
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 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					42
	snake_case_    =					42






	def __init__(	self :			List[Any]							,A :			UNetaDModel							,A :			ScoreSdeVeScheduler			):
					super().__init__()
					self.register_modules(unet=A							,scheduler=A			)







	@torch.no_grad()
	def __call__(	self :			List[str]							,A :			int = 1							,A :			int = 20_00							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			Optional[str] = "pil"							,A :			bool = True							,**A :			str							,):
					__A             =			self.unet.config.sample_size
					__A             =			(batch_size, 3, img_size, img_size)

					__A             =			self.unet

					__A             =			randn_tensor(A							,generator=A			) * self.scheduler.init_noise_sigma
					__A             =			sample.to(self.device			)

					self.scheduler.set_timesteps(A			)
					self.scheduler.set_sigmas(A			)

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

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

									# prediction step
									__A             =			model(A							,A			).sample
									__A             =			self.scheduler.step_pred(A							,A							,A							,generator=A			)

									__A      ,							__A             =			output.prev_sample, output.prev_sample_mean

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

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=A			)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE							:int										=    {'configuration_encoder_decoder': ['EncoderDecoderConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Dict										=    ['EncoderDecoderModel']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int										=    ['TFEncoderDecoderModel']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Optional[int]										=    ['FlaxEncoderDecoderModel']

if TYPE_CHECKING:
					from .configuration_encoder_decoder import EncoderDecoderConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encoder_decoder import EncoderDecoderModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
					import sys

					SCREAMING_SNAKE_CASE							:int										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
