



"""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


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



@require_torch
class      A__  (    unittest.TestCase				):




	'''simple docstring'''





	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Any:

				"""simple docstring"""

				return FSMTTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						str)							->			int:

				"""simple docstring"""

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

				__lowerCAmelCase :    List[Any]				=			bleu_data[pair]["src"]
				__lowerCAmelCase :    Optional[int]				=			bleu_data[pair]["tgt"]

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




"""simple docstring"""






def      _lowercase							(    __snake_case   ,__snake_case      )		->  int:
			return int((input_a, input_a).count(1      ) != 0      )


def      _lowercase							(    )		->  None:
			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))