import inspect
from typing import Optional, Tuple, Union

import torch

from ...models import UNet2DModel, VQModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import DDIMScheduler


class LDMPipeline(DiffusionPipeline):
    r"""
    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    Parameters:
        vqvae ([`VQModel`]):
            Vector-quantized (VQ) Model to encode and decode images to and from latent representations.
        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            [`DDIMScheduler`] is to be used in combination with `unet` to denoise the encoded image latens.
    """

    def __init__(self, vqvae: VQModel, unet: UNet2DModel, scheduler: DDIMScheduler):
        super().__init__()
        self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler)

    @torch.no_grad()
    def __call__(
        self,
        batch_size: int = 1,
        generator: Optional[torch.Generator] = None,
        eta: float = 0.0,
        num_inference_steps: int = 50,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        **kwargs,
    ) -> Union[Tuple, ImagePipelineOutput]:
        r"""
        Args:
            batch_size (`int`, *optional*, defaults to 1):
                Number of images to generate.
            generator (`torch.Generator`, *optional*):
                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                deterministic.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.

        Returns:
            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
            generated images.
        """

        latents = torch.randn(
            (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
            generator=generator,
        )
        latents = latents.to(self.device)

        self.scheduler.set_timesteps(num_inference_steps)

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())

        extra_kwargs = {}
        if accepts_eta:
            extra_kwargs["eta"] = eta

        for t in self.progress_bar(self.scheduler.timesteps):
            # predict the noise residual
            noise_prediction = self.unet(latents, t).sample
            # compute the previous noisy sample x_t -> x_t-1
            latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwargs).prev_sample

        # decode the image latents with the VAE
        image = self.vqvae.decode(latents).sample

        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).numpy()
        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=image)
