"""
InceptionNeXt implementation, paper: https://arxiv.org/abs/2303.16900

Some code is borrowed from timm: https://github.com/huggingface/pytorch-image-models
"""

from functools import partial

import torch
import torch.nn as nn

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import trunc_normal_, DropPath, to_2tuple
from ._manipulate import checkpoint_seq
from ._registry import register_model


class InceptionDWConv2d(nn.Module):
    """ Inception depthweise convolution
    """

    def __init__(self, in_channels, square_kernel_size=3, band_kernel_size=11, branch_ratio=0.125):
        super().__init__()

        gc = int(in_channels * branch_ratio)  # channel numbers of a convolution branch
        self.dwconv_hw = nn.Conv2d(gc, gc, square_kernel_size, padding=square_kernel_size // 2, groups=gc)
        self.dwconv_w = nn.Conv2d(
            gc, gc, kernel_size=(1, band_kernel_size), padding=(0, band_kernel_size // 2), groups=gc)
        self.dwconv_h = nn.Conv2d(
            gc, gc, kernel_size=(band_kernel_size, 1), padding=(band_kernel_size // 2, 0), groups=gc)
        self.split_indexes = (in_channels - 3 * gc, gc, gc, gc)

    def forward(self, x):
        x_id, x_hw, x_w, x_h = torch.split(x, self.split_indexes, dim=1)
        return torch.cat((
            x_id,
            self.dwconv_hw(x_hw),
            self.dwconv_w(x_w),
            self.dwconv_h(x_h)
            ), dim=1,
        )


class ConvMlp(nn.Module):
    """ MLP using 1x1 convs that keeps spatial dims
    copied from timm: https://github.com/huggingface/pytorch-image-models/blob/v0.6.11/timm/models/layers/mlp.py
    """

    def __init__(
            self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU,
            norm_layer=None, bias=True, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)

        self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0])
        self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
        self.act = act_layer()
        self.drop = nn.Dropout(drop)
        self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1])

    def forward(self, x):
        x = self.fc1(x)
        x = self.norm(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        return x


class MlpHead(nn.Module):
    """ MLP classification head
    """

    def __init__(
            self, dim, num_classes=1000, mlp_ratio=3, act_layer=nn.GELU,
            norm_layer=partial(nn.LayerNorm, eps=1e-6), drop=0., bias=True):
        super().__init__()
        hidden_features = int(mlp_ratio * dim)
        self.fc1 = nn.Linear(dim, hidden_features, bias=bias)
        self.act = act_layer()
        self.norm = norm_layer(hidden_features)
        self.fc2 = nn.Linear(hidden_features, num_classes, bias=bias)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = x.mean((2, 3))  # global average pooling
        x = self.fc1(x)
        x = self.act(x)
        x = self.norm(x)
        x = self.drop(x)
        x = self.fc2(x)
        return x


class MetaNeXtBlock(nn.Module):
    """ MetaNeXtBlock Block
    Args:
        dim (int): Number of input channels.
        drop_path (float): Stochastic depth rate. Default: 0.0
        ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
    """

    def __init__(
            self,
            dim,
            token_mixer=nn.Identity,
            norm_layer=nn.BatchNorm2d,
            mlp_layer=ConvMlp,
            mlp_ratio=4,
            act_layer=nn.GELU,
            ls_init_value=1e-6,
            drop_path=0.,

    ):
        super().__init__()
        self.token_mixer = token_mixer(dim)
        self.norm = norm_layer(dim)
        self.mlp = mlp_layer(dim, int(mlp_ratio * dim), act_layer=act_layer)
        self.gamma = nn.Parameter(ls_init_value * torch.ones(dim)) if ls_init_value else None
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

    def forward(self, x):
        shortcut = x
        x = self.token_mixer(x)
        x = self.norm(x)
        x = self.mlp(x)
        if self.gamma is not None:
            x = x.mul(self.gamma.reshape(1, -1, 1, 1))
        x = self.drop_path(x) + shortcut
        return x


class MetaNeXtStage(nn.Module):
    def __init__(
            self,
            in_chs,
            out_chs,
            ds_stride=2,
            depth=2,
            drop_path_rates=None,
            ls_init_value=1.0,
            token_mixer=nn.Identity,
            act_layer=nn.GELU,
            norm_layer=None,
            mlp_ratio=4,
    ):
        super().__init__()
        self.grad_checkpointing = False
        if ds_stride > 1:
            self.downsample = nn.Sequential(
                norm_layer(in_chs),
                nn.Conv2d(in_chs, out_chs, kernel_size=ds_stride, stride=ds_stride),
            )
        else:
            self.downsample = nn.Identity()

        drop_path_rates = drop_path_rates or [0.] * depth
        stage_blocks = []
        for i in range(depth):
            stage_blocks.append(MetaNeXtBlock(
                dim=out_chs,
                drop_path=drop_path_rates[i],
                ls_init_value=ls_init_value,
                token_mixer=token_mixer,
                act_layer=act_layer,
                norm_layer=norm_layer,
                mlp_ratio=mlp_ratio,
            ))
            in_chs = out_chs
        self.blocks = nn.Sequential(*stage_blocks)

    def forward(self, x):
        x = self.downsample(x)
        if self.grad_checkpointing and not torch.jit.is_scripting():
            x = checkpoint_seq(self.blocks, x)
        else:
            x = self.blocks(x)
        return x


class MetaNeXt(nn.Module):
    r""" MetaNeXt
        A PyTorch impl of : `InceptionNeXt: When Inception Meets ConvNeXt`  - https://arxiv.org/pdf/2203.xxxxx.pdf

    Args:
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        depths (tuple(int)): Number of blocks at each stage. Default: (3, 3, 9, 3)
        dims (tuple(int)): Feature dimension at each stage. Default: (96, 192, 384, 768)
        token_mixers: Token mixer function. Default: nn.Identity
        norm_layer: Normalziation layer. Default: nn.BatchNorm2d
        act_layer: Activation function for MLP. Default: nn.GELU
        mlp_ratios (int or tuple(int)): MLP ratios. Default: (4, 4, 4, 3)
        head_fn: classifier head
        drop_rate (float): Head dropout rate
        drop_path_rate (float): Stochastic depth rate. Default: 0.
        ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
    """

    def __init__(
            self,
            in_chans=3,
            num_classes=1000,
            depths=(3, 3, 9, 3),
            dims=(96, 192, 384, 768),
            token_mixers=nn.Identity,
            norm_layer=nn.BatchNorm2d,
            act_layer=nn.GELU,
            mlp_ratios=(4, 4, 4, 3),
            head_fn=MlpHead,
            drop_rate=0.,
            drop_path_rate=0.,
            ls_init_value=1e-6,
            **kwargs,
    ):
        super().__init__()

        num_stage = len(depths)
        if not isinstance(token_mixers, (list, tuple)):
            token_mixers = [token_mixers] * num_stage
        if not isinstance(mlp_ratios, (list, tuple)):
            mlp_ratios = [mlp_ratios] * num_stage

        self.num_classes = num_classes
        self.drop_rate = drop_rate
        self.stem = nn.Sequential(
            nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
            norm_layer(dims[0])
        )

        self.stages = nn.Sequential()
        dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
        stages = []
        prev_chs = dims[0]
        # feature resolution stages, each consisting of multiple residual blocks
        for i in range(num_stage):
            out_chs = dims[i]
            stages.append(MetaNeXtStage(
                prev_chs,
                out_chs,
                ds_stride=2 if i > 0 else 1,
                depth=depths[i],
                drop_path_rates=dp_rates[i],
                ls_init_value=ls_init_value,
                act_layer=act_layer,
                token_mixer=token_mixers[i],
                norm_layer=norm_layer,
                mlp_ratio=mlp_ratios[i],
            ))
            prev_chs = out_chs
        self.stages = nn.Sequential(*stages)
        self.num_features = prev_chs
        self.head = head_fn(self.num_features, num_classes, drop=drop_rate)
        self.apply(self._init_weights)

    @torch.jit.ignore
    def set_grad_checkpointing(self, enable=True):
        for s in self.stages:
            s.grad_checkpointing = enable

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'norm'}

    def forward_features(self, x):
        x = self.stem(x)
        x = self.stages(x)
        return x

    def forward_head(self, x):
        x = self.head(x)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.forward_head(x)
        return x

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv2d, nn.Linear)):
            trunc_normal_(m.weight, std=.02)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
        'crop_pct': 0.875, 'interpolation': 'bicubic',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'stem.0', 'classifier': 'head.fc',
        **kwargs
    }


default_cfgs = dict(
    inception_next_tiny=_cfg(
        url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_tiny.pth',
    ),
    inception_next_small=_cfg(
        url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_small.pth',
    ),
    inception_next_base=_cfg(
        url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base.pth',
    ),
    inception_next_base_384=_cfg(
        url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base_384.pth',
        input_size=(3, 384, 384), crop_pct=1.0,
    ),
)


@register_model
def inception_next_tiny(pretrained=False, **kwargs):
    model = MetaNeXt(
        depths=(3, 3, 9, 3), dims=(96, 192, 384, 768),
        token_mixers=InceptionDWConv2d,
        **kwargs
    )
    model.default_cfg = default_cfgs['inception_next_tiny']
    if pretrained:
        state_dict = torch.hub.load_state_dict_from_url(
            url=model.default_cfg['url'], map_location="cpu", check_hash=True)
        model.load_state_dict(state_dict)
    return model


@register_model
def inception_next_small(pretrained=False, **kwargs):
    model = MetaNeXt(
        depths=(3, 3, 27, 3), dims=(96, 192, 384, 768),
        token_mixers=InceptionDWConv2d,
        **kwargs
    )
    model.default_cfg = default_cfgs['inception_next_small']
    if pretrained:
        state_dict = torch.hub.load_state_dict_from_url(
            url=model.default_cfg['url'], map_location="cpu", check_hash=True)
        model.load_state_dict(state_dict)
    return model


@register_model
def inception_next_base(pretrained=False, **kwargs):
    model = MetaNeXt(
        depths=(3, 3, 27, 3), dims=(128, 256, 512, 1024),
        token_mixers=InceptionDWConv2d,
        **kwargs
    )
    model.default_cfg = default_cfgs['inception_next_base']
    if pretrained:
        state_dict = torch.hub.load_state_dict_from_url(
            url=model.default_cfg['url'], map_location="cpu", check_hash=True)
        model.load_state_dict(state_dict)
    return model


@register_model
def inception_next_base_384(pretrained=False, **kwargs):
    model = MetaNeXt(
        depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024],
        mlp_ratios=[4, 4, 4, 3],
        token_mixers=InceptionDWConv2d,
        **kwargs
    )
    model.default_cfg = default_cfgs['inception_next_base_384']
    if pretrained:
        state_dict = torch.hub.load_state_dict_from_url(
            url=model.default_cfg['url'], map_location="cpu", check_hash=True)
        model.load_state_dict(state_dict)
    return model