



'''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
)
_lowerCamelCase	:       Union[str, Any]      	=	logging.getLogger(__name__)







def 				__lowerCamelCase				(   )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  argparse.ArgumentParser(
       description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'    )
   parser.add_argument('--file_path'    ,						type=A__    ,						default='data/dump.txt'    ,						help='The path to the data.'    )
   parser.add_argument('--tokenizer_type'    ,						type=A__    ,						default='bert'    ,						choices=['bert', 'roberta', 'gpt2']    )
   parser.add_argument('--tokenizer_name'    ,						type=A__    ,						default='bert-base-uncased'    ,						help='The tokenizer to use.'    )
   parser.add_argument('--dump_file'    ,						type=A__    ,						default='data/dump'    ,						help='The dump file prefix.'    )
   UpperCamelCase					=  parser.parse_args()

   logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""    )
   if args.tokenizer_type == "bert":
      UpperCamelCase					=  BertTokenizer.from_pretrained(args.tokenizer_name    )
      UpperCamelCase					=  tokenizer.special_tokens_map['cls_token']  # `[CLS]`
      UpperCamelCase					=  tokenizer.special_tokens_map['sep_token']  # `[SEP]`
   elif args.tokenizer_type == "roberta":
      UpperCamelCase					=  RobertaTokenizer.from_pretrained(args.tokenizer_name    )
      UpperCamelCase					=  tokenizer.special_tokens_map['cls_token']  # `<s>`
      UpperCamelCase					=  tokenizer.special_tokens_map['sep_token']  # `</s>`
   elif args.tokenizer_type == "gpt2":
      UpperCamelCase					=  GPTaTokenizer.from_pretrained(args.tokenizer_name    )
      UpperCamelCase					=  tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
      UpperCamelCase					=  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					=  fp.readlines()

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

   UpperCamelCase					=  []
   UpperCamelCase					=  0
   UpperCamelCase					=  10_000
   UpperCamelCase					=  time.time()
   for text in data:
      UpperCamelCase					=  F"""{bos} {text.strip()} {sep}"""
      UpperCamelCase					=  tokenizer.encode(A__    ,						add_special_tokens=A__    )
      rslt.append(A__    )

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

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


if __name__ == "__main__":
   main()






'''simple docstring'''





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					analyze_text(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[int]							      =					list(""" """ + ascii_lowercase			)
    # what is our total sum of probabilities.
    lowerCAmelCase__ :  List[Any]							      =					sum(single_char_strings.values()			)

    # one length string
    lowerCAmelCase__ :  Optional[int]							      =					0
    # for each alpha we go in our dict and if it is in it we calculate entropy
    for ch in my_alphas:
        if ch in single_char_strings:
            lowerCAmelCase__ :  List[Any]							      =					single_char_strings[ch]
            lowerCAmelCase__ :  List[Any]							      =					my_str / all_sum
            my_fir_sum += prob * math.loga(UpperCamelCase			)  # entropy formula.

    # print entropy
    print(f"""{round(-1 * my_fir_sum			):.1f}"""			)

    # two len string
    lowerCAmelCase__ :  Dict							      =					sum(two_char_strings.values()			)
    lowerCAmelCase__ :  int							      =					0
    # for each alpha (two in size) calculate entropy.
    for cha in my_alphas:
        for cha in my_alphas:
            lowerCAmelCase__ :  Union[str, Any]							      =					cha + cha
            if sequence in two_char_strings:
                lowerCAmelCase__ :  Dict							      =					two_char_strings[sequence]
                lowerCAmelCase__ :  Tuple							      =					int(UpperCamelCase			) / all_sum
                my_sec_sum += prob * math.loga(UpperCamelCase			)

    # print second entropy
    print(f"""{round(-1 * my_sec_sum			):.1f}"""			)

    # print the difference between them
    print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)			):.1f}"""			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[Any]							      =					Counter()  # type: ignore
    lowerCAmelCase__ :  Tuple							      =					Counter()  # type: ignore
    single_char_strings[text[-1]] += 1

    # first case when we have space at start.
    two_char_strings[" " + text[0]] += 1
    for i in range(0							,    len(UpperCamelCase			) - 1			):
        single_char_strings[text[i]] += 1
        two_char_strings[text[i : i + 2]] += 1
    return single_char_strings, two_char_strings
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    import doctest

    doctest.testmod()
    # text = (
    #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
    #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
    #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
    #     "beloved evident through no service elderly is. Blind there if every no so "
    #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
    #     "of message cottage windows do besides against uncivil.  Delightful "
    #     "unreserved impossible few estimating men favourable see entreaties. She "
    #     "propriety immediate was improving. He or entrance humoured likewise "
    #     "moderate. Much nor game son say feel. Fat make met can must form into "
    #     "gate. Me we offending prevailed discovery. "
    # )

    # calculate_prob(text)


if __name__ == "__main__":
 main()


