from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNet2DModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
def _preprocess_image(image: Union[List, PIL.Image.Image, torch.Tensor]):
    if isinstance(image, torch.Tensor):
        return image
    elif isinstance(image, PIL.Image.Image):
        image = [image]

    if isinstance(image[0], PIL.Image.Image):
        w, h = image[0].size
        w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
        image = np.concatenate(image, axis=0)
        image = np.array(image).astype(np.float32) / 255.0
        image = image.transpose(0, 3, 1, 2)
        image = 2.0 * image - 1.0
        image = torch.from_numpy(image)
    elif isinstance(image[0], torch.Tensor):
        image = torch.cat(image, dim=0)
    return image


def _preprocess_mask(mask: Union[List, PIL.Image.Image, torch.Tensor]):
    if isinstance(mask, torch.Tensor):
        return mask
    elif isinstance(mask, PIL.Image.Image):
        mask = [mask]

    if isinstance(mask[0], PIL.Image.Image):
        w, h = mask[0].size
        w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
        mask = [np.array(m.convert("L").resize((w, h), resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask]
        mask = np.concatenate(mask, axis=0)
        mask = mask.astype(np.float32) / 255.0
        mask[mask < 0.5] = 0
        mask[mask >= 0.5] = 1
        mask = torch.from_numpy(mask)
    elif isinstance(mask[0], torch.Tensor):
        mask = torch.cat(mask, dim=0)
    return mask


class RePaintPipeline(DiffusionPipeline):
    unet: UNet2DModel
    scheduler: RePaintScheduler

    def __init__(self, unet, scheduler):
        super().__init__()
        self.register_modules(unet=unet, scheduler=scheduler)

    @torch.no_grad()
    def __call__(
        self,
        image: Union[torch.Tensor, PIL.Image.Image],
        mask_image: Union[torch.Tensor, PIL.Image.Image],
        num_inference_steps: int = 250,
        eta: float = 0.0,
        jump_length: int = 10,
        jump_n_sample: int = 10,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
    ) -> Union[ImagePipelineOutput, Tuple]:
        r"""
        Args:
            image (`torch.FloatTensor` or `PIL.Image.Image`):
                The original image to inpaint on.
            mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
                The mask_image where 0.0 values define which part of the original image to inpaint (change).
            num_inference_steps (`int`, *optional*, defaults to 1000):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            eta (`float`):
                The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 - 0.0 is DDIM
                and 1.0 is DDPM scheduler respectively.
            jump_length (`int`, *optional*, defaults to 10):
                The number of steps taken forward in time before going backward in time for a single jump ("j" in
                RePaint paper). Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
            jump_n_sample (`int`, *optional*, defaults to 10):
                The number of times we will make forward time jump for a given chosen time sample. Take a look at
                Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
            generator (`torch.Generator`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            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 [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.

        Returns:
            [`~pipelines.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.
        """

        original_image = image

        original_image = _preprocess_image(original_image)
        original_image = original_image.to(device=self.device, dtype=self.unet.dtype)
        mask_image = _preprocess_mask(mask_image)
        mask_image = mask_image.to(device=self.device, dtype=self.unet.dtype)

        batch_size = original_image.shape[0]

        # sample gaussian noise to begin the loop
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        image_shape = original_image.shape
        image = randn_tensor(image_shape, generator=generator, device=self.device, dtype=self.unet.dtype)

        # set step values
        self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self.device)
        self.scheduler.eta = eta

        t_last = self.scheduler.timesteps[0] + 1
        generator = generator[0] if isinstance(generator, list) else generator
        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
            if t < t_last:
                # predict the noise residual
                model_output = self.unet(image, t).sample
                # compute previous image: x_t -> x_t-1
                image = self.scheduler.step(model_output, t, image, original_image, mask_image, generator).prev_sample

            else:
                # compute the reverse: x_t-1 -> x_t
                image = self.scheduler.undo_step(image, t_last, generator)
            t_last = t

        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)
