
"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

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




class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				UNetaDModel
				UpperCAmelCase_    :				PNDMScheduler


				def __init__(    self		,     lowercase__		,     lowercase__      )   ->   Tuple:
									"""simple docstring"""







									super().__init__()

									SCREAMING_SNAKE_CASE           =     PNDMScheduler.from_config(scheduler.config      )

									self.register_modules(unet=lowercase__		,     scheduler=lowercase__      )




				@torch.no_grad()
				def __call__(    self		,     lowercase__ = 1		,     lowercase__ = 50		,     lowercase__ = None		,     lowercase__ = "pil"		,     lowercase__ = True		,     **lowercase__		,     )   ->   Union[ImagePipelineOutput, Tuple]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,     generator=lowercase__		,     device=self.device		,     )

									self.scheduler.set_timesteps(lowercase__      )
									for t in self.progress_bar(self.scheduler.timesteps      ):
														SCREAMING_SNAKE_CASE           =     self.unet(lowercase__		,     lowercase__      ).sample

														SCREAMING_SNAKE_CASE           =     self.scheduler.step(lowercase__		,     lowercase__		,     lowercase__      ).prev_sample

									SCREAMING_SNAKE_CASE           =     (image / 2 + 0.5).clamp(0		,     1      )
									SCREAMING_SNAKE_CASE           =     image.cpu().permute(0		,     2		,     3		,     1      ).numpy()
									if output_type == "pil":
														SCREAMING_SNAKE_CASE           =     self.numpy_to_pil(lowercase__      )

									if not return_dict:
														return (image,)

									return ImagePipelineOutput(images=lowercase__      )


"""simple docstring"""




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


snake_case			       =		TypeVar('T', bound='TaskTemplate')




@dataclass(frozen=__magic_name__				)
class 					UpperCamelCase     (	abc.ABC				):

				"""simple docstring"""



				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				ClassVar[Features]
				UpperCAmelCase_    :				ClassVar[Features]


				def 							A (    self		,     lowercase__      )   ->   T:
									"""simple docstring"""







									return copy.deepcopy(self      )


				@property
				def 							A (    self      )   ->   Features:
									"""simple docstring"""







									return Features(**self.input_schema		,     **self.label_schema      )


				@property
				@abc.abstractmethod
				def 							A (    self      )   ->   Dict[str, str]:
									"""simple docstring"""







									raise NotImplementedError




				@classmethod
				def 							A (    cls		,     lowercase__      )   ->   T:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     {f.name for f in dataclasses.fields(cls      )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )
