






'''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 a_    (    unittest.TestCase							):







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Tuple:
      """simple docstring"""






      return FSMTTokenizer.from_pretrained(__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      str:
      """simple docstring"""






      __snake_case				:      str     							= 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     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      List[str]:
      """simple docstring"""






      __snake_case				:      str     							= F"""facebook/wmt19-{pair}"""
      __snake_case				:      int     							= self.get_tokenizer(__a)
      __snake_case				:      Tuple     							= self.get_model(__a)

      __snake_case				:      Tuple     							= bleu_data[pair]['src']
      __snake_case				:      Union[str, Any]     							= bleu_data[pair]['tgt']

      __snake_case				:      List[str]     							= tokenizer(__a					,   return_tensors='pt'					,   truncation=__a					,   padding='longest').to(__a)
      __snake_case				:      Dict     							= model.generate(
          input_ids=batch.input_ids					,   num_beams=8					,   )
      __snake_case				:      Any     							= tokenizer.batch_decode(
          __a					,   skip_special_tokens=__a					,   clean_up_tokenization_spaces=__a)
      __snake_case				:      Tuple     							= calculate_bleu(__a					,   __a)
      print(__a)
      self.assertGreaterEqual(scores['bleu']					,   __a)







'''simple docstring'''






from functools import lru_cache




@lru_cache
def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   if num < 0:
      raise ValueError('Number should not be negative.'  )

   return 1 if num in (0, 1) else num * factorial(num - 1  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()