

"""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 	_lowerCAmelCase    :
							# setable values
							__lowerCAmelCase					:    Optional[jnp.ndarray]     =  None
							__lowerCAmelCase					:    Optional[jnp.ndarray]     =  None
							__lowerCAmelCase					:    Optional[jnp.ndarray]     =  None

							@classmethod
							def 					_lowerCAmelCase  ( cls       :    Any    )   ->    int:

									"""simple docstring"""
									return cls()




@dataclass
class 	_lowerCAmelCase    (      __snake_case       ):
							__lowerCAmelCase					:    ScoreSdeVeSchedulerState
							__lowerCAmelCase					:    jnp.ndarray
							__lowerCAmelCase					:    Optional[jnp.ndarray]     =  None




class 	_lowerCAmelCase    (      __snake_case       ,       __snake_case       ):

							@property
							def 					_lowerCAmelCase  ( self       :    int    )   ->    Dict:

									"""simple docstring"""
									return True

							@register_to_config
							def __init__( self       :    str ,		a       :    int = 2000 ,		a       :    float = 0.15 ,		a       :    float = 0.01 ,		a       :    float = 1_348.0 ,		a       :    float = 1E-5 ,		a       :    int = 1 ,		)   ->    int:

									"""simple docstring"""
									pass

							def 					_lowerCAmelCase  ( self       :    Dict    )   ->    Optional[Any]:

									"""simple docstring"""
									lowercase										=							ScoreSdeVeSchedulerState.create()
									return self.set_sigmas(
									    a ,		self.config.num_train_timesteps ,		self.config.sigma_min ,		self.config.sigma_max ,		self.config.sampling_eps ,		)

							def 					_lowerCAmelCase  ( self       :    Optional[int] ,		a       :    ScoreSdeVeSchedulerState ,		a       :    int ,		a       :    Tuple = () ,		a       :    float = None    )   ->    ScoreSdeVeSchedulerState:

									"""simple docstring"""
									lowercase										=							sampling_eps if sampling_eps is not None else self.config.sampling_eps

									lowercase										=							jnp.linspace(1 ,		a ,		a    )
									return state.replace(timesteps=a    )

							def 					_lowerCAmelCase  ( self       :    str ,		a       :    ScoreSdeVeSchedulerState ,		a       :    int ,		a       :    float = None ,		a       :    float = None ,		a       :    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(a ,		a ,		a    )

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

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

							def 					_lowerCAmelCase  ( self       :    Optional[int] ,		a       :    str ,		a       :    Union[str, Any] ,		a       :    Union[str, Any]    )   ->    Any:

									"""simple docstring"""
									return jnp.where(timesteps == 0 ,		jnp.zeros_like(a    ) ,		state.discrete_sigmas[timesteps - 1]    )

							def 					_lowerCAmelCase  ( self       :    List[Any] ,		a       :    ScoreSdeVeSchedulerState ,		a       :    jnp.ndarray ,		a       :    int ,		a       :    jnp.ndarray ,		a       :    random.KeyArray ,		a       :    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(a ,		a ,		a    )
									lowercase										=							jnp.zeros_like(a    )
									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(a ,		sample.shape    )
									lowercase										=							drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									lowercase										=							random.split(a ,		num=1    )
									lowercase										=							random.normal(key=a ,		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=a ,		prev_sample_mean=a ,		state=a    )

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    ScoreSdeVeSchedulerState ,		a       :    jnp.ndarray ,		a       :    jnp.ndarray ,		a       :    random.KeyArray ,		a       :    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(a ,		num=1    )
									lowercase										=							random.normal(key=a ,		shape=sample.shape    )

									# compute step size from the model_output, the noise, and the snr
									lowercase										=							jnp.linalg.norm(a    )
									lowercase										=							jnp.linalg.norm(a    )
									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(a ,		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=a ,		state=a    )

							def __len__( self       :    Tuple    )   ->    Optional[Any]:

									"""simple docstring"""
									return self.config.num_train_timesteps


"""simple docstring"""






from typing import Callable
def 		A_	(   __UpperCamelCase     :				Callable				):

		def wrapper(__UpperCamelCase     :				Optional[Any]				):
				lowercase										=							function_with_docstring.__doc__
				return target_function

		return wrapper