




def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			float:

	def get_matched_characters(__lowerCamelCase				,     __lowerCamelCase       ) -> str:
		a									=				[]
		a									=				min(len(_stra       )				,     len(_stra       )       ) // 2
		for i, l in enumerate(_stra       ):
			a									=				int(max(0				,     i - limit       )       )
			a									=				int(min(i + limit + 1				,     len(_stra       )       )       )
			if l in _stra[left:right]:
				matched.append(__lowerCamelCase       )
				a									=				f'{_stra[0:_stra.index(__lowerCamelCase       )]} {_stra[_stra.index(__lowerCamelCase       ) + 1:]}'

		return "".join(__lowerCamelCase       )

	# matching characters
	a									=				get_matched_characters(__lowerCamelCase				,     __lowerCamelCase       )
	a									=				get_matched_characters(__lowerCamelCase				,     __lowerCamelCase       )
	a									=				len(__lowerCamelCase       )

	# transposition
	a									=				(
	    len([(ca, ca) for ca, ca in zip(__lowerCamelCase				,     __lowerCamelCase       ) if ca != ca]       ) // 2
	)

	if not match_count:
		a									=				0.0
	else:
		a									=				(
		    1
		    / 3
		    * (
		        match_count / len(__lowerCamelCase       )
		        + match_count / len(__lowerCamelCase       )
		        + (match_count - transpositions) / match_count
		    )
		)

	# common prefix up to 4 characters
	a									=				0
	for ca, ca in zip(stra[:4]				,     stra[:4]       ):
		if ca == ca:
			prefix_len += 1
		else:
			break

	return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(jaro_winkler("hello", "world"))







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      (      __magic_name__					):
							UpperCamelCase__   	=    42
							UpperCamelCase__   	=    42
							def __init__(		self		:Any						, __magic_name__		:UNetaDModel						, __magic_name__		:ScoreSdeVeScheduler							):







								'''simple docstring'''







								super().__init__()
								self.register_modules(unet=__magic_name__						, scheduler=__magic_name__							)





							@torch.no_grad()
							def __call__(		self		:List[str]						, __magic_name__		:int = 1						, __magic_name__		:int = 2000						, __magic_name__		:Optional[Union[torch.Generator, List[torch.Generator]]] = None						, __magic_name__		:Optional[str] = "pil"						, __magic_name__		:bool = True						, **__magic_name__		:Union[str, Any]						, ):







								'''simple docstring'''







								a									=				self.unet.config.sample_size
								a									=				(batch_size, 3, img_size, img_size)

								a									=				self.unet

								a									=				randn_tensor(__magic_name__						, generator=__magic_name__							) * self.scheduler.init_noise_sigma
								a									=				sample.to(self.device							)

								self.scheduler.set_timesteps(__magic_name__							)
								self.scheduler.set_sigmas(__magic_name__							)

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

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

									# prediction step
									a									=				model(__magic_name__						, __magic_name__							).sample
									a									=				self.scheduler.step_pred(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)

									a							,					a									=				output.prev_sample, output.prev_sample_mean

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

								if not return_dict:
									return (sample,)

								return ImagePipelineOutput(images=__magic_name__							)

