





import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand






def   lowerCamelCase_		(       _UpperCamelCase				)	->		int:






				"""simple docstring"""


				return EnvironmentCommand()





class       __lowerCAmelCase (    _a  ):






			@staticmethod
			def     lowerCamelCase						(__magic_name__					)  ->     Any:


							'''simple docstring'''



							snake_case_		:					List[Any]			    =						parser.add_parser('''env'''      ,							help='''Print relevant system environment info.'''					)
							download_parser.set_defaults(func=__magic_name__					)






			def     lowerCamelCase						(self					)  ->     List[str]:


							'''simple docstring'''



							snake_case_		:					Tuple			    =						{
							    '''`datasets` version''': version,
							    '''Platform''': platform.platform(),
							    '''Python version''': platform.python_version(),
							    '''Huggingface_hub version''': huggingface_hub.__version__,
							    '''PyArrow version''': pyarrow.__version__,
							    '''Pandas version''': pandas.__version__,
							}

							print('''\nCopy-and-paste the text below in your GitHub issue.\n'''					)
							print(self.format_dict(__magic_name__					)					)

							return info






			@staticmethod
			def     lowerCamelCase						(__magic_name__					)  ->     List[str]:


							'''simple docstring'''



							return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]					) + "\n"












# 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       __lowerCAmelCase (    _a,  _a,  _a,  unittest.TestCase  ):
			lowerCamelCase_  :  List[str]     =   StableDiffusionControlNetInpaintPipeline
			lowerCamelCase_  :  List[Any]     =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase_  :  List[Any]     =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase_  :  Tuple     =   frozenset({'''control_image'''}  )  # skip `image` and `mask` for now, only test for control_image
			lowerCamelCase_  :  Any     =   TEXT_TO_IMAGE_IMAGE_PARAMS






			def     lowerCamelCase						(self					)  ->     Union[str, Any]:


							'''simple docstring'''



							torch.manual_seed(0					)
							snake_case_		:					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					)
							snake_case_		:					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)      ,							)
							torch.manual_seed(0					)
							snake_case_		:					int			    =						DDIMScheduler(
							    beta_start=0.00_085      ,							beta_end=0.012      ,							beta_schedule='''scaled_linear'''      ,							clip_sample=__magic_name__      ,							set_alpha_to_one=__magic_name__      ,							)
							torch.manual_seed(0					)
							snake_case_		:					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					)
							snake_case_		:					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      ,							)
							snake_case_		:					List[str]			    =						CLIPTextModel(__magic_name__					)
							snake_case_		:					Optional[int]			    =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

							snake_case_		:					Any			    =						{
							    '''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      ,							__magic_name__      ,							__magic_name__=0					)  ->     Any:


							'''simple docstring'''



							if str(__magic_name__					).startswith('''mps'''					):
											snake_case_		:					Union[str, Any]			    =						torch.manual_seed(__magic_name__					)
							else:
											snake_case_		:					str			    =						torch.Generator(device=__magic_name__					).manual_seed(__magic_name__					)

							snake_case_		:					int			    =						2
							snake_case_		:					Any			    =						randn_tensor(
							    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,							generator=__magic_name__      ,							device=torch.device(__magic_name__					)      ,							)
							snake_case_		:					List[Any]			    =						floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(__magic_name__					)					).to(__magic_name__					)
							snake_case_		:					Tuple			    =						init_image.cpu().permute(0      ,							2      ,							3      ,							1					)[0]

							snake_case_		:					Optional[Any]			    =						Image.fromarray(np.uinta(__magic_name__					)					).convert('''RGB'''					).resize((64, 64)					)
							snake_case_		:					str			    =						Image.fromarray(np.uinta(init_image + 4					)					).convert('''RGB'''					).resize((64, 64)					)

							snake_case_		:					List[str]			    =						{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							    '''image''': image,
							    '''mask_image''': mask_image,
							    '''control_image''': control_image,
							}

							return inputs






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''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     lowerCamelCase						(self					)  ->     str:


							'''simple docstring'''



							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							self._test_inference_batch_single_identical(expected_max_diff=2e-3					)




class       __lowerCAmelCase (    _a  ):
			lowerCamelCase_  :  Optional[int]     =   StableDiffusionControlNetInpaintPipeline
			lowerCamelCase_  :  Union[str, Any]     =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase_  :  Optional[int]     =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase_  :  Optional[int]     =   frozenset([]  )






			def     lowerCamelCase						(self					)  ->     Optional[int]:


							'''simple docstring'''



							torch.manual_seed(0					)
							snake_case_		:					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					)
							snake_case_		:					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)      ,							)
							torch.manual_seed(0					)
							snake_case_		:					Optional[int]			    =						DDIMScheduler(
							    beta_start=0.00_085      ,							beta_end=0.012      ,							beta_schedule='''scaled_linear'''      ,							clip_sample=__magic_name__      ,							set_alpha_to_one=__magic_name__      ,							)
							torch.manual_seed(0					)
							snake_case_		:					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					)
							snake_case_		:					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      ,							)
							snake_case_		:					Union[str, Any]			    =						CLIPTextModel(__magic_name__					)
							snake_case_		:					Optional[Any]			    =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

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




class       __lowerCAmelCase (    _a,  _a,  unittest.TestCase  ):
			lowerCamelCase_  :  Optional[int]     =   StableDiffusionControlNetInpaintPipeline
			lowerCamelCase_  :  Dict     =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase_  :  str     =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






			def     lowerCamelCase						(self					)  ->     Union[str, Any]:


							'''simple docstring'''



							torch.manual_seed(0					)
							snake_case_		:					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					)

							def init_weights(__magic_name__					):
											if isinstance(__magic_name__      ,							torch.nn.Convad					):
															torch.nn.init.normal(m.weight					)
															m.bias.data.fill_(1.0					)

							snake_case_		:					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)      ,							)
							controlneta.controlnet_down_blocks.apply(__magic_name__					)

							torch.manual_seed(0					)
							snake_case_		:					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(__magic_name__					)

							torch.manual_seed(0					)
							snake_case_		:					Optional[Any]			    =						DDIMScheduler(
							    beta_start=0.00_085      ,							beta_end=0.012      ,							beta_schedule='''scaled_linear'''      ,							clip_sample=__magic_name__      ,							set_alpha_to_one=__magic_name__      ,							)
							torch.manual_seed(0					)
							snake_case_		:					int			    =						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					)
							snake_case_		:					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      ,							)
							snake_case_		:					Optional[int]			    =						CLIPTextModel(__magic_name__					)
							snake_case_		:					Dict			    =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

							snake_case_		:					str			    =						MultiControlNetModel([controlneta, controlneta]					)

							snake_case_		:					List[Any]			    =						{
							    '''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      ,							__magic_name__      ,							__magic_name__=0					)  ->     str:


							'''simple docstring'''



							if str(__magic_name__					).startswith('''mps'''					):
											snake_case_		:					Dict			    =						torch.manual_seed(__magic_name__					)
							else:
											snake_case_		:					List[str]			    =						torch.Generator(device=__magic_name__					).manual_seed(__magic_name__					)

							snake_case_		:					Optional[int]			    =						2

							snake_case_		:					List[Any]			    =						[
							    randn_tensor(
							        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,							generator=__magic_name__      ,							device=torch.device(__magic_name__					)      ,							),
							    randn_tensor(
							        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,							generator=__magic_name__      ,							device=torch.device(__magic_name__					)      ,							),
							]
							snake_case_		:					Any			    =						floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(__magic_name__					)					).to(__magic_name__					)
							snake_case_		:					List[str]			    =						init_image.cpu().permute(0      ,							2      ,							3      ,							1					)[0]

							snake_case_		:					Union[str, Any]			    =						Image.fromarray(np.uinta(__magic_name__					)					).convert('''RGB'''					).resize((64, 64)					)
							snake_case_		:					List[str]			    =						Image.fromarray(np.uinta(init_image + 4					)					).convert('''RGB'''					).resize((64, 64)					)

							snake_case_		:					Dict			    =						{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							    '''image''': image,
							    '''mask_image''': mask_image,
							    '''control_image''': control_image,
							}

							return inputs






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.get_dummy_components()
							snake_case_		:					List[Any]			    =						self.pipeline_class(**__magic_name__					)
							pipe.to(__magic_name__					)

							snake_case_		:					Optional[int]			    =						10.0
							snake_case_		:					Dict			    =						4

							snake_case_		:					Optional[int]			    =						self.get_dummy_inputs(__magic_name__					)
							snake_case_		:					Any			    =						steps
							snake_case_		:					Union[str, Any]			    =						scale
							snake_case_		:					List[Any]			    =						pipe(**__magic_name__					)[0]

							snake_case_		:					Optional[int]			    =						self.get_dummy_inputs(__magic_name__					)
							snake_case_		:					Any			    =						steps
							snake_case_		:					int			    =						scale
							snake_case_		:					List[Any]			    =						pipe(**__magic_name__      ,							control_guidance_start=0.1      ,							control_guidance_end=0.2					)[0]

							snake_case_		:					Any			    =						self.get_dummy_inputs(__magic_name__					)
							snake_case_		:					Optional[int]			    =						steps
							snake_case_		:					Tuple			    =						scale
							snake_case_		:					List[str]			    =						pipe(**__magic_name__      ,							control_guidance_start=[0.1, 0.3]      ,							control_guidance_end=[0.2, 0.7]					)[0]

							snake_case_		:					str			    =						self.get_dummy_inputs(__magic_name__					)
							snake_case_		:					Optional[Any]			    =						steps
							snake_case_		:					Dict			    =						scale
							snake_case_		:					str			    =						pipe(**__magic_name__      ,							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					)  ->     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     lowerCamelCase						(self					)  ->     Union[str, Any]:


							'''simple docstring'''



							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)






			def     lowerCamelCase						(self					)  ->     int:


							'''simple docstring'''



							self._test_inference_batch_single_identical(expected_max_diff=2e-3					)






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							snake_case_		:					Optional[Any]			    =						self.get_dummy_components()
							snake_case_		:					Any			    =						self.pipeline_class(**__magic_name__					)
							pipe.to(__magic_name__					)
							pipe.set_progress_bar_config(disable=__magic_name__					)
							with tempfile.TemporaryDirectory() as tmpdir:
											try:
															# save_pretrained is not implemented for Multi-ControlNet
															pipe.save_pretrained(__magic_name__					)
											except NotImplementedError:
															pass




@slow
@require_torch_gpu
class       __lowerCAmelCase (    unittest.TestCase  ):






			def     lowerCamelCase						(self					)  ->     Any:


							'''simple docstring'''



							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			def     lowerCamelCase						(self					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					Tuple			    =						ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''					)

							snake_case_		:					Tuple			    =						StableDiffusionControlNetInpaintPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-inpainting'''      ,							safety_checker=__magic_name__      ,							controlnet=__magic_name__					)
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=__magic_name__					)

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

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

							snake_case_		:					List[str]			    =						'''pitch black hole'''

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

							snake_case_		:					Dict			    =						pipe(
							    __magic_name__      ,							image=__magic_name__      ,							mask_image=__magic_name__      ,							control_image=__magic_name__      ,							generator=__magic_name__      ,							output_type='''np'''      ,							num_inference_steps=3      ,							)

							snake_case_		:					List[str]			    =						output.images[0]

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

							snake_case_		:					Optional[int]			    =						load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'''					)

							assert np.abs(expected_image - image					).max() < 9e-2






			def     lowerCamelCase						(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					List[str]			    =						ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''					)

							snake_case_		:					List[Any]			    =						StableDiffusionControlNetInpaintPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-v1-5'''      ,							safety_checker=__magic_name__      ,							controlnet=__magic_name__					)
							snake_case_		:					Any			    =						DDIMScheduler.from_config(pipe.scheduler.config					)
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=__magic_name__					)

							snake_case_		:					List[Any]			    =						torch.Generator(device='''cpu'''					).manual_seed(33					)

							snake_case_		:					str			    =						load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'''					)
							snake_case_		:					List[Any]			    =						init_image.resize((512, 512)					)

							snake_case_		:					Dict			    =						load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''					)
							snake_case_		:					List[Any]			    =						mask_image.resize((512, 512)					)

							snake_case_		:					int			    =						'''a handsome man with ray-ban sunglasses'''

							def make_inpaint_condition(__magic_name__      ,							__magic_name__					):
											snake_case_		:					int			    =						np.array(image.convert('''RGB'''					)					).astype(np.floataa					) / 255.0
											snake_case_		:					Optional[int]			    =						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"
											snake_case_		:					Optional[int]			    =						-1.0  # set as masked pixel
											snake_case_		:					Dict			    =						np.expand_dims(__magic_name__      ,							0					).transpose(0      ,							3      ,							1      ,							2					)
											snake_case_		:					Optional[Any]			    =						torch.from_numpy(__magic_name__					)
											return image

							snake_case_		:					str			    =						make_inpaint_condition(__magic_name__      ,							__magic_name__					)

							snake_case_		:					Optional[Any]			    =						pipe(
							    __magic_name__      ,							image=__magic_name__      ,							mask_image=__magic_name__      ,							control_image=__magic_name__      ,							guidance_scale=9.0      ,							eta=1.0      ,							generator=__magic_name__      ,							num_inference_steps=20      ,							output_type='''np'''      ,							)
							snake_case_		:					List[str]			    =						output.images[0]

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

							snake_case_		:					List[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
