from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest



@require_tokenizers
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		SqueezeBertTokenizer
							_lowerCamelCase						=		SqueezeBertTokenizerFast
							_lowerCamelCase						=		True



							def        UpperCamelCase       (						self      ,				**lowercase_				):
													return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname      ,				**lowercase_				)



							@slow
							def        UpperCamelCase       (						self				):
													_snake_case						:     List[str]				=    SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless"				)

													_snake_case						:     str				=    tokenizer.encode("sequence builders"      ,				add_special_tokens=lowercase_				)
													_snake_case						:     List[Any]				=    tokenizer.encode("multi-sequence build"      ,				add_special_tokens=lowercase_				)

													_snake_case						:     Union[str, Any]				=    tokenizer.build_inputs_with_special_tokens(lowercase_				)
													_snake_case						:     Any				=    tokenizer.build_inputs_with_special_tokens(lowercase_      ,				lowercase_				)

													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
													]


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_						=					{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'HubertForCTC',
					    'HubertForSequenceClassification',
					    'HubertModel',
					    'HubertPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFHubertForCTC',
					    'TFHubertModel',
					    'TFHubertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

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


