


class 							lowerCamelCase   :


	def __init__(    self):
							__UpperCAmelCase			:							List[Any]	    =	''''''
							__UpperCAmelCase			:							Tuple	    =	''''''
							__UpperCAmelCase			:							Optional[Any]	    =	[]


	def  A(    self				,     lowercase__				,     lowercase__):
							if m == -1:
													return n + 1
							elif n == -1:
													return m + 1
							elif self.dp[m][n] > -1:
													return self.dp[m][n]
							else:
													if self.worda[m] == self.worda[n]:
																			__UpperCAmelCase			:							Tuple	    =	self.__min_dist_top_down_dp(m - 1				,     n - 1)
													else:
																			__UpperCAmelCase			:							Dict	    =	self.__min_dist_top_down_dp(lowercase__				,     n - 1)
																			__UpperCAmelCase			:							Optional[int]	    =	self.__min_dist_top_down_dp(m - 1				,     lowercase__)
																			__UpperCAmelCase			:							List[str]	    =	self.__min_dist_top_down_dp(m - 1				,     n - 1)
																			__UpperCAmelCase			:							Dict	    =	1 + min(lowercase__				,     lowercase__				,     lowercase__)

													return self.dp[m][n]


	def  A(    self				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	worda
							__UpperCAmelCase			:							Dict	    =	worda
							__UpperCAmelCase			:							str	    =	[[-1 for _ in range(len(lowercase__))] for _ in range(len(lowercase__))]

							return self.__min_dist_top_down_dp(len(lowercase__) - 1				,     len(lowercase__) - 1)


	def  A(    self				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	worda
							__UpperCAmelCase			:							List[str]	    =	worda
							__UpperCAmelCase			:							Optional[Any]	    =	len(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	len(lowercase__)
							__UpperCAmelCase			:							Dict	    =	[[0 for _ in range(n + 1)] for _ in range(m + 1)]

							for i in range(m + 1):
													for j in range(n + 1):
																			if i == 0:  # first string is empty
																									__UpperCAmelCase			:							Optional[int]	    =	j
																			elif j == 0:  # second string is empty
																									__UpperCAmelCase			:							List[str]	    =	i
																			elif worda[i - 1] == worda[j - 1]:  # last characters are equal
																									__UpperCAmelCase			:							Dict	    =	self.dp[i - 1][j - 1]
																			else:
																									__UpperCAmelCase			:							Optional[Any]	    =	self.dp[i][j - 1]
																									__UpperCAmelCase			:							int	    =	self.dp[i - 1][j]
																									__UpperCAmelCase			:							Dict	    =	self.dp[i - 1][j - 1]
																									__UpperCAmelCase			:							Any	    =	1 + min(lowercase__				,     lowercase__				,     lowercase__)
							return self.dp[m][n]


if __name__ == "__main__":
				lowerCAmelCase       		=    EditDistance()

				print("""****************** Testing Edit Distance DP Algorithm ******************""")
				print()

				lowerCAmelCase       		=    input("""Enter the first string: """).strip()
				lowerCAmelCase       		=    input("""Enter the second string: """).strip()

				print()
				print(F'The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}')
				print(F'The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}')
				print()
				print("""*************** End of Testing Edit Distance DP Algorithm ***************""")





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 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		UNetaDModel
	_lowerCAmelCase     :		ScoreSdeVeScheduler


	def __init__(    self				,     lowercase__				,     lowercase__):
							super().__init__()
							self.register_modules(unet=lowercase__				,     scheduler=lowercase__)


	@torch.no_grad()
	def __call__(    self				,     lowercase__ = 1				,     lowercase__ = 2_0_0_0				,     lowercase__ = None				,     lowercase__ = "pil"				,     lowercase__ = True				,     **lowercase__				,     ):
							__UpperCAmelCase			:							int	    =	self.unet.config.sample_size
							__UpperCAmelCase			:							Optional[int]	    =	(batch_size, 3, img_size, img_size)

							__UpperCAmelCase			:							int	    =	self.unet

							__UpperCAmelCase			:							Tuple	    =	randn_tensor(lowercase__				,     generator=lowercase__) * self.scheduler.init_noise_sigma
							__UpperCAmelCase			:							List[str]	    =	sample.to(self.device)

							self.scheduler.set_timesteps(lowercase__)
							self.scheduler.set_sigmas(lowercase__)

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

													# correction step
													for _ in range(self.scheduler.config.correct_steps):
																			__UpperCAmelCase			:							int	    =	self.unet(lowercase__				,     lowercase__).sample
																			__UpperCAmelCase			:							str	    =	self.scheduler.step_correct(lowercase__				,     lowercase__				,     generator=lowercase__).prev_sample

													# prediction step
													__UpperCAmelCase			:							Tuple	    =	model(lowercase__				,     lowercase__).sample
													__UpperCAmelCase			:							Dict	    =	self.scheduler.step_pred(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__)

													__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	output.prev_sample, output.prev_sample_mean

							__UpperCAmelCase			:							List[str]	    =	sample_mean.clamp(0				,     1)
							__UpperCAmelCase			:							Union[str, Any]	    =	sample.cpu().permute(0				,     2				,     3				,     1).numpy()
							if output_type == "pil":
													__UpperCAmelCase			:							List[Any]	    =	self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (sample,)

							return ImagePipelineOutput(images=lowercase__)

