






"""simple docstring"""







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=1024	,      lowerCAmelCase=1024	,      lowerCAmelCase=False	,      **lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			AutoTokenizer.from_pretrained(lowerCAmelCase		)
       UpperCAmelCase	    =			SeqaSeqDataset(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      type_path="""train"""	,      **lowerCAmelCase		)
       UpperCAmelCase	    =			tok.pad_token_id

       def get_lens(lowerCAmelCase		):
              UpperCAmelCase	    =			tqdm(
                  DataLoader(lowerCAmelCase	,      batch_size=512	,      num_workers=8	,      shuffle=lowerCAmelCase	,      collate_fn=ds.collate_fn		)	,      desc=str(ds.len_file		)	,      )
              UpperCAmelCase	    =			[]
              for batch in dl:
                     UpperCAmelCase	    =			batch["""input_ids"""].ne(lowerCAmelCase		).sum(1		).tolist()
                     UpperCAmelCase	    =			batch["""labels"""].ne(lowerCAmelCase		).sum(1		).tolist()
                     if consider_target:
                            for src, tgt in zip(lowerCAmelCase	,      lowerCAmelCase		):
                                   max_lens.append(max(lowerCAmelCase	,      lowerCAmelCase		)		)
                     else:
                            max_lens.extend(lowerCAmelCase		)
              return max_lens

       UpperCAmelCase	    =			get_lens(lowerCAmelCase		)
       UpperCAmelCase	    =			SeqaSeqDataset(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      type_path="""val"""	,      **lowerCAmelCase		)
       UpperCAmelCase	    =			get_lens(lowerCAmelCase		)
       pickle_save(lowerCAmelCase	,      train_ds.len_file		)
       pickle_save(lowerCAmelCase	,      val_ds.len_file		)


if __name__ == "__main__":
 fire.Fire(save_len_file)








"""simple docstring"""







from __future__ import annotations

import pandas as pd






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[0] * no_of_processes
       UpperCAmelCase	    =			[0] * no_of_processes
       # Copy the burst time into remaining_time[]
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			burst_time[i]

       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			999999999
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			False

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

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

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

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

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

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

                     if waiting_time[short] < 0:
                            UpperCAmelCase	    =			0

        # Increment time
              increment_time += 1
       return waiting_time






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[0] * no_of_processes
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			burst_time[i] + waiting_time[i]
       return turn_around_time






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			0
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			total_waiting_time + waiting_time[i]
              UpperCAmelCase	    =			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_	:    Optional[Any]            =      int(input())
 lowerCAmelCase_	:    List[Any]            =      [0] * no_of_processes
 lowerCAmelCase_	:    Optional[Any]            =      [0] * no_of_processes
 lowerCAmelCase_	:    int            =      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_	:    Optional[Any]            =      map(int, input().split())

 lowerCAmelCase_	:    str            =      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

 lowerCAmelCase_	:    str            =      burst_time
 lowerCAmelCase_	:    List[Any]            =      no_of_processes
 lowerCAmelCase_	:    int            =      waiting_time
 lowerCAmelCase_	:    int            =      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)
