




from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

a_ 		=  TypeVar("T", bound=bool)

class 			_lowerCamelCase    (      Generic[T] ):

      """simple docstring"""

      def __init__(  self       :			Any			,							snake_case       :			T					):
          __UpperCamelCase			=    value
          __UpperCamelCase			=    None
          __UpperCamelCase			=    None







      @property
      def    snake_case						(  self       :			Any					):
          return self._value







      @staticmethod
      def    snake_case						(  snake_case       :			RandomizedHeapNode[T] | None			,							snake_case       :			RandomizedHeapNode[T] | None					):
          if not roota:
              return roota

          if not roota:
              return roota

          if roota.value > roota.value:
              __UpperCamelCase		,   __UpperCamelCase			=    roota, roota

          if random.choice([True, False]					):
              __UpperCamelCase		,   __UpperCamelCase			=    roota.right, roota.left

          __UpperCamelCase			=    RandomizedHeapNode.merge(roota.left			,							snake_case					)

          return roota







class 			_lowerCamelCase    (      Generic[T] ):

      """simple docstring"""

      def __init__(  self       :			Dict			,							snake_case       :			Iterable[T] | None = ()					):
          __UpperCamelCase			=    None

          if data:
              for item in data:
                  self.insert(snake_case					)







      def    snake_case						(  self       :			Union[str, Any]			,							snake_case       :			T					):
          __UpperCamelCase			=    RandomizedHeapNode.merge(self._root			,							RandomizedHeapNode(snake_case					)					)







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    self.top()

          if self._root is None:
              return None

          __UpperCamelCase			=    RandomizedHeapNode.merge(self._root.left			,							self._root.right					)

          return result







      def    snake_case						(  self       :			Dict					):
          if not self._root:
              raise IndexError('''Can\'t get top element for the empty heap.'''					)
          return self._root.value







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    None







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    []
          while self:
              result.append(self.pop()					)

          return result







      def __bool__(  self       :			Tuple					):
          return self._root is not None


if __name__ == "__main__":
 import doctest

 doctest.testmod()











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :				List[Any]							={"""configuration_qdqbert""": ["""QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """QDQBertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				Dict							=[
	    """QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """QDQBertForMaskedLM""",
	    """QDQBertForMultipleChoice""",
	    """QDQBertForNextSentencePrediction""",
	    """QDQBertForQuestionAnswering""",
	    """QDQBertForSequenceClassification""",
	    """QDQBertForTokenClassification""",
	    """QDQBertLayer""",
	    """QDQBertLMHeadModel""",
	    """QDQBertModel""",
	    """QDQBertPreTrainedModel""",
	    """load_tf_weights_in_qdqbert""",
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

	UpperCAmelCase :				Optional[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
