import torch
import torch.nn as nn
import torchvision

import sys
import math

# from config import get_args
# global_args = get_args(sys.argv[1:])


def conv3x3(in_planes, out_planes, stride=1):
  """3x3 convolution with padding"""
  return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                   padding=1, bias=False)


def conv1x1(in_planes, out_planes, stride=1):
  """1x1 convolution"""
  return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


def get_sinusoid_encoding(n_position, feat_dim, wave_length=10000):
  # [n_position]
  positions = torch.arange(0, n_position)#.cuda()
  # [feat_dim]
  dim_range = torch.arange(0, feat_dim)#.cuda()
  dim_range = torch.pow(wave_length, 2 * (dim_range // 2) / feat_dim)
  # [n_position, feat_dim]
  angles = positions.unsqueeze(1) / dim_range.unsqueeze(0)
  angles = angles.float()
  angles[:, 0::2] = torch.sin(angles[:, 0::2])
  angles[:, 1::2] = torch.cos(angles[:, 1::2])
  return angles


class AsterBlock(nn.Module):

  def __init__(self, inplanes, planes, stride=1, downsample=None):
    super(AsterBlock, self).__init__()
    self.conv1 = conv1x1(inplanes, planes, stride)
    self.bn1 = nn.BatchNorm2d(planes)
    self.relu = nn.ReLU(inplace=True)
    self.conv2 = conv3x3(planes, planes)
    self.bn2 = nn.BatchNorm2d(planes)
    self.downsample = downsample
    self.stride = stride

  def forward(self, x):
    residual = x
    out = self.conv1(x)
    out = self.bn1(out)
    out = self.relu(out)
    out = self.conv2(out)
    out = self.bn2(out)

    if self.downsample is not None:
      residual = self.downsample(x)
    out += residual
    out = self.relu(out)
    return out


class ResNet_ASTER(nn.Module):
  """For aster or crnn
     borrowed from: https://github.com/ayumiymk/aster.pytorch
  """
  def __init__(self, in_channels=1, out_channel=512, n_group=1):
    super(ResNet_ASTER, self).__init__()
    self.n_group = n_group

    in_channels = in_channels
    self.layer0 = nn.Sequential(
        nn.Conv2d(in_channels, 32, kernel_size=(3, 3), stride=1, padding=1, bias=False),
        nn.BatchNorm2d(32),
        nn.ReLU(inplace=True))

    self.inplanes = 32
    self.layer1 = self._make_layer(32,  3, [2, 2]) # [16, 50]
    self.layer2 = self._make_layer(64,  4, [2, 2]) # [8, 25]
    self.layer3 = self._make_layer(128, 6, [2, 2]) # [4, 25]
    self.layer4 = self._make_layer(256, 6, [1, ]) # [2, 25]
    self.layer5 = self._make_layer(out_channel, 3, [1, 1]) # [1, 25]

    for m in self.modules():
      if isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
      elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)

  def _make_layer(self, planes, blocks, stride):
    downsample = None
    if stride != [1, 1] or self.inplanes != planes:
      downsample = nn.Sequential(
          conv1x1(self.inplanes, planes, stride),
          nn.BatchNorm2d(planes))

    layers = []
    layers.append(AsterBlock(self.inplanes, planes, stride, downsample))
    self.inplanes = planes
    for _ in range(1, blocks):
      layers.append(AsterBlock(self.inplanes, planes))
    return nn.Sequential(*layers)

  def forward(self, x):

    x0 = self.layer0(x)
    x1 = self.layer1(x0)
    x2 = self.layer2(x1)
    x3 = self.layer3(x2)
    x4 = self.layer4(x3)
    x5 = self.layer5(x4)

    return x5


def numel(model):
    return sum(p.numel() for p in model.parameters())

if __name__ == "__main__":
  x = torch.randn(3, 1, 64, 256)
  net = ResNet_ASTER()
  encoder_feat = net(x)
  print(encoder_feat.size())            # 3*512*h/4*w/4

  num_params = numel(net)
  print(f'Number of parameters: {num_params}')