'''simple docstring'''





from __future__ import annotations

from statistics import mean


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [0] * no_of_processes
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [0] * no_of_processes

     # Initialize remaining_time to waiting_time.

     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			Tuple    	=       burst_time[i]
     SCREAMING_SNAKE_CASE					:			list[int]    	=       []

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       0
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       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:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []
          SCREAMING_SNAKE_CASE					:			Dict    	=       -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:
               SCREAMING_SNAKE_CASE					:			Dict    	=       ready_process[0]
               for i in ready_process:
                    if remaining_time[i] < remaining_time[target_process]:
                         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       i
               total_time += burst_time[target_process]
               completed += 1
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       0
               SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                   total_time - arrival_time[target_process] - burst_time[target_process]
               )
          else:
               total_time += 1

     return waiting_time


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       [0] * no_of_processes
     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       burst_time[i] + waiting_time[i]
     return turn_around_time


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

 _lowerCAmelCase  :Optional[int]  				=    4
 _lowerCAmelCase  :Optional[int]  				=    [2, 5, 3, 7]
 _lowerCAmelCase  :List[str]  				=    [0, 0, 0, 0]
 _lowerCAmelCase  :Union[str, Any]  				=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
 _lowerCAmelCase  :Any  				=    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}""")




'''simple docstring'''





from typing import TYPE_CHECKING

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


_lowerCAmelCase  :int  				=    {
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :str  				=    ["""AlbertTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :Optional[int]  				=    ["""AlbertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :List[str]  				=    [
     """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """AlbertForMaskedLM""",
     """AlbertForMultipleChoice""",
     """AlbertForPreTraining""",
     """AlbertForQuestionAnswering""",
     """AlbertForSequenceClassification""",
     """AlbertForTokenClassification""",
     """AlbertModel""",
     """AlbertPreTrainedModel""",
     """load_tf_weights_in_albert""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :Any  				=    [
     """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFAlbertForMaskedLM""",
     """TFAlbertForMultipleChoice""",
     """TFAlbertForPreTraining""",
     """TFAlbertForQuestionAnswering""",
     """TFAlbertForSequenceClassification""",
     """TFAlbertForTokenClassification""",
     """TFAlbertMainLayer""",
     """TFAlbertModel""",
     """TFAlbertPreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :Optional[int]  				=    [
     """FlaxAlbertForMaskedLM""",
     """FlaxAlbertForMultipleChoice""",
     """FlaxAlbertForPreTraining""",
     """FlaxAlbertForQuestionAnswering""",
     """FlaxAlbertForSequenceClassification""",
     """FlaxAlbertForTokenClassification""",
     """FlaxAlbertModel""",
     """FlaxAlbertPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_albert import AlbertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_albert_fast import AlbertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_albert import (
      ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      AlbertForMaskedLM,
      AlbertForMultipleChoice,
      AlbertForPreTraining,
      AlbertForQuestionAnswering,
      AlbertForSequenceClassification,
      AlbertForTokenClassification,
      AlbertModel,
      AlbertPreTrainedModel,
      load_tf_weights_in_albert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_albert import (
      TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAlbertForMaskedLM,
      TFAlbertForMultipleChoice,
      TFAlbertForPreTraining,
      TFAlbertForQuestionAnswering,
      TFAlbertForSequenceClassification,
      TFAlbertForTokenClassification,
      TFAlbertMainLayer,
      TFAlbertModel,
      TFAlbertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
      FlaxAlbertPreTrainedModel,
  )
else:
 import sys

 _lowerCAmelCase  :Union[str, Any]  				=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



