





"""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 lowerCamelCase__  (    A__  ,				unittest.TestCase				):
   __UpperCAmelCase        =					MPNetTokenizer
   __UpperCAmelCase        =					MPNetTokenizerFast
   __UpperCAmelCase        =					True
   __UpperCAmelCase        =					True



   def    _UpperCAmelCase	(       self	) ->    int:







      """simple docstring"""
      super().setUp()

      lowercase    :			List[Any]  =       [
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "[PAD]",
          "[MASK]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      lowercase    :			int  =       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    _UpperCAmelCase	(       self      ,   snake_case	) ->    int:







      """simple docstring"""
      lowercase    :			List[str]  =       "UNwant\u00E9d,running"
      lowercase    :			List[str]  =       "unwanted, running"
      return input_text, output_text



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







      """simple docstring"""
      lowercase    :			str  =       self.tokenizer_class(self.vocab_file	)

      lowercase    :			Any  =       tokenizer.tokenize("""UNwant\u00E9d,running"""	)
      self.assertListEqual(snake_case      ,   ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]	)
      self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case	)      ,   [9, 6, 7, 1_2, 1_0, 1_1]	)





   @slow
   def    _UpperCAmelCase	(       self	) ->    int:







      """simple docstring"""
      lowercase    :			Union[str, Any]  =       self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""	)

      lowercase    :			Optional[int]  =       tokenizer.encode("""sequence builders"""      ,   add_special_tokens=snake_case	)
      lowercase    :			Optional[int]  =       tokenizer.encode("""multi-sequence build"""      ,   add_special_tokens=snake_case	)

      lowercase    :			Tuple  =       tokenizer.build_inputs_with_special_tokens(snake_case	)
      lowercase    :			Optional[int]  =       tokenizer.build_inputs_with_special_tokens(snake_case      ,   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_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase:	Union[str, Any]							      ={
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	Dict							      =["DebertaV2TokenizerFast"]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	Optional[Any]							      =[
       "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFDebertaV2ForMaskedLM",
       "TFDebertaV2ForQuestionAnswering",
       "TFDebertaV2ForSequenceClassification",
       "TFDebertaV2ForTokenClassification",
       "TFDebertaV2Model",
       "TFDebertaV2PreTrainedModel",
   ]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	str							      =[
       "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DebertaV2ForMaskedLM",
       "DebertaV2ForMultipleChoice",
       "DebertaV2ForQuestionAnswering",
       "DebertaV2ForSequenceClassification",
       "DebertaV2ForTokenClassification",
       "DebertaV2Model",
       "DebertaV2PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_deberta_va import (
       DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DebertaVaConfig,
       DebertaVaOnnxConfig,
   )
   from .tokenization_deberta_va import DebertaVaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta_va import (
          TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
          TFDebertaVaModel,
          TFDebertaVaPreTrainedModel,
      )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta_va import (
          DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaVaForMaskedLM,
          DebertaVaForMultipleChoice,
          DebertaVaForQuestionAnswering,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaModel,
          DebertaVaPreTrainedModel,
      )

else:
   import sys

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


