'''simple docstring'''

from __future__ import annotations

import pandas as pd







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				List[str]  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  [0] * no_of_processes
   UpperCAmelCase__							:				Optional[Any]   				=  [0] * no_of_processes
   # Copy the burst time into remaining_time[]
   for i in range(_A    ):
      UpperCAmelCase__							:				Dict   				=  burst_time[i]

   UpperCAmelCase__							:				Optional[int]   				=  0
   UpperCAmelCase__							:				List[Any]   				=  0
   UpperCAmelCase__							:				Any   				=  9_99_99_99_99
   UpperCAmelCase__							:				Optional[Any]   				=  0
   UpperCAmelCase__							:				Optional[Any]   				=  False

   # Process until all processes are completed
   while complete != no_of_processes:
      for j in range(_A    ):
         if arrival_time[j] <= increment_time and remaining_time[j] > 0:
            if remaining_time[j] < minm:
               UpperCAmelCase__							:				str   				=  remaining_time[j]
               UpperCAmelCase__							:				Dict   				=  j
               UpperCAmelCase__							:				str   				=  True

      if not check:
         increment_time += 1
         continue
      remaining_time[short] -= 1

      UpperCAmelCase__							:				Dict   				=  remaining_time[short]
      if minm == 0:
         UpperCAmelCase__							:				Dict   				=  9_99_99_99_99

      if remaining_time[short] == 0:
         complete += 1
         UpperCAmelCase__							:				Optional[int]   				=  False

         # Find finish time of current process
         UpperCAmelCase__							:				Optional[int]   				=  increment_time + 1

         # Calculate waiting time
         UpperCAmelCase__							:				Any   				=  finish_time - arrival_time[short]
         UpperCAmelCase__							:				Optional[int]   				=  finar - burst_time[short]

         if waiting_time[short] < 0:
            UpperCAmelCase__							:				str   				=  0

        # Increment time
      increment_time += 1
   return waiting_time







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				str  ,			_A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[Any]   				=  [0] * no_of_processes
   for i in range(_A    ):
      UpperCAmelCase__							:				Optional[int]   				=  burst_time[i] + waiting_time[i]
   return turn_around_time







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				List[Any]  ,			_A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				str   				=  0
   UpperCAmelCase__							:				str   				=  0
   for i in range(_A    ):
      UpperCAmelCase__							:				Any   				=  total_waiting_time + waiting_time[i]
      UpperCAmelCase__							:				List[Any]   				=  total_turn_around_time + turn_around_time[i]
   print(F'''Average waiting time = {total_waiting_time / no_of_processes:.5f}'''    )
   print('''Average turn around time ='''  ,			total_turn_around_time / no_of_processes    )


if __name__ == "__main__":
  print('Enter how many process you want to analyze')
  UpperCamelCase__     :	Tuple				=			int(input())
  UpperCamelCase__     :	Union[str, Any]				=			[0] * no_of_processes
  UpperCamelCase__     :	int				=			[0] * no_of_processes
  UpperCamelCase__     :	Union[str, Any]				=			list(range(1, no_of_processes + 1))

  for i in range(no_of_processes):
    print('Enter the arrival time and burst time for process:--' + str(i + 1))
    UpperCamelCase__     :	List[str]				=			map(int, input().split())

  UpperCamelCase__     :	Tuple				=			calculate_waitingtime(arrival_time, burst_time, no_of_processes)

  UpperCamelCase__     :	Dict				=			burst_time
  UpperCamelCase__     :	Union[str, Any]				=			no_of_processes
  UpperCamelCase__     :	Optional[int]				=			waiting_time
  UpperCamelCase__     :	Optional[int]				=			calculate_turnaroundtime(bt, n, wt)

  calculate_average_times(waiting_time, turn_around_time, no_of_processes)

  UpperCamelCase__     :	Dict				=			pd.DataFrame(
      list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
      columns=[
          'Process',
          'BurstTime',
          'ArrivalTime',
          'WaitingTime',
          'TurnAroundTime',
      ],
  )

  # Printing the dataFrame
  pd.set_option('display.max_rows', fcfs.shape[0] + 1)
  print(fcfs)





'''simple docstring'''

import torch

from transformers import AutoModel


class 				_lowercase	(      torch.nn.Module   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_="sayef/fsner-bert-base-uncased"							)			->			Dict:



         '''simple docstring'''



         super(lowerCamelCase_					,self							).__init__()

         UpperCAmelCase__							:				Union[str, Any]   				=  AutoModel.from_pretrained(lowerCamelCase_					,return_dict=lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  torch.nn.CosineSimilarity(3					,1e-08							)
         UpperCAmelCase__							:				int   				=  torch.nn.Softmax(dim=1							)




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         return self.bert(**lowerCamelCase_							).last_hidden_state




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return token_embeddings.sum(2					,keepdim=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_=1							)			->			int:



         '''simple docstring'''



         return self.softmax(T * self.cos(lowerCamelCase_					,lowerCamelCase_							)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  W_supports['''sizes'''].tolist()
         UpperCAmelCase__							:				Union[str, Any]   				=  W_supports['''start_token_id'''].item()
         UpperCAmelCase__							:				int   				=  W_supports['''end_token_id'''].item()

         del W_supports["sizes"]
         del W_supports["start_token_id"]
         del W_supports["end_token_id"]

         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  None
         UpperCAmelCase__							:				Tuple   				=  None

         UpperCAmelCase__							:				Dict   				=  W_supports['''input_ids'''] == start_token_id
         UpperCAmelCase__							:				Any   				=  W_supports['''input_ids'''] == end_token_id

         for i, size in enumerate(lowerCamelCase_							):
            if i == 0:
               UpperCAmelCase__							:				int   				=  0
            else:
               UpperCAmelCase__							:				str   				=  support_sizes[i - 1]

            UpperCAmelCase__							:				Dict   				=  S[s : s + size][start_token_masks[s : s + size]]
            UpperCAmelCase__							:				Union[str, Any]   				=  S[s : s + size][end_token_masks[s : s + size]]

            UpperCAmelCase__							:				int   				=  torch.matmul(q[i]					,s_start.T							).sum(1							).softmax(0							)
            UpperCAmelCase__							:				int   				=  torch.matmul(q[i]					,s_end.T							).sum(1							).softmax(0							)

            if p_starts is not None:
               UpperCAmelCase__							:				str   				=  torch.vstack((p_starts, p_start)							)
               UpperCAmelCase__							:				Optional[int]   				=  torch.vstack((p_ends, p_end)							)
            else:
               UpperCAmelCase__							:				Union[str, Any]   				=  p_start
               UpperCAmelCase__							:				int   				=  p_end

         return p_starts, p_ends




