




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(		_UpperCamelCase				, _UpperCamelCase				, unittest.TestCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		CycleDiffusionPipeline
 UpperCamelCase		  =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     "negative_prompt",
     "height",
     "width",
     "negative_prompt_embeds",
 }
 UpperCamelCase		  =		PipelineTesterMixin.required_optional_params - {"latents"}
 UpperCamelCase		  =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}  )
 UpperCamelCase		  =		IMAGE_TO_IMAGE_IMAGE_PARAMS
 UpperCamelCase		  =		IMAGE_TO_IMAGE_IMAGE_PARAMS

 def 							a__					(							self   :    List[str]    )  ->      List[str]:

       """simple docstring"""






       torch.manual_seed(0    )
       lowerCamelCase_							=       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		,				)
       lowerCamelCase_							=       DDIMScheduler(
           beta_start=0.00085		,				beta_end=0.012		,				beta_schedule='scaled_linear'		,				num_train_timesteps=1000		,				clip_sample=a_		,				set_alpha_to_one=a_		,				)
       torch.manual_seed(0    )
       lowerCamelCase_							=       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    )
       lowerCamelCase_							=       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		,				)
       lowerCamelCase_							=       CLIPTextModel(a_    )
       lowerCamelCase_							=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

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

 def 							a__					(							self   :    int		,				A_   :    Any		,				A_   :    List[str]=0    )  ->      str:

       """simple docstring"""






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

       """simple docstring"""






       lowerCamelCase_							=       "cpu"  # ensure determinism for the device-dependent torch.Generator

       lowerCamelCase_							=       self.get_dummy_components()
       lowerCamelCase_							=       CycleDiffusionPipeline(**a_    )
       lowerCamelCase_							=       pipe.to(a_    )
       pipe.set_progress_bar_config(disable=a_    )

       lowerCamelCase_							=       self.get_dummy_inputs(a_    )
       lowerCamelCase_							=       pipe(**a_    )
       lowerCamelCase_							=       output.images

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

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

       """simple docstring"""






       lowerCamelCase_							=       self.get_dummy_components()
       for name, module in components.items():
             if hasattr(a_		,				'half'    ):
                   lowerCamelCase_							=       module.half()
       lowerCamelCase_							=       CycleDiffusionPipeline(**a_    )
       lowerCamelCase_							=       pipe.to(a_    )
       pipe.set_progress_bar_config(disable=a_    )

       lowerCamelCase_							=       self.get_dummy_inputs(a_    )
       lowerCamelCase_							=       pipe(**a_    )
       lowerCamelCase_							=       output.images

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

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

       """simple docstring"""






       return super().test_save_load_local()

 @unittest.skip('non-deterministic pipeline'    )
 def 							a__					(							self   :    List[Any]    )  ->      List[Any]:

       """simple docstring"""






       return super().test_inference_batch_single_identical()

 @skip_mps
 def 							a__					(							self   :    Optional[int]    )  ->      Tuple:

       """simple docstring"""






       return super().test_dict_tuple_outputs_equivalent()

 @skip_mps
 def 							a__					(							self   :    Optional[int]    )  ->      str:

       """simple docstring"""






       return super().test_save_load_optional_components()





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

       """simple docstring"""






       return super().test_attention_slicing_forward_pass()







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



 '''simple docstring'''


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

       """simple docstring"""






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

 def 							a__					(							self   :    Dict    )  ->      List[str]:

       """simple docstring"""






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

       lowerCamelCase_							=       "CompVis/stable-diffusion-v1-4"
       lowerCamelCase_							=       DDIMScheduler.from_pretrained(a_		,				subfolder='scheduler'    )
       lowerCamelCase_							=       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()

       lowerCamelCase_							=       "A black colored car"
       lowerCamelCase_							=       "A blue colored car"

       lowerCamelCase_							=       torch.manual_seed(0    )
       lowerCamelCase_							=       pipe(
           prompt=a_		,				source_prompt=a_		,				image=a_		,				num_inference_steps=100		,				eta=0.1		,				strength=0.85		,				guidance_scale=3		,				source_guidance_scale=1		,				generator=a_		,				output_type='np'		,				)
       lowerCamelCase_							=       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 							a__					(							self   :    Dict    )  ->      str:

       """simple docstring"""






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

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

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

       lowerCamelCase_							=       "A black colored car"
       lowerCamelCase_							=       "A blue colored car"

       lowerCamelCase_							=       torch.manual_seed(0    )
       lowerCamelCase_							=       pipe(
           prompt=a_		,				source_prompt=a_		,				image=a_		,				num_inference_steps=100		,				eta=0.1		,				strength=0.85		,				guidance_scale=3		,				source_guidance_scale=1		,				generator=a_		,				output_type='np'		,				)
       lowerCamelCase_							=       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    )    )