



"""simple docstring"""




from __future__ import annotations

from statistics import mean

def 						_snake_case						(				SCREAMING_SNAKE_CASE__     :						list[int]						,  SCREAMING_SNAKE_CASE__     :						list[int]						,  SCREAMING_SNAKE_CASE__     :						int    )  ->       list[int]:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   [0] * no_of_processes
     _UpperCAmelCase      :     List[Any]        =   [0] * no_of_processes

     # Initialize remaining_time to waiting_time.

     for i in range(_snake_case    ):
          _UpperCAmelCase      :     Any        =   burst_time[i]
     _UpperCAmelCase      :     list[int]        =   []

     _UpperCAmelCase      :     List[Any]        =   0
     _UpperCAmelCase      :     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:
          _UpperCAmelCase      :     List[str]        =   []
          _UpperCAmelCase      :     List[str]        =   -1

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

          if len(_snake_case    ) > 0:
               _UpperCAmelCase      :     List[Any]        =   ready_process[0]
               for i in ready_process:
                    if remaining_time[i] < remaining_time[target_process]:
                         _UpperCAmelCase      :     Union[str, Any]        =   i
               total_time += burst_time[target_process]
               completed += 1
               _UpperCAmelCase      :     str        =   0
               _UpperCAmelCase      :     List[str]        =   (
                   total_time - arrival_time[target_process] - burst_time[target_process]
               )
          else:
               total_time += 1

     return waiting_time

def 						_snake_case						(				SCREAMING_SNAKE_CASE__     :						list[int]						,  SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						list[int]    )  ->       list[int]:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   [0] * no_of_processes
     for i in range(_snake_case    ):
          _UpperCAmelCase      :     int        =   burst_time[i] + waiting_time[i]
     return turn_around_time


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

 _lowerCAmelCase    : Any							=							4
 _lowerCAmelCase    : List[Any]							=							[2, 5, 3, 7]
 _lowerCAmelCase    : Optional[Any]							=							[0, 0, 0, 0]
 _lowerCAmelCase    : Optional[Any]							=							calculate_waitingtime(arrival_time, burst_time, no_of_processes)
 _lowerCAmelCase    : Optional[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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase    : Union[str, Any]							=							{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							["RoFormerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							[
     "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "RoFormerForCausalLM",
     "RoFormerForMaskedLM",
     "RoFormerForMultipleChoice",
     "RoFormerForQuestionAnswering",
     "RoFormerForSequenceClassification",
     "RoFormerForTokenClassification",
     "RoFormerLayer",
     "RoFormerModel",
     "RoFormerPreTrainedModel",
     "load_tf_weights_in_roformer",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Optional[int]							=							[
     "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFRoFormerForCausalLM",
     "TFRoFormerForMaskedLM",
     "TFRoFormerForMultipleChoice",
     "TFRoFormerForQuestionAnswering",
     "TFRoFormerForSequenceClassification",
     "TFRoFormerForTokenClassification",
     "TFRoFormerLayer",
     "TFRoFormerModel",
     "TFRoFormerPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : List[str]							=							[
     "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "FlaxRoFormerForMaskedLM",
     "FlaxRoFormerForMultipleChoice",
     "FlaxRoFormerForQuestionAnswering",
     "FlaxRoFormerForSequenceClassification",
     "FlaxRoFormerForTokenClassification",
     "FlaxRoFormerModel",
     "FlaxRoFormerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
 from .tokenization_roformer import RoFormerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roformer_fast import RoFormerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roformer import (
      ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      RoFormerForCausalLM,
      RoFormerForMaskedLM,
      RoFormerForMultipleChoice,
      RoFormerForQuestionAnswering,
      RoFormerForSequenceClassification,
      RoFormerForTokenClassification,
      RoFormerLayer,
      RoFormerModel,
      RoFormerPreTrainedModel,
      load_tf_weights_in_roformer,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roformer import (
      TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerLayer,
      TFRoFormerModel,
      TFRoFormerPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roformer import (
      FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
      FlaxRoFormerPreTrainedModel,
  )


else:
 import sys

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