




from __future__ import annotations

import pandas as pd




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       list[int]:
   snake_case				:    Union[str, Any]           =	[0] * no_of_processes
   snake_case				:    str           =	[0] * no_of_processes
   # Copy the burst time into remaining_time[]
   for i in range(lowercase					):
      snake_case				:    int           =	burst_time[i]

   snake_case				:    List[Any]           =	0
   snake_case				:    List[str]           =	0
   snake_case				:    Optional[Any]           =	999999999
   snake_case				:    Optional[Any]           =	0
   snake_case				:    Optional[Any]           =	False

   # Process until all processes are completed
   while complete != no_of_processes:
      for j in range(lowercase					):
         if arrival_time[j] <= increment_time and remaining_time[j] > 0:
            if remaining_time[j] < minm:
               snake_case				:    str           =	remaining_time[j]
               snake_case				:    int           =	j
               snake_case				:    List[Any]           =	True

      if not check:
         increment_time += 1
         continue
      remaining_time[short] -= 1

      snake_case				:    List[Any]           =	remaining_time[short]
      if minm == 0:
         snake_case				:    List[Any]           =	999999999

      if remaining_time[short] == 0:
         complete += 1
         snake_case				:    Optional[int]           =	False

         # Find finish time of current process
         snake_case				:    Any           =	increment_time + 1

         # Calculate waiting time
         snake_case				:    Tuple           =	finish_time - arrival_time[short]
         snake_case				:    int           =	finar - burst_time[short]

         if waiting_time[short] < 0:
            snake_case				:    Tuple           =	0

        # Increment time
      increment_time += 1
   return waiting_time




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       list[int]:
   snake_case				:    str           =	[0] * no_of_processes
   for i in range(lowercase					):
      snake_case				:    Dict           =	burst_time[i] + waiting_time[i]
   return turn_around_time




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       None:
   snake_case				:    List[str]           =	0
   snake_case				:    List[str]           =	0
   for i in range(lowercase					):
      snake_case				:    Optional[int]           =	total_waiting_time + waiting_time[i]
      snake_case				:    str           =	total_turn_around_time + turn_around_time[i]
   print(f"""Average waiting time = {total_waiting_time / no_of_processes:.5f}"""					)
   print("""Average turn around time ="""						,total_turn_around_time / no_of_processes					)


if __name__ == "__main__":
       print('Enter how many process you want to analyze')
       lowerCamelCase	:	List[Any]				=   int(input())
       lowerCamelCase	:	Optional[Any]				=   [0] * no_of_processes
       lowerCamelCase	:	str				=   [0] * no_of_processes
       lowerCamelCase	:	Optional[Any]				=   list(range(1, no_of_processes + 1))

       for i in range(no_of_processes):
              print('Enter the arrival time and burst time for process:--' + str(i + 1))
              lowerCamelCase	,       lowerCamelCase	:	Union[str, Any]				=   map(int, input().split())

       lowerCamelCase	:	Any				=   calculate_waitingtime(arrival_time, burst_time, no_of_processes)

       lowerCamelCase	:	Optional[int]				=   burst_time
       lowerCamelCase	:	int				=   no_of_processes
       lowerCamelCase	:	Union[str, Any]				=   waiting_time
       lowerCamelCase	:	Optional[Any]				=   calculate_turnaroundtime(bt, n, wt)

       calculate_average_times(waiting_time, turn_around_time, no_of_processes)

       lowerCamelCase	:	Optional[Any]				=   pd.DataFrame(
           list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
           columns=[
               'Process',
               'BurstTime',
               'ArrivalTime',
               'WaitingTime',
               'TurnAroundTime',
           ],
       )

       # Printing the dataFrame
       pd.set_option('display.max_rows', fcfs.shape[0] + 1)
       print(fcfs)











from typing import TYPE_CHECKING

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


lowerCamelCase	:	str				=   {
    '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	:	List[Any]				=   ['RoFormerTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           '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	:	Any				=   [
           '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	:	Union[str, Any]				=   [
           '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	:	Optional[int]				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





