

"""simple docstring"""






from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase





def 	lowercase      (   __UpperCamelCase						) ->      None:
 __magic_name__   ,						__magic_name__     =      analyze_text(__UpperCamelCase						)
 __magic_name__     =      list(''' ''' + ascii_lowercase						)
 # what is our total sum of probabilities.
 __magic_name__     =      sum(single_char_strings.values()						)

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

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

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

    # 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 	lowercase      (   __UpperCamelCase						) ->      tuple[dict, dict]:
 __magic_name__     =      Counter()  # type: ignore
 __magic_name__     =      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(__UpperCamelCase						) - 1						):
  single_char_strings[text[i]] += 1
  two_char_strings[text[i : i + 2]] += 1
 return single_char_strings, two_char_strings





def 	lowercase      (   ) ->      Dict:
 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"""






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       FunnelTokenizer
     _lowerCamelCase     =       FunnelTokenizerFast
     _lowerCamelCase     =       True
     _lowerCamelCase     =       True







     def 	lowerCAmelCase__    (      self       ):
      super().setUp()

      __magic_name__     =      [
          '''<unk>''',
          '''<cls>''',
          '''<sep>''',
          '''want''',
          '''##want''',
          '''##ed''',
          '''wa''',
          '''un''',
          '''runn''',
          '''##ing''',
          ''',''',
          '''low''',
          '''lowest''',
      ]
      __magic_name__     =      os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']       )
      with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
       vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return FunnelTokenizer.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return FunnelTokenizerFast.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      '''UNwant\u00E9d,running'''
      __magic_name__     =      '''unwanted, running'''
      return input_text, output_text







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.tokenizer_class(self.vocab_file       )

      __magic_name__     =      tokenizer.tokenize('''UNwant\u00E9d,running'''       )
      self.assertListEqual(UpperCamelCase_ ,						['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']       )
      self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_       ) ,						[7, 4, 5, 10, 8, 9]       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_tokenizers(do_lower_case=UpperCamelCase_       )
      for tokenizer in tokenizers:
       __magic_name__     =      tokenizer('''UNwant\u00E9d,running'''       )
       __magic_name__     =      len(inputs['''input_ids''']       ) - 1
       self.assertListEqual(inputs['''token_type_ids'''] ,						[2] + [0] * sentence_len       )

       __magic_name__     =      tokenizer('''UNwant\u00E9d,running''' ,						'''UNwant\u00E9d,running'''       )
       self.assertListEqual(inputs['''token_type_ids'''] ,						[2] + [0] * sentence_len + [1] * sentence_len       )

