
'''simple docstring'''






from __future__ import annotations

import pandas as pd







def     UpperCamelCase_      (   A__ :       list[int]							,   A__ :       list[int]							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       [0] * no_of_processes
  lowerCAmelCase_      :				List[str]      =       [0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(lowerCAmelCase__ ):
    lowerCAmelCase_      :				Union[str, Any]      =       burst_time[i]

  lowerCAmelCase_      :				int      =       0
  lowerCAmelCase_      :				List[str]      =       0
  lowerCAmelCase_      :				Any      =       9_99_99_99_99
  lowerCAmelCase_      :				Dict      =       0
  lowerCAmelCase_      :				Union[str, Any]      =       False

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

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

    lowerCAmelCase_      :				List[str]      =       remaining_time[short]
    if minm == 0:
      lowerCAmelCase_      :				List[Any]      =       9_99_99_99_99

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

      # Find finish time of current process
      lowerCAmelCase_      :				Optional[Any]      =       increment_time + 1

      # Calculate waiting time
      lowerCAmelCase_      :				Optional[Any]      =       finish_time - arrival_time[short]
      lowerCAmelCase_      :				List[Any]      =       finar - burst_time[short]

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

        # Increment time
    increment_time += 1
  return waiting_time







def     UpperCamelCase_      (   A__ :       list[int]							,   A__ :       int							,   A__ :       list[int] ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       list[int]							,   A__ :       list[int]							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[int]      =       0
  lowerCAmelCase_      :				Optional[Any]      =       0
  for i in range(lowerCAmelCase__ ):
    lowerCAmelCase_      :				Dict      =       total_waiting_time + waiting_time[i]
    lowerCAmelCase_      :				Union[str, Any]      =       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 :  Optional[Any]    							=				int(input())
  __A :  Union[str, Any]    							=				[0] * no_of_processes
  __A :  Any    							=				[0] * no_of_processes
  __A :  Union[str, 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))
    __A :  Any    							=				map(int, input().split())

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

  __A :  Dict    							=				burst_time
  __A :  Dict    							=				no_of_processes
  __A :  Optional[int]    							=				waiting_time
  __A :  Dict    							=				calculate_turnaroundtime(bt, n, wt)

  calculate_average_times(waiting_time, turn_around_time, no_of_processes)

  __A :  Tuple    							=				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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A :  List[Any]    							=				{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  pass

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Union[str, Any]    							=				[
      "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoCBertForCausalLM",
      "RoCBertForMaskedLM",
      "RoCBertForMultipleChoice",
      "RoCBertForPreTraining",
      "RoCBertForQuestionAnswering",
      "RoCBertForSequenceClassification",
      "RoCBertForTokenClassification",
      "RoCBertLayer",
      "RoCBertModel",
      "RoCBertPreTrainedModel",
      "load_tf_weights_in_roc_bert",
  ]

if TYPE_CHECKING:
  from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
  from .tokenization_roc_bert import RoCBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    raise OptionalDependencyNotAvailable()

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roc_bert import (
        ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertLayer,
        RoCBertModel,
        RoCBertPreTrainedModel,
        load_tf_weights_in_roc_bert,
    )


else:
  import sys

  __A :  int    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



