




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

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class        snake_case__				(a_  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				List[Any]									=   SqueezeBertTokenizer
				SCREAMING_SNAKE_CASE_			:				Tuple									=   SqueezeBertTokenizerFast
				SCREAMING_SNAKE_CASE_			:				str									=   True





				def 				__UpperCAmelCase				( self							:	Tuple   , **__lowerCamelCase							:	List[str]				)   ->	List[Any]:
							return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname   , **_a				)





				@slow
				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	List[str]:
							a       =     SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless"				)

							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
							]










from typing import TYPE_CHECKING

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


_snake_case				=			{
    "configuration_oneformer": ["ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "OneFormerConfig"],
    "processing_oneformer": ["OneFormerProcessor"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			["OneFormerImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "OneFormerForUniversalSegmentation",
          "OneFormerModel",
          "OneFormerPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      _snake_case				=			_LazyModule(__name__, globals()["__file__"], _import_structure)



