


from __future__ import annotations

import pandas as pd



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> list[int]:
				lowerCAmelCase_ :      Tuple    				=				[0] * no_of_processes
				lowerCAmelCase_ :      Any    				=				[0] * no_of_processes
				# Copy the burst time into remaining_time[]
				for i in range(__A ):
								lowerCAmelCase_ :      List[str]    				=				burst_time[i]

				lowerCAmelCase_ :      Any    				=				0
				lowerCAmelCase_ :      Optional[Any]    				=				0
				lowerCAmelCase_ :      str    				=				999_999_999
				lowerCAmelCase_ :      List[str]    				=				0
				lowerCAmelCase_ :      Tuple    				=				False

				# Process until all processes are completed
				while complete != no_of_processes:
								for j in range(__A ):
												if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																if remaining_time[j] < minm:
																				lowerCAmelCase_ :      Optional[Any]    				=				remaining_time[j]
																				lowerCAmelCase_ :      Optional[int]    				=				j
																				lowerCAmelCase_ :      Union[str, Any]    				=				True

								if not check:
												increment_time += 1
												continue
								remaining_time[short] -= 1

								lowerCAmelCase_ :      Optional[int]    				=				remaining_time[short]
								if minm == 0:
												lowerCAmelCase_ :      Optional[Any]    				=				999_999_999

								if remaining_time[short] == 0:
												complete += 1
												lowerCAmelCase_ :      Optional[int]    				=				False

												# Find finish time of current process
												lowerCAmelCase_ :      Dict    				=				increment_time + 1

												# Calculate waiting time
												lowerCAmelCase_ :      Tuple    				=				finish_time - arrival_time[short]
												lowerCAmelCase_ :      int    				=				finar - burst_time[short]

												if waiting_time[short] < 0:
																lowerCAmelCase_ :      List[Any]    				=				0

																    # Increment time
								increment_time += 1
				return waiting_time



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> list[int]:
				lowerCAmelCase_ :      List[Any]    				=				[0] * no_of_processes
				for i in range(__A ):
								lowerCAmelCase_ :      Union[str, Any]    				=				burst_time[i] + waiting_time[i]
				return turn_around_time



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> None:
				lowerCAmelCase_ :      Tuple    				=				0
				lowerCAmelCase_ :      Tuple    				=				0
				for i in range(__A ):
								lowerCAmelCase_ :      Any    				=				total_waiting_time + waiting_time[i]
								lowerCAmelCase_ :      Tuple    				=				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""")
						_UpperCAmelCase			:  Tuple     =int(input())
						_UpperCAmelCase			:  Dict     =[0] * no_of_processes
						_UpperCAmelCase			:  Any     =[0] * no_of_processes
						_UpperCAmelCase			:  Any     =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))
												_UpperCAmelCase     ,					_UpperCAmelCase			:  Optional[int]     =map(int, input().split())

						_UpperCAmelCase			:  str     =calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

						calculate_average_times(waiting_time, turn_around_time, no_of_processes)

						_UpperCAmelCase			:  str     =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)










import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCAmelCase__							)
class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  str					=							field(default="""automatic-speech-recognition""",						metadata={"""include_in_asdict_even_if_is_default""": True}							)
						SCREAMING_SNAKE_CASE__      :  ClassVar[Features]					=							Features({"""audio""": Audio()}							)
						SCREAMING_SNAKE_CASE__      :  ClassVar[Features]					=							Features({"""transcription""": Value("""string"""							)}							)
						SCREAMING_SNAKE_CASE__      :  str					=							"audio"
						SCREAMING_SNAKE_CASE__      :  str					=							"transcription"




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										if self.audio_column not in features:
														raise ValueError(f"""Column {self.audio_column} is not present in features."""							)
										if not isinstance(features[self.audio_column]   ,						__lowercase							):
														raise ValueError(f"""Column {self.audio_column} is not an Audio type."""							)
										lowerCAmelCase_ :      List[str]    				=				copy.deepcopy(self							)
										lowerCAmelCase_ :      Optional[Any]    				=				self.input_schema.copy()
										lowerCAmelCase_ :      Optional[Any]    				=				features[self.audio_column]
										lowerCAmelCase_ :      List[str]    				=				input_schema
										return task_template




						@property
						def 						lowercase_ (				self							)					->    Dict[str, str]:
										return {self.audio_column: "audio", self.transcription_column: "transcription"}