code: 
  
  
  """simple docstring"""
  
  
  
  
  # 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 __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   CommonSchedulerState
     lowerCAmelCase_				:   jnp.ndarray
  
     # setable values
     lowerCAmelCase_				:   jnp.ndarray
     lowerCAmelCase_				:   jnp.ndarray
     lowerCAmelCase_				:   Optional[int]     	=			None
  
  
  
  
     @classmethod
     def SCREAMING_SNAKE_CASE_      (		cls							:Optional[int]				,  UpperCamelCase__							:CommonSchedulerState				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:jnp.ndarray				,  ):
            return cls(
                common=UpperCamelCase__				,  final_alpha_cumprod=UpperCamelCase__				,  init_noise_sigma=UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )
  
  
  
  
  
  
  @dataclass
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   DDIMSchedulerState
  
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Union[str, Any]     	=			[e.name for e in FlaxKarrasDiffusionSchedulers]
  
     lowerCAmelCase_				:   jnp.dtype
  
  
  
  
     @property
     def SCREAMING_SNAKE_CASE_      (		self							:List[Any]       ):
            return True
  
  
  
  
     @register_to_config
     def __init__(		self							:int				,  UpperCamelCase__							:int = 1_000				,  UpperCamelCase__							:float = 0.0001				,  UpperCamelCase__							:float = 0.02				,  UpperCamelCase__							:str = "linear"				,  UpperCamelCase__							:Optional[jnp.ndarray] = None				,  UpperCamelCase__							:bool = True				,  UpperCamelCase__							:int = 0				,  UpperCamelCase__							:str = "epsilon"				,  UpperCamelCase__							:jnp.dtype = jnp.floataa				,  ):
            _a          =				dtype
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:Optional[CommonSchedulerState] = 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=UpperCamelCase__				,  final_alpha_cumprod=UpperCamelCase__				,  init_noise_sigma=UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:DDIMSchedulerState				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:Optional[int] = None       ):
            return sample
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:DDIMSchedulerState				,  UpperCamelCase__							:int				,  UpperCamelCase__							:Tuple = ()       ):
            _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				,  UpperCamelCase__       ) * step_ratio).round()[::-1] + self.config.steps_offset
  
            return state.replace(
                num_inference_steps=UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:DDIMSchedulerState				,  UpperCamelCase__							:Union[str, Any]				,  UpperCamelCase__							:Optional[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 SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:DDIMSchedulerState				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:int				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:float = 0.0				,  UpperCamelCase__							: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          =				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]				,  UpperCamelCase__       )
  
            _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(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )
            _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=UpperCamelCase__				,  state=UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  UpperCamelCase__							:DDIMSchedulerState				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:jnp.ndarray				,  ):
            return add_noise_common(state.common				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int				,  UpperCamelCase__							:DDIMSchedulerState				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:jnp.ndarray				,  UpperCamelCase__							:jnp.ndarray				,  ):
            return get_velocity_common(state.common				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )
  
  
  
  
     def __len__(		self							:Optional[Any]       ):
            return self.config.num_train_timesteps
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterator
  from typing import Any
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:Any       ):
            _a          =				data
            _a          =				None
  
  
  
  
     def __str__(		self							:Any       ):
            return f'{self.data}'
  
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Any       ):
            _a          =				None
            _a          =				None
  
  
  
  
     def __iter__(		self							:List[str]       ):
            _a          =				self.front
            while node:
                   yield node.data
                   _a          =				node.next
  
  
  
  
     def __len__(		self							:List[str]       ):
            return len(tuple(iter(self       )       )       )
  
  
  
  
     def __str__(		self							:Union[str, Any]       ):
            return " <- ".join(str(UpperCamelCase__       ) for item in self       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            return len(self       ) == 0
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:Any       ):
            _a          =				Node(UpperCamelCase__       )
            if self.is_empty():
                   _a          =				_a          =				node
            else:
                   assert isinstance(self.rear				,  UpperCamelCase__       )
                   _a          =				node
                   _a          =				node
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            if self.is_empty():
                   raise IndexError("dequeue from empty queue"       )
            assert isinstance(self.front				,  UpperCamelCase__       )
            _a          =				self.front
            _a          =				self.front.next
            if self.front is None:
                   _a          =				None
            return node.data
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            _a          =				_a          =				None
  
  
  if __name__ == "__main__":
     from doctest import testmod
  
     testmod()
  