





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    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   Optional[int])       ->str:



   '''simple docstring'''



   return FSMTTokenizer.from_pretrained(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   int)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =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 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   int)       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =F"""facebook/wmt19-{pair}"""
   lowerCamelCase__:      str			  =self.get_tokenizer(UpperCAmelCase_)
   lowerCamelCase__:      Optional[Any]			  =self.get_model(UpperCAmelCase_)

   lowerCamelCase__:      Optional[Any]			  =bleu_data[pair]["src"]
   lowerCamelCase__:      Optional[int]			  =bleu_data[pair]["tgt"]

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





"""simple docstring"""







from bisect import bisect
from itertools import accumulate


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_       )->      Union[str, Any]:
  '''simple docstring'''







  a						:		Any      							=			sorted(zip(A_  ,   A_       )  ,   key=lambda A_       : x[0] / x[1]  ,   reverse=A_       )
  a							,		a						:		int      							=			[i[0] for i in r], [i[1] for i in r]
  a						:		Union[str, Any]      							=			list(accumulate(A_       )       )
  a						:		Optional[Any]      							=			bisect(A_  ,   A_       )
  return (
      0
      if k == 0
      else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
      if k != n
      else sum(vl[:k]       )
  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






