






'''simple docstring'''




from __future__ import annotations

from statistics import mean

def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase       )       ->						list[int]:

      '''simple docstring'''


      _A			     =	[0] * no_of_processes
      _A			     =	[0] * no_of_processes

      # Initialize remaining_time to waiting_time.

      for i in range(__lowercase       ):
            _A			     =	burst_time[i]
      _A			     =	[]

      _A			     =	0
      _A			     =	0

      # When processes are not completed,
      # A process whose arrival time has passed \
      # and has remaining execution time is put into the ready_process.
      # The shortest process in the ready_process, target_process is executed.

      while completed != no_of_processes:
            _A			     =	[]
            _A			     =	-1

            for i in range(__lowercase       ):
                  if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                        ready_process.append(__lowercase       )

            if len(__lowercase       ) > 0:
                  _A			     =	ready_process[0]
                  for i in ready_process:
                        if remaining_time[i] < remaining_time[target_process]:
                              _A			     =	i
                  total_time += burst_time[target_process]
                  completed += 1
                  _A			     =	0
                  _A			     =	(
                      total_time - arrival_time[target_process] - burst_time[target_process]
                  )
            else:
                  total_time += 1

      return waiting_time

def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase       )       ->						list[int]:

      '''simple docstring'''


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


if __name__ == "__main__":
    print('''[TEST CASE 01]''')

    lowerCamelCase_												=					4
    lowerCamelCase_												=					[2, 5, 3, 7]
    lowerCamelCase_												=					[0, 0, 0, 0]
    lowerCamelCase_												=					calculate_waitingtime(arrival_time, burst_time, no_of_processes)
    lowerCamelCase_												=					calculate_turnaroundtime(
        burst_time, no_of_processes, waiting_time
    )

    # Printing the Result
    print('''PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time''')
    for i, process_id in enumerate(list(range(1, 5))):
        print(
            F"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
            F"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
        )
    print(F"""\nAverage waiting time = {mean(waiting_time):.5f}""")
    print(F"""Average turnaround time = {mean(turn_around_time):.5f}""")












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A									=				{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				["DistilBertTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DistilBertForMaskedLM",
         "DistilBertForMultipleChoice",
         "DistilBertForQuestionAnswering",
         "DistilBertForSequenceClassification",
         "DistilBertForTokenClassification",
         "DistilBertModel",
         "DistilBertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDistilBertForMaskedLM",
         "TFDistilBertForMultipleChoice",
         "TFDistilBertForQuestionAnswering",
         "TFDistilBertForSequenceClassification",
         "TFDistilBertForTokenClassification",
         "TFDistilBertMainLayer",
         "TFDistilBertModel",
         "TFDistilBertPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "FlaxDistilBertForMaskedLM",
         "FlaxDistilBertForMultipleChoice",
         "FlaxDistilBertForQuestionAnswering",
         "FlaxDistilBertForSequenceClassification",
         "FlaxDistilBertForTokenClassification",
         "FlaxDistilBertModel",
         "FlaxDistilBertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_distilbert import (
         DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DistilBertConfig,
         DistilBertOnnxConfig,
     )
     from .tokenization_distilbert import DistilBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_distilbert_fast import DistilBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_distilbert import (
              DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
              DistilBertModel,
              DistilBertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_distilbert import (
              TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDistilBertForMaskedLM,
              TFDistilBertForMultipleChoice,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertMainLayer,
              TFDistilBertModel,
              TFDistilBertPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_distilbert import (
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertModel,
              FlaxDistilBertPreTrainedModel,
          )

else:
     import sys

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

