

'''simple docstring'''
from statistics import mean

import numpy as np


def        _lowerCAmelCase					(						_UpperCamelCase						:     list							, _UpperCamelCase						:     list							, _UpperCamelCase						:     list							, _UpperCamelCase						:     int				)      ->			list:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =0
     # Number of processes finished
     _SCREAMING_SNAKE_CASE							    =0
     # Displays the finished process.
     # If it is 0, the performance is completed if it is 1, before the performance.
     _SCREAMING_SNAKE_CASE							    =[0] * no_of_process
     # List to include calculation results
     _SCREAMING_SNAKE_CASE							    =[0] * no_of_process

     # Sort by arrival time.
     _SCREAMING_SNAKE_CASE							    =[burst_time[i] for i in np.argsort(_UpperCamelCase				)]
     _SCREAMING_SNAKE_CASE							    =[process_name[i] for i in np.argsort(_UpperCamelCase				)]
     arrival_time.sort()

     while no_of_process > finished_process_count:
          _SCREAMING_SNAKE_CASE							    =0
          while finished_process[i] == 1:
               i += 1
          if current_time < arrival_time[i]:
               _SCREAMING_SNAKE_CASE							    =arrival_time[i]

          _SCREAMING_SNAKE_CASE							    =0
          # Index showing the location of the process being performed
          _SCREAMING_SNAKE_CASE							    =0
          # Saves the current response ratio.
          _SCREAMING_SNAKE_CASE							    =0
          for i in range(0							, _UpperCamelCase				):
               if finished_process[i] == 0 and arrival_time[i] <= current_time:
                    _SCREAMING_SNAKE_CASE							    =(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                        i
                    ]
               if response_ratio < temp:
                    _SCREAMING_SNAKE_CASE							    =temp
                    _SCREAMING_SNAKE_CASE							    =i

        # Calculate the turn around time
          _SCREAMING_SNAKE_CASE							    =current_time + burst_time[loc] - arrival_time[loc]
          current_time += burst_time[loc]
          # Indicates that the process has been performed.
          _SCREAMING_SNAKE_CASE							    =1
          # Increase finished_process_count by 1
          finished_process_count += 1

     return turn_around_time


def        _lowerCAmelCase					(						_UpperCamelCase						:     list							, _UpperCamelCase						:     list							, _UpperCamelCase						:     list							, _UpperCamelCase						:     int				)      ->			list:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[0] * no_of_process
     for i in range(0							, _UpperCamelCase				):
          _SCREAMING_SNAKE_CASE							    =turn_around_time[i] - burst_time[i]
     return waiting_time


if __name__ == "__main__":
     lowerCamelCase :      Dict					 =     5
     lowerCamelCase :      Optional[int]					 =     ["A", "B", "C", "D", "E"]
     lowerCamelCase :      Tuple					 =     [1, 2, 3, 4, 5]
     lowerCamelCase :      str					 =     [1, 2, 3, 4, 5]

     lowerCamelCase :      Tuple					 =     calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     lowerCamelCase :      Optional[Any]					 =     calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
     for i in range(0, no_of_process):
          print(
              f'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
              f'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
          )

     print(f'''average waiting time : {mean(waiting_time):.5f}''')
     print(f'''average turn around time : {mean(turn_around_time):.5f}''')



'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCamelCase :      Any					 =     {
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     ["FunnelTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[str]					 =     [
         "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FunnelBaseModel",
         "FunnelForMaskedLM",
         "FunnelForMultipleChoice",
         "FunnelForPreTraining",
         "FunnelForQuestionAnswering",
         "FunnelForSequenceClassification",
         "FunnelForTokenClassification",
         "FunnelModel",
         "FunnelPreTrainedModel",
         "load_tf_weights_in_funnel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFFunnelBaseModel",
         "TFFunnelForMaskedLM",
         "TFFunnelForMultipleChoice",
         "TFFunnelForPreTraining",
         "TFFunnelForQuestionAnswering",
         "TFFunnelForSequenceClassification",
         "TFFunnelForTokenClassification",
         "TFFunnelModel",
         "TFFunnelPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
     from .tokenization_funnel import FunnelTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_funnel_fast import FunnelTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_funnel import (
              FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              FunnelBaseModel,
              FunnelForMaskedLM,
              FunnelForMultipleChoice,
              FunnelForPreTraining,
              FunnelForQuestionAnswering,
              FunnelForSequenceClassification,
              FunnelForTokenClassification,
              FunnelModel,
              FunnelPreTrainedModel,
              load_tf_weights_in_funnel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_funnel import (
              TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFFunnelBaseModel,
              TFFunnelForMaskedLM,
              TFFunnelForMultipleChoice,
              TFFunnelForPreTraining,
              TFFunnelForQuestionAnswering,
              TFFunnelForSequenceClassification,
              TFFunnelForTokenClassification,
              TFFunnelModel,
              TFFunnelPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase :      Optional[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
