






# 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 	a_:



     """simple docstring"""



     __snake_case		:				Optional[jnp.ndarray]  					=None
     __snake_case		:				Optional[jnp.ndarray]  					=None
     __snake_case		:				Optional[jnp.ndarray]  					=None





     @classmethod
     def     __UpperCamelCase						(		cls					:	Optional[Any])							->	int:

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

@dataclass
class 	a_(		lowercase__	):



     """simple docstring"""



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

class 	a_(		lowercase__			,							lowercase__	):



     """simple docstring"""



     @property
     def     __UpperCamelCase						(		self					:	Tuple)							->	str:

      """simple docstring"""
      return True





     @register_to_config
     def __init__(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	int = 2_0_0_0  ,  lowerCAmelCase__					:	float = 0.15  ,  lowerCAmelCase__					:	float = 0.01  ,  lowerCAmelCase__					:	float = 13_48.0  ,  lowerCAmelCase__					:	float = 1e-5  ,  lowerCAmelCase__					:	int = 1  ,  )							->	str:

      """simple docstring"""
      pass





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	List[Any]:

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





     def     __UpperCamelCase						(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	ScoreSdeVeSchedulerState  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	Tuple = ()  ,  lowerCAmelCase__					:	float = None)							->	ScoreSdeVeSchedulerState:

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

      SCREAMING_SNAKE_CASE				    =		jnp.linspace(1  ,  lowerCAmelCase__  ,  lowerCAmelCase__)
      return state.replace(timesteps=lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	ScoreSdeVeSchedulerState  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	float = None  ,  lowerCAmelCase__					:	float = None  ,  lowerCAmelCase__					:	float = None  ,  )							->	ScoreSdeVeSchedulerState:

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

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

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





     def     __UpperCamelCase						(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	Dict)							->	Dict:

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





     def     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	ScoreSdeVeSchedulerState  ,  lowerCAmelCase__					:	jnp.ndarray  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	jnp.ndarray  ,  lowerCAmelCase__					:	random.KeyArray  ,  lowerCAmelCase__					:	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')

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

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

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





     def     __UpperCamelCase						(		self					:	Dict  ,  lowerCAmelCase__					:	ScoreSdeVeSchedulerState  ,  lowerCAmelCase__					:	jnp.ndarray  ,  lowerCAmelCase__					:	jnp.ndarray  ,  lowerCAmelCase__					:	random.KeyArray  ,  lowerCAmelCase__					:	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
      SCREAMING_SNAKE_CASE				    =		random.split(lowerCAmelCase__  ,  num=1)
      SCREAMING_SNAKE_CASE				    =		random.normal(key=lowerCAmelCase__  ,  shape=sample.shape)

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

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

      if not return_dict:
       return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=lowerCAmelCase__  ,  state=lowerCAmelCase__)





     def __len__(		self					:	Any)							->	List[Any]:

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







import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


lowercase_									=    set()
lowercase_									=    get_logger(__name__)
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[str] = None )				-> Optional[int]:





    '''simple docstring'''






    def decorator(SCREAMING_SNAKE_CASE__     :      Callable ):
        global _emitted_deprecation_warnings

        if inspect.isclass(SCREAMING_SNAKE_CASE__ ):
            A__        =  deprecated_class_or_function.__init__
            A__        =  deprecated_class_or_function.__name__
        else:
            A__        =  deprecated_class_or_function
            A__        =  deprecated_function.__name__
            # Support deprecating __init__ class method: class name instead
            A__        =  name if name != '__init__' else deprecated_function.__qualname__.split('.' )[-2]

        A__        =  (
            f'{name} is deprecated and will be removed in the next major version of datasets.' + f' {help_message}'
            if help_message
            else ''
        )

        @wraps(SCREAMING_SNAKE_CASE__ )
        def wrapper(*SCREAMING_SNAKE_CASE__     :      Tuple       ,     **SCREAMING_SNAKE_CASE__     :      Any ):
            A__        =  hash(SCREAMING_SNAKE_CASE__ )
            if func_hash not in _emitted_deprecation_warnings:
                warnings.warn(SCREAMING_SNAKE_CASE__       ,     category=SCREAMING_SNAKE_CASE__       ,     stacklevel=2 )
                _emitted_deprecation_warnings.add(SCREAMING_SNAKE_CASE__ )
            return deprecated_function(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__ )

        A__        =  'deprecated'

        if inspect.isclass(SCREAMING_SNAKE_CASE__ ):
            A__        =  wrapper
            return deprecated_class_or_function
        else:
            return wrapper

    return decorator


class 					A		(				enum.EnumMeta					):







    """simple docstring"""





    def __getattribute__( cls					:			Optional[Any],lowercase_					:			int						)->       Optional[Any]:





        '''simple docstring'''







        A__        =  super().__getattribute__(lowercase_						)
        if isinstance(lowercase_,enum.Enum						) and obj._on_access:
            obj._on_access()
        return obj


    def __getitem__( cls					:			int,lowercase_					:			Any						)->       Tuple:





        '''simple docstring'''







        A__        =  super().__getitem__(lowercase_						)
        if member._on_access:
            member._on_access()
        return member


    def __call__( cls					:			Optional[int],lowercase_					:			Tuple,lowercase_					:			List[Any]=None,*, lowercase_					:			Any=None,lowercase_					:			Optional[Any]=None,lowercase_					:			Any=None,lowercase_					:			Optional[int]=1						)->       List[str]:





        '''simple docstring'''







        A__        =  super().__call__(lowercase_,lowercase_,module=lowercase_,qualname=lowercase_,type=lowercase_,start=lowercase_						)
        if isinstance(lowercase_,enum.Enum						) and obj._on_access:
            obj._on_access()
        return obj




class 					A		(				enum.Enum       ,				metaclass=_UpperCAmelCase					):







    """simple docstring"""





    def __new__( cls					:			Any,lowercase_					:			str						)->       Any:





        '''simple docstring'''







        A__        =  object.__new__(cls						)
        A__        =  value
        A__        =  member.deprecate
        return member


    @property
    def 		snake_case__	( self					:			List[Any]						)->       List[Any]:





        '''simple docstring'''







        return ""


    def 		snake_case__	( self					:			List[str]						)->       Dict:





        '''simple docstring'''







        A__        =  F' {self.help_message}' if self.help_message else ''
        warnings.warn(
            F'\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'
            + help_message,lowercase_,stacklevel=3,)

