




"""simple docstring"""






def    UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					if number < 0 or shift_amount < 0:
										raise ValueError('''both inputs must be positive integers'''    )

					_lowerCAmelCase:Any									=  str(bin(snake_case    )    )
					binary_number += "0" * shift_amount
					return binary_number





def    UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					if number < 0 or shift_amount < 0:
										raise ValueError('''both inputs must be positive integers'''    )

					_lowerCAmelCase:int									=  str(bin(snake_case    )    )[2:]
					if shift_amount >= len(snake_case    ):
										return "0b0"
					_lowerCAmelCase:int									=  binary_number[: len(snake_case    ) - shift_amount]
					return "0b" + shifted_binary_number





def    UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					if number >= 0:  # Get binary representation of positive number
										_lowerCAmelCase:List[str]									=  '''0''' + str(bin(snake_case    )    ).strip('''-'''    )[2:]
					else:  # Get binary (2's complement) representation of negative number
										_lowerCAmelCase:List[str]									=  len(bin(snake_case    )[3:]    )  # Find 2's complement of number
										_lowerCAmelCase:Dict									=  bin(abs(snake_case    ) - (1 << binary_number_length)    )[3:]
										_lowerCAmelCase:int									=  (
										    '''1''' + '''0''' * (binary_number_length - len(snake_case    )) + binary_number
										)

					if shift_amount >= len(snake_case    ):
										return "0b" + binary_number[0] * len(snake_case    )
					return (
					    "0b"
					    + binary_number[0] * shift_amount
					    + binary_number[: len(snake_case    ) - shift_amount]
					)


if __name__ == "__main__":
				import doctest

				doctest.testmod()












"""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   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Union[str, Any]     ,a__      :	List[str]=2000     ,a__      :	Tuple=0.1     ,a__      :	Union[str, Any]=20     ,a__      :	int=1E-3)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  None
											_lowerCAmelCase:List[Any]									=  None
											_lowerCAmelCase:Dict									=  None





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Any     ,a__      :	Union[str, torch.device] = None)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:str									=  torch.linspace(1     ,self.config.sampling_eps     ,a__     ,device=a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	str     ,a__      :	List[Any]     ,a__      :	Union[str, Any]     ,a__      :	Any=None)						->       Tuple:




											"""simple docstring"""







											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
											_lowerCAmelCase:Union[str, Any]									=  (
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											_lowerCAmelCase:Dict									=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
											_lowerCAmelCase:Optional[Any]									=  std.flatten()
											while len(std.shape) < len(score.shape):
																_lowerCAmelCase:str									=  std.unsqueeze(-1)
											_lowerCAmelCase:Optional[int]									=  -score / std

											# compute
											_lowerCAmelCase:Optional[Any]									=  -1.0 / len(self.timesteps)

											_lowerCAmelCase:Union[str, Any]									=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											_lowerCAmelCase:Any									=  beta_t.flatten()
											while len(beta_t.shape) < len(x.shape):
																_lowerCAmelCase:List[str]									=  beta_t.unsqueeze(-1)
											_lowerCAmelCase:Tuple									=  -0.5 * beta_t * x

											_lowerCAmelCase:str									=  torch.sqrt(a__)
											_lowerCAmelCase:Union[str, Any]									=  drift - diffusion**2 * score
											_lowerCAmelCase:List[str]									=  x + drift * dt

											# add noise
											_lowerCAmelCase:Any									=  randn_tensor(x.shape     ,layout=x.layout     ,generator=a__     ,device=x.device     ,dtype=x.dtype)
											_lowerCAmelCase:Union[str, Any]									=  x_mean + diffusion * math.sqrt(-dt) * noise

											return x, x_mean







						def __len__(							self      :	int)						->       Tuple:




											"""simple docstring"""







											return self.config.num_train_timesteps






