

'''simple docstring'''







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
@flax.struct.dataclass
class 					__a   :
						# setable values
						__SCREAMING_SNAKE_CASE     :					Optional[jnp.ndarray]  =      None
						__SCREAMING_SNAKE_CASE     :					Optional[jnp.ndarray]  =      None
						__SCREAMING_SNAKE_CASE     :					Optional[jnp.ndarray]  =      None

						@classmethod
						def 	_UpperCAmelCase							(		cls	:						str)						->Any:

							"""simple docstring"""



							return cls()






@dataclass
class 					__a   (		_snake_case  ):
						__SCREAMING_SNAKE_CASE     :					ScoreSdeVeSchedulerState
						__SCREAMING_SNAKE_CASE     :					jnp.ndarray
						__SCREAMING_SNAKE_CASE     :					Optional[jnp.ndarray]  =      None






class 					__a   (		_snake_case							,_snake_case  ):

						@property
						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->Tuple:

							"""simple docstring"""



							return True

						@register_to_config
						def __init__(		self	:						List[str]    ,			lowercase__	:						int = 20_00    ,			lowercase__	:						float = 0.15    ,			lowercase__	:						float = 0.01    ,			lowercase__	:						float = 1348.0    ,			lowercase__	:						float = 1e-5    ,			lowercase__	:						int = 1    ,			)						->Union[str, Any]:

							"""simple docstring"""



							pass

						def 	_UpperCAmelCase							(		self	:						Tuple)						->Union[str, Any]:

							"""simple docstring"""



							_lowercase							=     ScoreSdeVeSchedulerState.create()
							return self.set_sigmas(
							    lowercase__    ,			self.config.num_train_timesteps    ,			self.config.sigma_min    ,			self.config.sigma_max    ,			self.config.sampling_eps    ,			)

						def 	_UpperCAmelCase							(		self	:						int    ,			lowercase__	:						ScoreSdeVeSchedulerState    ,			lowercase__	:						int    ,			lowercase__	:						Tuple = ()    ,			lowercase__	:						float = None)						->ScoreSdeVeSchedulerState:

							"""simple docstring"""



							_lowercase							=     sampling_eps if sampling_eps is not None else self.config.sampling_eps

							_lowercase							=     jnp.linspace(1    ,			lowercase__    ,			lowercase__)
							return state.replace(timesteps=lowercase__)

						def 	_UpperCAmelCase							(		self	:						str    ,			lowercase__	:						ScoreSdeVeSchedulerState    ,			lowercase__	:						int    ,			lowercase__	:						float = None    ,			lowercase__	:						float = None    ,			lowercase__	:						float = None    ,			)						->ScoreSdeVeSchedulerState:

							"""simple docstring"""



							_lowercase							=     sigma_min if sigma_min is not None else self.config.sigma_min
							_lowercase							=     sigma_max if sigma_max is not None else self.config.sigma_max
							_lowercase							=     sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if state.timesteps is None:
								_lowercase							=     self.set_timesteps(lowercase__    ,			lowercase__    ,			lowercase__)

							_lowercase							=     jnp.exp(jnp.linspace(jnp.log(lowercase__)    ,			jnp.log(lowercase__)    ,			lowercase__))
							_lowercase							=     jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps])

							return state.replace(discrete_sigmas=lowercase__    ,			sigmas=lowercase__)

						def 	_UpperCAmelCase							(		self	:						Optional[Any]    ,			lowercase__	:						List[str]    ,			lowercase__	:						Any    ,			lowercase__	:						List[str])						->Tuple:

							"""simple docstring"""



							return jnp.where(timesteps == 0    ,			jnp.zeros_like(lowercase__)    ,			state.discrete_sigmas[timesteps - 1])

						def 	_UpperCAmelCase							(		self	:						int    ,			lowercase__	:						ScoreSdeVeSchedulerState    ,			lowercase__	:						jnp.ndarray    ,			lowercase__	:						int    ,			lowercase__	:						jnp.ndarray    ,			lowercase__	:						random.KeyArray    ,			lowercase__	:						bool = True    ,			)						->Union[FlaxSdeVeOutput, Tuple]:

							"""simple docstring"""



							if state.timesteps is None:
								raise ValueError(
								    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""")

							_lowercase							=     timestep * jnp.ones(
							    sample.shape[0]    ,			)
							_lowercase							=     (timestep * (len(state.timesteps) - 1)).long()

							_lowercase							=     state.discrete_sigmas[timesteps]
							_lowercase							=     self.get_adjacent_sigma(lowercase__    ,			lowercase__    ,			lowercase__)
							_lowercase							=     jnp.zeros_like(lowercase__)
							_lowercase							=     (sigma**2 - adjacent_sigma**2) ** 0.5

							# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
							# also equation 47 shows the analog from SDE models to ancestral sampling methods
							_lowercase							=     diffusion.flatten()
							_lowercase							=     broadcast_to_shape_from_left(lowercase__    ,			sample.shape)
							_lowercase							=     drift - diffusion**2 * model_output

							#  equation 6: sample noise for the diffusion term of
							_lowercase							=     random.split(lowercase__    ,			num=1)
							_lowercase							=     random.normal(key=lowercase__    ,			shape=sample.shape)
							_lowercase							=     sample - drift  # subtract because `dt` is a small negative timestep
							# TODO is the variable diffusion the correct scaling term for the noise?
							_lowercase							=     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

							if not return_dict:
								return (prev_sample, prev_sample_mean, state)

							return FlaxSdeVeOutput(prev_sample=lowercase__    ,			prev_sample_mean=lowercase__    ,			state=lowercase__)

						def 	_UpperCAmelCase							(		self	:						int    ,			lowercase__	:						ScoreSdeVeSchedulerState    ,			lowercase__	:						jnp.ndarray    ,			lowercase__	:						jnp.ndarray    ,			lowercase__	:						random.KeyArray    ,			lowercase__	:						bool = True    ,			)						->Union[FlaxSdeVeOutput, Tuple]:

							"""simple docstring"""



							if state.timesteps is None:
								raise ValueError(
								    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""")

							# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
							# sample noise for correction
							_lowercase							=     random.split(lowercase__    ,			num=1)
							_lowercase							=     random.normal(key=lowercase__    ,			shape=sample.shape)

							# compute step size from the model_output, the noise, and the snr
							_lowercase							=     jnp.linalg.norm(lowercase__)
							_lowercase							=     jnp.linalg.norm(lowercase__)
							_lowercase							=     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
							_lowercase							=     step_size * jnp.ones(sample.shape[0])

							# compute corrected sample: model_output term and noise term
							_lowercase							=     step_size.flatten()
							_lowercase							=     broadcast_to_shape_from_left(lowercase__    ,			sample.shape)
							_lowercase							=     sample + step_size * model_output
							_lowercase							=     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

							if not return_dict:
								return (prev_sample, state)

							return FlaxSdeVeOutput(prev_sample=lowercase__    ,			state=lowercase__)


						def __len__(		self	:						Optional[Any])						->Union[str, Any]:

							"""simple docstring"""



							return self.config.num_train_timesteps







'''simple docstring'''







from __future__ import annotations

import math
import random






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     d * e - 1
	_lowercase							=     0
	_lowercase							=     0
	while p == 0:
		_lowercase							=     random.randint(2							,       n - 1	)
		_lowercase							=     k
		while True:
			if t % 2 == 0:
				_lowercase							=     t // 2
				_lowercase							=     (g**t) % n
				_lowercase							=     math.gcd(x - 1							,       snake_case_	)
				if x > 1 and y > 1:
					_lowercase							=     y
					_lowercase							=     n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




