

from __future__ import annotations

from random import random



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__ = None      ):
						A__							:   Optional[int]					       =		value
						A__							:   Any					       =		random()
						A__							:   Node | None					       =		None
						A__							:   Node | None					       =		None






					def __repr__(			self      ):
						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      ):
						A__							:   str					       =		str(self.value      ) + """ """
						A__							:   Tuple					       =		str(self.left or """"""      )
						A__							:   int					       =		str(self.right or """"""      )
						return value + left + right



def 						UpperCamelCase (lowercase_:			Node | None   ,				lowercase_:			int )					->				tuple[Node | None, Node | None]:
	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:
			A__       ,       A__							:   List[str]					       =		split(root.left   ,				lowercase_ )
			return left, root
		else:
			A__       ,       A__							:   Tuple					       =		split(root.right   ,				lowercase_ )
			return root, right

def 						UpperCamelCase (lowercase_:			Node | None   ,				lowercase_:			Node | None )					->				Node | None:
	if (not left) or (not right):  # If one node is None, return the other
		return left or right
	elif left.prior < right.prior:
		A__							:   int					       =		merge(left.right   ,				lowercase_ )
		return left
	else:
		A__							:   Optional[int]					       =		merge(lowercase_   ,				right.left )
		return right

def 						UpperCamelCase (lowercase_:			Node | None   ,				lowercase_:			int )					->				Node | None:
	A__							:   Any					       =		Node(lowercase_ )
	A__       ,       A__							:   Dict					       =		split(lowercase_   ,				lowercase_ )
	return merge(merge(lowercase_   ,				lowercase_ )   ,				lowercase_ )

def 						UpperCamelCase (lowercase_:			Node | None   ,				lowercase_:			int )					->				Node | None:
	A__       ,       A__							:   List[Any]					       =		split(lowercase_   ,				value - 1 )
	A__       ,       A__							:   int					       =		split(lowercase_   ,				lowercase_ )
	return merge(lowercase_   ,				lowercase_ )

def 						UpperCamelCase (lowercase_:			Node | None )					->				None:
	if not root:  # None
		return
	else:
		inorder(root.left )
		print(root.value   ,				end=""",""" )
		inorder(root.right )

def 						UpperCamelCase (lowercase_:			Node | None   ,				lowercase_:			str )					->				Node | None:
	for arg in args.split():
		if arg[0] == "+":
			A__							:   Any					       =		insert(lowercase_   ,				int(arg[1:] ) )

		elif arg[0] == "-":
			A__							:   Union[str, Any]					       =		erase(lowercase_   ,				int(arg[1:] ) )

		else:
			print("""Unknown command""" )

	return root

def 						UpperCamelCase ()					->				None:
	A__							:   Optional[int]					       =		None
	print(
	    """enter numbers to create a tree, + value to add value into treap, """
	    """- value to erase all nodes with value. 'q' to quit. """ )

	A__							:   int					       =		input()
	while args != "q":
		A__							:   Tuple					       =		interact_treap(lowercase_   ,				lowercase_ )
		print(lowercase_ )
		A__							:   List[str]					       =		input()

	print("""good by!""" )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_	:			List[Any]  	=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['GPTNeoXTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[int]  	=      [
	    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXForCausalLM',
	    'GPTNeoXForQuestionAnswering',
	    'GPTNeoXForSequenceClassification',
	    'GPTNeoXForTokenClassification',
	    'GPTNeoXLayer',
	    'GPTNeoXModel',
	    'GPTNeoXPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	A_	:			Any  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



