

"""simple docstring"""





from __future__ import annotations

import pandas as pd
def   _A  (  UpperCamelCase_					:	list[int],      UpperCamelCase_					:	list[int],      UpperCamelCase_					:	int)     ->						list[int]:
   '''simple docstring'''


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

   __lowercase								=						0
   __lowercase								=						0
   __lowercase								=						999999999
   __lowercase								=						0
   __lowercase								=						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:
               __lowercase								=						remaining_time[j]
               __lowercase								=						j
               __lowercase								=						True

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

      __lowercase								=						remaining_time[short]
      if minm == 0:
         __lowercase								=						999999999

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

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

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

         if waiting_time[short] < 0:
            __lowercase								=						0

        # Increment time
      increment_time += 1
   return waiting_time
def   _A  (  UpperCamelCase_					:	list[int],      UpperCamelCase_					:	int,      UpperCamelCase_					:	list[int])     ->						list[int]:
   '''simple docstring'''


   __lowercase								=						[0] * no_of_processes
   for i in range(_A):
      __lowercase								=						burst_time[i] + waiting_time[i]
   return turn_around_time
def   _A  (  UpperCamelCase_					:	list[int],      UpperCamelCase_					:	list[int],      UpperCamelCase_					:	int)     ->						None:
   '''simple docstring'''


   __lowercase								=						0
   __lowercase								=						0
   for i in range(_A):
      __lowercase								=						total_waiting_time + waiting_time[i]
      __lowercase								=						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							=							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"""





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_a							=							5_00_00
_a							=							50_00

_a				,						_a							=							os.path.split(__file__)
_a							=							os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	List[str])     ->						List[str]:
   '''simple docstring'''


   for i in range(UpperCamelCase_):
      __lowercase								=						dataset[i]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	int)     ->						Dict:
   '''simple docstring'''


   for i in range(0,      len(UpperCamelCase_),      UpperCamelCase_):
      __lowercase								=						dataset[i : i + batch_size]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	Any,      UpperCamelCase_					:	Optional[int])     ->						List[str]:
   '''simple docstring'''


   with dataset.formatted_as(type=UpperCamelCase_):
      for i in range(UpperCamelCase_):
         __lowercase								=						dataset[i]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	str,      UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Union[str, Any])     ->						Dict:
   '''simple docstring'''


   with dataset.formatted_as(type=UpperCamelCase_):
      for i in range(0,      UpperCamelCase_,      UpperCamelCase_):
         __lowercase								=						dataset[i : i + batch_size]




def   _A  (  )     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						{"num examples": SPEED_TEST_N_EXAMPLES}
   __lowercase								=						[
       (read, {"length": SMALL_TEST}),
       (read, {"length": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
       (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
       (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
       (read_formatted, {"type": "torch", "length": SMALL_TEST}),
       (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
   ]

   __lowercase								=						[
       (read, {"length": SMALL_TEST}),
       (read, {"length": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
       (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print("generating dataset")
      __lowercase								=						datasets.Features(
          {"list": datasets.Sequence(datasets.Value("float32")), "numbers": datasets.Value("float32")})
      __lowercase								=						generate_example_dataset(
          os.path.join(UpperCamelCase_,      "dataset.arrow"),      UpperCamelCase_,      num_examples=UpperCamelCase_,      seq_shapes={"list": (100,)},      )
      print("first set of iterations")
      for func, kwargs in functions:
         print(func.__name__,      str(UpperCamelCase_))
         __lowercase								=						func(UpperCamelCase_,      **UpperCamelCase_)

      print("shuffling dataset")
      __lowercase								=						dataset.shuffle()
      print("Second set of iterations (after shuffling")
      for func, kwargs in functions_shuffled:
         print("shuffled ",      func.__name__,      str(UpperCamelCase_))
         __lowercase								=						func(
             UpperCamelCase_,      **UpperCamelCase_)

   with open(UpperCamelCase_,      "wb") as f:
      f.write(json.dumps(UpperCamelCase_).encode("utf-8"))


if __name__ == "__main__":  # useful to run the profiler
   benchmark_iterating()




