from __future__ import annotations

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

lowercase__      :    Union[str, Any]          =     TypeVar('''T''', bound=bool)




class        lowercase_  (  Generic[T]						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE    )						->None:
								lowerCAmelCase     		=		value
								lowerCAmelCase     		=		None
								lowerCAmelCase     		=		None


	@property
	def       SCREAMING_SNAKE_CASE_    (							self    )						->T:
								return self._value




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

								if not roota:
															return roota

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

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

								lowerCAmelCase     		=		RandomizedHeapNode.merge(roota.left		,      __SCREAMING_SNAKE_CASE    )

								return roota




class        lowercase_  (  Generic[T]						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE = ()    )						->None:
								lowerCAmelCase     		=		None

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


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->None:
								lowerCAmelCase     		=		RandomizedHeapNode.merge(self._root		,      RandomizedHeapNode(__SCREAMING_SNAKE_CASE    )    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->T | None:
								lowerCAmelCase     		=		self.top()

								if self._root is None:
															return None

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

								return result


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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->None:
								lowerCAmelCase     		=		None


	def       SCREAMING_SNAKE_CASE_    (							self    )						->list[Any]:
								lowerCAmelCase     		=		[]
								while self:
															result.append(self.pop()    )

								return result




	def __bool__(							self    )						->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_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__      :    Dict          =     {
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    Dict          =     [
	    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SamModel''',
	    '''SamPreTrainedModel''',
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    str          =     [
	    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSamModel''',
	    '''TFSamPreTrainedModel''',
	]
try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    Dict          =     ['''SamImageProcessor''']


if TYPE_CHECKING:
	from .configuration_sam import (
	    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SamConfig,
	    SamMaskDecoderConfig,
	    SamPromptEncoderConfig,
	    SamVisionConfig,
	)
	from .processing_sam import SamProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_sam import SamImageProcessor

else:
	import sys

	lowercase__      :    Dict          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



