






'''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    _snake_case   :

					"""simple docstring"""





					_UpperCamelCase		 =      None
					_UpperCamelCase		 =      None
					_UpperCamelCase		 =      None




					@classmethod
					def  __SCREAMING_SNAKE_CASE (     cls     )      ->	List[Any]:
										return cls()



@dataclass
class    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      None



class    _snake_case   (				snake_case     ,					snake_case					):

					"""simple docstring"""





					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Any:
										return True




					@register_to_config
					def __init__(     self   , UpperCAmelCase__ = 2000   , UpperCAmelCase__ = 0.1_5   , UpperCAmelCase__ = 0.0_1   , UpperCAmelCase__ = 1_3_4_8.0   , UpperCAmelCase__ = 1e-5   , UpperCAmelCase__ = 1   , )      ->	Optional[int]:
										pass




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[Any]:
										a_									=   ScoreSdeVeSchedulerState.create()
										return self.set_sigmas(
										    UpperCAmelCase__   , self.config.num_train_timesteps   , self.config.sigma_min   , self.config.sigma_max   , self.config.sampling_eps   , )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = ()   , UpperCAmelCase__ = None     )      ->	ScoreSdeVeSchedulerState:
										a_									=   sampling_eps if sampling_eps is not None else self.config.sampling_eps

										a_									=   jnp.linspace(1   , UpperCAmelCase__   , UpperCAmelCase__     )
										return state.replace(timesteps=UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = None   , UpperCAmelCase__ = None   , UpperCAmelCase__ = None   , )      ->	ScoreSdeVeSchedulerState:
										a_									=   sigma_min if sigma_min is not None else self.config.sigma_min
										a_									=   sigma_max if sigma_max is not None else self.config.sigma_max
										a_									=   sampling_eps if sampling_eps is not None else self.config.sampling_eps
										if state.timesteps is None:
															a_									=   self.set_timesteps(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )

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

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




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	str:
										return jnp.where(timesteps == 0   , jnp.zeros_like(UpperCAmelCase__     )   , state.discrete_sigmas[timesteps - 1]     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = True   , )      ->	Union[FlaxSdeVeOutput, Tuple]:
										if state.timesteps is None:
															raise ValueError(
															    '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'     )

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

										a_									=   state.discrete_sigmas[timesteps]
										a_									=   self.get_adjacent_sigma(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )
										a_									=   jnp.zeros_like(UpperCAmelCase__     )
										a_									=   (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
										a_									=   diffusion.flatten()
										a_									=   broadcast_to_shape_from_left(UpperCAmelCase__   , sample.shape     )
										a_									=   drift - diffusion**2 * model_output

										#  equation 6: sample noise for the diffusion term of
										a_									=   random.split(UpperCAmelCase__   , num=1     )
										a_									=   random.normal(key=UpperCAmelCase__   , shape=sample.shape     )
										a_									=   sample - drift  # subtract because `dt` is a small negative timestep
										# TODO is the variable diffusion the correct scaling term for the noise?
										a_									=   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=UpperCAmelCase__   , prev_sample_mean=UpperCAmelCase__   , state=UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = True   , )      ->	Union[FlaxSdeVeOutput, Tuple]:
										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
										a_									=   random.split(UpperCAmelCase__   , num=1     )
										a_									=   random.normal(key=UpperCAmelCase__   , shape=sample.shape     )

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

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

										if not return_dict:
															return (prev_sample, state)

										return FlaxSdeVeOutput(prev_sample=UpperCAmelCase__   , state=UpperCAmelCase__     )




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









'''simple docstring'''
def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round((float(_UpperCAmelCase ) * 9 / 5) + 3_2					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round(float(_UpperCAmelCase ) + 2_7_3.1_5					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round((float(_UpperCAmelCase ) * 9 / 5) + 4_9_1.6_7					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round((float(_UpperCAmelCase ) - 3_2) * 5 / 9					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round(((float(_UpperCAmelCase ) - 3_2) * 5 / 9) + 2_7_3.1_5					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round(float(_UpperCAmelCase ) + 4_5_9.6_7					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round(float(_UpperCAmelCase ) - 2_7_3.1_5					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round(((float(_UpperCAmelCase ) - 2_7_3.1_5) * 9 / 5) + 3_2					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round((float(_UpperCAmelCase ) * 9 / 5)					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round((float(_UpperCAmelCase ) - 4_9_1.6_7) * 5 / 9					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round(float(_UpperCAmelCase ) - 4_5_9.6_7					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round((float(_UpperCAmelCase ) * 5 / 9)					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round((float(_UpperCAmelCase ) * 1.2_5 + 2_7_3.1_5)					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round((float(_UpperCAmelCase ) * 2.2_5 + 3_2)					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round((float(_UpperCAmelCase ) * 1.2_5)					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = 2 )				->				float:






					"""simple docstring"""







					return round((float(_UpperCAmelCase ) * 2.2_5 + 3_2 + 4_5_9.6_7)					,  _UpperCAmelCase )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

