# 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 torch
import torch.nn as nn
from timm.models.layers import DropPath, trunc_normal_

from mobileclip.modules.common.mobileone import MobileOneBlock


class ConvFFN(nn.Module):
    """Convolutional FFN Module."""

    def __init__(
        self,
        in_channels: int,
        context_size: int,
        hidden_channels: int | None = None,
        out_channels: int | None = None,
        act_layer: nn.Module = nn.GELU,
        drop: float = 0.0,
    ) -> None:
        """Build convolutional FFN module.

        Args:
            in_channels: Number of input channels.
            context_size: Context size for 1D signals.
            hidden_channels: Number of channels after expansion. Default: None
            out_channels: Number of output channels. Default: None
            act_layer: Activation layer. Default: ``GELU``
            drop: Dropout rate. Default: ``0.0``.
        """
        super().__init__()
        out_channels = out_channels or in_channels
        hidden_channels = hidden_channels or in_channels
        self.conv = nn.Sequential()
        self.conv.add_module(
            "conv",
            nn.Conv2d(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=(1, int(context_size)),
                padding=(0, int(context_size // 2)),
                groups=in_channels,
                bias=False,
            ),
        )
        self.conv.add_module("bn", nn.BatchNorm2d(num_features=out_channels))
        self.fc1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=1)
        self.act = act_layer()
        self.fc2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=1)
        self.drop = nn.Dropout(drop)
        self.apply(self._init_weights)

    def _init_weights(self, m: nn.Module) -> None:
        if isinstance(m, nn.Conv2d):
            trunc_normal_(m.weight, std=0.02)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.conv(x)
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class RepMixer(nn.Module):
    """Reparameterizable token mixer.

    For more details, please refer to our paper: `FastViT: A Fast Hybrid Vision Transformer using Structural
    Reparameterization <https://arxiv.org/pdf/2303.14189.pdf>`_
    """

    def __init__(
        self,
        dim,
        kernel_size=3,
        use_layer_scale=True,
        layer_scale_init_value=1e-5,
        inference_mode: bool = False,
    ):
        """Build RepMixer Module.

        Args:
            dim: Input feature map dimension. :math:`C_{in}` from an expected input of size :math:`(B, C_{in}, H, W)`.
            kernel_size: Kernel size for spatial mixing. Default: 3
            use_layer_scale: If True, learnable layer scale is used. Default: ``True``
            layer_scale_init_value: Initial value for layer scale. Default: 1e-5
            inference_mode: If True, instantiates model in inference mode. Default: ``False``
        """
        super().__init__()
        self.dim = dim
        self.kernel_size = kernel_size
        self.inference_mode = inference_mode

        if inference_mode:
            self.reparam_conv = nn.Conv2d(
                in_channels=self.dim,
                out_channels=self.dim,
                kernel_size=(1, self.kernel_size),
                stride=1,
                padding=(0, self.kernel_size // 2),
                groups=self.dim,
                bias=True,
            )
        else:
            self.norm = MobileOneBlock(
                dim,
                dim,
                (1, kernel_size),
                padding=(0, kernel_size // 2),
                groups=dim,
                use_act=False,
                use_scale_branch=False,
                num_conv_branches=0,
            )
            self.mixer = MobileOneBlock(
                dim,
                dim,
                (1, kernel_size),
                padding=(0, kernel_size // 2),
                groups=dim,
                use_act=False,
            )
            self.use_layer_scale = use_layer_scale
            if use_layer_scale:
                self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if hasattr(self, "reparam_conv"):
            x = self.reparam_conv(x)
            return x
        else:
            if self.use_layer_scale:
                x = x + self.layer_scale * (self.mixer(x) - self.norm(x))
            else:
                x = x + self.mixer(x) - self.norm(x)
            return x

    def reparameterize(self) -> None:
        """Reparameterize mixer and norm into a single convolutional layer for efficient inference."""
        if self.inference_mode:
            return

        self.mixer.reparameterize()
        self.norm.reparameterize()

        if self.use_layer_scale:
            w = self.mixer.id_tensor + self.layer_scale.unsqueeze(-1) * (
                self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
            )
            b = torch.squeeze(self.layer_scale) * (self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias)
        else:
            w = self.mixer.id_tensor + self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
            b = self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias

        self.reparam_conv = nn.Conv2d(
            in_channels=self.dim,
            out_channels=self.dim,
            kernel_size=(1, self.kernel_size),
            stride=1,
            padding=(0, self.kernel_size // 2),
            groups=self.dim,
            bias=True,
        )
        self.reparam_conv.weight.data = w
        self.reparam_conv.bias.data = b

        for para in self.parameters():
            para.detach_()
        self.__delattr__("mixer")
        self.__delattr__("norm")
        if self.use_layer_scale:
            self.__delattr__("layer_scale")


class RepMixerBlock(nn.Module):
    """Implementation of Metaformer block with RepMixer as token mixer.

    For more details on Metaformer structure, please refer to: `MetaFormer Is Actually What You Need for Vision
    <https://arxiv.org/pdf/2111.11418.pdf>`_
    """

    def __init__(
        self,
        dim: int,
        kernel_size: int = 11,
        mlp_ratio: float = 4.0,
        act_layer: nn.Module = nn.GELU,
        drop: float = 0.0,
        drop_path: float = 0.0,
        use_layer_scale: bool = True,
        layer_scale_init_value: float = 1e-5,
        inference_mode: bool = False,
        *args,
        **kwargs,
    ):
        """Build RepMixer Block.

        Args:
            dim: Number of embedding dimensions.
            kernel_size: Kernel size for repmixer. Default: 3
            mlp_ratio: MLP expansion ratio. Default: 4.0
            act_layer: Activation layer. Default: ``nn.GELU``
            drop: Dropout rate. Default: 0.0
            drop_path: Drop path rate. Default: 0.0
            use_layer_scale: Flag to turn on layer scale. Default: ``True``
            layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
            inference_mode: Flag to instantiate block in inference mode. Default: ``False``
        """
        super().__init__()

        self.token_mixer = RepMixer(
            dim,
            kernel_size=kernel_size,
            use_layer_scale=use_layer_scale,
            layer_scale_init_value=layer_scale_init_value,
            inference_mode=inference_mode,
        )

        assert mlp_ratio > 0, f"MLP ratio should be greater than 0, found: {mlp_ratio}"
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.convffn = ConvFFN(
            in_channels=dim,
            context_size=kernel_size,
            hidden_channels=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )

        # Drop Path
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()

        # Layer Scale
        self.use_layer_scale = use_layer_scale
        if use_layer_scale:
            self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)

    def forward(self, x, *args, **kwargs):
        if x.dim() == 3:
            # B, C, D --- where C is the context length
            # Convert to B, D, C --- to match RepMixer impl.
            x = x.permute(0, 2, 1)
            x = torch.unsqueeze(x, dim=2)
        else:
            raise ValueError(f"Expected tensor of dim=3, obtained tensor of dim={x.dim()}")

        if self.use_layer_scale:
            x = self.token_mixer(x)
            x = x + self.drop_path(self.layer_scale * self.convffn(x))
        else:
            x = self.token_mixer(x)
            x = x + self.drop_path(self.convffn(x))

        # Convert tensors back
        x = x.squeeze(dim=2).permute(0, 2, 1)
        return x
