



"""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


lowercase__    :  List[Any]							    =	get_tests_dir('''fixtures/test_sentencepiece.model''')

lowercase__    :  List[str]							    =	{'''target_lang''': '''fi''', '''source_lang''': '''en'''}
lowercase__    :  Optional[Any]							    =	'''>>zh<<'''
lowercase__    :  Union[str, Any]							    =	'''Helsinki-NLP/'''

if is_torch_available():
     lowercase__    :  Optional[int]							    =	'''pt'''
elif is_tf_available():
     lowercase__    :  str							    =	'''tf'''
else:
     lowercase__    :  Any							    =	'''jax'''




@require_sentencepiece
class        _UpperCAmelCase       (			__a    ,     unittest.TestCase):
 _lowerCAmelCase   :			str							=		MarianTokenizer
 _lowerCAmelCase   :			Dict							=		False
 _lowerCAmelCase   :			int							=		True

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

     snake_case_ :     int				=	MarianTokenizer.from_pretrained(self.tmpdirname				)
     tokenizer.save_pretrained(self.tmpdirname				)

 def 						_snake_case					(     self		:       Dict  ,     **lowercase_		:       List[Any]				):
     return MarianTokenizer.from_pretrained(self.tmpdirname  ,     **_A				)

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       Union[str, Any]				):
     return (
         "This is a test",
         "This is a test",
     )

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Dict				=	'''</s>'''
     snake_case_ :     Union[str, Any]				=	0

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

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     str				=	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(_A				)  ,     9				)

 def 						_snake_case					(     self		:       Optional[int]				):
     self.assertEqual(self.get_tokenizer().vocab_size  ,     9				)

 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_ :     int				=	MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de"				)
     snake_case_ :     str				=	en_de_tokenizer(['''I am a small frog''']  ,     return_tensors=_A				)
     self.assertIsInstance(_A  ,     _A				)
     snake_case_ :     str				=	[38, 121, 14, 697, 38848, 0]
     self.assertListEqual(_A  ,     batch.input_ids[0]				)

     snake_case_ :     List[Any]				=	tempfile.mkdtemp()
     en_de_tokenizer.save_pretrained(_A				)
     snake_case_ :     Dict				=	[x.name for x in Path(_A				).glob('''*'''				)]
     self.assertIn('''source.spm'''  ,     _A				)
     MarianTokenizer.from_pretrained(_A				)

 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     List[str]				=	self.get_tokenizer()

     snake_case_ :     Tuple				=	tok(
         ['''I am a small frog''' * 1000, '''I am a small frog''']  ,     padding=_A  ,     truncation=_A  ,     return_tensors=_A				)
     self.assertIsInstance(_A  ,     _A				)
     self.assertEqual(batch.input_ids.shape  ,     (2, 512)				)

 def 						_snake_case					(     self		:       Union[str, Any]				):
     snake_case_ :     Optional[int]				=	self.get_tokenizer()
     snake_case_ :     Union[str, Any]				=	tok(['''I am a tiny frog''', '''I am a small frog''']  ,     padding=_A  ,     return_tensors=_A				)
     self.assertIsInstance(_A  ,     _A				)
     self.assertEqual(batch_smaller.input_ids.shape  ,     (2, 10)				)

 @slow
 def 						_snake_case					(     self		:       Union[str, Any]				):
     snake_case_ :     Any				=	{'''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=_A  ,     model_name='''Helsinki-NLP/opus-mt-en-de'''  ,     revision='''1a8c2263da11e68e50938f97e10cd57820bd504c'''  ,     decode_kwargs={'''use_source_tokenizer''': True}  ,     )






 def 						_snake_case					(     self		:       Any				):
     snake_case_ :     List[str]				=	MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''				)

     snake_case_ :     Tuple				=	'''Tämä on testi'''
     snake_case_ :     int				=	'''This is a test'''

     snake_case_ :     str				=	[76, 7, 2047, 2]
     snake_case_ :     Optional[Any]				=	[69, 12, 11, 940, 2]

     snake_case_ :     int				=	tokenizer(_A				).input_ids
     self.assertListEqual(_A  ,     _A				)

     snake_case_ :     List[str]				=	tokenizer(text_target=_A				).input_ids
     self.assertListEqual(_A  ,     _A				)

     snake_case_ :     List[str]				=	tokenizer.decode(_A  ,     skip_special_tokens=_A				)
     self.assertEqual(_A  ,     _A				)









'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__    					=      {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''DistilBertTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DistilBertForMaskedLM''',
      '''DistilBertForMultipleChoice''',
      '''DistilBertForQuestionAnswering''',
      '''DistilBertForSequenceClassification''',
      '''DistilBertForTokenClassification''',
      '''DistilBertModel''',
      '''DistilBertPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFDistilBertForMaskedLM''',
      '''TFDistilBertForMultipleChoice''',
      '''TFDistilBertForQuestionAnswering''',
      '''TFDistilBertForSequenceClassification''',
      '''TFDistilBertForTokenClassification''',
      '''TFDistilBertMainLayer''',
      '''TFDistilBertModel''',
      '''TFDistilBertPreTrainedModel''',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''FlaxDistilBertForMaskedLM''',
      '''FlaxDistilBertForMultipleChoice''',
      '''FlaxDistilBertForQuestionAnswering''',
      '''FlaxDistilBertForSequenceClassification''',
      '''FlaxDistilBertForTokenClassification''',
      '''FlaxDistilBertModel''',
      '''FlaxDistilBertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_distilbert import (
      DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DistilBertConfig,
      DistilBertOnnxConfig,
  )
  from .tokenization_distilbert import DistilBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_distilbert_fast import DistilBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_distilbert import (
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DistilBertForMaskedLM,
        DistilBertForMultipleChoice,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
        DistilBertForTokenClassification,
        DistilBertModel,
        DistilBertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_distilbert import (
        TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDistilBertForMaskedLM,
        TFDistilBertForMultipleChoice,
        TFDistilBertForQuestionAnswering,
        TFDistilBertForSequenceClassification,
        TFDistilBertForTokenClassification,
        TFDistilBertMainLayer,
        TFDistilBertModel,
        TFDistilBertPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
        FlaxDistilBertPreTrainedModel,
    )

else:
  import sys

  UpperCamelCase__    					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
