
'''simple docstring'''







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class UpperCAmelCase__	:





  """simple docstring"""

  __UpperCAmelCase				:				Optional[int]   =					None
  __UpperCAmelCase				:				Optional[jnp.ndarray]   =					None
  __UpperCAmelCase				:				Optional[jnp.ndarray]   =					None  # sigma(t_i)


  @classmethod
  def   __lowercase		(		cls      :     Tuple       ):


     '''simple docstring'''






     return cls()







@dataclass
class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				jnp.ndarray
  __UpperCAmelCase				:				jnp.ndarray
  __UpperCAmelCase				:				KarrasVeSchedulerState







class UpperCAmelCase__	( lowercase__    ,				lowercase__	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     return True


  @register_to_config
  def __init__(		self      :     Union[str, Any]							,_a      :     float = 0.02							,_a      :     float = 100							,_a      :     float = 1.007							,_a      :     float = 80							,_a      :     float = 0.05							,_a      :     float = 50							,):


     '''simple docstring'''






     pass


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     return KarrasVeSchedulerState.create()


  def   __lowercase		(		self      :     List[Any]							,_a      :     KarrasVeSchedulerState							,_a      :     int							,_a      :     Tuple = ()       ):


     '''simple docstring'''






     _a				:	Dict			=   jnp.arange(0							,_a       )[::-1].copy()
     _a				:	Dict			=   [
         (
             self.config.sigma_max**2
             * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
         )
         for i in timesteps
     ]

     return state.replace(
         num_inference_steps=_a							,schedule=jnp.array(_a							,dtype=jnp.floataa       )							,timesteps=_a							,)


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     KarrasVeSchedulerState							,_a      :     jnp.ndarray							,_a      :     float							,_a      :     random.KeyArray							,):


     '''simple docstring'''






     if self.config.s_min <= sigma <= self.config.s_max:
        _a				:	int			=   min(self.config.s_churn / state.num_inference_steps							,2**0.5 - 1       )
     else:
        _a				:	Union[str, Any]			=   0

     # sample eps ~ N(0, S_noise^2 * I)
     _a				:	Any			=   random.split(_a							,num=1       )
     _a				:	List[Any]			=   self.config.s_noise * random.normal(key=_a							,shape=sample.shape       )
     _a				:	Dict			=   sigma + gamma * sigma
     _a				:	int			=   sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

     return sample_hat, sigma_hat


  def   __lowercase		(		self      :     Tuple							,_a      :     KarrasVeSchedulerState							,_a      :     jnp.ndarray							,_a      :     float							,_a      :     float							,_a      :     jnp.ndarray							,_a      :     bool = True							,):


     '''simple docstring'''






     _a				:	Optional[int]			=   sample_hat + sigma_hat * model_output
     _a				:	Any			=   (sample_hat - pred_original_sample) / sigma_hat
     _a				:	Dict			=   sample_hat + (sigma_prev - sigma_hat) * derivative

     if not return_dict:
        return (sample_prev, derivative, state)

     return FlaxKarrasVeOutput(prev_sample=_a							,derivative=_a							,state=_a       )


  def   __lowercase		(		self      :     List[str]							,_a      :     KarrasVeSchedulerState							,_a      :     jnp.ndarray							,_a      :     float							,_a      :     float							,_a      :     jnp.ndarray							,_a      :     jnp.ndarray							,_a      :     jnp.ndarray							,_a      :     bool = True							,):


     '''simple docstring'''






     _a				:	Optional[Any]			=   sample_prev + sigma_prev * model_output
     _a				:	int			=   (sample_prev - pred_original_sample) / sigma_prev
     _a				:	Optional[int]			=   sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

     if not return_dict:
        return (sample_prev, derivative, state)

     return FlaxKarrasVeOutput(prev_sample=_a							,derivative=_a							,state=_a       )


  def   __lowercase		(		self      :     List[str]							,_a      :     KarrasVeSchedulerState							,_a      :     Tuple							,_a      :     Dict							,_a      :     List[Any]       ):


     '''simple docstring'''






     raise NotImplementedError()





'''simple docstring'''







def 					UpperCAmelCase_		(__a      :    list ,	__a      :    list ,	__a      :    int ):






   """simple docstring"""

   _a				:	Optional[Any]			=   len(__a )
   _a				:	int			=   [[0] * n for i in range(__a )]
   for i in range(__a ):
      _a				:	Tuple			=   y_points[i]

   for i in range(2 ,	__a ):
      for j in range(__a ,	__a ):
         _a				:	Tuple			=   (
             (xa - x_points[j - i + 1]) * q[j][i - 1]
             - (xa - x_points[j]) * q[j - 1][i - 1]
         ) / (x_points[j] - x_points[j - i + 1])

   return [q[n - 1][n - 1], q]


if __name__ == "__main__":
  import doctest

  doctest.testmod()



