



"""simple docstring"""
from typing import List, Optional, Union

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


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   ["image_processor", "tokenizer"]
    lowercase__      =   "BridgeTowerImageProcessor"
    lowercase__      =   ("RobertaTokenizer", "RobertaTokenizerFast")

    def __init__(				self			:				Dict ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				str):







      """simple docstring"""







      super().__init__(lowerCAmelCase_ ,  lowerCAmelCase_)

    def __call__(				self			:				str ,  lowerCAmelCase_			:				Optional[int] ,  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_			:				Optional[bool] = None ,  lowerCAmelCase_			:				bool = False ,  lowerCAmelCase_			:				bool = False ,  lowerCAmelCase_			:				bool = False ,  lowerCAmelCase_			:				bool = False ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				Optional[Union[str, TensorType]] = None ,  **lowerCAmelCase_			:				str ,  ):







      """simple docstring"""







      lowercase_								= self.tokenizer(
          text=lowerCAmelCase_ ,  add_special_tokens=lowerCAmelCase_ ,  padding=lowerCAmelCase_ ,  truncation=lowerCAmelCase_ ,  max_length=lowerCAmelCase_ ,  stride=lowerCAmelCase_ ,  pad_to_multiple_of=lowerCAmelCase_ ,  return_token_type_ids=lowerCAmelCase_ ,  return_attention_mask=lowerCAmelCase_ ,  return_overflowing_tokens=lowerCAmelCase_ ,  return_special_tokens_mask=lowerCAmelCase_ ,  return_offsets_mapping=lowerCAmelCase_ ,  return_length=lowerCAmelCase_ ,  verbose=lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )
      # add pixel_values + pixel_mask
      lowercase_								= self.image_processor(
          lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_ ,  do_normalize=lowerCAmelCase_ ,  do_center_crop=lowerCAmelCase_ ,  **lowerCAmelCase_)
      encoding.update(lowerCAmelCase_)

      return encoding

    def 					_UpperCAmelCase	(				self			:				Any ,  *lowerCAmelCase_			:				Optional[int] ,  **lowerCAmelCase_			:				Dict):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Tuple ,  *lowerCAmelCase_			:				List[str] ,  **lowerCAmelCase_			:				str):







      """simple docstring"""







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




    @property
    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      lowercase_								= self.tokenizer.model_input_names
      lowercase_								= self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))






"""simple docstring"""
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						None:







  '''simple docstring'''


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

  # one length string
  lowercase_								= 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_								= single_char_strings[ch]
      lowercase_								= 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_								= sum(two_char_strings.values()							)
  lowercase_								= 0
  # for each alpha (two in size) calculate entropy.
  for cha in my_alphas:
    for cha in my_alphas:
      lowercase_								= cha + cha
      if sequence in two_char_strings:
        lowercase_								= two_char_strings[sequence]
        lowercase_								= 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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple[dict, dict]:







  '''simple docstring'''


  lowercase_								= Counter()  # type: ignore
  lowercase_								= 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 			_SCREAMING_SNAKE_CASE						() ->						str:







  '''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()

