
'''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 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					42
					lowerCAmelCase_					       =					42






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					@torch.no_grad()
					def __call__(       self    ,							__SCREAMING_SNAKE_CASE = 1    ,							__SCREAMING_SNAKE_CASE = 20_00    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = "pil"    ,							__SCREAMING_SNAKE_CASE = True    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





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

											lowercase_     :					Dict													=	self.unet

											lowercase_     :					Union[str, Any]													=	randn_tensor(__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		) * self.scheduler.init_noise_sigma
											lowercase_     :					int													=	sample.to(self.device		)

											self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)
											self.scheduler.set_sigmas(__SCREAMING_SNAKE_CASE		)

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

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

																	# prediction step
																	lowercase_     :					List[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		).sample
																	lowercase_     :					str													=	self.scheduler.step_pred(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		)

																	lowercase_						,			lowercase_     :					int													=	output.prev_sample, output.prev_sample_mean

											lowercase_     :					Optional[Any]													=	sample_mean.clamp(0    ,							1		)
											lowercase_     :					Dict													=	sample.cpu().permute(0    ,							2    ,							3    ,							1		).numpy()
											if output_type == "pil":
																	lowercase_     :					List[str]													=	self.numpy_to_pil(__SCREAMING_SNAKE_CASE		)

											if not return_dict:
																	return (sample,)

											return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE		)





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase			:  Tuple   							=	{"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase			:  str   							=	[
							    "VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTMSNModel",
							    "ViTMSNForImageClassification",
							    "ViTMSNPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_msn import (
														    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTMSNForImageClassification,
														    ViTMSNModel,
														    ViTMSNPreTrainedModel,
														)

else:
							import sys

							_lowercase			:  int   							=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



