'''simple docstring'''


class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  pass







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  pass







class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				[
           [],
           [],
           [],
       ]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       try:
            if len(self.queues[priority]						) >= 1_0_0:
                 raise OverflowError('Maximum queue size is 100'						)
            self.queues[priority].append(snake_case_						)
       except IndexError:
            raise ValueError('Valid priorities are 0, 1, and 2'						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       for queue in self.queues:
            if queue:
                 return queue.pop(0						)
       raise UnderFlowError('All queues are empty'						)






  def __str__(			self						):




       '''simple docstring'''

       return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues						)						)







class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				[]


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       if len(self.queue						) == 1_0_0:
            raise OverFlowError('Maximum queue size is 100'						)
       self.queue.append(snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       if not self.queue:
            raise UnderFlowError('The queue is empty'						)
       else:
            UpperCAmelCase_							:       List[Any]       =				min(self.queue						)
            self.queue.remove(snake_case_						)
            return data






  def __str__(			self						):




       '''simple docstring'''

       return str(self.queue						)







def  _lowerCamelCase   (				):



     """simple docstring"""





     UpperCAmelCase_							:       Union[str, Any]       =				FixedPriorityQueue()
     fpq.enqueue(0     ,					10       )
     fpq.enqueue(1     ,					70       )
     fpq.enqueue(0     ,					100       )
     fpq.enqueue(2     ,					1       )
     fpq.enqueue(2     ,					5       )
     fpq.enqueue(1     ,					7       )
     fpq.enqueue(2     ,					4       )
     fpq.enqueue(1     ,					64       )
     fpq.enqueue(0     ,					128       )
     print(lowerCamelCase_       )
     print(fpq.dequeue()       )
     print(fpq.dequeue()       )
     print(fpq.dequeue()       )
     print(fpq.dequeue()       )
     print(fpq.dequeue()       )
     print(lowerCamelCase_       )
     print(fpq.dequeue()       )
     print(fpq.dequeue()       )
     print(fpq.dequeue()       )
     print(fpq.dequeue()       )
     print(fpq.dequeue()       )


def  _lowerCamelCase   (				):



     """simple docstring"""





     UpperCAmelCase_							:       List[str]       =				ElementPriorityQueue()
     epq.enqueue(10       )
     epq.enqueue(70       )
     epq.enqueue(100       )
     epq.enqueue(1       )
     epq.enqueue(5       )
     epq.enqueue(7       )
     epq.enqueue(4       )
     epq.enqueue(64       )
     epq.enqueue(128       )
     print(lowerCamelCase_       )
     print(epq.dequeue()       )
     print(epq.dequeue()       )
     print(epq.dequeue()       )
     print(epq.dequeue()       )
     print(epq.dequeue()       )
     print(lowerCamelCase_       )
     print(epq.dequeue()       )
     print(epq.dequeue()       )
     print(epq.dequeue()       )
     print(epq.dequeue()       )
     print(epq.dequeue()       )


if __name__ == "__main__":
 fixed_priority_queue()
 element_priority_queue()







'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


snake_case__					:      Optional[int]				      =  '''1'''
snake_case__					:      str				      =  '''0'''
snake_case__					:      List[str]				      =  '''1'''

snake_case__					:      List[str]				      =  ort.SessionOptions()
snake_case__					:      str				      =  ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
snake_case__					:      Dict				      =  ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
snake_case__					:      Dict				      =  ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
snake_case__					:      str				      =  ort.RunOptions()

snake_case__					:      List[Any]				      =  128
snake_case__					:      Union[str, Any]				      =  1
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Union[str, Any]				      =  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...''')
snake_case__					:      Union[str, Any]				      =  time.time()
snake_case__					:      str				      =  2000
snake_case__					:      Tuple				      =  {}
for iter in range(max_iters):
 snake_case__					:      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))






