
from __future__ import annotations

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

__a        = TypeVar('T', bound=bool)
class 			lowercase__(    Generic[T]							):



	"""simple docstring"""




	def __init__(	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      T	)	->					None:
					lowercase_					  = value
					lowercase_					  = None
					lowercase_					  = None
	@property
	def 						_lowercase  (	self  :      List[Any]	)	->					T:
					return self._value




	@staticmethod
	def 						_lowercase  (	SCREAMING_SNAKE_CASE_  :      RandomizedHeapNode[T] | None	,							SCREAMING_SNAKE_CASE_  :      RandomizedHeapNode[T] | None	)	->					RandomizedHeapNode[T] | None:
					if not roota:
									return roota

					if not roota:
									return roota

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

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

					lowercase_					  = RandomizedHeapNode.merge(roota.left	,							SCREAMING_SNAKE_CASE_	)

					return roota

class 			lowercase__(    Generic[T]							):



	"""simple docstring"""




	def __init__(	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Iterable[T] | None = ()	)	->					None:
					lowercase_					  = None

					if data:
									for item in data:
													self.insert(SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      T	)	->					None:
					lowercase_					  = RandomizedHeapNode.merge(self._root	,							RandomizedHeapNode(SCREAMING_SNAKE_CASE_	)	)
	def 						_lowercase  (	self  :      Tuple	)	->					T | None:
					lowercase_					  = self.top()

					if self._root is None:
									return None

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

					return result
	def 						_lowercase  (	self  :      List[Any]	)	->					T:
					if not self._root:
									raise IndexError('''Can\'t get top element for the empty heap.'''	)
					return self._root.value
	def 						_lowercase  (	self  :      str	)	->					None:
					lowercase_					  = None
	def 						_lowercase  (	self  :      int	)	->					list[Any]:
					lowercase_					  = []
					while self:
									result.append(self.pop()	)

					return result




	def __bool__(	self  :      Optional[int]	)	->					bool:
					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, is_vision_available


__a        = {
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DonutSwinModel',
						    'DonutSwinPreTrainedModel',
						]

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = ['DonutFeatureExtractor']
						__a        = ['DonutImageProcessor']


if TYPE_CHECKING:
						from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
						from .processing_donut import DonutProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_donut_swin import (
												    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DonutSwinModel,
												    DonutSwinPreTrainedModel,
												)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_donut import DonutFeatureExtractor
												from .image_processing_donut import DonutImageProcessor

else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




