



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__)



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]