




# 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 							__SCREAMING_SNAKE_CASE				:
					_lowerCamelCase						    =					None
					_lowerCamelCase						    =					None
					_lowerCamelCase						    =					None

					@classmethod
					def       lowercase_				(							cls   ):







						'''simple docstring'''




						return cls()


@dataclass
class 							__SCREAMING_SNAKE_CASE				(						_UpperCAmelCase    ):
					_lowerCamelCase						    =					42
					_lowerCamelCase						    =					42
					_lowerCamelCase						    =					None


class 							__SCREAMING_SNAKE_CASE				(						_UpperCAmelCase  ,	_UpperCAmelCase    ):

					@property
					def       lowercase_				(							self   ):







						'''simple docstring'''




						return True

					@register_to_config
					def __init__(							self					,			__lowercase = 2_000					,			__lowercase = 0.15					,			__lowercase = 0.01					,			__lowercase = 1_348.0					,			__lowercase = 1E-5					,			__lowercase = 1					,			):







						'''simple docstring'''




						pass

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				ScoreSdeVeSchedulerState.create()
						return self.set_sigmas(
						    lowercase_					,			self.config.num_train_timesteps					,			self.config.sigma_min					,			self.config.sigma_max					,			self.config.sampling_eps					,			)

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase = ()					,			__lowercase = None   ):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				sampling_eps if sampling_eps is not None else self.config.sampling_eps

						lowerCamelCase_				:						List[Any]				  =				jnp.linspace(1					,			lowercase_					,			lowercase_   )
						return state.replace(timesteps=lowercase_   )

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				sigma_min if sigma_min is not None else self.config.sigma_min
						lowerCamelCase_				:						Tuple				  =				sigma_max if sigma_max is not None else self.config.sigma_max
						lowerCamelCase_				:						Any				  =				sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
							lowerCamelCase_				:						int				  =				self.set_timesteps(lowercase_					,			lowercase_					,			lowercase_   )

						lowerCamelCase_				:						Tuple				  =				jnp.exp(jnp.linspace(jnp.log(lowercase_   )					,			jnp.log(lowercase_   )					,			lowercase_   )   )
						lowerCamelCase_				:						str				  =				jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]   )

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

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						return jnp.where(timesteps == 0					,			jnp.zeros_like(lowercase_   )					,			state.discrete_sigmas[timesteps - 1]   )

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase = True					,			):







						'''simple docstring'''




						if state.timesteps is None:
							raise ValueError(
							    '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'   )

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

						lowerCamelCase_				:						str				  =				state.discrete_sigmas[timesteps]
						lowerCamelCase_				:						str				  =				self.get_adjacent_sigma(lowercase_					,			lowercase_					,			lowercase_   )
						lowerCamelCase_				:						Optional[Any]				  =				jnp.zeros_like(lowercase_   )
						lowerCamelCase_				:						Optional[Any]				  =				(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
						lowerCamelCase_				:						List[str]				  =				diffusion.flatten()
						lowerCamelCase_				:						str				  =				broadcast_to_shape_from_left(lowercase_					,			sample.shape   )
						lowerCamelCase_				:						List[str]				  =				drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						lowerCamelCase_				:						str				  =				random.split(lowercase_					,			num=1   )
						lowerCamelCase_				:						Optional[Any]				  =				random.normal(key=lowercase_					,			shape=sample.shape   )
						lowerCamelCase_				:						Tuple				  =				sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						lowerCamelCase_				:						Optional[int]				  =				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=lowercase_					,			prev_sample_mean=lowercase_					,			state=lowercase_   )

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase = True					,			):







						'''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
						lowerCamelCase_				:						Union[str, Any]				  =				random.split(lowercase_					,			num=1   )
						lowerCamelCase_				:						Union[str, Any]				  =				random.normal(key=lowercase_					,			shape=sample.shape   )

						# compute step size from the model_output, the noise, and the snr
						lowerCamelCase_				:						int				  =				jnp.linalg.norm(lowercase_   )
						lowerCamelCase_				:						Optional[int]				  =				jnp.linalg.norm(lowercase_   )
						lowerCamelCase_				:						List[Any]				  =				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
						lowerCamelCase_				:						Dict				  =				step_size * jnp.ones(sample.shape[0]   )

						# compute corrected sample: model_output term and noise term
						lowerCamelCase_				:						List[str]				  =				step_size.flatten()
						lowerCamelCase_				:						Any				  =				broadcast_to_shape_from_left(lowercase_					,			sample.shape   )
						lowerCamelCase_				:						Dict				  =				sample + step_size * model_output
						lowerCamelCase_				:						Any				  =				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
							return (prev_sample, state)

						return FlaxSdeVeOutput(prev_sample=lowercase_					,			state=lowercase_   )





					def __len__(							self   ):







						'''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,)

