
"""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__      :	List[str]          =    json.load(f)







@require_torch
class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Dict:
          return FSMTTokenizer.from_pretrained(__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Optional[int]:
          UpperCAmelCase__							: Tuple 							=						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  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Optional[int]:
          # 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__							: Optional[Any] 							=						F"facebook/wmt19-{pair}"
          UpperCAmelCase__							: List[Any] 							=						self.get_tokenizer(__UpperCamelCase  )
          UpperCAmelCase__							: str 							=						self.get_model(__UpperCamelCase  )

          UpperCAmelCase__							: Union[str, Any] 							=						bleu_data[pair]["src"]
          UpperCAmelCase__							: List[str] 							=						bleu_data[pair]["tgt"]

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





"""simple docstring"""




from timeit import timeit



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if number < 0:
          raise ValueError("the value of input must not be negative"		)
     UpperCAmelCase__							: Tuple 							=						0
     while number:
          number &= number - 1
          result += 1
     return result



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if number < 0:
          raise ValueError("the value of input must not be negative"		)
     UpperCAmelCase__							: Union[str, Any] 							=						0
     while number:
          if number % 2 == 1:
               result += 1
          number >>= 1
     return result



def 							a__ (				):






     '''simple docstring'''

     def do_benchmark(lowerCAmelCase	:       int		) -> None:
          UpperCAmelCase__							: Dict 							=						"import __main__ as z"
          print(F"Benchmark when {number = }:"		)
          print(F"{get_set_bits_count_using_modulo_operator(lowerCAmelCase		) = }"		)
          UpperCAmelCase__							: Tuple 							=						timeit("z.get_set_bits_count_using_modulo_operator(25)"       ,    setup=lowerCAmelCase		)
          print(F"timeit() runs in {timing} seconds"		)
          print(F"{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase		) = }"		)
          UpperCAmelCase__							: Any 							=						timeit(
              "z.get_set_bits_count_using_brian_kernighans_algorithm(25)"       ,    setup=lowerCAmelCase       ,    )
          print(F"timeit() runs in {timing} seconds"		)

     for number in (25, 37, 58, 0):
          do_benchmark(lowerCAmelCase		)
          print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()



