





from __future__ import annotations

import pandas as pd







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list[int]:
  __snake_case:						Tuple   =						[0] * no_of_processes
  __snake_case:						Dict   =						[0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(SCREAMING_SNAKE_CASE__):
    __snake_case:						List[str]   =						burst_time[i]

  __snake_case:						List[str]   =						0
  __snake_case:						Dict   =						0
  __snake_case:						int   =						9_9999_9999
  __snake_case:						str   =						0
  __snake_case:						Tuple   =						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:
          __snake_case:						Optional[int]   =						remaining_time[j]
          __snake_case:						Optional[Any]   =						j
          __snake_case:						int   =						True

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

    __snake_case:						int   =						remaining_time[short]
    if minm == 0:
      __snake_case:						Optional[Any]   =						9_9999_9999

    if remaining_time[short] == 0:
      complete += 1
      __snake_case:						List[Any]   =						False

      # Find finish time of current process
      __snake_case:						List[Any]   =						increment_time + 1

      # Calculate waiting time
      __snake_case:						int   =						finish_time - arrival_time[short]
      __snake_case:						Dict   =						finar - burst_time[short]

      if waiting_time[short] < 0:
        __snake_case:						Tuple   =						0

        # Increment time
    increment_time += 1
  return waiting_time







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list[int]:
  __snake_case:						Optional[int]   =						[0] * no_of_processes
  for i in range(SCREAMING_SNAKE_CASE__):
    __snake_case:						Any   =						burst_time[i] + waiting_time[i]
  return turn_around_time







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    None:
  __snake_case:						int   =						0
  __snake_case:						Optional[int]   =						0
  for i in range(SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]   =						total_waiting_time + waiting_time[i]
    __snake_case:						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       :   Any				       =							int(input())
      __UpperCAmelCase       :   int				       =							[0] * no_of_processes
      __UpperCAmelCase       :   Optional[Any]				       =							[0] * no_of_processes
      __UpperCAmelCase       :   List[str]				       =							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       :   Dict				       =							map(int, input().split())

      __UpperCAmelCase       :   Optional[Any]				       =							calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

      calculate_average_times(waiting_time, turn_around_time, no_of_processes)

      __UpperCAmelCase       :   Optional[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)













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),
             ]		)






