






# 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 __lowerCamelCase			(				UpperCamelCase_):





	"""simple docstring"""






	@classmethod
	def 	UpperCamelCase   (							cls   ):
							"""simple docstring"""







							return cls

	@slow
	@require_torch_gpu
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_auto_remove_tmp_dir()
							_UpperCAmelCase     =      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
							_UpperCAmelCase     =      get_gpu_count()
							_UpperCAmelCase     =      F"""\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        """.split()
							_UpperCAmelCase     =      [sys.executable] + distributed_args + args
							execute_subprocess_async(__SCREAMING_SNAKE_CASE	,   env=self.get_env()   )

							_UpperCAmelCase     =      os.path.join(__SCREAMING_SNAKE_CASE	,   'test_bleu.json'   )
							_UpperCAmelCase     =      load_json(__SCREAMING_SNAKE_CASE   )
							# print(metrics)
							self.assertGreaterEqual(metrics['bleu']	,   25   )








import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir





@dataclass
class __lowerCamelCase			:





	"""simple docstring"""






	UpperCamelCase__								=				42





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece.model'   )
							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   0   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   1   )

							self.assertIn(
							    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
							    ' which is not implemented in the fast tokenizers.'	,   str(w[0].message   )	,   )
