
'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case				:   str     =				{
    """configuration_blenderbot_small""": [
        """BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlenderbotSmallConfig""",
        """BlenderbotSmallOnnxConfig""",
    ],
    """tokenization_blenderbot_small""": ["""BlenderbotSmallTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   List[Any]     =				["""BlenderbotSmallTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   List[Any]     =				[
						    """BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """BlenderbotSmallForCausalLM""",
						    """BlenderbotSmallForConditionalGeneration""",
						    """BlenderbotSmallModel""",
						    """BlenderbotSmallPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Tuple     =				[
						    """TFBlenderbotSmallForConditionalGeneration""",
						    """TFBlenderbotSmallModel""",
						    """TFBlenderbotSmallPreTrainedModel""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   List[str]     =				[
						    """FlaxBlenderbotSmallForConditionalGeneration""",
						    """FlaxBlenderbotSmallModel""",
						    """FlaxBlenderbotSmallPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_blenderbot_small import (
						    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    BlenderbotSmallConfig,
						    BlenderbotSmallOnnxConfig,
						)
						from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_blenderbot_small import (
												    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BlenderbotSmallForCausalLM,
												    BlenderbotSmallForConditionalGeneration,
												    BlenderbotSmallModel,
												    BlenderbotSmallPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_blenderbot_small import (
												    TFBlenderbotSmallForConditionalGeneration,
												    TFBlenderbotSmallModel,
												    TFBlenderbotSmallPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_blenderbot_small import (
												    FlaxBlenderbotSmallForConditionalGeneration,
												    FlaxBlenderbotSmallModel,
												    FlaxBlenderbotSmallPreTrainedModel,
												)

else:
						import sys

						_snake_case				:   Dict     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

'''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 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Dict       =					DistilBertTokenizer
		a							:					Tuple       =					DistilBertTokenizerFast
		a							:					List[str]       =					True




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= DistilBertTokenizer.from_pretrained("distilbert-base-uncased"      )

								_SCREAMING_SNAKE_CASE				= tokenizer.encode("sequence builders"					,						add_special_tokens=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.encode("multi-sequence build"					,						add_special_tokens=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase					,						UpperCamelCase      )

								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
								]