






"""simple docstring"""







from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase

def _a			(		_snake_case							):







      """simple docstring"""






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

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

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

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

    # 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			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	Counter()  # type: ignore
      UpperCAmelCase          =	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(_snake_case							) - 1							):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings

def _a			(		):







      """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 io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_UpperCamelCase					      =  get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
  _UpperCamelCase					      =  json.load(f)







@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self     ,A ):
          return FSMTTokenizer.from_pretrained(A )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	FSMTForConditionalGeneration.from_pretrained(A ).to(A )
          if torch_device == "cuda":
                model.half()
          return model






    @parameterized.expand(
        [
            ["""en-ru""", 26.0],
            ["""ru-en""", 22.0],
            ["""en-de""", 22.0],
            ["""de-en""", 29.0],
        ] )
    @slow
    def     _UpperCamelCase							(			self     ,A     ,A ):
          # note: this test is not testing the best performance since it only evals a small batch
          # but it should be enough to detect a regression in the output quality
          UpperCAmelCase          =	F'''facebook/wmt19-{pair}'''
          UpperCAmelCase          =	self.get_tokenizer(A )
          UpperCAmelCase          =	self.get_model(A )

          UpperCAmelCase          =	bleu_data[pair]["""src"""]
          UpperCAmelCase          =	bleu_data[pair]["""tgt"""]

          UpperCAmelCase          =	tokenizer(A     ,return_tensors="""pt"""     ,truncation=A     ,padding="""longest""" ).to(A )
          UpperCAmelCase          =	model.generate(
              input_ids=batch.input_ids     ,num_beams=8     ,)
          UpperCAmelCase          =	tokenizer.batch_decode(
              A     ,skip_special_tokens=A     ,clean_up_tokenization_spaces=A )
          UpperCAmelCase          =	calculate_bleu(A     ,A )
          print(A )
          self.assertGreaterEqual(scores["""bleu"""]     ,A )



