
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
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_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class       a_   ( UpperCAmelCase_				, unittest.TestCase						):
			a  : Any				      =       PaintByExamplePipeline
			a  : Union[str, Any]				      =       IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
			a  : Any				      =       IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			a  : Optional[Any]				      =       frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






			def       UpperCamelCase_							(			self     ):
									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							, )
									_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							, )
									torch.manual_seed(0     )
									_lowercase									=				CLIPVisionConfig(
									    hidden_size=32							, projection_dim=32							, intermediate_size=37							, layer_norm_eps=1E-05							, num_attention_heads=4							, num_hidden_layers=5							, image_size=32							, patch_size=4							, )
									_lowercase									=				PaintByExampleImageEncoder(__UpperCamelCase							, proj_size=32     )
									_lowercase									=				CLIPImageProcessor(crop_size=32							, size=32     )

									_lowercase									=				{
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """image_encoder""": image_encoder,
									    """safety_checker""": None,
									    """feature_extractor""": feature_extractor,
									}
									return components






			def       UpperCamelCase_							(			self							, __UpperCamelCase     ):
									_lowercase									=				np.array(image.convert("""RGB"""     )     )
									_lowercase									=				image[None].transpose(0							, 3							, 1							, 2     )
									_lowercase									=				torch.from_numpy(__UpperCamelCase     ).to(dtype=torch.floataa     ) / 127.5 - 1.0
									return image






			def       UpperCamelCase_							(			self							, __UpperCamelCase="cpu"							, __UpperCamelCase=0     ):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									_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)     )
									_lowercase									=				Image.fromarray(np.uinta(__UpperCamelCase     )     ).convert("""RGB"""     ).resize((32, 32)     )

									if str(__UpperCamelCase     ).startswith("""mps"""     ):
															_lowercase									=				torch.manual_seed(__UpperCamelCase     )
									else:
															_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )
									_lowercase									=				{
									    """example_image""": example_image,
									    """image""": init_image,
									    """mask_image""": mask_image,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									}
									return inputs






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.get_dummy_components()

									# make sure here that pndm scheduler skips prk
									_lowercase									=				PaintByExamplePipeline(**__UpperCamelCase     )
									_lowercase									=				pipe.to("""cpu"""     )
									pipe.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				self.get_dummy_inputs()
									_lowercase									=				pipe(**__UpperCamelCase     )
									_lowercase									=				output.images

									_lowercase									=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_lowercase									=				np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]     )

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






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				"""cpu"""
									_lowercase									=				self.get_dummy_inputs()
									inputs.pop("""mask_image"""     )
									_lowercase									=				self.convert_to_pt(inputs.pop("""image"""     )     )
									_lowercase									=				image.clamp(0							, 1     ) / 2

									# make sure here that pndm scheduler skips prk
									_lowercase									=				PaintByExamplePipeline(**self.get_dummy_components()     )
									_lowercase									=				pipe.to(__UpperCamelCase     )
									pipe.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				pipe(image=__UpperCamelCase							, mask_image=mask_image[:, 0]							, **__UpperCamelCase     )
									_lowercase									=				output.images

									_lowercase									=				image.cpu().permute(0							, 2							, 3							, 1     )[0]
									_lowercase									=				mask_image.cpu().permute(0							, 2							, 3							, 1     )[0]

									_lowercase									=				Image.fromarray(np.uinta(__UpperCamelCase     )     ).convert("""RGB"""     )
									_lowercase									=				Image.fromarray(np.uinta(__UpperCamelCase     )     ).convert("""RGB"""     )

									_lowercase									=				pipe(**self.get_dummy_inputs()     )
									_lowercase									=				output.images

									assert out_a.shape == (1, 64, 64, 3)
									assert np.abs(out_a.flatten() - out_a.flatten()     ).max() < 5E-2






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







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






			def       UpperCamelCase_							(			self     ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def       UpperCamelCase_							(			self     ):
									# make sure here that pndm scheduler skips prk
									_lowercase									=				load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/paint_by_example/dog_in_bucket.png"""     )
									_lowercase									=				load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/paint_by_example/mask.png"""     )
									_lowercase									=				load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/paint_by_example/panda.jpg"""     )

									_lowercase									=				PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""     )
									_lowercase									=				pipe.to(__UpperCamelCase     )
									pipe.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				torch.manual_seed(321     )
									_lowercase									=				pipe(
									    image=__UpperCamelCase							, mask_image=__UpperCamelCase							, example_image=__UpperCamelCase							, generator=__UpperCamelCase							, guidance_scale=5.0							, num_inference_steps=50							, output_type="""np"""							, )

									_lowercase									=				output.images

									_lowercase									=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									_lowercase									=				np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]     )

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



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class       a_   ( _a				, unittest.TestCase						):
			a  : List[str]				      =       UnCLIPPipeline
			a  : Optional[int]				      =       TEXT_TO_IMAGE_PARAMS - {
			    '''negative_prompt''',
			    '''height''',
			    '''width''',
			    '''negative_prompt_embeds''',
			    '''guidance_scale''',
			    '''prompt_embeds''',
			    '''cross_attention_kwargs''',
			}
			a  : Optional[int]				      =       TEXT_TO_IMAGE_BATCH_PARAMS
			a  : List[Any]				      =       [
			    '''generator''',
			    '''return_dict''',
			    '''prior_num_inference_steps''',
			    '''decoder_num_inference_steps''',
			    '''super_res_num_inference_steps''',
			]
			a  : Dict				      =       False






			@property
			def       UpperCamelCase_							(			self     ):
									return 32






			@property
			def       UpperCamelCase_							(			self     ):
									return 32






			@property
			def       UpperCamelCase_							(			self     ):
									return self.time_input_dim






			@property
			def       UpperCamelCase_							(			self     ):
									return self.time_input_dim * 4






			@property
			def       UpperCamelCase_							(			self     ):
									return 100






			@property
			def       UpperCamelCase_							(			self     ):
									_lowercase									=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
									return tokenizer






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )
									_lowercase									=				CLIPTextConfig(
									    bos_token_id=0							, eos_token_id=2							, hidden_size=self.text_embedder_hidden_size							, projection_dim=self.text_embedder_hidden_size							, intermediate_size=37							, layer_norm_eps=1E-05							, num_attention_heads=4							, num_hidden_layers=5							, pad_token_id=1							, vocab_size=1_000							, )
									return CLIPTextModelWithProjection(__UpperCamelCase     )






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )

									_lowercase									=				{
									    """num_attention_heads""": 2,
									    """attention_head_dim""": 12,
									    """embedding_dim""": self.text_embedder_hidden_size,
									    """num_layers""": 1,
									}

									_lowercase									=				PriorTransformer(**__UpperCamelCase     )
									return model






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )

									_lowercase									=				{
									    """clip_embeddings_dim""": self.text_embedder_hidden_size,
									    """time_embed_dim""": self.time_embed_dim,
									    """cross_attention_dim""": self.cross_attention_dim,
									}

									_lowercase									=				UnCLIPTextProjModel(**__UpperCamelCase     )
									return model






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )

									_lowercase									=				{
									    """sample_size""": 32,
									    # RGB in channels
									    """in_channels""": 3,
									    # Out channels is double in channels because predicts mean and variance
									    """out_channels""": 6,
									    """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,
									    """cross_attention_dim""": self.cross_attention_dim,
									    """attention_head_dim""": 4,
									    """resnet_time_scale_shift""": """scale_shift""",
									    """class_embed_type""": """identity""",
									}

									_lowercase									=				UNetaDConditionModel(**__UpperCamelCase     )
									return model






			@property
			def       UpperCamelCase_							(			self     ):
									return {
									    "sample_size": 64,
									    "layers_per_block": 1,
									    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
									    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
									    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    "in_channels": 6,
									    "out_channels": 3,
									}






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )

									_lowercase									=				UNetaDModel(**self.dummy_super_res_kwargs     )
									return model






			@property
			def       UpperCamelCase_							(			self     ):
									# seeded differently to get different unet than `self.dummy_super_res_first`
									torch.manual_seed(1     )

									_lowercase									=				UNetaDModel(**self.dummy_super_res_kwargs     )
									return model






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.dummy_prior
									_lowercase									=				self.dummy_decoder
									_lowercase									=				self.dummy_text_proj
									_lowercase									=				self.dummy_text_encoder
									_lowercase									=				self.dummy_tokenizer
									_lowercase									=				self.dummy_super_res_first
									_lowercase									=				self.dummy_super_res_last

									_lowercase									=				UnCLIPScheduler(
									    variance_type="""fixed_small_log"""							, prediction_type="""sample"""							, num_train_timesteps=1_000							, clip_sample_range=5.0							, )

									_lowercase									=				UnCLIPScheduler(
									    variance_type="""learned_range"""							, prediction_type="""epsilon"""							, num_train_timesteps=1_000							, )

									_lowercase									=				UnCLIPScheduler(
									    variance_type="""fixed_small_log"""							, prediction_type="""epsilon"""							, num_train_timesteps=1_000							, )

									_lowercase									=				{
									    """prior""": prior,
									    """decoder""": decoder,
									    """text_proj""": text_proj,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """super_res_first""": super_res_first,
									    """super_res_last""": super_res_last,
									    """prior_scheduler""": prior_scheduler,
									    """decoder_scheduler""": decoder_scheduler,
									    """super_res_scheduler""": super_res_scheduler,
									}

									return components






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase=0     ):
									if str(__UpperCamelCase     ).startswith("""mps"""     ):
															_lowercase									=				torch.manual_seed(__UpperCamelCase     )
									else:
															_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )
									_lowercase									=				{
									    """prompt""": """horse""",
									    """generator""": generator,
									    """prior_num_inference_steps""": 2,
									    """decoder_num_inference_steps""": 2,
									    """super_res_num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}
									return inputs






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				"""cpu"""

									_lowercase									=				self.get_dummy_components()

									_lowercase									=				self.pipeline_class(**__UpperCamelCase     )
									_lowercase									=				pipe.to(__UpperCamelCase     )

									pipe.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				pipe(**self.get_dummy_inputs(__UpperCamelCase     )     )
									_lowercase									=				output.images

									_lowercase									=				pipe(
									    **self.get_dummy_inputs(__UpperCamelCase     )							, return_dict=__UpperCamelCase							, )[0]

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

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

									_lowercase									=				np.array(
									    [
									        0.99_97,
									        0.99_88,
									        0.00_28,
									        0.99_97,
									        0.99_84,
									        0.99_65,
									        0.00_29,
									        0.99_86,
									        0.00_25,
									    ]     )

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






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				torch.device("""cpu"""     )



									class       a_   :
												a  : int				      =       1







									_lowercase									=				self.get_dummy_components()

									_lowercase									=				self.pipeline_class(**__UpperCamelCase     )
									_lowercase									=				pipe.to(__UpperCamelCase     )

									_lowercase									=				components["""prior"""]
									_lowercase									=				components["""decoder"""]
									_lowercase									=				components["""super_res_first"""]
									_lowercase									=				components["""tokenizer"""]
									_lowercase									=				components["""text_encoder"""]

									_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(0     )
									_lowercase									=				prior.dtype
									_lowercase									=				1

									_lowercase									=				(batch_size, prior.config.embedding_dim)
									_lowercase									=				pipe.prepare_latents(
									    __UpperCamelCase							, dtype=__UpperCamelCase							, device=__UpperCamelCase							, generator=__UpperCamelCase							, latents=__UpperCamelCase							, scheduler=DummyScheduler()     )
									_lowercase									=				(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
									_lowercase									=				pipe.prepare_latents(
									    __UpperCamelCase							, dtype=__UpperCamelCase							, device=__UpperCamelCase							, generator=__UpperCamelCase							, latents=__UpperCamelCase							, scheduler=DummyScheduler()     )

									_lowercase									=				(
									    batch_size,
									    super_res_first.config.in_channels // 2,
									    super_res_first.config.sample_size,
									    super_res_first.config.sample_size,
									)
									_lowercase									=				pipe.prepare_latents(
									    __UpperCamelCase							, dtype=__UpperCamelCase							, device=__UpperCamelCase							, generator=__UpperCamelCase							, latents=__UpperCamelCase							, scheduler=DummyScheduler()     )

									pipe.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				"""this is a prompt example"""

									_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(0     )
									_lowercase									=				pipe(
									    [prompt]							, generator=__UpperCamelCase							, prior_num_inference_steps=2							, decoder_num_inference_steps=2							, super_res_num_inference_steps=2							, prior_latents=__UpperCamelCase							, decoder_latents=__UpperCamelCase							, super_res_latents=__UpperCamelCase							, output_type="""np"""							, )
									_lowercase									=				output.images

									_lowercase									=				tokenizer(
									    __UpperCamelCase							, padding="""max_length"""							, max_length=tokenizer.model_max_length							, return_tensors="""pt"""							, )
									_lowercase									=				text_encoder(text_inputs.input_ids     )
									_lowercase									=				text_inputs.attention_mask

									_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(0     )
									_lowercase									=				pipe(
									    generator=__UpperCamelCase							, prior_num_inference_steps=2							, decoder_num_inference_steps=2							, super_res_num_inference_steps=2							, prior_latents=__UpperCamelCase							, decoder_latents=__UpperCamelCase							, super_res_latents=__UpperCamelCase							, text_model_output=__UpperCamelCase							, text_attention_mask=__UpperCamelCase							, output_type="""np"""							, )[0]

									# make sure passing text embeddings manually is identical
									assert np.abs(image - image_from_text     ).max() < 1E-4






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									_lowercase									=				torch_device == """cpu"""

									self._test_attention_slicing_forward_pass(test_max_difference=__UpperCamelCase							, expected_max_diff=0.01     )






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									_lowercase									=				torch_device == """cpu"""
									_lowercase									=				True
									_lowercase									=				[
									    """prior_num_inference_steps""",
									    """decoder_num_inference_steps""",
									    """super_res_num_inference_steps""",
									]

									self._test_inference_batch_single_identical(
									    test_max_difference=__UpperCamelCase							, relax_max_difference=__UpperCamelCase							, additional_params_copy_to_batched_inputs=__UpperCamelCase							, )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				[
									    """prior_num_inference_steps""",
									    """decoder_num_inference_steps""",
									    """super_res_num_inference_steps""",
									]

									if torch_device == "mps":
															# TODO: MPS errors with larger batch sizes
															_lowercase									=				[2, 3]
															self._test_inference_batch_consistent(
															    batch_sizes=__UpperCamelCase							, additional_params_copy_to_batched_inputs=__UpperCamelCase							, )
									else:
															self._test_inference_batch_consistent(
															    additional_params_copy_to_batched_inputs=__UpperCamelCase     )






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									return super().test_dict_tuple_outputs_equivalent()






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									return super().test_save_load_local()






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									return super().test_save_load_optional_components()







@nightly
class       a_   ( unittest.TestCase						):






			def       UpperCamelCase_							(			self     ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/unclip/karlo_v1_alpha_horse_cpu.npy"""     )

									_lowercase									=				UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""     )
									pipeline.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				torch.manual_seed(0     )
									_lowercase									=				pipeline(
									    """horse"""							, num_images_per_prompt=1							, generator=__UpperCamelCase							, output_type="""np"""							, )

									_lowercase									=				output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image     ).max() < 1E-1







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






			def       UpperCamelCase_							(			self     ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/unclip/karlo_v1_alpha_horse_fp16.npy"""     )

									_lowercase									=				UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""							, torch_dtype=torch.floataa     )
									_lowercase									=				pipeline.to(__UpperCamelCase     )
									pipeline.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				torch.Generator(device="""cpu"""     ).manual_seed(0     )
									_lowercase									=				pipeline(
									    """horse"""							, generator=__UpperCamelCase							, output_type="""np"""							, )

									_lowercase									=				output.images[0]

									assert image.shape == (256, 256, 3)

									assert_mean_pixel_difference(__UpperCamelCase							, __UpperCamelCase     )






			def       UpperCamelCase_							(			self     ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_lowercase									=				UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""							, torch_dtype=torch.floataa     )
									_lowercase									=				pipe.to(__UpperCamelCase     )
									pipe.set_progress_bar_config(disable=__UpperCamelCase     )
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									_lowercase									=				pipe(
									    """horse"""							, num_images_per_prompt=1							, prior_num_inference_steps=2							, decoder_num_inference_steps=2							, super_res_num_inference_steps=2							, output_type="""np"""							, )

									_lowercase									=				torch.cuda.max_memory_allocated()
									# make sure that less than 7 GB is allocated
									assert mem_bytes < 7 * 10**9