

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							:  Optional[Any]            =						logging.getLogger(__name__)







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			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=SCREAMING_SNAKE_CASE__    ,     default="data/dump.txt"    ,     help="The path to the data."   )
						parser.add_argument("--tokenizer_type"    ,     type=SCREAMING_SNAKE_CASE__    ,     default="bert"    ,     choices=["bert", "roberta", "gpt2"]   )
						parser.add_argument("--tokenizer_name"    ,     type=SCREAMING_SNAKE_CASE__    ,     default="bert-base-uncased"    ,     help="The tokenizer to use."   )
						parser.add_argument("--dump_file"    ,     type=SCREAMING_SNAKE_CASE__    ,     default="data/dump"    ,     help="The dump file prefix."   )
						SCREAMING_SNAKE_CASE_:			Any  		=	parser.parse_args()

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

						logger.info("Start encoding"   )
						logger.info(f"{len(SCREAMING_SNAKE_CASE__   )} examples to process."   )

						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[]
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	0
						SCREAMING_SNAKE_CASE_:			int  		=	1_00_00
						SCREAMING_SNAKE_CASE_:			str  		=	time.time()
						for text in data:
												SCREAMING_SNAKE_CASE_:			Dict  		=	f"{bos} {text.strip()} {sep}"
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer.encode(SCREAMING_SNAKE_CASE__    ,     add_special_tokens=SCREAMING_SNAKE_CASE__   )
												rslt.append(SCREAMING_SNAKE_CASE__   )

												iter += 1
												if iter % interval == 0:
																		SCREAMING_SNAKE_CASE_:			int  		=	time.time()
																		logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"   )
																		SCREAMING_SNAKE_CASE_:			Dict  		=	time.time()
						logger.info("Finished binarization"   )
						logger.info(f"{len(SCREAMING_SNAKE_CASE__   )} examples processed."   )

						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	f"{args.dump_file}.{args.tokenizer_name}.pickle"
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer.vocab_size
						if vocab_size < (1 << 16):
												SCREAMING_SNAKE_CASE_:			int  		=	[np.uintaa(SCREAMING_SNAKE_CASE__   ) for d in rslt]
						else:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	[np.intaa(SCREAMING_SNAKE_CASE__   ) for d in rslt]
						random.shuffle(rslt_   )
						logger.info(f"Dump to {dp_file}"   )
						with open(SCREAMING_SNAKE_CASE__    ,     "wb"   ) as handle:
												pickle.dump(rslt_    ,     SCREAMING_SNAKE_CASE__    ,     protocol=pickle.HIGHEST_PROTOCOL   )


if __name__ == "__main__":
						main()









def       A_				(   _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()






