
from __future__ import annotations


def 						UpperCAmelCase			(	a_						)		->	bool:







				"""simple docstring"""



				return len(set(snake_case__						)						) == len(snake_case__						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





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


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



@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	FSMTForConditionalGeneration.from_pretrained(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
          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,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Any:
          # 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    :  int							=	F"""facebook/wmt19-{pair}"""
          UpperCamelCase    :  Optional[int]							=	self.get_tokenizer(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	self.get_model(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	bleu_data[pair]['src']
          UpperCamelCase    :  Tuple							=	bleu_data[pair]['tgt']

          UpperCamelCase    :  List[Any]							=	tokenizer(SCREAMING_SNAKE_CASE_,			return_tensors='pt',			truncation=SCREAMING_SNAKE_CASE_,			padding='longest'       ).to(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	model.generate(
              input_ids=batch.input_ids,			num_beams=8,			)
          UpperCamelCase    :  Tuple							=	tokenizer.batch_decode(
              SCREAMING_SNAKE_CASE_,			skip_special_tokens=SCREAMING_SNAKE_CASE_,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	calculate_bleu(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          print(SCREAMING_SNAKE_CASE_       )
          self.assertGreaterEqual(scores['bleu'],			SCREAMING_SNAKE_CASE_       )



