






'''simple docstring'''

import argparse


A:      List[Any]  				=				"docs/source/_static/js/custom.js"






def 	_UpperCAmelCase    (  a		:	Optional[Any]							)			->			Optional[Any]:





						"""simple docstring"""
						with open(a						,			encoding='utf-8'						,			newline='\n'							) as f:
												lowercase_      :   List[Any]        =			f.readlines()
						lowercase_      :   Dict        =			0

						# First let's put the right version
						while not lines[index].startswith('const stableVersion ='							):
												index += 1
						lowercase_      :   Dict        =			f"const stableVersion = \"v{version}\"\n"

						# Then update the dictionary
						while not lines[index].startswith('const versionMapping = {'							):
												index += 1

						# We go until the end
						while not lines[index].startswith('}'							):
												index += 1
						# We add the new version at the end
						lines[index - 1] += f"    \"v{version}\": \"v{version}\",\n"

						with open(a						,			'w'						,			encoding='utf-8'						,			newline='\n'							) as f:
												f.writelines(a							)


if __name__ == "__main__":
						A:      str  				=				argparse.ArgumentParser()
						parser.add_argument("--version", help="Release version.")
						A:      List[str]  				=				parser.parse_args()
						update_custom_js(args.version)








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






@require_torch
class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Tuple:
							return FSMTTokenizer.from_pretrained(_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Optional[int]:
							lowercase_      :   str        =			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        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  Optional[int]:
							# 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
							lowercase_      :   Optional[Any]        =			f"facebook/wmt19-{pair}"
							lowercase_      :   str        =			self.get_tokenizer(_lowercase       )
							lowercase_      :   Any        =			self.get_model(_lowercase       )

							lowercase_      :   Any        =			bleu_data[pair]['src']
							lowercase_      :   Any        =			bleu_data[pair]['tgt']

							lowercase_      :   Dict        =			tokenizer(_lowercase  ,      return_tensors='pt'  ,      truncation=_lowercase  ,      padding='longest'       ).to(_lowercase       )
							lowercase_      :   str        =			model.generate(
							    input_ids=batch.input_ids  ,      num_beams=8  ,      )
							lowercase_      :   Any        =			tokenizer.batch_decode(
							    _lowercase  ,      skip_special_tokens=_lowercase  ,      clean_up_tokenization_spaces=_lowercase       )
							lowercase_      :   Union[str, Any]        =			calculate_bleu(_lowercase  ,      _lowercase       )
							print(_lowercase       )
							self.assertGreaterEqual(scores['bleu']  ,      _lowercase       )
