3	)






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					self._test_inference_batch_single_identical(expected_max_diff=2E-3	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







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




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






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







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

					SCREAMING_SNAKE_CASE_						:Dict										=      StableDiffusionControlNetInpaintPipeline.from_pretrained(
					    'runwayml/stable-diffusion-inpainting'      ,       safety_checker=SCREAMING_SNAKE_CASE      ,       controlnet=SCREAMING_SNAKE_CASE	)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

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

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_inpaint/input_bench_mask.png'	).resize((512, 512)	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      'pitch black hole'

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

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      pipe(
					    SCREAMING_SNAKE_CASE      ,       image=SCREAMING_SNAKE_CASE      ,       mask_image=SCREAMING_SNAKE_CASE      ,       control_image=SCREAMING_SNAKE_CASE      ,       generator=SCREAMING_SNAKE_CASE      ,       output_type='np'      ,       num_inference_steps=3      ,       )

					SCREAMING_SNAKE_CASE_						:str										=      output.images[0]

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

					SCREAMING_SNAKE_CASE_						: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  :							Optional[int]	):




					"""simple docstring"""







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

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      StableDiffusionControlNetInpaintPipeline.from_pretrained(
					    'runwayml/stable-diffusion-v1-5'      ,       safety_checker=SCREAMING_SNAKE_CASE      ,       controlnet=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      DDIMScheduler.from_config(pipe.scheduler.config	)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      torch.Generator(device='cpu'	).manual_seed(33	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      init_image.resize((512, 512)	)

					SCREAMING_SNAKE_CASE_						:Any										=      load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      mask_image.resize((512, 512)	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      'a handsome man with ray-ban sunglasses'

					def make_inpaint_condition(SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							List[str]	):
							SCREAMING_SNAKE_CASE_						:List[Any]										=      np.array(image.convert('RGB'	)	).astype(np.floataa	) / 2_55.0
							SCREAMING_SNAKE_CASE_						:Any										=      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"
							SCREAMING_SNAKE_CASE_						:List[Any]										=      -1.0  # set as masked pixel
							SCREAMING_SNAKE_CASE_						:Dict										=      np.expand_dims(SCREAMING_SNAKE_CASE      ,       0	).transpose(0      ,       3      ,       1      ,       2	)
							SCREAMING_SNAKE_CASE_						:List[Any]										=      torch.from_numpy(SCREAMING_SNAKE_CASE	)
							return image

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      make_inpaint_condition(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

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

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

					SCREAMING_SNAKE_CASE_						:Any										=      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








"""simple docstring"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(inputs > threshold).type(inputs.type() )
   if mask.sum() < nb_min:
    # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
    __A						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							Optional[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Any      =						1
   return mask



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							List[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Tuple      =						1
   return mask





