





"""simple docstring"""





from __future__ import annotations

import pandas as pd
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->list[int]:





  """simple docstring"""


  lowerCAmelCase__     :int											=				[0] * no_of_processes
  lowerCAmelCase__     :int											=				[0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(_SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__     :Union[str, Any]											=				burst_time[i]

  lowerCAmelCase__     :Union[str, Any]											=				0
  lowerCAmelCase__     :List[Any]											=				0
  lowerCAmelCase__     :List[Any]											=				9_9999_9999
  lowerCAmelCase__     :Union[str, Any]											=				0
  lowerCAmelCase__     :Dict											=				False

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

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

    lowerCAmelCase__     :Optional[int]											=				remaining_time[short]
    if minm == 0:
      lowerCAmelCase__     :str											=				9_9999_9999

    if remaining_time[short] == 0:
      complete += 1
      lowerCAmelCase__     :str											=				False

      # Find finish time of current process
      lowerCAmelCase__     :Optional[Any]											=				increment_time + 1

      # Calculate waiting time
      lowerCAmelCase__     :Union[str, Any]											=				finish_time - arrival_time[short]
      lowerCAmelCase__     :Union[str, Any]											=				finar - burst_time[short]

      if waiting_time[short] < 0:
        lowerCAmelCase__     :List[Any]											=				0

        # Increment time
    increment_time += 1
  return waiting_time
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->list[int]:





  """simple docstring"""


  lowerCAmelCase__     :Any											=				[0] * no_of_processes
  for i in range(_SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__     :Any											=				burst_time[i] + waiting_time[i]
  return turn_around_time
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->None:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				0
  lowerCAmelCase__     :Optional[Any]											=				0
  for i in range(_SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__     :Tuple											=				total_waiting_time + waiting_time[i]
    lowerCAmelCase__     :Tuple											=				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""")
  __A											=						int(input())
  __A											=						[0] * no_of_processes
  __A											=						[0] * no_of_processes
  __A											=						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))
    __A							,				__A											=						map(int, input().split())

  __A											=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

  calculate_average_times(waiting_time, turn_around_time, no_of_processes)

  __A											=						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"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A											=						{"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """SEWForCTC""",
      """SEWForSequenceClassification""",
      """SEWModel""",
      """SEWPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

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