





'''simple docstring'''





from __future__ import annotations

import pandas as pd






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Dict						    =							[0] * no_of_processes
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[0] * no_of_processes
     # Copy the burst time into remaining_time[]
     for i in range(__lowerCamelCase	):
          _SCREAMING_SNAKE_CASE       :	Dict						    =							burst_time[i]

     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							0
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							0
     _SCREAMING_SNAKE_CASE       :	Any						    =							999999999
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							0
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							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:
                         _SCREAMING_SNAKE_CASE       :	str						    =							remaining_time[j]
                         _SCREAMING_SNAKE_CASE       :	Dict						    =							j
                         _SCREAMING_SNAKE_CASE       :	str						    =							True

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

          _SCREAMING_SNAKE_CASE       :	Dict						    =							remaining_time[short]
          if minm == 0:
               _SCREAMING_SNAKE_CASE       :	Dict						    =							999999999

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

               # Find finish time of current process
               _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							increment_time + 1

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

               if waiting_time[short] < 0:
                    _SCREAMING_SNAKE_CASE       :	str						    =							0

        # Increment time
          increment_time += 1
     return waiting_time






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[0] * no_of_processes
     for i in range(__lowerCamelCase	):
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							burst_time[i] + waiting_time[i]
     return turn_around_time






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	str						    =							0
     _SCREAMING_SNAKE_CASE       :	str						    =							0
     for i in range(__lowerCamelCase	):
          _SCREAMING_SNAKE_CASE       :	Any						    =							total_waiting_time + waiting_time[i]
          _SCREAMING_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')
       UpperCamelCase__      =int(input())
       UpperCamelCase__      =[0] * no_of_processes
       UpperCamelCase__      =[0] * no_of_processes
       UpperCamelCase__      =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))
              UpperCamelCase__      =map(int, input().split())

       UpperCamelCase__      =calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

       calculate_average_times(waiting_time, turn_around_time, no_of_processes)

       UpperCamelCase__      =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)






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     # Construct model
     if gpta_config_file == "":
          _SCREAMING_SNAKE_CASE       :	str						    =							GPTaConfig()
     else:
          _SCREAMING_SNAKE_CASE       :	int						    =							GPTaConfig.from_json_file(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							GPTaModel(__lowerCamelCase	)

     # Load weights from numpy
     load_tf_weights_in_gpta(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	)

     # Save pytorch-model
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""	)
     torch.save(model.state_dict(),       __lowerCamelCase	)
     print(f"""Save configuration file to {pytorch_config_dump_path}"""	)
     with open(__lowerCamelCase,       "w",       encoding="utf-8"	) as f:
          f.write(config.to_json_string()	)


if __name__ == "__main__":
       UpperCamelCase__      =argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       parser.add_argument(
           '--gpt2_config_file',
           default='',
           type=str,
           help=(
               'An optional config json file corresponding to the pre-trained OpenAI model. \n'
               'This specifies the model architecture.'
           ),
       )
       UpperCamelCase__      =parser.parse_args()
       convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)