



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
)
A__  :  Union[str, Any]								=					logging.getLogger(__name__)


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     str									= 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_			:     Tuple									= parser.parse_args()

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

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

		lowerCAmelCase_			:     Optional[Any]									= []
		lowerCAmelCase_			:     List[Any]									= 0
		lowerCAmelCase_			:     Dict									= 10000
		lowerCAmelCase_			:     int									= time.time()
		for text in data:
				lowerCAmelCase_			:     Tuple									= f"""{bos} {text.strip()} {sep}"""
				lowerCAmelCase_			:     int									= tokenizer.encode(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase )
				rslt.append(__UpperCamelCase )

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

		lowerCAmelCase_			:     Tuple									= f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
		lowerCAmelCase_			:     Union[str, Any]									= tokenizer.vocab_size
		if vocab_size < (1 << 16):
				lowerCAmelCase_			:     Union[str, Any]									= [np.uintaa(__UpperCamelCase ) for d in rslt]
		else:
				lowerCAmelCase_			:     Any									= [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()













from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




