





'''simple docstring'''

from statistics import mean

import numpy as np

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					list:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       0
   # Number of processes finished
   __SCREAMING_SNAKE_CASE      							=       0
   # Displays the finished process.
   # If it is 0, the performance is completed if it is 1, before the performance.
   __SCREAMING_SNAKE_CASE      							=       [0] * no_of_process
   # List to include calculation results
   __SCREAMING_SNAKE_CASE      							=       [0] * no_of_process

   # Sort by arrival time.
   __SCREAMING_SNAKE_CASE      							=       [burst_time[i] for i in np.argsort(__UpperCAmelCase      )]
   __SCREAMING_SNAKE_CASE      							=       [process_name[i] for i in np.argsort(__UpperCAmelCase      )]
   arrival_time.sort()

   while no_of_process > finished_process_count:
      __SCREAMING_SNAKE_CASE      							=       0
      while finished_process[i] == 1:
         i += 1
      if current_time < arrival_time[i]:
         __SCREAMING_SNAKE_CASE      							=       arrival_time[i]

      __SCREAMING_SNAKE_CASE      							=       0
      # Index showing the location of the process being performed
      __SCREAMING_SNAKE_CASE      							=       0
      # Saves the current response ratio.
      __SCREAMING_SNAKE_CASE      							=       0
      for i in range(0							,	__UpperCAmelCase      ):
         if finished_process[i] == 0 and arrival_time[i] <= current_time:
            __SCREAMING_SNAKE_CASE      							=       (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                i
            ]
         if response_ratio < temp:
            __SCREAMING_SNAKE_CASE      							=       temp
            __SCREAMING_SNAKE_CASE      							=       i

        # Calculate the turn around time
      __SCREAMING_SNAKE_CASE      							=       current_time + burst_time[loc] - arrival_time[loc]
      current_time += burst_time[loc]
      # Indicates that the process has been performed.
      __SCREAMING_SNAKE_CASE      							=       1
      # Increase finished_process_count by 1
      finished_process_count += 1

   return turn_around_time

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					list:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       [0] * no_of_process
   for i in range(0							,	__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       turn_around_time[i] - burst_time[i]
   return waiting_time


if __name__ == "__main__":
    a        =						5
    a        =						["A", "B", "C", "D", "E"]
    a        =						[1, 2, 3, 4, 5]
    a        =						[1, 2, 3, 4, 5]

    a        =						calculate_turn_around_time(
        process_name, arrival_time, burst_time, no_of_process
    )
    a        =						calculate_waiting_time(
        process_name, turn_around_time, burst_time, no_of_process
    )

    print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
    for i in range(0, no_of_process):
        print(
            F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
            F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
        )

    print(F'''average waiting time : {mean(waiting_time):.5f}''')
    print(F'''average turn around time : {mean(turn_around_time):.5f}''')












'''simple docstring'''

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 						__magic_name__       (							__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   def wrapper(*__UpperCAmelCase							,	**__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer()
      __SCREAMING_SNAKE_CASE      							=       func(*__UpperCAmelCase							,	**__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer() - starttime
      return delta

   __SCREAMING_SNAKE_CASE      							=       func.__name__

   return wrapper

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []
   __SCREAMING_SNAKE_CASE      							=       seq_shapes or {}
   for i in range(__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       {}
      for col_id, (k, v) in enumerate(features.items()      ):
         if isinstance(__UpperCAmelCase							,	_ArrayXD      ):
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*v.shape      ).astype(v.dtype      )
         elif isinstance(__UpperCAmelCase							,	datasets.Value      ):
            if v.dtype == "string":
               __SCREAMING_SNAKE_CASE      							=       """The small grey turtle was surprisingly fast when challenged."""
            else:
               __SCREAMING_SNAKE_CASE      							=       np.random.randint(10							,	size=1      ).astype(v.dtype      ).item()
         elif isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
            while isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
               __SCREAMING_SNAKE_CASE      							=       v.feature
            __SCREAMING_SNAKE_CASE      							=       seq_shapes[k]
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*__UpperCAmelCase      ).astype(v.dtype      )
         __SCREAMING_SNAKE_CASE      							=       data

      dummy_data.append((i, example)      )

   return dummy_data







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       generate_examples(__UpperCAmelCase							,	num_examples=__UpperCAmelCase							,	seq_shapes=__UpperCAmelCase      )

   with ArrowWriter(features=__UpperCAmelCase							,	path=__UpperCAmelCase      ) as writer:
      for key, record in dummy_data:
         __SCREAMING_SNAKE_CASE      							=       features.encode_example(__UpperCAmelCase      )
         writer.write(__UpperCAmelCase      )

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       writer.finalize()

   if not num_final_examples == num_examples:
      raise ValueError(
          f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""      )

   __SCREAMING_SNAKE_CASE      							=       datasets.Dataset.from_file(filename=__UpperCAmelCase							,	info=datasets.DatasetInfo(features=__UpperCAmelCase      )      )

   return dataset





