






from typing import TYPE_CHECKING

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


lowerCamelCase			:							Optional[Any]       ={
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase			:							Tuple       =[
     '''DPRContextEncoderTokenizerFast''',
     '''DPRQuestionEncoderTokenizerFast''',
     '''DPRReaderTokenizerFast''',
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase			:							Optional[Any]       =[
     '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''DPRContextEncoder''',
     '''DPRPretrainedContextEncoder''',
     '''DPRPreTrainedModel''',
     '''DPRPretrainedQuestionEncoder''',
     '''DPRPretrainedReader''',
     '''DPRQuestionEncoder''',
     '''DPRReader''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase			:							str       =[
     '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFDPRContextEncoder''',
     '''TFDPRPretrainedContextEncoder''',
     '''TFDPRPretrainedQuestionEncoder''',
     '''TFDPRPretrainedReader''',
     '''TFDPRQuestionEncoder''',
     '''TFDPRReader''',
 ]


if TYPE_CHECKING:
 from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
 from .tokenization_dpr import (
     DPRContextEncoderTokenizer,
     DPRQuestionEncoderTokenizer,
     DPRReaderOutput,
     DPRReaderTokenizer,
 )

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_dpr_fast import (
      DPRContextEncoderTokenizerFast,
      DPRQuestionEncoderTokenizerFast,
      DPRReaderTokenizerFast,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dpr import (
      DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
      DPRContextEncoder,
      DPRPretrainedContextEncoder,
      DPRPreTrainedModel,
      DPRPretrainedQuestionEncoder,
      DPRPretrainedReader,
      DPRQuestionEncoder,
      DPRReader,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_dpr import (
      TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDPRContextEncoder,
      TFDPRPretrainedContextEncoder,
      TFDPRPretrainedQuestionEncoder,
      TFDPRPretrainedReader,
      TFDPRQuestionEncoder,
      TFDPRReader,
  )

else:
 import sys

 lowerCamelCase			:							str       =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







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     __a						(					A__ ,     unittest.TestCase ):
 _lowerCAmelCase : str 				=				MPNetTokenizer
 _lowerCAmelCase : List[str] 				=				MPNetTokenizerFast
 _lowerCAmelCase : List[Any] 				=				True
 _lowerCAmelCase : Union[str, Any] 				=				True







 def       __lowercase      (	self      :     List[Any]		):


      '''simple docstring'''






      super().setUp()

      UpperCamelCase__    :     List[Any]        =						[
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "[PAD]",
          "[MASK]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      UpperCamelCase__    :     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       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     List[str]		):


      '''simple docstring'''






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







 def       __lowercase      (	self      :     Any		):


      '''simple docstring'''






      UpperCamelCase__    :     str        =						self.tokenizer_class(self.vocab_file		)

      UpperCamelCase__    :     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      :     str		):


      '''simple docstring'''






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

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

      UpperCamelCase__    :     Tuple        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Optional[int]        =						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]