


"""simple docstring"""






class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__     ):
								# we need a list not a string, so do something to change the type
								SCREAMING_SNAKE_CASE__     							=				arr.split(","     )






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				[int(self.array[0]     )] * len(self.array     )
								SCREAMING_SNAKE_CASE__     							=				[int(self.array[0]     )] * len(self.array     )
								for i in range(1       ,		len(self.array     )     ):
												SCREAMING_SNAKE_CASE__     							=				max(
												    int(self.array[i]     ) + sum_value[i - 1]       ,		int(self.array[i]     )     )
												SCREAMING_SNAKE_CASE__     							=				max(sum_value[i]       ,		rear[i - 1]     )
								return rear[len(self.array     ) - 1]


if __name__ == "__main__":
	_lowerCamelCase     =  input('please input some numbers:')
	_lowerCamelCase     =  SubArray(whole_array)
	_lowerCamelCase     =  array.solve_sub_array()
	print(('the results is:', re))




"""simple docstring"""






# 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 			lowerCamelCase_		(			lowercase       ,  lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					1



				@register_to_config
				def __init__(						self       ,		UpperCAmelCase__=2000       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=20       ,		UpperCAmelCase__=1e-3     ):
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				torch.linspace(1       ,		self.config.sampling_eps       ,		UpperCAmelCase__       ,		device=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__=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
								SCREAMING_SNAKE_CASE__     							=				(
								    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
								)
								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
								SCREAMING_SNAKE_CASE__     							=				std.flatten()
								while len(std.shape     ) < len(score.shape     ):
												SCREAMING_SNAKE_CASE__     							=				std.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-score / std

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

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

								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				drift - diffusion**2 * score
								SCREAMING_SNAKE_CASE__     							=				x + drift * dt

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

								return x, x_mean






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