
"""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 .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left



@flax.struct.dataclass
class 				UpperCAmelCase_	:
 """simple docstring"""




 UpperCamelCase_						:     Optional[jnp.ndarray]				   =    None
 UpperCamelCase_						:     Optional[jnp.ndarray]				   =    None
 UpperCamelCase_						:     Optional[jnp.ndarray]				   =    None




 @classmethod
 def   a      ( cls       : Dict )->					Any:



      """simple docstring"""

      return cls()



@dataclass
class 				UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 UpperCamelCase_						:     ScoreSdeVeSchedulerState
 UpperCamelCase_						:     jnp.ndarray
 UpperCamelCase_						:     Optional[jnp.ndarray]				   =    None



class 				UpperCAmelCase_	(lowerCamelCase_  , lowerCamelCase_						):
 """simple docstring"""




 @property
 def   a      ( self       : Any )->					str:



      """simple docstring"""

      return True




 @register_to_config
 def __init__( self       : List[str]     ,   a_       : int = 20_00     ,   a_       : float = 0.15     ,   a_       : float = 0.01     ,   a_       : float = 1348.0     ,   a_       : float = 1E-5     ,   a_       : int = 1     ,   )->					str:



      """simple docstring"""

      pass




 def   a      ( self       : Union[str, Any] )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    List[Any]    					=  ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          a_     ,   self.config.num_train_timesteps     ,   self.config.sigma_min     ,   self.config.sigma_max     ,   self.config.sampling_eps     ,   )




 def   a      ( self       : Optional[Any]     ,   a_       : ScoreSdeVeSchedulerState     ,   a_       : int     ,   a_       : Tuple = ()     ,   a_       : float = None )->					ScoreSdeVeSchedulerState:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  sampling_eps if sampling_eps is not None else self.config.sampling_eps

      UpperCAmelCase_						:    List[Any]    					=  jnp.linspace(1     ,   a_     ,   a_ )
      return state.replace(timesteps=a_ )




 def   a      ( self       : Optional[int]     ,   a_       : ScoreSdeVeSchedulerState     ,   a_       : int     ,   a_       : float = None     ,   a_       : float = None     ,   a_       : float = None     ,   )->					ScoreSdeVeSchedulerState:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  sigma_min if sigma_min is not None else self.config.sigma_min
      UpperCAmelCase_						:    Union[str, Any]    					=  sigma_max if sigma_max is not None else self.config.sigma_max
      UpperCAmelCase_						:    List[str]    					=  sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
           UpperCAmelCase_						:    Any    					=  self.set_timesteps(a_     ,   a_     ,   a_ )

      UpperCAmelCase_						:    Tuple    					=  jnp.exp(jnp.linspace(jnp.log(a_ )     ,   jnp.log(a_ )     ,   a_ ) )
      UpperCAmelCase_						:    Optional[int]    					=  jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps] )

      return state.replace(discrete_sigmas=a_     ,   sigmas=a_ )




 def   a      ( self       : Tuple     ,   a_       : str     ,   a_       : List[Any]     ,   a_       : List[str] )->					Any:



      """simple docstring"""

      return jnp.where(timesteps == 0     ,   jnp.zeros_like(a_ )     ,   state.discrete_sigmas[timesteps - 1] )




 def   a      ( self       : Any     ,   a_       : ScoreSdeVeSchedulerState     ,   a_       : jnp.ndarray     ,   a_       : int     ,   a_       : jnp.ndarray     ,   a_       : random.KeyArray     ,   a_       : bool = True     ,   )->					Union[FlaxSdeVeOutput, Tuple]:



      """simple docstring"""

      if state.timesteps is None:
           raise ValueError(
               """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )

      UpperCAmelCase_						:    Any    					=  timestep * jnp.ones(
          sample.shape[0]     ,   )
      UpperCAmelCase_						:    Union[str, Any]    					=  (timestep * (len(state.timesteps ) - 1)).long()

      UpperCAmelCase_						:    List[str]    					=  state.discrete_sigmas[timesteps]
      UpperCAmelCase_						:    List[str]    					=  self.get_adjacent_sigma(a_     ,   a_     ,   a_ )
      UpperCAmelCase_						:    Any    					=  jnp.zeros_like(a_ )
      UpperCAmelCase_						:    List[str]    					=  (sigma**2 - adjacent_sigma**2) ** 0.5

      # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
      # also equation 47 shows the analog from SDE models to ancestral sampling methods
      UpperCAmelCase_						:    Any    					=  diffusion.flatten()
      UpperCAmelCase_						:    Optional[int]    					=  broadcast_to_shape_from_left(a_     ,   sample.shape )
      UpperCAmelCase_						:    str    					=  drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      UpperCAmelCase_						:    List[Any]    					=  random.split(a_     ,   num=1 )
      UpperCAmelCase_						:    Any    					=  random.normal(key=a_     ,   shape=sample.shape )
      UpperCAmelCase_						:    str    					=  sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      UpperCAmelCase_						:    Dict    					=  prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
           return (prev_sample, prev_sample_mean, state)

      return FlaxSdeVeOutput(prev_sample=a_     ,   prev_sample_mean=a_     ,   state=a_ )




 def   a      ( self       : Tuple     ,   a_       : ScoreSdeVeSchedulerState     ,   a_       : jnp.ndarray     ,   a_       : jnp.ndarray     ,   a_       : random.KeyArray     ,   a_       : bool = True     ,   )->					Union[FlaxSdeVeOutput, Tuple]:



      """simple docstring"""

      if state.timesteps is None:
           raise ValueError(
               """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )

      # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
      # sample noise for correction
      UpperCAmelCase_						:    Optional[int]    					=  random.split(a_     ,   num=1 )
      UpperCAmelCase_						:    Optional[int]    					=  random.normal(key=a_     ,   shape=sample.shape )

      # compute step size from the model_output, the noise, and the snr
      UpperCAmelCase_						:    str    					=  jnp.linalg.norm(a_ )
      UpperCAmelCase_						:    int    					=  jnp.linalg.norm(a_ )
      UpperCAmelCase_						:    Optional[Any]    					=  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
      UpperCAmelCase_						:    List[Any]    					=  step_size * jnp.ones(sample.shape[0] )

      # compute corrected sample: model_output term and noise term
      UpperCAmelCase_						:    Optional[int]    					=  step_size.flatten()
      UpperCAmelCase_						:    Optional[Any]    					=  broadcast_to_shape_from_left(a_     ,   sample.shape )
      UpperCAmelCase_						:    List[Any]    					=  sample + step_size * model_output
      UpperCAmelCase_						:    Any    					=  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
           return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=a_     ,   state=a_ )






 def __len__( self       : Optional[Any] )->					Tuple:



      """simple docstring"""

      return self.config.num_train_timesteps



"""simple docstring"""







from __future__ import annotations



class 		UpperCAmelCase_	:
 """simple docstring"""




 def __init__( self       : Any     ,   a_       : int = 0 )->					None:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  value
      UpperCAmelCase_						:    Node | None    					=  None
      UpperCAmelCase_						:    Node | None    					=  None




def 			A_			(					lowercase						, lowercase      )     ->							Node | None:


     """simple docstring"""







     if treea is None:
          return treea
     if treea is None:
          return treea

     UpperCAmelCase_						:    int    					=  treea.value + treea.value
     UpperCAmelCase_						:    List[str]    					=  merge_two_binary_trees(treea.left						, treea.left      )
     UpperCAmelCase_						:    List[str]    					=  merge_two_binary_trees(treea.right						, treea.right      )
     return treea




def 			A_			(					lowercase      )     ->							None:


     """simple docstring"""







     if root:
          print(root.value      )
          print_preorder(root.left      )
          print_preorder(root.right      )


if __name__ == "__main__":
       lowercase_						    =					Node(1)
       lowercase_						    =					Node(2)
       lowercase_						    =					Node(3)
       lowercase_						    =					Node(4)

       lowercase_						    =					Node(2)
       lowercase_						    =					Node(4)
       lowercase_						    =					Node(6)
       lowercase_						    =					Node(9)
       lowercase_						    =					Node(5)

       print("Tree1 is: ")
       print_preorder(treea)
       print("Tree2 is: ")
       print_preorder(treea)
       lowercase_						    =					merge_two_binary_trees(treea, treea)
       print("Merged Tree is: ")
       print_preorder(merged_tree)

