




from __future__ import annotations

import pandas as pd







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       list[int]:



       """simple docstring"""

       A__       =    [0] * no_of_processes
       A__       =    [0] * no_of_processes
       # Copy the burst time into remaining_time[]
       for i in range(lowercase_						):
              A__       =    burst_time[i]

       A__       =    0
       A__       =    0
       A__       =    999_999_999
       A__       =    0
       A__       =    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:
                                   A__       =    remaining_time[j]
                                   A__       =    j
                                   A__       =    True

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

              A__       =    remaining_time[short]
              if minm == 0:
                     A__       =    999_999_999

              if remaining_time[short] == 0:
                     complete += 1
                     A__       =    False

                     # Find finish time of current process
                     A__       =    increment_time + 1

                     # Calculate waiting time
                     A__       =    finish_time - arrival_time[short]
                     A__       =    finar - burst_time[short]

                     if waiting_time[short] < 0:
                            A__       =    0

        # Increment time
              increment_time += 1
       return waiting_time







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       list[int]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       None:



       """simple docstring"""

       A__       =    0
       A__       =    0
       for i in range(lowercase_						):
              A__       =    total_waiting_time + waiting_time[i]
              A__       =    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	:		str							       =   int(input())
  _lowerCamelCase	:		Optional[int]							       =   [0] * no_of_processes
  _lowerCamelCase	:		str							       =   [0] * no_of_processes
  _lowerCamelCase	:		Tuple							       =   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	:		List[str]							       =   calculate_waitingtime(arrival_time, burst_time, no_of_processes)

  _lowerCamelCase	:		Any							       =   burst_time
  _lowerCamelCase	:		Optional[Any]							       =   no_of_processes
  _lowerCamelCase	:		Any							       =   waiting_time
  _lowerCamelCase	:		Optional[int]							       =   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)






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A		       =						{
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['VivitImageProcessor']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'VivitModel',
       'VivitPreTrainedModel',
       'VivitForVideoClassification',
   ]


if TYPE_CHECKING:
   from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_vivit import VivitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vivit import (
          VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          VivitForVideoClassification,
          VivitModel,
          VivitPreTrainedModel,
      )


else:
   import sys

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





