




'''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  :				Dict									=  json.load(f)






@require_torch
class 						a				(   unittest.TestCase     ):


				def   A_ (		self    :					int       ,				lowercase_    :					int					):
									return FSMTTokenizer.from_pretrained(lowercase_					)


				def   A_ (		self    :					Tuple       ,				lowercase_    :					List[Any]					):
									snake_case_									=				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   A_ (		self    :					Optional[Any]       ,				lowercase_    :					List[Any]       ,				lowercase_    :					List[str]					):
									# 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
									snake_case_									=				F"facebook/wmt19-{pair}"
									snake_case_									=				self.get_tokenizer(lowercase_					)
									snake_case_									=				self.get_model(lowercase_					)

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

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







'''simple docstring'''

import re

from filelock import FileLock


try:
							import nltk

							a  :				Union[str, Any]									=  True
except (ImportError, ModuleNotFoundError):
							a  :				Any									=  False

if NLTK_AVAILABLE:
							with FileLock('.lock') as lock:
														nltk.download('punkt', quiet=True)


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					re.sub('''<n>''',      '''''',      __UpperCAmelCase	)  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(__UpperCAmelCase	)	)

