code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  from ...models import UNetaDConditionModel, VQModel
  from ...pipelines import DiffusionPipeline
  from ...pipelines.pipeline_utils import ImagePipelineOutput
  from ...schedulers import DDPMScheduler
  from ...utils import (
      is_accelerate_available,
      is_accelerate_version,
      logging,
      randn_tensor,
      replace_example_docstring,
  )
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  UpperCAmelCase__      =							'''
      Examples:
          ```py
          >>> import torch
          >>> import numpy as np
  
          >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
          >>> from transformers import pipeline
          >>> from diffusers.utils import load_image
  
  
          >>> def make_hint(image, depth_estimator):
          ...     image = depth_estimator(image)["depth"]
          ...     image = np.array(image)
          ...     image = image[:, :, None]
          ...     image = np.concatenate([image, image, image], axis=2)
          ...     detected_map = torch.from_numpy(image).float() / 255.0
          ...     hint = detected_map.permute(2, 0, 1)
          ...     return hint
  
  
          >>> depth_estimator = pipeline("depth-estimation")
  
          >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
          ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
          ... )
          >>> pipe_prior = pipe_prior.to("cuda")
  
          >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
          ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
          ... )
          >>> pipe = pipe.to("cuda")
  
          >>> img = load_image(
          ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          ...     "/kandinsky/cat.png"
          ... ).resize((768, 768))
  
  
          >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
  
          >>> prompt = "A robot, 4k photo"
          >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
  
          >>> generator = torch.Generator(device="cuda").manual_seed(43)
  
          >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
          >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)
  
          >>> images = pipe(
          ...     image=img,
          ...     strength=0.5,
          ...     image_embeds=img_emb.image_embeds,
          ...     negative_image_embeds=negative_emb.image_embeds,
          ...     hint=hint,
          ...     num_inference_steps=50,
          ...     generator=generator,
          ...     height=768,
          ...     width=768,
          ... ).images
  
          >>> images[0].save("robot_cat.png")
          ```
  '''
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   str,_SCREAMING_SNAKE_CASE				:   Tuple,_SCREAMING_SNAKE_CASE				:   int=8   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  height // scale_factor**2
  			if height % scale_factor**2 != 0:
  						new_height += 1
  			__A=  width // scale_factor**2
  			if width % scale_factor**2 != 0:
  						new_width += 1
  			return new_height * scale_factor, new_width * scale_factor
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   str,_SCREAMING_SNAKE_CASE				:   Any=512,_SCREAMING_SNAKE_CASE				:   Optional[Any]=512   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  pil_image.resize((w, h),resample=Image.BICUBIC,reducing_gap=1   )
  			__A=  np.array(pil_image.convert('RGB'   )   )
  			__A=  arr.astype(np.floataa   ) / 1_27.5 - 1
  			__A=  np.transpose(_SCREAMING_SNAKE_CASE,[2, 0, 1]   )
  			__A=  torch.from_numpy(_SCREAMING_SNAKE_CASE   ).unsqueeze(0   )
  			return image
  
  
  
  
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Optional[Any] ,						lowerCAmelCase_       : UNetaDConditionModel ,						lowerCAmelCase_       : DDPMScheduler ,						lowerCAmelCase_       : VQModel ,						)			->		Optional[Any]:
  						super().__init__()
  
  						self.register_modules(
  						    unet=lowerCAmelCase_ ,						scheduler=lowerCAmelCase_ ,						movq=lowerCAmelCase_ ,						)
  						__A=  2 ** (len(self.movq.config.block_out_channels  ) - 1)
  			def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Optional[int]  )			->		Union[str, Any]:
  						# get the original timestep using init_timestep
  						__A=  min(int(num_inference_steps * strength  ) ,						lowerCAmelCase_  )
  
  						__A=  max(num_inference_steps - init_timestep ,						0  )
  						__A=  self.scheduler.timesteps[t_start:]
  
  						return timesteps, num_inference_steps - t_start
  			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : int=None  )			->		List[Any]:
  						if not isinstance(lowerCAmelCase_ ,						(torch.Tensor, PIL.Image.Image, list)  ):
  									raise ValueError(
  									    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase_  )}"""  )
  
  						__A=  image.to(device=lowerCAmelCase_ ,						dtype=lowerCAmelCase_  )
  
  						__A=  batch_size * num_images_per_prompt
  
  						if image.shape[1] == 4:
  									__A=  image
  
  						else:
  									if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ) and len(lowerCAmelCase_  ) != batch_size:
  												raise ValueError(
  												    F"""You have passed a list of generators of length {len(lowerCAmelCase_  )}, but requested an effective batch"""
  												    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""  )
  
  									elif isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  												__A=  [
  												    self.movq.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(lowerCAmelCase_  )
  												]
  												__A=  torch.cat(lowerCAmelCase_ ,						dim=0  )
  									else:
  												__A=  self.movq.encode(lowerCAmelCase_  ).latent_dist.sample(lowerCAmelCase_  )
  
  									__A=  self.movq.config.scaling_factor * init_latents
  
  						__A=  torch.cat([init_latents] ,						dim=0  )
  
  						__A=  init_latents.shape
  						__A=  randn_tensor(lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						device=lowerCAmelCase_ ,						dtype=lowerCAmelCase_  )
  
  						# get latents
  						__A=  self.scheduler.add_noise(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  						__A=  init_latents
  
  						return latents
  			def 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : Any=0  )			->		Tuple:
  						if is_accelerate_available():
  									from accelerate import cpu_offload
  						else:
  									raise ImportError('Please install accelerate via `pip install accelerate`'  )
  
  						__A=  torch.device(F"""cuda:{gpu_id}"""  )
  
  						__A=  [
  						    self.unet,
  						    self.movq,
  						]
  						for cpu_offloaded_model in models:
  									if cpu_offloaded_model is not None:
  												cpu_offload(lowerCAmelCase_ ,						lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : List[Any]=0  )			->		int:
  						if is_accelerate_available() and is_accelerate_version('>=' ,						'0.17.0.dev0'  ):
  									from accelerate import cpu_offload_with_hook
  						else:
  									raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'  )
  
  						__A=  torch.device(F"""cuda:{gpu_id}"""  )
  
  						if self.device.type != "cpu":
  									self.to('cpu' ,						silence_dtype_warnings=lowerCAmelCase_  )
  									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  						__A=  None
  						for cpu_offloaded_model in [self.unet, self.movq]:
  									__A,  __A=  cpu_offload_with_hook(lowerCAmelCase_ ,						lowerCAmelCase_ ,						prev_module_hook=lowerCAmelCase_  )
  
  						# We'll offload the last model manually.
  						__A=  hook
  			@property
  			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  			def 				lowerCAmelCase    (     self       : Tuple  )			->		Union[str, Any]:
  						if not hasattr(self.unet ,						'_hf_hook'  ):
  									return self.device
  						for module in self.unet.modules():
  									if (
  									    hasattr(lowerCAmelCase_ ,						'_hf_hook'  )
  									    and hasattr(module._hf_hook ,						'execution_device'  )
  									    and module._hf_hook.execution_device is not None
  									):
  												return torch.device(module._hf_hook.execution_device  )
  						return self.device
  
  			@torch.no_grad()
  			@replace_example_docstring(lowerCAmelCase_  )
  			def __call__(     self       : Optional[int] ,						lowerCAmelCase_       : Union[torch.FloatTensor, List[torch.FloatTensor]] ,						lowerCAmelCase_       : Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] ,						lowerCAmelCase_       : Union[torch.FloatTensor, List[torch.FloatTensor]] ,						lowerCAmelCase_       : torch.FloatTensor ,						lowerCAmelCase_       : int = 512 ,						lowerCAmelCase_       : int = 512 ,						lowerCAmelCase_       : int = 100 ,						lowerCAmelCase_       : float = 4.0 ,						lowerCAmelCase_       : float = 0.3 ,						lowerCAmelCase_       : int = 1 ,						lowerCAmelCase_       : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,						lowerCAmelCase_       : Optional[str] = "pil" ,						lowerCAmelCase_       : bool = True ,						)			->		Optional[Any]:
  						__A=  self._execution_device
  
  						__A=  guidance_scale > 1.0
  
  						if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  									__A=  torch.cat(lowerCAmelCase_ ,						dim=0  )
  						if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  									__A=  torch.cat(lowerCAmelCase_ ,						dim=0  )
  						if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  									__A=  torch.cat(lowerCAmelCase_ ,						dim=0  )
  
  						__A=  image_embeds.shape[0]
  
  						if do_classifier_free_guidance:
  									__A=  image_embeds.repeat_interleave(lowerCAmelCase_ ,						dim=0  )
  									__A=  negative_image_embeds.repeat_interleave(lowerCAmelCase_ ,						dim=0  )
  									__A=  hint.repeat_interleave(lowerCAmelCase_ ,						dim=0  )
  
  						__A=  torch.cat([negative_image_embeds, image_embeds] ,						dim=0  ).to(dtype=self.unet.dtype ,						device=lowerCAmelCase_  )
  						__A=  torch.cat([hint, hint] ,						dim=0  ).to(dtype=self.unet.dtype ,						device=lowerCAmelCase_  )
  
  						if not isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  									__A=  [image]
  						if not all(isinstance(lowerCAmelCase_ ,						(PIL.Image.Image, torch.Tensor)  ) for i in image  ):
  									raise ValueError(
  									    F"""Input is in incorrect format: {[type(lowerCAmelCase_  ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""  )
  
  						__A=  torch.cat([prepare_image(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_  ) for i in image] ,						dim=0  )
  						__A=  image.to(dtype=image_embeds.dtype ,						device=lowerCAmelCase_  )
  
  						__A=  self.movq.encode(lowerCAmelCase_  )['latents']
  						__A=  latents.repeat_interleave(lowerCAmelCase_ ,						dim=0  )
  						self.scheduler.set_timesteps(lowerCAmelCase_ ,						device=lowerCAmelCase_  )
  						__A,  __A=  self.get_timesteps(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_  )
  						__A=  timesteps[:1].repeat(batch_size * num_images_per_prompt  )
  						__A,  __A=  downscale_height_and_width(lowerCAmelCase_ ,						lowerCAmelCase_ ,						self.movq_scale_factor  )
  						__A=  self.prepare_latents(
  						    lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						image_embeds.dtype ,						lowerCAmelCase_ ,						lowerCAmelCase_  )
  						for i, t in enumerate(self.progress_bar(lowerCAmelCase_  )  ):
  									# expand the latents if we are doing classifier free guidance
  									__A=  torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
  
  									__A=  {'image_embeds': image_embeds, 'hint': hint}
  									__A=  self.unet(
  									    sample=lowerCAmelCase_ ,						timestep=lowerCAmelCase_ ,						encoder_hidden_states=lowerCAmelCase_ ,						added_cond_kwargs=lowerCAmelCase_ ,						return_dict=lowerCAmelCase_ ,						)[0]
  
  									if do_classifier_free_guidance:
  												__A,  __A=  noise_pred.split(latents.shape[1] ,						dim=1  )
  												__A,  __A=  noise_pred.chunk(2  )
  												__A,  __A=  variance_pred.chunk(2  )
  												__A=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  												__A=  torch.cat([noise_pred, variance_pred_text] ,						dim=1  )
  
  									if not (
  									    hasattr(self.scheduler.config ,						'variance_type'  )
  									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
  									):
  												__A,  __A=  noise_pred.split(latents.shape[1] ,						dim=1  )
  
  									# compute the previous noisy sample x_t -> x_t-1
  
  									__A=  self.scheduler.step(
  									    lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						)[0]
  
  						# post-processing
  						__A=  self.movq.decode(lowerCAmelCase_ ,						force_not_quantize=lowerCAmelCase_  )['sample']
  
  						if output_type not in ["pt", "np", "pil"]:
  									raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""  )
  
  						if output_type in ["np", "pil"]:
  									__A=  image * 0.5 + 0.5
  									__A=  image.clamp(0 ,						1  )
  									__A=  image.cpu().permute(0 ,						2 ,						3 ,						1  ).float().numpy()
  
  						if output_type == "pil":
  									__A=  self.numpy_to_pil(lowerCAmelCase_  )
  
  						if not return_dict:
  									return (image,)
  
  						return ImagePipelineOutput(images=lowerCAmelCase_  )
  
  
  
  
style_context: 
  
  
  '''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 	a__				(				a_			,     a_			,     a_			,     unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Optional[int]							= StableDiffusionControlNetInpaintPipeline
  			A	:							List[str]							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  			A	:							List[Any]							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  			A	:							Any							= frozenset({'''control_image'''}      )  # skip `image` and `mask` for now, only test for control_image
  			A	:							List[Any]							= TEXT_TO_IMAGE_IMAGE_PARAMS
  			def 				lowerCAmelCase    (     self       : Tuple  )			->		List[str]:
  						torch.manual_seed(0  )
  						__A=  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  )
  						__A=  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  )
  						__A=  DDIMScheduler(
  						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
  						torch.manual_seed(0  )
  						__A=  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  )
  						__A=  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 ,						)
  						__A=  CLIPTextModel(lowerCAmelCase_  )
  						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )
  
  						__A=  {
  						    'unet': unet,
  						    'controlnet': controlnet,
  						    'scheduler': scheduler,
  						    'vae': vae,
  						    'text_encoder': text_encoder,
  						    'tokenizer': tokenizer,
  						    'safety_checker': None,
  						    'feature_extractor': None,
  						}
  						return components
  			def 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : int=0  )			->		str:
  						if str(lowerCAmelCase_  ).startswith('mps'  ):
  									__A=  torch.manual_seed(lowerCAmelCase_  )
  						else:
  									__A=  torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )
  
  						__A=  2
  						__A=  randn_tensor(
  						    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						)
  						__A=  floats_tensor((1, 3, 32, 32) ,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
  						__A=  init_image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]
  
  						__A=  Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  ).resize((64, 64)  )
  						__A=  Image.fromarray(np.uinta(init_image + 4  )  ).convert('RGB'  ).resize((64, 64)  )
  
  						__A=  {
  						    '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 				lowerCAmelCase    (     self       : Tuple  )			->		Optional[int]:
  						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 				lowerCAmelCase    (     self       : Optional[int]  )			->		str:
  						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )
  
  			def 				lowerCAmelCase    (     self       : List[Any]  )			->		str:
  						self._test_inference_batch_single_identical(expected_max_diff=2E-3  )
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							int							= StableDiffusionControlNetInpaintPipeline
  			A	:							List[str]							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  			A	:							Tuple							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  			A	:							int							= frozenset([]      )
  			def 				lowerCAmelCase    (     self       : int  )			->		List[Any]:
  						torch.manual_seed(0  )
  						__A=  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  )
  						__A=  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  )
  						__A=  DDIMScheduler(
  						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
  						torch.manual_seed(0  )
  						__A=  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  )
  						__A=  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 ,						)
  						__A=  CLIPTextModel(lowerCAmelCase_  )
  						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )
  
  						__A=  {
  						    'unet': unet,
  						    'controlnet': controlnet,
  						    'scheduler': scheduler,
  						    'vae': vae,
  						    'text_encoder': text_encoder,
  						    'tokenizer': tokenizer,
  						    'safety_checker': None,
  						    'feature_extractor': None,
  						}
  						return components
  
  
  class 	a__				(				a_			,     a_			,     unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Tuple							= StableDiffusionControlNetInpaintPipeline
  			A	:							int							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  			A	:							Optional[int]							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  			def 				lowerCAmelCase    (     self       : List[Any]  )			->		int:
  						torch.manual_seed(0  )
  						__A=  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(lowerCAmelCase_       : Optional[int]  ):
  									if isinstance(lowerCAmelCase_ ,						torch.nn.Convad  ):
  												torch.nn.init.normal(m.weight  )
  												m.bias.data.fill_(1.0  )
  
  						__A=  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(lowerCAmelCase_  )
  
  						torch.manual_seed(0  )
  						__A=  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(lowerCAmelCase_  )
  
  						torch.manual_seed(0  )
  						__A=  DDIMScheduler(
  						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
  						torch.manual_seed(0  )
  						__A=  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  )
  						__A=  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 ,						)
  						__A=  CLIPTextModel(lowerCAmelCase_  )
  						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )
  
  						__A=  MultiControlNetModel([controlneta, controlneta]  )
  
  						__A=  {
  						    'unet': unet,
  						    'controlnet': controlnet,
  						    'scheduler': scheduler,
  						    'vae': vae,
  						    'text_encoder': text_encoder,
  						    'tokenizer': tokenizer,
  						    'safety_checker': None,
  						    'feature_extractor': None,
  						}
  						return components
  			def 				lowerCAmelCase    (     self       : Union[str, Any] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : List[Any]=0  )			->		List[Any]:
  						if str(lowerCAmelCase_  ).startswith('mps'  ):
  									__A=  torch.manual_seed(lowerCAmelCase_  )
  						else:
  									__A=  torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )
  
  						__A=  2
  
  						__A=  [
  						    randn_tensor(
  						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						),
  						    randn_tensor(
  						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						),
  						]
  						__A=  floats_tensor((1, 3, 32, 32) ,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
  						__A=  init_image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]
  
  						__A=  Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  ).resize((64, 64)  )
  						__A=  Image.fromarray(np.uinta(init_image + 4  )  ).convert('RGB'  ).resize((64, 64)  )
  
  						__A=  {
  						    '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 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Any:
  						__A=  self.get_dummy_components()
  						__A=  self.pipeline_class(**lowerCAmelCase_  )
  						pipe.to(lowerCAmelCase_  )
  
  						__A=  10.0
  						__A=  4
  
  						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
  						__A=  steps
  						__A=  scale
  						__A=  pipe(**lowerCAmelCase_  )[0]
  
  						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
  						__A=  steps
  						__A=  scale
  						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=0.1 ,						control_guidance_end=0.2  )[0]
  
  						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
  						__A=  steps
  						__A=  scale
  						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=[0.1, 0.3] ,						control_guidance_end=[0.2, 0.7]  )[0]
  
  						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
  						__A=  steps
  						__A=  scale
  						__A=  pipe(**lowerCAmelCase_ ,						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 				lowerCAmelCase    (     self       : str  )			->		Any:
  						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 				lowerCAmelCase    (     self       : Any  )			->		int:
  						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )
  			def 				lowerCAmelCase    (     self       : Dict  )			->		Dict:
  						self._test_inference_batch_single_identical(expected_max_diff=2E-3  )
  
  			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Optional[int]:
  						__A=  self.get_dummy_components()
  						__A=  self.pipeline_class(**lowerCAmelCase_  )
  						pipe.to(lowerCAmelCase_  )
  						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
  						with tempfile.TemporaryDirectory() as tmpdir:
  									try:
  												# save_pretrained is not implemented for Multi-ControlNet
  												pipe.save_pretrained(lowerCAmelCase_  )
  									except NotImplementedError:
  												pass
  
  
  @slow
  @require_torch_gpu
  class 	a__				(				unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Tuple:
  						super().tearDown()
  						gc.collect()
  						torch.cuda.empty_cache()
  			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Tuple:
  						__A=  ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'  )
  
  						__A=  StableDiffusionControlNetInpaintPipeline.from_pretrained(
  						    'runwayml/stable-diffusion-inpainting' ,						safety_checker=lowerCAmelCase_ ,						controlnet=lowerCAmelCase_  )
  						pipe.enable_model_cpu_offload()
  						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
  
  						__A=  torch.Generator(device='cpu'  ).manual_seed(0  )
  						__A=  load_image(
  						    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'  ).resize((512, 512)  )
  
  						__A=  load_image(
  						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
  						    '/stable_diffusion_inpaint/input_bench_mask.png'  ).resize((512, 512)  )
  
  						__A=  'pitch black hole'
  
  						__A=  load_image(
  						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'  ).resize((512, 512)  )
  
  						__A=  pipe(
  						    lowerCAmelCase_ ,						image=lowerCAmelCase_ ,						mask_image=lowerCAmelCase_ ,						control_image=lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						output_type='np' ,						num_inference_steps=3 ,						)
  
  						__A=  output.images[0]
  
  						assert image.shape == (512, 512, 3)
  
  						__A=  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 				lowerCAmelCase    (     self       : str  )			->		Tuple:
  						__A=  ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'  )
  
  						__A=  StableDiffusionControlNetInpaintPipeline.from_pretrained(
  						    'runwayml/stable-diffusion-v1-5' ,						safety_checker=lowerCAmelCase_ ,						controlnet=lowerCAmelCase_  )
  						__A=  DDIMScheduler.from_config(pipe.scheduler.config  )
  						pipe.enable_model_cpu_offload()
  						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
  
  						__A=  torch.Generator(device='cpu'  ).manual_seed(33  )
  
  						__A=  load_image(
  						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'  )
  						__A=  init_image.resize((512, 512)  )
  
  						__A=  load_image(
  						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'  )
  						__A=  mask_image.resize((512, 512)  )
  
  						__A=  'a handsome man with ray-ban sunglasses'
  
  						def make_inpaint_condition(lowerCAmelCase_       : int ,						lowerCAmelCase_       : str  ):
  									__A=  np.array(image.convert('RGB'  )  ).astype(np.floataa  ) / 2_55.0
  									__A=  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"
  									__A=  -1.0  # set as masked pixel
  									__A=  np.expand_dims(lowerCAmelCase_ ,						0  ).transpose(0 ,						3 ,						1 ,						2  )
  									__A=  torch.from_numpy(lowerCAmelCase_  )
  									return image
  
  						__A=  make_inpaint_condition(lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  						__A=  pipe(
  						    lowerCAmelCase_ ,						image=lowerCAmelCase_ ,						mask_image=lowerCAmelCase_ ,						control_image=lowerCAmelCase_ ,						guidance_scale=9.0 ,						eta=1.0 ,						generator=lowerCAmelCase_ ,						num_inference_steps=20 ,						output_type='np' ,						)
  						__A=  output.images[0]
  
  						assert image.shape == (512, 512, 3)
  
  						__A=  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
  
  
  
  