




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A        =    {
    "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
    "processing_layoutlmv2": ["LayoutLMv2Processor"],
    "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    ["LayoutLMv2TokenizerFast"]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    ["LayoutLMv2FeatureExtractor"]
					__A        =    ["LayoutLMv2ImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    [
					    "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "LayoutLMv2ForQuestionAnswering",
					    "LayoutLMv2ForSequenceClassification",
					    "LayoutLMv2ForTokenClassification",
					    "LayoutLMv2Layer",
					    "LayoutLMv2Model",
					    "LayoutLMv2PreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaLayer,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)
else:
					import sys

					__A        =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE__   :			List[Any]												=    TypeVar("T")




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				return (position - 1) // 2




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				return (2 * position) + 1




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				return (2 * position) + 2

class    lowerCAmelCase__     (	Generic[T]			):






		def __init__(		self		:							List[str]  )     ->							None:
						__lowerCamelCase    					=	[]
						__lowerCamelCase    					=	{}
						__lowerCamelCase    					=	0






		def __len__(		self		:							Optional[int]  )     ->							int:
						return self.elements






		def __repr__(		self		:							Optional[int]  )     ->							str:
						return str(self.heap  )






		def  __A       (		self		:							Union[str, Any]  )     ->							bool:
						# Check if the priority queue is empty
						return self.elements == 0






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Add an element with given priority to the queue
						self.heap.append((elem, weight)  )
						__lowerCamelCase    					=	self.elements
						self.elements += 1
						self._bubble_up(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Any  )     ->							T:
						# Remove and return the element with lowest weight (highest priority)
						if self.elements > 1:
										self._swap_nodes(0							,			self.elements - 1  )
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap.pop()
						del self.position_map[elem]
						self.elements -= 1
						if self.elements > 0:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[0]
										self._bubble_down(SCREAMING_SNAKE_CASE__  )
						return elem






		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Update the weight of the given key
						__lowerCamelCase    					=	self.position_map[elem]
						__lowerCamelCase    					=	(elem, weight)
						if position > 0:
										__lowerCamelCase    					=	get_parent_position(SCREAMING_SNAKE_CASE__  )
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[parent_position]
										if parent_weight > weight:
														self._bubble_up(SCREAMING_SNAKE_CASE__  )
										else:
														self._bubble_down(SCREAMING_SNAKE_CASE__  )
						else:
										self._bubble_down(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							T  )     ->							None:
						# Place a node at the proper position (upward movement) [to be used internally
						# only]
						__lowerCamelCase    					=	self.position_map[elem]
						if curr_pos == 0:
										return None
						__lowerCamelCase    					=	get_parent_position(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[curr_pos]
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[parent_position]
						if parent_weight > weight:
										self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										return self._bubble_up(SCREAMING_SNAKE_CASE__  )
						return None






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							T  )     ->							None:
						# Place a node at the proper position (downward movement) [to be used
						# internally only]
						__lowerCamelCase    					=	self.position_map[elem]
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[curr_pos]
						__lowerCamelCase    					=	get_child_left_position(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	get_child_right_position(SCREAMING_SNAKE_CASE__  )
						if child_left_position < self.elements and child_right_position < self.elements:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_left_position]
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_right_position]
										if child_right_weight < child_left_weight and child_right_weight < weight:
														self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														return self._bubble_down(SCREAMING_SNAKE_CASE__  )
						if child_left_position < self.elements:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_left_position]
										if child_left_weight < weight:
														self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														return self._bubble_down(SCREAMING_SNAKE_CASE__  )
						else:
										return None
						if child_right_position < self.elements:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_right_position]
										if child_right_weight < weight:
														self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														return self._bubble_down(SCREAMING_SNAKE_CASE__  )
						return None







		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Swap the nodes at the given positions
						__lowerCamelCase    					=	self.heap[nodea_pos][0]
						__lowerCamelCase    					=	self.heap[nodea_pos][0]
						__lowerCamelCase					,    __lowerCamelCase    					=	(
						    self.heap[nodea_pos],
						    self.heap[nodea_pos],
						)
						__lowerCamelCase    					=	nodea_pos
						__lowerCamelCase    					=	nodea_pos





class    lowerCAmelCase__     (	Generic[T]			):






		def __init__(		self		:							Tuple  )     ->							None:
						__lowerCamelCase    					=	{}
						__lowerCamelCase    					=	0






		def __repr__(		self		:							Optional[int]  )     ->							str:
						return str(self.connections  )






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






		def  __A       (		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							T  )     ->							None:
						# Add a node in the graph if it is not in the graph
						if node not in self.connections:
										__lowerCamelCase    					=	{}
										self.nodes += 1







		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Add an edge between 2 nodes in the graph
						self.add_node(SCREAMING_SNAKE_CASE__  )
						self.add_node(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	weight
						__lowerCamelCase    					=	weight





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

				__lowerCamelCase    					=	MinPriorityQueue()
				for node, weight in dist.items():
								priority_queue.push(__lowerCAmelCase		,  __lowerCAmelCase							)

				if priority_queue.is_empty():
								return dist, parent

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

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



