




"""simple docstring"""




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


a_            =   logging.getLogger(__name__)







def       __UpperCAmelCase      (				):
			__lowercase	:       Union[str, Any]				=   argparse.ArgumentParser(description='''Train a unigram tokenizer on the wikitext dataset.'''					)
			parser.add_argument(
			    '''--dataset_name'''					,							type=__UpperCamelCase					,							default='''wikitext'''					,							help='''Name of the training. Explore datasets at: hf.co/datasets.'''					,							)
			parser.add_argument(
			    '''--dataset_config'''					,							type=__UpperCamelCase					,							default='''wikitext-103-raw-v1'''					,							help='''Configuration name of the dataset.'''					)
			parser.add_argument(
			    '''--batch_size'''					,							type=__UpperCamelCase					,							default=10_00					,							help='''Batch size during training.'''					,							)
			parser.add_argument(
			    '''--vocab_size'''					,							type=__UpperCamelCase					,							default=1_00_48					,							help='''Size of the desired vocabulary.'''					,							)
			parser.add_argument(
			    '''--limit'''					,							default=__UpperCamelCase					,							type=__UpperCamelCase					,							help='''Limit the number of shards (used for debugging).'''					,							)
			parser.add_argument(
			    '''--export_to_hub'''					,							action='''store_true'''					,							)

			__lowercase	:       str				=   parser.parse_args()
			return args







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       Dict				=   datasets.load_dataset(args.dataset_name					,							args.dataset_config					,							split='''train'''					)

			if args.limit is not None:
						__lowercase	:       Tuple				=   min(len(__UpperCamelCase					)					,							args.limit					)
						__lowercase	:       Any				=   dataset.select(range(__UpperCamelCase					)					)
						logger.info(f"""Limiting the dataset to {args.limit} entries."""					)

			def batch_iterator():
						for i in range(0					,							len(__UpperCamelCase					)					,							args.batch_size					):
									yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
			__lowercase	:       Optional[Any]				=   Tokenizer(Unigram()					)
			__lowercase	:       Optional[int]				=   normalizers.Sequence([normalizers.Replace('''``'''					,							'''"'''					), normalizers.Replace('''\'\''''					,							'''"'''					)]					)
			__lowercase	:       List[Any]				=   pre_tokenizers.Metaspace()

			# Prepare the trainer.
			__lowercase	:       Any				=   UnigramTrainer(
			    unk_token='''<unk>'''					,							special_tokens=['''[CLS]''', '''[SEP]''', '''<unk>''', '''<pad>''', '''[MASK]''']					,							vocab_size=args.vocab_size					,							)

			logger.info('''Training the tokenizer.'''					)
			tokenizer.train_from_iterator(batch_iterator()					,							trainer=__UpperCamelCase					)
			logger.info('''Tokenizer training complete!'''					)

			__lowercase	:       Union[str, Any]				=   tokenizer.token_to_id('''[CLS]'''					)
			__lowercase	:       int				=   tokenizer.token_to_id('''[SEP]'''					)
			__lowercase	:       Optional[int]				=   processors.TemplateProcessing(
			    single='''[CLS]:0 $A:0 [SEP]:0'''					,							pair='''[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'''					,							special_tokens=[
			        ('''[CLS]''', cls_token_id),
			        ('''[SEP]''', sep_token_id),
			    ]					,							)
			__lowercase	:       Optional[Any]				=   decoders.Metaspace()

			if args.export_to_hub:
						logger.info('''Exporting the trained tokenzier to Hub.'''					)
						__lowercase	:       str				=   AlbertTokenizerFast(tokenizer_object=__UpperCamelCase					)
						new_tokenizer.push_to_hub('''unigram-tokenizer-dataset'''					)


if __name__ == "__main__":
			a_            =   parse_args()
			main(args)









"""simple docstring"""




from __future__ import annotations







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       List[Any]				=   [0] * len(__UpperCamelCase					)
			for i in range(1					,							len(__UpperCamelCase					)					):
						__lowercase	:       Optional[Any]				=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(__UpperCamelCase					)
			]







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)


if __name__ == "__main__":
			# process id's
			a_            =   [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
			a_            =   [1_9, 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
			a_            =   calculate_waiting_times(duration_times)
			a_            =   calculate_turnaround_times(duration_times, waiting_times)

			# get the average times
			a_            =   calculate_average_waiting_time(waiting_times)
			a_            =   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}")



