#!/usr/bin/env python3

"""
Implementation of entropy of information
https://en.wikipedia.org/wiki/Entropy_(information_theory)
"""
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def calculate_prob(text: str) -> None:
 """
 This method takes path and two dict as argument
 and than calculates entropy of them.
 :param dict:
 :param dict:
 :return: Prints
 1) Entropy of information based on 1 alphabet
 2) Entropy of information based on couples of 2 alphabet
 3) print Entropy of H(X n∣Xn−1)

 Text from random books. Also, random quotes.
 >>> text = ("Behind Winston’s back the voice "
 ... "from the telescreen was still "
 ... "babbling and the overfulfilment")
 >>> calculate_prob(text)
 4.0
 6.0
 2.0

 >>> text = ("The Ministry of Truth—Minitrue, in Newspeak [Newspeak was the official"
 ... "face in elegant lettering, the three")
 >>> calculate_prob(text)
 4.0
 5.0
 1.0
 >>> 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)
 4.0
 7.0
 3.0
 """
 single_char_strings, two_char_strings = analyze_text(text)
 my_alphas = list(" " + ascii_lowercase)
 # what is our total sum of probabilities.
 all_sum = sum(single_char_strings.values())

 # one length string
 my_fir_sum = 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:
 my_str = single_char_strings[ch]
 prob = my_str / all_sum
 my_fir_sum += prob * math.log2(prob) # entropy formula.

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

 # two len string
 all_sum = sum(two_char_strings.values())
 my_sec_sum = 0
 # for each alpha (two in size) calculate entropy.
 for ch0 in my_alphas:
 for ch1 in my_alphas:
 sequence = ch0 + ch1
 if sequence in two_char_strings:
 my_str = two_char_strings[sequence]
 prob = int(my_str) / all_sum
 my_sec_sum += prob * math.log2(prob)

 # 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 analyze_text(text: str) -> tuple[dict, dict]:
 """
 Convert text input into two dicts of counts.
 The first dictionary stores the frequency of single character strings.
 The second dictionary stores the frequency of two character strings.
 """
 single_char_strings = Counter() # type: ignore
 two_char_strings = 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(text) - 1):
 single_char_strings[text[i]] += 1
 two_char_strings[text[i : i + 2]] += 1
 return single_char_strings, two_char_strings


def main():
 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()