code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import os
  import unittest
  
  from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer
  
  from ...test_tokenization_common import TokenizerTesterMixin
  class 	a__				(				_UpperCAmelCase			,     unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Any							= BertweetTokenizer
  			A	:							str							= False
  			def 				lowerCAmelCase    (     self       : str  )			->		List[str]:
  						super().setUp()
  
  						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  						__A=  ['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
  						__A=  dict(zip(lowerCAmelCase_ ,						range(len(lowerCAmelCase_  )  )  )  )
  						__A=  ['#version: 0.2', 'a m</w>']
  						__A=  {'unk_token': '<unk>'}
  
  						__A=  os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['vocab_file']  )
  						__A=  os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['merges_file']  )
  						with open(self.vocab_file ,						'w' ,						encoding='utf-8'  ) as fp:
  									for token in vocab_tokens:
  												fp.write(F"""{token} {vocab_tokens[token]}\n"""  )
  						with open(self.merges_file ,						'w' ,						encoding='utf-8'  ) as fp:
  									fp.write('\n'.join(lowerCAmelCase_  )  )
  			def 				lowerCAmelCase    (     self       : Union[str, Any] ,						**lowerCAmelCase_       : Dict  )			->		Tuple:
  						kwargs.update(self.special_tokens_map  )
  						return BertweetTokenizer.from_pretrained(self.tmpdirname ,						**lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Dict ,						lowerCAmelCase_       : Any  )			->		Dict:
  						__A=  'I am VinAI Research'
  						__A=  'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
  						return input_text, output_text
  
  			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		Any:
  						__A=  BertweetTokenizer(self.vocab_file ,						self.merges_file ,						**self.special_tokens_map  )
  						__A=  'I am VinAI Research'
  						__A=  'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
  						__A=  tokenizer.tokenize(lowerCAmelCase_  )
  						self.assertListEqual(lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  						__A=  tokens + [tokenizer.unk_token]
  
  						__A=  [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
  						self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_  ) ,						lowerCAmelCase_  )
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  # This model implementation is heavily based on:
  
  import gc
  import random
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      ControlNetModel,
      DDIMScheduler,
      StableDiffusionControlNetInpaintPipeline,
      UNetaDConditionModel,
  )
  from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
  from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import (
      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
      TEXT_TO_IMAGE_IMAGE_PARAMS,
  )
  from ..test_pipelines_common import (
      PipelineKarrasSchedulerTesterMixin,
      PipelineLatentTesterMixin,
      PipelineTesterMixin,
  )
  
  
  enable_full_determinism()
  class 	a__				(				a_			,     a_			,     a_			,     unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Optional[int]							= StableDiffusionControlNetInpaintPipeline
  			A	:							List[str]							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  			A	:							List[Any]							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  			A	:							Any							= frozenset({'''control_image'''}      )  # skip `image` and `mask` for now, only test for control_image
  			A	:							List[Any]							= TEXT_TO_IMAGE_IMAGE_PARAMS
  			def 				lowerCAmelCase    (     self       : Tuple  )			->		List[str]:
  						torch.manual_seed(0  )
  						__A=  UNetaDConditionModel(
  						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						sample_size=32 ,						in_channels=9 ,						out_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,						cross_attention_dim=32 ,						)
  						torch.manual_seed(0  )
  						__A=  ControlNetModel(
  						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						in_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						cross_attention_dim=32 ,						conditioning_embedding_out_channels=(16, 32) ,						)
  						torch.manual_seed(0  )
  						__A=  DDIMScheduler(
  						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
  						torch.manual_seed(0  )
  						__A=  AutoencoderKL(
  						    block_out_channels=[32, 64] ,						in_channels=3 ,						out_channels=3 ,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,						latent_channels=4 ,						)
  						torch.manual_seed(0  )
  						__A=  CLIPTextConfig(
  						    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_000 ,						)
  						__A=  CLIPTextModel(lowerCAmelCase_  )
  						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )
  
  						__A=  {
  						    'unet': unet,
  						    'controlnet': controlnet,
  						    'scheduler': scheduler,
  						    'vae': vae,
  						    'text_encoder': text_encoder,
  						    'tokenizer': tokenizer,
  						    'safety_checker': None,
  						    'feature_extractor': None,
  						}
  						return components
  			def 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : int=0  )			->		str:
  						if str(lowerCAmelCase_  ).startswith('mps'  ):
  									__A=  torch.manual_seed(lowerCAmelCase_  )
  						else:
  									__A=  torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )
  
  						__A=  2
  						__A=  randn_tensor(
  						    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						)
  						__A=  floats_tensor((1, 3, 32, 32) ,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
  						__A=  init_image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]
  
  						__A=  Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  ).resize((64, 64)  )
  						__A=  Image.fromarray(np.uinta(init_image + 4  )  ).convert('RGB'  ).resize((64, 64)  )
  
  						__A=  {
  						    'prompt': 'A painting of a squirrel eating a burger',
  						    'generator': generator,
  						    'num_inference_steps': 2,
  						    'guidance_scale': 6.0,
  						    'output_type': 'numpy',
  						    'image': image,
  						    'mask_image': mask_image,
  						    'control_image': control_image,
  						}
  
  						return inputs
  			def 				lowerCAmelCase    (     self       : Tuple  )			->		Optional[int]:
  						return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3  )
  			@unittest.skipIf(
  			    torch_device != 'cuda' or not is_xformers_available() ,						reason='XFormers attention is only available with CUDA and `xformers` installed' ,						)
  			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		str:
  						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )
  
  			def 				lowerCAmelCase    (     self       : List[Any]  )			->		str:
  						self._test_inference_batch_single_identical(expected_max_diff=2E-3  )
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							int							= StableDiffusionControlNetInpaintPipeline
  			A	:							List[str]							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  			A	:							Tuple							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  			A	:							int							= frozenset([]      )
  			def 				lowerCAmelCase    (     self       : int  )			->		List[Any]:
  						torch.manual_seed(0  )
  						__A=  UNetaDConditionModel(
  						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						sample_size=32 ,						in_channels=4 ,						out_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,						cross_attention_dim=32 ,						)
  						torch.manual_seed(0  )
  						__A=  ControlNetModel(
  						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						in_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						cross_attention_dim=32 ,						conditioning_embedding_out_channels=(16, 32) ,						)
  						torch.manual_seed(0  )
  						__A=  DDIMScheduler(
  						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
  						torch.manual_seed(0  )
  						__A=  AutoencoderKL(
  						    block_out_channels=[32, 64] ,						in_channels=3 ,						out_channels=3 ,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,						latent_channels=4 ,						)
  						torch.manual_seed(0  )
  						__A=  CLIPTextConfig(
  						    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_000 ,						)
  						__A=  CLIPTextModel(lowerCAmelCase_  )
  						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )
  
  						__A=  {
  						    'unet': unet,
  						    'controlnet': controlnet,
  						    'scheduler': scheduler,
  						    'vae': vae,
  						    'text_encoder': text_encoder,
  						    'tokenizer': tokenizer,
  						    'safety_checker': None,
  						    'feature_extractor': None,
  						}
  						return components
  
  
  class 	a__				(				a_			,     a_			,     unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Tuple							= StableDiffusionControlNetInpaintPipeline
  			A	:							int							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  			A	:							Optional[int]							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  			def 				lowerCAmelCase    (     self       : List[Any]  )			->		int:
  						torch.manual_seed(0  )
  						__A=  UNetaDConditionModel(
  						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						sample_size=32 ,						in_channels=9 ,						out_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,						cross_attention_dim=32 ,						)
  						torch.manual_seed(0  )
  
  						def init_weights(lowerCAmelCase_       : Optional[int]  ):
  									if isinstance(lowerCAmelCase_ ,						torch.nn.Convad  ):
  												torch.nn.init.normal(m.weight  )
  												m.bias.data.fill_(1.0  )
  
  						__A=  ControlNetModel(
  						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						in_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						cross_attention_dim=32 ,						conditioning_embedding_out_channels=(16, 32) ,						)
  						controlneta.controlnet_down_blocks.apply(lowerCAmelCase_  )
  
  						torch.manual_seed(0  )
  						__A=  ControlNetModel(
  						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						in_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						cross_attention_dim=32 ,						conditioning_embedding_out_channels=(16, 32) ,						)
  						controlneta.controlnet_down_blocks.apply(lowerCAmelCase_  )
  
  						torch.manual_seed(0  )
  						__A=  DDIMScheduler(
  						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
  						torch.manual_seed(0  )
  						__A=  AutoencoderKL(
  						    block_out_channels=[32, 64] ,						in_channels=3 ,						out_channels=3 ,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,						latent_channels=4 ,						)
  						torch.manual_seed(0  )
  						__A=  CLIPTextConfig(
  						    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_000 ,						)
  						__A=  CLIPTextModel(lowerCAmelCase_  )
  						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )
  
  						__A=  MultiControlNetModel([controlneta, controlneta]  )
  
  						__A=  {
  						    'unet': unet,
  						    'controlnet': controlnet,
  						    'scheduler': scheduler,
  						    'vae': vae,
  						    'text_encoder': text_encoder,
  						    'tokenizer': tokenizer,
  						    'safety_checker': None,
  						    'feature_extractor': None,
  						}
  						return components
  			def 				lowerCAmelCase    (     self       : Union[str, Any] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : List[Any]=0  )			->		List[Any]:
  						if str(lowerCAmelCase_  ).startswith('mps'  ):
  									__A=  torch.manual_seed(lowerCAmelCase_  )
  						else:
  									__A=  torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )
  
  						__A=  2
  
  						__A=  [
  						    randn_tensor(
  						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						),
  						    randn_tensor(
  						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						),
  						]
  						__A=  floats_tensor((1, 3, 32, 32) ,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
  						__A=  init_image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]
  
  						__A=  Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  ).resize((64, 64)  )
  						__A=  Image.fromarray(np.uinta(init_image + 4  )  ).convert('RGB'  ).resize((64, 64)  )
  
  						__A=  {
  						    'prompt': 'A painting of a squirrel eating a burger',
  						    'generator': generator,
  						    'num_inference_steps': 2,
  						    'guidance_scale': 6.0,
  						    'output_type': 'numpy',
  						    'image': image,
  						    'mask_image': mask_image,
  						    'control_image': control_image,
  						}
  
  						return inputs
  			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Any:
  						__A=  self.get_dummy_components()
  						__A=  self.pipeline_class(**lowerCAmelCase_  )
  						pipe.to(lowerCAmelCase_  )
  
  						__A=  10.0
  						__A=  4
  
  						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
  						__A=  steps
  						__A=  scale
  						__A=  pipe(**lowerCAmelCase_  )[0]
  
  						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
  						__A=  steps
  						__A=  scale
  						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=0.1 ,						control_guidance_end=0.2  )[0]
  
  						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
  						__A=  steps
  						__A=  scale
  						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=[0.1, 0.3] ,						control_guidance_end=[0.2, 0.7]  )[0]
  
  						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
  						__A=  steps
  						__A=  scale
  						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=0.4 ,						control_guidance_end=[0.5, 0.8]  )[0]
  
  						# make sure that all outputs are different
  						assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
  						assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
  						assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
  			def 				lowerCAmelCase    (     self       : str  )			->		Any:
  						return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3  )
  			@unittest.skipIf(
  			    torch_device != 'cuda' or not is_xformers_available() ,						reason='XFormers attention is only available with CUDA and `xformers` installed' ,						)
  			def 				lowerCAmelCase    (     self       : Any  )			->		int:
  						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )
  			def 				lowerCAmelCase    (     self       : Dict  )			->		Dict:
  						self._test_inference_batch_single_identical(expected_max_diff=2E-3  )
  
  			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Optional[int]:
  						__A=  self.get_dummy_components()
  						__A=  self.pipeline_class(**lowerCAmelCase_  )
  						pipe.to(lowerCAmelCase_  )
  						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
  						with tempfile.TemporaryDirectory() as tmpdir:
  									try:
  												# save_pretrained is not implemented for Multi-ControlNet
  												pipe.save_pretrained(lowerCAmelCase_  )
  									except NotImplementedError:
  												pass
  
  
  @slow
  @require_torch_gpu
  class 	a__				(				unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Tuple:
  						super().tearDown()
  						gc.collect()
  						torch.cuda.empty_cache()
  			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Tuple:
  						__A=  ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'  )
  
  						__A=  StableDiffusionControlNetInpaintPipeline.from_pretrained(
  						    'runwayml/stable-diffusion-inpainting' ,						safety_checker=lowerCAmelCase_ ,						controlnet=lowerCAmelCase_  )
  						pipe.enable_model_cpu_offload()
  						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
  
  						__A=  torch.Generator(device='cpu'  ).manual_seed(0  )
  						__A=  load_image(
  						    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'  ).resize((512, 512)  )
  
  						__A=  load_image(
  						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
  						    '/stable_diffusion_inpaint/input_bench_mask.png'  ).resize((512, 512)  )
  
  						__A=  'pitch black hole'
  
  						__A=  load_image(
  						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'  ).resize((512, 512)  )
  
  						__A=  pipe(
  						    lowerCAmelCase_ ,						image=lowerCAmelCase_ ,						mask_image=lowerCAmelCase_ ,						control_image=lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						output_type='np' ,						num_inference_steps=3 ,						)
  
  						__A=  output.images[0]
  
  						assert image.shape == (512, 512, 3)
  
  						__A=  load_numpy(
  						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'  )
  
  						assert np.abs(expected_image - image  ).max() < 9E-2
  
  			def 				lowerCAmelCase    (     self       : str  )			->		Tuple:
  						__A=  ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'  )
  
  						__A=  StableDiffusionControlNetInpaintPipeline.from_pretrained(
  						    'runwayml/stable-diffusion-v1-5' ,						safety_checker=lowerCAmelCase_ ,						controlnet=lowerCAmelCase_  )
  						__A=  DDIMScheduler.from_config(pipe.scheduler.config  )
  						pipe.enable_model_cpu_offload()
  						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
  
  						__A=  torch.Generator(device='cpu'  ).manual_seed(33  )
  
  						__A=  load_image(
  						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'  )
  						__A=  init_image.resize((512, 512)  )
  
  						__A=  load_image(
  						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'  )
  						__A=  mask_image.resize((512, 512)  )
  
  						__A=  'a handsome man with ray-ban sunglasses'
  
  						def make_inpaint_condition(lowerCAmelCase_       : int ,						lowerCAmelCase_       : str  ):
  									__A=  np.array(image.convert('RGB'  )  ).astype(np.floataa  ) / 2_55.0
  									__A=  np.array(image_mask.convert('L'  )  ).astype(np.floataa  ) / 2_55.0
  
  									assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
  									__A=  -1.0  # set as masked pixel
  									__A=  np.expand_dims(lowerCAmelCase_ ,						0  ).transpose(0 ,						3 ,						1 ,						2  )
  									__A=  torch.from_numpy(lowerCAmelCase_  )
  									return image
  
  						__A=  make_inpaint_condition(lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  						__A=  pipe(
  						    lowerCAmelCase_ ,						image=lowerCAmelCase_ ,						mask_image=lowerCAmelCase_ ,						control_image=lowerCAmelCase_ ,						guidance_scale=9.0 ,						eta=1.0 ,						generator=lowerCAmelCase_ ,						num_inference_steps=20 ,						output_type='np' ,						)
  						__A=  output.images[0]
  
  						assert image.shape == (512, 512, 3)
  
  						__A=  load_numpy(
  						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'  )
  
  						assert np.abs(expected_image - image  ).max() < 9E-2
  
  
  
  