

from typing import TYPE_CHECKING

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


lowercase_   							=  {
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  ['ElectraTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  [
      'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ElectraForCausalLM',
      'ElectraForMaskedLM',
      'ElectraForMultipleChoice',
      'ElectraForPreTraining',
      'ElectraForQuestionAnswering',
      'ElectraForSequenceClassification',
      'ElectraForTokenClassification',
      'ElectraModel',
      'ElectraPreTrainedModel',
      'load_tf_weights_in_electra',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  [
      'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFElectraForMaskedLM',
      'TFElectraForMultipleChoice',
      'TFElectraForPreTraining',
      'TFElectraForQuestionAnswering',
      'TFElectraForSequenceClassification',
      'TFElectraForTokenClassification',
      'TFElectraModel',
      'TFElectraPreTrainedModel',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  [
      'FlaxElectraForCausalLM',
      'FlaxElectraForMaskedLM',
      'FlaxElectraForMultipleChoice',
      'FlaxElectraForPreTraining',
      'FlaxElectraForQuestionAnswering',
      'FlaxElectraForSequenceClassification',
      'FlaxElectraForTokenClassification',
      'FlaxElectraModel',
      'FlaxElectraPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
  from .tokenization_electra import ElectraTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_electra_fast import ElectraTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_electra import (
        ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        ElectraForCausalLM,
        ElectraForMaskedLM,
        ElectraForMultipleChoice,
        ElectraForPreTraining,
        ElectraForQuestionAnswering,
        ElectraForSequenceClassification,
        ElectraForTokenClassification,
        ElectraModel,
        ElectraPreTrainedModel,
        load_tf_weights_in_electra,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_electra import (
        TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFElectraForMaskedLM,
        TFElectraForMultipleChoice,
        TFElectraForPreTraining,
        TFElectraForQuestionAnswering,
        TFElectraForSequenceClassification,
        TFElectraForTokenClassification,
        TFElectraModel,
        TFElectraPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_electra import (
        FlaxElectraForCausalLM,
        FlaxElectraForMaskedLM,
        FlaxElectraForMultipleChoice,
        FlaxElectraForPreTraining,
        FlaxElectraForQuestionAnswering,
        FlaxElectraForSequenceClassification,
        FlaxElectraForTokenClassification,
        FlaxElectraModel,
        FlaxElectraPreTrainedModel,
    )

else:
  import sys

  lowercase_   							=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





from __future__ import annotations

from statistics import mean
def 	a (     A__      :  list[int] ,  A__      :  list[int] ,  A__      :  int	)       -> list[int]:





      """simple docstring"""



      _lowercase      			=[0] * no_of_processes
      _lowercase      			=[0] * no_of_processes

      # Initialize remaining_time to waiting_time.

      for i in range(A__	):
            _lowercase      			=burst_time[i]
      _lowercase      			=[]

      _lowercase      			=0
      _lowercase      			=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:
            _lowercase      			=[]
            _lowercase      			=-1

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

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

      return waiting_time
def 	a (     A__      :  list[int] ,  A__      :  int ,  A__      :  list[int]	)       -> list[int]:





      """simple docstring"""



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


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

  lowercase_   							=  4
  lowercase_   							=  [2, 5, 3, 7]
  lowercase_   							=  [0, 0, 0, 0]
  lowercase_   							=  calculate_waitingtime(arrival_time, burst_time, no_of_processes)
  lowercase_   							=  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}")


