
"""simple docstring"""







import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerCamelCase__           =	logging.getLogger(__name__)
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Union[str, Any]		    =   argparse.ArgumentParser(
	    description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'	)
	parser.add_argument('--file_path' ,						type=_UpperCamelCase ,						default='data/dump.txt' ,						help='The path to the data.'	)
	parser.add_argument('--tokenizer_type' ,						type=_UpperCamelCase ,						default='bert' ,						choices=['bert', 'roberta', 'gpt2']	)
	parser.add_argument('--tokenizer_name' ,						type=_UpperCamelCase ,						default='bert-base-uncased' ,						help='The tokenizer to use.'	)
	parser.add_argument('--dump_file' ,						type=_UpperCamelCase ,						default='data/dump' ,						help='The dump file prefix.'	)
	__lowerCAmelCase    :		Optional[Any]		    =   parser.parse_args()

	logger.info(F"Loading Tokenizer ({args.tokenizer_name})"	)
	if args.tokenizer_type == "bert":
		__lowerCAmelCase    :		str		    =   BertTokenizer.from_pretrained(args.tokenizer_name	)
		__lowerCAmelCase    :		int		    =   tokenizer.special_tokens_map['cls_token']  # `[CLS]`
		__lowerCAmelCase    :		Any		    =   tokenizer.special_tokens_map['sep_token']  # `[SEP]`
	elif args.tokenizer_type == "roberta":
		__lowerCAmelCase    :		Dict		    =   RobertaTokenizer.from_pretrained(args.tokenizer_name	)
		__lowerCAmelCase    :		Optional[int]		    =   tokenizer.special_tokens_map['cls_token']  # `<s>`
		__lowerCAmelCase    :		int		    =   tokenizer.special_tokens_map['sep_token']  # `</s>`
	elif args.tokenizer_type == "gpt2":
		__lowerCAmelCase    :		Optional[Any]		    =   GPTaTokenizer.from_pretrained(args.tokenizer_name	)
		__lowerCAmelCase    :		Optional[Any]		    =   tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
		__lowerCAmelCase    :		Any		    =   tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

	logger.info(F"Loading text from {args.file_path}"	)
	with open(args.file_path ,						'r' ,						encoding='utf8'	) as fp:
		__lowerCAmelCase    :		int		    =   fp.readlines()

	logger.info('Start encoding'	)
	logger.info(F"{len(_UpperCamelCase	)} examples to process."	)

	__lowerCAmelCase    :		str		    =   []
	__lowerCAmelCase    :		Tuple		    =   0
	__lowerCAmelCase    :		Tuple		    =   1_0000
	__lowerCAmelCase    :		Any		    =   time.time()
	for text in data:
		__lowerCAmelCase    :		int		    =   F"{bos} {text.strip()} {sep}"
		__lowerCAmelCase    :		str		    =   tokenizer.encode(_UpperCamelCase ,						add_special_tokens=_UpperCamelCase	)
		rslt.append(_UpperCamelCase	)

		iter += 1
		if iter % interval == 0:
			__lowerCAmelCase    :		Optional[int]		    =   time.time()
			logger.info(F"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"	)
			__lowerCAmelCase    :		Union[str, Any]		    =   time.time()
	logger.info('Finished binarization'	)
	logger.info(F"{len(_UpperCamelCase	)} examples processed."	)

	__lowerCAmelCase    :		str		    =   F"{args.dump_file}.{args.tokenizer_name}.pickle"
	__lowerCAmelCase    :		Tuple		    =   tokenizer.vocab_size
	if vocab_size < (1 << 16):
		__lowerCAmelCase    :		Any		    =   [np.uintaa(_UpperCamelCase	) for d in rslt]
	else:
		__lowerCAmelCase    :		Dict		    =   [np.intaa(_UpperCamelCase	) for d in rslt]
	random.shuffle(rslt_	)
	logger.info(F"Dump to {dp_file}"	)
	with open(_UpperCamelCase ,						'wb'	) as handle:
		pickle.dump(rslt_ ,						_UpperCamelCase ,						protocol=pickle.HIGHEST_PROTOCOL	)


if __name__ == "__main__":
						main()

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):

	if density <= 0:
		raise ValueError('Impossible fluid density'	)
	if bulk_modulus <= 0:
		raise ValueError('Impossible bulk modulus'	)

	return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
							import doctest

							doctest.testmod()