# Copyright 2023 ETH Zurich Computer Vision Lab and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
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]):
    warnings.warn(
        "The preprocess method is deprecated and will be removed in a future version. Please"
        " use VaeImageProcessor.preprocess instead",
        FutureWarning,
    )
    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._execution_device, dtype=self.unet.dtype)
        mask_image = _preprocess_mask(mask_image)
        mask_image = mask_image.to(device=self._execution_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._execution_device, dtype=self.unet.dtype)

        # set step values
        self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self._execution_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)
