import torch
import torch.nn as nn
from torch_geometric.nn.conv import GCNConv
from .utils import left_bias, right_bias, build_ocr_graph
from .base import CNN, MindNetLSTM
import torchvision.models as models


class SingleMindNet(nn.Module):
    """
    Base ToM net. Supports any subset of modalities
    """
    def __init__(self, hidden_dim, device, resnet=False, dropout=0.1, mods=['rgb', 'pose', 'gaze', 'ocr', 'bbox']):
        super(SingleMindNet, self).__init__()

        # ---- Images ----#
        if resnet: 
            resnet = models.resnet34(weights="IMAGENET1K_V1")
            self.cnn = nn.Sequential(
                *(list(resnet.children())[:-1])
            )
            for param in self.cnn.parameters():
                param.requires_grad = False
            self.rgb_ff = nn.Linear(512, hidden_dim)
        else:
            self.cnn = CNN(hidden_dim)
            self.rgb_ff = nn.Linear(hidden_dim, hidden_dim)

        # ---- OCR and bbox -----#
        self.ocr_x, self.ocr_edge_index, self.ocr_edge_attr = build_ocr_graph(device)
        self.ocr_gnn = GCNConv(-1, hidden_dim)
        self.bbox_ff = nn.Linear(108, hidden_dim)

        # ---- Others ----#
        self.act = nn.GELU()
        self.dropout = nn.Dropout(dropout)
        self.device = device

        # ---- Mind nets ----#
        self.mind_net = MindNetLSTM(hidden_dim, dropout, mods)
        
        self.left = nn.Linear(hidden_dim, 27)
        self.right = nn.Linear(hidden_dim, 27)
        self.left.bias.data = torch.tensor(left_bias).log()
        self.right.bias.data = torch.tensor(right_bias).log()

    def forward(self, images, poses, gazes, bboxes, ocr_tensor=None):

        assert images is not None
        assert poses is not None
        assert gazes is not None
        assert bboxes is not None
        
        batch_size, sequence_len, channels, height, width = images.shape

        bbox_feat = self.act(self.bbox_ff(bboxes))
        
        rgb_feat = []
        for i in range(sequence_len):
            images_i = images[:,i]
            img_i_feat = self.cnn(images_i)
            img_i_feat = img_i_feat.view(batch_size, -1)
            rgb_feat.append(img_i_feat)
        rgb_feat = torch.stack(rgb_feat, 1)
        rgb_feat = self.dropout(self.act(self.rgb_ff(rgb_feat)))

        ocr_feat = self.dropout(self.act(self.ocr_gnn(self.ocr_x, 
                                                      self.ocr_edge_index,
                                                      self.ocr_edge_attr)))
        ocr_feat = ocr_feat.mean(0).unsqueeze(0).repeat(batch_size, sequence_len, 1)

        out_left, cell_left, feats_left = self.mind_net(rgb_feat, ocr_feat, poses[:, :, 0, :], gazes[:, :, 0, :], bbox_feat)
        out_right, cell_right, feats_right = self.mind_net(rgb_feat, ocr_feat, poses[:, :, 1, :], gazes[:, :, 1, :], bbox_feat)

        return self.left(out_left), self.right(out_right), [out_left, cell_left, out_right, cell_right] + feats_left + feats_right




if __name__ == "__main__":

    def count_parameters(model):
        import numpy as np 
        model_parameters = filter(lambda p: p.requires_grad, model.parameters())
        return sum([np.prod(p.size()) for p in model_parameters])
    
    images = torch.ones(3, 22, 3, 128, 128)
    poses = torch.ones(3, 22, 2, 75)
    gazes = torch.ones(3, 22, 2, 3)
    bboxes = torch.ones(3, 22, 108)
    model = SingleMindNet(64, 'cpu', False, 0.5)
    print(count_parameters(model))
    breakpoint()
    out = model(images, poses, gazes, bboxes, None)
    print(out[0].shape)