


# 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   (       UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   unittest.TestCase    ):
		_a			:						str    			=			StableDiffusionControlNetInpaintPipeline
		_a			:						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			:						Optional[Any]    			=			TEXT_TO_IMAGE_IMAGE_PARAMS
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									torch.manual_seed(0		)
									_UpperCAmelCase					=    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		)
									_UpperCAmelCase					=    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		)
									_UpperCAmelCase					=    DDIMScheduler(
									    beta_start=0.00085    ,					beta_end=0.012    ,					beta_schedule='scaled_linear'    ,					clip_sample=_SCREAMING_SNAKE_CASE    ,					set_alpha_to_one=_SCREAMING_SNAKE_CASE    ,					)
									torch.manual_seed(0		)
									_UpperCAmelCase					=    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		)
									_UpperCAmelCase					=    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    ,					)
									_UpperCAmelCase					=    CLIPTextModel(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

									_UpperCAmelCase					=    {
									    'unet': unet,
									    'controlnet': controlnet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=0		)				-> Any:




									"""simple docstring"""





									if str(_SCREAMING_SNAKE_CASE		).startswith('mps'		):
																_UpperCAmelCase					=    torch.manual_seed(_SCREAMING_SNAKE_CASE		)
									else:
																_UpperCAmelCase					=    torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    2
									_UpperCAmelCase					=    randn_tensor(
									    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,					generator=_SCREAMING_SNAKE_CASE    ,					device=torch.device(_SCREAMING_SNAKE_CASE		)    ,					)
									_UpperCAmelCase					=    floats_tensor((1, 3, 32, 32)    ,					rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    init_image.cpu().permute(0    ,					2    ,					3    ,					1		)[0]

									_UpperCAmelCase					=    Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE		)		).convert('RGB'		).resize((64, 64)		)
									_UpperCAmelCase					=    Image.fromarray(np.uinta(init_image + 4		)		).convert('RGB'		).resize((64, 64)		)

									_UpperCAmelCase					=    {
									    '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 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""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 			UpperCAmelCase__       (    self		)				-> Dict:




									"""simple docstring"""





									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3		)






		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									self._test_inference_batch_single_identical(expected_max_diff=2e-3		)



class 	__a   (       UpperCAmelCase    ):
		_a			:						List[str]    			=			StableDiffusionControlNetInpaintPipeline
		_a			:						Optional[Any]    			=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
		_a			:						Optional[int]    			=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_a			:						Tuple    			=			frozenset([]    )
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									torch.manual_seed(0		)
									_UpperCAmelCase					=    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		)
									_UpperCAmelCase					=    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		)
									_UpperCAmelCase					=    DDIMScheduler(
									    beta_start=0.00085    ,					beta_end=0.012    ,					beta_schedule='scaled_linear'    ,					clip_sample=_SCREAMING_SNAKE_CASE    ,					set_alpha_to_one=_SCREAMING_SNAKE_CASE    ,					)
									torch.manual_seed(0		)
									_UpperCAmelCase					=    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		)
									_UpperCAmelCase					=    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    ,					)
									_UpperCAmelCase					=    CLIPTextModel(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

									_UpperCAmelCase					=    {
									    'unet': unet,
									    'controlnet': controlnet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components



class 	__a   (       UpperCAmelCase					,   UpperCAmelCase					,   unittest.TestCase    ):
		_a			:						Tuple    			=			StableDiffusionControlNetInpaintPipeline
		_a			:						Tuple    			=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
		_a			:						List[Any]    			=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									torch.manual_seed(0		)
									_UpperCAmelCase					=    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(_SCREAMING_SNAKE_CASE		):
																if isinstance(_SCREAMING_SNAKE_CASE    ,					torch.nn.Convad		):
																							torch.nn.init.normal(m.weight		)
																							m.bias.data.fill_(1.0		)

									_UpperCAmelCase					=    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(_SCREAMING_SNAKE_CASE		)

									torch.manual_seed(0		)
									_UpperCAmelCase					=    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(_SCREAMING_SNAKE_CASE		)

									torch.manual_seed(0		)
									_UpperCAmelCase					=    DDIMScheduler(
									    beta_start=0.00085    ,					beta_end=0.012    ,					beta_schedule='scaled_linear'    ,					clip_sample=_SCREAMING_SNAKE_CASE    ,					set_alpha_to_one=_SCREAMING_SNAKE_CASE    ,					)
									torch.manual_seed(0		)
									_UpperCAmelCase					=    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		)
									_UpperCAmelCase					=    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    ,					)
									_UpperCAmelCase					=    CLIPTextModel(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

									_UpperCAmelCase					=    MultiControlNetModel([controlneta, controlneta]		)

									_UpperCAmelCase					=    {
									    'unet': unet,
									    'controlnet': controlnet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=0		)				-> List[str]:




									"""simple docstring"""





									if str(_SCREAMING_SNAKE_CASE		).startswith('mps'		):
																_UpperCAmelCase					=    torch.manual_seed(_SCREAMING_SNAKE_CASE		)
									else:
																_UpperCAmelCase					=    torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    2

									_UpperCAmelCase					=    [
									    randn_tensor(
									        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,					generator=_SCREAMING_SNAKE_CASE    ,					device=torch.device(_SCREAMING_SNAKE_CASE		)    ,					),
									    randn_tensor(
									        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,					generator=_SCREAMING_SNAKE_CASE    ,					device=torch.device(_SCREAMING_SNAKE_CASE		)    ,					),
									]
									_UpperCAmelCase					=    floats_tensor((1, 3, 32, 32)    ,					rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    init_image.cpu().permute(0    ,					2    ,					3    ,					1		)[0]

									_UpperCAmelCase					=    Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE		)		).convert('RGB'		).resize((64, 64)		)
									_UpperCAmelCase					=    Image.fromarray(np.uinta(init_image + 4		)		).convert('RGB'		).resize((64, 64)		)

									_UpperCAmelCase					=    {
									    '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 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									_UpperCAmelCase					=    self.get_dummy_components()
									_UpperCAmelCase					=    self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
									pipe.to(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    10.0
									_UpperCAmelCase					=    4

									_UpperCAmelCase					=    self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    steps
									_UpperCAmelCase					=    scale
									_UpperCAmelCase					=    pipe(**_SCREAMING_SNAKE_CASE		)[0]

									_UpperCAmelCase					=    self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    steps
									_UpperCAmelCase					=    scale
									_UpperCAmelCase					=    pipe(**_SCREAMING_SNAKE_CASE    ,					control_guidance_start=0.1    ,					control_guidance_end=0.2		)[0]

									_UpperCAmelCase					=    self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    steps
									_UpperCAmelCase					=    scale
									_UpperCAmelCase					=    pipe(**_SCREAMING_SNAKE_CASE    ,					control_guidance_start=[0.1, 0.3]    ,					control_guidance_end=[0.2, 0.7]		)[0]

									_UpperCAmelCase					=    self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    steps
									_UpperCAmelCase					=    scale
									_UpperCAmelCase					=    pipe(**_SCREAMING_SNAKE_CASE    ,					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 			UpperCAmelCase__       (    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 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3		)
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									self._test_inference_batch_single_identical(expected_max_diff=2e-3		)






		def 			UpperCAmelCase__       (    self		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    self.get_dummy_components()
									_UpperCAmelCase					=    self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
									pipe.to(_SCREAMING_SNAKE_CASE		)
									pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)
									with tempfile.TemporaryDirectory() as tmpdir:
																try:
																							# save_pretrained is not implemented for Multi-ControlNet
																							pipe.save_pretrained(_SCREAMING_SNAKE_CASE		)
																except NotImplementedError:
																							pass



@slow
@require_torch_gpu
class 	__a   (       unittest.TestCase    ):
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									_UpperCAmelCase					=    ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'		)

									_UpperCAmelCase					=    StableDiffusionControlNetInpaintPipeline.from_pretrained(
									    'runwayml/stable-diffusion-inpainting'    ,					safety_checker=_SCREAMING_SNAKE_CASE    ,					controlnet=_SCREAMING_SNAKE_CASE		)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    torch.Generator(device='cpu'		).manual_seed(0		)
									_UpperCAmelCase					=    load_image(
									    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'		).resize((512, 512)		)

									_UpperCAmelCase					=    load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
									    '/stable_diffusion_inpaint/input_bench_mask.png'		).resize((512, 512)		)

									_UpperCAmelCase					=    'pitch black hole'

									_UpperCAmelCase					=    load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'		).resize((512, 512)		)

									_UpperCAmelCase					=    pipe(
									    _SCREAMING_SNAKE_CASE    ,					image=_SCREAMING_SNAKE_CASE    ,					mask_image=_SCREAMING_SNAKE_CASE    ,					control_image=_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE    ,					output_type='np'    ,					num_inference_steps=3    ,					)

									_UpperCAmelCase					=    output.images[0]

									assert image.shape == (512, 512, 3)

									_UpperCAmelCase					=    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 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'		)

									_UpperCAmelCase					=    StableDiffusionControlNetInpaintPipeline.from_pretrained(
									    'runwayml/stable-diffusion-v1-5'    ,					safety_checker=_SCREAMING_SNAKE_CASE    ,					controlnet=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    DDIMScheduler.from_config(pipe.scheduler.config		)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    torch.Generator(device='cpu'		).manual_seed(33		)

									_UpperCAmelCase					=    load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'		)
									_UpperCAmelCase					=    init_image.resize((512, 512)		)

									_UpperCAmelCase					=    load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'		)
									_UpperCAmelCase					=    mask_image.resize((512, 512)		)

									_UpperCAmelCase					=    'a handsome man with ray-ban sunglasses'

									def make_inpaint_condition(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    np.array(image.convert('RGB'		)		).astype(np.floataa		) / 255.0
																_UpperCAmelCase					=    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"
																_UpperCAmelCase					=    -1.0  # set as masked pixel
																_UpperCAmelCase					=    np.expand_dims(_SCREAMING_SNAKE_CASE    ,					0		).transpose(0    ,					3    ,					1    ,					2		)
																_UpperCAmelCase					=    torch.from_numpy(_SCREAMING_SNAKE_CASE		)
																return image

									_UpperCAmelCase					=    make_inpaint_condition(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    pipe(
									    _SCREAMING_SNAKE_CASE    ,					image=_SCREAMING_SNAKE_CASE    ,					mask_image=_SCREAMING_SNAKE_CASE    ,					control_image=_SCREAMING_SNAKE_CASE    ,					guidance_scale=9.0    ,					eta=1.0    ,					generator=_SCREAMING_SNAKE_CASE    ,					num_inference_steps=20    ,					output_type='np'    ,					)
									_UpperCAmelCase					=    output.images[0]

									assert image.shape == (512, 512, 3)

									_UpperCAmelCase					=    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




import warnings

from .generation import GenerationMixin

class 	__a   (       UpperCAmelCase    ):
		# warning at import time
		warnings.warn(
		    'Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will '
		    'be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead.'					,   UpperCAmelCase					,   )
