





'''simple docstring'''


from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase_   :   int       =					TypeVar("T")







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    int:
   return (position - 1) // 2







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    int:
   return (2 * position) + 1







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    int:
   return (2 * position) + 2






class       UpperCamelCase__  (		Generic[T]     ):







 def __init__(				self			:   Union[str, Any]		):




    '''simple docstring'''
    a__     = []
    a__     = {}
    a__     = 0







 def __len__(				self			:   List[str]		):




    '''simple docstring'''
    return self.elements







 def __repr__(				self			:   str		):




    '''simple docstring'''
    return str(self.heap		)







 def 						__a   (				self			:   List[Any]		):




    '''simple docstring'''
    return self.elements == 0







 def 						__a   (				self			:   Tuple , lowerCamelCase			:   T , lowerCamelCase			:   int		):




    '''simple docstring'''
    self.heap.append((elem, weight)		)
    a__     = self.elements
    self.elements += 1
    self._bubble_up(lowerCamelCase		)







 def 						__a   (				self			:   Any		):




    '''simple docstring'''
    # Remove and return the element with lowest weight (highest priority)
    if self.elements > 1:
       self._swap_nodes(0 , self.elements - 1		)
    a__   ,				a__     = self.heap.pop()
    del self.position_map[elem]
    self.elements -= 1
    if self.elements > 0:
       a__   ,				a__     = self.heap[0]
       self._bubble_down(lowerCamelCase		)
    return elem







 def 						__a   (				self			:   Dict , lowerCamelCase			:   T , lowerCamelCase			:   int		):




    '''simple docstring'''
    a__     = self.position_map[elem]
    a__     = (elem, weight)
    if position > 0:
       a__     = get_parent_position(lowerCamelCase		)
       a__   ,				a__     = self.heap[parent_position]
       if parent_weight > weight:
          self._bubble_up(lowerCamelCase		)
       else:
          self._bubble_down(lowerCamelCase		)
    else:
       self._bubble_down(lowerCamelCase		)







 def 						__a   (				self			:   Optional[int] , lowerCamelCase			:   T		):




    '''simple docstring'''
    a__     = self.position_map[elem]
    if curr_pos == 0:
       return None
    a__     = get_parent_position(lowerCamelCase		)
    a__   ,				a__     = self.heap[curr_pos]
    a__   ,				a__     = self.heap[parent_position]
    if parent_weight > weight:
       self._swap_nodes(lowerCamelCase , lowerCamelCase		)
       return self._bubble_up(lowerCamelCase		)
    return None







 def 						__a   (				self			:   Optional[int] , lowerCamelCase			:   T		):




    '''simple docstring'''
    a__     = self.position_map[elem]
    a__   ,				a__     = self.heap[curr_pos]
    a__     = get_child_left_position(lowerCamelCase		)
    a__     = get_child_right_position(lowerCamelCase		)
    if child_left_position < self.elements and child_right_position < self.elements:
       a__   ,				a__     = self.heap[child_left_position]
       a__   ,				a__     = self.heap[child_right_position]
       if child_right_weight < child_left_weight and child_right_weight < weight:
          self._swap_nodes(lowerCamelCase , lowerCamelCase		)
          return self._bubble_down(lowerCamelCase		)
    if child_left_position < self.elements:
       a__   ,				a__     = self.heap[child_left_position]
       if child_left_weight < weight:
          self._swap_nodes(lowerCamelCase , lowerCamelCase		)
          return self._bubble_down(lowerCamelCase		)
    else:
       return None
    if child_right_position < self.elements:
       a__   ,				a__     = self.heap[child_right_position]
       if child_right_weight < weight:
          self._swap_nodes(lowerCamelCase , lowerCamelCase		)
          return self._bubble_down(lowerCamelCase		)
    return None







 def 						__a   (				self			:   Tuple , lowerCamelCase			:   int , lowerCamelCase			:   int		):




    '''simple docstring'''
    a__     = self.heap[nodea_pos][0]
    a__     = self.heap[nodea_pos][0]
    a__   ,				a__     = (
        self.heap[nodea_pos],
        self.heap[nodea_pos],
    )
    a__     = nodea_pos
    a__     = nodea_pos






class       UpperCamelCase__  (		Generic[T]     ):







 def __init__(				self			:   Optional[Any]		):




    '''simple docstring'''
    a__     = {}
    a__     = 0







 def __repr__(				self			:   int		):




    '''simple docstring'''
    return str(self.connections		)







 def __len__(				self			:   str		):




    '''simple docstring'''
    return self.nodes







 def 						__a   (				self			:   Optional[Any] , lowerCamelCase			:   T		):




    '''simple docstring'''
    # Add a node in the graph if it is not in the graph
    if node not in self.connections:
       a__     = {}
       self.nodes += 1







 def 						__a   (				self			:   List[str] , lowerCamelCase			:   T , lowerCamelCase			:   T , lowerCamelCase			:   int		):




    '''simple docstring'''
    self.add_node(lowerCamelCase		)
    self.add_node(lowerCamelCase		)
    a__     = weight
    a__     = weight







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

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

   if priority_queue.is_empty():
      return dist, parent

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

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










'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


lowerCAmelCase_   :   Optional[int]       =					"1"
lowerCAmelCase_   :   List[str]       =					"0"
lowerCAmelCase_   :   Tuple       =					"1"

lowerCAmelCase_   :   str       =					ort.SessionOptions()
lowerCAmelCase_   :   Tuple       =					ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowerCAmelCase_   :   Any       =					["TensorrtExecutionProvider", "CUDAExecutionProvider"]
lowerCAmelCase_   :   str       =					ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowerCAmelCase_   :   Tuple       =					ort.RunOptions()

lowerCAmelCase_   :   Optional[int]       =					128
lowerCAmelCase_   :   Union[str, Any]       =					1
lowerCAmelCase_   :   List[str]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   Optional[Any]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   str       =					np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
lowerCAmelCase_   :   Tuple       =					time.time()
lowerCAmelCase_   :   int       =					2000
lowerCAmelCase_   :   Optional[Any]       =					{}
for iter in range(max_iters):
		lowerCAmelCase_   :   List[str]       =					sess.run(
		    None,
		    {
		        sess.get_inputs()[0].name: input_ids,
		        sess.get_inputs()[1].name: attention_mask,
		        sess.get_inputs()[2].name: token_type_ids,
		    },
		    run_options=run_opt,
		)
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))



