

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_   (  __lowercase					):
      UpperCamelCase_      :		UNetaDModel
      UpperCamelCase_      :		ScoreSdeVeScheduler



      def __init__(      self				:    List[str]      , A__				:    UNetaDModel      , A__				:    ScoreSdeVeScheduler		)   ->					Union[str, Any]:
        super().__init__()
        self.register_modules(unet=A__      , scheduler=A__		)







      @torch.no_grad()
      def __call__(      self				:    Tuple      , A__				:    int = 1      , A__				:    int = 2000      , A__				:    Optional[Union[torch.Generator, List[torch.Generator]]] = None      , A__				:    Optional[str] = "pil"      , A__				:    bool = True      , **A__				:    Optional[Any]      , )   ->					Union[ImagePipelineOutput, Tuple]:
        _snake_case						=							self.unet.config.sample_size
        _snake_case						=							(batch_size, 3, img_size, img_size)

        _snake_case						=							self.unet

        _snake_case						=							randn_tensor(A__      , generator=A__		) * self.scheduler.init_noise_sigma
        _snake_case						=							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		)		):
          _snake_case						=							self.scheduler.sigmas[i] * torch.ones(shape[0]      , device=self.device		)

          # correction step
          for _ in range(self.scheduler.config.correct_steps		):
            _snake_case						=							self.unet(A__      , A__		).sample
            _snake_case						=							self.scheduler.step_correct(A__      , A__      , generator=A__		).prev_sample

          # prediction step
          _snake_case						=							model(A__      , A__		).sample
          _snake_case						=							self.scheduler.step_pred(A__      , A__      , A__      , generator=A__		)

          _snake_case,					_snake_case						=							output.prev_sample, output.prev_sample_mean

        _snake_case						=							sample_mean.clamp(0      , 1		)
        _snake_case						=							sample.cpu().permute(0      , 2      , 3      , 1		).numpy()
        if output_type == "pil":
          _snake_case						=							self.numpy_to_pil(A__		)

        if not return_dict:
          return (sample,)

        return ImagePipelineOutput(images=A__		)




from __future__ import annotations



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     bool:



  """simple docstring"""






  _snake_case						=							get_failure_array(_UpperCamelCase					)

  # 2) Step through text searching for pattern
  _snake_case,					_snake_case						=							0, 0  # index into text, pattern
  while i < len(_UpperCamelCase					):
    if pattern[j] == text[i]:
      if j == (len(_UpperCamelCase					) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _snake_case						=							failure[j - 1]
      continue
    i += 1
  return False



def 				snake_case_(_UpperCamelCase					)							->     list[int]:



  """simple docstring"""






  _snake_case						=							[0]
  _snake_case						=							0
  _snake_case						=							1
  while j < len(_UpperCamelCase					):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _snake_case						=							failure[i - 1]
      continue
    j += 1
    failure.append(_UpperCamelCase					)
  return failure


if __name__ == "__main__":
 # Test 1)
 __A      		=     '''abc1abc12'''
 __A      		=     '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 __A      		=     '''alskfjaldsk23adsfabcabc'''
 assert kmp(pattern, texta) and not kmp(pattern, texta)

 # Test 2)
 __A      		=     '''ABABX'''
 __A      		=     '''ABABZABABYABABX'''
 assert kmp(pattern, text)

 # Test 3)
 __A      		=     '''AAAB'''
 __A      		=     '''ABAAAAAB'''
 assert kmp(pattern, text)

 # Test 4)
 __A      		=     '''abcdabcy'''
 __A      		=     '''abcxabcdabxabcdabcdabcy'''
 assert kmp(pattern, text)

 # Test 5)
 __A      		=     '''aabaabaaa'''
 assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]

