



'''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 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  42
 UpperCAmelCase							=  42







 def __init__(							self  ,a_  ,a_							)		->   str:
        super().__init__()
        self.register_modules(unet=a_  ,scheduler=a_							)







 @torch.no_grad()
 def __call__(							self  ,a_ = 1  ,a_ = 2_000  ,a_ = None  ,a_ = "pil"  ,a_ = True  ,**a_  ,)		->   Union[ImagePipelineOutput, Tuple]:
        _UpperCAmelCase  :						Optional[Any]			     = self.unet.config.sample_size
        _UpperCAmelCase  :						Dict			     = (batch_size, 3, img_size, img_size)

        _UpperCAmelCase  :						str			     = self.unet

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

        self.scheduler.set_timesteps(a_							)
        self.scheduler.set_sigmas(a_							)

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

               # correction step
               for _ in range(self.scheduler.config.correct_steps							):
                      _UpperCAmelCase  :						Optional[int]			     = self.unet(a_  ,a_							).sample
                      _UpperCAmelCase  :						List[Any]			     = self.scheduler.step_correct(a_  ,a_  ,generator=a_							).prev_sample

               # prediction step
               _UpperCAmelCase  :						Optional[int]			     = model(a_  ,a_							).sample
               _UpperCAmelCase  :						Tuple			     = self.scheduler.step_pred(a_  ,a_  ,a_  ,generator=a_							)

               _UpperCAmelCase     ,_UpperCAmelCase  :						str			     = output.prev_sample, output.prev_sample_mean

        _UpperCAmelCase  :						Any			     = sample_mean.clamp(0  ,1							)
        _UpperCAmelCase  :						List[Any]			     = sample.cpu().permute(0  ,2  ,3  ,1							).numpy()
        if output_type == "pil":
               _UpperCAmelCase  :						int			     = self.numpy_to_pil(a_							)

        if not return_dict:
               return (sample,)

        return ImagePipelineOutput(images=a_							)








'''simple docstring'''






from __future__ import annotations

import typing
from collections import Counter




def     snake_case_					(     lowerCAmelCase_		)->		typing.Counter[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						typing.Counter[int]			     = Counter()
       for base in range(1       ,   max_perimeter + 1		):
              for perpendicular in range(lowerCAmelCase_       ,   max_perimeter + 1		):
                     _UpperCAmelCase  :						List[str]			     = (base * base + perpendicular * perpendicular) ** 0.5
                     if hypotenuse == int(lowerCAmelCase_		):
                            _UpperCAmelCase  :						Optional[Any]			     = int(base + perpendicular + hypotenuse		)
                            if perimeter > max_perimeter:
                                   continue
                            triplets[perimeter] += 1
       return triplets




def     snake_case_					(     lowerCAmelCase_ = 1000		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = pythagorean_triple(lowerCAmelCase_		)
       return triplets.most_common(1		)[0][0]


if __name__ == "__main__":
   print(f"""Perimeter {solution()} has maximum solutions""")



