





"""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


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 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   List[str]							)     ->				List[str]:




										'''simple docstring'''



										return FSMTTokenizer.from_pretrained(lowerCamelCase_							)






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_							).to(lowerCamelCase_							)
										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		:   Tuple,			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   Dict							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : str         =   f"""facebook/wmt19-{pair}"""
										_UpperCamelCase      : Tuple         =   self.get_tokenizer(lowerCamelCase_							)
										_UpperCamelCase      : str         =   self.get_model(lowerCamelCase_							)

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

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













"""simple docstring"""




UpperCamelCase_									=[
    """DownloadConfig""",
    """DownloadManager""",
    """DownloadMode""",
    """StreamingDownloadManager""",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager



