


from statistics import mean

import numpy as np

def 	_a     ( SCREAMING_SNAKE_CASE		: Tuple			,       SCREAMING_SNAKE_CASE		: Optional[Any]			,       SCREAMING_SNAKE_CASE		: Optional[Any]			,       SCREAMING_SNAKE_CASE		: List[Any]   ):







 """simple docstring"""



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

 # Sort by arrival time.
 UpperCamelCase__    :			Any      				=      [burst_time[i] for i in np.argsort(lowerCamelCase_   )]
 UpperCamelCase__    :			Dict      				=      [process_name[i] for i in np.argsort(lowerCamelCase_   )]
 arrival_time.sort()

 while no_of_process > finished_process_count:
  UpperCamelCase__    :			Dict      				=      0
  while finished_process[i] == 1:
   i += 1
  if current_time < arrival_time[i]:
   UpperCamelCase__    :			List[str]      				=      arrival_time[i]

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

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

 return turn_around_time

def 	_a     ( SCREAMING_SNAKE_CASE		: Tuple			,       SCREAMING_SNAKE_CASE		: Any			,       SCREAMING_SNAKE_CASE		: int			,       SCREAMING_SNAKE_CASE		: str   ):







 """simple docstring"""



 UpperCamelCase__    :			List[Any]      				=      [0] * no_of_process
 for i in range(0			,       lowerCamelCase_   ):
  UpperCamelCase__    :			Any      				=      turn_around_time[i] - burst_time[i]
 return waiting_time


if __name__ == "__main__":
       __UpperCamelCase				:				int  				= 5
       __UpperCamelCase				:				Any  				= ['A', 'B', 'C', 'D', 'E']
       __UpperCamelCase				:				Optional[int]  				= [1, 2, 3, 4, 5]
       __UpperCamelCase				:				Union[str, Any]  				= [1, 2, 3, 4, 5]

       __UpperCamelCase				:				Tuple  				= calculate_turn_around_time(
           process_name, arrival_time, burst_time, no_of_process
       )
       __UpperCamelCase				:				str  				= 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}")










from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Dict,  lowerCamelCase						:    pyspark.sql.DataFrame,  lowerCamelCase						:    Optional[NamedSplit] = None,  lowerCamelCase						:    Optional[Features] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = "arrow",  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(
								    split=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  keep_in_memory=lowerCamelCase,  streaming=lowerCamelCase,  **lowerCamelCase,  )
								lowercase__    =			load_from_cache_file
								lowercase__    =			file_format
								lowercase__    =			Spark(
								    df=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  working_dir=lowerCamelCase,  **lowerCamelCase,  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								if self.streaming:
												return self.builder.as_streaming_dataset(split=self.split  )
								lowercase__    =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
								self.builder.download_and_prepare(
								    download_mode=lowerCamelCase,  file_format=self._file_format,  )
								return self.builder.as_dataset(split=self.split  )




