


"""simple docstring"""







from __future__ import annotations

import pandas as pd


def 					lowercase      (    A_  ,       A_  ,       A_	)->				list[int]:
  '''simple docstring'''







  a						:		int											=      [0] * no_of_processes
  a						:		Dict											=      [0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(_A	):
    a						:		List[str]											=      burst_time[i]

  a						:		Any											=      0
  a						:		int											=      0
  a						:		Optional[int]											=      999_999_999
  a						:		Optional[Any]											=      0
  a						:		int											=      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:
          a						:		Any											=      remaining_time[j]
          a						:		Optional[Any]											=      j
          a						:		List[Any]											=      True

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

    a						:		List[str]											=      remaining_time[short]
    if minm == 0:
      a						:		Any											=      999_999_999

    if remaining_time[short] == 0:
      complete += 1
      a						:		List[Any]											=      False

      # Find finish time of current process
      a						:		List[str]											=      increment_time + 1

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

      if waiting_time[short] < 0:
        a						:		int											=      0

        # Increment time
    increment_time += 1
  return waiting_time


def 					lowercase      (    A_  ,       A_  ,       A_	)->				list[int]:
  '''simple docstring'''







  a						:		List[Any]											=      [0] * no_of_processes
  for i in range(_A	):
    a						:		Any											=      burst_time[i] + waiting_time[i]
  return turn_around_time


def 					lowercase      (    A_  ,       A_  ,       A_	)->				None:
  '''simple docstring'''







  a						:		str											=      0
  a						:		Any											=      0
  for i in range(_A	):
    a						:		Union[str, Any]											=      total_waiting_time + waiting_time[i]
    a						:		Optional[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""")
 __lowercase    =				int(input())
 __lowercase    =				[0] * no_of_processes
 __lowercase    =				[0] * no_of_processes
 __lowercase    =				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))
  __lowercase    =				map(int, input().split())

 __lowercase    =				calculate_waitingtime(arrival_time, burst_time, no_of_processes)

 __lowercase    =				burst_time
 __lowercase    =				no_of_processes
 __lowercase    =				waiting_time
 __lowercase    =				calculate_turnaroundtime(bt, n, wt)

 calculate_average_times(waiting_time, turn_around_time, no_of_processes)

 __lowercase    =				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 CamembertForMaskedLM, CamembertTokenizer


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=5	)->				Union[str, Any]:
  '''simple docstring'''







  assert masked_input.count("<mask>"	) == 1
  a						:		List[str]											=      torch.tensor(tokenizer.encode(A_  ,       add_special_tokens=A_	)	).unsqueeze(0	)  # Batch size 1
  a						:		Dict											=      model(A_	)[0]  # The last hidden-state is the first element of the output tuple
  a						:		int											=      (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  a						:		Optional[Any]											=      logits[0, masked_index, :]
  a						:		Dict											=      logits.softmax(dim=0	)
  a							,		a						:		Any											=      prob.topk(k=A_  ,       dim=0	)
  a						:		Optional[Any]											=      " ".join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A_	)	)]	)
  a						:		str											=      tokenizer.mask_token
  a						:		Any											=      []
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "	)	):
    a						:		Dict											=      predicted_token_bpe.replace("\u2581"  ,       " "	)
    if " {0}".format(A_	) in masked_input:
      topk_filled_outputs.append(
          (
              masked_input.replace(" {0}".format(A_	)  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
    else:
      topk_filled_outputs.append(
          (
              masked_input.replace(A_  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
  return topk_filled_outputs


__lowercase    =				CamembertTokenizer.from_pretrained("""camembert-base""")
__lowercase    =				CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

__lowercase    =				"""Le camembert est <mask> :)"""







print(fill_mask(masked_input, model, tokenizer, topk=3))






