import os
import torch
import cv2
from PIL import Image
import numpy as np
from huggingface_hub import hf_hub_url, cached_download
from .rrdbnet_arch import RRDBNet
from .utils import (
    pad_reflect,
    split_image_into_overlapping_patches,
    stich_together,
    unpad_image,
)

HF_MODELS = {
    2: dict(
        repo_id="sberbank-ai/Real-ESRGAN",
        filename="RealESRGAN_x2.pth",
    ),
    4: dict(
        repo_id="sberbank-ai/Real-ESRGAN",
        filename="RealESRGAN_x4.pth",
    ),
    6: dict(
        repo_id="alicangonullu/ESRGAN-Ulti",
        filename="RealESRGAN_x4plus.pth",
    ),
    8: dict(
        repo_id="sberbank-ai/Real-ESRGAN",
        filename="RealESRGAN_x8.pth",
    ),
}

class RealESRGAN:
    def __init__(self, device, anime=False, scale=4):
        self.device = device
        self.scale = scale
        if anime:
            self.model = RRDBNet(
                num_in_ch=3,
                num_out_ch=3,
                num_feat=64,
                num_block=6,
                num_grow_ch=32,
                scale=scale,
            )
        else:
            self.model = RRDBNet(
                num_in_ch=3,
                num_out_ch=3,
                num_feat=64,
                num_block=23,
                num_grow_ch=32,
                scale=scale,
            )

    def load_weights(self, model_path, download=True):
        if not os.path.exists(model_path) and download:
            assert self.scale in [2, 4, 8], "You can download models only with scales: 2, 4, 8"
            config = HF_MODELS[self.scale]
            cache_dir = os.path.dirname(model_path)
            local_filename = os.path.basename(model_path)
            config_file_url = hf_hub_url(repo_id=config["repo_id"], filename=config["filename"])
            cached_download(config_file_url, cache_dir=cache_dir, force_filename=local_filename)
            print("Weights downloaded to:", os.path.join(cache_dir, local_filename))

        loadnet = torch.load(model_path)
        if "params" in loadnet:
            self.model.load_state_dict(loadnet["params"], strict=True)
        elif "params_ema" in loadnet:
            self.model.load_state_dict(loadnet["params_ema"], strict=True)
        else:
            self.model.load_state_dict(loadnet, strict=True)
        self.model.eval()
        self.model.to(self.device)

    @torch.cuda.amp.autocast()
    def predict(self, lr_image, batch_size=4, patches_size=192, padding=24, pad_size=15):
        scale = self.scale
        device = self.device
        lr_image = np.array(lr_image)
        lr_image = pad_reflect(lr_image, pad_size)

        patches, p_shape = split_image_into_overlapping_patches(
            lr_image, patch_size=patches_size, padding_size=padding
        )
        img = torch.FloatTensor(patches / 255).permute((0, 3, 1, 2)).to(device).detach()

        with torch.no_grad():
            res = self.model(img[0:batch_size])
            for i in range(batch_size, img.shape[0], batch_size):
                res = torch.cat((res, self.model(img[i : i + batch_size])), 0)

        sr_image = res.permute((0, 2, 3, 1)).clamp_(0, 1).cpu()
        np_sr_image = sr_image.numpy()

        padded_size_scaled = tuple(np.multiply(p_shape[0:2], scale)) + (3,)
        scaled_image_shape = tuple(np.multiply(lr_image.shape[0:2], scale)) + (3,)
        np_sr_image = stich_together(
            np_sr_image,
            padded_image_shape=padded_size_scaled,
            target_shape=scaled_image_shape,
            padding_size=padding * scale,
        )
        sr_img = (np_sr_image * 255).astype(np.uint8)
        sr_img = unpad_image(sr_img, pad_size * scale)
        sr_img = Image.fromarray(sr_img)
        return sr_img

    def face_enhance(self, img, scale=4):
        from gfpgan import GFPGANer
        face_enhancer = GFPGANer(
            model_path=r"C:\Users\Admin\Downloads\Term 3\Big Data Capstone Project\Real-ESRGAN-GFP\Img-Upscale-AI\model\GFPGANv1.3.pth",
            upscale=scale,
            arch="clean",
            channel_multiplier=2,
        )
        _, _, output = face_enhancer.enhance(
            img, has_aligned=False, only_center_face=False, paste_back=True
        )
        return output