
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 		__magic_name__   (						snake_case,				unittest.TestCase    ):
		_lowerCAmelCase		=	MPNetTokenizer
		_lowerCAmelCase		=	MPNetTokenizerFast
		_lowerCAmelCase		=	True
		_lowerCAmelCase		=	True





		def       _A							(							self    :	int				):
							super().setUp()

							lowerCAmelCase		:					str  	=     [
							    '''[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       _A							(							self    :	int						,  lowerCamelCase__    :	Optional[int]				):
							lowerCAmelCase		:					List[Any]  	=     '''UNwant\u00E9d,running'''
							lowerCAmelCase		:					Tuple  	=     '''unwanted, running'''
							return input_text, output_text





		def       _A							(							self    :	Dict				):
							lowerCAmelCase		:					int  	=     self.tokenizer_class(self.vocab_file				)

							lowerCAmelCase		:					Dict  	=     tokenizer.tokenize('''UNwant\u00E9d,running'''				)
							self.assertListEqual(lowerCamelCase__						,  ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']				)
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__				)						,  [9, 6, 7, 1_2, 1_0, 1_1]				)





		@slow
		def       _A							(							self    :	str				):
							lowerCAmelCase		:					Optional[int]  	=     self.tokenizer_class.from_pretrained('''microsoft/mpnet-base'''				)

							lowerCAmelCase		:					List[str]  	=     tokenizer.encode('''sequence builders'''						,  add_special_tokens=lowerCamelCase__				)
							lowerCAmelCase		:					Dict  	=     tokenizer.encode('''multi-sequence build'''						,  add_special_tokens=lowerCamelCase__				)

							lowerCAmelCase		:					Dict  	=     tokenizer.build_inputs_with_special_tokens(lowerCamelCase__				)
							lowerCAmelCase		:					str  	=     tokenizer.build_inputs_with_special_tokens(lowerCamelCase__						,  lowerCamelCase__				)

							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


__SCREAMING_SNAKE_CASE			:   List[str]         =     {'configuration_vilt': ['VILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViltConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Any         =     ['ViltFeatureExtractor']
		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     ['ViltImageProcessor']
		__SCREAMING_SNAKE_CASE			:   str         =     ['ViltProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Dict         =     [
		    'VILT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViltForImageAndTextRetrieval',
		    'ViltForImagesAndTextClassification',
		    'ViltForTokenClassification',
		    'ViltForMaskedLM',
		    'ViltForQuestionAnswering',
		    'ViltLayer',
		    'ViltModel',
		    'ViltPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     _LazyModule(__name__, globals()['__file__'], _import_structure)
