

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      ):
	UpperCAmelCase__ =      MPNetTokenizer
	UpperCAmelCase__ =      MPNetTokenizerFast
	UpperCAmelCase__ =      True
	UpperCAmelCase__ =      True
	def 					_lowercase    (   self		):






				'''simple docstring'''







				super().setUp()

				UpperCAmelCase       		=    [
				    '''[UNK]''',
				    '''[CLS]''',
				    '''[SEP]''',
				    '''[PAD]''',
				    '''[MASK]''',
				    '''want''',
				    '''##want''',
				    '''##ed''',
				    '''wa''',
				    '''un''',
				    '''runn''',
				    '''##ing''',
				    ''',''',
				    '''low''',
				    '''lowest''',
				]
				UpperCAmelCase       		=    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							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    '''UNwant\u00E9d,running'''
				UpperCAmelCase       		=    '''unwanted, running'''
				return input_text, output_text
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.tokenizer_class(self.vocab_file		)

				UpperCAmelCase       		=    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, 1_2, 1_0, 1_1]		)







	@slow
	def 					_lowercase    (   self		):






				'''simple docstring'''







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

				UpperCAmelCase       		=    tokenizer.encode('''sequence builders'''							, add_special_tokens=SCREAMING_SNAKE_CASE_		)
				UpperCAmelCase       		=    tokenizer.encode('''multi-sequence build'''							, add_special_tokens=SCREAMING_SNAKE_CASE_		)

				UpperCAmelCase       		=    tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_		)
				UpperCAmelCase       		=    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]










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__A    :   Tuple          =       {
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   List[str]          =       [
				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "MobileViTV2ForImageClassification",
				    "MobileViTV2ForSemanticSegmentation",
				    "MobileViTV2Model",
				    "MobileViTV2PreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				__A    :   Tuple          =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



