
from __future__ import annotations

import pandas as pd






def    lowerCamelCase__				(				__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Tuple				):




			"""simple docstring"""


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

			lowerCAmelCase_						=	0
			lowerCAmelCase_						=	0
			lowerCAmelCase_						=	999999999
			lowerCAmelCase_						=	0
			lowerCAmelCase_						=	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:
															lowerCAmelCase_						=	remaining_time[j]
															lowerCAmelCase_						=	j
															lowerCAmelCase_						=	True

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

						lowerCAmelCase_						=	remaining_time[short]
						if minm == 0:
									lowerCAmelCase_						=	999999999

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

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

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

									if waiting_time[short] < 0:
												lowerCAmelCase_						=	0

												# Increment time
						increment_time += 1
			return waiting_time






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


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






def    lowerCamelCase__				(				__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  Any				):




			"""simple docstring"""


			lowerCAmelCase_						=	0
			lowerCAmelCase_						=	0
			for i in range(SCREAMING_SNAKE_CASE__				):
						lowerCAmelCase_						=	total_waiting_time + waiting_time[i]
						lowerCAmelCase_						=	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   =     int(input())
			_A   =     [0] * no_of_processes
			_A   =     [0] * no_of_processes
			_A   =     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   =     map(int, input().split())

			_A   =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

			_A   =     burst_time
			_A   =     no_of_processes
			_A   =     waiting_time
			_A   =     calculate_turnaroundtime(bt, n, wt)

			calculate_average_times(waiting_time, turn_around_time, no_of_processes)

			_A   =     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)










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase       :   Optional[int]				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   Optional[int]				       =							{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """ibert"""




       def __init__(  self :    Dict							,				A :    Union[str, Any]=30_522							,				A :    List[Any]=768							,				A :    List[Any]=12							,				A :    Optional[int]=12							,				A :    Optional[Any]=3_072							,				A :    int="gelu"							,				A :    str=0.1							,				A :    List[Any]=0.1							,				A :    Optional[Any]=512							,				A :    int=2							,				A :    Union[str, Any]=0.02							,				A :    List[str]=1E-12							,				A :    Optional[int]=1							,				A :    Optional[int]=0							,				A :    List[str]=2							,				A :    str="absolute"							,				A :    Any=False							,				A :    Optional[Any]="none"							,				**A :    Any							,				):
         super().__init__(pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				**A		)

         __snake_case:						List[Any]   =						vocab_size
         __snake_case:						Optional[Any]   =						hidden_size
         __snake_case:						List[str]   =						num_hidden_layers
         __snake_case:						Tuple   =						num_attention_heads
         __snake_case:						List[str]   =						hidden_act
         __snake_case:						Optional[Any]   =						intermediate_size
         __snake_case:						Tuple   =						hidden_dropout_prob
         __snake_case:						List[str]   =						attention_probs_dropout_prob
         __snake_case:						Any   =						max_position_embeddings
         __snake_case:						int   =						type_vocab_size
         __snake_case:						List[str]   =						initializer_range
         __snake_case:						List[Any]   =						layer_norm_eps
         __snake_case:						Optional[int]   =						position_embedding_type
         __snake_case:						str   =						quant_mode
         __snake_case:						Optional[int]   =						force_dequant







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__ (  self :    Tuple		):
         if self.task == "multiple-choice":
           __snake_case:						List[Any]   =						{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
           __snake_case:						List[str]   =						{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
             ]		)






