


'''simple docstring'''


from statistics import mean

import numpy as np


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Optional[int]      =							0
     # Number of processes finished
     __A	:       List[Any]      =							0
     # Displays the finished process.
     # If it is 0, the performance is completed if it is 1, before the performance.
     __A	:       Tuple      =							[0] * no_of_process
     # List to include calculation results
     __A	:       Optional[int]      =							[0] * no_of_process

     # Sort by arrival time.
     __A	:       List[Any]      =							[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE							)]
     __A	:       int      =							[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE							)]
     arrival_time.sort()

     while no_of_process > finished_process_count:
          __A	:       Optional[int]      =							0
          while finished_process[i] == 1:
               i += 1
          if current_time < arrival_time[i]:
               __A	:       Dict      =							arrival_time[i]

          __A	:       str      =							0
          # Index showing the location of the process being performed
          __A	:       Tuple      =							0
          # Saves the current response ratio.
          __A	:       Any      =							0
          for i in range(0       ,					SCREAMING_SNAKE_CASE							):
               if finished_process[i] == 0 and arrival_time[i] <= current_time:
                    __A	:       Dict      =							(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                        i
                    ]
               if response_ratio < temp:
                    __A	:       List[Any]      =							temp
                    __A	:       Union[str, Any]      =							i

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

     return turn_around_time


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Dict      =							[0] * no_of_process
     for i in range(0       ,					SCREAMING_SNAKE_CASE							):
          __A	:       Union[str, Any]      =							turn_around_time[i] - burst_time[i]
     return waiting_time


if __name__ == "__main__":
      _UpperCamelCase 							=  5
      _UpperCamelCase 							=  ["""A""", """B""", """C""", """D""", """E"""]
      _UpperCamelCase 							=  [1, 2, 3, 4, 5]
      _UpperCamelCase 							=  [1, 2, 3, 4, 5]

      _UpperCamelCase 							=  calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      _UpperCamelCase 							=  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}""")





'''simple docstring'''


import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							BigBirdConfig.from_json_file(SCREAMING_SNAKE_CASE							)
     print(f"Building PyTorch model from configuration: {config}"							)

     if is_trivia_qa:
          __A	:       List[str]      =							BigBirdForQuestionAnswering(SCREAMING_SNAKE_CASE							)
     else:
          __A	:       List[Any]      =							BigBirdForPreTraining(SCREAMING_SNAKE_CASE							)

     # Load weights from tf checkpoint
     load_tf_weights_in_big_bird(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					is_trivia_qa=SCREAMING_SNAKE_CASE							)

     # Save pytorch-model
     print(f"Save PyTorch model to {pytorch_dump_path}"							)
     model.save_pretrained(SCREAMING_SNAKE_CASE							)


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 path."""
      )
      parser.add_argument(
          """--big_bird_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained BERT 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."""
      )
      parser.add_argument(
          """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
      )
      _UpperCamelCase 							=  parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
      )

