



'''simple docstring'''





from __future__ import annotations

import pandas as pd





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					list[int]:
      snake_case__    :       Optional[int]       		= [0] * no_of_processes
      snake_case__    :       Dict       		= [0] * no_of_processes
      # Copy the burst time into remaining_time[]
      for i in range(_lowerCAmelCase						):
            snake_case__    :       str       		= burst_time[i]

      snake_case__    :       List[str]       		= 0
      snake_case__    :       Tuple       		= 0
      snake_case__    :       Optional[Any]       		= 999_999_999
      snake_case__    :       Optional[int]       		= 0
      snake_case__    :       List[str]       		= False

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

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

            snake_case__    :       int       		= remaining_time[short]
            if minm == 0:
                  snake_case__    :       List[Any]       		= 999_999_999

            if remaining_time[short] == 0:
                  complete += 1
                  snake_case__    :       Optional[Any]       		= False

                  # Find finish time of current process
                  snake_case__    :       str       		= increment_time + 1

                  # Calculate waiting time
                  snake_case__    :       Optional[Any]       		= finish_time - arrival_time[short]
                  snake_case__    :       Tuple       		= finar - burst_time[short]

                  if waiting_time[short] < 0:
                        snake_case__    :       Any       		= 0

        # Increment time
            increment_time += 1
      return waiting_time





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					list[int]:
      snake_case__    :       str       		= [0] * no_of_processes
      for i in range(_lowerCAmelCase						):
            snake_case__    :       Union[str, Any]       		= burst_time[i] + waiting_time[i]
      return turn_around_time





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					None:
      snake_case__    :       Union[str, Any]       		= 0
      snake_case__    :       Union[str, Any]       		= 0
      for i in range(_lowerCAmelCase						):
            snake_case__    :       int       		= total_waiting_time + waiting_time[i]
            snake_case__    :       List[Any]       		= 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")
       __a  			=						int(input())
       __a  			=						[0] * no_of_processes
       __a  			=						[0] * no_of_processes
       __a  			=						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))
              __a			,		__a  			=						map(int, input().split())

       __a  			=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)

       __a  			=						burst_time
       __a  			=						no_of_processes
       __a  			=						waiting_time
       __a  			=						calculate_turnaroundtime(bt, n, wt)

       calculate_average_times(waiting_time, turn_around_time, no_of_processes)

       __a  			=						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)





'''simple docstring'''





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


__a  			=						"3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
       import torch

       print("Torch version:", torch.__version__)
       print("Cuda available:", torch.cuda.is_available())
       print("Cuda version:", torch.version.cuda)
       print("CuDNN version:", torch.backends.cudnn.version())
       print("Number of GPUs available:", torch.cuda.device_count())
       print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
       print("Torch version:", None)

try:
       import deepspeed

       print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
       print("DeepSpeed version:", None)

try:
       import tensorflow as tf

       print("TensorFlow version:", tf.__version__)
       print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
       print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
       print("TensorFlow version:", None)
