




"""simple docstring"""




from __future__ import annotations

from statistics import mean



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      list[int]:
			lowerCamelCase       =      [0] * no_of_processes
			lowerCamelCase       =      [0] * no_of_processes

			# Initialize remaining_time to waiting_time.

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

						if len(snake_case__       ) > 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__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      list[int]:
			lowerCamelCase       =      [0] * no_of_processes
			for i in range(snake_case__       ):
						lowerCamelCase       =      burst_time[i] + waiting_time[i]
			return turn_around_time


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

					lowerCAmelCase							:    int  							=							4
					lowerCAmelCase							:    List[Any]  							=							[2, 5, 3, 7]
					lowerCAmelCase							:    List[str]  							=							[0, 0, 0, 0]
					lowerCAmelCase							:    Union[str, Any]  							=							calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					lowerCAmelCase							:    Optional[int]  							=							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}""")









"""simple docstring"""




import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCAmelCase							:    List[str]  							=							4
lowerCAmelCase							:    List[str]  							=							3






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				pass




def 					a__				(       snake_case__       )		->      Dict:
			for shard in shards:
						for i in range(snake_case__       ):
									yield {"i": i, "shard": shard}



def 					a__				(       )		->      List[Any]:
			lowerCamelCase       =      int(os.environ["""RANK"""]       )
			lowerCamelCase       =      int(os.environ["""WORLD_SIZE"""]       )

			lowerCamelCase       =      ArgumentParser()
			parser.add_argument("""--streaming""" ,				type=snake_case__       )
			parser.add_argument("""--local_rank""" ,				type=snake_case__       )
			parser.add_argument("""--num_workers""" ,				type=snake_case__ ,				default=0       )
			lowerCamelCase       =      parser.parse_args()
			lowerCamelCase       =      args.streaming
			lowerCamelCase       =      args.num_workers

			lowerCamelCase       =      {"""shards""": [F'shard_{shard_idx}' for shard_idx in range(snake_case__       )]}
			lowerCamelCase       =      IterableDataset.from_generator(snake_case__ ,				gen_kwargs=snake_case__       )
			if not streaming:
						lowerCamelCase       =      Dataset.from_list(list(snake_case__       )       )

			lowerCamelCase       =      split_dataset_by_node(snake_case__ ,				rank=snake_case__ ,				world_size=snake_case__       )
			lowerCamelCase       =      torch.utils.data.DataLoader(snake_case__ ,				num_workers=snake_case__       )

			lowerCamelCase       =      NUM_SHARDS * NUM_ITEMS_PER_SHARD
			lowerCamelCase       =      full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)       )

			lowerCamelCase       =      sum(1 for _ in dataloader       )
			if local_size != expected_local_size:
						raise FailedTestError(F'local_size {local_size} != expected_local_size {expected_local_size}'       )


if __name__ == "__main__":
					main()



