



"""simple docstring"""







from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =DistilBertTokenizer
							_SCREAMING_SNAKE_CASE    =DistilBertTokenizerFast
							_SCREAMING_SNAKE_CASE    =True




							@slow
							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''    )

												a__          =  tokenizer.encode('''sequence builders'''				,		add_special_tokens=__A    )
												a__          =  tokenizer.encode('''multi-sequence build'''				,		add_special_tokens=__A    )

												a__          =  tokenizer.build_inputs_with_special_tokens(__A    )
												a__          =  tokenizer.build_inputs_with_special_tokens(__A				,		__A    )

												assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
												assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
												    tokenizer.sep_token_id
												]










"""simple docstring"""







from typing import TYPE_CHECKING

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


__a       :	str             = {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	__a       :	Dict             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


