





from __future__ import annotations






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							list[int]      ):
		SCREAMING_SNAKE_CASE__								= [0] * len(UpperCamelCase__      )
		for i in range(1   ,				len(UpperCamelCase__      )      ):
				SCREAMING_SNAKE_CASE__								= duration_times[i - 1] + waiting_times[i - 1]
		return waiting_times






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							list[int]   ,				UpperCamelCase__:							list[int]      ):
		return [
		    duration_time + waiting_times[i]
		    for i, duration_time in enumerate(UpperCamelCase__      )
		]






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							list[int]      ):
		return sum(UpperCamelCase__      ) / len(UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							list[int]      ):
		return sum(UpperCamelCase__      ) / len(UpperCamelCase__      )


if __name__ == "__main__":
	# process id's
	_lowerCamelCase							=     [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
	_lowerCamelCase							=     [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
	_lowerCamelCase							=     calculate_waiting_times(duration_times)
	_lowerCamelCase							=     calculate_turnaround_times(duration_times, waiting_times)

	# get the average times
	_lowerCamelCase							=     calculate_average_waiting_time(waiting_times)
	_lowerCamelCase							=     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

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Union[str, Any]      ):
		# Construct configuration depending on size
		SCREAMING_SNAKE_CASE__								= {"""small""": (512, 8, 24), """medium""": (1_024, 8, 36), """large""": (1_536, 16, 48)}
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE__								= ImageGPTConfig(n_embd=UpperCamelCase__   ,				n_layer=UpperCamelCase__   ,				n_head=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= ImageGPTForCausalLM(UpperCamelCase__      )

		# Load weights from numpy
		load_tf_weights_in_imagegpt(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__      )

		# Save pytorch-model
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + CONFIG_NAME
		print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''      )
		torch.save(model.state_dict()   ,				UpperCamelCase__      )
		print(f'''Save configuration file to {pytorch_config_dump_path}'''      )
		with open(UpperCamelCase__   ,				"""w"""   ,				encoding="""utf-8"""      ) as f:
				f.write(config.to_json_string()      )


if __name__ == "__main__":
	_lowerCamelCase							=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--imagegpt_checkpoint_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to the TensorFlow checkpoint path.',
	)
	parser.add_argument(
	    '--model_size',
	    default=None,
	    type=str,
	    required=True,
	    help='Size of the model (can be either \'small\', \'medium\' or \'large\').',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	_lowerCamelCase							=     parser.parse_args()
	convert_imagegpt_checkpoint_to_pytorch(
	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
	)