





'''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


lowerCAmelCase_					:    Tuple      	=			get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
					lowerCAmelCase_					:    Dict      	=			json.load(f)
@require_torch
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					Optional[Any]					):
								return FSMTTokenizer.from_pretrained(__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					List[str]					):
								_a								= FSMTForConditionalGeneration.from_pretrained(__a					).to(__a					)
								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     :					Optional[int]					,				__a     :					Union[str, Any]					,				__a     :					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
								_a								= f'facebook/wmt19-{pair}'
								_a								= self.get_tokenizer(__a					)
								_a								= self.get_model(__a					)

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

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







'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      str						)	->	list:
				if n_term == "":
								return []
				_a								= []
				for temp in range(int(lowercase						)						):
								series.append(F'1/{temp + 1}' if series else "1"						)
				return series


if __name__ == "__main__":
					lowerCAmelCase_					:    Union[str, Any]      	=			input('Enter the last number (nth term) of the Harmonic Series')
					print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
					print(harmonic_series(nth_term))
