




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
    from .autoencoder_kl import AutoencoderKL
    from .controlnet import ControlNetModel
    from .dual_transformer_ad import DualTransformeraDModel
    from .modeling_utils import ModelMixin
    from .prior_transformer import PriorTransformer
    from .ta_film_transformer import TaFilmDecoder
    from .transformer_ad import TransformeraDModel
    from .unet_ad import UNetaDModel
    from .unet_ad import UNetaDModel
    from .unet_ad_condition import UNetaDConditionModel
    from .unet_ad_condition import UNetaDConditionModel
    from .vq_model import VQModel

if is_flax_available():
    from .controlnet_flax import FlaxControlNetModel
    from .unet_ad_condition_flax import FlaxUNetaDConditionModel
    from .vae_flax import FlaxAutoencoderKL












'''simple docstring'''

import re

from filelock import FileLock


try:
							import nltk

							a  :				Union[str, Any]									=  True
except (ImportError, ModuleNotFoundError):
							a  :				Any									=  False

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


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					re.sub('''<n>''',      '''''',      __UpperCAmelCase	)  # 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(__UpperCAmelCase	)	)

