

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_   :
 _lowerCAmelCase       		=  42
 _lowerCAmelCase       		=  42

 # setable values
 _lowerCAmelCase       		=  42
 _lowerCAmelCase       		=  42
 _lowerCAmelCase       		=  None




 @classmethod
 def 						a					( cls     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     ):
   return cls(
       common=UpperCamelCase__     ,     final_alpha_cumprod=UpperCamelCase__     ,     init_noise_sigma=UpperCamelCase__     ,     timesteps=UpperCamelCase__     ,     )



@dataclass
class     lowerCamelCase_   ( _SCREAMING_SNAKE_CASE):
 _lowerCAmelCase       		=  42



class     lowerCamelCase_   ( _SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE):
 _lowerCAmelCase       		=  [e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCAmelCase       		=  42




 @property
 def 						a					( self       ):
   return True




 @register_to_config
 def __init__( self     ,     _UpperCAmelCase = 1_000     ,     _UpperCAmelCase = 0.0_0_0_1     ,     _UpperCAmelCase = 0.0_2     ,     _UpperCAmelCase = "linear"     ,     _UpperCAmelCase = None     ,     _UpperCAmelCase = True     ,     _UpperCAmelCase = 0     ,     _UpperCAmelCase = "epsilon"     ,     _UpperCAmelCase = jnp.floataa     ,     ):
   __UpperCAmelCase  :     int		=    dtype




 def 						a					( self     ,     _UpperCAmelCase = None       ):
   if common is None:
     __UpperCAmelCase  :     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.
   __UpperCAmelCase  :     Any		=    (
       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
   __UpperCAmelCase  :     Any		=    jnp.array(1.0     ,     dtype=self.dtype       )

   __UpperCAmelCase  :     List[str]		=    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 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase = None       ):
   return sample




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase = ()       ):
   __UpperCAmelCase  :     Union[str, 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
   __UpperCAmelCase  :     Dict		=    (jnp.arange(0     ,     UpperCamelCase__       ) * step_ratio).round()[::-1] + self.config.steps_offset

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




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Union[str, Any]		=    state.common.alphas_cumprod[timestep]
   __UpperCAmelCase  :     Tuple		=    jnp.where(
       prev_timestep >= 0     ,     state.common.alphas_cumprod[prev_timestep]     ,     state.final_alpha_cumprod       )
   __UpperCAmelCase  :     int		=    1 - alpha_prod_t
   __UpperCAmelCase  :     Optional[Any]		=    1 - alpha_prod_t_prev

   __UpperCAmelCase  :     List[Any]		=    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

   return variance




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase = 0.0     ,     _UpperCAmelCase = 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)
   __UpperCAmelCase  :     str		=    timestep - self.config.num_train_timesteps // state.num_inference_steps

   __UpperCAmelCase  :     List[str]		=    state.common.alphas_cumprod
   __UpperCAmelCase  :     Dict		=    state.final_alpha_cumprod

   # 2. compute alphas, betas
   __UpperCAmelCase  :     Optional[int]		=    alphas_cumprod[timestep]
   __UpperCAmelCase  :     Optional[Any]		=    jnp.where(prev_timestep >= 0     ,     alphas_cumprod[prev_timestep]     ,     UpperCamelCase__       )

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

   # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   __UpperCAmelCase  :     str		=    (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
   __UpperCAmelCase  :     Tuple		=    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 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     ):
   return add_noise_common(state.common     ,     UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__       )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     ):
   return get_velocity_common(state.common     ,     UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__       )




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








def    _snake_case		(snake_case_				:    list						,					snake_case_				:    list     )       ->		list:
  __UpperCAmelCase  :     int		=    len(snake_case_     )
  __UpperCAmelCase  :     int		=    len(snake_case_     )
  __UpperCAmelCase  :     int		=    (
      first_input_list_length
      if first_input_list_length > second_input_list_length
      else second_input_list_length
  )
  __UpperCAmelCase  :     list		=    []
  for char_count in range(snake_case_     ):
    if char_count < first_input_list_length:
      output_result_list.append(first_input_list[char_count]     )
    if char_count < second_input_list_length:
      output_result_list.append(second_input_list[char_count]     )

  return output_result_list


if __name__ == "__main__":
      print(alternative_list_arrange(['''A''', '''B''', '''C'''], [1, 2, 3, 4, 5]), end=''' ''')





