code: 
  # 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      __lowercase	:
  	# setable values
  	_A   			=      None
  	_A   			=      None
  	_A   			=      None
  
  
  
  
  
  
  	@classmethod
  	def 			_a(cls							:   int						)   -> Optional[int]:
  							return cls()
  
  
  
  
  @dataclass
  class      __lowercase	(	__snake_case					):
  	_A   			=      42
  	_A   			=      42
  	_A   			=      None
  
  
  
  
  class      __lowercase	(	__snake_case      ,   __snake_case					):
  
  
  
  
  
  
  	@property
  	def 			_a(self							:   List[str]						)   -> Union[str, Any]:
  							return True
  
  
  
  
  
  
  	@register_to_config
  	def __init__(self							:   Optional[Any]			,  snake_case							:   int = 2000			,  snake_case							:   float = 0.15			,  snake_case							:   float = 0.01			,  snake_case							:   float = 13_48.0			,  snake_case							:   float = 1e-5			,  snake_case							:   int = 1			,  )   -> Union[str, Any]:
  							pass
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> List[str]:
  							_lowercase					:  Dict    			=      ScoreSdeVeSchedulerState.create()
  							return self.set_sigmas(
  							    snake_case			,  self.config.num_train_timesteps			,  self.config.sigma_min			,  self.config.sigma_max			,  self.config.sampling_eps			,  )
  
  
  
  
  
  
  	def 			_a(self							:   Tuple			,  snake_case							:   ScoreSdeVeSchedulerState			,  snake_case							:   int			,  snake_case							:   Tuple = ()			,  snake_case							:   float = None						)   -> ScoreSdeVeSchedulerState:
  							_lowercase					:  Optional[Any]    			=      sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
  							_lowercase					:  str    			=      jnp.linspace(1			,  snake_case			,  snake_case						)
  							return state.replace(timesteps=snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   str			,  snake_case							:   ScoreSdeVeSchedulerState			,  snake_case							:   int			,  snake_case							:   float = None			,  snake_case							:   float = None			,  snake_case							:   float = None			,  )   -> ScoreSdeVeSchedulerState:
  							_lowercase					:  Union[str, Any]    			=      sigma_min if sigma_min is not None else self.config.sigma_min
  							_lowercase					:  str    			=      sigma_max if sigma_max is not None else self.config.sigma_max
  							_lowercase					:  Union[str, Any]    			=      sampling_eps if sampling_eps is not None else self.config.sampling_eps
  							if state.timesteps is None:
  													_lowercase					:  Dict    			=      self.set_timesteps(snake_case			,  snake_case			,  snake_case						)
  
  							_lowercase					:  List[Any]    			=      jnp.exp(jnp.linspace(jnp.log(snake_case						)			,  jnp.log(snake_case						)			,  snake_case						)						)
  							_lowercase					:  List[str]    			=      jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]						)
  
  							return state.replace(discrete_sigmas=snake_case			,  sigmas=snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Dict			,  snake_case							:   Dict			,  snake_case							:   Dict			,  snake_case							:   str						)   -> Dict:
  							return jnp.where(timesteps == 0			,  jnp.zeros_like(snake_case						)			,  state.discrete_sigmas[timesteps - 1]						)
  
  
  
  
  
  
  	def 			_a(self							:   List[str]			,  snake_case							:   ScoreSdeVeSchedulerState			,  snake_case							:   jnp.ndarray			,  snake_case							:   int			,  snake_case							:   jnp.ndarray			,  snake_case							:   random.KeyArray			,  snake_case							:   bool = 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"						)
  
  							_lowercase					:  str    			=      timestep * jnp.ones(
  							    sample.shape[0]			,  )
  							_lowercase					:  Tuple    			=      (timestep * (len(state.timesteps						) - 1)).long()
  
  							_lowercase					:  str    			=      state.discrete_sigmas[timesteps]
  							_lowercase					:  List[str]    			=      self.get_adjacent_sigma(snake_case			,  snake_case			,  snake_case						)
  							_lowercase					:  Dict    			=      jnp.zeros_like(snake_case						)
  							_lowercase					:  str    			=      (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					:  str    			=      diffusion.flatten()
  							_lowercase					:  Optional[Any]    			=      broadcast_to_shape_from_left(snake_case			,  sample.shape						)
  							_lowercase					:  int    			=      drift - diffusion**2 * model_output
  
  							#  equation 6: sample noise for the diffusion term of
  							_lowercase					:  Dict    			=      random.split(snake_case			,  num=1						)
  							_lowercase					:  int    			=      random.normal(key=snake_case			,  shape=sample.shape						)
  							_lowercase					:  Optional[Any]    			=      sample - drift  # subtract because `dt` is a small negative timestep
  							# TODO is the variable diffusion the correct scaling term for the noise?
  							_lowercase					:  Optional[Any]    			=      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=snake_case			,  prev_sample_mean=snake_case			,  state=snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[int]			,  snake_case							:   ScoreSdeVeSchedulerState			,  snake_case							:   jnp.ndarray			,  snake_case							:   jnp.ndarray			,  snake_case							:   random.KeyArray			,  snake_case							:   bool = 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
  							_lowercase					:  List[Any]    			=      random.split(snake_case			,  num=1						)
  							_lowercase					:  Union[str, Any]    			=      random.normal(key=snake_case			,  shape=sample.shape						)
  
  							# compute step size from the model_output, the noise, and the snr
  							_lowercase					:  Optional[Any]    			=      jnp.linalg.norm(snake_case						)
  							_lowercase					:  int    			=      jnp.linalg.norm(snake_case						)
  							_lowercase					:  Optional[int]    			=      (self.config.snr * noise_norm / grad_norm) ** 2 * 2
  							_lowercase					:  Union[str, Any]    			=      step_size * jnp.ones(sample.shape[0]						)
  
  							# compute corrected sample: model_output term and noise term
  							_lowercase					:  str    			=      step_size.flatten()
  							_lowercase					:  int    			=      broadcast_to_shape_from_left(snake_case			,  sample.shape						)
  							_lowercase					:  List[Any]    			=      sample + step_size * model_output
  							_lowercase					:  Optional[Any]    			=      prev_sample_mean + ((step_size * 2) ** 0.5) * noise
  
  							if not return_dict:
  													return (prev_sample, state)
  
  							return FlaxSdeVeOutput(prev_sample=snake_case			,  state=snake_case						)
  
  
  
  
  
  
  	def __len__(self							:   Optional[Any]						)   -> str:
  							return self.config.num_train_timesteps
  
  
  
  
  
style_context: 
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> bool:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if not isinstance(_UpperCAmelCase   , _UpperCAmelCase     ):
  												_lowercase					:  Optional[int]    			=      f"""Input value of [number={number}] must be an integer"""
  												raise TypeError(_UpperCAmelCase     )
  						if number < 0 or number % 2 == 1:
  												return False
  						_lowercase					:  List[str]    			=      int(number**0.5     )
  						return number == number_sqrt * (number_sqrt + 1)
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  
  
  
  