import random

import PIL.Image
import cv2
import numpy as np
import torch
from diffusers import PNDMScheduler, DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, \
    EulerAncestralDiscreteScheduler
from loguru import logger

from lama_cleaner.model.base import InpaintModel
from lama_cleaner.schema import Config, SDSampler


#
#
# def preprocess_image(image):
#     w, h = image.size
#     w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
#     image = image.resize((w, h), resample=PIL.Image.LANCZOS)
#     image = np.array(image).astype(np.float32) / 255.0
#     image = image[None].transpose(0, 3, 1, 2)
#     image = torch.from_numpy(image)
#     # [-1, 1]
#     return 2.0 * image - 1.0
#
#
# def preprocess_mask(mask):
#     mask = mask.convert("L")
#     w, h = mask.size
#     w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
#     mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
#     mask = np.array(mask).astype(np.float32) / 255.0
#     mask = np.tile(mask, (4, 1, 1))
#     mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
#     mask = 1 - mask  # repaint white, keep black
#     mask = torch.from_numpy(mask)
#     return mask

class CPUTextEncoderWrapper:
    def __init__(self, text_encoder, torch_dtype):
        self.text_encoder = text_encoder.to(torch.device('cpu'), non_blocking=True)
        self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True)
        self.torch_dtype = torch_dtype

    def __call__(self, x):
        input_device = x.device
        return [self.text_encoder(x.to(self.text_encoder.device))[0].to(input_device).to(self.torch_dtype)]


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

    def init_model(self, device: torch.device, **kwargs):
        from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline

        model_kwargs = {"local_files_only": kwargs['sd_run_local']}
        if kwargs['sd_disable_nsfw']:
            logger.info("Disable Stable Diffusion Model NSFW checker")
            model_kwargs.update(dict(
                safety_checker=None,
            ))

        use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
        torch_dtype = torch.float16 if use_gpu else torch.float32
        self.model = StableDiffusionInpaintPipeline.from_pretrained(
            self.model_id_or_path,
            revision="fp16" if use_gpu else "main",
            torch_dtype=torch_dtype,
            use_auth_token=kwargs["hf_access_token"],
            **model_kwargs
        )
        # https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
        self.model.enable_attention_slicing()
        # https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
        if kwargs['sd_enable_xformers']:
            self.model.enable_xformers_memory_efficient_attention()
        self.model = self.model.to(device)

        if kwargs['sd_cpu_textencoder']:
            logger.info("Run Stable Diffusion TextEncoder on CPU")
            self.model.text_encoder = CPUTextEncoderWrapper(self.model.text_encoder, torch_dtype)

        self.callback = kwargs.pop("callback", None)

    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
        """

        # image = norm_img(image)  # [0, 1]
        # image = image * 2 - 1  # [0, 1] -> [-1, 1]

        # resize to latent feature map size
        # h, w = mask.shape[:2]
        # mask = cv2.resize(mask, (h // 8, w // 8), interpolation=cv2.INTER_AREA)
        # mask = norm_img(mask)
        #
        # image = torch.from_numpy(image).unsqueeze(0).to(self.device)
        # mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)

        scheduler_kwargs = dict(
            beta_schedule="scaled_linear",
            beta_start=0.00085,
            beta_end=0.012,
            num_train_timesteps=1000,
        )

        if config.sd_sampler == SDSampler.ddim:
            scheduler = DDIMScheduler(
                **scheduler_kwargs,
                clip_sample=False,
                set_alpha_to_one=False,
            )
        elif config.sd_sampler == SDSampler.pndm:
            scheduler = PNDMScheduler(**scheduler_kwargs, skip_prk_steps=True)
        elif config.sd_sampler == SDSampler.k_lms:
            scheduler = LMSDiscreteScheduler(**scheduler_kwargs)
        elif config.sd_sampler == SDSampler.k_euler:
            scheduler = EulerDiscreteScheduler(**scheduler_kwargs)
        elif config.sd_sampler == SDSampler.k_euler_a:
            scheduler = EulerAncestralDiscreteScheduler(**scheduler_kwargs)
        else:
            raise ValueError(config.sd_sampler)

        self.model.scheduler = scheduler

        seed = config.sd_seed
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)

        if config.sd_mask_blur != 0:
            k = 2 * config.sd_mask_blur + 1
            mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]

        _kwargs = {
            self.image_key: PIL.Image.fromarray(image),
        }

        img_h, img_w = image.shape[:2]

        output = self.model(
            prompt=config.prompt,
            negative_prompt=config.negative_prompt,
            mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
            strength=config.sd_strength,
            num_inference_steps=config.sd_steps,
            guidance_scale=config.sd_guidance_scale,
            output_type="np.array",
            callback=self.callback,
            height=img_h,
            width=img_w,
            **_kwargs
        ).images[0]

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

    @torch.no_grad()
    def __call__(self, image, mask, config: Config):
        """
        images: [H, W, C] RGB, not normalized
        masks: [H, W]
        return: BGR IMAGE
        """
        img_h, img_w = image.shape[:2]

        # boxes = boxes_from_mask(mask)
        if config.use_croper:
            logger.info("use croper")
            l, t, w, h = (
                config.croper_x,
                config.croper_y,
                config.croper_width,
                config.croper_height,
            )
            r = l + w
            b = t + h

            l = max(l, 0)
            r = min(r, img_w)
            t = max(t, 0)
            b = min(b, img_h)

            crop_img = image[t:b, l:r, :]
            crop_mask = mask[t:b, l:r]

            crop_image = self._pad_forward(crop_img, crop_mask, config)

            inpaint_result = image[:, :, ::-1]
            inpaint_result[t:b, l:r, :] = crop_image
        else:
            inpaint_result = self._pad_forward(image, mask, config)

        return inpaint_result

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

        if config.sd_mask_blur != 0:
            k = 2 * config.sd_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


class SD15(SD):
    model_id_or_path = "runwayml/stable-diffusion-inpainting"
    image_key = "image"