





'''simple docstring'''


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 	__a(SCREAMING_SNAKE_CASE_  :					str    ):



    '''simple docstring'''





    _lowerCAmelCase			,			_lowerCAmelCase				=      analyze_text(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      list(" " + ascii_lowercase    )
    # what is our total sum of probabilities.
    _lowerCAmelCase				=      sum(single_char_strings.values()    )

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

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

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

    # 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(SCREAMING_SNAKE_CASE_  :					str    ):



    '''simple docstring'''





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


def 	__a():



    '''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()









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							str         =					"speech_to_text_2"
 __lowerCamelCase						:							Union[str, Any]         =					["past_key_values"]
 __lowerCamelCase						:							Tuple         =					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}




 def __init__(	self			,     _lowerCAmelCase=10000			,     _lowerCAmelCase=6			,     _lowerCAmelCase=2048			,     _lowerCAmelCase=4			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=True			,     _lowerCAmelCase="relu"			,     _lowerCAmelCase=256			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=2			,     _lowerCAmelCase=True			,     _lowerCAmelCase=1			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1024			,     **_lowerCAmelCase			,     )    ->							Dict:
     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      d_model
     _lowerCAmelCase				=      decoder_ffn_dim
     _lowerCAmelCase				=      decoder_layers
     _lowerCAmelCase				=      decoder_attention_heads
     _lowerCAmelCase				=      dropout
     _lowerCAmelCase				=      attention_dropout
     _lowerCAmelCase				=      activation_dropout
     _lowerCAmelCase				=      activation_function
     _lowerCAmelCase				=      init_std
     _lowerCAmelCase				=      decoder_layerdrop
     _lowerCAmelCase				=      use_cache
     _lowerCAmelCase				=      decoder_layers
     _lowerCAmelCase				=      scale_embedding  # scale factor will be sqrt(d_model) if True
     _lowerCAmelCase				=      max_target_positions

     super().__init__(
         pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     decoder_start_token_id=_lowerCAmelCase			,     **_lowerCAmelCase			,     )


