



"""simple docstring"""


from __future__ import annotations

import pandas as pd







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						list[int]					,     _SCREAMING_SNAKE_CASE   :						list[int]					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    [0] * no_of_processes
				_UpperCAmelCase					=    [0] * no_of_processes
				# Copy the burst time into remaining_time[]
				for i in range(_SCREAMING_SNAKE_CASE				):
								_UpperCAmelCase					=    burst_time[i]

				_UpperCAmelCase					=    0
				_UpperCAmelCase					=    0
				_UpperCAmelCase					=    9_9999_9999
				_UpperCAmelCase					=    0
				_UpperCAmelCase					=    False

				# Process until all processes are completed
				while complete != no_of_processes:
								for j in range(_SCREAMING_SNAKE_CASE				):
												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					=    9_9999_9999

								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  lowercase	(      _SCREAMING_SNAKE_CASE   :						list[int]					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						list[int]				):






				'''simple docstring'''



				_UpperCAmelCase					=    [0] * no_of_processes
				for i in range(_SCREAMING_SNAKE_CASE				):
								_UpperCAmelCase					=    burst_time[i] + waiting_time[i]
				return turn_around_time







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						list[int]					,     _SCREAMING_SNAKE_CASE   :						list[int]					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    0
				_UpperCAmelCase					=    0
				for i in range(_SCREAMING_SNAKE_CASE				):
								_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")
					__A   :							Union[str, Any]										=   int(input())
					__A   :							Optional[int]										=   [0] * no_of_processes
					__A   :							List[str]										=   [0] * no_of_processes
					__A   :							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))
										__A						,							__A   :							Optional[Any]										=   map(int, input().split())

					__A   :							List[Any]										=   calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					__A   :							Any										=   burst_time
					__A   :							List[Any]										=   no_of_processes
					__A   :							Union[str, Any]										=   waiting_time
					__A   :							Optional[int]										=   calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					__A   :							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)








"""simple docstring"""


from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 			_a (					lowerCAmelCase):
			"""simple docstring"""



			def __init__(		self     :    List[str]				,   __UpperCamelCase     :    NestedDataStructureLike[PathLike]				,   __UpperCamelCase     :    Optional[NamedSplit] = None				,   __UpperCamelCase     :    Optional[Features] = None				,   __UpperCamelCase     :    str = None				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    Optional[int] = None				,   **__UpperCamelCase     :    Optional[Any]				,   )->Any:
							super().__init__(
							    __UpperCamelCase				,   split=__UpperCamelCase				,   features=__UpperCamelCase				,   cache_dir=__UpperCamelCase				,   keep_in_memory=__UpperCamelCase				,   streaming=__UpperCamelCase				,   num_proc=__UpperCamelCase				,   **__UpperCamelCase				,   )
							_UpperCAmelCase					=    path_or_paths if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else {self.split: path_or_paths}
							_UpperCAmelCase					=    Text(
							    cache_dir=__UpperCamelCase				,   data_files=__UpperCamelCase				,   features=__UpperCamelCase				,   **__UpperCamelCase				,   )



			def  lowercase__						(		self     :    Optional[Any]   )->str:
							# Build iterable dataset
							if self.streaming:
											_UpperCAmelCase					=    self.builder.as_streaming_dataset(split=self.split   )
							# Build regular (map-style) dataset
							else:
											_UpperCAmelCase					=    None
											_UpperCAmelCase					=    None
											_UpperCAmelCase					=    None
											_UpperCAmelCase					=    None

											self.builder.download_and_prepare(
											    download_config=__UpperCamelCase				,   download_mode=__UpperCamelCase				,   verification_mode=__UpperCamelCase				,   base_path=__UpperCamelCase				,   num_proc=self.num_proc				,   )
											_UpperCAmelCase					=    self.builder.as_dataset(
											    split=self.split				,   verification_mode=__UpperCamelCase				,   in_memory=self.keep_in_memory   )
							return dataset



