# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# For licensing see accompanying LICENSE file.
# Copyright (C) 2024 Apple Inc. All Rights Reserved.

from __future__ import annotations

import copy

import torch
import torch.nn as nn
import torch.nn.functional as F

__all__ = ["MobileOneBlock", "reparameterize_model"]


class SEBlock(nn.Module):
    """Squeeze and Excite module.

    Pytorch implementation of `Squeeze-and-Excitation Networks` - https://arxiv.org/pdf/1709.01507.pdf
    """

    def __init__(self, in_channels: int, rd_ratio: float = 0.0625) -> None:
        """Construct a Squeeze and Excite Module.

        Args:
            in_channels: Number of input channels.
            rd_ratio: Input channel reduction ratio.
        """
        super().__init__()
        self.reduce = nn.Conv2d(
            in_channels=in_channels,
            out_channels=int(in_channels * rd_ratio),
            kernel_size=1,
            stride=1,
            bias=True,
        )
        self.expand = nn.Conv2d(
            in_channels=int(in_channels * rd_ratio),
            out_channels=in_channels,
            kernel_size=1,
            stride=1,
            bias=True,
        )

    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        """Apply forward pass."""
        _b, c, h, w = inputs.size()
        x = F.avg_pool2d(inputs, kernel_size=[h, w])
        x = self.reduce(x)
        x = F.relu(x)
        x = self.expand(x)
        x = torch.sigmoid(x)
        x = x.view(-1, c, 1, 1)
        return inputs * x


class MobileOneBlock(nn.Module):
    """MobileOne building block.

    This block has a multi-branched architecture at train-time and plain-CNN style architecture at inference time For
    more details, please refer to our paper: `An Improved One millisecond Mobile Backbone` -
    https://arxiv.org/pdf/2206.04040.pdf
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        padding: int = 0,
        dilation: int = 1,
        groups: int = 1,
        inference_mode: bool = False,
        use_se: bool = False,
        use_act: bool = True,
        use_scale_branch: bool = True,
        num_conv_branches: int = 1,
        activation: nn.Module = nn.GELU(),
    ) -> None:
        """Construct a MobileOneBlock module.

        Args:
            in_channels: Number of channels in the input.
            out_channels: Number of channels produced by the block.
            kernel_size: Size of the convolution kernel.
            stride: Stride size.
            padding: Zero-padding size.
            dilation: Kernel dilation factor.
            groups: Group number.
            inference_mode: If True, instantiates model in inference mode.
            use_se: Whether to use SE-ReLU activations.
            use_act: Whether to use activation. Default: ``True``
            use_scale_branch: Whether to use scale branch. Default: ``True``
            num_conv_branches: Number of linear conv branches.
        """
        super().__init__()
        self.inference_mode = inference_mode
        self.groups = groups
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.kernel_size = kernel_size
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.num_conv_branches = num_conv_branches

        # Check if SE-ReLU is requested
        if use_se:
            self.se = SEBlock(out_channels)
        else:
            self.se = nn.Identity()

        if use_act:
            self.activation = activation
        else:
            self.activation = nn.Identity()

        if inference_mode:
            self.reparam_conv = nn.Conv2d(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=kernel_size,
                stride=stride,
                padding=padding,
                dilation=dilation,
                groups=groups,
                bias=True,
            )
        else:
            # Re-parameterizable skip connection
            self.rbr_skip = (
                nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else None
            )

            # Re-parameterizable conv branches
            if num_conv_branches > 0:
                rbr_conv = list()
                for _ in range(self.num_conv_branches):
                    rbr_conv.append(self._conv_bn(kernel_size=kernel_size, padding=padding))
                self.rbr_conv = nn.ModuleList(rbr_conv)
            else:
                self.rbr_conv = None

            # Re-parameterizable scale branch
            self.rbr_scale = None
            if not isinstance(kernel_size, int):
                kernel_size = kernel_size[0]
            if (kernel_size > 1) and use_scale_branch:
                self.rbr_scale = self._conv_bn(kernel_size=1, padding=0)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Apply forward pass."""
        # Inference mode forward pass.
        if self.inference_mode:
            return self.activation(self.se(self.reparam_conv(x)))

        # Multi-branched train-time forward pass.
        # Skip branch output
        identity_out = 0
        if self.rbr_skip is not None:
            identity_out = self.rbr_skip(x)

        # Scale branch output
        scale_out = 0
        if self.rbr_scale is not None:
            scale_out = self.rbr_scale(x)

        # Other branches
        out = scale_out + identity_out
        if self.rbr_conv is not None:
            for ix in range(self.num_conv_branches):
                out += self.rbr_conv[ix](x)

        return self.activation(self.se(out))

    def reparameterize(self):
        """Following works like `RepVGG: Making VGG-style ConvNets Great Again` - https://arxiv.org/pdf/2101.03697.pdf.
        We re-parameterize multi-branched architecture used at training time to obtain a plain CNN-like
        structure for inference.
        """
        if self.inference_mode:
            return
        kernel, bias = self._get_kernel_bias()
        self.reparam_conv = nn.Conv2d(
            in_channels=self.in_channels,
            out_channels=self.out_channels,
            kernel_size=self.kernel_size,
            stride=self.stride,
            padding=self.padding,
            dilation=self.dilation,
            groups=self.groups,
            bias=True,
        )
        self.reparam_conv.weight.data = kernel
        self.reparam_conv.bias.data = bias

        # Delete un-used branches
        for para in self.parameters():
            para.detach_()
        self.__delattr__("rbr_conv")
        self.__delattr__("rbr_scale")
        if hasattr(self, "rbr_skip"):
            self.__delattr__("rbr_skip")

        self.inference_mode = True

    def _get_kernel_bias(self) -> tuple[torch.Tensor, torch.Tensor]:
        """Method to obtain re-parameterized kernel and bias. Reference:
        https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83.

        Returns:
            Tuple of (kernel, bias) after fusing branches.
        """
        # get weights and bias of scale branch
        kernel_scale = 0
        bias_scale = 0
        if self.rbr_scale is not None:
            kernel_scale, bias_scale = self._fuse_bn_tensor(self.rbr_scale)
            # Pad scale branch kernel to match conv branch kernel size.
            pad = self.kernel_size // 2
            kernel_scale = torch.nn.functional.pad(kernel_scale, [pad, pad, pad, pad])

        # get weights and bias of skip branch
        kernel_identity = 0
        bias_identity = 0
        if self.rbr_skip is not None:
            kernel_identity, bias_identity = self._fuse_bn_tensor(self.rbr_skip)

        # get weights and bias of conv branches
        kernel_conv = 0
        bias_conv = 0
        if self.rbr_conv is not None:
            for ix in range(self.num_conv_branches):
                _kernel, _bias = self._fuse_bn_tensor(self.rbr_conv[ix])
                kernel_conv += _kernel
                bias_conv += _bias

        kernel_final = kernel_conv + kernel_scale + kernel_identity
        bias_final = bias_conv + bias_scale + bias_identity
        return kernel_final, bias_final

    def _fuse_bn_tensor(self, branch: nn.Sequential | nn.BatchNorm2d) -> tuple[torch.Tensor, torch.Tensor]:
        """Method to fuse batchnorm layer with preceeding conv layer. Reference:
        https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95.

        Args:
            branch: Sequence of ops to be fused.

        Returns:
            Tuple of (kernel, bias) after fusing batchnorm.
        """
        if isinstance(branch, nn.Sequential):
            kernel = branch.conv.weight
            running_mean = branch.bn.running_mean
            running_var = branch.bn.running_var
            gamma = branch.bn.weight
            beta = branch.bn.bias
            eps = branch.bn.eps
        else:
            assert isinstance(branch, nn.BatchNorm2d)
            if not hasattr(self, "id_tensor"):
                input_dim = self.in_channels // self.groups

                kernel_size = self.kernel_size
                if isinstance(self.kernel_size, int):
                    kernel_size = (self.kernel_size, self.kernel_size)

                kernel_value = torch.zeros(
                    (self.in_channels, input_dim, kernel_size[0], kernel_size[1]),
                    dtype=branch.weight.dtype,
                    device=branch.weight.device,
                )
                for i in range(self.in_channels):
                    kernel_value[i, i % input_dim, kernel_size[0] // 2, kernel_size[1] // 2] = 1
                self.id_tensor = kernel_value
            kernel = self.id_tensor
            running_mean = branch.running_mean
            running_var = branch.running_var
            gamma = branch.weight
            beta = branch.bias
            eps = branch.eps
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape(-1, 1, 1, 1)
        return kernel * t, beta - running_mean * gamma / std

    def _conv_bn(self, kernel_size: int, padding: int) -> nn.Sequential:
        """Helper method to construct conv-batchnorm layers.

        Args:
            kernel_size: Size of the convolution kernel.
            padding: Zero-padding size.

        Returns:
            Conv-BN module.
        """
        mod_list = nn.Sequential()
        mod_list.add_module(
            "conv",
            nn.Conv2d(
                in_channels=self.in_channels,
                out_channels=self.out_channels,
                kernel_size=kernel_size,
                stride=self.stride,
                padding=padding,
                groups=self.groups,
                bias=False,
            ),
        )
        mod_list.add_module("bn", nn.BatchNorm2d(num_features=self.out_channels))
        return mod_list


def reparameterize_model(model: torch.nn.Module) -> nn.Module:
    """Method returns a model where a multi-branched structure used in training is re-parameterized into a single branch
    for inference.

    Args:
        model: MobileOne model in train mode.

    Returns:
        MobileOne model in inference mode.
    """
    # Avoid editing original graph
    model = copy.deepcopy(model)
    for module in model.modules():
        if hasattr(module, "reparameterize"):
            module.reparameterize()
    return model
