






'''simple docstring'''




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  A_						(a_				):
							"""simple docstring"""




							a__ 		=						42
							a__ 		=						42

							def __init__(		self      :Any							,				lowerCAmelCase__      :UNetaDModel							,				lowerCAmelCase__      :ScoreSdeVeScheduler					)					->	int:


										'''simple docstring'''




										super().__init__()
										self.register_modules(unet=lowerCAmelCase__							,				scheduler=lowerCAmelCase__					)






							@torch.no_grad()
							def __call__(		self      :List[str]							,				lowerCAmelCase__      :int = 1							,				lowerCAmelCase__      :int = 2_000							,				lowerCAmelCase__      :Optional[Union[torch.Generator, List[torch.Generator]]] = None							,				lowerCAmelCase__      :Optional[str] = "pil"							,				lowerCAmelCase__      :bool = True							,				**lowerCAmelCase__      :Union[str, Any]							,				)					->	List[Any]:


										'''simple docstring'''




										snake_case_					:       List[Any] =							self.unet.config.sample_size
										snake_case_					:       Optional[int] =							(batch_size, 3, img_size, img_size)

										snake_case_					:       List[str] =							self.unet

										snake_case_					:       Optional[Any] =							randn_tensor(lowerCAmelCase__							,				generator=lowerCAmelCase__					) * self.scheduler.init_noise_sigma
										snake_case_					:       Union[str, Any] =							sample.to(self.device					)

										self.scheduler.set_timesteps(lowerCAmelCase__					)
										self.scheduler.set_sigmas(lowerCAmelCase__					)

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

													# correction step
													for _ in range(self.scheduler.config.correct_steps					):
																snake_case_					:       List[Any] =							self.unet(lowerCAmelCase__							,				lowerCAmelCase__					).sample
																snake_case_					:       str =							self.scheduler.step_correct(lowerCAmelCase__							,				lowerCAmelCase__							,				generator=lowerCAmelCase__					).prev_sample

													# prediction step
													snake_case_					:       Optional[Any] =							model(lowerCAmelCase__							,				lowerCAmelCase__					).sample
													snake_case_					:       Tuple =							self.scheduler.step_pred(lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__							,				generator=lowerCAmelCase__					)

													snake_case_,						snake_case_					:       Union[str, Any] =							output.prev_sample, output.prev_sample_mean

										snake_case_					:       Union[str, Any] =							sample_mean.clamp(0							,				1					)
										snake_case_					:       Union[str, Any] =							sample.cpu().permute(0							,				2							,				3							,				1					).numpy()
										if output_type == "pil":
													snake_case_					:       str =							self.numpy_to_pil(lowerCAmelCase__					)

										if not return_dict:
													return (sample,)

										return ImagePipelineOutput(images=lowerCAmelCase__					)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase   :			Optional[int]          = {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase   :			int          = [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase   :			List[str]          = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

