code: 
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_    :					Tuple             ={
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
  	if not is_tokenizers_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	A_    :					Any             =["""GPT2TokenizerFast"""]
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	A_    :					List[str]             =[
  	    """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """GPT2DoubleHeadsModel""",
  	    """GPT2ForQuestionAnswering""",
  	    """GPT2ForSequenceClassification""",
  	    """GPT2ForTokenClassification""",
  	    """GPT2LMHeadModel""",
  	    """GPT2Model""",
  	    """GPT2PreTrainedModel""",
  	    """load_tf_weights_in_gpt2""",
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	A_    :					int             =[
  	    """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """TFGPT2DoubleHeadsModel""",
  	    """TFGPT2ForSequenceClassification""",
  	    """TFGPT2LMHeadModel""",
  	    """TFGPT2MainLayer""",
  	    """TFGPT2Model""",
  	    """TFGPT2PreTrainedModel""",
  	]
  
  try:
  	if not is_keras_nlp_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	A_    :					str             =["""TFGPT2Tokenizer"""]
  
  try:
  	if not is_flax_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	A_    :					List[str]             =["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
  	from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
  	from .tokenization_gpta import GPTaTokenizer
  
  	try:
  		if not is_tokenizers_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_gpta_fast import GPTaTokenizerFast
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_gpta import (
  		    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    GPTaDoubleHeadsModel,
  		    GPTaForQuestionAnswering,
  		    GPTaForSequenceClassification,
  		    GPTaForTokenClassification,
  		    GPTaLMHeadModel,
  		    GPTaModel,
  		    GPTaPreTrainedModel,
  		    load_tf_weights_in_gpta,
  		)
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_gpta import (
  		    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    TFGPTaDoubleHeadsModel,
  		    TFGPTaForSequenceClassification,
  		    TFGPTaLMHeadModel,
  		    TFGPTaMainLayer,
  		    TFGPTaModel,
  		    TFGPTaPreTrainedModel,
  		)
  
  	try:
  		if not is_keras_nlp_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_gpta_tf import TFGPTaTokenizer
  
  	try:
  		if not is_flax_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
  	import sys
  
  	A_    :					Any             =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
style_context: 
  
  
  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        lowercase_				( UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		MPNetTokenizer
  							snake_case_        =		MPNetTokenizerFast
  							snake_case_        =		True
  							snake_case_        =		True
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().setUp()
  
  													a_					=		[
  													    """[UNK]""",
  													    """[CLS]""",
  													    """[SEP]""",
  													    """[PAD]""",
  													    """[MASK]""",
  													    """want""",
  													    """##want""",
  													    """##ed""",
  													    """wa""",
  													    """un""",
  													    """runn""",
  													    """##ing""",
  													    """,""",
  													    """low""",
  													    """lowest""",
  													]
  													a_					=		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			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		"""UNwant\u00E9d,running"""
  													a_					=		"""unwanted, running"""
  													return input_text, output_text
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class(self.vocab_file		)
  
  													a_					=		tokenizer.tokenize("""UNwant\u00E9d,running"""		)
  													self.assertListEqual(_UpperCAmelCase			,    ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]		)
  													self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)			,    [9, 6, 7, 12, 10, 11]		)
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""		)
  
  													a_					=		tokenizer.encode("""sequence builders"""			,    add_special_tokens=_UpperCAmelCase		)
  													a_					=		tokenizer.encode("""multi-sequence build"""			,    add_special_tokens=_UpperCAmelCase		)
  
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		)
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  													assert encoded_sentence == [0] + text + [2]
  													assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]