code: import warnings
  
  from ...utils import logging
  from .image_processing_levit import LevitImageProcessor
  
  
  _SCREAMING_SNAKE_CASE   						=    logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							lowerCAmelCase_    ):
  
  
  
  
      def __init__(					self  :   Optional[Any]      ,      *lowerCAmelCase  :   List[Any]      ,      **lowerCAmelCase  :   int   )	-> Tuple:
        warnings.warn(
            "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use LevitImageProcessor instead."      ,      __SCREAMING_SNAKE_CASE      ,      )
        super().__init__(*__SCREAMING_SNAKE_CASE      ,      **__SCREAMING_SNAKE_CASE   )
  
  
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
  
  