from asyncio.log import logger
import math
import random
import torch
import numpy as np
from basicsr.utils.registry import ARCH_REGISTRY
from basicsr.archs.arch_util import ResidualBlockNoBN, flow_warp, make_layer, default_init_weights

from torch import nn
from torch.nn import functional as F

from .stylegan2_clean_arch import StyleGAN2GeneratorClean


class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
    """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).

    It is the clean version without custom compiled CUDA extensions used in StyleGAN2.

    Args:
        out_size (int): The spatial size of outputs.
        num_style_feat (int): Channel number of style features. Default: 512.
        num_mlp (int): Layer number of MLP style layers. Default: 8.
        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
        narrow (float): The narrow ratio for channels. Default: 1.
        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
    """

    def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1, sft_half=False):
        super(StyleGAN2GeneratorCSFT, self).__init__(
            out_size,
            num_style_feat=num_style_feat,
            num_mlp=num_mlp,
            channel_multiplier=channel_multiplier,
            narrow=narrow)
        self.sft_half = sft_half

    def forward(self,
                styles,
                conditions,
                input_is_latent=False,
                noise=None,
                randomize_noise=True,
                truncation=1,
                truncation_latent=None,
                inject_index=None,
                return_latents=False):
        """Forward function for StyleGAN2GeneratorCSFT.

        Args:
            styles (list[Tensor]): Sample codes of styles.
            conditions (list[Tensor]): SFT conditions to generators.
            input_is_latent (bool): Whether input is latent style. Default: False.
            noise (Tensor | None): Input noise or None. Default: None.
            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
            truncation (float): The truncation ratio. Default: 1.
            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
            inject_index (int | None): The injection index for mixing noise. Default: None.
            return_latents (bool): Whether to return style latents. Default: False.
        """
        # style codes -> latents with Style MLP layer
        if not input_is_latent:
            styles = [self.style_mlp(s) for s in styles]
        # noises
        if noise is None:
            if randomize_noise is True:
                noise = [None] * self.num_layers  # for each style conv layer
            elif randomize_noise == 'zero':
                noise = [torch.zeros_like(getattr(self.noises, f'noise{i}')) for i in range(self.num_layers)]
            else:  # use the stored noise
                noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
        # style truncation
        if truncation < 1:
            style_truncation = []
            for style in styles:
                style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
            styles = style_truncation
        # get style latents with injection
        if len(styles) == 1:
            inject_index = self.num_latent

            if styles[0].ndim < 3:
                # repeat latent code for all the layers
                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
            else:  # used for encoder with different latent code for each layer
                latent = styles[0]
        elif len(styles) == 2:  # mixing noises
            if inject_index is None:
                inject_index = random.randint(1, self.num_latent - 1)
            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
            latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
            latent = torch.cat([latent1, latent2], 1)

        # main generation
        out = self.constant_input(latent.shape[0])
        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
        skip = self.to_rgb1(out, latent[:, 1])

        i = 1
        for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2],
                                                        self.style_convs[1::2], noise[1::2], noise[2::2], self.to_rgbs):
            out = conv1(out, latent[:, i], noise=noise1)

            # the conditions may have fewer levels
            if i < len(conditions):
                # SFT part to combine the conditions
                if self.sft_half:  # only apply SFT to half of the channels
                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
                    out_sft = out_sft * conditions[i - 1] + conditions[i]
                    out = torch.cat([out_same, out_sft], dim=1)
                else:  # apply SFT to all the channels
                    out = out * conditions[i - 1] + conditions[i]

            out = conv2(out, latent[:, i + 1], noise=noise2)
            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
            i += 2

        image = skip

        if return_latents:
            return image, latent
        else:
            return image, None


class ResBlock(nn.Module):
    """Residual block with bilinear upsampling/downsampling.

    Args:
        in_channels (int): Channel number of the input.
        out_channels (int): Channel number of the output.
        mode (str): Upsampling/downsampling mode. Options: down | up. Default: down.
    """

    def __init__(self, in_channels, out_channels, mode='down'):
        super(ResBlock, self).__init__()

        self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
        self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1)
        self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False)
        if mode == 'down':
            self.scale_factor = 0.5
        elif mode == 'up':
            self.scale_factor = 2

    def forward(self, x):
        out = F.leaky_relu_(self.conv1(x), negative_slope=0.2)
        # upsample/downsample
        out = F.interpolate(out, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
        out = F.leaky_relu_(self.conv2(out), negative_slope=0.2)
        # skip
        x = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
        skip = self.skip(x)
        out = out + skip
        return out

def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module
class ConvReLUConv(nn.Module):
    """Residual block with bilinear upsampling/downsampling.

    Args:
        in_channels (int): Channel number of the input.
        out_channels (int): Channel number of the output.
        mode (str): Upsampling/downsampling mode. Options: down | up. Default: down.
    """

    def __init__(self, in_channels, out_channels, mode=None, zero_out_init=False):
        super(ConvReLUConv, self).__init__()

        self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
        self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1)
        # self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False)
        self.mode = mode
        if mode == 'down':
            self.scale_factor = 0.5
        elif mode == 'up':
            self.scale_factor = 2
        else:
            self.scale_factor = 1
        if zero_out_init:
            self.conv2 = zero_module(self.conv2)
    def forward(self, x):
        out = F.leaky_relu_(self.conv1(x), negative_slope=0.2)
        # upsample/downsample
        if self.mode:
            out = F.interpolate(out, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
        out = self.conv2(out)
        # skip
        # x = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
        # skip = self.skip(x)
        # out = out +
        return out


@ARCH_REGISTRY.register()
class GFPGANv1Clean_UnetWindow(nn.Module):
    """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.

    It is the clean version without custom compiled CUDA extensions used in StyleGAN2.

    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.

    Args:
        out_size (int): The spatial size of outputs.
        num_style_feat (int): Channel number of style features. Default: 512.
        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
        fix_decoder (bool): Whether to fix the decoder. Default: True.

        num_mlp (int): Layer number of MLP style layers. Default: 8.
        input_is_latent (bool): Whether input is latent style. Default: False.
        different_w (bool): Whether to use different latent w for different layers. Default: False.
        narrow (float): The narrow ratio for channels. Default: 1.
        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
    """

    def __init__(
            self,
            out_size,
            num_style_feat=512,
            channel_multiplier=1,
            decoder_load_path=None,
            fix_decoder=True,
            GFPGANv1_load_path=None,
            # for stylegan decoder
            num_mlp=8,
            input_is_latent=False,
            different_w=False,
            narrow=1,
            sft_half=False,
            return_rgb=True,
            randomize_noise= "zero",
            # for recurrent
            zero_init=False,
            zero_out_init=True,
            temp_type='unet_window'

            ):

        super(GFPGANv1Clean_UnetWindow, self).__init__()
        self.input_is_latent = input_is_latent
        self.different_w = different_w
        self.num_style_feat = num_style_feat
        self.return_rgb = return_rgb
        self.randomize_noise = randomize_noise
        self.temp_type = temp_type
        self.zero_out_init = zero_out_init
        unet_narrow = narrow * 0.5  # by default, use a half of input channels
        channels = {
            '4': int(512 * unet_narrow),
            '8': int(512 * unet_narrow),
            '16': int(512 * unet_narrow),
            '32': int(512 * unet_narrow),
            '64': int(256 * channel_multiplier * unet_narrow),
            '128': int(128 * channel_multiplier * unet_narrow),
            '256': int(64 * channel_multiplier * unet_narrow),
            '512': int(32 * channel_multiplier * unet_narrow),
            '1024': int(16 * channel_multiplier * unet_narrow)
        }

        self.log_size = int(math.log(out_size, 2))
        first_out_size = 2**(int(math.log(out_size, 2)))
        self.out_size = out_size
        self.conv_body_first = nn.Conv2d(3, channels[f'{first_out_size}'], 1)

        # downsample
        in_channels = channels[f'{first_out_size}']
        self.conv_body_down = nn.ModuleList()
        self.window_down = nn.ModuleList()
        for i in range(self.log_size, 2, -1):
            out_channels = channels[f'{2**(i - 1)}']
            self.conv_body_down.append(ResBlock(in_channels, out_channels, mode='down'))
            self.window_down.append(ConvReLUConv(out_channels*3, out_channels, zero_out_init=zero_out_init))
            # self.recurrent_forward.append(ConvReLUConv(sft_out_channels*3, sft_out_channels,zero_out_init=zero_out_init))
            in_channels = out_channels

        self.final_conv = nn.Conv2d(in_channels, channels['4'], 3, 1, 1)

        # upsample
        in_channels = channels['4']
        self.conv_body_up = nn.ModuleList()
        for i in range(3, self.log_size + 1):
            out_channels = channels[f'{2**i}']
            self.conv_body_up.append(ResBlock(in_channels, out_channels, mode='up'))
            in_channels = out_channels

        # to RGB
        self.toRGB = nn.ModuleList()
        for i in range(3, self.log_size + 1):
            self.toRGB.append(nn.Conv2d(channels[f'{2**i}'], 3, 1))

        if different_w:
            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
        else:
            linear_out_channel = num_style_feat

        self.final_linear = nn.Linear(channels['4'] * 4 * 4, linear_out_channel)

        # the decoder: stylegan2 generator with SFT modulations
        self.stylegan_decoder = StyleGAN2GeneratorCSFT(
            out_size=out_size,
            num_style_feat=num_style_feat,
            num_mlp=num_mlp,
            channel_multiplier=channel_multiplier,
            narrow=narrow,
            sft_half=sft_half)

        # load pre-trained stylegan2 model if necessary
        if decoder_load_path:
            self.stylegan_decoder.load_state_dict(
                torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema'])
        # fix decoder without updating params
        if fix_decoder:
            for _, param in self.stylegan_decoder.named_parameters():
                param.requires_grad = False

        # for SFT modulations (scale and shift)
        self.condition_scale = nn.ModuleList()
        self.condition_shift = nn.ModuleList()
        self.recurrent_backward = nn.ModuleList()
        self.recurrent_forward = nn.ModuleList()
        for i in range(3, self.log_size + 1):
            out_channels = channels[f'{2**i}']
            if sft_half:
                sft_out_channels = out_channels
            else:
                sft_out_channels = out_channels * 2
            self.condition_scale.append(
                nn.Sequential(
                    nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True),
                    nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1)))
            self.condition_shift.append(
                nn.Sequential(
                    nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True),
                    nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1)))
            self.recurrent_forward.append(ConvReLUConv(sft_out_channels*3, sft_out_channels,zero_out_init=zero_out_init))
            # assert temp_type == 'recurrent' or temp_type == 'window' or self.temp_type == 'none'
            if temp_type == 'recurrent':
                self.recurrent_backward.append(ConvReLUConv(sft_out_channels + sft_out_channels, sft_out_channels, zero_out_init=zero_out_init))
                self.recurrent_backward.append(ConvReLUConv(sft_out_channels + sft_out_channels, sft_out_channels,zero_out_init=zero_out_init))
                self.recurrent_forward.append(ConvReLUConv(sft_out_channels + sft_out_channels, sft_out_channels,zero_out_init=zero_out_init))
                self.recurrent_forward.append(ConvReLUConv(sft_out_channels + sft_out_channels, sft_out_channels,zero_out_init=zero_out_init))
                if zero_init:
                    print("Warning: this implementation has bugs")
                    exit()
                    default_init_weights([self.recurrent_backward, self.recurrent_forward], scale=0.0, bias_fill=0.0)
            elif temp_type == 'window':
                self.recurrent_forward.append(ConvReLUConv(sft_out_channels*3, sft_out_channels,zero_out_init=zero_out_init))
                self.recurrent_forward.append(ConvReLUConv(sft_out_channels*3, sft_out_channels,zero_out_init=zero_out_init))
            elif temp_type == 'unet_window':
                pass
                # self.recurrent_forward.append(ConvReLUConv(sft_out_channels*3, sft_out_channels,zero_out_init=zero_out_init))
            elif self.temp_type == 'none':
                pass

        if GFPGANv1_load_path is not None:
            self.load_state_dict(
                torch.load(GFPGANv1_load_path, map_location=lambda storage, loc: storage)['params_ema'], strict=False)

    def temp_window(self, conditions_list):
        out_pyramid_list = [] # n, 14, b, c, h, w
        conditions = conditions_list[-1]
        n = len(conditions_list)
        for i in range(n): # different time
            out_pyramid_list_i = []
            for j in range(len(conditions_list[-1])): # different res
                out_pyramid_list_i.append(torch.zeros_like(conditions_list[i][j]))
            out_pyramid_list.append(out_pyramid_list_i)
        # Pyramid_list = []

        assert len(self.recurrent_forward) == len(conditions), f"number of recurrent module, {len(self.recurrent_forward)} == {len(conditions)}"
        for j in range(len(conditions)): # different res
            for i in range(n): # different time
                # if i == 0:
                feat_fuse = torch.cat([conditions_list[np.clip(i-1, 0, n-1)][j], conditions_list[i][j], conditions_list[np.clip(i+1, 0, n-1)][j]], dim=1)
                out_pyramid_list[i][j] = self.recurrent_forward[j](feat_fuse) + conditions_list[i][j]
                # out_pyramid_list[i][j] = (conditions_list[np.clip(i-1, 0, n-1)][j] + conditions_list[i][j] + conditions_list[np.clip(i+1, 0, n-1)][j])/3.0
                # out_pyramid_list[i][j] = self.recurrent_forward[j](feat_fuse) + conditions_list[i][j]
        return out_pyramid_list
                # out_pyramid_list[i][j] = feat_prop
    def unet_window(self, temporal_feature, index):

        n,c,h,w = temporal_feature.size() # 1,2,3,4,5
        padded_input = torch.concat([temporal_feature[0:1, ...], temporal_feature, temporal_feature[n-1:, ...]], dim=0) # 1123455
        concat_padded_input = torch.concat([padded_input[0:n, ...], padded_input[1:n+1, ...], padded_input[2:n+2, ...]], dim=1)
        # tensor([[1, 1, 2],
        # [1, 2, 3],
        # [2, 3, 4],
        # [3, 4, 5],
        # [4, 5, 5]])
        # feat_fuse = torch.cat([], dim=1)
        out_feature = self.recurrent_forward[index](concat_padded_input) + temporal_feature
        return out_feature
    def unet_window_down(self, temporal_feature, index):

        n,c,h,w = temporal_feature.size() # 1,2,3,4,5
        padded_input = torch.concat([temporal_feature[0:1, ...], temporal_feature, temporal_feature[n-1:, ...]], dim=0) # 1123455
        concat_padded_input = torch.concat([padded_input[0:n, ...], padded_input[1:n+1, ...], padded_input[2:n+2, ...]], dim=1)
        # tensor([[1, 1, 2],
        # [1, 2, 3],
        # [2, 3, 4],
        # [3, 4, 5],
        # [4, 5, 5]])
        # feat_fuse = torch.cat([], dim=1)
        out_feature = self.window_down[index](concat_padded_input) + temporal_feature
        return out_feature


    def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True, interpolate=False):
        """Forward function for GFPGANv1Clean.

        Args:
            x (Tensor): Input images.
            return_latents (bool): Whether to return style latents. Default: False.
            return_rgb (bool): Whether return intermediate rgb images. Default: True.
            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
        """
        # import pdb; pdb.set_trace()

        # Preprocessing
        b, n, c, h, w = x.size()
        assert b==1, "only support batch size=1"
        x = x[0, ...]
        x = x*2.0-1.0

        conditions = []
        unet_skips = []
        out_rgbs = []
        interpolate = False
        if x.shape[2] != self.out_size:
            interpolate = True
            x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
            # x = F.interpolate(x, scale_factor=2)
        # encoder
        feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2)
        for i in range(self.log_size - 2):
            feat = self.conv_body_down[i](feat)
            feat = self.unet_window_down(feat, i)
            unet_skips.insert(0, feat)
        feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2)

        # style code
        style_code = self.final_linear(feat.view(feat.size(0), -1))
        if self.different_w:
            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)

        # decode
        for i in range(self.log_size - 2):
            # add unet skip
            feat = feat + unet_skips[i]
            # ResUpLayer
            feat = self.conv_body_up[i](feat)
            # Temporal window
            feat = self.unet_window(feat, i)
            # generate scale and shift for SFT layers
            scale = self.condition_scale[i](feat)
            conditions.append(scale.clone())
            shift = self.condition_shift[i](feat)
            conditions.append(shift.clone())
            # generate rgb images
            if return_rgb:
                out_rgbs.append(self.toRGB[i](feat))

        # decoder
        if not randomize_noise:
            self.randomize_noise = False
        image, _ = self.stylegan_decoder([style_code],
                                        conditions,
                                        return_latents=return_latents,
                                        input_is_latent=self.input_is_latent,
                                        randomize_noise=self.randomize_noise)
            # out_list.append(image)


        out = torch.unsqueeze(image, 0)
        out = (out+1.0)/2.0


        return out
