

'''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     lowercase_		:

  '''simple docstring'''





  __lowerCAmelCase   :	Optional[jnp.ndarray]      					=				None
  __lowerCAmelCase   :	Optional[jnp.ndarray]      					=				None
  __lowerCAmelCase   :	Optional[jnp.ndarray]      					=				None
  @classmethod
  def   snake_case_     (       cls       )							->			Tuple:


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




@dataclass
class     lowercase_		(						a	):

  '''simple docstring'''





  __lowerCAmelCase   :	ScoreSdeVeSchedulerState
  __lowerCAmelCase   :	jnp.ndarray
  __lowerCAmelCase   :	Optional[jnp.ndarray]      					=				None




class     lowercase_		(						a	,    a	):

  '''simple docstring'''





  @property
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    return True
  @register_to_config
  def __init__(       self				,	a_ = 2_0_0_0				,	a_ = 0.15				,	a_ = 0.01				,	a_ = 1348.0				,	a_ = 1E-5				,	a_ = 1				,	)							->			str:


    """simple docstring"""
    pass
  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		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   snake_case_     (       self				,	a_				,	a_				,	a_ = ()				,	a_ = None       )							->			ScoreSdeVeSchedulerState:


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

    UpperCAmelCase  							=		jnp.linspace(1				,	a_				,	a_       )
    return state.replace(timesteps=a_       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = None				,	a_ = None				,	a_ = None				,	)							->			ScoreSdeVeSchedulerState:


    """simple docstring"""
    UpperCAmelCase  							=		sigma_min if sigma_min is not None else self.config.sigma_min
    UpperCAmelCase  							=		sigma_max if sigma_max is not None else self.config.sigma_max
    UpperCAmelCase  							=		sampling_eps if sampling_eps is not None else self.config.sampling_eps
    if state.timesteps is None:
      UpperCAmelCase  							=		self.set_timesteps(a_				,	a_				,	a_       )

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

    return state.replace(discrete_sigmas=a_				,	sigmas=a_       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_       )							->			int:


    """simple docstring"""
    return jnp.where(timesteps == 0				,	jnp.zeros_like(a_       )				,	state.discrete_sigmas[timesteps - 1]       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_				,	a_				,	a_ = 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'       )

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

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

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

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

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


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





'''simple docstring'''




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				str  ):
  UpperCAmelCase  							=		str(SCREAMING_SNAKE_CASE  ).strip()
  if not bin_string:
    raise ValueError('Empty string was passed to the function'  )
  UpperCAmelCase  							=		bin_string[0] == '-'
  if is_negative:
    UpperCAmelCase  							=		bin_string[1:]
  if not all(char in '01' for char in bin_string  ):
    raise ValueError('Non-binary value was passed to the function'  )
  UpperCAmelCase  							=		0
  for char in bin_string:
    UpperCAmelCase  							=		2 * decimal_number + int(SCREAMING_SNAKE_CASE  )
  return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
  from doctest import testmod

  testmod()


