






# 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 copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__UpperCAmelCase      =				logging.get_logger(__name__)
class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				Union[str, Any]  					='''speech-encoder-decoder'''
     __snake_case		:				List[Any]  					=True





     def __init__(		self					:	Any  ,  **lowerCAmelCase__					:	List[Any])							->	Tuple:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      if "encoder" not in kwargs or "decoder" not in kwargs:
       raise ValueError(
           f'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
           f''' `decoder` sub-configurations are passed, but only {kwargs}''')

      SCREAMING_SNAKE_CASE				    =		kwargs.pop('encoder')
      SCREAMING_SNAKE_CASE				    =		encoder_config.pop('model_type')
      SCREAMING_SNAKE_CASE				    =		kwargs.pop('decoder')
      SCREAMING_SNAKE_CASE				    =		decoder_config.pop('model_type')

      SCREAMING_SNAKE_CASE				    =		AutoConfig.for_model(lowerCAmelCase__  ,  **lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		AutoConfig.for_model(lowerCAmelCase__  ,  **lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		True





     @classmethod
     def     __UpperCamelCase						(		cls					:	str  ,  lowerCAmelCase__					:	PretrainedConfig  ,  lowerCAmelCase__					:	PretrainedConfig  ,  **lowerCAmelCase__					:	List[str])							->	PretrainedConfig:

      """simple docstring"""
      logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config')
      SCREAMING_SNAKE_CASE				    =		True
      SCREAMING_SNAKE_CASE				    =		True

      return cls(encoder=encoder_config.to_dict()  ,  decoder=decoder_config.to_dict()  ,  **lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	str)							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		copy.deepcopy(self.__dict__)
      SCREAMING_SNAKE_CASE				    =		self.encoder.to_dict()
      SCREAMING_SNAKE_CASE				    =		self.decoder.to_dict()
      SCREAMING_SNAKE_CASE				    =		self.__class__.model_type
      return output




