






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

import math
from typing import Union

import torch

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



class 							SCREAMING_SNAKE_CASE__		(       __SCREAMING_SNAKE_CASE				,     __SCREAMING_SNAKE_CASE		):





							"""simple docstring"""






							a_			=	1



							@register_to_config
							def __init__(      self  :						str							,	__A  :						Dict=2_0_0_0							,	__A  :						Union[str, Any]=0.1							,	__A  :						Union[str, Any]=2_0							,	__A  :						Optional[int]=1e-3 ):
								snake_case__				:   Any     =						None
								snake_case__				:   Union[str, Any]     =						None
								snake_case__				:   Optional[Any]     =						None



							def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Optional[Any]							,	__A  :						Union[str, torch.device] = None ):
								snake_case__				:   List[Any]     =						torch.linspace(1							,	self.config.sampling_eps							,	__snake_case							,	device=__snake_case )



							def  _lowercase  (      self  :						Any							,	__A  :						str							,	__A  :						Any							,	__A  :						Dict							,	__A  :						int=None ):
								if self.timesteps is None:
									raise ValueError(
									    "`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler" )

								# TODO(Patrick) better comments + non-PyTorch
								# postprocess model score
								snake_case__				:   Any     =						(
								    -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
								)
								snake_case__				:   str     =						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
								snake_case__				:   Optional[Any]     =						std.flatten()
								while len(std.shape ) < len(score.shape ):
									snake_case__				:   Any     =						std.unsqueeze(-1 )
								snake_case__				:   Optional[int]     =						-score / std

								# compute
								snake_case__				:   List[str]     =						-1.0 / len(self.timesteps )

								snake_case__				:   Optional[int]     =						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
								snake_case__				:   int     =						beta_t.flatten()
								while len(beta_t.shape ) < len(x.shape ):
									snake_case__				:   Any     =						beta_t.unsqueeze(-1 )
								snake_case__				:   List[Any]     =						-0.5 * beta_t * x

								snake_case__				:   List[Any]     =						torch.sqrt(__snake_case )
								snake_case__				:   Union[str, Any]     =						drift - diffusion**2 * score
								snake_case__				:   Optional[int]     =						x + drift * dt

								# add noise
								snake_case__				:   Tuple     =						randn_tensor(x.shape							,	layout=x.layout							,	generator=__snake_case							,	device=x.device							,	dtype=x.dtype )
								snake_case__				:   str     =						x_mean + diffusion * math.sqrt(-dt ) * noise

								return x, x_mean






							def __len__(      self  :						Tuple ):
								return self.config.num_train_timesteps











def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	snake_case__				:   Any     =						[0] * len(snake_case_     )

	for i in range(1  ,						len(snake_case_     )     ):
		# use last results for better performance - dynamic programming
		snake_case__				:   Union[str, Any]     =						prefix_result[i - 1]
		while j > 0 and input_string[i] != input_string[j]:
			snake_case__				:   str     =						prefix_result[j - 1]

		if input_string[i] == input_string[j]:
			j += 1
		snake_case__				:   int     =						j

	return prefix_result







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	return max(prefix_function(snake_case_     )     )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



