



'''simple docstring'''

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

_A				:    Optional[Any]     						=TypeVar('''T''')







def 						__UpperCamelCase				(							_lowercase					) ->			int:
	return (position - 1) // 2







def 						__UpperCamelCase				(							_lowercase					) ->			int:
	return (2 * position) + 1







def 						__UpperCamelCase				(							_lowercase					) ->			int:
	return (2 * position) + 2



class 		lowerCamelCase__      (						Generic[T]     ):




					'''simple docstring'''



					def __init__(  self				:	Any					)  ->       None:



						'''simple docstring'''





						_lowercase					:       list[tuple[T, int]]       =		[]
						_lowercase					:       dict[T, int]       =		{}
						_lowercase					:       int       =		0




					def __len__(  self				:	List[str]					)  ->       int:



						'''simple docstring'''





						return self.elements




					def __repr__(  self				:	List[str]					)  ->       str:



						'''simple docstring'''





						return str(self.heap					)




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       bool:



						'''simple docstring'''





						return self.elements == 0




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	T     ,  UpperCamelCase_				:	int					)  ->       None:



						'''simple docstring'''





						self.heap.append((elem, weight)					)
						_lowercase					:       Tuple       =		self.elements
						self.elements += 1
						self._bubble_up(UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	int					)  ->       T:



						'''simple docstring'''





						if self.elements > 1:
							self._swap_nodes(0     ,  self.elements - 1					)
						_lowercase					:       Union[str, Any]       =		self.heap.pop()
						del self.position_map[elem]
						self.elements -= 1
						if self.elements > 0:
							_lowercase					:       List[str]       =		self.heap[0]
							self._bubble_down(UpperCamelCase_					)
						return elem




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	T     ,  UpperCamelCase_				:	int					)  ->       None:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		self.position_map[elem]
						_lowercase					:       Optional[Any]       =		(elem, weight)
						if position > 0:
							_lowercase					:       Optional[Any]       =		get_parent_position(UpperCamelCase_					)
							_lowercase					:       str       =		self.heap[parent_position]
							if parent_weight > weight:
								self._bubble_up(UpperCamelCase_					)
							else:
								self._bubble_down(UpperCamelCase_					)
						else:
							self._bubble_down(UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	T					)  ->       None:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		self.position_map[elem]
						if curr_pos == 0:
							return None
						_lowercase					:       Optional[Any]       =		get_parent_position(UpperCamelCase_					)
						_lowercase					:       List[str]       =		self.heap[curr_pos]
						_lowercase					:       List[Any]       =		self.heap[parent_position]
						if parent_weight > weight:
							self._swap_nodes(UpperCamelCase_     ,  UpperCamelCase_					)
							return self._bubble_up(UpperCamelCase_					)
						return None




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	T					)  ->       None:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		self.position_map[elem]
						_lowercase					:       Optional[Any]       =		self.heap[curr_pos]
						_lowercase					:       Dict       =		get_child_left_position(UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		get_child_right_position(UpperCamelCase_					)
						if child_left_position < self.elements and child_right_position < self.elements:
							_lowercase					:       List[Any]       =		self.heap[child_left_position]
							_lowercase					:       int       =		self.heap[child_right_position]
							if child_right_weight < child_left_weight and child_right_weight < weight:
								self._swap_nodes(UpperCamelCase_     ,  UpperCamelCase_					)
								return self._bubble_down(UpperCamelCase_					)
						if child_left_position < self.elements:
							_lowercase					:       Dict       =		self.heap[child_left_position]
							if child_left_weight < weight:
								self._swap_nodes(UpperCamelCase_     ,  UpperCamelCase_					)
								return self._bubble_down(UpperCamelCase_					)
						else:
							return None
						if child_right_position < self.elements:
							_lowercase					:       Optional[Any]       =		self.heap[child_right_position]
							if child_right_weight < weight:
								self._swap_nodes(UpperCamelCase_     ,  UpperCamelCase_					)
								return self._bubble_down(UpperCamelCase_					)
						return None




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	int					)  ->       None:



						'''simple docstring'''





						_lowercase					:       Dict       =		self.heap[nodea_pos][0]
						_lowercase					:       List[Any]       =		self.heap[nodea_pos][0]
						_lowercase					:       Any       =		(
						    self.heap[nodea_pos],
						    self.heap[nodea_pos],
						)
						_lowercase					:       str       =		nodea_pos
						_lowercase					:       Optional[int]       =		nodea_pos






class 		lowerCamelCase__      (						Generic[T]     ):




					'''simple docstring'''



					def __init__(  self				:	Any					)  ->       None:



						'''simple docstring'''





						_lowercase					:       dict[T, dict[T, int]]       =		{}
						_lowercase					:       int       =		0




					def __repr__(  self				:	Tuple					)  ->       str:



						'''simple docstring'''





						return str(self.connections					)




					def __len__(  self				:	int					)  ->       int:



						'''simple docstring'''





						return self.nodes




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  UpperCamelCase_				:	T					)  ->       None:



						'''simple docstring'''





						if node not in self.connections:
							_lowercase					:       Union[str, Any]       =		{}
							self.nodes += 1




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	T     ,  UpperCamelCase_				:	T     ,  UpperCamelCase_				:	int					)  ->       None:



						'''simple docstring'''





						self.add_node(UpperCamelCase_					)
						self.add_node(UpperCamelCase_					)
						_lowercase					:       int       =		weight
						_lowercase					:       Tuple       =		weight







def 						__UpperCamelCase				(							_lowercase,				) ->			tuple[dict[T, int], dict[T, T | None]]:
	_lowercase					:       dict[T, int]       =		{node: maxsize for node in graph.connections}
	_lowercase					:       dict[T, T | None]       =		{node: None for node in graph.connections}

	_lowercase					:       MinPriorityQueue[T]       =		MinPriorityQueue()
	for node, weight in dist.items():
		priority_queue.push(_lowercase,				_lowercase					)

	if priority_queue.is_empty():
		return dist, parent

	# initialization
	_lowercase					:       Dict       =		priority_queue.extract_min()
	_lowercase					:       Any       =		0
	for neighbour in graph.connections[node]:
		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
			_lowercase					:       Any       =		dist[node] + graph.connections[node][neighbour]
			priority_queue.update_key(_lowercase,				dist[neighbour]					)
			_lowercase					:       str       =		node

    # running prim's algorithm
	while not priority_queue.is_empty():
		_lowercase					:       Dict       =		priority_queue.extract_min()
		for neighbour in graph.connections[node]:
			if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
				_lowercase					:       Tuple       =		dist[node] + graph.connections[node][neighbour]
				priority_queue.update_key(_lowercase,				dist[neighbour]					)
				_lowercase					:       Dict       =		node
	return dist, parent





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A				:    Optional[Any]     						={'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Tuple     						=['''XLNetTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=['''XLNetTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Any     						=[
		    '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XLNetForMultipleChoice''',
		    '''XLNetForQuestionAnswering''',
		    '''XLNetForQuestionAnsweringSimple''',
		    '''XLNetForSequenceClassification''',
		    '''XLNetForTokenClassification''',
		    '''XLNetLMHeadModel''',
		    '''XLNetModel''',
		    '''XLNetPreTrainedModel''',
		    '''load_tf_weights_in_xlnet''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=[
		    '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFXLNetForMultipleChoice''',
		    '''TFXLNetForQuestionAnsweringSimple''',
		    '''TFXLNetForSequenceClassification''',
		    '''TFXLNetForTokenClassification''',
		    '''TFXLNetLMHeadModel''',
		    '''TFXLNetMainLayer''',
		    '''TFXLNetModel''',
		    '''TFXLNetPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlnet import XLNetTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlnet_fast import XLNetTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xlnet import (
				    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XLNetForMultipleChoice,
				    XLNetForQuestionAnswering,
				    XLNetForQuestionAnsweringSimple,
				    XLNetForSequenceClassification,
				    XLNetForTokenClassification,
				    XLNetLMHeadModel,
				    XLNetModel,
				    XLNetPreTrainedModel,
				    load_tf_weights_in_xlnet,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_xlnet import (
				    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXLNetForMultipleChoice,
				    TFXLNetForQuestionAnsweringSimple,
				    TFXLNetForSequenceClassification,
				    TFXLNetForTokenClassification,
				    TFXLNetLMHeadModel,
				    TFXLNetMainLayer,
				    TFXLNetModel,
				    TFXLNetPreTrainedModel,
				)

else:
		import sys

		_A				:    str     						=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
