
from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__A				=TypeVar('''T''')





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				return (position - 1) // 2





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				return (2 * position) + 1





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				return (2 * position) + 2




class     _SCREAMING_SNAKE_CASE     (      Generic[T]						):



	def __init__(    self )				->       None:
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			{}
					lowerCamelCase_						=			0



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



	def __repr__(    self )				->       str:
					return str(self.heap )



	def     SCREAMING_SNAKE_CASE_(    self )				->       bool:
					# Check if the priority queue is empty
					return self.elements == 0



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       None:
					# Add an element with given priority to the queue
					self.heap.append((elem, weight) )
					lowerCamelCase_						=			self.elements
					self.elements += 1
					self._bubble_up(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       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(lowercase )
					return elem



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       None:
					# Update the weight of the given key
					lowerCamelCase_						=			self.position_map[elem]
					lowerCamelCase_						=			(elem, weight)
					if position > 0:
									lowerCamelCase_						=			get_parent_position(lowercase )
									lowerCamelCase_	,    lowerCamelCase_						=			self.heap[parent_position]
									if parent_weight > weight:
													self._bubble_up(lowercase )
									else:
													self._bubble_down(lowercase )
					else:
									self._bubble_down(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       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(lowercase )
					lowerCamelCase_	,    lowerCamelCase_						=			self.heap[curr_pos]
					lowerCamelCase_	,    lowerCamelCase_						=			self.heap[parent_position]
					if parent_weight > weight:
									self._swap_nodes(lowercase						,  lowercase )
									return self._bubble_up(lowercase )
					return None



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       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(lowercase )
					lowerCamelCase_						=			get_child_right_position(lowercase )
					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(lowercase						,  lowercase )
													return self._bubble_down(lowercase )
					if child_left_position < self.elements:
									lowerCamelCase_	,    lowerCamelCase_						=			self.heap[child_left_position]
									if child_left_weight < weight:
													self._swap_nodes(lowercase						,  lowercase )
													return self._bubble_down(lowercase )
					else:
									return None
					if child_right_position < self.elements:
									lowerCamelCase_	,    lowerCamelCase_						=			self.heap[child_right_position]
									if child_right_weight < weight:
													self._swap_nodes(lowercase						,  lowercase )
													return self._bubble_down(lowercase )
					return None



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       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     _SCREAMING_SNAKE_CASE     (      Generic[T]						):



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



	def __repr__(    self )				->       str:
					return str(self.connections )



	def __len__(    self )				->       int:
					return self.nodes



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase )				->       None:
					# Add an edge between 2 nodes in the graph
					self.add_node(lowercase )
					self.add_node(lowercase )
					lowerCamelCase_						=			weight
					lowerCamelCase_						=			weight





def 			lowerCamelCase_ (				lowerCamelCase__	, ):
				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




from typing import TYPE_CHECKING

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


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

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


