


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_				:			List[str]   =	logging.get_logger(__name__)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					List[Any]			       =						'''encoder-decoder'''
      snake_case__    :					Any			       =						True
      def __init__(	self    :	Union[str, Any]					,							**SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      Union[str, Any]:
             super().__init__(**SCREAMING_SNAKE_CASE__					)
             assert (
                 "encoder" in kwargs and "decoder" in kwargs
             ), "Config has to be initialized with encoder and decoder config"
             a_ :		Dict							=  kwargs.pop('encoder'					)
             a_ :		Optional[int]							=  encoder_config.pop('model_type'					)
             a_ :		str							=  kwargs.pop('decoder'					)
             a_ :		str							=  decoder_config.pop('model_type'					)

             from ..auto.configuration_auto import AutoConfig

             a_ :		str							=  AutoConfig.for_model(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
             a_ :		Tuple							=  AutoConfig.for_model(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
             a_ :		Any							=  True
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	PretrainedConfig					,							SCREAMING_SNAKE_CASE__    :	PretrainedConfig					,							**SCREAMING_SNAKE_CASE__    :	Dict					)		->      PretrainedConfig:
             logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'					)
             a_ :		str							=  True
             a_ :		int							=  True

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

      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      int:
             a_ :		Any							=  copy.deepcopy(self.__dict__					)
             a_ :		int							=  self.encoder.to_dict()
             a_ :		Tuple							=  self.decoder.to_dict()
             a_ :		Union[str, Any]							=  self.__class__.model_type
             return output






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__      ):
      snake_case__    :					Dict					       =						1
      @register_to_config
      def __init__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Tuple=2_0_0_0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=2_0					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=1E-3					)		->      Optional[int]:
             a_ :		Tuple							=  None
             a_ :		int							=  None
             a_ :		Tuple							=  None
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, torch.device] = None					)		->      List[str]:
             a_ :		Tuple							=  torch.linspace(1					,							self.config.sampling_eps					,							SCREAMING_SNAKE_CASE__					,							device=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Tuple=None					)		->      Tuple:
             if self.timesteps is None:
                    raise ValueError(
                        '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'					)

             # TODO(Patrick) better comments + non-PyTorch
             # postprocess model score
             a_ :		Tuple							=  (
                 -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
             )
             a_ :		int							=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
             a_ :		Dict							=  std.flatten()
             while len(std.shape					) < len(score.shape					):
                    a_ :		str							=  std.unsqueeze(-1					)
             a_ :		List[str]							=  -score / std

             # compute
             a_ :		List[str]							=  -1.0 / len(self.timesteps					)

             a_ :		Optional[int]							=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
             a_ :		Optional[Any]							=  beta_t.flatten()
             while len(beta_t.shape					) < len(x.shape					):
                    a_ :		List[str]							=  beta_t.unsqueeze(-1					)
             a_ :		Optional[Any]							=  -0.5 * beta_t * x

             a_ :		Tuple							=  torch.sqrt(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  drift - diffusion**2 * score
             a_ :		List[str]							=  x + drift * dt

             # add noise
             a_ :		Optional[Any]							=  randn_tensor(x.shape					,							layout=x.layout					,							generator=SCREAMING_SNAKE_CASE__					,							device=x.device					,							dtype=x.dtype					)
             a_ :		Optional[Any]							=  x_mean + diffusion * math.sqrt(-dt					) * noise

             return x, x_mean

      def __len__(	self    :	int					)		->      Tuple:
             return self.config.num_train_timesteps


