


'''simple docstring'''



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        UpperCAmelCase_  (_UpperCAmelCase ,		unittest.TestCase  ):





				"""simple docstring"""






				lowerCamelCase  : List[Any]												=      MPNetTokenizer
				lowerCamelCase  : Tuple												=      MPNetTokenizerFast
				lowerCamelCase  : Optional[int]												=      True
				lowerCamelCase  : str												=      True







				def 				lowercase_    (							self     )    ->					Any:
									super().setUp()

									__lowerCamelCase       :     Any        =		[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    '[PAD]',
									    '[MASK]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									__lowerCamelCase       :     List[str]        =		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 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:
									__lowerCamelCase       :     Optional[Any]        =		'UNwant\u00E9d,running'
									__lowerCamelCase       :     str        =		'unwanted, running'
									return input_text, output_text







				def 				lowercase_    (							self     )    ->					Optional[int]:
									__lowerCamelCase       :     Optional[int]        =		self.tokenizer_class(self.vocab_file     )

									__lowerCamelCase       :     List[Any]        =		tokenizer.tokenize('UNwant\u00E9d,running'     )
									self.assertListEqual(SCREAMING_SNAKE_CASE_      ,					['un', '##want', '##ed', ',', 'runn', '##ing']     )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_     )      ,					[9, 6, 7, 12, 10, 11]     )







				@slow
				def 				lowercase_    (							self     )    ->					Any:
									__lowerCamelCase       :     Any        =		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'     )

									__lowerCamelCase       :     Any        =		tokenizer.encode('sequence builders'      ,					add_special_tokens=SCREAMING_SNAKE_CASE_     )
									__lowerCamelCase       :     Optional[int]        =		tokenizer.encode('multi-sequence build'      ,					add_special_tokens=SCREAMING_SNAKE_CASE_     )

									__lowerCamelCase       :     List[Any]        =		tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_     )
									__lowerCamelCase       :     Optional[Any]        =		tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )

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










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__		: str												=  {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: List[str]												=  [
          """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """HubertForCTC""",
          """HubertForSequenceClassification""",
          """HubertModel""",
          """HubertPreTrainedModel""",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: Optional[int]												=  [
          """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFHubertForCTC""",
          """TFHubertModel""",
          """TFHubertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_hubert import (
                HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                HubertForCTC,
                HubertForSequenceClassification,
                HubertModel,
                HubertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_hubert import (
                TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFHubertForCTC,
                TFHubertModel,
                TFHubertPreTrainedModel,
            )


else:
      import sys

      A__		: Optional[int]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






