


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A	:		Optional[int]    =       logging.get_logger(__name__)






def 			UpperCamelCase (     __magic_name__					:							Dict					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		DPTConfig()

		if "large" in checkpoint_url:
				lowercase__					      =		1024
				lowercase__					      =		4096
				lowercase__					      =		24
				lowercase__					      =		16
				lowercase__					      =		[5, 11, 17, 23]
				lowercase__					      =		[256, 512, 1024, 1024]
				lowercase__					      =		(1, 384, 384)

		if "ade" in checkpoint_url:
				lowercase__					      =		True

				lowercase__					      =		150
				lowercase__					      =		"""huggingface/label-files"""
				lowercase__					      =		"""ade20k-id2label.json"""
				lowercase__					      =		json.load(open(cached_download(hf_hub_url(__magic_name__   ,   __magic_name__   ,   repo_type="""dataset"""					)					)   ,   """r"""					)					)
				lowercase__					      =		{int(__magic_name__					): v for k, v in idalabel.items()}
				lowercase__					      =		idalabel
				lowercase__					      =		{v: k for k, v in idalabel.items()}
				lowercase__					      =		[1, 150, 480, 480]

		return config, expected_shape






def 			UpperCamelCase (     __magic_name__					:							Dict					)       ->  Optional[Any]:
		"""simple docstring"""




		lowercase__					      =		["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
		for k in ignore_keys:
				state_dict.pop(__magic_name__   ,   __magic_name__					)






def 			UpperCamelCase (     __magic_name__					:							List[str]					)       ->  int:
		"""simple docstring"""




		if (
		    "pretrained.model" in name
		    and "cls_token" not in name
		    and "pos_embed" not in name
		    and "patch_embed" not in name
		):
				lowercase__					      =		name.replace("""pretrained.model"""   ,   """dpt.encoder"""					)
		if "pretrained.model" in name:
				lowercase__					      =		name.replace("""pretrained.model"""   ,   """dpt.embeddings"""					)
		if "patch_embed" in name:
				lowercase__					      =		name.replace("""patch_embed"""   ,   """patch_embeddings"""					)
		if "pos_embed" in name:
				lowercase__					      =		name.replace("""pos_embed"""   ,   """position_embeddings"""					)
		if "attn.proj" in name:
				lowercase__					      =		name.replace("""attn.proj"""   ,   """attention.output.dense"""					)
		if "proj" in name and "project" not in name:
				lowercase__					      =		name.replace("""proj"""   ,   """projection"""					)
		if "blocks" in name:
				lowercase__					      =		name.replace("""blocks"""   ,   """layer"""					)
		if "mlp.fc1" in name:
				lowercase__					      =		name.replace("""mlp.fc1"""   ,   """intermediate.dense"""					)
		if "mlp.fc2" in name:
				lowercase__					      =		name.replace("""mlp.fc2"""   ,   """output.dense"""					)
		if "norm1" in name:
				lowercase__					      =		name.replace("""norm1"""   ,   """layernorm_before"""					)
		if "norm2" in name:
				lowercase__					      =		name.replace("""norm2"""   ,   """layernorm_after"""					)
		if "scratch.output_conv" in name:
				lowercase__					      =		name.replace("""scratch.output_conv"""   ,   """head"""					)
		if "scratch" in name:
				lowercase__					      =		name.replace("""scratch"""   ,   """neck"""					)
		if "layer1_rn" in name:
				lowercase__					      =		name.replace("""layer1_rn"""   ,   """convs.0"""					)
		if "layer2_rn" in name:
				lowercase__					      =		name.replace("""layer2_rn"""   ,   """convs.1"""					)
		if "layer3_rn" in name:
				lowercase__					      =		name.replace("""layer3_rn"""   ,   """convs.2"""					)
		if "layer4_rn" in name:
				lowercase__					      =		name.replace("""layer4_rn"""   ,   """convs.3"""					)
		if "refinenet" in name:
				lowercase__					      =		int(name[len("""neck.refinenet"""					) : len("""neck.refinenet"""					) + 1]					)
				# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
				lowercase__					      =		name.replace(f'''refinenet{layer_idx}'''   ,   f'''fusion_stage.layers.{abs(layer_idx-4					)}'''					)
		if "out_conv" in name:
				lowercase__					      =		name.replace("""out_conv"""   ,   """projection"""					)
		if "resConfUnit1" in name:
				lowercase__					      =		name.replace("""resConfUnit1"""   ,   """residual_layer1"""					)
		if "resConfUnit2" in name:
				lowercase__					      =		name.replace("""resConfUnit2"""   ,   """residual_layer2"""					)
		if "conv1" in name:
				lowercase__					      =		name.replace("""conv1"""   ,   """convolution1"""					)
		if "conv2" in name:
				lowercase__					      =		name.replace("""conv2"""   ,   """convolution2"""					)
		# readout blocks
		if "pretrained.act_postprocess1.0.project.0" in name:
				lowercase__					      =		name.replace("""pretrained.act_postprocess1.0.project.0"""   ,   """neck.reassemble_stage.readout_projects.0.0"""					)
		if "pretrained.act_postprocess2.0.project.0" in name:
				lowercase__					      =		name.replace("""pretrained.act_postprocess2.0.project.0"""   ,   """neck.reassemble_stage.readout_projects.1.0"""					)
		if "pretrained.act_postprocess3.0.project.0" in name:
				lowercase__					      =		name.replace("""pretrained.act_postprocess3.0.project.0"""   ,   """neck.reassemble_stage.readout_projects.2.0"""					)
		if "pretrained.act_postprocess4.0.project.0" in name:
				lowercase__					      =		name.replace("""pretrained.act_postprocess4.0.project.0"""   ,   """neck.reassemble_stage.readout_projects.3.0"""					)
		# resize blocks
		if "pretrained.act_postprocess1.3" in name:
				lowercase__					      =		name.replace("""pretrained.act_postprocess1.3"""   ,   """neck.reassemble_stage.layers.0.projection"""					)
		if "pretrained.act_postprocess1.4" in name:
				lowercase__					      =		name.replace("""pretrained.act_postprocess1.4"""   ,   """neck.reassemble_stage.layers.0.resize"""					)
		if "pretrained.act_postprocess2.3" in name:
				lowercase__					      =		name.replace("""pretrained.act_postprocess2.3"""   ,   """neck.reassemble_stage.layers.1.projection"""					)
		if "pretrained.act_postprocess2.4" in name:
				lowercase__					      =		name.replace("""pretrained.act_postprocess2.4"""   ,   """neck.reassemble_stage.layers.1.resize"""					)
		if "pretrained.act_postprocess3.3" in name:
				lowercase__					      =		name.replace("""pretrained.act_postprocess3.3"""   ,   """neck.reassemble_stage.layers.2.projection"""					)
		if "pretrained.act_postprocess4.3" in name:
				lowercase__					      =		name.replace("""pretrained.act_postprocess4.3"""   ,   """neck.reassemble_stage.layers.3.projection"""					)
		if "pretrained.act_postprocess4.4" in name:
				lowercase__					      =		name.replace("""pretrained.act_postprocess4.4"""   ,   """neck.reassemble_stage.layers.3.resize"""					)
		if "pretrained" in name:
				lowercase__					      =		name.replace("""pretrained"""   ,   """dpt"""					)
		if "bn" in name:
				lowercase__					      =		name.replace("""bn"""   ,   """batch_norm"""					)
		if "head" in name:
				lowercase__					      =		name.replace("""head"""   ,   """head.head"""					)
		if "encoder.norm" in name:
				lowercase__					      =		name.replace("""encoder.norm"""   ,   """layernorm"""					)
		if "auxlayer" in name:
				lowercase__					      =		name.replace("""auxlayer"""   ,   """auxiliary_head.head"""					)

		return name






def 			UpperCamelCase (     __magic_name__					:							Dict   ,   __magic_name__					:							List[str]					)       ->  Optional[Any]:
		"""simple docstring"""




		for i in range(config.num_hidden_layers					):
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				lowercase__					      =		state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''					)
				lowercase__					      =		state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''					)
				# next, add query, keys and values (in that order) to the state dict
				lowercase__					      =		in_proj_weight[: config.hidden_size, :]
				lowercase__					      =		in_proj_bias[: config.hidden_size]
				lowercase__					      =		in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowercase__					      =		in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowercase__					      =		in_proj_weight[
				    -config.hidden_size :, :
				]
				lowercase__					      =		in_proj_bias[-config.hidden_size :]






def 			UpperCamelCase (     )       ->  Union[str, Any]:
		"""simple docstring"""




		lowercase__					      =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowercase__					      =		Image.open(requests.get(__magic_name__   ,   stream=__magic_name__					).raw					)
		return im






@torch.no_grad()
def 			UpperCamelCase (     __magic_name__					:							Dict   ,   __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Union[str, Any]   ,   __magic_name__					:							Optional[Any]					)       ->  Any:
		"""simple docstring"""




		lowercase__      ,     lowercase__					      =		get_dpt_config(__magic_name__					)
		# load original state_dict from URL
		lowercase__					      =		torch.hub.load_state_dict_from_url(__magic_name__   ,   map_location="""cpu"""					)
		# remove certain keys
		remove_ignore_keys_(__magic_name__					)
		# rename keys
		for key in state_dict.copy().keys():
				lowercase__					      =		state_dict.pop(__magic_name__					)
				lowercase__					      =		val
		# read in qkv matrices
		read_in_q_k_v(__magic_name__   ,   __magic_name__					)

		# load HuggingFace model
		lowercase__					      =		DPTForSemanticSegmentation(__magic_name__					) if """ade""" in checkpoint_url else DPTForDepthEstimation(__magic_name__					)
		model.load_state_dict(__magic_name__					)
		model.eval()

		# Check outputs on an image
		lowercase__					      =		480 if """ade""" in checkpoint_url else 384
		lowercase__					      =		DPTImageProcessor(size=__magic_name__					)

		lowercase__					      =		prepare_img()
		lowercase__					      =		image_processor(__magic_name__   ,   return_tensors="""pt"""					)

		# forward pass
		lowercase__					      =		model(**__magic_name__					).logits if """ade""" in checkpoint_url else model(**__magic_name__					).predicted_depth

		# Assert logits
		lowercase__					      =		torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]					)
		if "ade" in checkpoint_url:
				lowercase__					      =		torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]					)
		assert outputs.shape == torch.Size(__magic_name__					)
		assert (
		    torch.allclose(outputs[0, 0, :3, :3]   ,   __magic_name__   ,   atol=1E-4					)
		    if "ade" in checkpoint_url
		    else torch.allclose(outputs[0, :3, :3]   ,   __magic_name__					)
		)

		Path(__magic_name__					).mkdir(exist_ok=__magic_name__					)
		print(f'''Saving model to {pytorch_dump_folder_path}'''					)
		model.save_pretrained(__magic_name__					)
		print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
		image_processor.save_pretrained(__magic_name__					)

		if push_to_hub:
				print("""Pushing model to hub..."""					)
				model.push_to_hub(
				    repo_path_or_name=Path(__magic_name__   ,   __magic_name__					)   ,   organization="""nielsr"""   ,   commit_message="""Add model"""   ,   use_temp_dir=__magic_name__   ,   )
				image_processor.push_to_hub(
				    repo_path_or_name=Path(__magic_name__   ,   __magic_name__					)   ,   organization="""nielsr"""   ,   commit_message="""Add image processor"""   ,   use_temp_dir=__magic_name__   ,   )


if __name__ == "__main__":
	A	:		Optional[int]    =       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--checkpoint_url',
	    default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
	    type=str,
	    help='URL of the original DPT checkpoint you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	)
	parser.add_argument(
	    '--model_name',
	    default='dpt-large',
	    type=str,
	    help='Name of the model, in case you\'re pushing to the hub.',
	)

	A	:		Optional[int]    =       parser.parse_args()
	convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)










import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			StableDiffusionInpaintPipeline
					A__      =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					A__      =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__      =			frozenset(
					    []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					A__      =			frozenset([]							)

					def   lowerCamelCase__					(self   :						Tuple		)						->       List[Any]:




							"""simple docstring"""


							torch.manual_seed(0		)
							lowercase__					      =		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				, attention_head_dim=(2, 4)				, use_linear_projection=_UpperCAmelCase				, )
							lowercase__					      =		PNDMScheduler(skip_prk_steps=_UpperCAmelCase		)
							torch.manual_seed(0		)
							lowercase__					      =		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				, sample_size=128				, )
							torch.manual_seed(0		)
							lowercase__					      =		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				, hidden_act="""gelu"""				, projection_dim=512				, )
							lowercase__					      =		CLIPTextModel(_UpperCAmelCase		)
							lowercase__					      =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

							lowercase__					      =		{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Optional[Any]=0		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		floats_tensor((1, 3, 32, 32)				, rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
							lowercase__					      =		image.cpu().permute(0				, 2				, 3				, 1		)[0]
							lowercase__					      =		Image.fromarray(np.uinta(_UpperCAmelCase		)		).convert("""RGB"""		).resize((64, 64)		)
							lowercase__					      =		Image.fromarray(np.uinta(image + 4		)		).convert("""RGB"""		).resize((64, 64)		)
							if str(_UpperCAmelCase		).startswith("""mps"""		):
									lowercase__					      =		torch.manual_seed(_UpperCAmelCase		)
							else:
									lowercase__					      =		torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
							lowercase__					      =		{
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": init_image,
							    """mask_image""": mask_image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """output_type""": """numpy""",
							}
							return inputs

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase__					      =		self.get_dummy_components()
							lowercase__					      =		StableDiffusionInpaintPipeline(**_UpperCAmelCase		)
							lowercase__					      =		sd_pipe.to(_UpperCAmelCase		)
							sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

							lowercase__					      =		self.get_dummy_inputs(_UpperCAmelCase		)
							lowercase__					      =		sd_pipe(**_UpperCAmelCase		).images
							lowercase__					      =		image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							lowercase__					      =		np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]		)

							assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2


					def   lowerCamelCase__					(self   :						str		)						->       int:




							"""simple docstring"""


							super().test_inference_batch_single_identical(expected_max_diff=3E-3		)






@slow
@require_torch_gpu
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def   lowerCamelCase__					(self   :						str		)						->       Optional[Any]:




							"""simple docstring"""


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

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




							"""simple docstring"""


							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)
							lowercase__					      =		load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
							    """/yellow_cat_sitting_on_a_park_bench.npy"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase				, safety_checker=_UpperCAmelCase		)
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, output_type="""np"""				, )
							lowercase__					      =		output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image		).max() < 9E-3

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)
							lowercase__					      =		load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
							    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(
							    _UpperCAmelCase				, torch_dtype=torch.floataa				, safety_checker=_UpperCAmelCase				, )
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, output_type="""np"""				, )
							lowercase__					      =		output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image		).max() < 5E-1


					def   lowerCamelCase__					(self   :						List[str]		)						->       Optional[Any]:




							"""simple docstring"""


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		PNDMScheduler.from_pretrained(_UpperCAmelCase				, subfolder="""scheduler"""		)
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(
							    _UpperCAmelCase				, safety_checker=_UpperCAmelCase				, scheduler=_UpperCAmelCase				, torch_dtype=torch.floataa				, )
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing(1		)
							pipe.enable_sequential_cpu_offload()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, num_inference_steps=2				, output_type="""np"""				, )

							lowercase__					      =		torch.cuda.max_memory_allocated()
							# make sure that less than 2.65 GB is allocated
							assert mem_bytes < 2.65 * 10**9

