






'''simple docstring'''

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
     from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__           =  get_tests_dir('fixtures/test_sentencepiece.model')

SCREAMING_SNAKE_CASE__           =  {'target_lang': 'fi', 'source_lang': 'en'}
SCREAMING_SNAKE_CASE__           =  '>>zh<<'
SCREAMING_SNAKE_CASE__           =  'Helsinki-NLP/'

if is_torch_available():
     SCREAMING_SNAKE_CASE__           =  'pt'
elif is_tf_available():
     SCREAMING_SNAKE_CASE__           =  'tf'
else:
     SCREAMING_SNAKE_CASE__           =  'jax'






@require_sentencepiece
class 							a_		(     _UpperCamelCase      ,			unittest.TestCase							):
  lowercase							= MarianTokenizer
  lowercase							= False
  lowercase							= True

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



        """simple docstring"""






        super().setUp()
        UpperCamelCase							    =   ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
        UpperCamelCase							    =   dict(zip(_SCREAMING_SNAKE_CASE     ,						range(len(_SCREAMING_SNAKE_CASE ) ) ) )
        UpperCamelCase							    =   Path(self.tmpdirname )
        save_json(_SCREAMING_SNAKE_CASE     ,						save_dir / VOCAB_FILES_NAMES["""vocab"""] )
        save_json(_SCREAMING_SNAKE_CASE     ,						save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""] )
        if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
              copyfile(_SCREAMING_SNAKE_CASE     ,						save_dir / VOCAB_FILES_NAMES["""source_spm"""] )
              copyfile(_SCREAMING_SNAKE_CASE     ,						save_dir / VOCAB_FILES_NAMES["""target_spm"""] )

        UpperCamelCase							    =   MarianTokenizer.from_pretrained(self.tmpdirname )
        tokenizer.save_pretrained(self.tmpdirname )

  def     A__		( self     ,						**_SCREAMING_SNAKE_CASE )    -> MarianTokenizer:



        """simple docstring"""






        return MarianTokenizer.from_pretrained(self.tmpdirname     ,						**_SCREAMING_SNAKE_CASE )

  def     A__		( self     ,						_SCREAMING_SNAKE_CASE )    -> Optional[Any]:



        """simple docstring"""






        return (
            "This is a test",
            "This is a test",
        )

  def     A__		( self )    -> Any:



        """simple docstring"""






        UpperCamelCase							    =   """</s>"""
        UpperCamelCase							    =   0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE )     ,						_SCREAMING_SNAKE_CASE )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE )     ,						_SCREAMING_SNAKE_CASE )

  def     A__		( self )    -> Dict:



        """simple docstring"""






        UpperCamelCase							    =   list(self.get_tokenizer().get_vocab().keys() )

        self.assertEqual(vocab_keys[0]     ,						"""</s>""" )
        self.assertEqual(vocab_keys[1]     ,						"""<unk>""" )
        self.assertEqual(vocab_keys[-1]     ,						"""<pad>""" )
        self.assertEqual(len(_SCREAMING_SNAKE_CASE )     ,						9 )

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



        """simple docstring"""






        self.assertEqual(self.get_tokenizer().vocab_size     ,						9 )

  def     A__		( self )    -> List[Any]:



        """simple docstring"""






        UpperCamelCase							    =   MarianTokenizer.from_pretrained(F"{ORG_NAME}opus-mt-en-de" )
        UpperCamelCase							    =   en_de_tokenizer(["""I am a small frog"""]     ,						return_tensors=_SCREAMING_SNAKE_CASE )
        self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE )
        UpperCamelCase							    =   [38, 121, 14, 697, 38848, 0]
        self.assertListEqual(_SCREAMING_SNAKE_CASE     ,						batch.input_ids[0] )

        UpperCamelCase							    =   tempfile.mkdtemp()
        en_de_tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
        UpperCamelCase							    =   [x.name for x in Path(_SCREAMING_SNAKE_CASE ).glob("""*""" )]
        self.assertIn("""source.spm"""     ,						_SCREAMING_SNAKE_CASE )
        MarianTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )

  def     A__		( self )    -> Tuple:



        """simple docstring"""






        UpperCamelCase							    =   self.get_tokenizer()

        UpperCamelCase							    =   tok(
            ["""I am a small frog""" * 1000, """I am a small frog"""]     ,						padding=_SCREAMING_SNAKE_CASE     ,						truncation=_SCREAMING_SNAKE_CASE     ,						return_tensors=_SCREAMING_SNAKE_CASE )
        self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE )
        self.assertEqual(batch.input_ids.shape     ,						(2, 512) )

  def     A__		( self )    -> Any:



        """simple docstring"""






        UpperCamelCase							    =   self.get_tokenizer()
        UpperCamelCase							    =   tok(["""I am a tiny frog""", """I am a small frog"""]     ,						padding=_SCREAMING_SNAKE_CASE     ,						return_tensors=_SCREAMING_SNAKE_CASE )
        self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE )
        self.assertEqual(batch_smaller.input_ids.shape     ,						(2, 10) )

  @slow
  def     A__		( self )    -> int:



        """simple docstring"""






        UpperCamelCase							    =   {"""input_ids""": [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=_SCREAMING_SNAKE_CASE     ,						model_name="""Helsinki-NLP/opus-mt-en-de"""     ,						revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""     ,						decode_kwargs={"""use_source_tokenizer""": True}     ,						)


  def     A__		( self )    -> Tuple:



        """simple docstring"""






        UpperCamelCase							    =   MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs""" )

        UpperCamelCase							    =   """Tämä on testi"""
        UpperCamelCase							    =   """This is a test"""

        UpperCamelCase							    =   [76, 7, 2047, 2]
        UpperCamelCase							    =   [69, 12, 11, 940, 2]

        UpperCamelCase							    =   tokenizer(_SCREAMING_SNAKE_CASE ).input_ids
        self.assertListEqual(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE )

        UpperCamelCase							    =   tokenizer(text_target=_SCREAMING_SNAKE_CASE ).input_ids
        self.assertListEqual(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE )

        UpperCamelCase							    =   tokenizer.decode(_SCREAMING_SNAKE_CASE     ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )
        self.assertEqual(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE )






import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

a__		     =     logging.getLogger()





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					__UpperCamelCase					:	Dict             =      {}
					__UpperCamelCase					:	Dict             =      os.path.join(snake_case		,	"""all_results.json"""				)
					if os.path.exists(snake_case				):
										with open(snake_case		,	"""r"""				) as f:
															__UpperCamelCase					:	List[Any]             =      json.load(snake_case				)
					else:
										raise ValueError(F'''can\'t find {path}'''				)
					return results


a__		     =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Dict	)	->       Dict:







						"""simple docstring"""







						import xla_spawn

						__UpperCamelCase					:	Dict             =      self.get_auto_remove_tmp_dir()
						__UpperCamelCase					:	str             =      F'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

						with patch.object(lowerCAmelCase					,     """argv"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Union[str, Any]             =      time()
											xla_spawn.main()
											__UpperCamelCase					:	List[str]             =      time()

											__UpperCamelCase					:	Optional[int]             =      get_results(lowerCAmelCase	)
											self.assertGreaterEqual(result["""eval_accuracy"""]					,     0.75	)

											# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
											self.assertLess(end - start					,     500	)






	def 					lowerCamelCase__     (					self		:						int	)	->       List[Any]:







						"""simple docstring"""







						import xla_spawn

						__UpperCamelCase					:	List[Any]             =      """
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            """.split()
						with patch.object(lowerCAmelCase					,     """argv"""					,     lowerCAmelCase	):
											xla_spawn.main()






