from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath
import torch.utils.checkpoint as checkpoint
import numpy as np

class Block(nn.Module):

    def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=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. 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 ConvNeXt(nn.Module):
    def __init__(self, in_chans=3, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
                 drop_path_rate=0., layer_scale_init_value=1e-6, out_features=['C1','C2','C3']):
        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.num_layers = len(depths)
        num_features = [int(dims[i] * 2 ** i) for i in range(self.num_layers)]
        self.num_features = num_features
        self._out_features = out_features

        self._out_feature_strides = {}
        self._out_feature_channels = {}

        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
        strides = [4, 4, 4, 4]
        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]

            stage_name = "C"+str(i)

            self._out_feature_channels[stage_name] = dims[i]
            self._out_feature_strides[stage_name] = strides[i] * 2 ** i

        norm_layer = partial(LayerNorm, eps=1e-6, data_format="channels_first")
        for i_layer in range(4):
            layer = norm_layer(dims[i_layer])
            layer_name = f'norm{i_layer}'
            self.add_module(layer_name, layer)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, (nn.Conv2d, nn.Linear)):
            trunc_normal_(m.weight, std=.02)
            nn.init.constant_(m.bias, 0)

    def init_weights(self, pretrained=None):
        """Initialize the weights in backbone.
        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Defaults to None.
        """

        def _init_weights(m):
            if isinstance(m, nn.Linear):
                trunc_normal_(m.weight, std=.02)
                if isinstance(m, nn.Linear) and m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.LayerNorm):
                nn.init.constant_(m.bias, 0)
                nn.init.constant_(m.weight, 1.0)

        self.apply(_init_weights)

    def forward_features(self, x):
        outs = {}
        for i in range(4):
            x = self.downsample_layers[i](x)
            x = self.stages[i](x)
            stage_name = "C"+str(i)
            if stage_name in self._out_features:
                norm_layer = getattr(self, f'norm{i}')
                x_out = norm_layer(x)
                out = x_out.contiguous()
                outs[stage_name] = out

        return outs  # {"stage%d" % (i+2,): out for i, out in enumerate(outs)} #tuple(outs)
    def forward_rfp(self,x,r):
        outs = {}
        for i in range(4):
            x = self.downsample_layers[i](x)
            if i>0:
                x=x+r[i-1]
            x = self.stages[i](x)
            stage_name = "C"+str(i)
            if stage_name in self._out_features:
                norm_layer = getattr(self, f'norm{i}')
                x_out = norm_layer(x)
                out = x_out.contiguous()
                outs[stage_name] = out
        return outs
        
    def forward(self, x,r=[]):
        if r:
            x=self.forward_rfp(x,r)
        else:
            x = self.forward_features(x)
        return x
    
    


class LayerNorm(nn.Module):

    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


