


"""simple docstring"""




from __future__ import annotations
def     UpperCAmelCase_ (							__snake_case						)					->      list[int]:







 """simple docstring"""


 _lowercase											=[0] * len(__snake_case						)
 for i in range(1					,						len(__snake_case						)						):
  _lowercase											=duration_times[i - 1] + waiting_times[i - 1]
 return waiting_times
def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      list[int]:







 """simple docstring"""


 return [
     duration_time + waiting_times[i]
     for i, duration_time in enumerate(__snake_case						)
 ]
def     UpperCAmelCase_ (							__snake_case						)					->      float:







 """simple docstring"""


 return sum(__snake_case						) / len(__snake_case						)
def     UpperCAmelCase_ (							__snake_case						)					->      float:







 """simple docstring"""


 return sum(__snake_case						) / len(__snake_case						)


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}''')





import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      Tuple:







 """simple docstring"""


 _lowercase											=BertConfig.from_pretrained(
     '''bert-large-cased'''					,						vocab_size=__snake_case					,						max_position_embeddings=512					,						is_decoder=__snake_case					,						add_cross_attention=__snake_case					,						)
 _lowercase											=bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 _lowercase											=BertGenerationConfig(**__snake_case						)
 if is_encoder:
  _lowercase											=BertGenerationEncoder(__snake_case						)
 else:
  _lowercase											=BertGenerationDecoder(__snake_case						)
 print(F"Building PyTorch model from configuration: {config}"						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     __snake_case					,						__snake_case					,						model_class='''bert'''					,						is_encoder_named_decoder=__snake_case					,						is_encoder=__snake_case					,						)

 # Save pytorch-model
 print(F"Save PyTorch model and config to {pytorch_dump_path}"						)
 model.save_pretrained(__snake_case						)


if __name__ == "__main__":
  UpperCAmelCase__            =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--is_encoder_named_decoder''',
      action='''store_true''',
      help='''If decoder has to be renamed to encoder in PyTorch model.''',
  )
  parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
  parser.add_argument('''--vocab_size''', default=5_0358, type=int, help='''Vocab size of model''')
  UpperCAmelCase__            =							parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )

