



"""simple docstring"""






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import (
	    VersatileDiffusionDualGuidedPipeline,
	    VersatileDiffusionImageVariationPipeline,
	    VersatileDiffusionPipeline,
	    VersatileDiffusionTextToImagePipeline,
	)
else:
	from .modeling_text_unet import UNetFlatConditionModel
	from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
	from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
	from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
	from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline




"""simple docstring"""






import re

from filelock import FileLock


try:
	import nltk

	__snake_case							:     Any   =					True
except (ImportError, ModuleNotFoundError):
	__snake_case							:     Optional[int]   =					False

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


def     _lowercase       (						__snake_case			)		->     str:
			re.sub("<n>"      ,""      ,__snake_case			)  # 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(__snake_case			)			)