




from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   None:
   snake_case	,					snake_case      :				Dict				=       analyze_text(__lowercase     )
   snake_case      :				List[Any]				=       list(""" """ + ascii_lowercase     )
   # what is our total sum of probabilities.
   snake_case      :				Any				=       sum(single_char_strings.values()     )

   # one length string
   snake_case      :				Optional[Any]				=       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      :				Union[str, Any]				=       single_char_strings[ch]
         snake_case      :				str				=       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      :				List[Any]				=       sum(two_char_strings.values()     )
   snake_case      :				Optional[int]				=       0
   # for each alpha (two in size) calculate entropy.
   for cha in my_alphas:
      for cha in my_alphas:
         snake_case      :				Tuple				=       cha + cha
         if sequence in two_char_strings:
            snake_case      :				Union[str, Any]				=       two_char_strings[sequence]
            snake_case      :				Dict				=       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 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   tuple[dict, dict]:
   snake_case      :				List[Any]				=       Counter()  # type: ignore
   snake_case      :				Optional[Any]				=       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 						SCREAMING_SNAKE_CASE__					(    )  ->   Optional[Any]:
   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()












'''simple docstring'''




import re

from filelock import FileLock


try:
  import nltk

  __SCREAMING_SNAKE_CASE     :Optional[int]       =				True
except (ImportError, ModuleNotFoundError):
  __SCREAMING_SNAKE_CASE     :str       =				False

if NLTK_AVAILABLE:
  with FileLock('''.lock''') as lock:
    nltk.download('''punkt''', quiet=True)
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       str:


     '''simple docstring'''



     re.sub("<n>"			,     ""			,     __lowercase       )  # remove pegasus newline char
     assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
     return "\n".join(nltk.sent_tokenize(__lowercase       )       )




