import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__magic_name__								=data_utils.TransfoXLTokenizer
__magic_name__								=data_utils.TransfoXLCorpus
__magic_name__								=data_utils
__magic_name__								=data_utils







def  __UpperCamelCase    (						A ,							A ,							A ,							A							):
					if transfo_xl_dataset_file:
										# Convert a pre-processed corpus (see original TensorFlow repo)
										with open(A ,							'''rb'''							) as fp:
															UpperCamelCase__										=      pickle.load(A ,							encoding='''latin1'''							)
										# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
										UpperCamelCase__										=      pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
										print(f"Save vocabulary to {pytorch_vocab_dump_path}"							)
										UpperCamelCase__										=      corpus.vocab.__dict__
										torch.save(A ,							A							)

										UpperCamelCase__										=      corpus.__dict__
										corpus_dict_no_vocab.pop('''vocab''' ,							A							)
										UpperCamelCase__										=      pytorch_dump_folder_path + '''/''' + CORPUS_NAME
										print(f"Save dataset to {pytorch_dataset_dump_path}"							)
										torch.save(A ,							A							)

					if tf_checkpoint_path:
										# Convert a pre-trained TensorFlow model
										UpperCamelCase__										=      os.path.abspath(A							)
										UpperCamelCase__										=      os.path.abspath(A							)

										print(f"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."							)
										# Initialise PyTorch model
										if transfo_xl_config_file == "":
															UpperCamelCase__										=      TransfoXLConfig()
										else:
															UpperCamelCase__										=      TransfoXLConfig.from_json_file(A							)
										print(f"Building PyTorch model from configuration: {config}"							)
										UpperCamelCase__										=      TransfoXLLMHeadModel(A							)

										UpperCamelCase__										=      load_tf_weights_in_transfo_xl(A ,							A ,							A							)
										# Save pytorch-model
										UpperCamelCase__										=      os.path.join(A ,							A							)
										UpperCamelCase__										=      os.path.join(A ,							A							)
										print(f"Save PyTorch model to {os.path.abspath(A							)}"							)
										torch.save(model.state_dict() ,							A							)
										print(f"Save configuration file to {os.path.abspath(A							)}"							)
										with open(A ,							'''w''' ,							encoding='''utf-8'''							) as f:
															f.write(config.to_json_string()							)


if __name__ == "__main__":
					__magic_name__								=argparse.ArgumentParser()
					parser.add_argument(
					    '''--pytorch_dump_folder_path''',
					    default=None,
					    type=str,
					    required=True,
					    help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
					)
					parser.add_argument(
					    '''--tf_checkpoint_path''',
					    default='''''',
					    type=str,
					    help='''An optional path to a TensorFlow checkpoint path to be converted.''',
					)
					parser.add_argument(
					    '''--transfo_xl_config_file''',
					    default='''''',
					    type=str,
					    help=(
					        '''An optional config json file corresponding to the pre-trained BERT model. \n'''
					        '''This specifies the model architecture.'''
					    ),
					)
					parser.add_argument(
					    '''--transfo_xl_dataset_file''',
					    default='''''',
					    type=str,
					    help='''An optional dataset file to be converted in a vocabulary.''',
					)
					__magic_name__								=parser.parse_args()
					convert_transfo_xl_checkpoint_to_pytorch(
					    args.tf_checkpoint_path,
					    args.transfo_xl_config_file,
					    args.pytorch_dump_folder_path,
					    args.transfo_xl_dataset_file,
					)




from __future__ import annotations

import pandas as pd







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      [0] * no_of_processes
     UpperCamelCase__										=      [0] * no_of_processes
     # Copy the burst time into remaining_time[]
     for i in range(A							):
          UpperCamelCase__										=      burst_time[i]

     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      999999999
     UpperCamelCase__										=      0
     UpperCamelCase__										=      False

     # Process until all processes are completed
     while complete != no_of_processes:
          for j in range(A							):
               if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                    if remaining_time[j] < minm:
                         UpperCamelCase__										=      remaining_time[j]
                         UpperCamelCase__										=      j
                         UpperCamelCase__										=      True

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

          UpperCamelCase__										=      remaining_time[short]
          if minm == 0:
               UpperCamelCase__										=      999999999

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

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

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

               if waiting_time[short] < 0:
                    UpperCamelCase__										=      0

        # Increment time
          increment_time += 1
     return waiting_time







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      [0] * no_of_processes
     for i in range(A							):
          UpperCamelCase__										=      burst_time[i] + waiting_time[i]
     return turn_around_time







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     for i in range(A							):
          UpperCamelCase__										=      total_waiting_time + waiting_time[i]
          UpperCamelCase__										=      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''')
     __magic_name__								=int(input())
     __magic_name__								=[0] * no_of_processes
     __magic_name__								=[0] * no_of_processes
     __magic_name__								=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))
          __magic_name__		,  __magic_name__								=map(int, input().split())

     __magic_name__								=calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     __magic_name__								=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)



