


'''simple docstring'''



from statistics import mean

import numpy as np


def    lowerCamelCase__   ( A     :    list   ,     A     :    list   ,     A     :    list   ,     A     :    int  ):




						'''simple docstring'''






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

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

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

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

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

						return turn_around_time


def    lowerCamelCase__   ( A     :    list   ,     A     :    list   ,     A     :    list   ,     A     :    int  ):




						'''simple docstring'''






						UpperCAmelCase         =			[0] * no_of_process
						for i in range(0   ,     A  ):
												UpperCAmelCase         =			turn_around_time[i] - burst_time[i]
						return waiting_time


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

	_lowercase    :    int			     =  calculate_turn_around_time(
	    process_name, arrival_time, burst_time, no_of_process
	)
	_lowercase    :    List[str]			     =  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    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    int   ,     A     :    List[str]   ,     A     :    Optional[int]  ):




						'''simple docstring'''






						UpperCAmelCase         =			BigBirdConfig.from_json_file(A  )
						print(f"""Building PyTorch model from configuration: {config}"""  )

						if is_trivia_qa:
												UpperCAmelCase         =			BigBirdForQuestionAnswering(A  )
						else:
												UpperCAmelCase         =			BigBirdForPreTraining(A  )

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

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


if __name__ == "__main__":
	_lowercase    :    List[str]			     =  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."""
	)
	_lowercase    :    Any			     =  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
	)





