import numpy as np
import utils.weight_init as weight_init
import torch
import torch.nn.functional as F
from torch import nn
from bodyseg.backbone.backbone import Backbone, get_norm, Conv2d

class BasicStem(nn.Module):
    def __init__(self, in_channels=3, out_channels=64, norm="BN"):
        """
        Args:
            norm (str or callable): a callable that takes the number of
                channels and return a `nn.Module`, or a pre-defined string
                (one of {"FrozenBN", "BN", "GN"}).
        """
        super().__init__()
        self.conv1 = Conv2d(
            in_channels,
            out_channels,
            kernel_size=7,
            stride=2,
            padding=3,
            bias=False,
            norm=get_norm(norm, out_channels),
        )
        weight_init.c2_msra_fill(self.conv1)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu_(x)
        x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
        return x

    @property
    def out_channels(self):
        return self.conv1.out_channels

    @property
    def stride(self):
        return 4  # = stride 2 conv -> stride 2 max pool

class ResNetBlockBase(nn.Module):
    def __init__(self, in_channels, out_channels, stride):
        """
        The `__init__` method of any subclass should also contain these arguments.

        Args:
            in_channels (int):
            out_channels (int):
            stride (int):
        """
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.stride = stride

class BottleneckBlock(ResNetBlockBase):
    def __init__(
        self,
        in_channels,
        out_channels,
        *,
        bottleneck_channels,
        stride=1,
        num_groups=1,
        norm="BN",
        stride_in_1x1=False,
        dilation=1,
    ):
        """
        Args:
            norm (str or callable): a callable that takes the number of
                channels and return a `nn.Module`, or a pre-defined string
                (one of {"FrozenBN", "BN", "GN"}).
            stride_in_1x1 (bool): when stride==2, whether to put stride in the
                first 1x1 convolution or the bottleneck 3x3 convolution.
        """
        super().__init__(in_channels, out_channels, stride)

        if in_channels != out_channels:
            self.shortcut = Conv2d(
                in_channels,
                out_channels,
                kernel_size=1,
                stride=stride,
                bias=False,
                norm=get_norm(norm, out_channels),
            )
        else:
            self.shortcut = None

        # The original MSRA ResNet models have stride in the first 1x1 conv
        # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have
        # stride in the 3x3 conv
        stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)

        self.conv1 = Conv2d(
            in_channels,
            bottleneck_channels,
            kernel_size=1,
            stride=stride_1x1,
            bias=False,
            norm=get_norm(norm, bottleneck_channels),
        )

        self.conv2 = Conv2d(
            bottleneck_channels,
            bottleneck_channels,
            kernel_size=3,
            stride=stride_3x3,
            padding=1 * dilation,
            bias=False,
            groups=num_groups,
            dilation=dilation,
            norm=get_norm(norm, bottleneck_channels),
        )

        self.conv3 = Conv2d(
            bottleneck_channels,
            out_channels,
            kernel_size=1,
            bias=False,
            norm=get_norm(norm, out_channels),
        )

        for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]:
            if layer is not None:  # shortcut can be None
                weight_init.c2_msra_fill(layer)

        # Zero-initialize the last normalization in each residual branch,
        # so that at the beginning, the residual branch starts with zeros,
        # and each residual block behaves like an identity.
        # See Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour":
        # "For BN layers, the learnable scaling coefficient γ is initialized
        # to be 1, except for each residual block's last BN
        # where γ is initialized to be 0."

        # nn.init.constant_(self.conv3.norm.weight, 0)
        # TODO this somehow hurts performance when training GN models from scratch.
        # Add it as an option when we need to use this code to train a backbone.

    def forward(self, x):
        out = self.conv1(x)
        out = F.relu_(out)

        out = self.conv2(out)
        out = F.relu_(out)

        out = self.conv3(out)

        if self.shortcut is not None:
            shortcut = self.shortcut(x)
        else:
            shortcut = x

        out += shortcut
        out = F.relu_(out)
        return out

def make_stage(block_class, num_blocks, first_stride, **kwargs):
    """
    Create a resnet stage by creating many blocks.
    Args:
        block_class (class): a subclass of ResNetBlockBase
        num_blocks (int):
        first_stride (int): the stride of the first block. The other blocks will have stride=1.
            A `stride` argument will be passed to the block constructor.
        kwargs: other arguments passed to the block constructor.

    Returns:
        list[nn.Module]: a list of block module.
    """
    blocks = []
    for i in range(num_blocks):
        blocks.append(block_class(stride=first_stride if i == 0 else 1, **kwargs))
        kwargs["in_channels"] = kwargs["out_channels"]
    return blocks

class ResNet(Backbone):
    def __init__(self, stem, stages, num_classes=None, out_features=None):
        """
        Args:
            stem (nn.Module): a stem module
            stages (list[list[ResNetBlock]]): several (typically 4) stages,
                each contains multiple :class:`ResNetBlockBase`.
            num_classes (None or int): if None, will not perform classification.
            out_features (list[str]): name of the layers whose outputs should
                be returned in forward. Can be anything in "stem", "linear", or "res2" ...
                If None, will return the output of the last layer.
        """
        super(ResNet, self).__init__()
        self.stem = stem
        self.num_classes = num_classes

        current_stride = self.stem.stride
        self._out_feature_strides = {"stem": current_stride}
        self._out_feature_channels = {"stem": self.stem.out_channels}

        self.stages = []
        self.names = []
        for i, blocks in enumerate(stages):
            for block in blocks:
                assert isinstance(block, ResNetBlockBase), block
                curr_channels = block.out_channels
            stage = nn.Sequential(*blocks)
            name = "res" + str(i + 2)
            self.stages.append(stage)
            self.names.append(name)
            self._out_feature_strides[name] = current_stride = int(
                current_stride * np.prod([k.stride for k in blocks])
            )
            self._out_feature_channels[name] = blocks[-1].out_channels
        self.stages = nn.ModuleList(self.stages)

        if num_classes is not None:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
            self.linear = nn.Linear(curr_channels, num_classes)

            # Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour":
            # "The 1000-way fully-connected layer is initialized by
            # drawing weights from a zero-mean Gaussian with standard deviation of 0.01."
            nn.init.normal_(self.linear.weight, stddev=0.01)
            name = "linear"

        if out_features is None:
            out_features = [name]
        self._out_features = out_features
        assert len(self._out_features)
        for out_feature in self._out_features:
            assert out_feature in self.names, "Available children: {}".format(", ".join(self.names))

    def forward(self, x):
        outputs = {}
        x = self.stem(x)
        if "stem" in self._out_features:
            outputs["stem"] = x
        for ix, stage in enumerate(self.stages):
            name = self.names[ix]
            x = stage(x)
            if name in self._out_features:
                outputs[name] = x
        if self.num_classes is not None:
            x = self.avgpool(x)
            x = self.linear(x)
            if "linear" in self._out_features:
                outputs["linear"] = x
        return outputs

def build_resnet_backbone(in_channels=3):
    norm = "BN"
    stem = BasicStem(
        in_channels=in_channels,
        out_channels=64,
        norm=norm,
    )

    # fmt: off
    out_features = ["res2", "res3", "res4"]
    depth = 101
    num_groups = 1
    bottleneck_channels = 64
    in_channels = 64
    out_channels = 256
    stride_in_1x1 = True
    res5_dilation = 1
    # fmt: on
    assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation)

    num_blocks_per_stage = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3]}[depth]

    stages = []

    # Avoid creating variables without gradients
    # It consumes extra memory and may cause allreduce to fail
    out_stage_idx = [{"res2": 2, "res3": 3, "res4": 4, "res5": 5}[f] for f in out_features]
    max_stage_idx = max(out_stage_idx)
    for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)):
        dilation = res5_dilation if stage_idx == 5 else 1
        first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2
        stage_kargs = dict()
        stage_kargs.update({
            "num_blocks": num_blocks_per_stage[idx],
            "first_stride": first_stride,
            "in_channels": in_channels,
            "bottleneck_channels": bottleneck_channels,
            "out_channels": out_channels,
            "num_groups": num_groups,
            "norm": norm,
            "stride_in_1x1": stride_in_1x1,
            "dilation": dilation,
        })
        stage_kargs["block_class"] = BottleneckBlock
        blocks = make_stage(**stage_kargs)
        in_channels = out_channels
        out_channels *= 2
        bottleneck_channels *= 2
        stages.append(blocks)
    return ResNet(stem, stages, out_features=out_features)