




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 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase  ,	unittest.TestCase    ):
					_lowerCamelCase						    =					MPNetTokenizer
					_lowerCamelCase						    =					MPNetTokenizerFast
					_lowerCamelCase						    =					True
					_lowerCamelCase						    =					True

					def       lowercase_				(							self   ):







						'''simple docstring'''




						super().setUp()

						lowerCamelCase_				:						Tuple				  =				[
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						lowerCamelCase_				:						Union[str, Any]				  =				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					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				'UNwant\u00E9d,running'
						lowerCamelCase_				:						List[str]				  =				'unwanted, running'
						return input_text, output_text

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				self.tokenizer_class(self.vocab_file   )

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





					@slow
					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				self.tokenizer_class.from_pretrained('microsoft/mpnet-base'   )

						lowerCamelCase_				:						Optional[Any]				  =				tokenizer.encode('sequence builders'					,			add_special_tokens=__lowercase   )
						lowerCamelCase_				:						str				  =				tokenizer.encode('multi-sequence build'					,			add_special_tokens=__lowercase   )

						lowerCamelCase_				:						int				  =				tokenizer.build_inputs_with_special_tokens(__lowercase   )
						lowerCamelCase_				:						Dict				  =				tokenizer.build_inputs_with_special_tokens(__lowercase					,			__lowercase   )

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






from typing import TYPE_CHECKING

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


UpperCamelCase__						=		{
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		['LayoutLMTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'LayoutLMForMaskedLM',
							    'LayoutLMForSequenceClassification',
							    'LayoutLMForTokenClassification',
							    'LayoutLMForQuestionAnswering',
							    'LayoutLMModel',
							    'LayoutLMPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFLayoutLMForMaskedLM',
							    'TFLayoutLMForSequenceClassification',
							    'TFLayoutLMForTokenClassification',
							    'TFLayoutLMForQuestionAnswering',
							    'TFLayoutLMMainLayer',
							    'TFLayoutLMModel',
							    'TFLayoutLMPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
							from .tokenization_layoutlm import LayoutLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlm import (
														    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMForMaskedLM,
														    LayoutLMForQuestionAnswering,
														    LayoutLMForSequenceClassification,
														    LayoutLMForTokenClassification,
														    LayoutLMModel,
														    LayoutLMPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_layoutlm import (
														    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLayoutLMForMaskedLM,
														    TFLayoutLMForQuestionAnswering,
														    TFLayoutLMForSequenceClassification,
														    TFLayoutLMForTokenClassification,
														    TFLayoutLMMainLayer,
														    TFLayoutLMModel,
														    TFLayoutLMPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase__						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
