





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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     42
 lowercase_						=     42
 lowercase_						=     None



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     2

 @register_to_config
 def __init__(self							:  Optional[int]			,					UpperCAmelCase_							:  float = 0.02			,					UpperCAmelCase_							:  float = 100			,					UpperCAmelCase_							:  float = 1.007			,					UpperCAmelCase_							:  float = 80			,					UpperCAmelCase_							:  float = 0.05			,					UpperCAmelCase_							:  float = 50			,					)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Dict         =sigma_max

   # setable values
   lowerCamelCase__: int         =None
   lowerCamelCase__: np.IntTensor         =None
   lowerCamelCase__: torch.FloatTensor         =None  # sigma(t_i)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  Optional[int] = None)			->torch.FloatTensor:



   '''simple docstring'''



   return sample

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Union[str, torch.device] = None)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =num_inference_steps
   lowerCamelCase__: List[Any]         =np.arange(0			,					self.num_inference_steps)[::-1].copy()
   lowerCamelCase__: Union[str, Any]         =torch.from_numpy(UpperCAmelCase_).to(UpperCAmelCase_)
   lowerCamelCase__: Union[str, Any]         =[
       (
           self.config.sigma_max**2
           * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
       )
       for i in self.timesteps
   ]
   lowerCamelCase__: int         =torch.tensor(UpperCAmelCase_			,					dtype=torch.floataa			,					device=UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str]			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  float			,					UpperCAmelCase_							:  Optional[torch.Generator] = None)			->Tuple[torch.FloatTensor, float]:



   '''simple docstring'''



   if self.config.s_min <= sigma <= self.config.s_max:
     lowerCamelCase__: List[Any]         =min(self.config.s_churn / self.num_inference_steps			,					2**0.5 - 1)
   else:
     lowerCamelCase__: Any         =0

   # sample eps ~ N(0, S_noise^2 * I)
   lowerCamelCase__: Any         =self.config.s_noise * randn_tensor(sample.shape			,					generator=UpperCAmelCase_).to(sample.device)
   lowerCamelCase__: Optional[Any]         =sigma + gamma * sigma
   lowerCamelCase__: str         =sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

   return sample_hat, sigma_hat

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any]			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  float			,					UpperCAmelCase_							:  float			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  bool = True			,					)			->Union[KarrasVeOutput, Tuple]:



   '''simple docstring'''



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

   if not return_dict:
     return (sample_prev, derivative)

   return KarrasVeOutput(
       prev_sample=UpperCAmelCase_			,					derivative=UpperCAmelCase_			,					pred_original_sample=UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str]			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  float			,					UpperCAmelCase_							:  float			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  torch.FloatTensor			,					UpperCAmelCase_							:  bool = True			,					)			->Union[KarrasVeOutput, Tuple]:



   '''simple docstring'''



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

   if not return_dict:
     return (sample_prev, derivative)

   return KarrasVeOutput(
       prev_sample=UpperCAmelCase_			,					derivative=UpperCAmelCase_			,					pred_original_sample=UpperCAmelCase_)







 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  Optional[int])			->Any:



   '''simple docstring'''



   raise NotImplementedError()






from collections.abc import Sequence
from queue import Queue






class     __a			:



	def __init__(    self				:     str					,						snake_case_				:     List[str]					,						snake_case_				:     Tuple					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[Any]=None					,						snake_case_				:     Dict=None)->				Optional[int]:
						__lowerCAmelCase						 =start
						__lowerCAmelCase						 =end
						__lowerCAmelCase						 =val
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =left
						__lowerCAmelCase						 =right



	def __repr__(    self				:     List[Any])->				Dict:
						return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""






class     __a			:



	def __init__(    self				:     List[str]					,						snake_case_				:     Sequence					,						snake_case_				:     int)->				str:
						__lowerCAmelCase						 =collection
						__lowerCAmelCase						 =function
						if self.collection:
											__lowerCAmelCase						 =self._build_tree(0					,						len(snake_case_) - 1)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[int])->				Optional[int]:
						self._update_tree(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     Any					,						snake_case_				:     Union[str, Any])->				Union[str, Any]:
						return self._query_range(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     List[str]					,						snake_case_				:     List[str])->				List[Any]:
						if start == end:
											return SegmentTreeNode(snake_case_					,						snake_case_					,						self.collection[start])
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =self._build_tree(snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self._build_tree(mid + 1					,						snake_case_)
						return SegmentTreeNode(snake_case_					,						snake_case_					,						self.fn(left.val					,						right.val)					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     int					,						snake_case_				:     int)->				List[Any]:
						if node.start == i and node.end == i:
											__lowerCAmelCase						 =val
											return
						if i <= node.mid:
											self._update_tree(node.left					,						snake_case_					,						snake_case_)
						else:
											self._update_tree(node.right					,						snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self.fn(node.left.val					,						node.right.val)



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     str					,						snake_case_				:     List[Any])->				Optional[int]:
						if node.start == i and node.end == j:
											return node.val

						if i <= node.mid:
											if j <= node.mid:
																# range in left child tree
																return self._query_range(node.left					,						snake_case_					,						snake_case_)
											else:
																# range in left child tree and right child tree
																return self.fn(
																    self._query_range(node.left					,						snake_case_					,						node.mid)					,						self._query_range(node.right					,						node.mid + 1					,						snake_case_)					,						)
						else:
											# range in right child tree
											return self._query_range(node.right					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str])->				Tuple:
						if self.root is not None:
											__lowerCAmelCase						 =Queue()
											queue.put(self.root)
											while not queue.empty():
																__lowerCAmelCase						 =queue.get()
																yield node

																if node.left is not None:
																					queue.put(node.left)

																if node.right is not None:
																					queue.put(node.right)


if __name__ == "__main__":
				import operator

				for fn in [operator.add, max, min]:
								print('''*''' * 50)
								lowercase_													=      SegmentTree([2, 1, 5, 3, 4], fn)
								for node in arr.traverse():
												print(node)
								print()

								arr.update(1, 5)
								for node in arr.traverse():
												print(node)
								print()

								print(arr.query_range(3, 4))  # 7
								print(arr.query_range(2, 2))  # 5
								print(arr.query_range(1, 3))  # 13
								print()




