





from typing import TYPE_CHECKING

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


lowercase				:	Union[str, Any]      						=  {
    """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:
    lowercase				:	Optional[int]      						=  ["""BlenderbotSmallTokenizerFast"""]

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

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Union[str, Any]      						=  [
        """TFBlenderbotSmallForConditionalGeneration""",
        """TFBlenderbotSmallModel""",
        """TFBlenderbotSmallPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Any      						=  [
        """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

    lowercase				:	Optional[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









import re

from filelock import FileLock


try:
    import nltk

    lowercase				:	List[str]      						=  True
except (ImportError, ModuleNotFoundError):
    lowercase				:	Tuple      						=  False

if NLTK_AVAILABLE:
    with FileLock(""".lock""") as lock:
        nltk.download("""punkt""", quiet=True)



def A_     (		A__    )  ->		str:
       re.sub('<n>'		,				''		,				A__    )  # remove pegasus newline char
       assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
       return "\n".join(nltk.sent_tokenize(A__    )    )


