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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 AltDiffusionPipelineOutput(BaseOutput):
    """

    Output class for Alt Diffusion pipelines.



    Args:

        images (`List[PIL.Image.Image]` or `np.ndarray`)

            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,

            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.

        nsfw_content_detected (`List[bool]`)

            List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"

            (nsfw) content, or `None` if safety checking could not be performed.

    """

    images: Union[List[PIL.Image.Image], np.ndarray]
    nsfw_content_detected: 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_img2img import AltDiffusionImg2ImgPipeline