
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


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






@require_torch
class     UpperCAmelCase						(		unittest.TestCase					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]       ,		snake_case__						:   int					)						->      Dict:


          '''simple docstring'''


          return FSMTTokenizer.from_pretrained(__lowerCAmelCase					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   Union[str, Any]					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  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 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   List[Any]       ,		snake_case__						:   List[Any]					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= f"""facebook/wmt19-{pair}"""
          snake_case			:  Any     							= self.get_tokenizer(__lowerCAmelCase					)
          snake_case			:  List[str]     							= self.get_model(__lowerCAmelCase					)

          snake_case			:  Dict     							= bleu_data[pair]["src"]
          snake_case			:  Union[str, Any]     							= bleu_data[pair]["tgt"]

          snake_case			:  Dict     							= tokenizer(__lowerCAmelCase       ,		return_tensors="pt"       ,		truncation=__lowerCAmelCase       ,		padding="longest"					).to(__lowerCAmelCase					)
          snake_case			:  int     							= model.generate(
              input_ids=batch.input_ids       ,		num_beams=8       ,		)
          snake_case			:  int     							= tokenizer.batch_decode(
              __lowerCAmelCase       ,		skip_special_tokens=__lowerCAmelCase       ,		clean_up_tokenization_spaces=__lowerCAmelCase					)
          snake_case			:  Tuple     							= calculate_bleu(__lowerCAmelCase       ,		__lowerCAmelCase					)
          print(__lowerCAmelCase					)
          self.assertGreaterEqual(scores["bleu"]       ,		__lowerCAmelCase					)












def   __lowerCamelCase   (				__a  :str       )						->							bool:






		"""simple docstring"""

		A__     =  0
		for ch in input_str:
				A__     =  ord(__a       )
				A__     =  pow(2       ,	__a       )

				# If we already turned on bit for current character's unicode
				if bitmap >> ch_unicode & 1 == 1:
						return False
				bitmap |= ch_bit_index_on
		return True


if __name__ == "__main__":
					import doctest

					doctest.testmod()



