code: from sklearn.metrics import mean_absolute_error
  
  import datasets
  
  
  _SCREAMING_SNAKE_CASE   						=    """\
  @article{scikit-learn,
   title={Scikit-learn: Machine Learning in {P}ython},
   author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
   journal={Journal of Machine Learning Research},
   volume={12},
   pages={2825--2830},
   year={2011}
  }
  """
  
  _SCREAMING_SNAKE_CASE   						=    """\
  Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
  values.
  """
  
  
  _SCREAMING_SNAKE_CASE   						=    """
  Args:
      predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
          Estimated target values.
      references: array-like of shape (n_samples,) or (n_samples, n_outputs)
          Ground truth (correct) target values.
      sample_weight: array-like of shape (n_samples,), default=None
          Sample weights.
      multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
          Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
  
                   \"raw_values\" : Returns a full set of errors in case of multioutput input.
  
                   \"uniform_average\" : Errors of all outputs are averaged with uniform weight.
  
  Returns:
      mae : mean absolute error.
          If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
          MAE output is non-negative floating point. The best value is 0.0.
  Examples:
  
      >>> mae_metric = datasets.load_metric(\"mae\")
      >>> predictions = [2.5, 0.0, 2, 8]
      >>> references = [3, -0.5, 2, 7]
      >>> results = mae_metric.compute(predictions=predictions, references=references)
      >>> print(results)
      {'mae': 0.5}
  
      If you're using multi-dimensional lists, then set the config as follows :
  
      >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
      >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
      >>> references = [[0, 2], [-1, 2], [8, -5]]
      >>> results = mae_metric.compute(predictions=predictions, references=references)
      >>> print(results)
      {'mae': 0.75}
      >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
      >>> print(results)
      {'mae': array([0.5, 1. ])}
  """
  
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,     _KWARGS_DESCRIPTION    )
  class 					UpperCAmelCase_				(							datasets.Metric    ):
  
  
  
  
      def 						__lowerCamelCase (					self  :   Dict   )	-> Union[str, Any]:
        return datasets.MetricInfo(
            description=_DESCRIPTION      ,      citation=_CITATION      ,      inputs_description=_KWARGS_DESCRIPTION      ,      features=datasets.Features(self._get_feature_types()   )      ,      reference_urls=[
                "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"
            ]      ,      )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> Optional[int]:
        if self.config_name == "multilist":
          return {
              "predictions": datasets.Sequence(datasets.Value("float"   )   ),
              "references": datasets.Sequence(datasets.Value("float"   )   ),
          }
        else:
          return {
              "predictions": datasets.Value("float"   ),
              "references": datasets.Value("float"   ),
          }
  
  
  
  
      def 						__lowerCamelCase (					self  :   str      ,      lowerCAmelCase  :   Optional[int]      ,      lowerCAmelCase  :   Union[str, Any]      ,      lowerCAmelCase  :   int=None      ,      lowerCAmelCase  :   List[str]="uniform_average"   )	-> int:
        a__     		=     mean_absolute_error(lowerCAmelCase      ,      lowerCAmelCase      ,      sample_weight=lowerCAmelCase      ,      multioutput=lowerCAmelCase   )
  
        return {"mae": mae_score}
  
  
style_context: # 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 					UpperCAmelCase_				(							_a     ,     _a     ,     _a     ,     unittest.TestCase    ):
      UpperCAmelCase_			:       Dict						 =							StableDiffusionControlNetInpaintPipeline
      UpperCAmelCase_			:       Tuple						 =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase_			:       Optional[Any]						 =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase_			:       Any						 =							frozenset({'control_image'}    )  # skip `image` and `mask` for now, only test for control_image
      UpperCAmelCase_			:       str						 =							TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> Union[str, Any]:
        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=0.00_085      ,      beta_end=0.012      ,      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-0_5      ,      num_attention_heads=4      ,      num_hidden_layers=5      ,      pad_token_id=1      ,      vocab_size=1000      ,      )
        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  :   Tuple      ,      lowerCAmelCase  :   Optional[Any]      ,      lowerCAmelCase  :   Dict=0   )	-> Optional[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   )      ,      )
        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  :   Any   )	-> Tuple:
        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  :   List[Any]   )	-> Optional[int]:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any   )	-> Optional[int]:
        self._test_inference_batch_single_identical(expected_max_diff=2e-3   )
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							_a    ):
      UpperCAmelCase_			:       Tuple						 =							StableDiffusionControlNetInpaintPipeline
      UpperCAmelCase_			:       List[Any]						 =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase_			:       Dict						 =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase_			:       Tuple						 =							frozenset([]    )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int   )	-> Dict:
        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=0.00_085      ,      beta_end=0.012      ,      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-0_5      ,      num_attention_heads=4      ,      num_hidden_layers=5      ,      pad_token_id=1      ,      vocab_size=1000      ,      )
        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 					UpperCAmelCase_				(							_a     ,     _a     ,     unittest.TestCase    ):
      UpperCAmelCase_			:       Union[str, Any]						 =							StableDiffusionControlNetInpaintPipeline
      UpperCAmelCase_			:       Any						 =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase_			:       Optional[int]						 =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  
  
  
  
      def 						__lowerCamelCase (					self  :   Dict   )	-> 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   )
  
        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=0.00_085      ,      beta_end=0.012      ,      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-0_5      ,      num_attention_heads=4      ,      num_hidden_layers=5      ,      pad_token_id=1      ,      vocab_size=1000      ,      )
        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  :   Optional[int]      ,      lowerCAmelCase  :   Dict      ,      lowerCAmelCase  :   Any=0   )	-> List[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   )      ,      ),
            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  :   Optional[Any]   )	-> List[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  :   Tuple   )	-> 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   )	-> Dict:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any   )	-> List[str]:
        self._test_inference_batch_single_identical(expected_max_diff=2e-3   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]   )	-> Any:
        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 					UpperCAmelCase_				(							unittest.TestCase    ):
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]   )	-> Tuple:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
  
  
  
  
      def 						__lowerCamelCase (					self  :   Union[str, Any]   )	-> List[Any]:
        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  :   Dict   )	-> List[str]:
        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  :   Any      ,      lowerCAmelCase  :   Optional[int]   ):
          a__     		=     np.array(image.convert("RGB"   )   ).astype(np.floataa   ) / 255.0
          a__     		=     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"
          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
  
  