



# 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    :
   SCREAMING_SNAKE_CASE__					      =  42
   SCREAMING_SNAKE_CASE__					      =  42

   # setable values
   SCREAMING_SNAKE_CASE__					      =  42
   SCREAMING_SNAKE_CASE__					      =  42
   SCREAMING_SNAKE_CASE__					      =  None





   @classmethod
   def 						__lowerCAmelCase				(						cls							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  ):
         return cls(
             common=_lowerCAmelCase							,  final_alpha_cumprod=_lowerCAmelCase							,  init_noise_sigma=_lowerCAmelCase							,  timesteps=_lowerCAmelCase							,  )






@dataclass
class 		UpperCAmelCase    (    snake_case_					):
   SCREAMING_SNAKE_CASE__					      =  42






class 		UpperCAmelCase    (    snake_case_							,snake_case_					):
   SCREAMING_SNAKE_CASE__					      =  [e.name for e in FlaxKarrasDiffusionSchedulers]

   SCREAMING_SNAKE_CASE__					      =  42





   @property
   def 						__lowerCAmelCase				(						self      ):
         return True





   @register_to_config
   def __init__(						self							,  _lowerCAmelCase = 1_000							,  _lowerCAmelCase = 0.0_001							,  _lowerCAmelCase = 0.02							,  _lowerCAmelCase = "linear"							,  _lowerCAmelCase = None							,  _lowerCAmelCase = True							,  _lowerCAmelCase = 0							,  _lowerCAmelCase = "epsilon"							,  _lowerCAmelCase = jnp.floataa							,  ):
         _lowerCAmelCase			      =	dtype





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase = None      ):
         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 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase = None      ):
         return sample





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase = ()      ):
         _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 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         _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 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase = 0.0							,  _lowerCAmelCase = 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)
         _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 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  ):
         return add_noise_common(state.common							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  ):
         return get_velocity_common(state.common							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase      )





   def __len__(						self      ):
         return self.config.num_train_timesteps




def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						str					)->str:
      return " ".join(sorted(set(sentence.split()					)					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()