




"""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__       (			__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE    ):


    """simple docstring"""





    __UpperCAmelCase :     Dict       =      1



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



    def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	Union[str, torch.device] = None						):
      __lowercase				:		List[Any]												=				torch.linspace(1    ,   self.config.sampling_eps    ,   __snake_case    ,   device=__snake_case						)



    def       snake_case    (     self					:	Any    ,   lowercase__					:	str    ,   lowercase__					:	Any    ,   lowercase__					:	Dict    ,   lowercase__					:	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
      __lowercase				:		Any												=				(
          -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      __lowercase				:		str												=				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff						)						)
      __lowercase				:		Optional[Any]												=				std.flatten()
      while len(std.shape						) < len(score.shape						):
        __lowercase				:		Any												=				std.unsqueeze(-1						)
      __lowercase				:		Optional[int]												=				-score / std

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

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

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

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

      return x, x_mean



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






"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->int:






		"""simple docstring"""


		return abs(_lowerCamelCase				) if a == 0 else greatest_common_divisor(b % a,  _lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->int:






		"""simple docstring"""


		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				__lowercase						,__lowercase				:		Any												=				y, x % y
		return abs(_lowerCamelCase				)

def 						snake_case__       (					)			->Optional[int]:






		"""simple docstring"""


		try:
				__lowercase				:		Optional[int]												=				input("Enter two integers separated by comma (,): "				).split(","				)
				__lowercase				:		Optional[Any]												=				int(nums[0]				)
				__lowercase				:		str												=				int(nums[1]				)
				print(
				    F'greatest_common_divisor({num_a}, {num_a}) = '
				    F'{greatest_common_divisor(_lowerCamelCase,  _lowerCamelCase				)}'				)
				print(F'By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(_lowerCamelCase,  _lowerCamelCase				)}'				)
		except (IndexError, UnboundLocalError, ValueError):
				print("Wrong input"				)


if __name__ == "__main__":
						main()
