


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def        UpperCamelCase_(   lowerCamelCase_				)					->		None:
    _lowercase :							Any   	=	analyze_text(lowerCamelCase_				)
    _lowercase :							str   	=	list(' ' + ascii_lowercase				)
    # what is our total sum of probabilities.
    _lowercase :							str   	=	sum(single_char_strings.values()				)

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






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class _lowerCamelCase(		_a     ):
       lowercase_				:			int									=    ["""image_processor""", """tokenizer"""]
       lowercase_				:			List[str]									=    """CLIPImageProcessor"""
       lowercase_				:			Union[str, Any]									=    ("""CLIPTokenizer""", """CLIPTokenizerFast""")
       def __init__(      self,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                   ' instead.',   lowerCamelCase,   )
               _lowercase :							Dict   	=	kwargs.pop('feature_extractor')

           _lowercase :							int   	=	image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError('You need to specify an `image_processor`.')
           if tokenizer is None:
               raise ValueError('You need to specify a `tokenizer`.')

           super().__init__(lowerCamelCase,   lowerCamelCase)
       def __call__(      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     Tuple:




           """simple docstring"""

           if text is None and images is None:
               raise ValueError('You have to specify either text or images. Both cannot be none.')

           if text is not None:
               _lowercase :							Optional[Any]   	=	self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)

           if images is not None:
               _lowercase :							List[Any]   	=	self.image_processor(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)

           if text is not None and images is not None:
               _lowercase :							int   	=	image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**lowerCamelCase),   tensor_type=lowerCamelCase)
       def     UpperCamelCase  (      self,   *lowerCamelCase,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           return self.tokenizer.batch_decode(*lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   *lowerCamelCase,   **lowerCamelCase) ->     int:




           """simple docstring"""

           return self.tokenizer.decode(*lowerCamelCase,   **lowerCamelCase)
       @property
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							int   	=	self.tokenizer.model_input_names
           _lowercase :							Dict   	=	self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
       @property
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           warnings.warn(
               '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.',   lowerCamelCase,   )
           return self.image_processor_class




       @property
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           warnings.warn(
               '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.',   lowerCamelCase,   )
           return self.image_processor


