



'''simple docstring'''





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_		:
    __UpperCAmelCase							=	42
    __UpperCAmelCase							=	42

    # setable values
    __UpperCAmelCase							=	42
    __UpperCAmelCase							=	42
    __UpperCAmelCase							=	None





    @classmethod
    def 					__snake_case       (    cls    :    str,    _snake_case    :    Dict,    _snake_case    :    Optional[int],    _snake_case    :    List[str],    _snake_case    :    List[str],    ):



       '''simple docstring'''







       return cls(
           common=lowercase_,    final_alpha_cumprod=lowercase_,    init_noise_sigma=lowercase_,    timesteps=lowercase_,    )





@dataclass
class 							lowerCAmelCase_		(	SCREAMING_SNAKE_CASE_						):
    __UpperCAmelCase							=	42





class 							lowerCAmelCase_		(	SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_						):
    __UpperCAmelCase							=	[e.name for e in FlaxKarrasDiffusionSchedulers]

    __UpperCAmelCase							=	42





    @property
    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       return True





    @register_to_config
    def __init__(    self    :    Optional[Any],    _snake_case    :    List[Any] = 1_000,    _snake_case    :    Tuple = 0.0001,    _snake_case    :    Any = 0.02,    _snake_case    :    Any = "linear",    _snake_case    :    List[Any] = None,    _snake_case    :    List[str] = True,    _snake_case    :    Optional[int] = 0,    _snake_case    :    Tuple = "epsilon",    _snake_case    :    int = jnp.floataa,    ):



       '''simple docstring'''







       snake_case   :							Optional[Any]						  =dtype





    def 					__snake_case       (    self    :    int,    _snake_case    :    Optional[int] = None     ):



       '''simple docstring'''







       if common is None:
          snake_case   :							Optional[int]						  =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.
       snake_case   :							int						  =(
           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
       snake_case   :							Any						  =jnp.array(1.0,    dtype=self.dtype     )

       snake_case   :							int						  =jnp.arange(0,    self.config.num_train_timesteps     ).round()[::-1]

       return DDIMSchedulerState.create(
           common=lowercase_,    final_alpha_cumprod=lowercase_,    init_noise_sigma=lowercase_,    timesteps=lowercase_,    )





    def 					__snake_case       (    self    :    int,    _snake_case    :    Union[str, Any],    _snake_case    :    int,    _snake_case    :    int = None     ):



       '''simple docstring'''







       return sample





    def 					__snake_case       (    self    :    Optional[Any],    _snake_case    :    str,    _snake_case    :    Optional[Any],    _snake_case    :    Union[str, Any] = ()     ):



       '''simple docstring'''







       snake_case   :							List[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
       snake_case   :							Optional[int]						  =(jnp.arange(0,    lowercase_     ) * step_ratio).round()[::-1] + self.config.steps_offset

       return state.replace(
           num_inference_steps=lowercase_,    timesteps=lowercase_,    )





    def 					__snake_case       (    self    :    Dict,    _snake_case    :    Union[str, Any],    _snake_case    :    Optional[Any],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  =state.common.alphas_cumprod[timestep]
       snake_case   :							str						  =jnp.where(
           prev_timestep >= 0,    state.common.alphas_cumprod[prev_timestep],    state.final_alpha_cumprod     )
       snake_case   :							Dict						  =1 - alpha_prod_t
       snake_case   :							Tuple						  =1 - alpha_prod_t_prev

       snake_case   :							Any						  =(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

       return variance





    def 					__snake_case       (    self    :    Optional[Any],    _snake_case    :    Any,    _snake_case    :    Any,    _snake_case    :    Union[str, Any],    _snake_case    :    Any,    _snake_case    :    Dict = 0.0,    _snake_case    :    List[str] = 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)
       snake_case   :							str						  =timestep - self.config.num_train_timesteps // state.num_inference_steps

       snake_case   :							Optional[int]						  =state.common.alphas_cumprod
       snake_case   :							Optional[Any]						  =state.final_alpha_cumprod

       # 2. compute alphas, betas
       snake_case   :							Optional[int]						  =alphas_cumprod[timestep]
       snake_case   :							Tuple						  =jnp.where(prev_timestep >= 0,    alphas_cumprod[prev_timestep],    lowercase_     )

       snake_case   :							Optional[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":
          snake_case   :							Tuple						  =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          snake_case   :							List[str]						  =model_output
       elif self.config.prediction_type == "sample":
          snake_case   :							List[Any]						  =model_output
          snake_case   :							Union[str, Any]						  =(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
          snake_case   :							Tuple						  =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          snake_case   :							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)
       snake_case   :							Tuple						  =self._get_variance(lowercase_,    lowercase_,    lowercase_     )
       snake_case   :							Any						  =eta * variance ** 0.5

       # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       snake_case   :							Union[str, 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
       snake_case   :							Optional[Any]						  =alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if not return_dict:
          return (prev_sample, state)

       return FlaxDDIMSchedulerOutput(prev_sample=lowercase_,    state=lowercase_     )





    def 					__snake_case       (    self    :    Any,    _snake_case    :    Optional[Any],    _snake_case    :    Dict,    _snake_case    :    Any,    _snake_case    :    str,    ):



       '''simple docstring'''







       return add_noise_common(state.common,    lowercase_,    lowercase_,    lowercase_     )





    def 					__snake_case       (    self    :    int,    _snake_case    :    Any,    _snake_case    :    List[Any],    _snake_case    :    Optional[Any],    _snake_case    :    Union[str, Any],    ):



       '''simple docstring'''







       return get_velocity_common(state.common,    lowercase_,    lowercase_,    lowercase_     )






    def __len__(    self    :    Tuple     ):



       '''simple docstring'''







       return self.config.num_train_timesteps










'''simple docstring'''





def 		_a   (  lowerCamelCase_							):
   return "".join(chr(ord(lowerCamelCase_							) + 32							) if '''A''' <= char <= '''Z''' else char for char in word							)


if __name__ == "__main__":
      from doctest import testmod

      testmod()





