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_ (						SCREAMING_SNAKE_CASE       ,     unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_	 =					MPNetTokenizer
 UpperCAmelCase_	 =					MPNetTokenizerFast
 UpperCAmelCase_	 =					True
 UpperCAmelCase_	 =					True




 def 		__snake_case						(			self					:  str):







        '''simple docstring'''



        super().setUp()

        lowerCAmelCase__     	=					[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        lowerCAmelCase__     	=					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 		__snake_case						(			self					:  List[str]			,    lowercase__					:  Tuple):







        '''simple docstring'''



        lowerCAmelCase__     	=					'UNwant\u00E9d,running'
        lowerCAmelCase__     	=					'unwanted, running'
        return input_text, output_text




 def 		__snake_case						(			self					:  int):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.tokenizer_class(self.vocab_file)

        lowerCAmelCase__     	=					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 		__snake_case						(			self					:  List[str]):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.tokenizer_class.from_pretrained('microsoft/mpnet-base')

        lowerCAmelCase__     	=					tokenizer.encode('sequence builders'			,    add_special_tokens=lowercase__)
        lowerCAmelCase__     	=					tokenizer.encode('multi-sequence build'			,    add_special_tokens=lowercase__)

        lowerCAmelCase__     	=					tokenizer.build_inputs_with_special_tokens(lowercase__)
        lowerCAmelCase__     	=					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,
)


lowerCAmelCase__							= {
    '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__							= ['DebertaV2TokenizerFast']

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= [
        '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__							= [
        '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__							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
