


import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     CustomConfig





     def __init__(self						,	UpperCAmelCase			)	->  Dict:
      super().__init__(UpperCAmelCase			)
      _lowercase				 =torch.nn.Linear(config.hidden_size						,	config.hidden_size			)





     def 	__A   (self						,	UpperCAmelCase			)	->  int:
      return self.linear(UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase			)	->  Optional[int]:
      pass







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     NoSuperInitConfig





     def __init__(self						,	UpperCAmelCase			)	->  Optional[int]:
      super().__init__(UpperCAmelCase			)
      _lowercase				 =torch.nn.Linear(config.attribute						,	config.attribute			)





     def 	__A   (self						,	UpperCAmelCase			)	->  Union[str, Any]:
      return self.linear(UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase			)	->  Tuple:
      pass





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  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      super().__init__()

      _lowercase				 =PNDMScheduler.from_config(scheduler.config			)

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





     @torch.no_grad()
     def __call__(self						,	UpperCAmelCase = 1						,	UpperCAmelCase = 5_0						,	UpperCAmelCase = None						,	UpperCAmelCase = "pil"						,	UpperCAmelCase = True						,	**UpperCAmelCase						,	)	->  Union[ImagePipelineOutput, Tuple]:
      _lowercase				 =randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,	generator=UpperCAmelCase						,	device=self.device						,	)

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

       _lowercase				 =self.scheduler.step(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			).prev_sample

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

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase			)

