




"""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


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


@require_torch
class _lowerCamelCase    (					unittest.TestCase						):






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		List[str]   )			->						Union[str, Any]:

         """simple docstring"""
         return FSMTTokenizer.from_pretrained(UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		List[Any]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	FSMTForConditionalGeneration.from_pretrained(UpperCamelCase   ).to(UpperCamelCase   )
         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 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		Tuple   )			->						Dict:

         """simple docstring"""
         # 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
         lowerCAmelCase__      :			Optional[int]   =	f"""facebook/wmt19-{pair}"""
         lowerCAmelCase__      :			List[str]   =	self.get_tokenizer(UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	self.get_model(UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	bleu_data[pair]["""src"""]
         lowerCAmelCase__      :			List[str]   =	bleu_data[pair]["""tgt"""]

         lowerCAmelCase__      :			Any   =	tokenizer(UpperCamelCase   ,     return_tensors="""pt"""   ,     truncation=UpperCamelCase   ,     padding="""longest"""   ).to(UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	model.generate(
             input_ids=batch.input_ids   ,     num_beams=8   ,     )
         lowerCAmelCase__      :			Optional[int]   =	tokenizer.batch_decode(
             UpperCamelCase   ,     skip_special_tokens=UpperCamelCase   ,     clean_up_tokenization_spaces=UpperCamelCase   )
         lowerCAmelCase__      :			Optional[Any]   =	calculate_bleu(UpperCamelCase   ,     UpperCamelCase   )
         print(UpperCamelCase   )
         self.assertGreaterEqual(scores["""bleu"""]   ,     UpperCamelCase   )











"""simple docstring"""


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 	lowercase_				(			__UpperCAmelCase				)						->    None:
      lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	analyze_text(__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	list(""" """ + ascii_lowercase				)
      # what is our total sum of probabilities.
      lowerCAmelCase__      :			List[str]   =	sum(single_char_strings.values()				)

      # one length string
      lowerCAmelCase__      :			List[str]   =	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:
                  lowerCAmelCase__      :			List[Any]   =	single_char_strings[ch]
                  lowerCAmelCase__      :			int   =	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
      lowerCAmelCase__      :			Tuple   =	sum(two_char_strings.values()				)
      lowerCAmelCase__      :			str   =	0
      # for each alpha (two in size) calculate entropy.
      for cha in my_alphas:
            for cha in my_alphas:
                  lowerCAmelCase__      :			Optional[int]   =	cha + cha
                  if sequence in two_char_strings:
                        lowerCAmelCase__      :			int   =	two_char_strings[sequence]
                        lowerCAmelCase__      :			str   =	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]:
      lowerCAmelCase__      :			Any   =	Counter()  # type: ignore
      lowerCAmelCase__      :			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_				(			)						->    Any:
      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()





