





from statistics import mean

import numpy as np

def   a_   (			lowerCAmelCase_				:      list,     lowerCAmelCase_				:      list,     lowerCAmelCase_				:      list,     lowerCAmelCase_				:      int     ):
   __lowerCAmelCase							     =     0
   # Number of processes finished
   __lowerCAmelCase							     =     0
   # Displays the finished process.
   # If it is 0, the performance is completed if it is 1, before the performance.
   __lowerCAmelCase							     =     [0] * no_of_process
   # List to include calculation results
   __lowerCAmelCase							     =     [0] * no_of_process

   # Sort by arrival time.
   __lowerCAmelCase							     =     [burst_time[i] for i in np.argsort(lowerCAmelCase_     )]
   __lowerCAmelCase							     =     [process_name[i] for i in np.argsort(lowerCAmelCase_     )]
   arrival_time.sort()

   while no_of_process > finished_process_count:
      __lowerCAmelCase							     =     0
      while finished_process[i] == 1:
         i += 1
      if current_time < arrival_time[i]:
         __lowerCAmelCase							     =     arrival_time[i]

      __lowerCAmelCase							     =     0
      # Index showing the location of the process being performed
      __lowerCAmelCase							     =     0
      # Saves the current response ratio.
      __lowerCAmelCase							     =     0
      for i in range(0,     lowerCAmelCase_     ):
         if finished_process[i] == 0 and arrival_time[i] <= current_time:
            __lowerCAmelCase							     =     (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                i
            ]
         if response_ratio < temp:
            __lowerCAmelCase							     =     temp
            __lowerCAmelCase							     =     i

        # Calculate the turn around time
      __lowerCAmelCase							     =     current_time + burst_time[loc] - arrival_time[loc]
      current_time += burst_time[loc]
      # Indicates that the process has been performed.
      __lowerCAmelCase							     =     1
      # Increase finished_process_count by 1
      finished_process_count += 1

   return turn_around_time

def   a_   (			lowerCAmelCase_				:      list,     lowerCAmelCase_				:      list,     lowerCAmelCase_				:      list,     lowerCAmelCase_				:      int     ):
   __lowerCAmelCase							     =     [0] * no_of_process
   for i in range(0,     lowerCAmelCase_     ):
      __lowerCAmelCase							     =     turn_around_time[i] - burst_time[i]
   return waiting_time


if __name__ == "__main__":
     _snake_case  :   Union[str, Any]						   =  5
     _snake_case  :   List[Any]						   =  ['A', 'B', 'C', 'D', 'E']
     _snake_case  :   Optional[int]						   =  [1, 2, 3, 4, 5]
     _snake_case  :   int						   =  [1, 2, 3, 4, 5]

     _snake_case  :   Optional[int]						   =  calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     _snake_case  :   int						   =  calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
     for i in range(0, no_of_process):
          print(
              F"""{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"""
              F"""{turn_around_time[i]}\t\t\t{waiting_time[i]}"""
          )

     print(F"""average waiting time : {mean(waiting_time):.5f}""")
     print(F"""average turn around time : {mean(turn_around_time):.5f}""")








import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Tuple     ):
   # Initialise PyTorch model.
   # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
   # TapasConfig to False.

   # initialize configuration from json file
   __lowerCAmelCase							     =     TapasConfig.from_json_file(lowerCAmelCase_     )
   # set absolute/relative position embeddings parameter
   __lowerCAmelCase							     =     reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WTQ":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     True
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     0.66_4694
      __lowerCAmelCase							     =     0.20_7951
      __lowerCAmelCase							     =     0.12_1194
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     False
      __lowerCAmelCase							     =     0.035_2513

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     False
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     36.4519
      __lowerCAmelCase							     =     0.90_3421
      __lowerCAmelCase							     =     222.088
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     0.76_3141

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "TABFACT":
      __lowerCAmelCase							     =     TapasForSequenceClassification(config=lowerCAmelCase_     )
   elif task == "MLM":
      __lowerCAmelCase							     =     TapasForMaskedLM(config=lowerCAmelCase_     )
   elif task == "INTERMEDIATE_PRETRAINING":
      __lowerCAmelCase							     =     TapasModel(config=lowerCAmelCase_     )
   else:
      raise ValueError(F"""Task {task} not supported."""     )

   print(F"""Building PyTorch model from configuration: {config}"""     )
   # Load weights from tf checkpoint
   load_tf_weights_in_tapas(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

   # Save pytorch-model (weights and configuration)
   print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
   model.save_pretrained(lowerCAmelCase_     )

   # Save tokenizer files
   print(F"""Save tokenizer files to {pytorch_dump_path}"""     )
   __lowerCAmelCase							     =     TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt',     model_max_length=512     )
   tokenizer.save_pretrained(lowerCAmelCase_     )

   print('Used relative position embeddings:',     model.config.reset_position_index_per_cell     )


if __name__ == "__main__":
     _snake_case  :   Union[str, Any]						   =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
     )
     parser.add_argument(
         '--reset_position_index_per_cell',
         default=False,
         action='store_true',
         help='Whether to use relative position embeddings or not. Defaults to True.',
     )
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--tapas_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained TAPAS model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     _snake_case  :   Optional[Any]						   =  parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.task,
         args.reset_position_index_per_cell,
         args.tf_checkpoint_path,
         args.tapas_config_file,
         args.pytorch_dump_path,
     )

