



from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__):
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         A__		 =					DDIMScheduler.from_config(scheduler.config)

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




     @torch.no_grad()
     def __call__(				self ,						a__ = 1 ,						a__ = None ,						a__ = 0.0 ,						a__ = 5_0 ,						a__ = None ,						a__ = "pil" ,						a__ = True ,						):

         # Sample gaussian noise to begin loop
         if isinstance(self.unet.config.sample_size ,						a__):
             A__		 =					(
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size,
                 self.unet.config.sample_size,
             )
         else:
             A__		 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

         if isinstance(a__ ,						a__) and len(a__) != batch_size:
             raise ValueError(
                 F"You have passed a list of generators of length {len(a__)}, but requested an effective batch"
                 F" size of {batch_size}. Make sure the batch size matches the length of the generators.")

         A__		 =					randn_tensor(a__ ,						generator=a__ ,						device=self.device ,						dtype=self.unet.dtype)

         # set step values
         self.scheduler.set_timesteps(a__)

         for t in self.progress_bar(self.scheduler.timesteps):
             # 1. predict noise model_output
             A__		 =					self.unet(a__ ,						a__).sample

             # 2. predict previous mean of image x_t-1 and add variance depending on eta
             # eta corresponds to η in paper and should be between [0, 1]
             # do x_t -> x_t-1
             A__		 =					self.scheduler.step(
                 a__ ,						a__ ,						a__ ,						eta=a__ ,						use_clipped_model_output=a__ ,						generator=a__).prev_sample

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

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=a__)





def 		lowerCAmelCase__    (					UpperCamelCase_				:       float ,       UpperCamelCase_				:       int	)->  float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase_	) ,       UpperCamelCase_	)
    return number - int(UpperCamelCase_	)


if __name__ == "__main__":
   print(decimal_isolate(1.53, 0))
   print(decimal_isolate(35.345, 1))
   print(decimal_isolate(35.345, 2))
   print(decimal_isolate(35.345, 3))
   print(decimal_isolate(-14.789, 3))
   print(decimal_isolate(0, 2))
   print(decimal_isolate(-14.123, 1))
   print(decimal_isolate(-14.123, 2))
   print(decimal_isolate(-14.123, 3))
