



'''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
)
UpperCamelCase__:     List[str]										=    logging.getLogger(__name__)


def 				snake_case_  (      )   ->     Any:

							'''simple docstring'''





							UpperCAmelCase				: Dict											=	argparse.ArgumentParser(
							    description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''						)
							parser.add_argument('''--file_path'''     ,   type=_lowerCAmelCase     ,   default='''data/dump.txt'''     ,   help='''The path to the data.'''						)
							parser.add_argument('''--tokenizer_type'''     ,   type=_lowerCAmelCase     ,   default='''bert'''     ,   choices=['''bert''', '''roberta''', '''gpt2''']						)
							parser.add_argument('''--tokenizer_name'''     ,   type=_lowerCAmelCase     ,   default='''bert-base-uncased'''     ,   help='''The tokenizer to use.'''						)
							parser.add_argument('''--dump_file'''     ,   type=_lowerCAmelCase     ,   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				: Optional[int]											=	BertTokenizer.from_pretrained(args.tokenizer_name						)
														UpperCAmelCase				: str											=	tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
														UpperCAmelCase				: List[str]											=	tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
							elif args.tokenizer_type == "roberta":
														UpperCAmelCase				: Any											=	RobertaTokenizer.from_pretrained(args.tokenizer_name						)
														UpperCAmelCase				: List[str]											=	tokenizer.special_tokens_map['''cls_token''']  # `<s>`
														UpperCAmelCase				: Dict											=	tokenizer.special_tokens_map['''sep_token''']  # `</s>`
							elif args.tokenizer_type == "gpt2":
														UpperCAmelCase				: Optional[int]											=	GPTaTokenizer.from_pretrained(args.tokenizer_name						)
														UpperCAmelCase				: Optional[int]											=	tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
														UpperCAmelCase				: 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:
														UpperCAmelCase				: Optional[Any]											=	fp.readlines()

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

							UpperCAmelCase				: List[Any]											=	[]
							UpperCAmelCase				: Any											=	0
							UpperCAmelCase				: Optional[Any]											=	10000
							UpperCAmelCase				: List[Any]											=	time.time()
							for text in data:
														UpperCAmelCase				: Union[str, Any]											=	f"""{bos} {text.strip()} {sep}"""
														UpperCAmelCase				: Union[str, Any]											=	tokenizer.encode(_lowerCAmelCase     ,   add_special_tokens=_lowerCAmelCase						)
														rslt.append(_lowerCAmelCase						)

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

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


if __name__ == "__main__":
						main()










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__:     Union[str, Any]										=    {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:     List[str]										=    [
						    "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LiltForQuestionAnswering",
						    "LiltForSequenceClassification",
						    "LiltForTokenClassification",
						    "LiltModel",
						    "LiltPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__:     Optional[Any]										=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





