import torch
import torch.nn as nn

from models.auto_encoder import AutoEncoder
from models.hpm import HorizontalPyramidMatching
from models.part_net import PartNet


class RGBPartNet(nn.Module):
    def __init__(
            self,
            ae_in_channels: int = 3,
            ae_in_size: tuple[int, int] = (64, 48),
            ae_feature_channels: int = 64,
            f_a_c_p_dims: tuple[int, int, int] = (128, 128, 64),
            hpm_use_1x1conv: bool = False,
            hpm_scales: tuple[int, ...] = (1, 2, 4),
            hpm_use_avg_pool: bool = True,
            hpm_use_max_pool: bool = True,
            tfa_squeeze_ratio: int = 4,
            tfa_num_parts: int = 16,
            embedding_dims: int = 256,
            image_log_on: bool = False
    ):
        super().__init__()
        self.h, self.w = ae_in_size
        (self.f_a_dim, self.f_c_dim, self.f_p_dim) = f_a_c_p_dims
        self.hpm_num_parts = sum(hpm_scales)
        self.image_log_on = image_log_on

        self.ae = AutoEncoder(
            ae_in_channels, ae_in_size, ae_feature_channels, f_a_c_p_dims
        )
        self.pn_in_channels = ae_feature_channels * 2
        self.pn = PartNet(
            self.pn_in_channels, tfa_squeeze_ratio, tfa_num_parts
        )
        self.hpm = HorizontalPyramidMatching(
            ae_feature_channels * 2, self.pn_in_channels, hpm_use_1x1conv,
            hpm_scales, hpm_use_avg_pool, hpm_use_max_pool
        )
        self.num_total_parts = self.hpm_num_parts + tfa_num_parts
        empty_fc = torch.empty(self.num_total_parts,
                               self.pn_in_channels, embedding_dims)
        self.fc_mat = nn.Parameter(empty_fc)

    def fc(self, x):
        return x @ self.fc_mat

    def forward(self, x_c1, x_c2=None):
        # Step 1: Disentanglement
        # n, t, c, h, w
        ((x_c, x_p), ae_losses, images) = self._disentangle(x_c1, x_c2)

        # Step 2.a: Static Gait Feature Aggregation & HPM
        # n, c, h, w
        x_c = self.hpm(x_c)
        # p, n, c

        # Step 2.b: FPFE & TFA (Dynamic Gait Feature Aggregation)
        # n, t, c, h, w
        x_p = self.pn(x_p)
        # p, n, c

        # Step 3: Cat feature map together and fc
        x = torch.cat((x_c, x_p))
        x = self.fc(x)

        if self.training:
            return x.transpose(0, 1), ae_losses, images
        else:
            return x.unsqueeze(1).view(-1)

    def _disentangle(self, x_c1_t2, x_c2_t2=None):
        n, t, c, h, w = x_c1_t2.size()
        device = x_c1_t2.device
        if self.training:
            x_c1_t1 = x_c1_t2[:, torch.randperm(t), :, :, :]
            ((f_a_, f_c_, f_p_), losses) = self.ae(x_c1_t2, x_c1_t1, x_c2_t2)
            # Decode features
            x_c = self._decode_cano_feature(f_c_, n, t, device)
            x_p_ = self._decode_pose_feature(f_p_, n, t, device)
            x_p = x_p_.view(n, t, self.pn_in_channels, self.h // 4, self.w // 4)

            i_a, i_c, i_p = None, None, None
            if self.image_log_on:
                with torch.no_grad():
                    i_a = self._decode_appr_feature(f_a_, n, t, device)
                    # Continue decoding canonical features
                    i_c = self.ae.decoder.trans_conv3(x_c)
                    i_c = torch.sigmoid(self.ae.decoder.trans_conv4(i_c))
                    i_p_ = self.ae.decoder.trans_conv3(x_p_)
                    i_p_ = torch.sigmoid(self.ae.decoder.trans_conv4(i_p_))
                    i_p = i_p_.view(n, t, c, h, w)

            return (x_c, x_p), losses, (i_a, i_c, i_p)

        else:  # evaluating
            f_c_, f_p_ = self.ae(x_c1_t2)
            x_c = self._decode_cano_feature(f_c_, n, t, device)
            x_p_ = self._decode_pose_feature(f_p_, n, t, device)
            x_p = x_p_.view(n, t, self.pn_in_channels, self.h // 4, self.w // 4)
            return (x_c, x_p), None, None

    def _decode_appr_feature(self, f_a_, n, t, device):
        # Decode appearance features
        f_a = f_a_.view(n, t, -1)
        x_a = self.ae.decoder(
            f_a.mean(1),
            torch.zeros((n, self.f_c_dim), device=device),
            torch.zeros((n, self.f_p_dim), device=device)
        )
        return x_a

    def _decode_cano_feature(self, f_c_, n, t, device):
        # Decode average canonical features to higher dimension
        f_c = f_c_.view(n, t, -1)
        x_c = self.ae.decoder(
            torch.zeros((n, self.f_a_dim), device=device),
            f_c.mean(1),
            torch.zeros((n, self.f_p_dim), device=device),
            is_feature_map=True
        )
        return x_c

    def _decode_pose_feature(self, f_p_, n, t, device):
        # Decode pose features to images
        x_p_ = self.ae.decoder(
            torch.zeros((n * t, self.f_a_dim), device=device),
            torch.zeros((n * t, self.f_c_dim), device=device),
            f_p_,
            is_feature_map=True
        )
        return x_p_