


'''simple docstring'''




import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser





def 	a_					( lowerCamelCase	:     Any=None	, lowerCamelCase	:     int=None				):
   return field(default_factory=lambda: default	, metadata=lowerCamelCase				)

@dataclass
class 		UpperCAmelCase_      :
     lowerCamelCase				:	str		=     field(
         metadata={'''help''': '''The csv file to plot.'''}       ,	)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}       ,	)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}       ,	)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Disable logarithmic scale when plotting'''}       ,	)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
         }       ,	)
     lowerCamelCase				:	Optional[str]		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}       ,	)
     lowerCamelCase				:	Optional[List[str]]		=     list_field(
         default=__lowercase       ,	metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}		)





def 	a_					( lowerCamelCase	:     Union[str, Any]				):
   try:
      int(lowerCamelCase				)
      return True
   except ValueError:
      return False





def 	a_					( lowerCamelCase	:     List[Any]				):
   try:
      float(lowerCamelCase				)
      return True
   except ValueError:
      return False

class 		UpperCAmelCase_      :





     def __init__( self       :      int    ,      UpperCAmelCase__       :      Union[str, Any]    )				->  List[str]:
        lowerCAmelCase     =			args
        lowerCAmelCase     =			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

        with open(self.args.csv_file    ,      newline=''    ) as csv_file:
           lowerCAmelCase     =			csv.DictReader(UpperCAmelCase__    )
           for row in reader:
              lowerCAmelCase     =			row['model']
              self.result_dict[model_name]["bsz"].append(int(row['batch_size']    )    )
              self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']    )    )
              if can_convert_to_int(row['result']    ):
                 # value is not None
                 lowerCAmelCase     =			int(row['result']    )
              elif can_convert_to_float(row['result']    ):
                 # value is not None
                 lowerCAmelCase     =			float(row['result']    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Dict:
        lowerCAmelCase     ,  lowerCAmelCase     =			plt.subplots()
        lowerCAmelCase     =			'Time usage' if self.args.is_time else 'Memory usage'
        lowerCAmelCase     =			title_str + ' for training' if self.args.is_train else title_str + ' for inference'

        if not self.args.no_log_scale:
           # set logarithm scales
           ax.set_xscale('log'    )
           ax.set_yscale('log'    )

        for axis in [ax.xaxis, ax.yaxis]:
           axis.set_major_formatter(ScalarFormatter()    )

        for model_name_idx, model_name in enumerate(self.result_dict.keys()    ):
           lowerCAmelCase     =			sorted(set(self.result_dict[model_name]['bsz']    )    )
           lowerCAmelCase     =			sorted(set(self.result_dict[model_name]['seq_len']    )    )
           lowerCAmelCase     =			self.result_dict[model_name]['result']

           ((lowerCAmelCase)     ,  (lowerCAmelCase))     =			(
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           lowerCAmelCase     =			(
               model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
           )

           for inner_loop_value in inner_loop_array:
              if self.args.plot_along_batch:
                 lowerCAmelCase     =			np.asarray(
                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,      dtype=UpperCAmelCase__    ,      )
              else:
                 lowerCAmelCase     =			np.asarray(
                     [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]    ,      dtype=np.floataa    ,      )

              ((lowerCAmelCase)     ,  (lowerCAmelCase))     =			(
                  ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
              )

              lowerCAmelCase     =			np.asarray(UpperCAmelCase__    ,      UpperCAmelCase__    )[: len(UpperCAmelCase__    )]
              plt.scatter(
                  UpperCAmelCase__    ,      UpperCAmelCase__    ,      label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''    )
              plt.plot(UpperCAmelCase__    ,      UpperCAmelCase__    ,      '--'    )

           title_str += F''' {label_model_name} vs.'''

        lowerCAmelCase     =			title_str[:-4]
        lowerCAmelCase     =			'Time in s' if self.args.is_time else 'Memory in MB'

        # plot
        plt.title(UpperCAmelCase__    )
        plt.xlabel(UpperCAmelCase__    )
        plt.ylabel(UpperCAmelCase__    )
        plt.legend()

        if self.args.figure_png_file is not None:
           plt.savefig(self.args.figure_png_file    )
        else:
           plt.show()





def 	a_					( ):
   lowerCAmelCase     =			HfArgumentParser(lowerCamelCase				)
   lowerCAmelCase     =			parser.parse_args_into_dataclasses()[0]
   lowerCAmelCase     =			Plot(args=lowerCamelCase				)
   plot.plot()


if __name__ == "__main__":
       main()









'''simple docstring'''




from __future__ import annotations

from statistics import mean





def 	a_					( lowerCamelCase	:     list[int]	, lowerCamelCase	:     list[int]	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			[0] * no_of_processes
   lowerCAmelCase     =			[0] * no_of_processes

   # Initialize remaining_time to waiting_time.

   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			burst_time[i]
   lowerCAmelCase     =			[]

   lowerCAmelCase     =			0
   lowerCAmelCase     =			0

   # When processes are not completed,
   # A process whose arrival time has passed \
   # and has remaining execution time is put into the ready_process.
   # The shortest process in the ready_process, target_process is executed.

   while completed != no_of_processes:
      lowerCAmelCase     =			[]
      lowerCAmelCase     =			-1

      for i in range(lowerCamelCase				):
         if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
            ready_process.append(lowerCamelCase				)

      if len(lowerCamelCase				) > 0:
         lowerCAmelCase     =			ready_process[0]
         for i in ready_process:
            if remaining_time[i] < remaining_time[target_process]:
               lowerCAmelCase     =			i
         total_time += burst_time[target_process]
         completed += 1
         lowerCAmelCase     =			0
         lowerCAmelCase     =			(
             total_time - arrival_time[target_process] - burst_time[target_process]
         )
      else:
         total_time += 1

   return waiting_time





def 	a_					( lowerCamelCase	:     list[int]	, lowerCamelCase	:     int	, lowerCamelCase	:     list[int]				):
   lowerCAmelCase     =			[0] * no_of_processes
   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			burst_time[i] + waiting_time[i]
   return turn_around_time


if __name__ == "__main__":
       print("""[TEST CASE 01]""")

       __snake_case    						=4
       __snake_case    						=[2, 5, 3, 7]
       __snake_case    						=[0, 0, 0, 0]
       __snake_case    						=calculate_waitingtime(arrival_time, burst_time, no_of_processes)
       __snake_case    						=calculate_turnaroundtime(
           burst_time, no_of_processes, waiting_time
       )

       # Printing the Result
       print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
       for i, process_id in enumerate(list(range(1, 5))):
              print(
                  F'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
                  F'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
              )
       print(F'''\nAverage waiting time = {mean(waiting_time):.5f}''')
       print(F'''Average turnaround time = {mean(turn_around_time):.5f}''')





