


# 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       _UpperCamelCase    (					__snake_case    ,		__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 1

				@register_to_config
				def __init__(						self				,   a__=2000				,   a__=0.1				,   a__=20				,   a__=1e-3   )	->				Any:
					A								=				None
					A								=				None
					A								=				None

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				Any:
					A								=				torch.linspace(1				,   self.config.sampling_eps				,   a__				,   device=a__   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__=None   )	->				str:
					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(a__   )
					A								=				drift - diffusion**2 * score
					A								=				x + drift * dt

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

					return x, x_mean

				def __len__(						self   )	->				Any:
					return self.config.num_train_timesteps







import math





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   bool:





	"""simple docstring"""







	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5							,					int(math.sqrt(UpperCamelCase__						) + 1						)							,					6						):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def   _lowerCAmelCase				(   UpperCamelCase__:     float = 0.1						) ->   int:





	"""simple docstring"""







	A								=				3
	A								=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1							,					(j + 2) * (j + 2)							,					j + 1						):
			primes += is_prime(UpperCamelCase__						)
		j += 2
	return j


if __name__ == "__main__":
				import doctest

				doctest.testmod()



