

from __future__ import annotations

from random import random

class     __UpperCamelCase     :




	def __init__(		self						:    Optional[Any]		,						lowerCAmelCase						:    int | None = None				):


		'''simple docstring'''







		UpperCAmelCase_												=      value
		UpperCAmelCase_												=      random()
		UpperCAmelCase_												=      None
		UpperCAmelCase_												=      None




	def __repr__(		self						:    Any				):


		'''simple docstring'''







		from pprint import pformat

		if self.left is None and self.right is None:
			return F"\'{self.value}: {self.prior:.5}\'"
		else:
			return pformat(
			    {F"{self.value}: {self.prior:.5}": (self.left, self.right)}		,						indent=1				)




	def __str__(		self						:    List[str]				):


		'''simple docstring'''







		UpperCAmelCase_												=      str(self.value				) + " "
		UpperCAmelCase_												=      str(self.left or ""				)
		UpperCAmelCase_												=      str(self.right or ""				)
		return value + left + right



def    __lowerCAmelCase   (    A							,  A     ):
	if root is None:  # None tree is split into 2 Nones
		return None, None
	elif root.value is None:
		return None, None
	else:
		if value < root.value:
			UpperCAmelCase_					, UpperCAmelCase_												=      split(root.left							,  _lowercase     )
			return left, root
		else:
			UpperCAmelCase_					, UpperCAmelCase_												=      split(root.right							,  _lowercase     )
			return root, right



def    __lowerCAmelCase   (    A							,  A     ):
	if (not left) or (not right):  # If one node is None, return the other
		return left or right
	elif left.prior < right.prior:
		UpperCAmelCase_												=      merge(left.right							,  _lowercase     )
		return left
	else:
		UpperCAmelCase_												=      merge(_lowercase							,  right.left     )
		return right



def    __lowerCAmelCase   (    A							,  A     ):
	UpperCAmelCase_												=      Node(_lowercase     )
	UpperCAmelCase_					, UpperCAmelCase_												=      split(_lowercase							,  _lowercase     )
	return merge(merge(_lowercase							,  _lowercase     )							,  _lowercase     )



def    __lowerCAmelCase   (    A							,  A     ):
	UpperCAmelCase_					, UpperCAmelCase_												=      split(_lowercase							,  value - 1     )
	UpperCAmelCase_					, UpperCAmelCase_												=      split(_lowercase							,  _lowercase     )
	return merge(_lowercase							,  _lowercase     )



def    __lowerCAmelCase   (    A     ):
	if not root:  # None
		return
	else:
		inorder(root.left     )
		print(root.value							,  end=","     )
		inorder(root.right     )



def    __lowerCAmelCase   (    A							,  A     ):
	for arg in args.split():
		if arg[0] == "+":
			UpperCAmelCase_												=      insert(_lowercase							,  int(arg[1:]     )     )

		elif arg[0] == "-":
			UpperCAmelCase_												=      erase(_lowercase							,  int(arg[1:]     )     )

		else:
			print("Unknown command"     )

	return root



def    __lowerCAmelCase   (    ):
	UpperCAmelCase_												=      None
	print(
	    "enter numbers to create a tree, + value to add value into treap, "
	    "- value to erase all nodes with value. \'q\' to quit. "     )

	UpperCAmelCase_												=      input()
	while args != "q":
		UpperCAmelCase_												=      interact_treap(_lowercase							,  _lowercase     )
		print(_lowercase     )
		UpperCAmelCase_												=      input()

	print("good by!"     )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()

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       _lowerCAmelCase  :

 """simple docstring"""
 # setable values
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None  # sigma(t_i)

 @classmethod
 def      __lowerCAmelCase  (						cls			:			Optional[Any]   ):






      """simple docstring"""



      return cls()




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					KarrasVeSchedulerState




class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      return True

 @register_to_config
 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			float = 0.02					, SCREAMING_SNAKE_CASE__			:			float = 1_00					, SCREAMING_SNAKE_CASE__			:			float = 1.007					, SCREAMING_SNAKE_CASE__			:			float = 80					, SCREAMING_SNAKE_CASE__			:			float = 0.05					, SCREAMING_SNAKE_CASE__			:			float = 50					, ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      return KarrasVeSchedulerState.create()

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Tuple = ()   ):






      """simple docstring"""



      UpperCamelCase						    =					jnp.arange(0					, SCREAMING_SNAKE_CASE__   )[::-1].copy()
      UpperCamelCase						    =					[
          (
              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=SCREAMING_SNAKE_CASE__					, schedule=jnp.array(SCREAMING_SNAKE_CASE__					, dtype=jnp.floataa   )					, timesteps=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			random.KeyArray					, ):






      """simple docstring"""



      if self.config.s_min <= sigma <= self.config.s_max:
           UpperCamelCase						    =					min(self.config.s_churn / state.num_inference_steps					, 2**0.5 - 1   )
      else:
           UpperCamelCase						    =					0

      # sample eps ~ N(0, S_noise^2 * I)
      UpperCamelCase						    =					random.split(SCREAMING_SNAKE_CASE__					, num=1   )
      UpperCamelCase						    =					self.config.s_noise * random.normal(key=SCREAMING_SNAKE_CASE__					, shape=sample.shape   )
      UpperCamelCase						    =					sigma + gamma * sigma
      UpperCamelCase						    =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_hat + sigma_hat * model_output
      UpperCamelCase						    =					(sample_hat - pred_original_sample) / sigma_hat
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * derivative

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

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

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_prev + sigma_prev * model_output
      UpperCamelCase						    =					(sample_prev - pred_original_sample) / sigma_prev
      UpperCamelCase						    =					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=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      raise NotImplementedError()


