code: 
  
  
  
  
  """simple docstring"""
  
  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			):
  			a__ :      Dict								=     StableDiffusionControlNetInpaintPipeline
  			a__ :      List[Any]								=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  			a__ :      List[Any]								=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  			a__ :      List[Any]								=     frozenset({"""control_image"""}			)  # skip `image` and `mask` for now, only test for control_image
  			a__ :      Optional[int]								=     TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					torch.manual_seed(0							)
  					snake_case		:						int								=				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							)
  					snake_case		:						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							)
  					snake_case		:						Optional[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							)
  					snake_case		:						Optional[int]								=				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							)
  					snake_case		:						Optional[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				, )
  					snake_case		:						int								=				CLIPTextModel(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[int]								=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
  
  					snake_case		:						List[Any]								=				{
  					    "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				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=0							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if str(SCREAMING_SNAKE_CASE							).startswith("mps"							):
  							snake_case		:						Optional[int]								=				torch.manual_seed(SCREAMING_SNAKE_CASE							)
  					else:
  							snake_case		:						str								=				torch.Generator(device=SCREAMING_SNAKE_CASE							).manual_seed(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Dict								=				2
  					snake_case		:						List[Any]								=				randn_tensor(
  					    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=SCREAMING_SNAKE_CASE				, device=torch.device(SCREAMING_SNAKE_CASE							)				, )
  					snake_case		:						Optional[int]								=				floats_tensor((1, 3, 32, 32)				, rng=random.Random(SCREAMING_SNAKE_CASE							)							).to(SCREAMING_SNAKE_CASE							)
  					snake_case		:						str								=				init_image.cpu().permute(0				, 2				, 3				, 1							)[0]
  
  					snake_case		:						Union[str, Any]								=				Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE							)							).convert("RGB"							).resize((64, 64)							)
  					snake_case		:						Union[str, Any]								=				Image.fromarray(np.uinta(init_image + 4							)							).convert("RGB"							).resize((64, 64)							)
  
  					snake_case		:						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 							lowerCamelCase_				(     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 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3							)
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					self._test_inference_batch_single_identical(expected_max_diff=2E-3							)
  
  
  
  
  
  
  
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Any								=     StableDiffusionControlNetInpaintPipeline
  			a__ :      Optional[int]								=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  			a__ :      int								=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  			a__ :      List[str]								=     frozenset([]			)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					torch.manual_seed(0							)
  					snake_case		:						Optional[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							)
  					snake_case		:						Optional[int]								=				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							)
  					snake_case		:						Optional[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							)
  					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							)
  					snake_case		:						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				, )
  					snake_case		:						Any								=				CLIPTextModel(SCREAMING_SNAKE_CASE							)
  					snake_case		:						str								=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
  
  					snake_case		:						int								=				{
  					    "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			):
  			a__ :      str								=     StableDiffusionControlNetInpaintPipeline
  			a__ :      List[Any]								=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  			a__ :      Any								=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					torch.manual_seed(0							)
  					snake_case		:						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							):
  							if isinstance(SCREAMING_SNAKE_CASE				, torch.nn.Convad							):
  									torch.nn.init.normal(m.weight							)
  									m.bias.data.fill_(1.0							)
  
  					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							)
  					snake_case		:						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(SCREAMING_SNAKE_CASE							)
  
  					torch.manual_seed(0							)
  					snake_case		:						str								=				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							)
  					snake_case		:						int								=				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							)
  					snake_case		:						Optional[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				, )
  					snake_case		:						List[Any]								=				CLIPTextModel(SCREAMING_SNAKE_CASE							)
  					snake_case		:						int								=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
  
  					snake_case		:						Tuple								=				MultiControlNetModel([controlneta, controlneta]							)
  
  					snake_case		:						List[str]								=				{
  					    "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				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=0							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if str(SCREAMING_SNAKE_CASE							).startswith("mps"							):
  							snake_case		:						int								=				torch.manual_seed(SCREAMING_SNAKE_CASE							)
  					else:
  							snake_case		:						List[str]								=				torch.Generator(device=SCREAMING_SNAKE_CASE							).manual_seed(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Optional[Any]								=				2
  
  					snake_case		:						Optional[Any]								=				[
  					    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							)				, ),
  					]
  					snake_case		:						Optional[int]								=				floats_tensor((1, 3, 32, 32)				, rng=random.Random(SCREAMING_SNAKE_CASE							)							).to(SCREAMING_SNAKE_CASE							)
  					snake_case		:						List[Any]								=				init_image.cpu().permute(0				, 2				, 3				, 1							)[0]
  
  					snake_case		:						List[Any]								=				Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE							)							).convert("RGB"							).resize((64, 64)							)
  					snake_case		:						str								=				Image.fromarray(np.uinta(init_image + 4							)							).convert("RGB"							).resize((64, 64)							)
  
  					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 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				self.get_dummy_components()
  					snake_case		:						int								=				self.pipeline_class(**SCREAMING_SNAKE_CASE							)
  					pipe.to(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						List[Any]								=				10.0
  					snake_case		:						str								=				4
  
  					snake_case		:						Tuple								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[int]								=				steps
  					snake_case		:						Any								=				scale
  					snake_case		:						Union[str, Any]								=				pipe(**SCREAMING_SNAKE_CASE							)[0]
  
  					snake_case		:						str								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[Any]								=				steps
  					snake_case		:						Optional[Any]								=				scale
  					snake_case		:						Dict								=				pipe(**SCREAMING_SNAKE_CASE				, control_guidance_start=0.1				, control_guidance_end=0.2							)[0]
  
  					snake_case		:						Union[str, Any]								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[int]								=				steps
  					snake_case		:						Optional[int]								=				scale
  					snake_case		:						List[str]								=				pipe(**SCREAMING_SNAKE_CASE				, control_guidance_start=[0.1, 0.3]				, control_guidance_end=[0.2, 0.7]							)[0]
  
  					snake_case		:						Union[str, Any]								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Dict								=				steps
  					snake_case		:						Optional[int]								=				scale
  					snake_case		:						Union[str, Any]								=				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 							lowerCamelCase_				(     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 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					self._test_inference_batch_single_identical(expected_max_diff=2E-3							)
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[str]								=				self.get_dummy_components()
  					snake_case		:						Union[str, Any]								=				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 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().tearDown()
  					gc.collect()
  					torch.cuda.empty_cache()
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Union[str, Any]								=				ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"							)
  
  					snake_case		:						List[Any]								=				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							)
  
  					snake_case		:						Optional[int]								=				torch.Generator(device="cpu"							).manual_seed(0							)
  					snake_case		:						int								=				load_image(
  					    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"							).resize((512, 512)							)
  
  					snake_case		:						int								=				load_image(
  					    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
  					    "/stable_diffusion_inpaint/input_bench_mask.png"							).resize((512, 512)							)
  
  					snake_case		:						Tuple								=				"pitch black hole"
  
  					snake_case		:						Optional[int]								=				load_image(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"							).resize((512, 512)							)
  
  					snake_case		:						Optional[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				, )
  
  					snake_case		:						Optional[Any]								=				output.images[0]
  
  					assert image.shape == (512, 512, 3)
  
  					snake_case		:						Union[str, Any]								=				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							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"							)
  
  					snake_case		:						Tuple								=				StableDiffusionControlNetInpaintPipeline.from_pretrained(
  					    "runwayml/stable-diffusion-v1-5"				, safety_checker=SCREAMING_SNAKE_CASE				, controlnet=SCREAMING_SNAKE_CASE							)
  					snake_case		:						Tuple								=				DDIMScheduler.from_config(pipe.scheduler.config							)
  					pipe.enable_model_cpu_offload()
  					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						List[str]								=				torch.Generator(device="cpu"							).manual_seed(33							)
  
  					snake_case		:						Any								=				load_image(
  					    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"							)
  					snake_case		:						List[str]								=				init_image.resize((512, 512)							)
  
  					snake_case		:						Optional[Any]								=				load_image(
  					    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"							)
  					snake_case		:						Any								=				mask_image.resize((512, 512)							)
  
  					snake_case		:						int								=				"a handsome man with ray-ban sunglasses"
  
  					def make_inpaint_condition(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  							snake_case		:						Optional[int]								=				np.array(image.convert("RGB"							)							).astype(np.floataa							) / 255.0
  							snake_case		:						int								=				np.array(image_mask.convert("L"							)							).astype(np.floataa							) / 255.0
  
  							assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
  							snake_case		:						Union[str, Any]								=				-1.0  # set as masked pixel
  							snake_case		:						List[str]								=				np.expand_dims(SCREAMING_SNAKE_CASE				, 0							).transpose(0				, 3				, 1				, 2							)
  							snake_case		:						Optional[Any]								=				torch.from_numpy(SCREAMING_SNAKE_CASE							)
  							return image
  
  					snake_case		:						Any								=				make_inpaint_condition(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						List[Any]								=				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"				, )
  					snake_case		:						Optional[Any]								=				output.images[0]
  
  					assert image.shape == (512, 512, 3)
  
  					snake_case		:						Tuple								=				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
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Tuple								=     ["""image_processor""", """tokenizer"""]
  			a__ :      List[Any]								=     """EfficientNetImageProcessor"""
  			a__ :      str								=     ("""BertTokenizer""", """BertTokenizerFast""")
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().__init__(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  
  			def __call__(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE="max_length"				, SCREAMING_SNAKE_CASE=64				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if text is None and images is None:
  							raise ValueError("You have to specify either text or images. Both cannot be none."							)
  
  					if text is not None:
  							snake_case		:						Dict								=				self.tokenizer(
  							    SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE				, max_length=SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if images is not None:
  							snake_case		:						Tuple								=				self.image_processor(SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if text is not None and images is not None:
  							snake_case		:						Dict								=				image_features.pixel_values
  							return encoding
  					elif text is not None:
  							return encoding
  					else:
  							return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE							)				, tensor_type=SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				self.tokenizer.model_input_names
  					snake_case		:						Any								=				self.image_processor.model_input_names
  					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
  
  