

'''simple docstring'''




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     )							->				List[Any]:


    """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     )							->				Optional[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   , )							->				List[str]:


    """simple docstring"""
    pass
  def 						snake_case_			(     self     )							->				str:


    """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_     )							->				Optional[Any]:


    """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     )							->				List[Any]:


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





'''simple docstring'''




from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Optional[Any]						):
  if is_torch_available():
    import torch

    if isinstance(SCREAMING_SNAKE_CASE       ,   torch.Tensor						):
      return True

  return isinstance(SCREAMING_SNAKE_CASE       ,   np.ndarray						)



class        lowercase_	(		a ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	fields(self     )

    # Safety and consistency checks
    if not len(a_     ):
      raise ValueError(F'''{self.__class__.__name__} has no fields.'''     )

    UpperCAmelCase													=	getattr(self   , class_fields[0].name     )
    UpperCAmelCase													=	all(getattr(self   , field.name     ) is None for field in class_fields[1:]     )

    if other_fields_are_none and isinstance(a_   , a_     ):
      for key, value in first_field.items():
        UpperCAmelCase													=	value
    else:
      for field in class_fields:
        UpperCAmelCase													=	getattr(self   , field.name     )
        if v is not None:
          UpperCAmelCase													=	v
  def __delitem__(     self   , *a_   , **a_     )							->				int:


    """simple docstring"""
    raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				Tuple:


    """simple docstring"""
    raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				int:


    """simple docstring"""
    raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				Dict:


    """simple docstring"""
    raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''     )
  def __getitem__(     self   , a_     )							->				Tuple:


    """simple docstring"""
    if isinstance(a_   , a_     ):
      UpperCAmelCase													=	dict(self.items()     )
      return inner_dict[k]
    else:
      return self.to_tuple()[k]
  def __setattr__(     self   , a_   , a_     )							->				Dict:


    """simple docstring"""
    if name in self.keys() and value is not None:
      # Don't call self.__setitem__ to avoid recursion errors
      super().__setitem__(a_   , a_     )
    super().__setattr__(a_   , a_     )
  def __setitem__(     self   , a_   , a_     )							->				str:


    """simple docstring"""
    # Will raise a KeyException if needed
    super().__setitem__(a_   , a_     )
    # Don't call self.__setattr__ to avoid recursion errors
    super().__setattr__(a_   , a_     )





  def 						snake_case_			(     self     )							->				Tuple[Any]:


    """simple docstring"""
    return tuple(self[k] for k in self.keys()     )


