import torch
import torch.nn as nn
from pathlib import Path
import zipfile
from ..simple_models import Size
from typing import Type, Any, Callable, Union, List, Optional
import urllib.request

# This file is modified from https://github.com/tonylins/simclr-converter


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


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


class BasicBlock(nn.Module):
    expansion = 1
    __constants__ = ["downsample"]

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None,
    ):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    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 += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4
    __constants__ = ["downsample"]

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None,
    ):
        super(Bottleneck, self).__init__()
        self.downsample = (
            downsample  # hack: moving downsample to the first to make order correct
        )
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.0)) * groups
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)

        self.stride = stride

    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)
        out = self.relu(out)

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

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    input_size = (3, 224, 224)

    def __init__(
        self,
        block: Type[Union[BasicBlock, Bottleneck]],
        layers: List[int],
        num_classes: int = 1000,
        zero_init_residual: bool = False,
        groups: int = 1,
        width_per_group: int = 64,
        replace_stride_with_dilation: Optional[List[bool]] = None,
        norm_layer: Optional[Callable[..., nn.Module]] = None,
        width_mult: int = 1,
    ):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64 * width_mult
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(
            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64 * width_mult, layers[0])
        self.layer2 = self._make_layer(
            block,
            128 * width_mult,
            layers[1],
            stride=2,
            dilate=replace_stride_with_dilation[0],
        )
        self.layer3 = self._make_layer(
            block,
            256 * width_mult,
            layers[2],
            stride=2,
            dilate=replace_stride_with_dilation[1],
        )
        self.layer4 = self._make_layer(
            block,
            512 * width_mult,
            layers[3],
            stride=2,
            dilate=replace_stride_with_dilation[2],
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion * width_mult, num_classes)

        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.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(
            block(
                self.inplanes,
                planes,
                stride,
                downsample,
                self.groups,
                self.base_width,
                previous_dilation,
                norm_layer,
            )
        )
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(
                block(
                    self.inplanes,
                    planes,
                    groups=self.groups,
                    base_width=self.base_width,
                    dilation=self.dilation,
                    norm_layer=norm_layer,
                )
            )

        return nn.Sequential(*layers)

    def _forward_impl(self, x, apply_fc=True):
        # See note [TorchScript super()]
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        if apply_fc:
            x = self.fc(x)

        return x

    def forward(self, x):
        return self._forward_impl(x)

    @property
    def linear_output(self):
        return self.fc


def resnet50x1(**kwargs):
    return ResNet(Bottleneck, [3, 4, 6, 3], width_mult=1)


def resnet50x2(**kwargs):
    return ResNet(Bottleneck, [3, 4, 6, 3], width_mult=2)


def resnet50x4(**kwargs):
    return ResNet(Bottleneck, [3, 4, 6, 3], width_mult=4)


class SimCLRNet(ResNet):
    def __init__(
        self,
        input_size: Size,
        output_size: Size,
        width_mult: int,
        ckpt_dir: Optional[str] = None,
    ):
        # TODO: remove kwargs?
        assert width_mult in [1, 2, 4]
        assert input_size == self.input_size
        super().__init__(Bottleneck, [3, 4, 6, 3], width_mult=width_mult)

        if ckpt_dir is not None:
            ckpt_dir = Path(ckpt_dir)
            ckpt_path = ckpt_dir / f"resnet50-{width_mult}x.pth"
            if not ckpt_path.exists():
                # Given link from SimCLR repo is "https://storage.cloud.google.com/simclr-gcs/checkpoints/ResNet50_{width_mult}x.zip"
                # But we need to change the link or else we'll just get html
                url = f"https://storage.googleapis.com/simclr-gcs/checkpoints/ResNet50_{width_mult}x.zip"
                tf_ckpt_path = (
                    ckpt_dir / f"ResNet50_{width_mult}x" / "model.ckpt-225206"
                )
                zipfile_path = ckpt_dir / f"checkpoints_ResNet50_{width_mult}x.zip"

                if tf_ckpt_path.exists():
                    from .convert import convert

                    convert(str(tf_ckpt_path), str(ckpt_path))
                elif not zipfile_path.exists():
                    print(
                        f"Downloading SimCLR checkpoint from {url} into {zipfile_path}"
                    )
                    urllib.request.urlretrieve(url, zipfile_path)
                    with zipfile.ZipFile(zipfile_path, "r") as zip_ref:
                        zip_ref.extractall(ckpt_dir)
                    from .convert import convert

                    convert(str(tf_ckpt_path), str(ckpt_path))
                else:
                    with zipfile.ZipFile(zipfile_path, "r") as zip_ref:
                        zip_ref.extractall(ckpt_dir)
                    from .convert import convert

                    convert(str(tf_ckpt_path), str(ckpt_path))

            ckpt = torch.load(ckpt_path)
            self.load_state_dict(ckpt["state_dict"])