

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


@require_torch
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    def 				snake_case_    (				self						,       a):
         return FSMTTokenizer.from_pretrained(_lowerCamelCase)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Dict				=	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 				snake_case_    (				self						,       a						,       a):
         # 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__   :       List[str]				=	f"""facebook/wmt19-{pair}"""
         lowercase__   :       Union[str, Any]				=	self.get_tokenizer(_lowerCamelCase)
         lowercase__   :       List[str]				=	self.get_model(_lowerCamelCase)

         lowercase__   :       Optional[Any]				=	bleu_data[pair]['''src''']
         lowercase__   :       Dict				=	bleu_data[pair]['''tgt''']

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




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   float      ):



     '''simple docstring'''





     if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
          raise ValueError('Length must be a positive.'      )
     return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   float      ):



     '''simple docstring'''





     if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
          raise ValueError('Length must be a positive.'      )
     return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

