

"""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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class        _lowercase					(     unittest.TestCase     ):







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,						return_dict=UpperCamelCase_       ).to(UpperCamelCase_       )
      __magic_name__     =      AutoTokenizer.from_pretrained('''google/mt5-small'''       )

      __magic_name__     =      tokenizer('''Hello there''' ,						return_tensors='''pt'''       ).input_ids
      __magic_name__     =      tokenizer('''Hi I am''' ,						return_tensors='''pt'''       ).input_ids

      __magic_name__     =      model(input_ids.to(UpperCamelCase_       ) ,						labels=labels.to(UpperCamelCase_       )       ).loss
      __magic_name__     =      -(labels.shape[-1] * loss.item())

      __magic_name__     =      -8_4.9_1_2_7
      self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )

