




# 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 						SCREAMING_SNAKE_CASE__		:
     lowercase_   :			CommonSchedulerState
     lowercase_   :			jnp.ndarray

     # setable values
     lowercase_   :			jnp.ndarray
     lowercase_   :			jnp.ndarray
     lowercase_   :			Optional[int]   =   None
     @classmethod
     def 					A__			(	cls  :		Optional[int]					,	__lowerCamelCase  :		CommonSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	):



       """simple docstring"""
       return cls(
           common=__lowerCamelCase					,	final_alpha_cumprod=__lowerCamelCase					,	init_noise_sigma=__lowerCamelCase					,	timesteps=__lowerCamelCase					,	)





@dataclass
class 						SCREAMING_SNAKE_CASE__		(_a ):
     lowercase_   :			DDIMSchedulerState





class 						SCREAMING_SNAKE_CASE__		(_a						,				_a ):
     lowercase_   :			str   =   [e.name for e in FlaxKarrasDiffusionSchedulers]

     lowercase_   :			jnp.dtype
     @property
     def 					A__			(	self  :		List[str]	):



       """simple docstring"""
       return True
     @register_to_config
     def __init__(	self  :		Any					,	__lowerCamelCase  :		int = 10_00					,	__lowerCamelCase  :		float = 0.0001					,	__lowerCamelCase  :		float = 0.02					,	__lowerCamelCase  :		str = "linear"					,	__lowerCamelCase  :		Optional[jnp.ndarray] = None					,	__lowerCamelCase  :		bool = True					,	__lowerCamelCase  :		int = 0					,	__lowerCamelCase  :		str = "epsilon"					,	__lowerCamelCase  :		jnp.dtype = jnp.floataa					,	):



       """simple docstring"""
       lowerCAmelCase__								=			dtype
     def 					A__			(	self  :		List[Any]					,	__lowerCamelCase  :		Optional[CommonSchedulerState] = None	):



       """simple docstring"""
       if common is None:
         lowerCAmelCase__								=			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.
       lowerCAmelCase__								=			(
           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
       lowerCAmelCase__								=			jnp.array(1.0					,	dtype=self.dtype	)

       lowerCAmelCase__								=			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 					A__			(	self  :		Optional[int]					,	__lowerCamelCase  :		DDIMSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		Optional[int] = None	):



       """simple docstring"""
       return sample
     def 					A__			(	self  :		Optional[Any]					,	__lowerCamelCase  :		DDIMSchedulerState					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		Tuple = ()	):



       """simple docstring"""
       lowerCAmelCase__								=			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
       lowerCAmelCase__								=			(jnp.arange(0					,	__lowerCamelCase	) * step_ratio).round()[::-1] + self.config.steps_offset

       return state.replace(
           num_inference_steps=__lowerCamelCase					,	timesteps=__lowerCamelCase					,	)
     def 					A__			(	self  :		Tuple					,	__lowerCamelCase  :		DDIMSchedulerState					,	__lowerCamelCase  :		Dict					,	__lowerCamelCase  :		Tuple	):



       """simple docstring"""
       lowerCAmelCase__								=			state.common.alphas_cumprod[timestep]
       lowerCAmelCase__								=			jnp.where(
           prev_timestep >= 0					,	state.common.alphas_cumprod[prev_timestep]					,	state.final_alpha_cumprod	)
       lowerCAmelCase__								=			1 - alpha_prod_t
       lowerCAmelCase__								=			1 - alpha_prod_t_prev

       lowerCAmelCase__								=			(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

       return variance
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		DDIMSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		float = 0.0					,	__lowerCamelCase  :		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)
       lowerCAmelCase__								=			timestep - self.config.num_train_timesteps // state.num_inference_steps

       lowerCAmelCase__								=			state.common.alphas_cumprod
       lowerCAmelCase__								=			state.final_alpha_cumprod

       # 2. compute alphas, betas
       lowerCAmelCase__								=			alphas_cumprod[timestep]
       lowerCAmelCase__								=			jnp.where(prev_timestep >= 0					,	alphas_cumprod[prev_timestep]					,	__lowerCamelCase	)

       lowerCAmelCase__								=			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":
         lowerCAmelCase__								=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         lowerCAmelCase__								=			model_output
       elif self.config.prediction_type == "sample":
         lowerCAmelCase__								=			model_output
         lowerCAmelCase__								=			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
         lowerCAmelCase__								=			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         lowerCAmelCase__								=			(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)
       lowerCAmelCase__								=			self._get_variance(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase	)
       lowerCAmelCase__								=			eta * variance ** 0.5

       # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       lowerCAmelCase__								=			(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
       lowerCAmelCase__								=			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 					A__			(	self  :		Tuple					,	__lowerCamelCase  :		DDIMSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	):



       """simple docstring"""
       return add_noise_common(state.common					,	__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase	)
     def 					A__			(	self  :		List[str]					,	__lowerCamelCase  :		DDIMSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	):



       """simple docstring"""
       return get_velocity_common(state.common					,	__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase	)



     def __len__(	self  :		Tuple	):



       """simple docstring"""
       return self.config.num_train_timesteps







def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase							):
  assert isinstance(__lowerCAmelCase      ,   __lowerCAmelCase							), F"""The input value of [set_a={set_a}] is not a set"""
  assert isinstance(__lowerCAmelCase      ,   __lowerCAmelCase							), F"""The input value of [set_b={set_b}] is not a set"""

  return {a + b for a in set_a for b in set_b}


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

      testmod()

