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 					_UpperCamelCase :
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  42
  							_A  				=  42
  
  							# setable values
  							_A  				=  42
  							_A  				=  42
  							_A  				=  None
  
  
  
  
  
  
  
  							@classmethod
  							def 		_UpperCAmelCase	(		cls  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				int		,				SCREAMING_SNAKE_CASE_  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				Dict		,				):
  													return cls(
  													    common=lowerCamelCase__		,				final_alpha_cumprod=lowerCamelCase__		,				init_noise_sigma=lowerCamelCase__		,				timesteps=lowerCamelCase__		,				)
  
  
  
  
  @dataclass
  class 					_UpperCamelCase (      UpperCAmelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  42
  
  
  
  
  class 					_UpperCamelCase (      UpperCAmelCase__			,							UpperCAmelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  [e.name for e in FlaxKarrasDiffusionSchedulers]
  
  							_A  				=  42
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				int		):
  													return True
  
  
  
  
  
  
  
  							@register_to_config
  							def __init__(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				Dict = 1_0_0_0		,				SCREAMING_SNAKE_CASE_  :				str = 0.0001		,				SCREAMING_SNAKE_CASE_  :				int = 0.02		,				SCREAMING_SNAKE_CASE_  :				List[Any] = "linear"		,				SCREAMING_SNAKE_CASE_  :				List[Any] = None		,				SCREAMING_SNAKE_CASE_  :				Any = True		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = 0		,				SCREAMING_SNAKE_CASE_  :				Any = "epsilon"		,				SCREAMING_SNAKE_CASE_  :				List[str] = jnp.floataa		,				):
  													_a     =			dtype
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				int = None		):
  													if common is None:
  																			_a     =			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     =			(
  													    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     =			jnp.array(1.0		,				dtype=self.dtype		)
  
  													_a     =			jnp.arange(0		,				self.config.num_train_timesteps		).round()[::-1]
  
  													return DDIMSchedulerState.create(
  													    common=lowerCamelCase__		,				final_alpha_cumprod=lowerCamelCase__		,				init_noise_sigma=lowerCamelCase__		,				timesteps=lowerCamelCase__		,				)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				Dict		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				int = None		):
  													return sample
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				List[str] = ()		):
  													_a     =			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     =			(jnp.arange(0		,				lowerCamelCase__		) * step_ratio).round()[::-1] + self.config.steps_offset
  
  													return state.replace(
  													    num_inference_steps=lowerCamelCase__		,				timesteps=lowerCamelCase__		,				)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				Dict		,				SCREAMING_SNAKE_CASE_  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
  													_a     =			state.common.alphas_cumprod[timestep]
  													_a     =			jnp.where(
  													    prev_timestep >= 0		,				state.common.alphas_cumprod[prev_timestep]		,				state.final_alpha_cumprod		)
  													_a     =			1 - alpha_prod_t
  													_a     =			1 - alpha_prod_t_prev
  
  													_a     =			(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  													return variance
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				Optional[Any] = 0.0		,				SCREAMING_SNAKE_CASE_  :				Tuple = 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     =			timestep - self.config.num_train_timesteps // state.num_inference_steps
  
  													_a     =			state.common.alphas_cumprod
  													_a     =			state.final_alpha_cumprod
  
  													# 2. compute alphas, betas
  													_a     =			alphas_cumprod[timestep]
  													_a     =			jnp.where(prev_timestep >= 0		,				alphas_cumprod[prev_timestep]		,				lowerCamelCase__		)
  
  													_a     =			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     =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  																			_a     =			model_output
  													elif self.config.prediction_type == "sample":
  																			_a     =			model_output
  																			_a     =			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  													elif self.config.prediction_type == "v_prediction":
  																			_a     =			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  																			_a     =			(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     =			self._get_variance(lowerCamelCase__		,				lowerCamelCase__		,				lowerCamelCase__		)
  													_a     =			eta * variance ** 0.5
  
  													# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  													_a     =			(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     =			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
  													if not return_dict:
  																			return (prev_sample, state)
  
  													return FlaxDDIMSchedulerOutput(prev_sample=lowerCamelCase__		,				state=lowerCamelCase__		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				):
  													return add_noise_common(state.common		,				lowerCamelCase__		,				lowerCamelCase__		,				lowerCamelCase__		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				Any		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				Dict		,				):
  													return get_velocity_common(state.common		,				lowerCamelCase__		,				lowerCamelCase__		,				lowerCamelCase__		)
  
  
  
  
  
  
  
  							def __len__(		self  :				List[Any]		):
  													return self.config.num_train_timesteps
  
  
  
style_context: 
  
  
  from math import pi
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
  						return degree / (180 / pi)
  
  
  if __name__ == "__main__":
  		from doctest import testmod
  
  		testmod()
  
  
  