



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class lowerCAmelCase_       (   A_			):







	'''simple docstring'''




	A_       :     Union[List[PIL.Image.Image], np.ndarray]
	A_       :     Optional[List[bool]]


if is_transformers_available() and is_torch_available():
	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
	from .pipeline_alt_diffusion import AltDiffusionPipeline
	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline






from typing import TYPE_CHECKING

from ...utils import _LazyModule


_lowercase				:					List[Any]     					={"""tokenization_bert_japanese""": ["""BertJapaneseTokenizer""", """CharacterTokenizer""", """MecabTokenizer"""]}


if TYPE_CHECKING:
	from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer

else:
	import sys

	_lowercase				:					Union[str, Any]     					=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

