import torch
from torchvision.models.resnet import Bottleneck, BasicBlock, ResNet, model_urls
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
from torch.quantization import QuantStub, DeQuantStub, fuse_modules
from torch._jit_internal import Optional
from torch.hub import load_state_dict_from_url
from .utils import _replace_relu, quantize_model

__all__ = ['QuantizableResNet', 'resnet18', 'resnet50',
           'resnext101_32x8d']


quant_model_urls = {
    'resnet18_fbgemm':
        'https://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth',
    'resnet50_fbgemm':
        'https://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pth',
    'resnext101_32x8d_fbgemm':
        'https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pth',
}


class QuantizableBasicBlock(BasicBlock):
    def __init__(self, *args, **kwargs):
        super(QuantizableBasicBlock, self).__init__(*args, **kwargs)
        self.add_relu = torch.nn.quantized.FloatFunctional()

    def forward(self, x):
        identity = 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:
            identity = self.downsample(x)

        out = self.add_relu.add_relu(out, identity)

        return out

    def fuse_model(self):
        torch.quantization.fuse_modules(self, [['conv1', 'bn1', 'relu'],
                                               ['conv2', 'bn2']], inplace=True)
        if self.downsample:
            torch.quantization.fuse_modules(self.downsample, ['0', '1'], inplace=True)

class QuantizableBottleneck(Bottleneck):
    def __init__(self, *args, **kwargs):
        super(QuantizableBottleneck, self).__init__(*args, **kwargs)
        self.skip_add_relu = nn.quantized.FloatFunctional()
        self.relu1 = nn.ReLU(inplace=False)
        self.relu2 = nn.ReLU(inplace=False)

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu2(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)
        out = self.skip_add_relu.add_relu(out, identity)

        return out

    def fuse_model(self):
        fuse_modules(self, [['conv1', 'bn1', 'relu1'],
                            ['conv2', 'bn2', 'relu2'],
                            ['conv3', 'bn3']], inplace=True)
        if self.downsample:
            torch.quantization.fuse_modules(self.downsample, ['0', '1'], inplace=True)


class QuantizableResNet(ResNet):

    def __init__(self, *args, **kwargs):
        super(QuantizableResNet, self).__init__(*args, **kwargs)

        self.quant = torch.quantization.QuantStub()
        self.dequant = torch.quantization.DeQuantStub()

    def forward(self, x):
        x = self.quant(x)
        # Ensure scriptability
        # super(QuantizableResNet,self).forward(x)
        # is not scriptable
        x = self._forward_impl(x)
        x = self.dequant(x)
        return x

    def fuse_model(self):
        r"""Fuse conv/bn/relu modules in resnet models

        Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization.
        Model is modified in place.  Note that this operation does not change numerics
        and the model after modification is in floating point
        """

        fuse_modules(self, ['conv1', 'bn1', 'relu'], inplace=True)
        for m in self.modules():
            if type(m) == QuantizableBottleneck or type(m) == QuantizableBasicBlock:
                m.fuse_model()


def _resnet(arch, block, layers, pretrained, progress, quantize = False, **kwargs):
    model = QuantizableResNet(block, layers, **kwargs)
    _replace_relu(model)
    if quantize:
        # TODO use pretrained as a string to specify the backend
        backend = 'fbgemm'
        quantize_model(model, backend)
    else:
        assert pretrained in [True, False]

    if pretrained:
        if quantize:
            model_url = quant_model_urls[arch + '_' + backend]
        else:
            model_url = model_urls[arch]

        state_dict = load_state_dict_from_url(model_url,
                                              progress=progress)

        model.load_state_dict(state_dict)
    return model


# def resnet18(pretrained=False, progress=True, quantize=False, **kwargs):
#     r"""ResNet-18 model from
#     `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

#     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
#     """
#     return _resnet('resnet18', QuantizableBasicBlock, [2, 2, 2, 2], pretrained, progress,
#                    quantize, **kwargs)


# def resnet50(pretrained=False, progress=True, quantize=False, **kwargs):
#     r"""ResNet-50 model from
#     `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

#     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
#     """
#     return _resnet('resnet50', QuantizableBottleneck, [3, 4, 6, 3], pretrained, progress,
#                    quantize, **kwargs)


# def resnext101_32x8d(pretrained=False, progress=True, quantize=False, **kwargs):
#     r"""ResNeXt-101 32x8d model from
#     `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_

#     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
#     """
#     kwargs['groups'] = 32
#     kwargs['width_per_group'] = 8
#     return _resnet('resnext101_32x8d', QuantizableBottleneck, [3, 4, 23, 3],
#                    pretrained, progress, quantize, **kwargs)

def resnet18(pretrained=False, progress=True, **kwargs):
    r"""ResNet-18 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    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
    """
    return _resnet('resnet18', QuantizableBasicBlock, [2, 2, 2, 2], pretrained, progress,
                   **kwargs)


def resnet34(pretrained=False, progress=True, **kwargs):
    r"""ResNet-34 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    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
    """
    return _resnet('resnet34', QuantizableBasicBlock, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)


def resnet50(pretrained=False, progress=True, **kwargs):
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    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
    """
    return _resnet('resnet50', QuantizableBottleneck, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)


def resnet101(pretrained=False, progress=True, **kwargs):
    r"""ResNet-101 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    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
    """
    return _resnet('resnet101', QuantizableBottleneck, [3, 4, 23, 3], pretrained, progress,
                   **kwargs)


def resnet152(pretrained=False, progress=True, **kwargs):
    r"""ResNet-152 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    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
    """
    return _resnet('resnet152', QuantizableBottleneck, [3, 8, 36, 3], pretrained, progress,
                   **kwargs)


def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
    r"""ResNeXt-50 32x4d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_

    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
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 4
    return _resnet('resnext50_32x4d', QuantizableBottleneck, [3, 4, 6, 3],
                   pretrained, progress, **kwargs)


def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
    r"""ResNeXt-101 32x8d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_

    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
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 8
    return _resnet('resnext101_32x8d', QuantizableBottleneck, [3, 4, 23, 3],
                   pretrained, progress, **kwargs)


def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
    r"""Wide ResNet-50-2 model from
    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_

    The model is the same as ResNet except for the bottleneck number of channels
    which is twice larger in every block. The number of channels in outer 1x1
    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
    channels, and in Wide ResNet-50-2 has 2048-1024-2048.

    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
    """
    kwargs['width_per_group'] = 64 * 2
    return _resnet('wide_resnet50_2', QuantizableBottleneck, [3, 4, 6, 3],
                   pretrained, progress, **kwargs)


def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
    r"""Wide ResNet-101-2 model from
    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_

    The model is the same as ResNet except for the bottleneck number of channels
    which is twice larger in every block. The number of channels in outer 1x1
    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
    channels, and in Wide ResNet-50-2 has 2048-1024-2048.

    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
    """
    kwargs['width_per_group'] = 64 * 2
    return _resnet('wide_resnet101_2', QuantizableBottleneck, [3, 4, 23, 3],
                   pretrained, progress, **kwargs)