

'''simple docstring'''



import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()



class lowerCamelCase			(  lowercase_	,						lowercase_	,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	AutoencoderKL
			__snake_case        =	"""sample"""
			__snake_case        =	1E-2


			@property
			def lowercase__						(				self	:						Optional[int]			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Optional[Any]     =4
									A__					:       Any     =3
									A__					:       Optional[int]     =(32, 32)

									A__					:       List[str]     =floats_tensor((batch_size, num_channels) + sizes			).to(_A			)

									return {"sample": image}


			@property
			def lowercase__						(				self	:						int			)      ->		Dict:





									'''simple docstring'''
									return (3, 32, 32)


			@property
			def lowercase__						(				self	:						Any			)      ->		Union[str, Any]:





									'''simple docstring'''
									return (3, 32, 32)


			def lowercase__						(				self	:						Any			)      ->		str:





									'''simple docstring'''
									A__					:       Optional[int]     ={
									    'block_out_channels': [32, 64],
									    'in_channels': 3,
									    'out_channels': 3,
									    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
									    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
									    'latent_channels': 4,
									}
									A__					:       int     =self.dummy_input
									return init_dict, inputs_dict


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





									'''simple docstring'''
									pass


			def lowercase__						(				self	:						Tuple			)      ->		Any:





									'''simple docstring'''
									pass


			@unittest.skipIf(torch_device == """mps"""		,      """Gradient checkpointing skipped on MPS"""			)
			def lowercase__						(				self	:						Optional[Any]			)      ->		List[Any]:





									'''simple docstring'''
									# enable deterministic behavior for gradient checkpointing
									A__					:       List[Any]     =self.prepare_init_args_and_inputs_for_common()
									A__					:       int     =self.model_class(**_A			)
									model.to(_A			)

									assert not model.is_gradient_checkpointing and model.training

									A__					:       List[Any]     =model(**_A			).sample
									# run the backwards pass on the model. For backwards pass, for simplicity purpose,
									# we won't calculate the loss and rather backprop on out.sum()
									model.zero_grad()

									A__					:       str     =torch.randn_like(_A			)
									A__					:       List[Any]     =(out - labels).mean()
									loss.backward()

									# re-instantiate the model now enabling gradient checkpointing
									A__					:       Optional[Any]     =self.model_class(**_A			)
									# clone model
									model_a.load_state_dict(model.state_dict()			)
									model_a.to(_A			)
									model_a.enable_gradient_checkpointing()

									assert model_a.is_gradient_checkpointing and model_a.training

									A__					:       List[Any]     =model_a(**_A			).sample
									# run the backwards pass on the model. For backwards pass, for simplicity purpose,
									# we won't calculate the loss and rather backprop on out.sum()
									model_a.zero_grad()
									A__					:       Dict     =(out_a - labels).mean()
									loss_a.backward()

									# compare the output and parameters gradients
									self.assertTrue((loss - loss_a).abs() < 1e-5			)
									A__					:       List[str]     =dict(model.named_parameters()			)
									A__					:       Union[str, Any]     =dict(model_a.named_parameters()			)
									for name, param in named_params.items():
															self.assertTrue(torch_all_close(param.grad.data		,      named_params_a[name].grad.data		,      atol=5e-5			)			)


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





									'''simple docstring'''
									A__					:       List[Any]     =AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""		,      output_loading_info=_A			)
									self.assertIsNotNone(_A			)
									self.assertEqual(len(loading_info["""missing_keys"""]			)		,      0			)

									model.to(_A			)
									A__					:       str     =model(**self.dummy_input			)

									assert image is not None, "Make sure output is not None"



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





									'''simple docstring'''
									A__					:       Optional[int]     =AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""			)
									A__					:       List[Any]     =model.to(_A			)
									model.eval()

									if torch_device == "mps":
															A__					:       str     =torch.manual_seed(0			)
									else:
															A__					:       str     =torch.Generator(device=_A			).manual_seed(0			)

									A__					:       Optional[int]     =torch.randn(
									    1		,      model.config.in_channels		,      model.config.sample_size		,      model.config.sample_size		,      generator=torch.manual_seed(0			)		,      )
									A__					:       List[Any]     =image.to(_A			)
									with torch.no_grad():
															A__					:       str     =model(_A		,      sample_posterior=_A		,      generator=_A			).sample

									A__					:       Tuple     =output[0, -1, -3:, -3:].flatten().cpu()

									# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
									# the expected output slices are not the same for CPU and GPU.
									if torch_device == "mps":
															A__					:       int     =torch.tensor(
															    [
															        -4.0078e-01,
															        -3.8323e-04,
															        -1.2681e-01,
															        -1.1462e-01,
															        2.0095e-01,
															        1.0893e-01,
															        -8.8247e-02,
															        -3.0361e-01,
															        -9.8644e-03,
															    ]			)
									elif torch_device == "cpu":
															A__					:       Any     =torch.tensor(
															    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]			)
									else:
															A__					:       int     =torch.tensor(
															    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]			)

									self.assertTrue(torch_all_close(_A		,      _A		,      rtol=1e-2			)			)



@slow
class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						Tuple			)      ->		List[Any]:





									'''simple docstring'''
									return f"gaussian_noise_s={seed}_shape={'_'.join([str(_A			) for s in shape]			)}.npy"


			def lowercase__						(				self	:						Optional[int]			)      ->		Dict:





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


			def lowercase__						(				self	:						str		,      lowerCAmelCase_	:						Optional[Any]=0		,      lowerCAmelCase_	:						List[str]=(4, 3, 5_12, 5_12)		,      lowerCAmelCase_	:						Union[str, Any]=False			)      ->		int:





									'''simple docstring'''
									A__					:       Optional[int]     =torch.floataa if fpaa else torch.floataa
									A__					:       Optional[Any]     =torch.from_numpy(load_hf_numpy(self.get_file_format(_A		,      _A			)			)			).to(_A			).to(_A			)
									return image


			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						int="CompVis/stable-diffusion-v1-4"		,      lowerCAmelCase_	:						Any=False			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       Dict     ='fp16' if fpaa else None
									A__					:       Any     =torch.floataa if fpaa else torch.floataa

									A__					:       Union[str, Any]     =AutoencoderKL.from_pretrained(
									    _A		,      subfolder="""vae"""		,      torch_dtype=_A		,      revision=_A		,      )
									model.to(_A			).eval()

									return model


			def lowercase__						(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Any=0			)      ->		Union[str, Any]:





									'''simple docstring'''
									if torch_device == "mps":
															return torch.manual_seed(_A			)
									return torch.Generator(device=_A			).manual_seed(_A			)


			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
			        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
			        # fmt: on
			    ]			)
			def lowercase__						(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Optional[int]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						Optional[Any]			)      ->		int:





									'''simple docstring'''
									A__					:       List[Any]     =self.get_sd_vae_model()
									A__					:       Optional[int]     =self.get_sd_image(_A			)
									A__					:       Any     =self.get_generator(_A			)

									with torch.no_grad():
															A__					:       Optional[Any]     =model(_A		,      generator=_A		,      sample_posterior=_A			).sample

									assert sample.shape == image.shape

									A__					:       Dict     =sample[-1, -2:, -2:, :2].flatten().float().cpu()
									A__					:       Tuple     =torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice			)

									assert torch_all_close(_A		,      _A		,      atol=3e-3			)


			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
			        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
			        # fmt: on
			    ]			)
			@require_torch_gpu
			def lowercase__						(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						str			)      ->		str:





									'''simple docstring'''
									A__					:       int     =self.get_sd_vae_model(fpaa=_A			)
									A__					:       List[Any]     =self.get_sd_image(_A		,      fpaa=_A			)
									A__					:       Any     =self.get_generator(_A			)

									with torch.no_grad():
															A__					:       Optional[int]     =model(_A		,      generator=_A		,      sample_posterior=_A			).sample

									assert sample.shape == image.shape

									A__					:       int     =sample[-1, -2:, :2, -2:].flatten().float().cpu()
									A__					:       Any     =torch.tensor(_A			)

									assert torch_all_close(_A		,      _A		,      atol=1e-2			)


			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
			        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
			        # fmt: on
			    ]			)
			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						Dict		,      lowerCAmelCase_	:						Dict			)      ->		Any:





									'''simple docstring'''
									A__					:       Any     =self.get_sd_vae_model()
									A__					:       Dict     =self.get_sd_image(_A			)

									with torch.no_grad():
															A__					:       Tuple     =model(_A			).sample

									assert sample.shape == image.shape

									A__					:       Tuple     =sample[-1, -2:, -2:, :2].flatten().float().cpu()
									A__					:       Tuple     =torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice			)

									assert torch_all_close(_A		,      _A		,      atol=3e-3			)


			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
			        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
			        # fmt: on
			    ]			)
			@require_torch_gpu
			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						Any		,      lowerCAmelCase_	:						Tuple			)      ->		str:





									'''simple docstring'''
									A__					:       Tuple     =self.get_sd_vae_model()
									A__					:       List[Any]     =self.get_sd_image(_A		,      shape=(3, 4, 64, 64)			)

									with torch.no_grad():
															A__					:       Tuple     =model.decode(_A			).sample

									assert list(sample.shape			) == [3, 3, 5_12, 5_12]

									A__					:       Any     =sample[-1, -2:, :2, -2:].flatten().cpu()
									A__					:       List[str]     =torch.tensor(_A			)

									assert torch_all_close(_A		,      _A		,      atol=1e-3			)


			@parameterized.expand(
			    [
			        # fmt: off
			        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
			        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
			        # fmt: on
			    ]			)
			@require_torch_gpu
			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						Dict			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Optional[int]     =self.get_sd_vae_model(fpaa=_A			)
									A__					:       List[str]     =self.get_sd_image(_A		,      shape=(3, 4, 64, 64)		,      fpaa=_A			)

									with torch.no_grad():
															A__					:       Tuple     =model.decode(_A			).sample

									assert list(sample.shape			) == [3, 3, 5_12, 5_12]

									A__					:       Optional[Any]     =sample[-1, -2:, :2, -2:].flatten().float().cpu()
									A__					:       Optional[Any]     =torch.tensor(_A			)

									assert torch_all_close(_A		,      _A		,      atol=5e-3			)


			@parameterized.expand([(13,), (16,), (27,)]			)
			@require_torch_gpu
			@unittest.skipIf(not is_xformers_available()		,      reason="""xformers is not required when using PyTorch 2.0."""			)
			def lowercase__						(				self	:						List[Any]		,      lowerCAmelCase_	:						Union[str, Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Any     =self.get_sd_vae_model(fpaa=_A			)
									A__					:       int     =self.get_sd_image(_A		,      shape=(3, 4, 64, 64)		,      fpaa=_A			)

									with torch.no_grad():
															A__					:       Optional[Any]     =model.decode(_A			).sample

									model.enable_xformers_memory_efficient_attention()
									with torch.no_grad():
															A__					:       Tuple     =model.decode(_A			).sample

									assert list(sample.shape			) == [3, 3, 5_12, 5_12]

									assert torch_all_close(_A		,      _A		,      atol=1e-1			)


			@parameterized.expand([(13,), (16,), (37,)]			)
			@require_torch_gpu
			@unittest.skipIf(not is_xformers_available()		,      reason="""xformers is not required when using PyTorch 2.0."""			)
			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						Optional[Any]			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.get_sd_vae_model()
									A__					:       Tuple     =self.get_sd_image(_A		,      shape=(3, 4, 64, 64)			)

									with torch.no_grad():
															A__					:       List[Any]     =model.decode(_A			).sample

									model.enable_xformers_memory_efficient_attention()
									with torch.no_grad():
															A__					:       Any     =model.decode(_A			).sample

									assert list(sample.shape			) == [3, 3, 5_12, 5_12]

									assert torch_all_close(_A		,      _A		,      atol=1e-2			)



			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
			        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
			        # fmt: on
			    ]			)
			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						Optional[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.get_sd_vae_model()
									A__					:       Union[str, Any]     =self.get_sd_image(_A			)
									A__					:       Any     =self.get_generator(_A			)

									with torch.no_grad():
															A__					:       Dict     =model.encode(_A			).latent_dist
															A__					:       Dict     =dist.sample(generator=_A			)

									assert list(sample.shape			) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

									A__					:       str     =sample[0, -1, -3:, -3:].flatten().cpu()
									A__					:       List[str]     =torch.tensor(_A			)

									A__					:       Any     =3e-3 if torch_device != 'mps' else 1e-2
									assert torch_all_close(_A		,      _A		,      atol=_A			)




'''simple docstring'''



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 lowerCamelCase			(  lowercase_	,						lowercase_	,						lowercase_	,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	StableDiffusionInstructPixaPixPipeline
			__snake_case        =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
			__snake_case        =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__snake_case        =	IMAGE_TO_IMAGE_IMAGE_PARAMS
			__snake_case        =	IMAGE_TO_IMAGE_IMAGE_PARAMS


			def lowercase__						(				self	:						Dict			)      ->		str:





									'''simple docstring'''
									torch.manual_seed(0			)
									A__					:       int     =UNetaDConditionModel(
									    block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=8		,      out_channels=4		,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")		,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")		,      cross_attention_dim=32		,      )
									A__					:       str     =PNDMScheduler(skip_prk_steps=lowerCAmelCase_			)
									torch.manual_seed(0			)
									A__					:       Dict     =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			)
									A__					:       List[Any]     =CLIPTextConfig(
									    bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1e-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=10_00		,      )
									A__					:       Tuple     =CLIPTextModel(lowerCAmelCase_			)
									A__					:       int     =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

									A__					:       Union[str, Any]     ={
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components


			def lowercase__						(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Optional[int]		,      lowerCAmelCase_	:						Dict=0			)      ->		str:





									'''simple docstring'''
									A__					:       Optional[Any]     =floats_tensor((1, 3, 32, 32)		,      rng=random.Random(lowerCAmelCase_			)			).to(lowerCAmelCase_			)
									A__					:       str     =image.cpu().permute(0		,      2		,      3		,      1			)[0]
									A__					:       List[str]     =Image.fromarray(np.uinta(lowerCAmelCase_			)			).convert("""RGB"""			)
									if str(lowerCAmelCase_			).startswith("""mps"""			):
															A__					:       Any     =torch.manual_seed(lowerCAmelCase_			)
									else:
															A__					:       int     =torch.Generator(device=lowerCAmelCase_			).manual_seed(lowerCAmelCase_			)
									A__					:       Optional[Any]     ={
									    """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	:						Any			)      ->		str:





									'''simple docstring'''
									A__					:       Any     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       Any     =self.get_dummy_components()
									A__					:       List[str]     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       Dict     =sd_pipe.to(lowerCAmelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       List[Any]     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       List[Any]     =sd_pipe(**lowerCAmelCase_			).images
									A__					:       Dict     =image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									A__					:       Tuple     =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			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Optional[int]     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       List[str]     =self.get_dummy_components()
									A__					:       Union[str, Any]     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       Union[str, Any]     =sd_pipe.to(lowerCAmelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Optional[Any]     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       Optional[int]     ="""french fries"""
									A__					:       Tuple     =sd_pipe(**lowerCAmelCase_		,      negative_prompt=lowerCAmelCase_			)
									A__					:       Union[str, Any]     =output.images
									A__					:       List[str]     =image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									A__					:       Tuple     =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	:						Dict			)      ->		Dict:





									'''simple docstring'''
									A__					:       str     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       str     =self.get_dummy_components()
									A__					:       List[Any]     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       Union[str, Any]     =sd_pipe.to(lowerCAmelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Tuple     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       Dict     =[inputs["""prompt"""]] * 2

									A__					:       Optional[int]     =np.array(inputs["""image"""]			).astype(np.floataa			) / 255.0
									A__					:       List[str]     =torch.from_numpy(lowerCAmelCase_			).unsqueeze(0			).to(lowerCAmelCase_			)
									A__					:       Union[str, Any]     =image / 2 + 0.5
									A__					:       Optional[int]     =image.permute(0		,      3		,      1		,      2			)
									A__					:       Dict     =image.repeat(2		,      1		,      1		,      1			)

									A__					:       int     =sd_pipe(**lowerCAmelCase_			).images
									A__					:       List[Any]     =image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									A__					:       List[Any]     =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	:						str			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Optional[Any]     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       List[str]     =self.get_dummy_components()
									A__					:       List[str]     =EulerAncestralDiscreteScheduler(
									    beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="""scaled_linear"""			)
									A__					:       str     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       int     =sd_pipe.to(lowerCAmelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Union[str, Any]     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       Optional[Any]     =sd_pipe(**lowerCAmelCase_			).images
									A__					:       Tuple     =image[0, -3:, -3:, -1]

									A__					:       List[str]     =[round(lowerCAmelCase_		,      4			) for x in image_slice.flatten().tolist()]
									print(""",""".join([str(lowerCAmelCase_			) for x in slice]			)			)

									assert image.shape == (1, 32, 32, 3)
									A__					:       Any     =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	:						List[Any]			)      ->		int:





									'''simple docstring'''
									super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



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





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.get_dummy_components()
									A__					:       Optional[Any]     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       Any     =VaeImageProcessor(do_resize=lowerCAmelCase_		,      do_normalize=lowerCAmelCase_			)
									A__					:       Dict     =pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       str     =pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_		,      input_image_type="""pt"""			)			)[0]

									A__					:       List[Any]     =components["""vae"""]
									A__					:       Dict     =self.get_dummy_inputs_by_type(lowerCAmelCase_		,      input_image_type="""pt"""			)

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

									A__					:       Optional[Any]     =pipe(**lowerCAmelCase_			)[0]

									A__					:       Dict     =np.abs(out - out_latents_inputs			).max()
									self.assertLess(lowerCAmelCase_		,      1e-4		,      """passing latents as image input generate different result from passing image"""			)



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



			'''simple docstring'''






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





									'''simple docstring'''
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						int=0			)      ->		List[str]:





									'''simple docstring'''
									A__					:       List[Any]     =torch.manual_seed(lowerCAmelCase_			)
									A__					:       Optional[Any]     =load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""			)
									A__					:       List[Any]     ={
									    """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	:						Optional[Any]			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       List[Any]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Optional[Any]     =self.get_inputs()
									A__					:       Optional[Any]     =pipe(**lowerCAmelCase_			).images
									A__					:       Tuple     =image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A__					:       Dict     =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	:						str			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       List[str]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_			)
									A__					:       str     =LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Union[str, Any]     =self.get_inputs()
									A__					:       Tuple     =pipe(**lowerCAmelCase_			).images
									A__					:       List[Any]     =image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A__					:       List[Any]     =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	:						Optional[Any]			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       List[str]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_			)
									A__					:       str     =DDIMScheduler.from_config(pipe.scheduler.config			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Optional[Any]     =self.get_inputs()
									A__					:       List[str]     =pipe(**lowerCAmelCase_			).images
									A__					:       List[Any]     =image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A__					:       Any     =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	:						Union[str, Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       int     =0

									def callback_fn(lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						torch.FloatTensor			) -> None:
															A__					:       Any     =True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																					A__					:       List[str]     =latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 64)
																					A__					:       Optional[Any]     =latents[0, -3:, -3:, -1]
																					A__					:       Tuple     =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:
																					A__					:       List[Any]     =latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 64)
																					A__					:       Dict     =latents[0, -3:, -3:, -1]
																					A__					:       List[Any]     =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

									A__					:       List[str]     =False

									A__					:       Optional[Any]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_		,      torch_dtype=torch.floataa			)
									A__					:       int     =pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Optional[Any]     =self.get_inputs()
									pipe(**lowerCAmelCase_		,      callback=lowerCAmelCase_		,      callback_steps=1			)
									assert callback_fn.has_been_called
									assert number_of_steps == 3


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





									'''simple docstring'''
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									A__					:       Dict     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_		,      torch_dtype=torch.floataa			)
									A__					:       Union[str, Any]     =pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing(1			)
									pipe.enable_sequential_cpu_offload()

									A__					:       List[str]     =self.get_inputs()
									A__					:       Dict     =pipe(**lowerCAmelCase_			)

									A__					:       List[str]     =torch.cuda.max_memory_allocated()
									# make sure that less than 2.2 GB is allocated
									assert mem_bytes < 2.2 * 10**9



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





									'''simple docstring'''
									A__					:       Tuple     =self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									A__					:       int     =inputs["""image"""].resize((5_04, 5_04)			)

									A__					:       Optional[int]     ="""timbrooks/instruct-pix2pix"""
									A__					:       List[Any]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    lowerCAmelCase_		,      safety_checker=lowerCAmelCase_		,      )
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Dict     =pipe(**lowerCAmelCase_			)
									A__					:       Dict     =output.images[0]

									A__					:       int     =image[2_55:2_58, 3_83:3_86, -1]

									assert image.shape == (5_04, 5_04, 3)
									A__					:       Dict     =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

