




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





@require_torch
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			List[Any]						):
       return FSMTTokenizer.from_pretrained(_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			int						):
       _A     =  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			:			Union[str, Any]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			List[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
       _A     =  F'''facebook/wmt19-{pair}'''
       _A     =  self.get_tokenizer(_UpperCAmelCase						)
       _A     =  self.get_model(_UpperCAmelCase						)

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

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







"""simple docstring"""

def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   int				)       ->   int:






      '''simple docstring'''


      return int((input_a, input_a).count(1				) != 0				)






def 		_snake_case	(   )       ->   None:






      '''simple docstring'''


      assert or_gate(0							,    0				) == 0
      assert or_gate(0							,    1				) == 1
      assert or_gate(1							,    0				) == 1
      assert or_gate(1							,    1				) == 1


if __name__ == "__main__":
       print(or_gate(0, 1))
       print(or_gate(1, 0))
       print(or_gate(0, 0))
       print(or_gate(1, 1))

