"""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
)
_a      :		Any           =   logging.getLogger(__name__)





def 							SCREAMING_SNAKE_CASE     (			)       ->      Union[str, Any]:
  _lowerCAmelCase						:       List[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=_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."""	)
  _lowerCAmelCase						:       int  			=     parser.parse_args()

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

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

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

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

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

import fire
import torch
from tqdm import tqdm

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				str = "cpu"    ,   lowerCAmelCase__      :				Union[str, None] = None						)						->   None:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= torch.load(lowerCAmelCase__    ,   map_location=lowerCAmelCase__						)
  for k, v in tqdm(state_dict.items()						):
    if not isinstance(lowerCAmelCase__    ,   torch.Tensor						):
      raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'						)
    lowerCAmelCase_   :				str								= v.half()
  if save_path is None:  # overwrite src_path
    lowerCAmelCase_   :				Dict								= src_path
  torch.save(lowerCAmelCase__    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     fire.Fire(convert)





