


"""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      lowerCamelCase__   (     A     ,    A     ,    unittest.TestCase				):







					"""simple docstring"""


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






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												torch.manual_seed(0  )
												__UpperCAmelCase   :						List[Any]      							=					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   :						List[Any]      							=					DDIMScheduler(
												    beta_start=0.00085 ,       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  )
												__UpperCAmelCase   :						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  )
												__UpperCAmelCase   :						Union[str, Any]      							=					CLIPTextConfig(
												    bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=32 ,       intermediate_size=37 ,       layer_norm_eps=1e-0_5 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=1_000 ,       )
												__UpperCAmelCase   :						Tuple      							=					CLIPTextModel(UpperCamelCase  )
												__UpperCAmelCase   :						int      							=					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    : List[str] ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : List[str]=0  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					floats_tensor((1, 3, 32, 32) ,       rng=random.Random(UpperCamelCase  )  ).to(UpperCamelCase  )
												__UpperCAmelCase   :						int      							=					image / 2 + 0.5
												if str(UpperCamelCase  ).startswith("""mps"""  ):
																			__UpperCAmelCase   :						Dict      							=					torch.manual_seed(UpperCamelCase  )
												else:
																			__UpperCAmelCase   :						Optional[Any]      							=					torch.Generator(device=UpperCamelCase  ).manual_seed(UpperCamelCase  )
												__UpperCAmelCase   :						str      							=					{
												    """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    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

												__UpperCAmelCase   :						Tuple      							=					self.get_dummy_components()
												__UpperCAmelCase   :						Union[str, Any]      							=					CycleDiffusionPipeline(**UpperCamelCase  )
												__UpperCAmelCase   :						Any      							=					pipe.to(UpperCamelCase  )
												pipe.set_progress_bar_config(disable=UpperCamelCase  )

												__UpperCAmelCase   :						str      							=					self.get_dummy_inputs(UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					pipe(**UpperCamelCase  )
												__UpperCAmelCase   :						Tuple      							=					output.images

												__UpperCAmelCase   :						Tuple      							=					images[0, -3:, -3:, -1]

												assert images.shape == (1, 32, 32, 3)
												__UpperCAmelCase   :						List[str]      							=					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    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					self.get_dummy_components()
												for name, module in components.items():
																			if hasattr(UpperCamelCase ,       """half"""  ):
																										__UpperCAmelCase   :						List[Any]      							=					module.half()
												__UpperCAmelCase   :						List[str]      							=					CycleDiffusionPipeline(**UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					pipe.to(UpperCamelCase  )
												pipe.set_progress_bar_config(disable=UpperCamelCase  )

												__UpperCAmelCase   :						Optional[int]      							=					self.get_dummy_inputs(UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					pipe(**UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					output.images

												__UpperCAmelCase   :						Union[str, Any]      							=					images[0, -3:, -3:, -1]

												assert images.shape == (1, 32, 32, 3)
												__UpperCAmelCase   :						List[Any]      							=					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    : str  ):
												'''simple docstring'''






												return super().test_save_load_local()






					@unittest.skip("""non-deterministic pipeline"""  )
					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												return super().test_inference_batch_single_identical()






					@skip_mps
					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






												return super().test_dict_tuple_outputs_equivalent()






					@skip_mps
					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												return super().test_save_load_optional_components()






					@skip_mps
					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												return super().test_attention_slicing_forward_pass()






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







					"""simple docstring"""


					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/cycle-diffusion/black_colored_car.png"""  )
												__UpperCAmelCase   :						List[str]      							=					load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""  )
												__UpperCAmelCase   :						Tuple      							=					init_image.resize((512, 512)  )

												__UpperCAmelCase   :						List[Any]      							=					"""CompVis/stable-diffusion-v1-4"""
												__UpperCAmelCase   :						Union[str, Any]      							=					DDIMScheduler.from_pretrained(UpperCamelCase ,       subfolder="""scheduler"""  )
												__UpperCAmelCase   :						List[str]      							=					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()

												__UpperCAmelCase   :						int      							=					"""A black colored car"""
												__UpperCAmelCase   :						str      							=					"""A blue colored car"""

												__UpperCAmelCase   :						List[Any]      							=					torch.manual_seed(0  )
												__UpperCAmelCase   :						Union[str, Any]      							=					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""" ,       )
												__UpperCAmelCase   :						Union[str, Any]      							=					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    : Optional[Any]  ):
												'''simple docstring'''






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

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

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

												__UpperCAmelCase   :						int      							=					"""A black colored car"""
												__UpperCAmelCase   :						List[str]      							=					"""A blue colored car"""

												__UpperCAmelCase   :						Dict      							=					torch.manual_seed(0  )
												__UpperCAmelCase   :						Tuple      							=					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""" ,       )
												__UpperCAmelCase   :						List[str]      							=					output.images

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




"""simple docstring"""






import re
def      lowerCamelCase       (     _UpperCamelCase		:      str							)		->       str:

							'''simple docstring'''



							if len(re.findall("""[ATCG]"""       ,			_UpperCamelCase							)							) != len(_UpperCamelCase							):
														raise ValueError("""Invalid Strand"""							)

							return dna.translate(dna.maketrans("""ATCG"""       ,			"""TAGC"""							)							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
