






from __future__ import annotations

from random import random




class 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE = None				) ->				Any:
								snake_case_				:							Tuple						 =       value
								snake_case_				:							Optional[Any]						 =       random()
								snake_case_				:							Node | None						 =       None
								snake_case_				:							Node | None						 =       None






						def __repr__(	self				) ->				str:
								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				) ->				str:
								snake_case_				:							Dict						 =       str(self.value				) + " "
								snake_case_				:							Tuple						 =       str(self.left or ""				)
								snake_case_				:							List[Any]						 =       str(self.right or ""				)
								return value + left + right




def      lowerCAmelCase__	(    _a       :			Node | None  ,      _a       :			int    ):
		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:
						snake_case_				:							List[Any]						 =       split(root.left  ,      _a    )
						return left, root
				else:
						snake_case_				:							Tuple						 =       split(root.right  ,      _a    )
						return root, right

def      lowerCAmelCase__	(    _a       :			Node | None  ,      _a       :			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:
				snake_case_				:							Tuple						 =       merge(left.right  ,      _a    )
				return left
		else:
				snake_case_				:							Union[str, Any]						 =       merge(_a  ,      right.left    )
				return right

def      lowerCAmelCase__	(    _a       :			Node | None  ,      _a       :			int    ):
		snake_case_				:							List[str]						 =       Node(_a    )
		snake_case_				:							Optional[int]						 =       split(_a  ,      _a    )
		return merge(merge(_a  ,      _a    )  ,      _a    )

def      lowerCAmelCase__	(    _a       :			Node | None  ,      _a       :			int    ):
		snake_case_				:							Optional[Any]						 =       split(_a  ,      value - 1    )
		snake_case_				:							List[Any]						 =       split(_a  ,      _a    )
		return merge(_a  ,      _a    )

def      lowerCAmelCase__	(    _a       :			Node | None    ):
		if not root:  # None
				return
		else:
				inorder(root.left    )
				print(root.value  ,      end=","    )
				inorder(root.right    )

def      lowerCAmelCase__	(    _a       :			Node | None  ,      _a       :			str    ):
		for arg in args.split():
				if arg[0] == "+":
						snake_case_				:							Union[str, Any]						 =       insert(_a  ,      int(arg[1:]    )    )

				elif arg[0] == "-":
						snake_case_				:							Any						 =       erase(_a  ,      int(arg[1:]    )    )

				else:
						print("Unknown command"    )

		return root

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

		snake_case_				:							Optional[Any]						 =       input()
		while args != "q":
				snake_case_				:							List[str]						 =       interact_treap(_a  ,      _a    )
				print(_a    )
				snake_case_				:							Optional[int]						 =       input()

		print("good by!"    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()








from typing import TYPE_CHECKING

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


lowercase  :							str			  =    {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							List[str]			  =    [
							    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BioGptForCausalLM''',
							    '''BioGptForTokenClassification''',
							    '''BioGptForSequenceClassification''',
							    '''BioGptModel''',
							    '''BioGptPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
