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
)
__lowercase										= logging.getLogger(__name__)
def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		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.'''     )
    __UpperCamelCase					:List[Any]        =		parser.parse_args()

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

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

    __UpperCamelCase					:List[str]        =		[]
    __UpperCamelCase					:Optional[int]        =		0
    __UpperCamelCase					:Optional[int]        =		10_000
    __UpperCamelCase					:Any        =		time.time()
    for text in data:
        __UpperCamelCase					:Optional[Any]        =		f"""{bos} {text.strip()} {sep}"""
        __UpperCamelCase					:Dict        =		tokenizer.encode(SCREAMING_SNAKE_CASE  ,      add_special_tokens=SCREAMING_SNAKE_CASE     )
        rslt.append(SCREAMING_SNAKE_CASE     )

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

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






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:int        =		ArgumentParser(
        description=(
            '''PyTorch TPU distributed training launch '''
            '''helper utility that will spawn up '''
            '''multiple distributed processes'''
        )     )

    # Optional arguments for the launch helper
    parser.add_argument('''--num_cores'''  ,      type=SCREAMING_SNAKE_CASE  ,      default=1  ,      help='''Number of TPU cores to use (1 or 8).'''     )

    # positional
    parser.add_argument(
        '''training_script'''  ,      type=SCREAMING_SNAKE_CASE  ,      help=(
            '''The full path to the single TPU training '''
            '''program/script to be launched in parallel, '''
            '''followed by all the arguments for the '''
            '''training script'''
        )  ,      )

    # rest from the training program
    parser.add_argument('''training_script_args'''  ,      nargs=SCREAMING_SNAKE_CASE     )

    return parser.parse_args()



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Tuple        =		parse_args()

    # Import training_script as a module.
    __UpperCamelCase					:Dict        =		Path(args.training_script     )
    sys.path.append(str(script_fpath.parent.resolve()     )     )
    __UpperCamelCase					:Any        =		script_fpath.stem
    __UpperCamelCase					:Union[str, Any]        =		importlib.import_module(SCREAMING_SNAKE_CASE     )

    # Patch sys.argv
    __UpperCamelCase					:Dict        =		[args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores     )]
    xmp.spawn(mod._mp_fn  ,      args=()  ,      nprocs=args.num_cores     )


if __name__ == "__main__":
    main()





