



from __future__ import annotations

def 	a_					(							_lowerCAmelCase	)				->				list[int]:
  __lowerCamelCase	:	Optional[Any]   	=			[0] * len(_lowerCAmelCase	)
  for i in range(1	,len(_lowerCAmelCase	)	):
    __lowerCamelCase	:	Any   	=			duration_times[i - 1] + waiting_times[i - 1]
  return waiting_times

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				list[int]:
  return [
      duration_time + waiting_times[i]
      for i, duration_time in enumerate(_lowerCAmelCase	)
  ]

def 	a_					(							_lowerCAmelCase	)				->				float:
  return sum(_lowerCAmelCase	) / len(_lowerCAmelCase	)

def 	a_					(							_lowerCAmelCase	)				->				float:
  return sum(_lowerCAmelCase	) / len(_lowerCAmelCase	)


if __name__ == "__main__":
   # process id's
   _UpperCamelCase							    =						[1, 2, 3]

   # ensure that we actually have processes
   if len(processes) == 0:
      print('Zero amount of processes')
      raise SystemExit(0)

   # duration time of all processes
   _UpperCamelCase							    =						[19, 8, 9]

   # ensure we can match each id to a duration time
   if len(duration_times) != len(processes):
      print('Unable to match all id\'s with their duration time')
      raise SystemExit(0)

   # get the waiting times and the turnaround times
   _UpperCamelCase							    =						calculate_waiting_times(duration_times)
   _UpperCamelCase							    =						calculate_turnaround_times(duration_times, waiting_times)

   # get the average times
   _UpperCamelCase							    =						calculate_average_waiting_time(waiting_times)
   _UpperCamelCase							    =						calculate_average_turnaround_time(turnaround_times)

   # print all the results
   print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
   for i, process in enumerate(processes):
      print(
          f'''{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'''
          f'''{turnaround_times[i]}'''
      )
   print(f'''Average waiting time = {average_waiting_time}''')
   print(f'''Average turn around time = {average_turnaround_time}''')











'''simple docstring'''





import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				List[Any]:
  # Initialize PyTorch model
  __lowerCamelCase	:	Union[str, Any]   	=			CanineConfig()
  __lowerCamelCase	:	Optional[Any]   	=			CanineModel(_lowerCAmelCase	)
  model.eval()

  print(F'Building PyTorch model from configuration: {config}'	)

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(_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
  __lowerCamelCase	:	str   	=			CanineTokenizer()
  print(F'Save tokenizer files to {pytorch_dump_path}'	)
  tokenizer.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
   _UpperCamelCase							    =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path',
       default=None,
       type=str,
       required=True,
       help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
   )
   parser.add_argument(
       '--pytorch_dump_path',
       default=None,
       type=str,
       required=True,
       help='Path to a folder where the PyTorch model will be placed.',
   )
   _UpperCamelCase							    =						parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






