# Copyright (c) Facebook, Inc. and its affiliates.

# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.


"""
Initial code copied from the official ConvNext repository:
https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py

Then modified to support VISSL format.
"""

# Copyright (c) Meta Platforms, Inc. and affiliates.

# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import List

import torch
import torch.nn as nn
import torch.nn.functional as F
from vissl.config import AttrDict
from vissl.models.model_helpers import DropPath, trunc_normal_
from vissl.models.trunks import register_model_trunk


class Block(nn.Module):
    r"""
    ConvNeXt Block. There are two equivalent implementations:

    (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv
        -> GELU -> 1x1 Conv; all in (N, C, H, W)

    (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last)
        -> Linear -> GELU -> Linear; Permute back

    We use (2) as we find it slightly faster in PyTorch

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

    def __init__(self, dim, drop_path=0.0, layer_scale_init_value=1e-6):
        super().__init__()
        self.dwconv = nn.Conv2d(
            dim, dim, kernel_size=(7, 7), padding=(3, 3), groups=dim
        )  # depthwise conv
        self.norm = LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(
            dim, 4 * dim
        )  # pointwise/1x1 convs, implemented with linear layers
        self.act = nn.GELU()
        self.pwconv2 = nn.Linear(4 * dim, dim)
        self.gamma = (
            nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
            if layer_scale_init_value > 0
            else None
        )
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()

    def forward(self, x):
        input = x
        x = self.dwconv(x)
        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
        x = self.norm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)
        if self.gamma is not None:
            x = self.gamma * x
        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)

        x = input + self.drop_path(x)
        return x


class LayerNorm(nn.Module):
    r"""
    LayerNorm that supports two data formats: channels_last (default) or
    channels_first.

    The ordering of the dimensions in the inputs. channels_last corresponds
    to inputs with shape (batch_size, height, width, channels) while
    channels_first corresponds to inputs with shape
    (batch_size, channels, height, width).
    """

    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError
        self.normalized_shape = (normalized_shape,)

    def forward(self, x):
        if self.data_format == "channels_last":
            return F.layer_norm(
                x, self.normalized_shape, self.weight, self.bias, self.eps
            )
        elif self.data_format == "channels_first":
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
            return x


class ConvNeXt(nn.Module):
    r"""ConvNeXt
        A PyTorch impl of : `A ConvNet for the 2020s`  -
          https://arxiv.org/pdf/2201.03545.pdf

    Args:
        depths: Number of blocks at each stage. Default: [3, 3, 9, 3]
        dims: Feature dimension at each stage. Default: [96, 192, 384, 768]
        in_chans: Number of input image channels. Default: 3
        drop_path_rate: Stochastic depth rate. Default: 0.
        layer_scale_init_value: Init value for Layer Scale. Default: 1e-6.
    """

    def __init__(
        self,
        depths: List[int],
        dims: List[int],
        in_chans: int = 3,
        drop_path_rate=0.0,
        layer_scale_init_value=1e-6,
    ):
        super().__init__()

        self.downsample_layers = (
            nn.ModuleList()
        )  # stem and 3 intermediate downsampling conv layers
        stem = nn.Sequential(
            nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
            LayerNorm(dims[0], eps=1e-6, data_format="channels_first"),
        )
        self.downsample_layers.append(stem)
        for i in range(3):
            downsample_layer = nn.Sequential(
                LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
                nn.Conv2d(dims[i], dims[i + 1], kernel_size=2, stride=2),
            )
            self.downsample_layers.append(downsample_layer)

        self.stages = (
            nn.ModuleList()
        )  # 4 feature resolution stages, each consisting of multiple residual blocks
        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
        cur = 0
        for i in range(4):
            stage = nn.Sequential(
                *[
                    Block(
                        dim=dims[i],
                        drop_path=dp_rates[cur + j],
                        layer_scale_init_value=layer_scale_init_value,
                    )
                    for j in range(depths[i])
                ]
            )
            self.stages.append(stage)
            cur += depths[i]

        self.norm = nn.LayerNorm(dims[-1], eps=1e-6)  # final norm layer
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv2d, nn.Linear)):
            trunc_normal_(m.weight, std=0.02)
            nn.init.constant_(m.bias, 0)

    def forward_features(self, x):
        for i in range(4):
            x = self.downsample_layers[i](x)
            x = self.stages[i](x)
        return self.norm(
            x.mean([-2, -1])
        )  # global average pooling, (N, C, H, W) -> (N, C)

    def forward(self, x, out_feat_keys: List[str] = None) -> List[torch.Tensor]:
        x = self.forward_features(x)
        return [x]


@register_model_trunk("convnext")
def ConvNeXtTrunk(model_config: AttrDict, model_name: str) -> nn.Module:
    trunk_config = model_config.TRUNK.CONVNEXT
    return ConvNeXt(
        depths=trunk_config.DEPTH,
        dims=trunk_config.DIMS,
        drop_path_rate=trunk_config.DROP_PATH_RATE,
    )
