'''simple docstring'''




import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image




def 				lowerCamelCase      ( UpperCAmelCase__			:   Union[str, Any] , UpperCAmelCase__			:   Dict=256					)     -> Optional[int]:
							lowercase_   :     Dict									=			min(img.size					)

							if s < target_image_size:
														raise ValueError(F'''min dim for image {s} < {target_image_size}'''					)

							lowercase_   :     Union[str, Any]									=			target_image_size / s
							lowercase_   :     Optional[int]									=			(round(r * img.size[1]					), round(r * img.size[0]					))
							lowercase_   :     Optional[int]									=			TF.resize(__snake_case , __snake_case , interpolation=PIL.Image.LANCZOS					)
							lowercase_   :     Optional[Any]									=			TF.center_crop(__snake_case , output_size=2 * [target_image_size]					)
							lowercase_   :     List[Any]									=			torch.unsqueeze(T.ToTensor()(__snake_case					) , 0					)
							return img




def 				lowerCamelCase      ( UpperCAmelCase__			:   List[Any]					)     -> Union[str, Any]:
							lowercase_   :     Optional[int]									=			2.0 * x - 1.0
							return x




def 				lowerCamelCase      ( UpperCAmelCase__			:   Any , UpperCAmelCase__			:   str=True , UpperCAmelCase__			:   Any="RGB"					)     -> Optional[Any]:
							lowercase_   :     int									=			x.detach().cpu()
							if process:
														lowercase_   :     Optional[int]									=			post_process_tensor(__snake_case					)
							lowercase_   :     List[Any]									=			x.numpy()
							if process:
														lowercase_   :     Optional[int]									=			(255 * x).astype(np.uinta					)
							lowercase_   :     int									=			Image.fromarray(__snake_case					)
							if not x.mode == mode:
														lowercase_   :     Optional[Any]									=			x.convert(__snake_case					)
							return x




def 				lowerCamelCase      ( UpperCAmelCase__			:   List[Any]					)     -> List[str]:
							lowercase_   :     str									=			torch.clamp(__snake_case , -1.0 , 1.0					)
							lowercase_   :     Dict									=			(x + 1.0) / 2.0
							lowercase_   :     List[str]									=			x.permute(1 , 2 , 0					)
							return x




def 				lowerCamelCase      ( UpperCAmelCase__			:   List[str]					)     -> List[Any]:
							lowercase_   :     Any									=			post_process_tensor(x.squeeze()					)
							return x.permute(2 , 0 , 1					).unsqueeze(0					)









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			)

