import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv2d, BatchNorm2d, PReLU, Sequential, Module
import math

from .helpers import get_blocks, bottleneck_IR, bottleneck_IR_SE

import sys, os
sys.path.append(os.path.dirname(__file__) + os.sep + '../')
from model import EqualLinear


"""
Modified from [pSp](https://github.com/eladrich/pixel2style2pixel)
"""


class GradualStyleBlock(Module):
    def __init__(self, in_c, out_c, spatial):
        super(GradualStyleBlock, self).__init__()
        self.out_c = out_c
        self.spatial = spatial
        num_pools = int(np.log2(spatial))
        modules = []
        modules += [Conv2d(in_c, out_c, kernel_size=3, stride=2, padding=1),
                    nn.LeakyReLU()]
        for i in range(num_pools - 1):
            modules += [
                Conv2d(out_c, out_c, kernel_size=3, stride=2, padding=1),
                nn.LeakyReLU()
            ]
        self.convs = nn.Sequential(*modules)
        self.linear = EqualLinear(out_c, out_c, lr_mul=1)

    def forward(self, x):
        x = self.convs(x)
        x = x.view(-1, self.out_c)
        x = self.linear(x)
        return x


class GradualStyleEncoder(Module):
    def __init__(self, num_layers, mode='ir', n_styles=18):
        super(GradualStyleEncoder, self).__init__()
        assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
        blocks = get_blocks(num_layers)
        if mode == 'ir':
            unit_module = bottleneck_IR
        elif mode == 'ir_se':
            unit_module = bottleneck_IR_SE
        self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
                                      BatchNorm2d(64),
                                      PReLU(64))
        modules = []
        for block in blocks:
            for bottleneck in block:
                modules.append(unit_module(bottleneck.in_channel,
                                           bottleneck.depth,
                                           bottleneck.stride))
        self.body = Sequential(*modules)

        self.styles = nn.ModuleList()
        self.style_count = n_styles  # opts.n_styles
        self.coarse_ind = 3
        self.middle_ind = 7
        for i in range(self.style_count):
            if i < self.coarse_ind:
                style = GradualStyleBlock(512, 512, 16)
            elif i < self.middle_ind:
                style = GradualStyleBlock(512, 512, 32)
            else:
                style = GradualStyleBlock(512, 512, 64)
            self.styles.append(style)
        self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0)
        self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0)

    def _upsample_add(self, x, y):
        '''Upsample and add two feature maps.
        Args:
          x: (Variable) top feature map to be upsampled.
          y: (Variable) lateral feature map.
        Returns:
          (Variable) added feature map.
        Note in PyTorch, when input size is odd, the upsampled feature map
        with `F.upsample(..., scale_factor=2, mode='nearest')`
        maybe not equal to the lateral feature map size.
        e.g.
        original input size: [N,_,15,15] ->
        conv2d feature map size: [N,_,8,8] ->
        upsampled feature map size: [N,_,16,16]
        So we choose bilinear upsample which supports arbitrary output sizes.
        '''
        _, _, H, W = y.size()
        return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True) + y

    def forward(self, x):
        x = self.input_layer(x)

        latents = []
        modulelist = list(self.body._modules.values())
        for i, l in enumerate(modulelist):
            x = l(x)
            if i == 6:
                c1 = x
            elif i == 20:
                c2 = x
            elif i == 23:
                c3 = x

        for j in range(self.coarse_ind):
            latents.append(self.styles[j](c3))

        p2 = self._upsample_add(c3, self.latlayer1(c2))
        for j in range(self.coarse_ind, self.middle_ind):
            latents.append(self.styles[j](p2))

        p1 = self._upsample_add(p2, self.latlayer2(c1))
        for j in range(self.middle_ind, self.style_count):
            latents.append(self.styles[j](p1))

        out = torch.stack(latents, dim=1)
        return out


def get_keys(d, name):
    if 'state_dict' in d:
        d = d['state_dict']
    d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name}
    return d_filt


class PSPEncoder(Module):
    def __init__(self, encoder_ckpt_path, output_size=1024):
        super(PSPEncoder, self).__init__()
        n_styles = int(math.log(output_size, 2)) * 2 - 2
        self.encoder = GradualStyleEncoder(50, 'ir_se', n_styles)

        print('Loading psp encoders weights from irse50!')
        encoder_ckpt = torch.load(encoder_ckpt_path, map_location='cpu')
        self.encoder.load_state_dict(get_keys(encoder_ckpt, 'encoder'), strict=True)
        self.latent_avg = encoder_ckpt['latent_avg']

        self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256))

    def forward(self, x):
        x = self.face_pool(x)
        codes = self.encoder(x)
        codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)
        return codes