import os

import numpy as np
import torch
from PIL import Image
from basicsr.utils.download_util import load_file_from_url

import modules.esrgam_model_arch as arch
from modules import shared, modelloader, images
from modules.devices import has_mps
from modules.paths import models_path
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import opts


def fix_model_layers(crt_model, pretrained_net):
    # this code is adapted from https://github.com/xinntao/ESRGAN
    if 'conv_first.weight' in pretrained_net:
        return pretrained_net

    if 'model.0.weight' not in pretrained_net:
        is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]
        if is_realesrgan:
            raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
        else:
            raise Exception("The file is not a ESRGAN model.")

    crt_net = crt_model.state_dict()
    load_net_clean = {}
    for k, v in pretrained_net.items():
        if k.startswith('module.'):
            load_net_clean[k[7:]] = v
        else:
            load_net_clean[k] = v
    pretrained_net = load_net_clean

    tbd = []
    for k, v in crt_net.items():
        tbd.append(k)

    # directly copy
    for k, v in crt_net.items():
        if k in pretrained_net and pretrained_net[k].size() == v.size():
            crt_net[k] = pretrained_net[k]
            tbd.remove(k)

    crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
    crt_net['conv_first.bias'] = pretrained_net['model.0.bias']

    for k in tbd.copy():
        if 'RDB' in k:
            ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
            if '.weight' in k:
                ori_k = ori_k.replace('.weight', '.0.weight')
            elif '.bias' in k:
                ori_k = ori_k.replace('.bias', '.0.bias')
            crt_net[k] = pretrained_net[ori_k]
            tbd.remove(k)

    crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
    crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
    crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
    crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
    crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
    crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
    crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
    crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
    crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
    crt_net['conv_last.bias'] = pretrained_net['model.10.bias']

    return crt_net

class UpscalerESRGAN(Upscaler):
    def __init__(self, dirname):
        self.name = "ESRGAN"
        self.model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
        self.model_name = "ESRGAN 4x"
        self.scalers = []
        self.user_path = dirname
        self.model_path = os.path.join(models_path, self.name)
        super().__init__()
        model_paths = self.find_models(ext_filter=[".pt", ".pth"])
        scalers = []
        if len(model_paths) == 0:
            scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
            scalers.append(scaler_data)
        for file in model_paths:
            if "http" in file:
                name = self.model_name
            else:
                name = modelloader.friendly_name(file)

            scaler_data = UpscalerData(name, file, self, 4)
            self.scalers.append(scaler_data)

    def do_upscale(self, img, selected_model):
        model = self.load_model(selected_model)
        if model is None:
            return img
        model.to(shared.device)
        img = esrgan_upscale(model, img)
        return img

    def load_model(self, path: str):
        if "http" in path:
            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
                                          file_name="%s.pth" % self.model_name,
                                          progress=True)
        else:
            filename = path
        if not os.path.exists(filename) or filename is None:
            print("Unable to load %s from %s" % (self.model_path, filename))
            return None

        pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
        crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)

        pretrained_net = fix_model_layers(crt_model, pretrained_net)
        crt_model.load_state_dict(pretrained_net)
        crt_model.eval()

        return crt_model


def upscale_without_tiling(model, img):
    img = np.array(img)
    img = img[:, :, ::-1]
    img = np.moveaxis(img, 2, 0) / 255
    img = torch.from_numpy(img).float()
    img = img.unsqueeze(0).to(shared.device)
    with torch.no_grad():
        output = model(img)
    output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
    output = 255. * np.moveaxis(output, 0, 2)
    output = output.astype(np.uint8)
    output = output[:, :, ::-1]
    return Image.fromarray(output, 'RGB')


def esrgan_upscale(model, img):
    if opts.ESRGAN_tile == 0:
        return upscale_without_tiling(model, img)

    grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
    newtiles = []
    scale_factor = 1

    for y, h, row in grid.tiles:
        newrow = []
        for tiledata in row:
            x, w, tile = tiledata

            output = upscale_without_tiling(model, tile)
            scale_factor = output.width // tile.width

            newrow.append([x * scale_factor, w * scale_factor, output])
        newtiles.append([y * scale_factor, h * scale_factor, newrow])

    newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
    output = images.combine_grid(newgrid)
    return output
