

'''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_				  =							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=snake_case					,				default="data/dump.txt"					,				help="The path to the data."       )
  parser.add_argument("--tokenizer_type"					,				type=snake_case					,				default="bert"					,				choices=["bert", "roberta", "gpt2"]       )
  parser.add_argument("--tokenizer_name"					,				type=snake_case					,				default="bert-base-uncased"					,				help="The tokenizer to use."       )
  parser.add_argument("--dump_file"					,				type=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:List[Any]  					=    tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
    SCREAMING_SNAKE_CASE:Union[str, Any]  					=    tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
  elif args.tokenizer_type == "roberta":
    SCREAMING_SNAKE_CASE:Union[str, Any]  					=    RobertaTokenizer.from_pretrained(args.tokenizer_name       )
    SCREAMING_SNAKE_CASE:Dict  					=    tokenizer.special_tokens_map["cls_token"]  # `<s>`
    SCREAMING_SNAKE_CASE:int  					=    tokenizer.special_tokens_map["sep_token"]  # `</s>`
  elif args.tokenizer_type == "gpt2":
    SCREAMING_SNAKE_CASE:Union[str, Any]  					=    GPTaTokenizer.from_pretrained(args.tokenizer_name       )
    SCREAMING_SNAKE_CASE:Union[str, Any]  					=    tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
    SCREAMING_SNAKE_CASE:str  					=    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:Dict  					=    fp.readlines()

  logger.info("Start encoding"       )
  logger.info(F'''{len(snake_case       )} examples to process.'''       )

  SCREAMING_SNAKE_CASE:List[str]  					=    []
  SCREAMING_SNAKE_CASE:List[Any]  					=    0
  SCREAMING_SNAKE_CASE:Optional[Any]  					=    10000
  SCREAMING_SNAKE_CASE:Optional[Any]  					=    time.time()
  for text in data:
    SCREAMING_SNAKE_CASE:Tuple  					=    F'''{bos} {text.strip()} {sep}'''
    SCREAMING_SNAKE_CASE:Dict  					=    tokenizer.encode(snake_case					,				add_special_tokens=snake_case       )
    rslt.append(snake_case       )

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

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


if __name__ == "__main__":
    main()











_lowercase: Dict      			=		[
    (1000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]






def     a(      A       :    str       )		->       int:
					"""simple docstring"""





					a					     =   {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
					a					     =   0
					a					     =   0
					while place < len(A       ):
										if (place + 1 < len(A       )) and (vals[roman[place]] < vals[roman[place + 1]]):
															total += vals[roman[place + 1]] - vals[roman[place]]
															place += 2
										else:
															total += vals[roman[place]]
															place += 1
					return total






def     a(      A       :    int       )		->       str:
					"""simple docstring"""





					a					     =   []
					for arabic, roman in ROMAN:
										((a)     ,				(a))					     =   divmod(A ,     A       )
										result.append(roman * factor       )
										if number == 0:
															break
					return "".join(A       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
