



"""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


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

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

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






@require_sentencepiece
class      UpperCAmelCase__		(      UpperCamelCase   ,unittest.TestCase     ):
  lowerCAmelCase_       :	Union[str, Any] 							=     MarianTokenizer
  lowerCAmelCase_       :	Dict 							=     False
  lowerCAmelCase_       :	str 							=     True



  def 					A_		( self  :    Optional[Any]				)			->						Optional[Any]:






         '''simple docstring'''

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

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



  def 					A_		( self  :    Optional[Any]      ,							**snake_case  :    Union[str, Any]				)			->						MarianTokenizer:






         '''simple docstring'''

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



  def 					A_		( self  :    int      ,							snake_case  :    str				)			->						int:






         '''simple docstring'''

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



  def 					A_		( self  :    List[str]				)			->						Dict:






         '''simple docstring'''

         A         =					'</s>'
         A         =					0

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



  def 					A_		( self  :    Any				)			->						int:






         '''simple docstring'''

         A         =					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(snake_case				)      ,							9				)



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






         '''simple docstring'''

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



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






         '''simple docstring'''

         A         =					MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""				)
         A         =					en_de_tokenizer(['I am a small frog']      ,							return_tensors=snake_case				)
         self.assertIsInstance(snake_case      ,							snake_case				)
         A         =					[38, 121, 14, 697, 38_848, 0]
         self.assertListEqual(snake_case      ,							batch.input_ids[0]				)

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



  def 					A_		( self  :    str				)			->						int:






         '''simple docstring'''

         A         =					self.get_tokenizer()

         A         =					tok(
             ['I am a small frog' * 1_000, 'I am a small frog']      ,							padding=snake_case      ,							truncation=snake_case      ,							return_tensors=snake_case				)
         self.assertIsInstance(snake_case      ,							snake_case				)
         self.assertEqual(batch.input_ids.shape      ,							(2, 512)				)



  def 					A_		( self  :    Any				)			->						Dict:






         '''simple docstring'''

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



  @slow
  def 					A_		( self  :    Optional[int]				)			->						Any:






         '''simple docstring'''

         A         =					{'input_ids': [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], '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=snake_case      ,							model_name='Helsinki-NLP/opus-mt-en-de'      ,							revision='1a8c2263da11e68e50938f97e10cd57820bd504c'      ,							decode_kwargs={'use_source_tokenizer': True}      ,							)



  def 					A_		( self  :    List[str]				)			->						int:






         '''simple docstring'''

         A         =					MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs'				)

         A         =					'Tämä on testi'
         A         =					'This is a test'

         A         =					[76, 7, 2_047, 2]
         A         =					[69, 12, 11, 940, 2]

         A         =					tokenizer(snake_case				).input_ids
         self.assertListEqual(snake_case      ,							snake_case				)

         A         =					tokenizer(text_target=snake_case				).input_ids
         self.assertListEqual(snake_case      ,							snake_case				)

         A         =					tokenizer.decode(snake_case      ,							skip_special_tokens=snake_case				)
         self.assertEqual(snake_case      ,							snake_case				)








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A    =			{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			['PLBartTokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'PLBartForCausalLM',
							    'PLBartForConditionalGeneration',
							    'PLBartForSequenceClassification',
							    'PLBartModel',
							    'PLBartPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_plbart import PLBartTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_plbart import (
														    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PLBartForCausalLM,
														    PLBartForConditionalGeneration,
														    PLBartForSequenceClassification,
														    PLBartModel,
														    PLBartPreTrainedModel,
														)


else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure)



