






'''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     :      List[Any]   					=get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
			_A     :      Optional[Any]   					=json.load(f)

@require_torch
class _lowercase     (    unittest.TestCase		):




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    List[Any]					):
														return FSMTTokenizer.from_pretrained(UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    Any					):
														lowerCamelCase__ :     str	  =	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:    List[str]      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    Optional[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
														lowerCamelCase__ :     int	  =	F'''facebook/wmt19-{pair}'''
														lowerCamelCase__ :     List[Any]	  =	self.get_tokenizer(UpperCamelCase__					)
														lowerCamelCase__ :     Optional[Any]	  =	self.get_model(UpperCamelCase__					)

														lowerCamelCase__ :     str	  =	bleu_data[pair]["""src"""]
														lowerCamelCase__ :     Dict	  =	bleu_data[pair]["""tgt"""]

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









'''simple docstring'''







from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_A     :      Optional[Any]   					=typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_A     :      Optional[int]   					=typing.Union[np.floataa, int, float]  # noqa: UP007





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return np.sqrt(np.sum((np.asarray(UpperCamelCase				) - np.asarray(UpperCamelCase				)) ** 2				)				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return sum((va - va) ** 2 for va, va in zip(UpperCamelCase    ,      UpperCamelCase				)				) ** (1 / 2)


if __name__ == "__main__":





			def 			SCREAMING_SNAKE_CASE_	()				->	None:
										from timeit import timeit

										print("""Without Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)
										print("""With Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)



			benchmark()

