




"""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      a__				(	__magic_name__							,    __magic_name__							,    unittest.TestCase   ):
			lowercase_    =	CycleDiffusionPipeline
			lowercase_    =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
			    "negative_prompt",
			    "height",
			    "width",
			    "negative_prompt_embeds",
			}
			lowercase_    =	PipelineTesterMixin.required_optional_params - {"latents"}
			lowercase_    =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}   )
			lowercase_    =	IMAGE_TO_IMAGE_IMAGE_PARAMS
			lowercase_    =	IMAGE_TO_IMAGE_IMAGE_PARAMS


			def       a_     (      self      :    Any):


						"""simple docstring"""




						torch.manual_seed(0)
						__UpperCAmelCase	: Optional[int]		=					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[str]		=					DDIMScheduler(
						    beta_start=0.00085      ,  beta_end=0.012      ,  beta_schedule="scaled_linear"      ,  num_train_timesteps=1000      ,  clip_sample=UpperCamelCase_      ,  set_alpha_to_one=UpperCamelCase_      ,  )
						torch.manual_seed(0)
						__UpperCAmelCase	: List[str]		=					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	: str		=					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	: Any		=					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

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


			def       a_     (      self      :    Any      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Dict=0):


						"""simple docstring"""




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


						"""simple docstring"""




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

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

						__UpperCAmelCase	: Tuple		=					self.get_dummy_inputs(UpperCamelCase_)
						__UpperCAmelCase	: str		=					pipe(**UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					output.images

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

						assert images.shape == (1, 32, 32, 3)
						__UpperCAmelCase	: Optional[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       a_     (      self      :    List[Any]):


						"""simple docstring"""




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

						__UpperCAmelCase	: List[Any]		=					self.get_dummy_inputs(UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					pipe(**UpperCamelCase_)
						__UpperCAmelCase	: Any		=					output.images

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

						assert images.shape == (1, 32, 32, 3)
						__UpperCAmelCase	: str		=					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      :    Union[str, Any]):


						"""simple docstring"""




						return super().test_save_load_local()


			@unittest.skip("non-deterministic pipeline")
			def       a_     (      self      :    str):


						"""simple docstring"""




						return super().test_inference_batch_single_identical()


			@skip_mps
			def       a_     (      self      :    int):


						"""simple docstring"""




						return super().test_dict_tuple_outputs_equivalent()


			@skip_mps
			def       a_     (      self      :    Dict):


						"""simple docstring"""




						return super().test_save_load_optional_components()


			@skip_mps
			def       a_     (      self      :    Dict):


						"""simple docstring"""




						return super().test_attention_slicing_forward_pass()






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


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




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


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




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

						__UpperCAmelCase	: Optional[Any]		=					"CompVis/stable-diffusion-v1-4"
						__UpperCAmelCase	: Optional[int]		=					DDIMScheduler.from_pretrained(UpperCamelCase_      ,  subfolder="scheduler")
						__UpperCAmelCase	: Dict		=					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	: Optional[Any]		=					"A black colored car"
						__UpperCAmelCase	: List[Any]		=					"A blue colored car"

						__UpperCAmelCase	: Dict		=					torch.manual_seed(0)
						__UpperCAmelCase	: 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	: 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       a_     (      self      :    List[Any]):


						"""simple docstring"""




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

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

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

						__UpperCAmelCase	: Union[str, Any]		=					"A black colored car"
						__UpperCAmelCase	: Optional[Any]		=					"A blue colored car"

						__UpperCAmelCase	: Union[str, Any]		=					torch.manual_seed(0)
						__UpperCAmelCase	: Optional[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	: Any		=					output.images

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








"""simple docstring"""





import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class      a__				(	__magic_name__							,    unittest.TestCase   ):
			lowercase_    =	LDMTextToImagePipeline
			lowercase_    =	TEXT_TO_IMAGE_PARAMS - {
			    "negative_prompt",
			    "negative_prompt_embeds",
			    "cross_attention_kwargs",
			    "prompt_embeds",
			}
			lowercase_    =	PipelineTesterMixin.required_optional_params - {
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			lowercase_    =	TEXT_TO_IMAGE_BATCH_PARAMS
			lowercase_    =	False


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						torch.manual_seed(0)
						__UpperCAmelCase	: 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      ,  )
						__UpperCAmelCase	: Dict		=					DDIMScheduler(
						    beta_start=0.00085      ,  beta_end=0.012      ,  beta_schedule="scaled_linear"      ,  clip_sample=UpperCamelCase_      ,  set_alpha_to_one=UpperCamelCase_      ,  )
						torch.manual_seed(0)
						__UpperCAmelCase	: Optional[int]		=					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	: List[str]		=					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	: List[str]		=					CLIPTextModel(UpperCamelCase_)
						__UpperCAmelCase	: Union[str, Any]		=					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

						__UpperCAmelCase	: Optional[int]		=					{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vqvae": vae,
						    "bert": text_encoder,
						    "tokenizer": tokenizer,
						}
						return components


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    Tuple=0):


						"""simple docstring"""




						if str(UpperCamelCase_).startswith("mps"):
									__UpperCAmelCase	: str		=					torch.manual_seed(UpperCamelCase_)
						else:
									__UpperCAmelCase	: Tuple		=					torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




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

						__UpperCAmelCase	: Union[str, Any]		=					self.get_dummy_components()
						__UpperCAmelCase	: Union[str, Any]		=					LDMTextToImagePipeline(**UpperCamelCase_)
						pipe.to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: int		=					self.get_dummy_inputs(UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					pipe(**UpperCamelCase_).images
						__UpperCAmelCase	: List[Any]		=					image[0, -3:, -3:, -1]

						assert image.shape == (1, 16, 16, 3)
						__UpperCAmelCase	: List[str]		=					np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3






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


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




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


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    int=torch.floataa      ,  UpperCamelCase_      :    Dict=0):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					torch.manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Optional[Any]		=					np.random.RandomState(UpperCamelCase_).standard_normal((1, 4, 32, 32))
						__UpperCAmelCase	: str		=					torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_      ,  dtype=UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

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

						assert image.shape == (1, 256, 256, 3)
						__UpperCAmelCase	: List[Any]		=					np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878])
						__UpperCAmelCase	: List[Any]		=					np.abs(expected_slice - image_slice).max()
						assert max_diff < 1e-3






@nightly
@require_torch_gpu
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




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


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Tuple      ,  UpperCamelCase_      :    int=torch.floataa      ,  UpperCamelCase_      :    Optional[int]=0):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					torch.manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					np.random.RandomState(UpperCamelCase_).standard_normal((1, 4, 32, 32))
						__UpperCAmelCase	: Optional[Any]		=					torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_      ,  dtype=UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 50,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: Union[str, Any]		=					self.get_inputs(UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					pipe(**UpperCamelCase_).images[0]

						__UpperCAmelCase	: int		=					load_numpy(
						    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy")
						__UpperCAmelCase	: int		=					np.abs(expected_image - image).max()
						assert max_diff < 1e-3


