



"""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 			lowercase__			(      A,					A,					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        lowerCamelCase_   (  self						)			->							int:
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		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	,       )
									_UpperCAmelCase							 =		DDIMScheduler(
									    beta_start=0.00085	,       beta_end=0.012	,       beta_schedule='scaled_linear'	,       num_train_timesteps=1000	,       clip_sample=snake_case	,       set_alpha_to_one=snake_case	,       )
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		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							 =		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							 =		CLIPTextModel(snake_case						)
									_UpperCAmelCase							 =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

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






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=0						)			->							Optional[Any]:
									_UpperCAmelCase							 =		floats_tensor((1, 3, 32, 32)	,       rng=random.Random(snake_case						)						).to(snake_case						)
									_UpperCAmelCase							 =		image / 2 + 0.5
									if str(snake_case						).startswith('mps'						):
											_UpperCAmelCase							 =		torch.manual_seed(snake_case						)
									else:
											_UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(snake_case						)
									_UpperCAmelCase							 =		{
									    '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        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		'cpu'  # ensure determinism for the device-dependent torch.Generator

									_UpperCAmelCase							 =		self.get_dummy_components()
									_UpperCAmelCase							 =		CycleDiffusionPipeline(**snake_case						)
									_UpperCAmelCase							 =		pipe.to(snake_case						)
									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case						)
									_UpperCAmelCase							 =		pipe(**snake_case						)
									_UpperCAmelCase							 =		output.images

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

									assert images.shape == (1, 32, 32, 3)
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		self.get_dummy_components()
									for name, module in components.items():
											if hasattr(snake_case	,       'half'						):
													_UpperCAmelCase							 =		module.half()
									_UpperCAmelCase							 =		CycleDiffusionPipeline(**snake_case						)
									_UpperCAmelCase							 =		pipe.to(snake_case						)
									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case						)
									_UpperCAmelCase							 =		pipe(**snake_case						)
									_UpperCAmelCase							 =		output.images

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

									assert images.shape == (1, 32, 32, 3)
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Any:
									return super().test_save_load_local()






							@unittest.skip('non-deterministic pipeline'						)
							def        lowerCamelCase_   (  self						)			->							str:
									return super().test_inference_batch_single_identical()






							@skip_mps
							def        lowerCamelCase_   (  self						)			->							Any:
									return super().test_dict_tuple_outputs_equivalent()






							@skip_mps
							def        lowerCamelCase_   (  self						)			->							Tuple:
									return super().test_save_load_optional_components()






							@skip_mps
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return super().test_attention_slicing_forward_pass()







@slow
@require_torch_gpu
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






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

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

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

									_UpperCAmelCase							 =		'A black colored car'
									_UpperCAmelCase							 =		'A blue colored car'

									_UpperCAmelCase							 =		torch.manual_seed(0						)
									_UpperCAmelCase							 =		pipe(
									    prompt=snake_case	,       source_prompt=snake_case	,       image=snake_case	,       num_inference_steps=100	,       eta=0.1	,       strength=0.85	,       guidance_scale=3	,       source_guidance_scale=1	,       generator=snake_case	,       output_type='np'	,       )
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/cycle-diffusion/black_colored_car.png'						)
									_UpperCAmelCase							 =		load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'						)
									_UpperCAmelCase							 =		init_image.resize((512, 512)						)

									_UpperCAmelCase							 =		'CompVis/stable-diffusion-v1-4'
									_UpperCAmelCase							 =		DDIMScheduler.from_pretrained(snake_case	,       subfolder='scheduler'						)
									_UpperCAmelCase							 =		CycleDiffusionPipeline.from_pretrained(snake_case	,       scheduler=snake_case	,       safety_checker=snake_case						)

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

									_UpperCAmelCase							 =		'A black colored car'
									_UpperCAmelCase							 =		'A blue colored car'

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

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







"""simple docstring"""





import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					DiTPipeline
							_UpperCAmelCase      				=					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							_UpperCAmelCase      				=					PipelineTesterMixin.required_optional_params - {
							    '''latents''',
							    '''num_images_per_prompt''',
							    '''callback''',
							    '''callback_steps''',
							}
							_UpperCAmelCase      				=					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							_UpperCAmelCase      				=					False






							def        lowerCamelCase_   (  self						)			->							str:
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		TransformeraDModel(
									    sample_size=16	,       num_layers=2	,       patch_size=4	,       attention_head_dim=8	,       num_attention_heads=2	,       in_channels=4	,       out_channels=8	,       attention_bias=snake_case	,       activation_fn='gelu-approximate'	,       num_embeds_ada_norm=1000	,       norm_type='ada_norm_zero'	,       norm_elementwise_affine=snake_case	,       )
									_UpperCAmelCase							 =		AutoencoderKL()
									_UpperCAmelCase							 =		DDIMScheduler()
									_UpperCAmelCase							 =		{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
									return components






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=0						)			->							Optional[Any]:
									if str(snake_case						).startswith('mps'						):
											_UpperCAmelCase							 =		torch.manual_seed(snake_case						)
									else:
											_UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(snake_case						)
									_UpperCAmelCase							 =		{
									    'class_labels': [1],
									    'generator': generator,
									    'num_inference_steps': 2,
									    'output_type': 'numpy',
									}
									return inputs






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		'cpu'

									_UpperCAmelCase							 =		self.get_dummy_components()
									_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
									pipe.to(snake_case						)
									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case						)
									_UpperCAmelCase							 =		pipe(**snake_case						).images
									_UpperCAmelCase							 =		image[0, -3:, -3:, -1]

									self.assertEqual(image.shape	,       (1, 16, 16, 3)						)
									_UpperCAmelCase							 =		np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]						)
									_UpperCAmelCase							 =		np.abs(image_slice.flatten() - expected_slice						).max()
									self.assertLessEqual(snake_case	,       1E-3						)






							def        lowerCamelCase_   (  self						)			->							Any:
									self._test_inference_batch_single_identical(relax_max_difference=snake_case	,       expected_max_diff=1E-3						)






							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()	,       reason='XFormers attention is only available with CUDA and `xformers` installed'	,       )
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)







@require_torch_gpu
@slow
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							int:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		torch.manual_seed(0						)

									_UpperCAmelCase							 =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'						)
									pipe.to('cuda'						)

									_UpperCAmelCase							 =		['vase', 'umbrella', 'white shark', 'white wolf']
									_UpperCAmelCase							 =		pipe.get_label_ids(snake_case						)

									_UpperCAmelCase							 =		pipe(snake_case	,       generator=snake_case	,       num_inference_steps=40	,       output_type='np'						).images

									for word, image in zip(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		load_numpy(
											    f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'						)
											assert np.abs((expected_image - image).max()						) < 1E-2






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'						)
									_UpperCAmelCase							 =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
									pipe.to('cuda'						)

									_UpperCAmelCase							 =		['vase', 'umbrella']
									_UpperCAmelCase							 =		pipe.get_label_ids(snake_case						)

									_UpperCAmelCase							 =		torch.manual_seed(0						)
									_UpperCAmelCase							 =		pipe(snake_case	,       generator=snake_case	,       num_inference_steps=25	,       output_type='np'						).images

									for word, image in zip(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    f'/dit/{word}_512.npy'						)

											assert np.abs((expected_image - image).max()						) < 1E-1


