



import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

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


enable_full_determinism()

class 	_A (			A__	,    A__	,    unittest.TestCase						):


						'''simple docstring'''





						_snake_case      :  Union[str, Any]   				=   CycleDiffusionPipeline
						_snake_case      :  int   				=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    """negative_prompt""",
						    """height""",
						    """width""",
						    """negative_prompt_embeds""",
						}
						_snake_case      :  Union[str, Any]   				=   PipelineTesterMixin.required_optional_params - {"""latents"""}
						_snake_case      :  int   				=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}						)
						_snake_case      :  List[str]   				=   IMAGE_TO_IMAGE_IMAGE_PARAMS
						_snake_case      :  int   				=   IMAGE_TO_IMAGE_IMAGE_PARAMS



						def      _snake_case						(						self						:				str						):






							'''simple docstring'''



							torch.manual_seed(0						)
							__lowercase									=      UNetaDConditionModel(
							    block_out_channels=(32, 64)				,   layers_per_block=2				,   sample_size=32				,   in_channels=4				,   out_channels=4				,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				,   cross_attention_dim=32				,   )
							__lowercase									=      DDIMScheduler(
							    beta_start=0.0_0085				,   beta_end=0.012				,   beta_schedule="scaled_linear"				,   num_train_timesteps=1_000				,   clip_sample=UpperCamelCase_				,   set_alpha_to_one=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									=      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=1_000				,   )
							__lowercase									=      CLIPTextModel(UpperCamelCase_						)
							__lowercase									=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

							__lowercase									=      {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components



						def      _snake_case						(						self						:				Tuple				,   lowerCamelCase						:				Optional[int]				,   lowerCamelCase						:				Any=0						):






							'''simple docstring'''



							__lowercase									=      floats_tensor((1, 3, 32, 32)				,   rng=random.Random(UpperCamelCase_						)						).to(UpperCamelCase_						)
							__lowercase									=      image / 2 + 0.5
							if str(UpperCamelCase_						).startswith("mps"						):
								__lowercase									=      torch.manual_seed(UpperCamelCase_						)
							else:
								__lowercase									=      torch.Generator(device=UpperCamelCase_						).manual_seed(UpperCamelCase_						)
							__lowercase									=      {
							    '''prompt''': '''An astronaut riding an elephant''',
							    '''source_prompt''': '''An astronaut riding a horse''',
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''eta''': 0.1,
							    '''strength''': 0.8,
							    '''guidance_scale''': 3,
							    '''source_guidance_scale''': 1,
							    '''output_type''': '''numpy''',
							}
							return inputs



						def      _snake_case						(						self						:				List[Any]						):






							'''simple docstring'''



							__lowercase									=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

							__lowercase									=      self.get_dummy_components()
							__lowercase									=      CycleDiffusionPipeline(**UpperCamelCase_						)
							__lowercase									=      pipe.to(UpperCamelCase_						)
							pipe.set_progress_bar_config(disable=UpperCamelCase_						)

							__lowercase									=      self.get_dummy_inputs(UpperCamelCase_						)
							__lowercase									=      pipe(**UpperCamelCase_						)
							__lowercase									=      output.images

							__lowercase									=      images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							__lowercase									=      np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]						)

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



						@unittest.skipIf(torch_device != "cuda"				,   "This test requires a GPU"						)
						def      _snake_case						(						self						:				Dict						):






							'''simple docstring'''



							__lowercase									=      self.get_dummy_components()
							for name, module in components.items():
								if hasattr(UpperCamelCase_				,   "half"						):
									__lowercase									=      module.half()
							__lowercase									=      CycleDiffusionPipeline(**UpperCamelCase_						)
							__lowercase									=      pipe.to(UpperCamelCase_						)
							pipe.set_progress_bar_config(disable=UpperCamelCase_						)

							__lowercase									=      self.get_dummy_inputs(UpperCamelCase_						)
							__lowercase									=      pipe(**UpperCamelCase_						)
							__lowercase									=      output.images

							__lowercase									=      images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							__lowercase									=      np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]						)

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



						@skip_mps
						def      _snake_case						(						self						:				List[Any]						):






							'''simple docstring'''



							return super().test_save_load_local()



						@unittest.skip("non-deterministic pipeline"						)
						def      _snake_case						(						self						:				Tuple						):






							'''simple docstring'''



							return super().test_inference_batch_single_identical()



						@skip_mps
						def      _snake_case						(						self						:				List[Any]						):






							'''simple docstring'''



							return super().test_dict_tuple_outputs_equivalent()



						@skip_mps
						def      _snake_case						(						self						:				Optional[Any]						):






							'''simple docstring'''



							return super().test_save_load_optional_components()



						@skip_mps
						def      _snake_case						(						self						:				Any						):






							'''simple docstring'''



							return super().test_attention_slicing_forward_pass()

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


						'''simple docstring'''





						def      _snake_case						(						self						:				Tuple						):






							'''simple docstring'''



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



						def      _snake_case						(						self						:				List[str]						):






							'''simple docstring'''



							__lowercase									=      load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/cycle-diffusion/black_colored_car.png"						)
							__lowercase									=      load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"						)
							__lowercase									=      init_image.resize((512, 512)						)

							__lowercase									=      '''CompVis/stable-diffusion-v1-4'''
							__lowercase									=      DDIMScheduler.from_pretrained(UpperCamelCase_				,   subfolder="scheduler"						)
							__lowercase									=      CycleDiffusionPipeline.from_pretrained(
							    UpperCamelCase_				,   scheduler=UpperCamelCase_				,   safety_checker=UpperCamelCase_				,   torch_dtype=torch.floataa				,   revision="fp16"						)

							pipe.to(UpperCamelCase_						)
							pipe.set_progress_bar_config(disable=UpperCamelCase_						)
							pipe.enable_attention_slicing()

							__lowercase									=      '''A black colored car'''
							__lowercase									=      '''A blue colored car'''

							__lowercase									=      torch.manual_seed(0						)
							__lowercase									=      pipe(
							    prompt=UpperCamelCase_				,   source_prompt=UpperCamelCase_				,   image=UpperCamelCase_				,   num_inference_steps=100				,   eta=0.1				,   strength=0.85				,   guidance_scale=3				,   source_guidance_scale=1				,   generator=UpperCamelCase_				,   output_type="np"				,   )
							__lowercase									=      output.images

							# the values aren't exactly equal, but the images look the same visually
							assert np.abs(image - expected_image						).max() < 5e-1



						def      _snake_case						(						self						:				Tuple						):






							'''simple docstring'''



							__lowercase									=      load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/cycle-diffusion/black_colored_car.png"						)
							__lowercase									=      load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"						)
							__lowercase									=      init_image.resize((512, 512)						)

							__lowercase									=      '''CompVis/stable-diffusion-v1-4'''
							__lowercase									=      DDIMScheduler.from_pretrained(UpperCamelCase_				,   subfolder="scheduler"						)
							__lowercase									=      CycleDiffusionPipeline.from_pretrained(UpperCamelCase_				,   scheduler=UpperCamelCase_				,   safety_checker=UpperCamelCase_						)

							pipe.to(UpperCamelCase_						)
							pipe.set_progress_bar_config(disable=UpperCamelCase_						)
							pipe.enable_attention_slicing()

							__lowercase									=      '''A black colored car'''
							__lowercase									=      '''A blue colored car'''

							__lowercase									=      torch.manual_seed(0						)
							__lowercase									=      pipe(
							    prompt=UpperCamelCase_				,   source_prompt=UpperCamelCase_				,   image=UpperCamelCase_				,   num_inference_steps=100				,   eta=0.1				,   strength=0.85				,   guidance_scale=3				,   source_guidance_scale=1				,   generator=UpperCamelCase_				,   output_type="np"				,   )
							__lowercase									=      output.images

							assert np.abs(image - expected_image						).max() < 2e-2







'''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   lowercase      (				A__      ,						A__      ,						A__      ,						unittest.TestCase   ):

		"""simple docstring"""

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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.manual_seed(0				)
							UpperCamelCase__  :Optional[Any]				   =					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       ,  )
							UpperCamelCase__  :List[str]				   =					PNDMScheduler(skip_prk_steps=UpperCamelCase_				)
							torch.manual_seed(0				)
							UpperCamelCase__  :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				)
							UpperCamelCase__  :int				   =					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=1000       ,  )
							UpperCamelCase__  :Tuple				   =					CLIPTextModel(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

							UpperCamelCase__  :Dict				   =					{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=0				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
							UpperCamelCase__  :List[Any]				   =					Image.fromarray(np.uinta(UpperCamelCase_				)				).convert('''RGB'''				)
							if str(UpperCamelCase_				).startswith('''mps'''				):
												UpperCamelCase__  :Tuple				   =					torch.manual_seed(UpperCamelCase_				)
							else:
												UpperCamelCase__  :List[Any]				   =					torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
							UpperCamelCase__  :List[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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :str				   =					self.get_dummy_components()
							UpperCamelCase__  :Optional[Any]				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :List[str]				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					sd_pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :str				   =					image[0, -3:, -3:, -1]
							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :Union[str, Any]				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :Optional[Any]				   =					self.get_dummy_components()
							UpperCamelCase__  :Any				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :Tuple				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Tuple				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					'''french fries'''
							UpperCamelCase__  :Any				   =					sd_pipe(**UpperCamelCase_       ,  negative_prompt=UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					output.images
							UpperCamelCase__  :Any				   =					image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :List[str]				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :Dict				   =					self.get_dummy_components()
							UpperCamelCase__  :str				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Dict				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					[inputs['''prompt''']] * 2

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

							UpperCamelCase__  :Optional[Any]				   =					sd_pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Any				   =					image[-1, -3:, -3:, -1]

							assert image.shape == (2, 32, 32, 3)
							UpperCamelCase__  :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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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

							UpperCamelCase__  :str				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					sd_pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Tuple				   =					image[0, -3:, -3:, -1]

							UpperCamelCase__  :Dict				   =					[round(UpperCamelCase_       ,  4				) for x in image_slice.flatten().tolist()]
							print(''','''.join([str(UpperCamelCase_				) for x in slice]				)				)

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :Union[str, 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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					self.get_dummy_components()
							UpperCamelCase__  :Optional[Any]				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					VaeImageProcessor(do_resize=UpperCamelCase_       ,  do_normalize=UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Optional[int]				   =					pipe(**self.get_dummy_inputs_by_type(UpperCamelCase_       ,  input_image_type='''pt'''				)				)[0]

							UpperCamelCase__  :List[Any]				   =					components['''vae''']
							UpperCamelCase__  :Optional[Any]				   =					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():
																	UpperCamelCase__  :List[str]				   =					vae.encode(inputs[image_param]				).latent_dist.mode()

							UpperCamelCase__  :str				   =					pipe(**UpperCamelCase_				)[0]

							UpperCamelCase__  :Optional[int]				   =					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   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_=0				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					torch.manual_seed(UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''				)
							UpperCamelCase__  :Dict				   =					{
							    '''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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_				)
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Optional[int]				   =					self.get_inputs()
							UpperCamelCase__  :str				   =					pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Optional[Any]				   =					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__  :str				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :int				   =					self.get_inputs()
							UpperCamelCase__  :List[str]				   =					pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :str				   =					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__  :Optional[int]				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_				)
							UpperCamelCase__  :str				   =					DDIMScheduler.from_config(pipe.scheduler.config				)
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Union[str, Any]				   =					self.get_inputs()
							UpperCamelCase__  :Dict				   =					pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Optional[int]				   =					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__  :Tuple				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					0

							def callback_fn(UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				) -> None:
												UpperCamelCase__  :Optional[Any]				   =					True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																	UpperCamelCase__  :Union[str, Any]				   =					latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	UpperCamelCase__  :Optional[Any]				   =					latents[0, -3:, -3:, -1]
																	UpperCamelCase__  :List[str]				   =					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:
																	UpperCamelCase__  :Union[str, Any]				   =					latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	UpperCamelCase__  :Union[str, Any]				   =					latents[0, -3:, -3:, -1]
																	UpperCamelCase__  :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

							UpperCamelCase__  :Union[str, Any]				   =					False

							UpperCamelCase__  :Union[str, Any]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_       ,  torch_dtype=torch.floataa				)
							UpperCamelCase__  :Dict				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Dict				   =					self.get_inputs()
							pipe(**UpperCamelCase_       ,  callback=UpperCamelCase_       ,  callback_steps=1				)
							assert callback_fn.has_been_called
							assert number_of_steps == 3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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

							UpperCamelCase__  :Union[str, Any]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_       ,  torch_dtype=torch.floataa				)
							UpperCamelCase__  :List[str]				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing(1				)
							pipe.enable_sequential_cpu_offload()

							UpperCamelCase__  :Any				   =					self.get_inputs()
							UpperCamelCase__  :Tuple				   =					pipe(**UpperCamelCase_				)

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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					self.get_inputs()
							# resize to resolution that is divisible by 8 but not 16 or 32
							UpperCamelCase__  :int				   =					inputs['''image'''].resize((504, 504)				)

							UpperCamelCase__  :Union[str, Any]				   =					'''timbrooks/instruct-pix2pix'''
							UpperCamelCase__  :str				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    UpperCamelCase_       ,  safety_checker=UpperCamelCase_       ,  )
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :List[str]				   =					pipe(**UpperCamelCase_				)
							UpperCamelCase__  :str				   =					output.images[0]

							UpperCamelCase__  :str				   =					image[255:258, 383:386, -1]

							assert image.shape == (504, 504, 3)
							UpperCamelCase__  :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