import random

import PIL
import PIL.Image
import cv2
import numpy as np
import torch
from diffusers import DiffusionPipeline
from loguru import logger

from lama_cleaner.helper import resize_max_size
from lama_cleaner.model.base import InpaintModel
from lama_cleaner.schema import Config


class PaintByExample(InpaintModel):
    pad_mod = 8
    min_size = 512

    def init_model(self, device: torch.device, **kwargs):
        fp16 = not kwargs.get('no_half', False)
        use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
        torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
        model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)}
        self.model = DiffusionPipeline.from_pretrained(
            "Fantasy-Studio/Paint-by-Example",
            torch_dtype=torch_dtype,
            **model_kwargs
        )
        self.model = self.model.to(device)
        self.model.enable_attention_slicing()
        # TODO: gpu_id
        if kwargs.get('cpu_offload', False) and torch.cuda.is_available():
            self.model.enable_sequential_cpu_offload(gpu_id=0)

    def forward(self, image, mask, config: Config):
        """Input image and output image have same size
        image: [H, W, C] RGB
        mask: [H, W, 1] 255 means area to repaint
        return: BGR IMAGE
        """
        seed = config.paint_by_example_seed
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)

        output = self.model(
            image=PIL.Image.fromarray(image),
            mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
            example_image=config.paint_by_example_example_image,
            num_inference_steps=config.paint_by_example_steps,
            output_type='np.array',
        ).images[0]

        output = (output * 255).round().astype("uint8")
        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
        return output

    def _scaled_pad_forward(self, image, mask, config: Config):
        longer_side_length = int(config.sd_scale * max(image.shape[:2]))
        origin_size = image.shape[:2]
        downsize_image = resize_max_size(image, size_limit=longer_side_length)
        downsize_mask = resize_max_size(mask, size_limit=longer_side_length)
        logger.info(
            f"Resize image to do paint_by_example: {image.shape} -> {downsize_image.shape}"
        )
        inpaint_result = self._pad_forward(downsize_image, downsize_mask, config)
        # only paste masked area result
        inpaint_result = cv2.resize(
            inpaint_result,
            (origin_size[1], origin_size[0]),
            interpolation=cv2.INTER_CUBIC,
        )
        original_pixel_indices = mask < 127
        inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
        return inpaint_result

    @torch.no_grad()
    def __call__(self, image, mask, config: Config):
        """
        images: [H, W, C] RGB, not normalized
        masks: [H, W]
        return: BGR IMAGE
        """
        if config.use_croper:
            crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
            crop_image = self._scaled_pad_forward(crop_img, crop_mask, config)
            inpaint_result = image[:, :, ::-1]
            inpaint_result[t:b, l:r, :] = crop_image
        else:
            inpaint_result = self._scaled_pad_forward(image, mask, config)

        return inpaint_result

    def forward_post_process(self, result, image, mask, config):
        if config.paint_by_example_match_histograms:
            result = self._match_histograms(result, image[:, :, ::-1], mask)

        if config.paint_by_example_mask_blur != 0:
            k = 2 * config.paint_by_example_mask_blur + 1
            mask = cv2.GaussianBlur(mask, (k, k), 0)
        return result, image, mask

    @staticmethod
    def is_downloaded() -> bool:
        # model will be downloaded when app start, and can't switch in frontend settings
        return True