




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		:  int	=   get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
     _UpperCAmelCase		:  Union[str, Any]	=   json.load(f)




@require_torch
class       __lowerCAmelCase				(							unittest.TestCase):






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    Any ):
         return FSMTTokenizer.from_pretrained(_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    Tuple ):
         lowercase			:str							=  FSMTForConditionalGeneration.from_pretrained(_lowerCAmelCase ).to(_lowerCAmelCase )
         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:    Dict				,    _lowerCAmelCase:    Optional[int]				,    _lowerCAmelCase:    str ):
         # 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
         lowercase			:int							=  F"facebook/wmt19-{pair}"
         lowercase			:Any							=  self.get_tokenizer(_lowerCAmelCase )
         lowercase			:List[Any]							=  self.get_model(_lowerCAmelCase )

         lowercase			:Optional[Any]							=  bleu_data[pair]["src"]
         lowercase			:Any							=  bleu_data[pair]["tgt"]

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









import torch



def 					UpperCAmelCase__     (   ):
  if torch.cuda.is_available():
    lowercase			:Optional[int]							=  torch.cuda.device_count()
  else:
    lowercase			:Dict							=  0
  print(F"Successfully ran on {num_gpus} GPUs"      )


if __name__ == "__main__":
     main()



