
'''simple docstring'''




def _lowerCAmelCase     (      _lowerCAmelCase = 1       ,    _lowerCAmelCase = 10_00    )->     int:
 __UpperCAmelCase					=   1
 __UpperCAmelCase					=   0

 for divide_by_number in range(SCREAMING_SNAKE_CASE_       ,    digit + 1    ):
  __UpperCAmelCase					=   []
  __UpperCAmelCase					=   numerator
  for _ in range(1       ,    digit + 1    ):
   if now_divide in has_been_divided:
    if longest_list_length < len(SCREAMING_SNAKE_CASE_    ):
     __UpperCAmelCase					=   len(SCREAMING_SNAKE_CASE_    )
     __UpperCAmelCase					=   divide_by_number
   else:
    has_been_divided.append(SCREAMING_SNAKE_CASE_    )
    __UpperCAmelCase					=   now_divide * 10 % divide_by_number

 return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()












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






@require_torch
class UpperCAmelCase							(		unittest.TestCase							):



    def 					__lowerCamelCase						(  self     ,			__A					):
     return FSMTTokenizer.from_pretrained(__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   FSMTForConditionalGeneration.from_pretrained(__A					).to(__A					)
     if torch_device == "cuda":
      model.half()
     return model




    @parameterized.expand(
        [
            ['en-ru', 2_6.0],
            ['ru-en', 2_2.0],
            ['en-de', 2_2.0],
            ['de-en', 2_9.0],
        ]					)
    @slow
    def 					__lowerCamelCase						(  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
     __UpperCAmelCase					=   f'facebook/wmt19-{pair}'
     __UpperCAmelCase					=   self.get_tokenizer(__A					)
     __UpperCAmelCase					=   self.get_model(__A					)

     __UpperCAmelCase					=   bleu_data[pair]['src']
     __UpperCAmelCase					=   bleu_data[pair]['tgt']

     __UpperCAmelCase					=   tokenizer(__A     ,			return_tensors='pt'     ,			truncation=__A     ,			padding='longest'					).to(__A					)
     __UpperCAmelCase					=   model.generate(
         input_ids=batch.input_ids     ,			num_beams=8     ,			)
     __UpperCAmelCase					=   tokenizer.batch_decode(
         __A     ,			skip_special_tokens=__A     ,			clean_up_tokenization_spaces=__A					)
     __UpperCAmelCase					=   calculate_bleu(__A     ,			__A					)
     print(__A					)
     self.assertGreaterEqual(scores['bleu']     ,			__A					)





