



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


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



@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




  '''simple docstring'''



  def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

      """simple docstring"""






      return FSMTTokenizer.from_pretrained(__lowerCamelCase				)



  def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

      """simple docstring"""






      lowerCAmelCase__  :       Optional[Any]				=  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 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

      """simple docstring"""






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

      lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''src''']
      lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''tgt''']

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       str				=  [0 for i in range(r + 1)]
				# nc0 = 1
				lowerCAmelCase__  :       Tuple				=  1
				for i in range(1 ,n + 1):
								# to compute current row from previous row.
								lowerCAmelCase__  :       Optional[int]				=  min(lowerCamelCase_ ,lowerCamelCase_)
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]

print(binomial_coefficient(n=1_0, r=5))

