

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def 			UpperCAmelCase__				(			_A      : str							):





  '''simple docstring'''







  a__,      a__  =analyze_text(_A							)
  a__  =list(''' ''' + ascii_lowercase							)
  # what is our total sum of probabilities.
  a__  =sum(single_char_strings.values()							)

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

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

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

    # 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 			UpperCAmelCase__				(			_A      : str							):





  '''simple docstring'''







  a__  =Counter()  # type: ignore
  a__  =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(_A							) - 1							):
    single_char_strings[text[i]] += 1
    two_char_strings[text[i : i + 2]] += 1
  return single_char_strings, two_char_strings




def 			UpperCAmelCase__				(			):





  '''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()



from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
