code: 
  
  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				(							snake_case_						,						snake_case_						,						snake_case_						,						unittest.TestCase ):
      lowerCAmelCase_							:		Tuple						=							StableDiffusionControlNetInpaintPipeline
      lowerCAmelCase_							:		Dict						=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      lowerCAmelCase_							:		Any						=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      lowerCAmelCase_							:		List[str]						=							frozenset({"control_image"} )  # skip `image` and `mask` for now, only test for control_image
      lowerCAmelCase_							:		int						=							TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          torch.manual_seed(0    )
          lowerCAmelCase				:			List[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    )
          lowerCAmelCase				:			Tuple    			=      ControlNetModel(
              block_out_channels=(32, 64)    ,				layers_per_block=2    ,				in_channels=4    ,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,				cross_attention_dim=32    ,				conditioning_embedding_out_channels=(16, 32)    ,				)
          torch.manual_seed(0    )
          lowerCAmelCase				:			Optional[int]    			=      DDIMScheduler(
              beta_start=0.0_00_85    ,				beta_end=0.0_12    ,				beta_schedule='scaled_linear'    ,				clip_sample=UpperCAmelCase_    ,				set_alpha_to_one=UpperCAmelCase_    ,				)
          torch.manual_seed(0    )
          lowerCAmelCase				:			Any    			=      AutoencoderKL(
              block_out_channels=[32, 64]    ,				in_channels=3    ,				out_channels=3    ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,				latent_channels=4    ,				)
          torch.manual_seed(0    )
          lowerCAmelCase				:			Dict    			=      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=1000    ,				)
          lowerCAmelCase				:			Tuple    			=      CLIPTextModel(UpperCAmelCase_    )
          lowerCAmelCase				:			Dict    			=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
  
          lowerCAmelCase				:			Dict    			=      {
              'unet': unet,
              'controlnet': controlnet,
              'scheduler': scheduler,
              'vae': vae,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'safety_checker': None,
              'feature_extractor': None,
          }
          return components
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    int=0    ):
          if str(UpperCAmelCase_    ).startswith('mps'    ):
              lowerCAmelCase				:			Union[str, Any]    			=      torch.manual_seed(UpperCAmelCase_    )
          else:
              lowerCAmelCase				:			Dict    			=      torch.Generator(device=UpperCAmelCase_    ).manual_seed(UpperCAmelCase_    )
  
          lowerCAmelCase				:			List[Any]    			=      2
          lowerCAmelCase				:			int    			=      randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,				generator=UpperCAmelCase_    ,				device=torch.device(UpperCAmelCase_    )    ,				)
          lowerCAmelCase				:			Any    			=      floats_tensor((1, 3, 32, 32)    ,				rng=random.Random(UpperCAmelCase_    )    ).to(UpperCAmelCase_    )
          lowerCAmelCase				:			Any    			=      init_image.cpu().permute(0    ,				2    ,				3    ,				1    )[0]
  
          lowerCAmelCase				:			Any    			=      Image.fromarray(np.uinta(UpperCAmelCase_    )    ).convert('RGB'    ).resize((64, 64)    )
          lowerCAmelCase				:			Dict    			=      Image.fromarray(np.uinta(init_image + 4    )    ).convert('RGB'    ).resize((64, 64)    )
  
          lowerCAmelCase				:			Optional[int]    			=      {
              'prompt': 'A painting of a squirrel eating a burger',
              'generator': generator,
              'num_inference_steps': 2,
              'guidance_scale': 6.0,
              'output_type': 'numpy',
              'image': image,
              'mask_image': mask_image,
              'control_image': control_image,
          }
  
          return inputs
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3    )
  
  
  
  
  
  
      @unittest.skipIf(
          torch_device != 'cuda' or not is_xformers_available()    ,				reason='XFormers attention is only available with CUDA and `xformers` installed'    ,				)
      def 	lowercase__ (	self			:    Optional[int]    ):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          self._test_inference_batch_single_identical(expected_max_diff=2E-3    )
  
  
  
  
  
  
  class    __A				(							snake_case_ ):
      lowerCAmelCase_							:		str						=							StableDiffusionControlNetInpaintPipeline
      lowerCAmelCase_							:		Union[str, Any]						=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      lowerCAmelCase_							:		Dict						=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      lowerCAmelCase_							:		Dict						=							frozenset([] )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          torch.manual_seed(0    )
          lowerCAmelCase				:			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    )
          lowerCAmelCase				:			str    			=      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    )
          lowerCAmelCase				:			Union[str, Any]    			=      DDIMScheduler(
              beta_start=0.0_00_85    ,				beta_end=0.0_12    ,				beta_schedule='scaled_linear'    ,				clip_sample=UpperCAmelCase_    ,				set_alpha_to_one=UpperCAmelCase_    ,				)
          torch.manual_seed(0    )
          lowerCAmelCase				:			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    )
          lowerCAmelCase				:			Dict    			=      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=1000    ,				)
          lowerCAmelCase				:			Optional[int]    			=      CLIPTextModel(UpperCAmelCase_    )
          lowerCAmelCase				:			int    			=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
  
          lowerCAmelCase				:			str    			=      {
              'unet': unet,
              'controlnet': controlnet,
              'scheduler': scheduler,
              'vae': vae,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'safety_checker': None,
              'feature_extractor': None,
          }
          return components
  
  
  
  
  
  
  class    __A				(							snake_case_						,						snake_case_						,						unittest.TestCase ):
      lowerCAmelCase_							:		str						=							StableDiffusionControlNetInpaintPipeline
      lowerCAmelCase_							:		str						=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      lowerCAmelCase_							:		List[str]						=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Union[str, Any]    ):
          torch.manual_seed(0    )
          lowerCAmelCase				:			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(UpperCAmelCase_			:    Union[str, Any]    ):
              if isinstance(UpperCAmelCase_    ,				torch.nn.Convad    ):
                  torch.nn.init.normal(m.weight    )
                  m.bias.data.fill_(1.0    )
  
          lowerCAmelCase				:			str    			=      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(UpperCAmelCase_    )
  
          torch.manual_seed(0    )
          lowerCAmelCase				:			List[str]    			=      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(UpperCAmelCase_    )
  
          torch.manual_seed(0    )
          lowerCAmelCase				:			Optional[int]    			=      DDIMScheduler(
              beta_start=0.0_00_85    ,				beta_end=0.0_12    ,				beta_schedule='scaled_linear'    ,				clip_sample=UpperCAmelCase_    ,				set_alpha_to_one=UpperCAmelCase_    ,				)
          torch.manual_seed(0    )
          lowerCAmelCase				:			Tuple    			=      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    )
          lowerCAmelCase				:			Optional[int]    			=      CLIPTextConfig(
              bos_token_id=0    ,				eos_token_id=2    ,				hidden_size=32    ,				intermediate_size=37    ,				layer_norm_eps=1E-05    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				pad_token_id=1    ,				vocab_size=1000    ,				)
          lowerCAmelCase				:			str    			=      CLIPTextModel(UpperCAmelCase_    )
          lowerCAmelCase				:			Optional[Any]    			=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
  
          lowerCAmelCase				:			Any    			=      MultiControlNetModel([controlneta, controlneta]    )
  
          lowerCAmelCase				:			Optional[Any]    			=      {
              'unet': unet,
              'controlnet': controlnet,
              'scheduler': scheduler,
              'vae': vae,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'safety_checker': None,
              'feature_extractor': None,
          }
          return components
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    Optional[int]=0    ):
          if str(UpperCAmelCase_    ).startswith('mps'    ):
              lowerCAmelCase				:			str    			=      torch.manual_seed(UpperCAmelCase_    )
          else:
              lowerCAmelCase				:			List[Any]    			=      torch.Generator(device=UpperCAmelCase_    ).manual_seed(UpperCAmelCase_    )
  
          lowerCAmelCase				:			Optional[Any]    			=      2
  
          lowerCAmelCase				:			int    			=      [
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,				generator=UpperCAmelCase_    ,				device=torch.device(UpperCAmelCase_    )    ,				),
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,				generator=UpperCAmelCase_    ,				device=torch.device(UpperCAmelCase_    )    ,				),
          ]
          lowerCAmelCase				:			Any    			=      floats_tensor((1, 3, 32, 32)    ,				rng=random.Random(UpperCAmelCase_    )    ).to(UpperCAmelCase_    )
          lowerCAmelCase				:			Union[str, Any]    			=      init_image.cpu().permute(0    ,				2    ,				3    ,				1    )[0]
  
          lowerCAmelCase				:			int    			=      Image.fromarray(np.uinta(UpperCAmelCase_    )    ).convert('RGB'    ).resize((64, 64)    )
          lowerCAmelCase				:			Optional[int]    			=      Image.fromarray(np.uinta(init_image + 4    )    ).convert('RGB'    ).resize((64, 64)    )
  
          lowerCAmelCase				:			List[Any]    			=      {
              'prompt': 'A painting of a squirrel eating a burger',
              'generator': generator,
              'num_inference_steps': 2,
              'guidance_scale': 6.0,
              'output_type': 'numpy',
              'image': image,
              'mask_image': mask_image,
              'control_image': control_image,
          }
  
          return inputs
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ):
          lowerCAmelCase				:			Tuple    			=      self.get_dummy_components()
          lowerCAmelCase				:			Optional[Any]    			=      self.pipeline_class(**UpperCAmelCase_    )
          pipe.to(UpperCAmelCase_    )
  
          lowerCAmelCase				:			str    			=      10.0
          lowerCAmelCase				:			List[str]    			=      4
  
          lowerCAmelCase				:			int    			=      self.get_dummy_inputs(UpperCAmelCase_    )
          lowerCAmelCase				:			Tuple    			=      steps
          lowerCAmelCase				:			Optional[int]    			=      scale
          lowerCAmelCase				:			Union[str, Any]    			=      pipe(**UpperCAmelCase_    )[0]
  
          lowerCAmelCase				:			int    			=      self.get_dummy_inputs(UpperCAmelCase_    )
          lowerCAmelCase				:			Dict    			=      steps
          lowerCAmelCase				:			int    			=      scale
          lowerCAmelCase				:			List[Any]    			=      pipe(**UpperCAmelCase_    ,				control_guidance_start=0.1    ,				control_guidance_end=0.2    )[0]
  
          lowerCAmelCase				:			int    			=      self.get_dummy_inputs(UpperCAmelCase_    )
          lowerCAmelCase				:			Union[str, Any]    			=      steps
          lowerCAmelCase				:			Tuple    			=      scale
          lowerCAmelCase				:			List[Any]    			=      pipe(**UpperCAmelCase_    ,				control_guidance_start=[0.1, 0.3]    ,				control_guidance_end=[0.2, 0.7]    )[0]
  
          lowerCAmelCase				:			Union[str, Any]    			=      self.get_dummy_inputs(UpperCAmelCase_    )
          lowerCAmelCase				:			Dict    			=      steps
          lowerCAmelCase				:			Dict    			=      scale
          lowerCAmelCase				:			int    			=      pipe(**UpperCAmelCase_    ,				control_guidance_start=0.4    ,				control_guidance_end=[0.5, 0.8]    )[0]
  
          # make sure that all outputs are different
          assert np.sum(np.abs(output_a - output_a    )    ) > 1E-3
          assert np.sum(np.abs(output_a - output_a    )    ) > 1E-3
          assert np.sum(np.abs(output_a - output_a    )    ) > 1E-3
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[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 	lowercase__ (	self			:    Optional[Any]    ):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
          self._test_inference_batch_single_identical(expected_max_diff=2E-3    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          lowerCAmelCase				:			List[str]    			=      self.get_dummy_components()
          lowerCAmelCase				:			List[str]    			=      self.pipeline_class(**UpperCAmelCase_    )
          pipe.to(UpperCAmelCase_    )
          pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
          with tempfile.TemporaryDirectory() as tmpdir:
              try:
                  # save_pretrained is not implemented for Multi-ControlNet
                  pipe.save_pretrained(UpperCAmelCase_    )
              except NotImplementedError:
                  pass
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class    __A				(							unittest.TestCase ):
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ):
          lowerCAmelCase				:			List[str]    			=      ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'    )
  
          lowerCAmelCase				:			Tuple    			=      StableDiffusionControlNetInpaintPipeline.from_pretrained(
              'runwayml/stable-diffusion-inpainting'    ,				safety_checker=UpperCAmelCase_    ,				controlnet=UpperCAmelCase_    )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
  
          lowerCAmelCase				:			Tuple    			=      torch.Generator(device='cpu'    ).manual_seed(0    )
          lowerCAmelCase				:			Tuple    			=      load_image(
              'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'    ).resize((512, 512)    )
  
          lowerCAmelCase				:			List[str]    			=      load_image(
              'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
              '/stable_diffusion_inpaint/input_bench_mask.png'    ).resize((512, 512)    )
  
          lowerCAmelCase				:			Optional[Any]    			=      'pitch black hole'
  
          lowerCAmelCase				:			Optional[int]    			=      load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'    ).resize((512, 512)    )
  
          lowerCAmelCase				:			Tuple    			=      pipe(
              UpperCAmelCase_    ,				image=UpperCAmelCase_    ,				mask_image=UpperCAmelCase_    ,				control_image=UpperCAmelCase_    ,				generator=UpperCAmelCase_    ,				output_type='np'    ,				num_inference_steps=3    ,				)
  
          lowerCAmelCase				:			Optional[int]    			=      output.images[0]
  
          assert image.shape == (512, 512, 3)
  
          lowerCAmelCase				:			List[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 	lowercase__ (	self			:    Any    ):
          lowerCAmelCase				:			str    			=      ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'    )
  
          lowerCAmelCase				:			Optional[Any]    			=      StableDiffusionControlNetInpaintPipeline.from_pretrained(
              'runwayml/stable-diffusion-v1-5'    ,				safety_checker=UpperCAmelCase_    ,				controlnet=UpperCAmelCase_    )
          lowerCAmelCase				:			Optional[int]    			=      DDIMScheduler.from_config(pipe.scheduler.config    )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
  
          lowerCAmelCase				:			str    			=      torch.Generator(device='cpu'    ).manual_seed(33    )
  
          lowerCAmelCase				:			List[Any]    			=      load_image(
              'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'    )
          lowerCAmelCase				:			List[Any]    			=      init_image.resize((512, 512)    )
  
          lowerCAmelCase				:			int    			=      load_image(
              'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'    )
          lowerCAmelCase				:			Optional[Any]    			=      mask_image.resize((512, 512)    )
  
          lowerCAmelCase				:			Tuple    			=      'a handsome man with ray-ban sunglasses'
  
          def make_inpaint_condition(UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    List[Any]    ):
              lowerCAmelCase				:			int    			=      np.array(image.convert('RGB'    )    ).astype(np.floataa    ) / 2_55.0
              lowerCAmelCase				:			Any    			=      np.array(image_mask.convert('L'    )    ).astype(np.floataa    ) / 2_55.0
  
              assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
              lowerCAmelCase				:			str    			=      -1.0  # set as masked pixel
              lowerCAmelCase				:			int    			=      np.expand_dims(UpperCAmelCase_    ,				0    ).transpose(0    ,				3    ,				1    ,				2    )
              lowerCAmelCase				:			Any    			=      torch.from_numpy(UpperCAmelCase_    )
              return image
  
          lowerCAmelCase				:			Dict    			=      make_inpaint_condition(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
          lowerCAmelCase				:			Dict    			=      pipe(
              UpperCAmelCase_    ,				image=UpperCAmelCase_    ,				mask_image=UpperCAmelCase_    ,				control_image=UpperCAmelCase_    ,				guidance_scale=9.0    ,				eta=1.0    ,				generator=UpperCAmelCase_    ,				num_inference_steps=20    ,				output_type='np'    ,				)
          lowerCAmelCase				:			Dict    			=      output.images[0]
  
          assert image.shape == (512, 512, 3)
  
          lowerCAmelCase				:			Any    			=      load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'    )
  
          assert np.abs(expected_image - image    ).max() < 9E-2
  
  
  
  
  
  
style_context: 
  
  import gym
  import numpy as np
  import torch
  from mujoco_py import GlfwContext
  
  from transformers import DecisionTransformerModel
  
  
  GlfwContext(offscreen=True)  # Create a window to init GLFW.
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> str:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Optional[Any]    			=      states.reshape(1,      -1,      model.config.state_dim				)
      lowerCAmelCase				:			List[Any]    			=      actions.reshape(1,      -1,      model.config.act_dim				)
      lowerCAmelCase				:			List[Any]    			=      returns_to_go.reshape(1,      -1,      1				)
      lowerCAmelCase				:			Dict    			=      timesteps.reshape(1,      -1				)
  
      if model.config.max_length is not None:
          lowerCAmelCase				:			Optional[Any]    			=      states[:, -model.config.max_length :]
          lowerCAmelCase				:			Union[str, Any]    			=      actions[:, -model.config.max_length :]
          lowerCAmelCase				:			Dict    			=      returns_to_go[:, -model.config.max_length :]
          lowerCAmelCase				:			List[str]    			=      timesteps[:, -model.config.max_length :]
  
          # pad all tokens to sequence length
          lowerCAmelCase				:			List[str]    			=      torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]				), torch.ones(states.shape[1]				)]				)
          lowerCAmelCase				:			List[Any]    			=      attention_mask.to(dtype=torch.long,      device=states.device				).reshape(1,      -1				)
          lowerCAmelCase				:			Any    			=      torch.cat(
              [
                  torch.zeros(
                      (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim),      device=states.device,      ),
                  states,
              ],      dim=1,      ).to(dtype=torch.floataa				)
          lowerCAmelCase				:			int    			=      torch.cat(
              [
                  torch.zeros(
                      (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim),      device=actions.device,      ),
                  actions,
              ],      dim=1,      ).to(dtype=torch.floataa				)
          lowerCAmelCase				:			Union[str, Any]    			=      torch.cat(
              [
                  torch.zeros(
                      (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1),      device=returns_to_go.device,      ),
                  returns_to_go,
              ],      dim=1,      ).to(dtype=torch.floataa				)
          lowerCAmelCase				:			Tuple    			=      torch.cat(
              [
                  torch.zeros(
                      (timesteps.shape[0], model.config.max_length - timesteps.shape[1]),      device=timesteps.device				),
                  timesteps,
              ],      dim=1,      ).to(dtype=torch.long				)
      else:
          lowerCAmelCase				:			Optional[Any]    			=      None
  
      lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			List[str]    			=      model(
          states=_UpperCAmelCase,      actions=_UpperCAmelCase,      rewards=_UpperCAmelCase,      returns_to_go=_UpperCAmelCase,      timesteps=_UpperCAmelCase,      attention_mask=_UpperCAmelCase,      return_dict=_UpperCAmelCase,      )
  
      return action_preds[0, -1]
  
  
  # build the environment
  
  __A   :     Dict      =			gym.make('''Hopper-v3''')
  __A   :     Any      =			env.observation_space.shape[0]
  __A   :     Optional[Any]      =			env.action_space.shape[0]
  __A   :     Any      =			1000
  __A   :     Tuple      =			'''cuda'''
  __A   :     Optional[int]      =			10_00.0  # normalization for rewards/returns
  __A   :     Optional[Any]      =			3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  __A   :     str      =			np.array(
      [
          1.31_12_79,
          -0.08_46_95_21,
          -0.5_38_27_19,
          -0.07_20_15_76,
          0.04_93_23_66,
          2.1_06_68_56,
          -0.15_01_73_54,
          0.00_87_83_45,
          -0.2_84_81_86,
          -0.18_54_00_96,
          -0.28_46_12_86,
      ]
  )
  __A   :     Dict      =			np.array(
      [
          0.17_79_07_51,
          0.05_44_46_21,
          0.21_29_71_39,
          0.14_53_04_19,
          0.6_12_44_44,
          0.85_17_44_65,
          1.4_51_52_52,
          0.6_75_16_96,
          1.53_62_39,
          1.6_16_07_46,
          5.6_07_25_36,
      ]
  )
  __A   :     int      =			torch.from_numpy(state_mean).to(device=device)
  __A   :     Tuple      =			torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  __A   :     List[Any]      =			DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
  __A   :     Any      =			model.to(device)
  model.eval()
  
  for ep in range(10):
   __A     ,      __A   :     Optional[int]      =			0, 0
   __A   :     List[str]      =			env.reset()
   __A   :     Optional[int]      =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   __A   :     str      =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   __A   :     Optional[int]      =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   __A   :     Any      =			torch.zeros(0, device=device, dtype=torch.floataa)
  
   __A   :     List[Any]      =			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
   for t in range(max_ep_len):
    env.render()
    # add padding
    __A   :     Union[str, Any]      =			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
    __A   :     List[Any]      =			torch.cat([rewards, torch.zeros(1, device=device)])
  
    __A   :     int      =			get_action(
        model,
        (states.to(dtype=torch.floataa) - state_mean) / state_std,
        actions.to(dtype=torch.floataa),
        rewards.to(dtype=torch.floataa),
        target_return.to(dtype=torch.floataa),
        timesteps.to(dtype=torch.long),
    )
    __A   :     List[str]      =			action
    __A   :     List[str]      =			action.detach().cpu().numpy()
  
    __A     ,      __A     ,      __A     ,      __A   :     List[str]      =			env.step(action)
  
    __A   :     Union[str, Any]      =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
    __A   :     Any      =			torch.cat([states, cur_state], dim=0)
    __A   :     Dict      =			reward
  
    __A   :     str      =			target_return[0, -1] - (reward / scale)
    __A   :     Dict      =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    __A   :     List[Any]      =			torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)
  
    episode_return += reward
    episode_length += 1
  
    if done:
     break
  
  
  
  
  
  