




import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 				__snake_case     (_a  ,		unittest.TestCase						):
				lowerCAmelCase__			=   MPNetTokenizer
				lowerCAmelCase__			=   MPNetTokenizerFast
				lowerCAmelCase__			=   True
				lowerCAmelCase__			=   True



				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			Dict:





								'''simple docstring'''




								super().setUp()

								_lowerCAmelCase			:			int        =					[
								    """[UNK]""",
								    """[CLS]""",
								    """[SEP]""",
								    """[PAD]""",
								    """[MASK]""",
								    """want""",
								    """##want""",
								    """##ed""",
								    """wa""",
								    """un""",
								    """runn""",
								    """##ing""",
								    """,""",
								    """low""",
								    """lowest""",
								]
								_lowerCAmelCase			:			Tuple        =					os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["""vocab_file"""]					)
								with open(self.vocab_file						,		"""w"""						,		encoding="""utf-8"""					) as vocab_writer:
												vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						str						,		_UpperCAmelCase					:						Optional[Any]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					"""UNwant\u00E9d,running"""
								_lowerCAmelCase			:			Optional[Any]        =					"""unwanted, running"""
								return input_text, output_text



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase			:			str        =					self.tokenizer_class(self.vocab_file					)

								_lowerCAmelCase			:			Dict        =					tokenizer.tokenize("""UNwant\u00E9d,running"""					)
								self.assertListEqual(_UpperCAmelCase						,		["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]					)
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)						,		[9, 6, 7, 12, 10, 11]					)



				@slow
				def 					SCREAMING_SNAKE_CASE				(					self					:						Tuple					)     ->			int:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""					)

								_lowerCAmelCase			:			Any        =					tokenizer.encode("""sequence builders"""						,		add_special_tokens=_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tokenizer.encode("""multi-sequence build"""						,		add_special_tokens=_UpperCAmelCase					)

								_lowerCAmelCase			:			Tuple        =					tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase					)
								_lowerCAmelCase			:			str        =					tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase						,		_UpperCAmelCase					)

								assert encoded_sentence == [0] + text + [2]
								assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]







# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					@classmethod
					def 	lowercase_							(				cls					):







						'''simple docstring'''




						return cls





					@slow
					@require_torch_gpu
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.get_auto_remove_tmp_dir()
						lowerCamelCase_							:		Tuple							   =							f"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						lowerCamelCase_							:		List[Any]							   =							get_gpu_count()
						lowerCamelCase_							:		Union[str, Any]							   =							f"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
						lowerCamelCase_							:		int							   =							[sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase ,       env=self.get_env()					)

						lowerCamelCase_							:		Any							   =							os.path.join(__lowercase ,       'test_bleu.json'					)
						lowerCamelCase_							:		List[Any]							   =							load_json(__lowercase					)
						# print(metrics)
						self.assertGreaterEqual(metrics['bleu'] ,       25					)
