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       							=    42
         _lowercase       							=    42
  
         # setable values
         _lowercase       							=    42
         _lowercase       							=    42
         _lowercase       							=    None
  
  
  
  
  
  
  
         @classmethod
         def 		snake_case_  ( cls:   str,A_:   CommonSchedulerState,A_:   jnp.ndarray,A_:   jnp.ndarray,A_:   jnp.ndarray,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return cls(
                  common=A_,final_alpha_cumprod=A_,init_noise_sigma=A_,timesteps=A_,)
  
  
  
  @dataclass
  class   __lowerCamelCase (_a	):
         _lowercase       							=    42
  
  
  
  class   __lowerCamelCase (_a							,			_a	):
         _lowercase       							=    [e.name for e in FlaxKarrasDiffusionSchedulers]
  
         _lowercase       							=    42
  
  
  
  
  
  
  
         @property
         def 		snake_case_  ( self:   Optional[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return True
  
  
  
  
  
  
  
         @register_to_config
         def __init__( self:   List[str],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,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = dtype
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple,A_:   Optional[CommonSchedulerState] = None				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              if common is None:
                   __UpperCamelCase   = 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.
              __UpperCamelCase   = (
                  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
              __UpperCamelCase   = jnp.array(1.0,dtype=self.dtype				)
  
              __UpperCamelCase   = 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 		snake_case_  ( self:   List[str],A_:   DDIMSchedulerState,A_:   jnp.ndarray,A_:   Optional[int] = None				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return sample
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple,A_:   DDIMSchedulerState,A_:   int,A_:   Tuple = ()				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = 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
              __UpperCamelCase   = (jnp.arange(0,A_				) * step_ratio).round()[::-1] + self.config.steps_offset
  
              return state.replace(
                  num_inference_steps=A_,timesteps=A_,)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   int,A_:   DDIMSchedulerState,A_:   List[str],A_:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = state.common.alphas_cumprod[timestep]
              __UpperCamelCase   = jnp.where(
                  prev_timestep >= 0,state.common.alphas_cumprod[prev_timestep],state.final_alpha_cumprod				)
              __UpperCamelCase   = 1 - alpha_prod_t
              __UpperCamelCase   = 1 - alpha_prod_t_prev
  
              __UpperCamelCase   = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
              return variance
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int],A_:   DDIMSchedulerState,A_:   jnp.ndarray,A_:   int,A_:   jnp.ndarray,A_:   float = 0.0,A_:   bool = True,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              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)
              __UpperCamelCase   = timestep - self.config.num_train_timesteps // state.num_inference_steps
  
              __UpperCamelCase   = state.common.alphas_cumprod
              __UpperCamelCase   = state.final_alpha_cumprod
  
              # 2. compute alphas, betas
              __UpperCamelCase   = alphas_cumprod[timestep]
              __UpperCamelCase   = jnp.where(prev_timestep >= 0,alphas_cumprod[prev_timestep],A_				)
  
              __UpperCamelCase   = 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":
                   __UpperCamelCase   = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                   __UpperCamelCase   = model_output
              elif self.config.prediction_type == "sample":
                   __UpperCamelCase   = model_output
                   __UpperCamelCase   = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
              elif self.config.prediction_type == "v_prediction":
                   __UpperCamelCase   = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                   __UpperCamelCase   = (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)
              __UpperCamelCase   = self._get_variance(A_,A_,A_				)
              __UpperCamelCase   = eta * variance ** 0.5
  
              # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
              __UpperCamelCase   = (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
              __UpperCamelCase   = 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 		snake_case_  ( self:   Tuple,A_:   DDIMSchedulerState,A_:   jnp.ndarray,A_:   jnp.ndarray,A_:   jnp.ndarray,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return add_noise_common(state.common,A_,A_,A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int],A_:   DDIMSchedulerState,A_:   jnp.ndarray,A_:   jnp.ndarray,A_:   jnp.ndarray,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return get_velocity_common(state.common,A_,A_,A_				)
  
  
  
  
  
  
  
         def __len__( self:   List[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return self.config.num_train_timesteps
  
  
  
style_context: 
  
  
  from ..utils import DummyObject, requires_backends
  
  
  
  class   __lowerCamelCase (metaclass=_a	):
         _lowercase       							=    ["""torch""", """transformers""", """k_diffusion"""]
  
  
  
  
  
  
  
         def __init__( self:   Tuple,*A_:   Union[str, Any],**A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              requires_backends(self,['torch', 'transformers', 'k_diffusion']				)
  
  
  
  
  
  
  
         @classmethod
         def 		snake_case_  ( cls:   int,*A_:   Optional[Any],**A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              requires_backends(cls,['torch', 'transformers', 'k_diffusion']				)
  
  
  
  
  
  
  
         @classmethod
         def 		snake_case_  ( cls:   int,*A_:   Tuple,**A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              requires_backends(cls,['torch', 'transformers', 'k_diffusion']				)
  
  
  