import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath
from functools import partial
from monai.networks.blocks.dynunet_block import UnetOutBlock
from monai.networks.blocks.unetr_block import UnetrBasicBlock, UnetrUpBlock
from typing import Union, Tuple

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)
            # print(self.weight.size())
            x = self.weight[:, None, None, None] * x + self.bias[:, None, None, None]

            return x

class ux_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., layer_scale_init_value=1e-6):
        super().__init__()
        self.dwconv = nn.Conv3d(dim, dim, kernel_size=7, padding=3, groups=dim)
        self.norm = LayerNorm(dim, eps=1e-6)
        # self.pwconv1 = nn.Linear(dim, 4 * dim)
        self.pwconv1 = nn.Conv3d(dim, 4 * dim, kernel_size=1, groups=dim)
        self.act = nn.GELU()
        # self.pwconv2 = nn.Linear(4 * dim, dim)
        self.pwconv2 = nn.Conv3d(4 * dim, dim, kernel_size=1, groups=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, 4, 1) # (N, C, H, W, D) -> (N, H, W, D, C)
        x = self.norm(x)
        x = x .permute(0, 4, 1, 2, 3)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)
        x = x.permute(0, 2, 3, 4, 1)
        if self.gamma is not None:
            x = self.gamma * x

        x = x.permute(0, 4, 1, 2, 3)
        x = input + self.drop_path(x)
        return x


class uxnet_conv(nn.Module):
    """
    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 (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
        drop_path_rate (float): Stochastic depth rate. Default: 0.
        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
        head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
    """
    def __init__(self, in_chans=1, depths=[2, 2, 2, 2], dims=[48, 96, 192, 384],
                 drop_path_rate=0., layer_scale_init_value=1e-6, out_indices=[0, 1, 2, 3]):
        super().__init__()

        self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers
        # stem = nn.Sequential(
        #     nn.Conv3d(in_chans, dims[0], kernel_size=7, stride=2, padding=3),
        #     LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
        # )
        stem = nn.Sequential(
              nn.Conv3d(in_chans, dims[0], kernel_size=7, stride=2, padding=3),
              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.Conv3d(dims[i], dims[i+1], kernel_size=2, stride=2),
            )
            self.downsample_layers.append(downsample_layer)

        self.stages = nn.ModuleList()
        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(
                *[ux_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.out_indices = out_indices

        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 forward_features(self, x):
        outs = []
        for i in range(4):
            # print(i)
            # print(x.size())
            x = self.downsample_layers[i](x)
            # print(x.size())
            x = self.stages[i](x)
            # print(x.size())
            if i in self.out_indices:
                norm_layer = getattr(self, f'norm{i}')
                x_out = norm_layer(x)
                outs.append(x_out)

        return tuple(outs)

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



class ProjectionHead(nn.Module):
    def __init__(self, dim_in, proj_dim=256, proj='convmlp', bn_type='torchbn'):
        super(ProjectionHead, self).__init__()


        if proj == 'linear':
            self.proj = nn.Conv2d(dim_in, proj_dim, kernel_size=1)
        elif proj == 'convmlp':
            self.proj = nn.Sequential(
                nn.Conv3d(dim_in, dim_in, kernel_size=1),
                nn.BatchNorm3d(dim_in),
                nn.ReLU(),
                nn.Conv3d(dim_in, proj_dim, kernel_size=1)
            )

    def forward(self, x):
        return F.normalize(self.proj(x), p=2, dim=1)



class UXNET(nn.Module):

    def __init__(
        self,
        in_chans=1,
        out_chans=13,
        depths=[2, 2, 2, 2],
        feat_size=[48, 96, 192, 384],
        drop_path_rate=0,
        layer_scale_init_value=1e-6,
        hidden_size: int = 768,
        norm_name: Union[Tuple, str] = "instance",
        conv_block: bool = True,
        res_block: bool = True,
        spatial_dims=3,
    ) -> None:
        """
        Args:
            in_channels: dimension of input channels.
            out_channels: dimension of output channels.
            img_size: dimension of input image.
            feature_size: dimension of network feature size.
            hidden_size: dimension of hidden layer.
            mlp_dim: dimension of feedforward layer.
            num_heads: number of attention heads.
            pos_embed: position embedding layer type.
            norm_name: feature normalization type and arguments.
            conv_block: bool argument to determine if convolutional block is used.
            res_block: bool argument to determine if residual block is used.
            dropout_rate: faction of the input units to drop.
            spatial_dims: number of spatial dims.

        """

        super().__init__()

        # in_channels: int,
        # out_channels: int,
        # img_size: Union[Sequence[int], int],
        # feature_size: int = 16,
        # if not (0 <= dropout_rate <= 1):
        #     raise ValueError("dropout_rate should be between 0 and 1.")
        #
        # if hidden_size % num_heads != 0:
        #     raise ValueError("hidden_size should be divisible by num_heads.")
        self.hidden_size = hidden_size
        # self.feature_size = feature_size
        self.in_chans = in_chans
        self.out_chans = out_chans
        self.depths = depths
        self.drop_path_rate = drop_path_rate
        self.feat_size = feat_size
        self.layer_scale_init_value = layer_scale_init_value
        self.out_indice = []
        for i in range(len(self.feat_size)):
            self.out_indice.append(i)

        self.spatial_dims = spatial_dims

        # self.classification = False
        # self.vit = ViT(
        #     in_channels=in_channels,
        #     img_size=img_size,
        #     patch_size=self.patch_size,
        #     hidden_size=hidden_size,
        #     mlp_dim=mlp_dim,
        #     num_layers=self.num_layers,
        #     num_heads=num_heads,
        #     pos_embed=pos_embed,
        #     classification=self.classification,
        #     dropout_rate=dropout_rate,
        #     spatial_dims=spatial_dims,
        # )
        self.uxnet_3d = uxnet_conv(
            in_chans= self.in_chans,
            depths=self.depths,
            dims=self.feat_size,
            drop_path_rate=self.drop_path_rate,
            layer_scale_init_value=1e-6,
            out_indices=self.out_indice
        )
        self.encoder1 = UnetrBasicBlock(
            spatial_dims=spatial_dims,
            in_channels=self.in_chans,
            out_channels=self.feat_size[0],
            kernel_size=3,
            stride=1,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.encoder2 = UnetrBasicBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[0],
            out_channels=self.feat_size[1],
            kernel_size=3,
            stride=1,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.encoder3 = UnetrBasicBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[1],
            out_channels=self.feat_size[2],
            kernel_size=3,
            stride=1,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.encoder4 = UnetrBasicBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[2],
            out_channels=self.feat_size[3],
            kernel_size=3,
            stride=1,
            norm_name=norm_name,
            res_block=res_block,
        )

        self.encoder5 = UnetrBasicBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[3],
            out_channels=self.hidden_size,
            kernel_size=3,
            stride=1,
            norm_name=norm_name,
            res_block=res_block,
        )

        self.decoder5 = UnetrUpBlock(
            spatial_dims=spatial_dims,
            in_channels=self.hidden_size,
            out_channels=self.feat_size[3],
            kernel_size=3,
            upsample_kernel_size=2,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.decoder4 = UnetrUpBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[3],
            out_channels=self.feat_size[2],
            kernel_size=3,
            upsample_kernel_size=2,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.decoder3 = UnetrUpBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[2],
            out_channels=self.feat_size[1],
            kernel_size=3,
            upsample_kernel_size=2,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.decoder2 = UnetrUpBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[1],
            out_channels=self.feat_size[0],
            kernel_size=3,
            upsample_kernel_size=2,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.decoder1 = UnetrBasicBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[0],
            out_channels=self.feat_size[0],
            kernel_size=3,
            stride=1,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.out = UnetOutBlock(spatial_dims=spatial_dims, in_channels=48, out_channels=self.out_chans)
        # self.conv_proj = ProjectionHead(dim_in=hidden_size)


    def proj_feat(self, x, hidden_size, feat_size):
        new_view = (x.size(0), *feat_size, hidden_size)
        x = x.view(new_view)
        new_axes = (0, len(x.shape) - 1) + tuple(d + 1 for d in range(len(feat_size)))
        x = x.permute(new_axes).contiguous()
        return x
    
    def forward(self, x_in):
        outs = self.uxnet_3d(x_in)
        # print(outs[0].size())
        # print(outs[1].size())
        # print(outs[2].size())
        # print(outs[3].size())
        enc1 = self.encoder1(x_in)
        # print(enc1.size())
        x2 = outs[0]
        enc2 = self.encoder2(x2)
        # print(enc2.size())
        x3 = outs[1]
        enc3 = self.encoder3(x3)
        # print(enc3.size())
        x4 = outs[2]
        enc4 = self.encoder4(x4)
        # print(enc4.size())
        # dec4 = self.proj_feat(outs[3], self.hidden_size, self.feat_size)
        enc_hidden = self.encoder5(outs[3])
        dec3 = self.decoder5(enc_hidden, enc4)
        dec2 = self.decoder4(dec3, enc3)
        dec1 = self.decoder3(dec2, enc2)
        dec0 = self.decoder2(dec1, enc1)
        out = self.decoder1(dec0)
        
        # feat = self.conv_proj(dec4)
        
        return self.out(out)

if __name__ == '__main__':
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = UXNET().to(device)
    x = torch.randn(4, 1, 64, 64, 64).to(device)
    y = model(x)
    print(y.size())