





'''simple docstring'''

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    A__,     A__    =      analyze_text(_lowerCamelCase	)
    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(_lowerCamelCase	)  # 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(_lowerCamelCase	) / all_sum
                my_sec_sum += prob * math.loga(_lowerCamelCase	)

    # 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    (							_lowerCamelCase : str	):
    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(_lowerCamelCase	) - 1	):
        single_char_strings[text[i]] += 1
        two_char_strings[text[i : i + 2]] += 1
    return single_char_strings, two_char_strings





def 						UpperCamelCase    (							):
    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'''

__lowerCAmelCase							:     Dict		     ="\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase							:     Optional[Any]		     =[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase							:     Tuple		     ={
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



