





"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput






@dataclass
class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	42
 _lowerCamelCase								=	42






class 						__snake_case   (						SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	1

 @register_to_config
 def __init__(       self   ,		__lowerCamelCase = 2000   ,		__lowerCamelCase = 0.1_5   ,		__lowerCamelCase = 0.0_1   ,		__lowerCamelCase = 1_3_4_8.0   ,		__lowerCamelCase = 1e-5   ,		__lowerCamelCase = 1   ,		):



     '''simple docstring'''




     __A       :		List[str]							=	sigma_max

     # setable values
     __A       :		Optional[Any]							=	None

     self.set_sigmas(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None    ):



     '''simple docstring'''




     return sample

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None    ):



     '''simple docstring'''




     __A       :		List[str]							=	sampling_eps if sampling_eps is not None else self.config.sampling_eps

     __A       :		Dict							=	torch.linspace(1   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		device=__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None    ):



     '''simple docstring'''




     __A       :		List[str]							=	sigma_min if sigma_min is not None else self.config.sigma_min
     __A       :		Optional[Any]							=	sigma_max if sigma_max is not None else self.config.sigma_max
     __A       :		Tuple							=	sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if self.timesteps is None:
         self.set_timesteps(__lowerCamelCase   ,		__lowerCamelCase    )

     __A       :		List[str]							=	sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
     __A       :		Optional[int]							=	torch.exp(torch.linspace(math.log(__lowerCamelCase    )   ,		math.log(__lowerCamelCase    )   ,		__lowerCamelCase    )    )
     __A       :		List[str]							=	torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     return torch.where(
         timesteps == 0   ,		torch.zeros_like(t.to(timesteps.device    )    )   ,		self.discrete_sigmas[timesteps - 1].to(timesteps.device    )   ,		)

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		):



     '''simple docstring'''




     if self.timesteps is None:
         raise ValueError(
             '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''    )

     __A       :		Union[str, Any]							=	timestep * torch.ones(
         sample.shape[0]   ,		device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
     __A       :		List[str]							=	(timestep * (len(self.timesteps    ) - 1)).long()

     # mps requires indices to be in the same device, so we use cpu as is the default with cuda
     __A       :		str							=	timesteps.to(self.discrete_sigmas.device    )

     __A       :		Tuple							=	self.discrete_sigmas[timesteps].to(sample.device    )
     __A       :		int							=	self.get_adjacent_sigma(__lowerCamelCase   ,		__lowerCamelCase    ).to(sample.device    )
     __A       :		str							=	torch.zeros_like(__lowerCamelCase    )
     __A       :		Optional[Any]							=	(sigma**2 - adjacent_sigma**2) ** 0.5

     # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
     # also equation 47 shows the analog from SDE models to ancestral sampling methods
     __A       :		int							=	diffusion.flatten()
     while len(diffusion.shape    ) < len(sample.shape    ):
         __A       :		Any							=	diffusion.unsqueeze(-1    )
     __A       :		Union[str, Any]							=	drift - diffusion**2 * model_output

     #  equation 6: sample noise for the diffusion term of
     __A       :		Union[str, Any]							=	randn_tensor(
         sample.shape   ,		layout=sample.layout   ,		generator=__lowerCamelCase   ,		device=sample.device   ,		dtype=sample.dtype    )
     __A       :		List[Any]							=	sample - drift  # subtract because `dt` is a small negative timestep
     # TODO is the variable diffusion the correct scaling term for the noise?
     __A       :		Optional[int]							=	prev_sample_mean + diffusion * noise  # add impact of diffusion field g

     if not return_dict:
         return (prev_sample, prev_sample_mean)

     return SdeVeOutput(prev_sample=__lowerCamelCase   ,		prev_sample_mean=__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		):



     '''simple docstring'''




     if self.timesteps is None:
         raise ValueError(
             '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''    )

     # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
     # sample noise for correction
     __A       :		Tuple							=	randn_tensor(sample.shape   ,		layout=sample.layout   ,		generator=__lowerCamelCase    ).to(sample.device    )

     # compute step size from the model_output, the noise, and the snr
     __A       :		str							=	torch.norm(model_output.reshape(model_output.shape[0]   ,		-1    )   ,		dim=-1    ).mean()
     __A       :		Any							=	torch.norm(noise.reshape(noise.shape[0]   ,		-1    )   ,		dim=-1    ).mean()
     __A       :		str							=	(self.config.snr * noise_norm / grad_norm) ** 2 * 2
     __A       :		Any							=	step_size * torch.ones(sample.shape[0]    ).to(sample.device    )
     # self.repeat_scalar(step_size, sample.shape[0])

     # compute corrected sample: model_output term and noise term
     __A       :		List[Any]							=	step_size.flatten()
     while len(step_size.shape    ) < len(sample.shape    ):
         __A       :		List[str]							=	step_size.unsqueeze(-1    )
     __A       :		List[str]							=	sample + step_size * model_output
     __A       :		Union[str, Any]							=	prev_sample_mean + ((step_size * 2) ** 0.5) * noise

     if not return_dict:
         return (prev_sample,)

     return SchedulerOutput(prev_sample=__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Dict							=	timesteps.to(original_samples.device    )
     __A       :		Union[str, Any]							=	self.discrete_sigmas.to(original_samples.device    )[timesteps]
     __A       :		Any							=	(
         noise * sigmas[:, None, None, None]
         if noise is not None
         else torch.randn_like(__lowerCamelCase    ) * sigmas[:, None, None, None]
     )
     __A       :		Optional[Any]							=	noise + original_samples
     return noisy_samples







 def __len__(       self    ):



     '''simple docstring'''




     return self.config.num_train_timesteps











"""simple docstring"""



def 						__lowercase	(  snake_case_    :    str							,snake_case_    :    str		)  ->float:



    '''simple docstring'''







    def get_matched_characters(snake_case_    :    str							,snake_case_    :    str		) -> str:
        __A       :		Any							=	[]
        __A       :		Any							=	min(len(_stra		)							,len(_stra		)		) // 2
        for i, l in enumerate(_stra		):
            __A       :		Dict							=	int(max(0							,i - limit		)		)
            __A       :		Tuple							=	int(min(i + limit + 1							,len(_stra		)		)		)
            if l in _stra[left:right]:
                matched.append(snake_case_		)
                __A       :		Any							=	F"""{_stra[0:_stra.index(snake_case_		)]} {_stra[_stra.index(snake_case_		) + 1:]}"""

        return "".join(snake_case_		)

    # matching characters
    __A       :		int							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		Tuple							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		str							=	len(snake_case_		)

    # transposition
    __A       :		Dict							=	(
        len([(ca, ca) for ca, ca in zip(snake_case_							,snake_case_		) if ca != ca]		) // 2
    )

    if not match_count:
        __A       :		List[str]							=	0.0
    else:
        __A       :		Tuple							=	(
            1
            / 3
            * (
                match_count / len(snake_case_		)
                + match_count / len(snake_case_		)
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __A       :		Tuple							=	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"""))




