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


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






@require_torch
class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		Union[str, Any]	):

											'''simple docstring'''
											return FSMTTokenizer.from_pretrained(__lowercase	)





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		List[Any]	):

											'''simple docstring'''
											__a       =		FSMTForConditionalGeneration.from_pretrained(__lowercase	).to(__lowercase	)
											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 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		List[str]      ,				__lowercase		:		Tuple	):

											'''simple docstring'''
											__a       =		F"facebook/wmt19-{pair}"
											__a       =		self.get_tokenizer(__lowercase	)
											__a       =		self.get_model(__lowercase	)

											__a       =		bleu_data[pair]["""src"""]
											__a       =		bleu_data[pair]["""tgt"""]

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





def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Tuple=False    ):
				"""simple docstring"""







				if isinstance(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ) and isinstance(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ):
								__a       =		len(set_a.intersection(_SCREAMING_SNAKE_CASE    )    )

								if alternative_union:
												__a       =		len(_SCREAMING_SNAKE_CASE    ) + len(_SCREAMING_SNAKE_CASE    )
								else:
												__a       =		len(set_a.union(_SCREAMING_SNAKE_CASE    )    )

								return intersection / union

				if isinstance(_SCREAMING_SNAKE_CASE				,     (list, tuple)    ) and isinstance(_SCREAMING_SNAKE_CASE				,     (list, tuple)    ):
								__a       =		[element for element in set_a if element in set_b]

								if alternative_union:
												__a       =		len(_SCREAMING_SNAKE_CASE    ) + len(_SCREAMING_SNAKE_CASE    )
												return len(_SCREAMING_SNAKE_CASE    ) / union
								else:
												__a       =		set_a + [element for element in set_b if element not in set_a]
												return len(_SCREAMING_SNAKE_CASE    ) / len(_SCREAMING_SNAKE_CASE    )

								return len(_SCREAMING_SNAKE_CASE    ) / len(_SCREAMING_SNAKE_CASE    )
				return None


if __name__ == "__main__":
						lowerCamelCase__			= {"""a""", """b""", """c""", """d""", """e"""}
						lowerCamelCase__			= {"""c""", """d""", """e""", """f""", """h""", """i"""}
						print(jaccard_similarity(set_a, set_b))
