




'''simple docstring'''



from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def   lowercase__(			__UpperCamelCase:					str    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						analyze_text(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Dict    					=						list(' ' + ascii_lowercase    )
      # what is our total sum of probabilities.
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						sum(single_char_strings.values()    )

      # one length string
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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:
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						single_char_strings[ch]
                  SCREAMING_SNAKE_CASE   :      Dict    					=						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
      SCREAMING_SNAKE_CASE   :      Tuple    					=						sum(two_char_strings.values()    )
      SCREAMING_SNAKE_CASE   :      int    					=						0
      # for each alpha (two in size) calculate entropy.
      for cha in my_alphas:
            for cha in my_alphas:
                  SCREAMING_SNAKE_CASE   :      Optional[int]    					=						cha + cha
                  if sequence in two_char_strings:
                        SCREAMING_SNAKE_CASE   :      Any    					=						two_char_strings[sequence]
                        SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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:					str    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						Counter()  # type: ignore
      SCREAMING_SNAKE_CASE   :      Tuple    					=						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__(			):

      """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'''
import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		CTRLTokenizer
					_UpperCamelCase							=		False
					_UpperCamelCase							=		False



					def     __snake_case						(	self     :						str)    ->							List[Any]:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								A_									= ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
								A_									= dict(zip(_lowercase      ,       range(len(_lowercase))))
								A_									= ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
								A_									= {'unk_token': '<unk>'}

								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['vocab_file'])
								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['merges_file'])
								with open(self.vocab_file      ,       'w'      ,       encoding='utf-8') as fp:
											fp.write(json.dumps(_lowercase) + '\n')
								with open(self.merges_file      ,       'w'      ,       encoding='utf-8') as fp:
											fp.write('\n'.join(_lowercase))



					def     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								kwargs.update(self.special_tokens_map)
								return CTRLTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Union[str, Any])    ->							Dict:
								A_									= 'adapt react readapt apt'
								A_									= 'adapt react readapt apt'
								return input_text, output_text



					def     __snake_case						(	self     :						Any)    ->							Any:
								A_									= CTRLTokenizer(self.vocab_file      ,       self.merges_file      ,       **self.special_tokens_map)
								A_									= 'adapt react readapt apt'
								A_									= 'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
								A_									= tokenizer.tokenize(_lowercase)
								self.assertListEqual(_lowercase      ,       _lowercase)

								A_									= tokens + [tokenizer.unk_token]

								A_									= [0, 1, 2, 4, 5, 1, 0, 3, 6]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase)      ,       _lowercase)




