import math
import torch.nn as nn
import models.basicblock as B


"""
# --------------------------------------------
# SR network with Residual in Residual Dense Block (RRDB)
# "ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks"
# --------------------------------------------
"""


class RRDB(nn.Module):
    """
    gc: number of growth channels
    nb: number of RRDB
    """
    def __init__(self, in_nc=3, out_nc=3, nc=64, nb=23, gc=32, upscale=4, act_mode='L', upsample_mode='upconv'):
        super(RRDB, self).__init__()
        assert 'R' in act_mode or 'L' in act_mode, 'Examples of activation function: R, L, BR, BL, IR, IL'

        n_upscale = int(math.log(upscale, 2))
        if upscale == 3:
            n_upscale = 1

        m_head = B.conv(in_nc, nc, mode='C')

        m_body = [B.RRDB(nc, gc=32, mode='C'+act_mode) for _ in range(nb)]
        m_body.append(B.conv(nc, nc, mode='C'))

        if upsample_mode == 'upconv':
            upsample_block = B.upsample_upconv
        elif upsample_mode == 'pixelshuffle':
            upsample_block = B.upsample_pixelshuffle
        elif upsample_mode == 'convtranspose':
            upsample_block = B.upsample_convtranspose
        else:
            raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))

        if upscale == 3:
            m_uper = upsample_block(nc, nc, mode='3'+act_mode)
        else:
            m_uper = [upsample_block(nc, nc, mode='2'+act_mode) for _ in range(n_upscale)]

        H_conv0 = B.conv(nc, nc, mode='C'+act_mode)
        H_conv1 = B.conv(nc, out_nc, mode='C')
        m_tail = B.sequential(H_conv0, H_conv1)

        self.model = B.sequential(m_head, B.ShortcutBlock(B.sequential(*m_body)), *m_uper, m_tail)

    def forward(self, x):
        x = self.model(x)
        return x