import torch
from torchvision.models import GoogLeNet
from torch.hub import load_state_dict_from_url

model_urls = {
    # GoogLeNet ported from TensorFlow
    'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth',
}

def googlenet(pretrained=False, progress=True, **kwargs):
    r"""GoogLeNet (Inception v1) model architecture from
    `"Going Deeper with Convolutions" <http://arxiv.org/abs/1409.4842>`_.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
        aux_logits (bool): If True, adds two auxiliary branches that can improve training.
            Default: *False* when pretrained is True otherwise *True*
        transform_input (bool): If True, preprocesses the input according to the method with which it
            was trained on ImageNet. Default: *False*
    """
    if pretrained:
        if 'transform_input' not in kwargs:
            kwargs['transform_input'] = True
        if 'aux_logits' not in kwargs:
            kwargs['aux_logits'] = False
        if kwargs['aux_logits']:
            warnings.warn('auxiliary heads in the pretrained googlenet model are NOT pretrained, '
                          'so make sure to train them')
        original_aux_logits = kwargs['aux_logits']
        kwargs['aux_logits'] = True
        kwargs['init_weights'] = False
        model = GoogLeNetForHDML(**kwargs)
        state_dict = load_state_dict_from_url(model_urls['googlenet'],
                                              progress=progress)
        model.load_state_dict(state_dict)
        if not original_aux_logits:
            model.aux_logits = False
            del model.aux1, model.aux2
        return model

    return GoogLeNetForHDML(**kwargs)


class GoogLeNetForHDML(GoogLeNet):
    def __init__(self, aux_logits=True, transform_input=False, init_weights=True):
        super(GoogLeNetForHDML, self).__init__(1000, aux_logits, transform_input, init_weights)

    def forward(self, x):
        if self.transform_input:
            x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
            x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
            x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
            x = torch.cat((x_ch0, x_ch1, x_ch2), 1)

        # N x 3 x 224 x 224
        x = self.conv1(x)
        # N x 64 x 112 x 112
        x = self.maxpool1(x)
        # N x 64 x 56 x 56
        x = self.conv2(x)
        # N x 64 x 56 x 56
        x = self.conv3(x)
        # N x 192 x 56 x 56
        x = self.maxpool2(x)

        # N x 192 x 28 x 28
        x = self.inception3a(x)
        # N x 256 x 28 x 28
        x = self.inception3b(x)
        # N x 480 x 28 x 28
        x = self.maxpool3(x)
        # N x 480 x 14 x 14
        x = self.inception4a(x)
        # N x 512 x 14 x 14
        if self.training and self.aux_logits:
            aux1 = self.aux1(x)

        x = self.inception4b(x)
        # N x 512 x 14 x 14
        x = self.inception4c(x)
        # N x 512 x 14 x 14
        x = self.inception4d(x)
        # N x 528 x 14 x 14
        if self.training and self.aux_logits:
            aux2 = self.aux2(x)

        x = self.inception4e(x)
        # N x 832 x 14 x 14
        x = self.maxpool4(x)
        # N x 832 x 7 x 7
        x = self.inception5a(x)
        # N x 832 x 7 x 7
        x = self.inception5b(x)
        # N x 1024 x 7 x 7

        x = self.avgpool(x)
        # N x 1024 x 1 x 1
        x = torch.flatten(x, 1)
        return x
