



'''simple docstring'''


from __future__ import annotations

import pandas as pd






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=[0] * no_of_processes
     a_       		=[0] * no_of_processes
     # Copy the burst time into remaining_time[]
     for i in range(lowercase__       ):
          a_       		=burst_time[i]

     a_       		=0
     a_       		=0
     a_       		=9_9_9_9_9_9_9_9_9
     a_       		=0
     a_       		=False

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

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

          a_       		=remaining_time[short]
          if minm == 0:
               a_       		=9_9_9_9_9_9_9_9_9

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

               # Find finish time of current process
               a_       		=increment_time + 1

               # Calculate waiting time
               a_       		=finish_time - arrival_time[short]
               a_       		=finar - burst_time[short]

               if waiting_time[short] < 0:
                    a_       		=0

        # Increment time
          increment_time += 1
     return waiting_time






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=[0] * no_of_processes
     for i in range(lowercase__       ):
          a_       		=burst_time[i] + waiting_time[i]
     return turn_around_time






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=0
     a_       		=0
     for i in range(lowercase__       ):
          a_       		=total_waiting_time + waiting_time[i]
          a_       		=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       ,      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'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase							       =			{
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''VisionEncoderDecoderModel''']

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''TFVisionEncoderDecoderModel''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
  from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
  import sys

  lowercase							       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


