




from __future__ import annotations

import pandas as pd

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			list[int]      ,				lowercase      :			list[int]      ,				lowercase      :			int	):


      '''simple docstring'''



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

      lowerCamelCase_							=       0
      lowerCamelCase_							=       0
      lowerCamelCase_							=       9_99_99_99_99
      lowerCamelCase_							=       0
      lowerCamelCase_							=       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:
                              lowerCamelCase_							=       remaining_time[j]
                              lowerCamelCase_							=       j
                              lowerCamelCase_							=       True

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

            lowerCamelCase_							=       remaining_time[short]
            if minm == 0:
                  lowerCamelCase_							=       9_99_99_99_99

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

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

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

                  if waiting_time[short] < 0:
                        lowerCamelCase_							=       0

        # Increment time
            increment_time += 1
      return waiting_time

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			list[int]      ,				lowercase      :			int      ,				lowercase      :			list[int]	):


      '''simple docstring'''



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

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			list[int]      ,				lowercase      :			list[int]      ,				lowercase      :			int	):


      '''simple docstring'''



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

 lowerCamelCase							:						Union[str, Any]					       =  calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

 calculate_average_times(waiting_time, turn_around_time, no_of_processes)

 lowerCamelCase							:						List[Any]					       =  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_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTBigCodeForSequenceClassification",
           "GPTBigCodeForTokenClassification",
           "GPTBigCodeForCausalLM",
           "GPTBigCodeModel",
           "GPTBigCodePreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_bigcode import (
                  GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTBigCodeForCausalLM,
                  GPTBigCodeForSequenceClassification,
                  GPTBigCodeForTokenClassification,
                  GPTBigCodeModel,
                  GPTBigCodePreTrainedModel,
              )


else:
       import sys

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