





"""simple docstring"""






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 					__UpperCAmelCase							(						_UpperCamelCase						,  _UpperCamelCase						,  unittest.TestCase  ):
				__lowerCamelCase				:	str				     =				CycleDiffusionPipeline
				__lowerCamelCase				:	Union[str, Any]				     =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    "negative_prompt",
				    "height",
				    "width",
				    "negative_prompt_embeds",
				}
				__lowerCamelCase				:	Dict				     =				PipelineTesterMixin.required_optional_params - {"latents"}
				__lowerCamelCase				:	Optional[Any]				     =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}  )
				__lowerCamelCase				:	Optional[Any]				     =				IMAGE_TO_IMAGE_IMAGE_PARAMS
				__lowerCamelCase				:	Optional[Any]				     =				IMAGE_TO_IMAGE_IMAGE_PARAMS
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							torch.manual_seed(0    )
							a__  :	List[str]		 =			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	, )
							a__  :	Optional[Any]		 =			DDIMScheduler(
							    beta_start=0.0_0085	, beta_end=0.012	, beta_schedule="scaled_linear"	, num_train_timesteps=10_00	, clip_sample=a_	, set_alpha_to_one=a_	, )
							torch.manual_seed(0    )
							a__  :	Any		 =			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__  :	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=10_00	, )
							a__  :	Optional[int]		 =			CLIPTextModel(a_    )
							a__  :	Tuple		 =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

							a__  :	Optional[int]		 =			{
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "safety_checker": None,
							    "feature_extractor": None,
							}
							return components
				def       UpperCAmelCase      (		self    :			int	, a_    :			Any	, a_    :			List[str]=0    )	->	str:



							'''simple docstring'''



							a__  :	Tuple		 =			floats_tensor((1, 3, 32, 32)	, rng=random.Random(a_    )    ).to(a_    )
							a__  :	List[str]		 =			image / 2 + 0.5
							if str(a_    ).startswith("mps"    ):
										a__  :	Optional[Any]		 =			torch.manual_seed(a_    )
							else:
										a__  :	Optional[Any]		 =			torch.Generator(device=a_    ).manual_seed(a_    )
							a__  :	Tuple		 =			{
							    "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       UpperCAmelCase      (		self    :			Dict    )	->	Dict:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			"cpu"  # ensure determinism for the device-dependent torch.Generator

							a__  :	Tuple		 =			self.get_dummy_components()
							a__  :	Optional[Any]		 =			CycleDiffusionPipeline(**a_    )
							a__  :	str		 =			pipe.to(a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[Any]		 =			self.get_dummy_inputs(a_    )
							a__  :	Optional[Any]		 =			pipe(**a_    )
							a__  :	Optional[int]		 =			output.images

							a__  :	Optional[int]		 =			images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							a__  :	int		 =			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       UpperCAmelCase      (		self    :			Dict    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_dummy_components()
							for name, module in components.items():
										if hasattr(a_	, "half"    ):
													a__  :	Tuple		 =			module.half()
							a__  :	Tuple		 =			CycleDiffusionPipeline(**a_    )
							a__  :	str		 =			pipe.to(a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Tuple		 =			self.get_dummy_inputs(a_    )
							a__  :	str		 =			pipe(**a_    )
							a__  :	Any		 =			output.images

							a__  :	str		 =			images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							a__  :	Tuple		 =			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       UpperCAmelCase      (		self    :			int    )	->	List[str]:



							'''simple docstring'''



							return super().test_save_load_local()
				@unittest.skip("non-deterministic pipeline"    )
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	List[Any]:



							'''simple docstring'''



							return super().test_inference_batch_single_identical()
				@skip_mps
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Tuple:



							'''simple docstring'''



							return super().test_dict_tuple_outputs_equivalent()
				@skip_mps
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	str:



							'''simple docstring'''



							return super().test_save_load_optional_components()






				@skip_mps
				def       UpperCAmelCase      (		self    :			Any    )	->	Optional[int]:



							'''simple docstring'''



							return super().test_attention_slicing_forward_pass()



@slow
@require_torch_gpu
class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	int:



							'''simple docstring'''



							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
				def       UpperCAmelCase      (		self    :			Dict    )	->	List[str]:



							'''simple docstring'''



							a__  :	List[Any]		 =			load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/cycle-diffusion/black_colored_car.png"    )
							a__  :	Optional[Any]		 =			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"    )
							a__  :	int		 =			init_image.resize((5_12, 5_12)    )

							a__  :	Any		 =			"CompVis/stable-diffusion-v1-4"
							a__  :	Optional[int]		 =			DDIMScheduler.from_pretrained(a_	, subfolder="scheduler"    )
							a__  :	Union[str, Any]		 =			CycleDiffusionPipeline.from_pretrained(
							    a_	, scheduler=a_	, safety_checker=a_	, torch_dtype=torch.floataa	, revision="fp16"    )

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

							a__  :	str		 =			"A black colored car"
							a__  :	Any		 =			"A blue colored car"

							a__  :	Any		 =			torch.manual_seed(0    )
							a__  :	Optional[Any]		 =			pipe(
							    prompt=a_	, source_prompt=a_	, image=a_	, num_inference_steps=1_00	, eta=0.1	, strength=0.85	, guidance_scale=3	, source_guidance_scale=1	, generator=a_	, output_type="np"	, )
							a__  :	Tuple		 =			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       UpperCAmelCase      (		self    :			Dict    )	->	str:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/cycle-diffusion/black_colored_car.png"    )
							a__  :	Union[str, Any]		 =			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"    )
							a__  :	Optional[int]		 =			init_image.resize((5_12, 5_12)    )

							a__  :	Union[str, Any]		 =			"CompVis/stable-diffusion-v1-4"
							a__  :	Tuple		 =			DDIMScheduler.from_pretrained(a_	, subfolder="scheduler"    )
							a__  :	int		 =			CycleDiffusionPipeline.from_pretrained(a_	, scheduler=a_	, safety_checker=a_    )

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

							a__  :	List[str]		 =			"A black colored car"
							a__  :	Optional[Any]		 =			"A blue colored car"

							a__  :	Dict		 =			torch.manual_seed(0    )
							a__  :	List[Any]		 =			pipe(
							    prompt=a_	, source_prompt=a_	, image=a_	, num_inference_steps=1_00	, eta=0.1	, strength=0.85	, guidance_scale=3	, source_guidance_scale=1	, generator=a_	, output_type="np"	, )
							a__  :	Dict		 =			output.images

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






"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__UpperCAmelCase						=			False



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Optional[Any]=32    )	->	Union[str, Any]:



							'''simple docstring'''



							set_seed(0    )
							a__  :	Any		 =			UNetaDModel(sample_size=a_	, in_channels=3	, out_channels=3    )
							a__  :	Union[str, Any]		 =			torch.optim.SGD(model.parameters()	, lr=0.0001    )
							return model, optimizer






				@slow
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Any:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							a__  :	Dict		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )
							a__  :	Optional[Any]		 =			DDIMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0    )
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).clip(-1	, 1    ).to(a_    ) for _ in range(4    )]
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).to(a_    ) for _ in range(4    )]
							a__  :	Optional[Any]		 =			[torch.randint(0	, 10_00	, (4,)    ).long().to(a_    ) for _ in range(4    )]

							# train with a DDPM scheduler
							a__ ,	a__  :	Union[str, Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	Optional[int]		 =			ddpm_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	List[str]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Optional[int]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							a__ ,	a__  :	Optional[Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	List[str]		 =			ddim_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	Optional[int]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Union[str, Any]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )
							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )