





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




def _a (  )	->    str:

       '''simple docstring'''






       SCREAMING_SNAKE_CASE__       :      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."       )
       SCREAMING_SNAKE_CASE__       :      List[str]     =						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__       :      Any     =						tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
       elif args.tokenizer_type == "roberta":
              SCREAMING_SNAKE_CASE__       :      int     =						RobertaTokenizer.from_pretrained(args.tokenizer_name       )
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						tokenizer.special_tokens_map["cls_token"]  # `<s>`
              SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer.special_tokens_map["sep_token"]  # `</s>`
       elif args.tokenizer_type == "gpt2":
              SCREAMING_SNAKE_CASE__       :      str     =						GPTaTokenizer.from_pretrained(args.tokenizer_name       )
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
              SCREAMING_SNAKE_CASE__       :      Tuple     =						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__       :      Optional[Any]     =						fp.readlines()

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

       SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[]
       SCREAMING_SNAKE_CASE__       :      Optional[int]     =						0
       SCREAMING_SNAKE_CASE__       :      List[Any]     =						1_00_00
       SCREAMING_SNAKE_CASE__       :      List[Any]     =						time.time()
       for text in data:
              SCREAMING_SNAKE_CASE__       :      Any     =						f'''{bos} {text.strip()} {sep}'''
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tokenizer.encode(SCREAMING_SNAKE_CASE__			,				add_special_tokens=SCREAMING_SNAKE_CASE__       )
              rslt.append(SCREAMING_SNAKE_CASE__       )

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

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













_lowerCamelCase :    dict[tuple[int, int, int], int]  							= {}




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  int       )	->    int:

							'''simple docstring'''






							if late == 3 or absent == 2:
														return 0

							# if we have no days left, and have not failed any other rules,
							# we have a prize string
							if days == 0:
														return 1

							# No easy solution, so now we need to do the recursive calculation

							# First, check if the combination is already in the cache, and
							# if yes, return the stored value from there since we already
							# know the number of possible prize strings from this point on
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						(days, absent, late)
							if key in cache:
														return cache[key]

							# now we calculate the three possible ways that can unfold from
							# this point on, depending on our attendance today

							# 1) if we are late (but not absent), the "absent" counter stays as
							# it is, but the "late" counter increases by one
							SCREAMING_SNAKE_CASE__       :      Tuple     =						_calculate(days - 1			,				SCREAMING_SNAKE_CASE__			,				late + 1       )

							# 2) if we are absent, the "absent" counter increases by 1, and the
							# "late" counter resets to 0
							SCREAMING_SNAKE_CASE__       :      Dict     =						_calculate(days - 1			,				absent + 1			,				0       )

							# 3) if we are on time, this resets the "late" counter and keeps the
							# absent counter
							SCREAMING_SNAKE_CASE__       :      Any     =						_calculate(days - 1			,				SCREAMING_SNAKE_CASE__			,				0       )

							SCREAMING_SNAKE_CASE__       :      str     =						state_late + state_absent + state_ontime

							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						prizestrings
							return prizestrings




def _a (  SCREAMING_SNAKE_CASE__      :  int = 30       )	->    int:

							'''simple docstring'''






							return _calculate(SCREAMING_SNAKE_CASE__			,				absent=0			,				late=0       )


if __name__ == "__main__":
		print(solution())






