
import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class    __A:

					def __init__(						self       :  Tuple ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Optional[Any]=sys.maxsize ):
												lowerCamelCase_					=       """bilinear"""
												lowerCamelCase_					=       max_size
												lowerCamelCase_					=       short_edge_length



					def __call__(						self       :  List[Any] ,       __UpperCamelCase       :  Any ):
												lowerCamelCase_					=       []
												for img in imgs:
																			lowerCamelCase_	, lowerCamelCase_					=       img.shape[:2]
																			# later: provide list and randomly choose index for resize
																			lowerCamelCase_					=       np.random.randint(self.short_edge_length[0] ,       self.short_edge_length[1] + 1 )
																			if size == 0:
																										return img
																			lowerCamelCase_					=       size * 1.0 / min(__UpperCamelCase ,       __UpperCamelCase )
																			if h < w:
																										lowerCamelCase_	, lowerCamelCase_					=       size, scale * w
																			else:
																										lowerCamelCase_	, lowerCamelCase_					=       scale * h, size
																			if max(__UpperCamelCase ,       __UpperCamelCase ) > self.max_size:
																										lowerCamelCase_					=       self.max_size * 1.0 / max(__UpperCamelCase ,       __UpperCamelCase )
																										lowerCamelCase_					=       newh * scale
																										lowerCamelCase_					=       neww * scale
																			lowerCamelCase_					=       int(neww + 0.5 )
																			lowerCamelCase_					=       int(newh + 0.5 )

																			if img.dtype == np.uinta:
																										lowerCamelCase_					=       Image.fromarray(__UpperCamelCase )
																										lowerCamelCase_					=       pil_image.resize((neww, newh) ,       PILImageResampling.BILINEAR )
																										lowerCamelCase_					=       np.asarray(__UpperCamelCase )
																			else:
																										lowerCamelCase_					=       img.permute(2 ,       0 ,       1 ).unsqueeze(0 )  # 3, 0, 1)  # hw(c) -> nchw
																										lowerCamelCase_					=       nn.functional.interpolate(
																										    __UpperCamelCase ,       (newh, neww) ,       mode=self.interp_method ,       align_corners=__UpperCamelCase ).squeeze(0 )
																			img_augs.append(__UpperCamelCase )

												return img_augs





class    __A:

					def __init__(						self       :  Optional[Any] ,       __UpperCamelCase       :  Tuple ):
												lowerCamelCase_					=       ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,       cfg.INPUT.MAX_SIZE_TEST )
												lowerCamelCase_					=       cfg.INPUT.FORMAT
												lowerCamelCase_					=       cfg.SIZE_DIVISIBILITY
												lowerCamelCase_					=       cfg.PAD_VALUE
												lowerCamelCase_					=       cfg.INPUT.MAX_SIZE_TEST
												lowerCamelCase_					=       cfg.MODEL.DEVICE
												lowerCamelCase_					=       torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) ,       1 ,       1 )
												lowerCamelCase_					=       torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) ,       1 ,       1 )
												lowerCamelCase_					=       lambda __UpperCamelCase : (x - self.pixel_mean) / self.pixel_std

					def  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  Any ):
												lowerCamelCase_					=       tuple(max(__UpperCamelCase ) for s in zip(*[img.shape for img in images] ) )
												lowerCamelCase_					=       [im.shape[-2:] for im in images]
												lowerCamelCase_					=       [
												    nn.functional.pad(
												        __UpperCamelCase ,       [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,       value=self.pad_value ,       )
												    for size, im in zip(__UpperCamelCase ,       __UpperCamelCase )
												]

												return torch.stack(__UpperCamelCase ), torch.tensor(__UpperCamelCase )



					def __call__(						self       :  str ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  Optional[Any]=False ):
												with torch.no_grad():
																			if not isinstance(__UpperCamelCase ,       __UpperCamelCase ):
																										lowerCamelCase_					=       [images]
																			if single_image:
																										assert len(__UpperCamelCase ) == 1
																			for i in range(len(__UpperCamelCase ) ):
																										if isinstance(images[i] ,       torch.Tensor ):
																																	images.insert(__UpperCamelCase ,       images.pop(__UpperCamelCase ).to(self.device ).float() )
																										elif not isinstance(images[i] ,       torch.Tensor ):
																																	images.insert(
																																	    __UpperCamelCase ,       torch.as_tensor(img_tensorize(images.pop(__UpperCamelCase ) ,       input_format=self.input_format ) )
																																	    .to(self.device )
																																	    .float() ,       )
            # resize smallest edge
																			lowerCamelCase_					=       torch.tensor([im.shape[:2] for im in images] )
																			lowerCamelCase_					=       self.aug(__UpperCamelCase )
																			# transpose images and convert to torch tensors
																			# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																			# now normalize before pad to avoid useless arithmetic
																			lowerCamelCase_					=       [self.normalizer(__UpperCamelCase ) for x in images]
																			# now pad them to do the following operations
																			lowerCamelCase_	, lowerCamelCase_					=       self.pad(__UpperCamelCase )
																			# Normalize

																			if self.size_divisibility > 0:
																										raise NotImplementedError()
																			# pad
																			lowerCamelCase_					=       torch.true_divide(__UpperCamelCase ,       __UpperCamelCase )
																			if single_image:
																										return images[0], sizes[0], scales_yx[0]
																			else:
																										return images, sizes, scales_yx



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Tuple							)	->     Dict:
							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[Any]      ,					UpperCAmelCase__					:   Tuple[int, int]							)	->     Any:
							assert torch.isfinite(UpperCAmelCase__							).all(), "Box tensor contains infinite or NaN!"
							lowerCamelCase_	, lowerCamelCase_					=       box_size
							tensor[:, 0].clamp_(min=0      ,					max=UpperCAmelCase__							)
							tensor[:, 1].clamp_(min=0      ,					max=UpperCAmelCase__							)
							tensor[:, 2].clamp_(min=0      ,					max=UpperCAmelCase__							)
							tensor[:, 3].clamp_(min=0      ,					max=UpperCAmelCase__							)







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,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class    __A(					UpperCAmelCase  ,		UpperCAmelCase  ,		UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     StableDiffusionInstructPixaPixPipeline
					SCREAMING_SNAKE_CASE           =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
					SCREAMING_SNAKE_CASE           =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					SCREAMING_SNAKE_CASE           =     IMAGE_TO_IMAGE_IMAGE_PARAMS
					SCREAMING_SNAKE_CASE           =     IMAGE_TO_IMAGE_IMAGE_PARAMS

					def  lowercase__   (						self       :  List[Any] ):
												torch.manual_seed(0 )
												lowerCamelCase_					=       UNetaDConditionModel(
												    block_out_channels=(3_2, 6_4) ,       layers_per_block=2 ,       sample_size=3_2 ,       in_channels=8 ,       out_channels=4 ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,       cross_attention_dim=3_2 ,       )
												lowerCamelCase_					=       PNDMScheduler(skip_prk_steps=__UpperCamelCase )
												torch.manual_seed(0 )
												lowerCamelCase_					=       AutoencoderKL(
												    block_out_channels=[3_2, 6_4] ,       in_channels=3 ,       out_channels=3 ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,       latent_channels=4 ,       )
												torch.manual_seed(0 )
												lowerCamelCase_					=       CLIPTextConfig(
												    bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=3_2 ,       intermediate_size=3_7 ,       layer_norm_eps=1E-05 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=1_0_0_0 ,       )
												lowerCamelCase_					=       CLIPTextModel(__UpperCamelCase )
												lowerCamelCase_					=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

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

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Any=0 ):
												lowerCamelCase_					=       floats_tensor((1, 3, 3_2, 3_2) ,       rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
												lowerCamelCase_					=       image.cpu().permute(0 ,       2 ,       3 ,       1 )[0]
												lowerCamelCase_					=       Image.fromarray(np.uinta(__UpperCamelCase ) ).convert("""RGB""" )
												if str(__UpperCamelCase ).startswith("""mps""" ):
																			lowerCamelCase_					=       torch.manual_seed(__UpperCamelCase )
												else:
																			lowerCamelCase_					=       torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
												lowerCamelCase_					=       {
												    """prompt""": """A painting of a squirrel eating a burger""",
												    """image""": image,
												    """generator""": generator,
												    """num_inference_steps""": 2,
												    """guidance_scale""": 6.0,
												    """image_guidance_scale""": 1,
												    """output_type""": """numpy""",
												}
												return inputs

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe.to(__UpperCamelCase )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[0, -3:, -3:, -1]
												assert image.shape == (1, 3_2, 3_2, 3)
												lowerCamelCase_					=       np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe.to(__UpperCamelCase )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )
												lowerCamelCase_					=       """french fries"""
												lowerCamelCase_					=       sd_pipe(**__UpperCamelCase ,       negative_prompt=__UpperCamelCase )
												lowerCamelCase_					=       output.images
												lowerCamelCase_					=       image[0, -3:, -3:, -1]

												assert image.shape == (1, 3_2, 3_2, 3)
												lowerCamelCase_					=       np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe.to(__UpperCamelCase )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )
												lowerCamelCase_					=       [inputs["""prompt"""]] * 2

												lowerCamelCase_					=       np.array(inputs["""image"""] ).astype(np.floataa ) / 255.0
												lowerCamelCase_					=       torch.from_numpy(__UpperCamelCase ).unsqueeze(0 ).to(__UpperCamelCase )
												lowerCamelCase_					=       image / 2 + 0.5
												lowerCamelCase_					=       image.permute(0 ,       3 ,       1 ,       2 )
												lowerCamelCase_					=       image.repeat(2 ,       1 ,       1 ,       1 )

												lowerCamelCase_					=       sd_pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[-1, -3:, -3:, -1]

												assert image.shape == (2, 3_2, 3_2, 3)
												lowerCamelCase_					=       np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       EulerAncestralDiscreteScheduler(
												    beta_start=0.00085 ,       beta_end=0.012 ,       beta_schedule="""scaled_linear""" )
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe.to(__UpperCamelCase )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												lowerCamelCase_					=       [round(__UpperCamelCase ,       4 ) for x in image_slice.flatten().tolist()]
												print(""",""".join([str(__UpperCamelCase ) for x in slice] ) )

												assert image.shape == (1, 3_2, 3_2, 3)
												lowerCamelCase_					=       np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3

					def  lowercase__   (						self       :  str ):
												super().test_inference_batch_single_identical(expected_max_diff=3E-3 )



					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       VaeImageProcessor(do_resize=__UpperCamelCase ,       do_normalize=__UpperCamelCase )
												lowerCamelCase_					=       pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       pipe(**self.get_dummy_inputs_by_type(__UpperCamelCase ,       input_image_type="""pt""" ) )[0]

												lowerCamelCase_					=       components["""vae"""]
												lowerCamelCase_					=       self.get_dummy_inputs_by_type(__UpperCamelCase ,       input_image_type="""pt""" )

												for image_param in self.image_latents_params:
																			if image_param in inputs.keys():
																										lowerCamelCase_					=       vae.encode(inputs[image_param] ).latent_dist.mode()

												lowerCamelCase_					=       pipe(**__UpperCamelCase )[0]

												lowerCamelCase_					=       np.abs(out - out_latents_inputs ).max()
												self.assertLess(__UpperCamelCase ,       1E-4 ,       """passing latents as image input generate different result from passing image""" )





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

					def  lowercase__   (						self       :  List[Any] ):
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  Union[str, Any]=0 ):
												lowerCamelCase_					=       torch.manual_seed(__UpperCamelCase )
												lowerCamelCase_					=       load_image(
												    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg""" )
												lowerCamelCase_					=       {
												    """prompt""": """turn him into a cyborg""",
												    """image""": image,
												    """generator""": generator,
												    """num_inference_steps""": 3,
												    """guidance_scale""": 7.5,
												    """image_guidance_scale""": 1.0,
												    """output_type""": """numpy""",
												}
												return inputs

					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

												lowerCamelCase_					=       self.get_inputs()
												lowerCamelCase_					=       pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )

												assert np.abs(expected_slice - image_slice ).max() < 1E-3

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase )
												lowerCamelCase_					=       LMSDiscreteScheduler.from_config(pipe.scheduler.config )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

												lowerCamelCase_					=       self.get_inputs()
												lowerCamelCase_					=       pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )

												assert np.abs(expected_slice - image_slice ).max() < 1E-3

					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase )
												lowerCamelCase_					=       DDIMScheduler.from_config(pipe.scheduler.config )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

												lowerCamelCase_					=       self.get_inputs()
												lowerCamelCase_					=       pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )

												assert np.abs(expected_slice - image_slice ).max() < 1E-3

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       0

												def callback_fn(__UpperCamelCase       :  int ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  torch.FloatTensor ) -> None:
																			lowerCamelCase_					=       True
																			nonlocal number_of_steps
																			number_of_steps += 1
																			if step == 1:
																										lowerCamelCase_					=       latents.detach().cpu().numpy()
																										assert latents.shape == (1, 4, 6_4, 6_4)
																										lowerCamelCase_					=       latents[0, -3:, -3:, -1]
																										lowerCamelCase_					=       np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )

																										assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
																			elif step == 2:
																										lowerCamelCase_					=       latents.detach().cpu().numpy()
																										assert latents.shape == (1, 4, 6_4, 6_4)
																										lowerCamelCase_					=       latents[0, -3:, -3:, -1]
																										lowerCamelCase_					=       np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )

																										assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2

												lowerCamelCase_					=       False

												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase ,       torch_dtype=torch.floataa )
												lowerCamelCase_					=       pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

												lowerCamelCase_					=       self.get_inputs()
												pipe(**__UpperCamelCase ,       callback=__UpperCamelCase ,       callback_steps=1 )
												assert callback_fn.has_been_called
												assert number_of_steps == 3

					def  lowercase__   (						self       :  int ):
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase ,       torch_dtype=torch.floataa )
												lowerCamelCase_					=       pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing(1 )
												pipe.enable_sequential_cpu_offload()

												lowerCamelCase_					=       self.get_inputs()
												lowerCamelCase_					=       pipe(**__UpperCamelCase )

												lowerCamelCase_					=       torch.cuda.max_memory_allocated()
												# make sure that less than 2.2 GB is allocated
												assert mem_bytes < 2.2 * 1_0**9



					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       self.get_inputs()
												# resize to resolution that is divisible by 8 but not 16 or 32
												lowerCamelCase_					=       inputs["""image"""].resize((5_0_4, 5_0_4) )

												lowerCamelCase_					=       """timbrooks/instruct-pix2pix"""
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    __UpperCamelCase ,       safety_checker=__UpperCamelCase ,       )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

												lowerCamelCase_					=       pipe(**__UpperCamelCase )
												lowerCamelCase_					=       output.images[0]

												lowerCamelCase_					=       image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

												assert image.shape == (5_0_4, 5_0_4, 3)
												lowerCamelCase_					=       np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3





