from torch import nn
from torch.autograd import Variable
import torch
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import numpy as np

# from models.vgg_tro_channel1 import vgg16_bn
from models.vgg_tro_channel3 import vgg16_bn, vgg19_bn

# torch.cuda.set_device(1)

DROP_OUT = False
LSTM = False
SUM_UP = True
PRE_TRAIN_VGG = True


class Encoder(nn.Module):
    def __init__(self, hidden_size, height, width, bgru, step, flip):
        super(Encoder, self).__init__()
        self.hidden_size = hidden_size
        self.height = height
        self.width = width
        self.bi = bgru
        self.step = step
        self.flip = flip
        self.n_layers = 2
        self.dropout = 0.5

        # self.layer = vgg16_bn(PRE_TRAIN_VGG)
        self.layer = vgg19_bn(PRE_TRAIN_VGG)

        if DROP_OUT:
            self.layer_dropout = nn.Dropout2d(p=0.5)
        if self.step is not None:
            # self.output_proj = nn.Linear((((((self.height-2)//2)-2)//2-2-2-2)//2)*128*self.step, self.hidden_size)
            self.output_proj = nn.Linear(
                self.height // 16 * 512 * self.step, self.height // 16 * 512
            )

        if LSTM:
            RNN = nn.LSTM
        else:
            RNN = nn.GRU

        if self.bi:  # 8: 3 MaxPool->2**3    128: last hidden_size of layer4
            self.rnn = RNN(
                self.height // 16 * 512,
                self.hidden_size,
                self.n_layers,
                dropout=self.dropout,
                bidirectional=True,
            )
            if SUM_UP:
                self.enc_out_merge = (
                    lambda x: x[:, :, : x.shape[-1] // 2] + x[:, :, x.shape[-1] // 2 :]
                )
                self.enc_hidden_merge = lambda x: (x[0] + x[1]).unsqueeze(0)
        else:
            self.rnn = RNN(
                self.height // 16 * 512,
                self.hidden_size,
                self.n_layers,
                dropout=self.dropout,
                bidirectional=False,
            )

    # (32, 1, 80, 1400)
    def forward(self, in_data, in_data_len, hidden=None):
        batch_size = in_data.shape[0]
        out = self.layer(in_data)  # torch.Size([32, 512, 4, 63])
        if DROP_OUT and self.training:
            out = self.layer_dropout(out)
        # out.register_hook(print)
        out = out.permute(3, 0, 2, 1)  # (width, batch, height, channels)
        out.contiguous()
        # out = out.view(-1, batch_size, (((((self.height-2)//2)-2)//2-2-2-2)//2)*128) # (t, b, f) (173, 32, 1024)
        out = out.reshape(-1, batch_size, self.height // 16 * 512)
        if self.step is not None:
            time_step, batch_size, n_feature = out.shape[0], out.shape[1], out.shape[2]
            out_short = Variable(
                torch.zeros(time_step // self.step, batch_size, n_feature * self.step)
            ).cuda()  # t//STEP, b, f*STEP
            for i in range(0, time_step // self.step):
                part_out = [out[j] for j in range(i * self.step, (i + 1) * self.step)]
                # reverse the image feature map
                out_short[i] = torch.cat(part_out, 1)  # b, f*STEP

            out = self.output_proj(out_short)  # t//STEP, b, hidden_size
        width = out.shape[0]
        src_len = in_data_len.numpy() * (width / self.width)
        src_len = src_len + 0.999  # in case of 0 length value from float to int
        src_len = src_len.astype("int")
        out = pack_padded_sequence(out, src_len.tolist(), batch_first=False)
        output, hidden = self.rnn(out, hidden)
        # output: t, b, f*2  hidden: 2, b, f
        output, output_len = pad_packed_sequence(output, batch_first=False)
        if self.bi and SUM_UP:
            output = self.enc_out_merge(output)
            # hidden = self.enc_hidden_merge(hidden)
        # # output: t, b, f    hidden:  b, f
        odd_idx = [1, 3, 5, 7, 9, 11]
        hidden_idx = odd_idx[: self.n_layers]
        final_hidden = hidden[hidden_idx]
        # if self.flip:
        #    hidden = output[-1]
        #    #hidden = hidden.permute(1, 0, 2) # b, 2, f
        #    #hidden = hidden.contiguous().view(batch_size, -1) # b, f*2
        # else:
        #    hidden = output[0] # b, f*2
        return output, final_hidden  # t, b, f*2    b, f*2

    # matrix: b, c, h, w    lens: list size of batch_size
    def conv_mask(self, matrix, lens):
        lens = np.array(lens)
        width = matrix.shape[-1]
        lens2 = lens * (width / self.width)
        lens2 = lens2 + 0.999  # in case le == 0
        lens2 = lens2.astype("int")
        matrix_new = matrix.permute(0, 3, 1, 2)  # b, w, c, h
        matrix_out = Variable(torch.zeros(matrix_new.shape)).cuda()
        for i, le in enumerate(lens2):
            if self.flip:
                matrix_out[i, -le:] = matrix_new[i, -le:]
            else:
                matrix_out[i, :le] = matrix_new[i, :le]
        matrix_out = matrix_out.permute(0, 2, 3, 1)  # b, c, h, w
        return matrix_out


if __name__ == "__main__":
    print(vgg16_bn())

numpy.array