code: 
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  
  
  
  
  @flax.struct.dataclass
  class     lowerCamelCase_			:
   _lowercase		:      CommonSchedulerState
   _lowercase		:      jnp.ndarray
  
   # setable values
   _lowercase		:      jnp.ndarray
   _lowercase		:      jnp.ndarray
   _lowercase		:      Optional[int]    				=							None
  
  
  
  
   @classmethod
   def 					lowerCAmelCase_					(    cls	:    int				,			__A	:    CommonSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
    return cls(
        common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)
  
  
  
  
  
  
  @dataclass
  class     lowerCamelCase_			(  _lowercase  ):
   _lowercase		:      DDIMSchedulerState
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase	,			_lowercase  ):
   _lowercase		:      Union[str, Any]    				=							[e.name for e in FlaxKarrasDiffusionSchedulers]
  
   _lowercase		:      jnp.dtype
  
  
  
  
   @property
   def 					lowerCAmelCase_					(    self	:    Tuple       ):
    return True
  
  
  
  
   @register_to_config
   def __init__(    self	:    Any				,			__A	:    int = 1000				,			__A	:    float = 0.0_0_0_1				,			__A	:    float = 0.0_2				,			__A	:    str = "linear"				,			__A	:    Optional[jnp.ndarray] = None				,			__A	:    bool = True				,			__A	:    int = 0				,			__A	:    str = "epsilon"				,			__A	:    jnp.dtype = jnp.floataa				,			):
    __A   : Optional[int]								= dtype
  
  
  
  
   def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    Optional[CommonSchedulerState] = None       ):
    if common is None:
     __A   : Any								= CommonSchedulerState.create(self       )
  
    # At every step in ddim, we are looking into the previous alphas_cumprod
    # For the final step, there is no previous alphas_cumprod because we are already at 0
    # `set_alpha_to_one` decides whether we set this parameter simply to one or
    # whether we use the final alpha of the "non-previous" one.
    __A   : Tuple								= (
        jnp.array(1.0				,			dtype=self.dtype       ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
    )
  
    # standard deviation of the initial noise distribution
    __A   : List[str]								= jnp.array(1.0				,			dtype=self.dtype       )
  
    __A   : Any								= jnp.arange(0				,			self.config.num_train_timesteps       ).round()[::-1]
  
    return DDIMSchedulerState.create(
        common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)
  
  
  
  
   def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    Optional[int] = None       ):
    return sample
  
  
  
  
   def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    DDIMSchedulerState				,			__A	:    int				,			__A	:    Tuple = ()       ):
    __A   : Union[str, Any]								= self.config.num_train_timesteps // num_inference_steps
    # creates integer timesteps by multiplying by ratio
    # rounding to avoid issues when num_inference_step is power of 3
    __A   : Optional[Any]								= (jnp.arange(0				,			__A       ) * step_ratio).round()[::-1] + self.config.steps_offset
  
    return state.replace(
        num_inference_steps=__A				,			timesteps=__A				,			)
  
  
  
  
   def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    DDIMSchedulerState				,			__A	:    str				,			__A	:    List[Any]       ):
    __A   : Tuple								= state.common.alphas_cumprod[timestep]
    __A   : Tuple								= jnp.where(
        prev_timestep >= 0				,			state.common.alphas_cumprod[prev_timestep]				,			state.final_alpha_cumprod       )
    __A   : List[Any]								= 1 - alpha_prod_t
    __A   : Tuple								= 1 - alpha_prod_t_prev
  
    __A   : List[Any]								= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
    return variance
  
  
  
  
   def 					lowerCAmelCase_					(    self	:    List[Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    int				,			__A	:    jnp.ndarray				,			__A	:    float = 0.0				,			__A	:    bool = True				,			):
    if state.num_inference_steps is None:
     raise ValueError(
         """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""       )
  
    # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
    # Ideally, read DDIM paper in-detail understanding
  
    # Notation (<variable name> -> <name in paper>
    # - pred_noise_t -> e_theta(x_t, t)
    # - pred_original_sample -> f_theta(x_t, t) or x_0
    # - std_dev_t -> sigma_t
    # - eta -> η
    # - pred_sample_direction -> "direction pointing to x_t"
    # - pred_prev_sample -> "x_t-1"
  
    # 1. get previous step value (=t-1)
    __A   : Optional[Any]								= timestep - self.config.num_train_timesteps // state.num_inference_steps
  
    __A   : Optional[int]								= state.common.alphas_cumprod
    __A   : Optional[int]								= state.final_alpha_cumprod
  
    # 2. compute alphas, betas
    __A   : Optional[int]								= alphas_cumprod[timestep]
    __A   : str								= jnp.where(prev_timestep >= 0				,			alphas_cumprod[prev_timestep]				,			__A       )
  
    __A   : Any								= 1 - alpha_prod_t
  
    # 3. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    if self.config.prediction_type == "epsilon":
     __A   : Any								= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     __A   : List[Any]								= model_output
    elif self.config.prediction_type == "sample":
     __A   : Dict								= model_output
     __A   : Tuple								= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
    elif self.config.prediction_type == "v_prediction":
     __A   : int								= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     __A   : str								= (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
    else:
     raise ValueError(
         F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
         """ `v_prediction`"""       )
  
    # 4. compute variance: "sigma_t(η)" -> see formula (16)
    # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
    __A   : Union[str, Any]								= self._get_variance(__A				,			__A				,			__A       )
    __A   : Any								= eta * variance ** 0.5
  
    # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    __A   : Any								= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
    # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    __A   : Optional[int]								= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
    if not return_dict:
     return (prev_sample, state)
  
    return FlaxDDIMSchedulerOutput(prev_sample=__A				,			state=__A       )
  
  
  
  
   def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
    return add_noise_common(state.common				,			__A				,			__A				,			__A       )
  
  
  
  
   def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
    return get_velocity_common(state.common				,			__A				,			__A				,			__A       )
  
  
  
  
   def __len__(    self	:    Union[str, Any]       ):
    return self.config.num_train_timesteps
  
  
  
  
  
  
style_context: 
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast
  from ...utils import logging
  
  
  UpperCAmelCase_       :				int		   =      logging.get_logger(__name__)
  
  UpperCAmelCase_       :				Any		   =      {
      '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
      # See all PLBART models at https://huggingface.co/models?filter=plbart
  }
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      str    				=							'''plbart'''
  	_lowercase		:      str    				=							['''past_key_values''']
  	_lowercase		:      Any    				=							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  
  
  	def __init__(    self	:    List[Any]				,			__A	:    str=5_0005				,			__A	:    Union[str, Any]=1024				,			__A	:    int=6				,			__A	:    Dict=3072				,			__A	:    Union[str, Any]=12				,			__A	:    Tuple=6				,			__A	:    Tuple=3072				,			__A	:    Optional[int]=12				,			__A	:    int=0.0				,			__A	:    List[Any]=0.0				,			__A	:    Union[str, Any]=True				,			__A	:    Any=True				,			__A	:    Optional[int]="gelu"				,			__A	:    Optional[int]=768				,			__A	:    Any=0.1				,			__A	:    Union[str, Any]=0.1				,			__A	:    Union[str, Any]=0.0				,			__A	:    int=0.0_2				,			__A	:    Union[str, Any]=0.0				,			__A	:    Tuple=True				,			__A	:    List[str]=1				,			__A	:    str=0				,			__A	:    List[Any]=2				,			__A	:    Any=2				,			**__A	:    Union[str, Any]				,			):
  		__A   : List[Any]								= vocab_size
  		__A   : str								= max_position_embeddings
  		__A   : Union[str, Any]								= d_model
  		__A   : List[str]								= encoder_ffn_dim
  		__A   : Optional[Any]								= encoder_layers
  		__A   : Tuple								= encoder_attention_heads
  		__A   : List[Any]								= decoder_ffn_dim
  		__A   : Optional[int]								= decoder_layers
  		__A   : Any								= decoder_attention_heads
  		__A   : Optional[int]								= dropout
  		__A   : Any								= attention_dropout
  		__A   : Tuple								= activation_dropout
  		__A   : List[Any]								= activation_function
  		__A   : Tuple								= init_std
  		__A   : List[Any]								= encoder_layerdrop
  		__A   : str								= decoder_layerdrop
  		__A   : Union[str, Any]								= classifier_dropout
  		__A   : Optional[Any]								= use_cache
  		__A   : List[str]								= encoder_layers
  		__A   : Optional[int]								= scale_embedding  # scale factor will be sqrt(d_model) if True
  		super().__init__(
  		    pad_token_id=__A				,			bos_token_id=__A				,			eos_token_id=__A				,			is_encoder_decoder=__A				,			forced_eos_token_id=__A				,			**__A				,			)
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Any       ):
  		return OrderedDict(
  		    [
  		        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
  		        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
  		    ]       )
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Tuple       ):
  		if self.use_past:
  			return OrderedDict(
  			    [
  			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
  			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			    ]       )
  		else:
  			return OrderedDict(
  			    [
  			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			    ]       )
  
  
  
  
  
  