


'''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    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				str:
														return FSMTTokenizer.from_pretrained(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														lowerCAmelCase__			  =      FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_  ).to(lowerCamelCase_  )
														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   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Tuple:
														# 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__			  =      F"""facebook/wmt19-{pair}"""
														lowerCAmelCase__			  =      self.get_tokenizer(lowerCamelCase_  )
														lowerCAmelCase__			  =      self.get_model(lowerCamelCase_  )

														lowerCAmelCase__			  =      bleu_data[pair]['''src''']
														lowerCAmelCase__			  =      bleu_data[pair]['''tgt''']

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



'''simple docstring'''






from collections.abc import Callable

import numpy as np


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       np.array:
							lowerCAmelCase__			  =      int(np.ceil((x_end - xa) / step_size			)			)
							lowerCAmelCase__			  =      np.zeros((n + 1,)			)
							lowerCAmelCase__			  =      ya
							lowerCAmelCase__			  =      xa

							for k in range(A			):
														lowerCAmelCase__			  =      y[k] + step_size * ode_func(A					,       y[k]			)
														lowerCAmelCase__			  =      y[k] + (
														    (step_size / 2) * (ode_func(A					,       y[k]			) + ode_func(x + step_size					,       A			))
														)
														x += step_size

							return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()