

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			UNetaDModel
				__SCREAMING_SNAKE_CASE :			ScoreSdeVeScheduler




				def __init__( self				,       snake_case				,       snake_case	):
											super().__init__()
											self.register_modules(unet=snake_case				,       scheduler=snake_case	)




				@torch.no_grad()
				def __call__( self				,       snake_case = 1				,       snake_case = 2000				,       snake_case = None				,       snake_case = "pil"				,       snake_case = True				,       **snake_case				,       ):
											snake_case_          =   self.unet.config.sample_size
											snake_case_          =   (batch_size, 3, img_size, img_size)

											snake_case_          =   self.unet

											snake_case_          =   randn_tensor(snake_case				,       generator=snake_case	) * self.scheduler.init_noise_sigma
											snake_case_          =   sample.to(self.device	)

											self.scheduler.set_timesteps(snake_case	)
											self.scheduler.set_sigmas(snake_case	)

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
																		snake_case_          =   self.scheduler.sigmas[i] * torch.ones(shape[0]				,       device=self.device	)

																		# correction step
																		for _ in range(self.scheduler.config.correct_steps	):
																									snake_case_          =   self.unet(snake_case				,       snake_case	).sample
																									snake_case_          =   self.scheduler.step_correct(snake_case				,       snake_case				,       generator=snake_case	).prev_sample

																		# prediction step
																		snake_case_          =   model(snake_case				,       snake_case	).sample
																		snake_case_          =   self.scheduler.step_pred(snake_case				,       snake_case				,       snake_case				,       generator=snake_case	)

																		snake_case_    ,   snake_case_          =   output.prev_sample, output.prev_sample_mean

											snake_case_          =   sample_mean.clamp(0				,       1	)
											snake_case_          =   sample.cpu().permute(0				,       2				,       3				,       1	).numpy()
											if output_type == "pil":
																		snake_case_          =   self.numpy_to_pil(snake_case	)

											if not return_dict:
																		return (sample,)

											return ImagePipelineOutput(images=snake_case	)







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel




