'''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       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       UNetaDModel
   A							:       ScoreSdeVeScheduler
   def __init__(self       ,				_a       ,				_a				)    ->     Union[str, Any]:
        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]:
        lowercase_					:       Any     				=					self.unet.config.sample_size
        lowercase_					:       List[Any]     				=					(batch_size, 3, img_size, img_size)

        lowercase_					:       Any     				=					self.unet

        lowercase_					:       Tuple     				=					randn_tensor(_a       ,				generator=_a				) * self.scheduler.init_noise_sigma
        lowercase_					:       Optional[int]     				=					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				)				):
             lowercase_					:       Optional[Any]     				=					self.scheduler.sigmas[i] * torch.ones(shape[0]       ,				device=self.device				)

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

             # prediction step
             lowercase_					:       List[str]     				=					model(_a       ,				_a				).sample
             lowercase_					:       List[str]     				=					self.scheduler.step_pred(_a       ,				_a       ,				_a       ,				generator=_a				)

             lowercase_					:       List[str]     				=					output.prev_sample, output.prev_sample_mean

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

        if not return_dict:
             return (sample,)

        return ImagePipelineOutput(images=_a				)





'''simple docstring'''




from __future__ import annotations

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     # preprocessing the first row
     for i in range(1  ,       len(matrix[0]							)							):
          matrix[0][i] += matrix[0][i - 1]

     # preprocessing the first column
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          for j in range(1  ,       len(matrix[0]							)							):
               matrix[i][j] += min(matrix[i - 1][j]  ,       matrix[i][j - 1]							)

     return matrix[-1][-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




