



from __future__ import annotations

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

UpperCAmelCase				:							str								=TypeVar("""T""", bound=bool)


class  _lowercase				(Generic[T] ):
    '''simple docstring'''







    def __init__(	self       ,	snake_case__      ):

         '''simple docstring'''


         UpperCamelCase_											=				value
         UpperCamelCase_											=				None
         UpperCamelCase_											=				None

    @property
    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         return self._value

    @staticmethod
    def  _lowerCamelCase   (	snake_case__       ,	snake_case__      ):

         '''simple docstring'''


         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  _lowercase				(Generic[T] ):
    '''simple docstring'''







    def __init__(	self       ,	snake_case__ = ()      ):

         '''simple docstring'''


         UpperCamelCase_											=				None

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

    def  _lowerCamelCase   (	self       ,	snake_case__      ):

         '''simple docstring'''


         UpperCamelCase_											=				RandomizedHeapNode.merge(self._root       ,	RandomizedHeapNode(snake_case__      )      )

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.top()

         if self._root is None:
              return None

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

         return result

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


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

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				None

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				[]
         while self:
              result.append(self.pop()      )

         return result

    def __bool__(	self      ):

         '''simple docstring'''


         return self._root is not None


if __name__ == "__main__":
 import doctest

 doctest.testmod()





from typing import TYPE_CHECKING

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


UpperCAmelCase				:							Optional[int]								={
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							Optional[int]								=["""RealmTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							Union[str, Any]								=[
	    """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RealmEmbedder""",
	    """RealmForOpenQA""",
	    """RealmKnowledgeAugEncoder""",
	    """RealmPreTrainedModel""",
	    """RealmReader""",
	    """RealmScorer""",
	    """load_tf_weights_in_realm""",
	]
	UpperCAmelCase				:							List[str]								=["""RealmRetriever"""]


if TYPE_CHECKING:
	from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
	from .tokenization_realm import RealmTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_realm import RealmTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_realm import (
		    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RealmEmbedder,
		    RealmForOpenQA,
		    RealmKnowledgeAugEncoder,
		    RealmPreTrainedModel,
		    RealmReader,
		    RealmScorer,
		    load_tf_weights_in_realm,
		)
		from .retrieval_realm import RealmRetriever


else:
	import sys

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