'''simple docstring'''




from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def       A_ (			_lowerCamelCase      :				str     ):
      _lowerCAmelCase		 = analyze_text(__lowerCAmelCase     )
      _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(__lowerCAmelCase     )  # 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(__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       A_ (			_lowerCamelCase      :				str     ):
      _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(__lowerCAmelCase     ) - 1     ):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings







def       A_ (			):
      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()













from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Tuple						=					['''image_processor''', '''tokenizer''']
  lowercase_						:			str						=					'''BlipImageProcessor'''
  lowercase_						:			str						=					('''BertTokenizer''', '''BertTokenizerFast''')

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[int]					=					False
     super().__init__(lowerCamelCase__   ,				lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					self.image_processor

  def __call__(    self						:	Optional[Any]   ,				lowerCamelCase__						:	ImageInput = None   ,				lowerCamelCase__						:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Union[bool, str, PaddingStrategy] = False   ,				lowerCamelCase__						:	Union[bool, str, TruncationStrategy] = None   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	int = 0   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	Optional[bool] = None   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Optional[Union[str, TensorType]] = None   ,				**lowerCamelCase__						:	int   ,				):
     """simple docstring"""

     if images is None and text is None:
        raise ValueError("""You have to specify either images or text."""			)

     # Get only text
     if images is None:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer
        __UpperCamelCase	:			Dict					=					self.tokenizer(
            text=lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				stride=lowerCamelCase__   ,				pad_to_multiple_of=lowerCamelCase__   ,				return_attention_mask=lowerCamelCase__   ,				return_overflowing_tokens=lowerCamelCase__   ,				return_special_tokens_mask=lowerCamelCase__   ,				return_offsets_mapping=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__   ,				return_length=lowerCamelCase__   ,				verbose=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
        return text_encoding

     # add pixel_values
     __UpperCamelCase	:			str					=					self.image_processor(lowerCamelCase__   ,				return_tensors=lowerCamelCase__			)

     if text is not None:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer(
            text=lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				stride=lowerCamelCase__   ,				pad_to_multiple_of=lowerCamelCase__   ,				return_attention_mask=lowerCamelCase__   ,				return_overflowing_tokens=lowerCamelCase__   ,				return_special_tokens_mask=lowerCamelCase__   ,				return_offsets_mapping=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__   ,				return_length=lowerCamelCase__   ,				verbose=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
     else:
        __UpperCamelCase	:			Optional[Any]					=					None

     if text_encoding is not None:
        encoding_image_processor.update(lowerCamelCase__			)

     return encoding_image_processor

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	Any   ,				**lowerCamelCase__						:	List[Any]			):
     """simple docstring"""

     return self.tokenizer.batch_decode(*lowerCamelCase__   ,				**lowerCamelCase__			)

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	int   ,				**lowerCamelCase__						:	Tuple			):
     """simple docstring"""

     return self.tokenizer.decode(*lowerCamelCase__   ,				**lowerCamelCase__			)

  @property
  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.tokenizer.model_input_names
     __UpperCamelCase	:			str					=					self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)



