"""simple docstring"""




import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__lowerCAmelCase     :				List[Any]    				=    data_utils.TransfoXLTokenizer
__lowerCAmelCase     :				Union[str, Any]    				=    data_utils.TransfoXLCorpus
__lowerCAmelCase     :				int    				=    data_utils
__lowerCAmelCase     :				Optional[int]    				=    data_utils







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		if transfo_xl_dataset_file:
				# Convert a pre-processed corpus (see original TensorFlow repo)
				with open(lowerCamelCase__      ,	"""rb"""  ) as fp:
						lowerCAmelCase__   = pickle.load(lowerCamelCase__      ,	encoding="""latin1"""  )
				# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
				lowerCAmelCase__   = pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
				print(f"""Save vocabulary to {pytorch_vocab_dump_path}"""  )
				lowerCAmelCase__   = corpus.vocab.__dict__
				torch.save(lowerCamelCase__      ,	lowerCamelCase__  )

				lowerCAmelCase__   = corpus.__dict__
				corpus_dict_no_vocab.pop("""vocab"""      ,	lowerCamelCase__  )
				lowerCAmelCase__   = pytorch_dump_folder_path + """/""" + CORPUS_NAME
				print(f"""Save dataset to {pytorch_dataset_dump_path}"""  )
				torch.save(lowerCamelCase__      ,	lowerCamelCase__  )

		if tf_checkpoint_path:
				# Convert a pre-trained TensorFlow model
				lowerCAmelCase__   = os.path.abspath(lowerCamelCase__  )
				lowerCAmelCase__   = os.path.abspath(lowerCamelCase__  )

				print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""  )
				# Initialise PyTorch model
				if transfo_xl_config_file == "":
						lowerCAmelCase__   = TransfoXLConfig()
				else:
						lowerCAmelCase__   = TransfoXLConfig.from_json_file(lowerCamelCase__  )
				print(f"""Building PyTorch model from configuration: {config}"""  )
				lowerCAmelCase__   = TransfoXLLMHeadModel(lowerCamelCase__  )

				lowerCAmelCase__   = load_tf_weights_in_transfo_xl(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )
				# Save pytorch-model
				lowerCAmelCase__   = os.path.join(lowerCamelCase__      ,	lowerCamelCase__  )
				lowerCAmelCase__   = os.path.join(lowerCamelCase__      ,	lowerCamelCase__  )
				print(f"""Save PyTorch model to {os.path.abspath(lowerCamelCase__  )}"""  )
				torch.save(model.state_dict()      ,	lowerCamelCase__  )
				print(f"""Save configuration file to {os.path.abspath(lowerCamelCase__  )}"""  )
				with open(lowerCamelCase__      ,	"""w"""      ,	encoding="""utf-8"""  ) as f:
						f.write(config.to_json_string()  )


if __name__ == "__main__":
			__lowerCAmelCase     :				Tuple    				=    argparse.ArgumentParser()
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path to the folder to store the PyTorch model or dataset/vocab.",
			)
			parser.add_argument(
			    "--tf_checkpoint_path",
			    default="",
			    type=str,
			    help="An optional path to a TensorFlow checkpoint path to be converted.",
			)
			parser.add_argument(
			    "--transfo_xl_config_file",
			    default="",
			    type=str,
			    help=(
			        "An optional config json file corresponding to the pre-trained BERT model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--transfo_xl_dataset_file",
			    default="",
			    type=str,
			    help="An optional dataset file to be converted in a vocabulary.",
			)
			__lowerCAmelCase     :				str    				=    parser.parse_args()
			convert_transfo_xl_checkpoint_to_pytorch(
			    args.tf_checkpoint_path,
			    args.transfo_xl_config_file,
			    args.pytorch_dump_folder_path,
			    args.transfo_xl_dataset_file,
			)





"""simple docstring"""




from __future__ import annotations

from statistics import mean







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [0] * no_of_processes
		lowerCAmelCase__   = [0] * no_of_processes

		# Initialize remaining_time to waiting_time.

		for i in range(lowerCamelCase__  ):
				lowerCAmelCase__   = burst_time[i]
		lowerCAmelCase__   = []

		lowerCAmelCase__   = 0
		lowerCAmelCase__   = 0

		# When processes are not completed,
		# A process whose arrival time has passed \
		# and has remaining execution time is put into the ready_process.
		# The shortest process in the ready_process, target_process is executed.

		while completed != no_of_processes:
				lowerCAmelCase__   = []
				lowerCAmelCase__   = -1

				for i in range(lowerCamelCase__  ):
						if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
								ready_process.append(lowerCamelCase__  )

				if len(lowerCamelCase__  ) > 0:
						lowerCAmelCase__   = ready_process[0]
						for i in ready_process:
								if remaining_time[i] < remaining_time[target_process]:
										lowerCAmelCase__   = i
						total_time += burst_time[target_process]
						completed += 1
						lowerCAmelCase__   = 0
						lowerCAmelCase__   = (
						    total_time - arrival_time[target_process] - burst_time[target_process]
						)
				else:
						total_time += 1

		return waiting_time







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [0] * no_of_processes
		for i in range(lowerCamelCase__  ):
				lowerCAmelCase__   = burst_time[i] + waiting_time[i]
		return turn_around_time


if __name__ == "__main__":
			print("[TEST CASE 01]")

			__lowerCAmelCase     :				str    				=    4
			__lowerCAmelCase     :				Dict    				=    [2, 5, 3, 7]
			__lowerCAmelCase     :				List[str]    				=    [0, 0, 0, 0]
			__lowerCAmelCase     :				Union[str, Any]    				=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
			__lowerCAmelCase     :				Union[str, Any]    				=    calculate_turnaroundtime(
			    burst_time, no_of_processes, waiting_time
			)

			# Printing the Result
			print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
			for i, process_id in enumerate(list(range(1, 5))):
						print(
						    F"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
						    F"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
						)
			print(F"\nAverage waiting time = {mean(waiting_time):.5f}")
			print(F"Average turnaround time = {mean(turn_around_time):.5f}")




