




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




@require_torch
class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	int    ,   lowercase__					:	str						):
						return FSMTTokenizer.from_pretrained(lowercase__						)



				def       snake_case    (     self					:	List[str]    ,   lowercase__					:	int						):
						__lowercase				:		Union[str, Any]												=				FSMTForConditionalGeneration.from_pretrained(lowercase__						).to(lowercase__						)
						if torch_device == "cuda":
								model.half()
						return model



				@parameterized.expand(
				    [
				        ["en-ru", 2_6.0],
				        ["ru-en", 2_2.0],
				        ["en-de", 2_2.0],
				        ["de-en", 2_9.0],
				    ]						)
				@slow
				def       snake_case    (     self					:	Dict    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	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
						__lowercase				:		str												=				f'facebook/wmt19-{pair}'
						__lowercase				:		Optional[int]												=				self.get_tokenizer(lowercase__						)
						__lowercase				:		Dict												=				self.get_model(lowercase__						)

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

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







"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		if a < 0:
				raise ValueError("Input value must be a positive integer"				)
		elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
				raise TypeError("Input value must be a 'int' type"				)
		return bin(_lowerCamelCase				).count("1"				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
