

'''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


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


@require_torch
class 					A__      (      unittest.TestCase			):



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[Any]:





           '''simple docstring'''
           return FSMTTokenizer.from_pretrained(UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[Any]:





           '''simple docstring'''
           A_						=      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 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      int:





           '''simple docstring'''
           A_						=      f'''facebook/wmt19-{pair}'''
           A_						=      self.get_tokenizer(UpperCamelCase__       )
           A_						=      self.get_model(UpperCamelCase__       )

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

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



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			bool:
       if num < 0:
              return False

       A_						=      num
       A_						=      0
       while num > 0:
              A_						=      rev_num * 10 + (num % 10)
              num //= 10

       return num_copy == rev_num


if __name__ == "__main__":
      import doctest

      doctest.testmod()
