code: 
  
  
  
  
  # 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 					snake_case__  ( UpperCamelCase__      ,    UpperCamelCase__      ,    UpperCamelCase__      ,    unittest.TestCase       ):
  	UpperCAmelCase   :							Tuple         = StableDiffusionControlNetInpaintPipeline
  	UpperCAmelCase   :							int         = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  	UpperCAmelCase   :							str         = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  	UpperCAmelCase   :							List[Any]         = frozenset({"""control_image"""}       )  # skip `image` and `mask` for now, only test for control_image
  	UpperCAmelCase   :							str         = TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  
  
  
  	def     A				(  self							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								torch.manual_seed(0							)
  								a_       :   Tuple			=	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_       :   Any			=	ControlNetModel(
  								    block_out_channels=(32, 64)				,    layers_per_block=2				,    in_channels=4				,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,    cross_attention_dim=32				,    conditioning_embedding_out_channels=(16, 32)				,    )
  								torch.manual_seed(0							)
  								a_       :   Dict			=	DDIMScheduler(
  								    beta_start=0.0_0085				,    beta_end=0.012				,    beta_schedule="""scaled_linear"""				,    clip_sample=lowerCAmelCase__				,    set_alpha_to_one=lowerCAmelCase__				,    )
  								torch.manual_seed(0							)
  								a_       :   List[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							)
  								a_       :   Union[str, Any]			=	CLIPTextConfig(
  								    bos_token_id=0				,    eos_token_id=2				,    hidden_size=32				,    intermediate_size=37				,    layer_norm_eps=1e-05				,    num_attention_heads=4				,    num_hidden_layers=5				,    pad_token_id=1				,    vocab_size=1000				,    )
  								a_       :   str			=	CLIPTextModel(lowerCAmelCase__							)
  								a_       :   List[Any]			=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
  
  								a_       :   List[str]			=	{
  								    "unet": unet,
  								    "controlnet": controlnet,
  								    "scheduler": scheduler,
  								    "vae": vae,
  								    "text_encoder": text_encoder,
  								    "tokenizer": tokenizer,
  								    "safety_checker": None,
  								    "feature_extractor": None,
  								}
  								return components
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_=0							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								if str(lowerCAmelCase__							).startswith("""mps"""							):
  															a_       :   int			=	torch.manual_seed(lowerCAmelCase__							)
  								else:
  															a_       :   List[str]			=	torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)
  
  								a_       :   Optional[int]			=	2
  								a_       :   str			=	randn_tensor(
  								    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,    generator=lowerCAmelCase__				,    device=torch.device(lowerCAmelCase__							)				,    )
  								a_       :   List[Any]			=	floats_tensor((1, 3, 32, 32)				,    rng=random.Random(lowerCAmelCase__							)							).to(lowerCAmelCase__							)
  								a_       :   List[Any]			=	init_image.cpu().permute(0				,    2				,    3				,    1							)[0]
  
  								a_       :   Tuple			=	Image.fromarray(np.uinta(lowerCAmelCase__							)							).convert("""RGB"""							).resize((64, 64)							)
  								a_       :   Union[str, Any]			=	Image.fromarray(np.uinta(init_image + 4							)							).convert("""RGB"""							).resize((64, 64)							)
  
  								a_       :   Optional[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     A				(  self							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3							)
  
  
  
  
  
  	@unittest.skipIf(
  	    torch_device != """cuda""" or not is_xformers_available()				,    reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,    )
  	def     A				(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3							)
  
  
  
  
  
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								self._test_inference_batch_single_identical(expected_max_diff=2e-3							)
  
  
  
  
  
  class 					snake_case__  ( UpperCamelCase__       ):
  	UpperCAmelCase   :							Optional[int]         = StableDiffusionControlNetInpaintPipeline
  	UpperCAmelCase   :							Optional[Any]         = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  	UpperCAmelCase   :							Optional[Any]         = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  	UpperCAmelCase   :							Optional[Any]         = frozenset([]       )
  
  
  
  
  
  	def     A				(  self							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								torch.manual_seed(0							)
  								a_       :   Tuple			=	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_       :   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							)
  								a_       :   Optional[int]			=	DDIMScheduler(
  								    beta_start=0.0_0085				,    beta_end=0.012				,    beta_schedule="""scaled_linear"""				,    clip_sample=lowerCAmelCase__				,    set_alpha_to_one=lowerCAmelCase__				,    )
  								torch.manual_seed(0							)
  								a_       :   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							)
  								a_       :   Union[str, Any]			=	CLIPTextConfig(
  								    bos_token_id=0				,    eos_token_id=2				,    hidden_size=32				,    intermediate_size=37				,    layer_norm_eps=1e-05				,    num_attention_heads=4				,    num_hidden_layers=5				,    pad_token_id=1				,    vocab_size=1000				,    )
  								a_       :   Optional[int]			=	CLIPTextModel(lowerCAmelCase__							)
  								a_       :   Optional[int]			=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
  
  								a_       :   str			=	{
  								    "unet": unet,
  								    "controlnet": controlnet,
  								    "scheduler": scheduler,
  								    "vae": vae,
  								    "text_encoder": text_encoder,
  								    "tokenizer": tokenizer,
  								    "safety_checker": None,
  								    "feature_extractor": None,
  								}
  								return components
  
  
  
  
  
  class 					snake_case__  ( UpperCamelCase__      ,    UpperCamelCase__      ,    unittest.TestCase       ):
  	UpperCAmelCase   :							Tuple         = StableDiffusionControlNetInpaintPipeline
  	UpperCAmelCase   :							Dict         = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  	UpperCAmelCase   :							int         = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								torch.manual_seed(0							)
  								a_       :   str			=	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_							):
  															if isinstance(lowerCAmelCase__				,    torch.nn.Convad							):
  																						torch.nn.init.normal(m.weight							)
  																						m.bias.data.fill_(1.0							)
  
  								a_       :   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(lowerCAmelCase__							)
  
  								torch.manual_seed(0							)
  								a_       :   Union[str, Any]			=	ControlNetModel(
  								    block_out_channels=(32, 64)				,    layers_per_block=2				,    in_channels=4				,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,    cross_attention_dim=32				,    conditioning_embedding_out_channels=(16, 32)				,    )
  								controlneta.controlnet_down_blocks.apply(lowerCAmelCase__							)
  
  								torch.manual_seed(0							)
  								a_       :   Union[str, Any]			=	DDIMScheduler(
  								    beta_start=0.0_0085				,    beta_end=0.012				,    beta_schedule="""scaled_linear"""				,    clip_sample=lowerCAmelCase__				,    set_alpha_to_one=lowerCAmelCase__				,    )
  								torch.manual_seed(0							)
  								a_       :   Optional[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							)
  								a_       :   Optional[Any]			=	CLIPTextConfig(
  								    bos_token_id=0				,    eos_token_id=2				,    hidden_size=32				,    intermediate_size=37				,    layer_norm_eps=1e-05				,    num_attention_heads=4				,    num_hidden_layers=5				,    pad_token_id=1				,    vocab_size=1000				,    )
  								a_       :   List[str]			=	CLIPTextModel(lowerCAmelCase__							)
  								a_       :   List[Any]			=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
  
  								a_       :   Union[str, Any]			=	MultiControlNetModel([controlneta, controlneta]							)
  
  								a_       :   int			=	{
  								    "unet": unet,
  								    "controlnet": controlnet,
  								    "scheduler": scheduler,
  								    "vae": vae,
  								    "text_encoder": text_encoder,
  								    "tokenizer": tokenizer,
  								    "safety_checker": None,
  								    "feature_extractor": None,
  								}
  								return components
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_=0							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								if str(lowerCAmelCase__							).startswith("""mps"""							):
  															a_       :   List[str]			=	torch.manual_seed(lowerCAmelCase__							)
  								else:
  															a_       :   Optional[Any]			=	torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)
  
  								a_       :   List[str]			=	2
  
  								a_       :   Optional[int]			=	[
  								    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_       :   List[str]			=	floats_tensor((1, 3, 32, 32)				,    rng=random.Random(lowerCAmelCase__							)							).to(lowerCAmelCase__							)
  								a_       :   Dict			=	init_image.cpu().permute(0				,    2				,    3				,    1							)[0]
  
  								a_       :   int			=	Image.fromarray(np.uinta(lowerCAmelCase__							)							).convert("""RGB"""							).resize((64, 64)							)
  								a_       :   str			=	Image.fromarray(np.uinta(init_image + 4							)							).convert("""RGB"""							).resize((64, 64)							)
  
  								a_       :   Optional[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     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Tuple			=	self.get_dummy_components()
  								a_       :   Union[str, Any]			=	self.pipeline_class(**lowerCAmelCase__							)
  								pipe.to(lowerCAmelCase__							)
  
  								a_       :   int			=	10.0
  								a_       :   int			=	4
  
  								a_       :   Dict			=	self.get_dummy_inputs(lowerCAmelCase__							)
  								a_       :   int			=	steps
  								a_       :   List[Any]			=	scale
  								a_       :   Tuple			=	pipe(**lowerCAmelCase__							)[0]
  
  								a_       :   Dict			=	self.get_dummy_inputs(lowerCAmelCase__							)
  								a_       :   Dict			=	steps
  								a_       :   int			=	scale
  								a_       :   int			=	pipe(**lowerCAmelCase__				,    control_guidance_start=0.1				,    control_guidance_end=0.2							)[0]
  
  								a_       :   Tuple			=	self.get_dummy_inputs(lowerCAmelCase__							)
  								a_       :   Optional[Any]			=	steps
  								a_       :   List[Any]			=	scale
  								a_       :   int			=	pipe(**lowerCAmelCase__				,    control_guidance_start=[0.1, 0.3]				,    control_guidance_end=[0.2, 0.7]							)[0]
  
  								a_       :   Any			=	self.get_dummy_inputs(lowerCAmelCase__							)
  								a_       :   int			=	steps
  								a_       :   Dict			=	scale
  								a_       :   Union[str, Any]			=	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     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3							)
  
  
  
  
  
  	@unittest.skipIf(
  	    torch_device != """cuda""" or not is_xformers_available()				,    reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,    )
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3							)
  
  
  
  
  
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								self._test_inference_batch_single_identical(expected_max_diff=2e-3							)
  
  
  
  
  
  	def     A				(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Union[str, Any]			=	self.get_dummy_components()
  								a_       :   Optional[Any]			=	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 					snake_case__  ( unittest.TestCase       ):
  
  
  
  
  
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().tearDown()
  								gc.collect()
  								torch.cuda.empty_cache()
  
  
  
  
  
  	def     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[int]			=	ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""							)
  
  								a_       :   Dict			=	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_       :   int			=	torch.Generator(device="""cpu"""							).manual_seed(0							)
  								a_       :   List[Any]			=	load_image(
  								    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""							).resize((512, 512)							)
  
  								a_       :   Union[str, Any]			=	load_image(
  								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
  								    """/stable_diffusion_inpaint/input_bench_mask.png"""							).resize((512, 512)							)
  
  								a_       :   Optional[Any]			=	"pitch black hole"
  
  								a_       :   Tuple			=	load_image(
  								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""							).resize((512, 512)							)
  
  								a_       :   List[str]			=	pipe(
  								    lowerCAmelCase__				,    image=lowerCAmelCase__				,    mask_image=lowerCAmelCase__				,    control_image=lowerCAmelCase__				,    generator=lowerCAmelCase__				,    output_type="""np"""				,    num_inference_steps=3				,    )
  
  								a_       :   Tuple			=	output.images[0]
  
  								assert image.shape == (512, 512, 3)
  
  								a_       :   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     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""							)
  
  								a_       :   Tuple			=	StableDiffusionControlNetInpaintPipeline.from_pretrained(
  								    """runwayml/stable-diffusion-v1-5"""				,    safety_checker=lowerCAmelCase__				,    controlnet=lowerCAmelCase__							)
  								a_       :   Optional[int]			=	DDIMScheduler.from_config(pipe.scheduler.config							)
  								pipe.enable_model_cpu_offload()
  								pipe.set_progress_bar_config(disable=lowerCAmelCase__							)
  
  								a_       :   int			=	torch.Generator(device="""cpu"""							).manual_seed(33							)
  
  								a_       :   Any			=	load_image(
  								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""							)
  								a_       :   Union[str, Any]			=	init_image.resize((512, 512)							)
  
  								a_       :   List[str]			=	load_image(
  								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""							)
  								a_       :   Optional[Any]			=	mask_image.resize((512, 512)							)
  
  								a_       :   str			=	"a handsome man with ray-ban sunglasses"
  
  								def make_inpaint_condition(UpperCamelCase_				,    UpperCamelCase_							):
  															a_       :   List[Any]			=	np.array(image.convert("""RGB"""							)							).astype(np.floataa							) / 255.0
  															a_       :   str			=	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_       :   Any			=	-1.0  # set as masked pixel
  															a_       :   int			=	np.expand_dims(lowerCAmelCase__				,    0							).transpose(0				,    3				,    1				,    2							)
  															a_       :   Tuple			=	torch.from_numpy(lowerCAmelCase__							)
  															return image
  
  								a_       :   List[str]			=	make_inpaint_condition(lowerCAmelCase__				,    lowerCAmelCase__							)
  
  								a_       :   int			=	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_       :   Dict			=	output.images[0]
  
  								assert image.shape == (512, 512, 3)
  
  								a_       :   Tuple			=	load_numpy(
  								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"""							)
  
  								assert np.abs(expected_image - image							).max() < 9e-2
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  SCREAMING_SNAKE_CASE : Optional[Any]          =    {
      "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
  }
  
  try:
  	if not is_sentencepiece_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Optional[int]          =    ["CamembertTokenizer"]
  
  try:
  	if not is_tokenizers_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Any          =    ["CamembertTokenizerFast"]
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Union[str, Any]          =    [
  	    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "CamembertForCausalLM",
  	    "CamembertForMaskedLM",
  	    "CamembertForMultipleChoice",
  	    "CamembertForQuestionAnswering",
  	    "CamembertForSequenceClassification",
  	    "CamembertForTokenClassification",
  	    "CamembertModel",
  	    "CamembertPreTrainedModel",
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : List[Any]          =    [
  	    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "TFCamembertForCausalLM",
  	    "TFCamembertForMaskedLM",
  	    "TFCamembertForMultipleChoice",
  	    "TFCamembertForQuestionAnswering",
  	    "TFCamembertForSequenceClassification",
  	    "TFCamembertForTokenClassification",
  	    "TFCamembertModel",
  	    "TFCamembertPreTrainedModel",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
  	try:
  		if not is_sentencepiece_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert import CamembertTokenizer
  
  	try:
  		if not is_tokenizers_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert_fast import CamembertTokenizerFast
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_camembert import (
  		    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    CamembertForCausalLM,
  		    CamembertForMaskedLM,
  		    CamembertForMultipleChoice,
  		    CamembertForQuestionAnswering,
  		    CamembertForSequenceClassification,
  		    CamembertForTokenClassification,
  		    CamembertModel,
  		    CamembertPreTrainedModel,
  		)
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_camembert import (
  		    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    TFCamembertForCausalLM,
  		    TFCamembertForMaskedLM,
  		    TFCamembertForMultipleChoice,
  		    TFCamembertForQuestionAnswering,
  		    TFCamembertForSequenceClassification,
  		    TFCamembertForTokenClassification,
  		    TFCamembertModel,
  		    TFCamembertPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	SCREAMING_SNAKE_CASE : Any          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  