






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()
__snake_case      :      Any             =       logging.get_logger(__name__)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]						)					->				Tuple:

						"""simple docstring"""



						lowerCAmelCase__        =  DPTConfig()

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

						if "ade" in checkpoint_url:
												lowerCAmelCase__        =  True

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

						return config, expected_shape







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]						)					->				Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__        =  ['pretrained.model.head.weight', 'pretrained.model.head.bias']
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase_      ,    UpperCamelCase_						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple						)					->				Optional[Any]:

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

						return name







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Tuple						)					->				int:

						"""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)
												lowerCAmelCase__        =  state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"						)
												lowerCAmelCase__        =  state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias"						)
												# next, add query, keys and values (in that order) to the state dict
												lowerCAmelCase__        =  in_proj_weight[: config.hidden_size, :]
												lowerCAmelCase__        =  in_proj_bias[: config.hidden_size]
												lowerCAmelCase__        =  in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowerCAmelCase__        =  in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowerCAmelCase__        =  in_proj_weight[
												    -config.hidden_size :, :
												]
												lowerCAmelCase__        =  in_proj_bias[-config.hidden_size :]







def 		_UpperCamelCase				(      )					->				List[str]:

						"""simple docstring"""



						lowerCAmelCase__        =  'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowerCAmelCase__        =  Image.open(requests.get(UpperCamelCase_      ,    stream=UpperCamelCase_						).raw						)
						return im







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   Optional[Any]						)					->				Union[str, Any]:

						"""simple docstring"""



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

						# load HuggingFace model
						lowerCAmelCase__        =  DPTForSemanticSegmentation(UpperCamelCase_						) if 'ade' in checkpoint_url else DPTForDepthEstimation(UpperCamelCase_						)
						model.load_state_dict(UpperCamelCase_						)
						model.eval()

						# Check outputs on an image
						lowerCAmelCase__        =  480 if 'ade' in checkpoint_url else 384
						lowerCAmelCase__        =  DPTImageProcessor(size=UpperCamelCase_						)

						lowerCAmelCase__        =  prepare_img()
						lowerCAmelCase__        =  image_processor(UpperCamelCase_      ,    return_tensors='pt'						)

						# forward pass
						lowerCAmelCase__        =  model(**UpperCamelCase_						).logits if 'ade' in checkpoint_url else model(**UpperCamelCase_						).predicted_depth

						# Assert logits
						lowerCAmelCase__        =  torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]						)
						if "ade" in checkpoint_url:
												lowerCAmelCase__        =  torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]						)
						assert outputs.shape == torch.Size(UpperCamelCase_						)
						assert (
						    torch.allclose(outputs[0, 0, :3, :3]      ,    UpperCamelCase_      ,    atol=1e-4						)
						    if "ade" in checkpoint_url
						    else torch.allclose(outputs[0, :3, :3]      ,    UpperCamelCase_						)
						)

						Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
						print(F"Saving model to {pytorch_dump_folder_path}"						)
						model.save_pretrained(UpperCamelCase_						)
						print(F"Saving image processor to {pytorch_dump_folder_path}"						)
						image_processor.save_pretrained(UpperCamelCase_						)

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


if __name__ == "__main__":
					__snake_case      :      Dict             =       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.""",
					)

					__snake_case      :      str             =       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 diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    int						=   KandinskyVaaInpaintPipeline
						_SCREAMING_SNAKE_CASE		:    int						=   ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   [
						    '''image_embeds''',
						    '''negative_image_embeds''',
						    '''image''',
						    '''mask_image''',
						]
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''latents''',
						    '''guidance_scale''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   False






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 32






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 32






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.time_input_dim






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.time_input_dim * 4






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 1_00






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)

												lowerCAmelCase__        =  {
												    'in_channels': 9,
												    # Out channels is double in channels because predicts mean and variance
												    'out_channels': 8,
												    'addition_embed_type': 'image',
												    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
												    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
												    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
												    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
												    'layers_per_block': 1,
												    'encoder_hid_dim': self.text_embedder_hidden_size,
												    'encoder_hid_dim_type': 'image_proj',
												    'cross_attention_dim': self.cross_attention_dim,
												    'attention_head_dim': 4,
												    'resnet_time_scale_shift': 'scale_shift',
												    'class_embed_type': None,
												}

												lowerCAmelCase__        =  UNetaDConditionModel(**_UpperCamelCase				)
												return model






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return {
												    "block_out_channels": [32, 64],
												    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
												    "in_channels": 3,
												    "latent_channels": 4,
												    "layers_per_block": 1,
												    "norm_num_groups": 8,
												    "norm_type": "spatial",
												    "num_vq_embeddings": 12,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)
												lowerCAmelCase__        =  VQModel(**self.dummy_movq_kwargs				)
												return model






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.dummy_unet
												lowerCAmelCase__        =  self.dummy_movq

												lowerCAmelCase__        =  DDIMScheduler(
												    num_train_timesteps=10_00       ,  beta_schedule='linear'       ,  beta_start=0.0_00_85       ,  beta_end=0.0_12       ,  clip_sample=_UpperCamelCase       ,  set_alpha_to_one=_UpperCamelCase       ,  steps_offset=1       ,  prediction_type='epsilon'       ,  thresholding=_UpperCamelCase       ,  )

												lowerCAmelCase__        =  {
												    'unet': unet,
												    'scheduler': scheduler,
												    'movq': movq,
												}

												return components






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=0				):






												"""simple docstring"""
												lowerCAmelCase__        =  floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(_UpperCamelCase				)				).to(_UpperCamelCase				)
												lowerCAmelCase__        =  floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(seed + 1				)				).to(
												    _UpperCamelCase				)
												# create init_image
												lowerCAmelCase__        =  floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_UpperCamelCase				)				).to(_UpperCamelCase				)
												lowerCAmelCase__        =  image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
												lowerCAmelCase__        =  Image.fromarray(np.uinta(_UpperCamelCase				)				).convert('RGB'				).resize((2_56, 2_56)				)
												# create mask
												lowerCAmelCase__        =  np.ones((64, 64)       ,  dtype=np.floataa				)
												lowerCAmelCase__        =  0

												if str(_UpperCamelCase				).startswith('mps'				):
																		lowerCAmelCase__        =  torch.manual_seed(_UpperCamelCase				)
												else:
																		lowerCAmelCase__        =  torch.Generator(device=_UpperCamelCase				).manual_seed(_UpperCamelCase				)
												lowerCAmelCase__        =  {
												    'image': init_image,
												    'mask_image': mask,
												    'image_embeds': image_embeds,
												    'negative_image_embeds': negative_image_embeds,
												    'generator': generator,
												    'height': 64,
												    'width': 64,
												    'num_inference_steps': 2,
												    'guidance_scale': 4.0,
												    'output_type': 'np',
												}
												return inputs






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  'cpu'

												lowerCAmelCase__        =  self.get_dummy_components()

												lowerCAmelCase__        =  self.pipeline_class(**_UpperCamelCase				)
												lowerCAmelCase__        =  pipe.to(_UpperCamelCase				)

												pipe.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  pipe(**self.get_dummy_inputs(_UpperCamelCase				)				)
												lowerCAmelCase__        =  output.images

												lowerCAmelCase__        =  pipe(
												    **self.get_dummy_inputs(_UpperCamelCase				)       ,  return_dict=_UpperCamelCase       ,  )[0]

												lowerCAmelCase__        =  image[0, -3:, -3:, -1]
												lowerCAmelCase__        =  image_from_tuple[0, -3:, -3:, -1]

												print(F"image.shape {image.shape}"				)

												assert image.shape == (1, 64, 64, 3)

												lowerCAmelCase__        =  np.array(
												    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]				)

												assert (
												    np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
												), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
												assert (
												    np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
												), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'				)

												lowerCAmelCase__        =  load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
												lowerCAmelCase__        =  np.ones((7_68, 7_68)       ,  dtype=np.floataa				)
												lowerCAmelCase__        =  0

												lowerCAmelCase__        =  'a hat'

												lowerCAmelCase__        =  KandinskyVaaPriorPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-2-prior'       ,  torch_dtype=torch.floataa				)
												pipe_prior.to(_UpperCamelCase				)

												lowerCAmelCase__        =  KandinskyVaaInpaintPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-2-decoder-inpaint'       ,  torch_dtype=torch.floataa				)
												lowerCAmelCase__        =  pipeline.to(_UpperCamelCase				)
												pipeline.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  torch.Generator(device='cpu'				).manual_seed(0				)
												lowerCAmelCase__  ,    lowerCAmelCase__        =  pipe_prior(
												    _UpperCamelCase       ,  generator=_UpperCamelCase       ,  num_inference_steps=5       ,  negative_prompt=''       ,  ).to_tuple()

												lowerCAmelCase__        =  pipeline(
												    image=_UpperCamelCase       ,  mask_image=_UpperCamelCase       ,  image_embeds=_UpperCamelCase       ,  negative_image_embeds=_UpperCamelCase       ,  generator=_UpperCamelCase       ,  num_inference_steps=1_00       ,  height=7_68       ,  width=7_68       ,  output_type='np'       ,  )

												lowerCAmelCase__        =  output.images[0]

												assert image.shape == (7_68, 7_68, 3)

												assert_mean_pixel_difference(_UpperCamelCase       ,  _UpperCamelCase				)





