
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 	a_		(	__lowercase       :     str			)			->  None:
			_snake_case     , _snake_case          =      analyze_text(__lowercase			)
			_snake_case          =      list(' ' + ascii_lowercase			)
			# what is our total sum of probabilities.
			_snake_case          =      sum(single_char_strings.values()			)

			# one length string
			_snake_case          =      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:
									_snake_case          =      single_char_strings[ch]
									_snake_case          =      my_str / all_sum
									my_fir_sum += prob * math.loga(__lowercase			)  # entropy formula.

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

			# two len string
			_snake_case          =      sum(two_char_strings.values()			)
			_snake_case          =      0
			# for each alpha (two in size) calculate entropy.
			for cha in my_alphas:
						for cha in my_alphas:
									_snake_case          =      cha + cha
									if sequence in two_char_strings:
												_snake_case          =      two_char_strings[sequence]
												_snake_case          =      int(__lowercase			) / all_sum
												my_sec_sum += prob * math.loga(__lowercase			)

    # 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 	a_		(	__lowercase       :     str			)			->  tuple[dict, dict]:
			_snake_case          =      Counter()  # type: ignore
			_snake_case          =      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(__lowercase			) - 1			):
						single_char_strings[text[i]] += 1
						two_char_strings[text[i : i + 2]] += 1
			return single_char_strings, two_char_strings






def 	a_		(	)			->  str:
			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()

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	:    Tuple          =      logging.getLogger(__name__)






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

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

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

			_snake_case          =      []
			_snake_case          =      0
			_snake_case          =      10_000
			_snake_case          =      time.time()
			for text in data:
						_snake_case          =      f'''{bos} {text.strip()} {sep}'''
						_snake_case          =      tokenizer.encode(__lowercase	, add_special_tokens=__lowercase			)
						rslt.append(__lowercase			)

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

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


if __name__ == "__main__":
			main()