


def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  float:
		"""simple docstring"""




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

				return "".join(__magic_name__					)

		# matching characters
		lowercase__					      =		get_matched_characters(__magic_name__   ,   __magic_name__					)
		lowercase__					      =		get_matched_characters(__magic_name__   ,   __magic_name__					)
		lowercase__					      =		len(__magic_name__					)

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

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

		# common prefix up to 4 characters
		lowercase__					      =		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 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			42
					A__      =			42

					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						UNetaDModel				, _UpperCAmelCase   :						ScoreSdeVeScheduler		)						->       List[str]:




							"""simple docstring"""


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


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




							"""simple docstring"""


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

							lowercase__					      =		self.unet

							lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase		) * self.scheduler.init_noise_sigma
							lowercase__					      =		sample.to(self.device		)

							self.scheduler.set_timesteps(_UpperCAmelCase		)
							self.scheduler.set_sigmas(_UpperCAmelCase		)

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

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

									# prediction step
									lowercase__					      =		model(_UpperCAmelCase				, _UpperCAmelCase		).sample
									lowercase__					      =		self.scheduler.step_pred(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		)

									lowercase__      ,     lowercase__					      =		output.prev_sample, output.prev_sample_mean

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

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)

