


# 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  snake_case_     (lowerCamelCase_			,		lowerCamelCase_   ):
					UpperCAmelCase__      :					str					   =    1
					@register_to_config
					def __init__(   self     :int    ,__snake_case     :Any=20_00    ,__snake_case     :str=0.1    ,__snake_case     :List[str]=20    ,__snake_case     :Dict=1E-3   )      ->					List[str]:
												a__    =  None
												a__    =  None
												a__    =  None
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :List[Any]    ,__snake_case     :Union[str, torch.device] = None   )      ->					Tuple:
												a__    =  torch.linspace(1    ,self.config.sampling_eps    ,__snake_case    ,device=__snake_case   )
					def 				lowerCamelCase__(   self     :str    ,__snake_case     :str    ,__snake_case     :str    ,__snake_case     :Union[str, Any]    ,__snake_case     :Optional[Any]=None   )      ->					Union[str, Any]:
												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
												a__    =  (
												    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
												)
												a__    =  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff   )   )
												a__    =  std.flatten()
												while len(std.shape   ) < len(score.shape   ):
																			a__    =  std.unsqueeze(-1   )
												a__    =  -score / std

												# compute
												a__    =  -1.0 / len(self.timesteps   )

												a__    =  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
												a__    =  beta_t.flatten()
												while len(beta_t.shape   ) < len(x.shape   ):
																			a__    =  beta_t.unsqueeze(-1   )
												a__    =  -0.5 * beta_t * x

												a__    =  torch.sqrt(__snake_case   )
												a__    =  drift - diffusion**2 * score
												a__    =  x + drift * dt

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

												return x, x_mean



					def __len__(   self     :Union[str, Any]   )      ->					Optional[int]:
												return self.config.num_train_timesteps








import unittest

from knapsack import greedy_knapsack as kp







class  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Union[str, Any]:
												a__    =  [10, 20, 30, 40, 50, 60]
												a__    =  [2, 4, 6, 8, 10, 12]
												a__    =  1_00
												self.assertEqual(kp.calc_profit(__snake_case    ,__snake_case    ,__snake_case   )    ,2_10   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[int]:
												self.assertRaisesRegex(__snake_case    ,'max_weight must greater than zero.'   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					int:
												self.assertRaisesRegex(__snake_case    ,'Weight can not be negative.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					List[str]:
												self.assertRaisesRegex(__snake_case    ,'Profit can not be negative.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[Any]:
												self.assertRaisesRegex(__snake_case    ,'max_weight must greater than zero.'   )



					def 				lowerCamelCase__(   self     :int   )      ->					List[Any]:
												self.assertRaisesRegex(
												    __snake_case    ,'The length of profit and weight must be same.'   )


if __name__ == "__main__":
						unittest.main()




