

from __future__ import annotations

import pandas as pd
def 	a (     A__      :  list[int] ,  A__      :  list[int] ,  A__      :  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 (     A__      :  list[int] ,  A__      :  int ,  A__      :  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 (     A__      :  list[int] ,  A__      :  list[int] ,  A__      :  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')
  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)





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_   							=  5_0_0_0_0_0

lowercase_							,		lowercase_   							=  os.path.split(__file__)
lowercase_   							=  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[str]	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=dataset.map(**A__	)
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[Any]	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=dataset.filter(**A__	)
def 	a (     )       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			={'num examples': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            _lowercase      			=datasets.Features({'text': datasets.Value('string'	), 'numbers': datasets.Value('float32'	)}	)
            _lowercase      			=generate_example_dataset(
                os.path.join(A__ ,  'dataset.arrow'	) ,  A__ ,  num_examples=A__	)

            _lowercase      			=transformers.AutoTokenizer.from_pretrained('bert-base-cased' ,  use_fast=A__	)

            def tokenize(A__      :  int	):
                  return tokenizer(examples['text']	)

            _lowercase      			=map(A__	)

            _lowercase      			=map(A__ ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='numpy'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='pandas'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='torch' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='tensorflow' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=A__ ,  batched=A__	)

            _lowercase      			=filter(A__	)

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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


