# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------

import torch
import torch.nn as nn
import torch.nn.functional as F
from itertools import repeat
import collections.abc

class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

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


def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image

    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class SwinMLPBlock(nn.Module):
    r""" Swin MLP Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        drop (float, optional): Dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.padding = [self.window_size - self.shift_size, self.shift_size,
                        self.window_size - self.shift_size, self.shift_size]  # P_l,P_r,P_t,P_b

        self.norm1 = norm_layer(dim)
        # use group convolution to implement multi-head MLP
        self.spatial_mlp = nn.Conv1d(self.num_heads * self.window_size ** 2,
                                     self.num_heads * self.window_size ** 2,
                                     kernel_size=1,
                                     groups=self.num_heads)

        self.drop_path = nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward(self, x):
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        shortcut = x
        x = self.norm1(x)
        x = x.view(B, H, W, C)

        # shift
        if self.shift_size > 0:
            P_l, P_r, P_t, P_b = self.padding
            shifted_x = F.pad(x, [0, 0, P_l, P_r, P_t, P_b], "constant", 0)
        else:
            shifted_x = x
        _, _H, _W, _ = shifted_x.shape

        # partition windows
        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

        # Window/Shifted-Window Spatial MLP
        x_windows_heads = x_windows.view(-1, self.window_size * self.window_size, self.num_heads, C // self.num_heads)
        x_windows_heads = x_windows_heads.transpose(1, 2)  # nW*B, nH, window_size*window_size, C//nH
        x_windows_heads = x_windows_heads.reshape(-1, self.num_heads * self.window_size * self.window_size,
                                                  C // self.num_heads)
        spatial_mlp_windows = self.spatial_mlp(x_windows_heads)  # nW*B, nH*window_size*window_size, C//nH
        spatial_mlp_windows = spatial_mlp_windows.view(-1, self.num_heads, self.window_size * self.window_size,
                                                       C // self.num_heads).transpose(1, 2)
        spatial_mlp_windows = spatial_mlp_windows.reshape(-1, self.window_size * self.window_size, C)

        # merge windows
        spatial_mlp_windows = spatial_mlp_windows.reshape(-1, self.window_size, self.window_size, C)
        shifted_x = window_reverse(spatial_mlp_windows, self.window_size, _H, _W)  # B H' W' C

        # reverse shift
        if self.shift_size > 0:
            P_l, P_r, P_t, P_b = self.padding
            x = shifted_x[:, P_t:-P_b, P_l:-P_r, :].contiguous()
        else:
            x = shifted_x
        x = x.view(B, H * W, C)

        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x


class PatchMerging(nn.Module):
    r""" Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = x.view(B, H, W, C)

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)

        return x

class BasicLayer(nn.Module):
    """ A basic Swin MLP layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        drop (float, optional): Dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., drop=0., drop_path=0.,
                 norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            SwinMLPBlock(dim=dim, input_resolution=input_resolution,
                         num_heads=num_heads, window_size=window_size,
                         shift_size=0 if (i % 2 == 0) else window_size // 2,
                         mlp_ratio=mlp_ratio,
                         drop=drop,
                         drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                         norm_layer=norm_layer)
            for i in range(depth)])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def forward(self, x):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        if self.downsample is not None:
            x = self.downsample(x)
        return x

def _ntuple(n):
    def parse(x):
        if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
            return tuple(x)
        return tuple(repeat(x, n))
    return parse
to_2tuple = _ntuple(2)

class PatchEmbed(nn.Module):
    r""" Image to Patch Embedding

    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
        if self.norm is not None:
            x = self.norm(x)
        return x

    def flops(self):
        Ho, Wo = self.patches_resolution
        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
        if self.norm is not None:
            flops += Ho * Wo * self.embed_dim
        return flops


class Model(nn.Module):
    r""" Swin MLP

    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin MLP layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        drop_rate (float): Dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
                 window_size=7, mlp_ratio=4., drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, patch_norm=True,
                 use_checkpoint=False, **kwargs):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        # build layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
                                                 patches_resolution[1] // (2 ** i_layer)),
                               depth=depths[i_layer],
                               num_heads=num_heads[i_layer],
                               window_size=window_size,
                               mlp_ratio=self.mlp_ratio,
                               drop=drop_rate,
                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                               norm_layer=norm_layer,
                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                               use_checkpoint=use_checkpoint)
            self.layers.append(layer)

        self.norm = norm_layer(self.num_features)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        x = self.patch_embed(x)
        x = self.pos_drop(x)

        for layer in self.layers:
            x = layer(x)

        x = self.norm(x)  # B L C
        x = self.avgpool(x.transpose(1, 2))  # B C 1
        x = torch.flatten(x, 1)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x
    
batch_size = 10
image_size = 224

def get_inputs():
    return [torch.rand(batch_size, 3, image_size, image_size)]

def get_init_inputs():
    return []

import torch
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
from itertools import repeat
import collections.abc
_cpp_src = r'''
#include <torch/extension.h>
torch::Tensor spatial_mlp_forward_cuda(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t H, int64_t W, int64_t num_heads, int64_t window_size, int64_t shift_size);
torch::Tensor patch_merge_norm_forward_cuda(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t H, int64_t W);
torch::Tensor spatial_mlp_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t H, int64_t W, int64_t num_heads, int64_t window_size, int64_t shift_size) { return spatial_mlp_forward_cuda(x, weight, bias, H, W, num_heads, window_size, shift_size); }
torch::Tensor patch_merge_norm_forward(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t H, int64_t W) { return patch_merge_norm_forward_cuda(x, gamma, beta, H, W); }
'''
_cuda_src = r'''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void spatial_mlp_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int B,int H,int W,int C,int heads,int ws,int shift) {
    long long total = (long long)B*H*W*C; int tid=blockIdx.x*blockDim.x+threadIdx.x; int stride=blockDim.x*gridDim.x; int cphead=C/heads; int ws2=ws*ws; int pad=(shift>0)?(ws-shift):0;
    for(long long idx=tid; idx<total; idx+=stride){int c=idx%C; long long t=idx/C; int ox=t%W; t/=W; int oy=t%H; int bb=t/H; int sy=oy+pad,sx=ox+pad; int wy=sy/ws,wx=sx/ws; int p=(sy-wy*ws)*ws+(sx-wx*ws); int head=c/cphead; float acc=b[head*ws2+p]; const float* ww=w+(head*ws2+p)*ws2;
        #pragma unroll
        for(int q=0;q<64;++q){ if(q>=ws2) break; int qy=q/ws,qx=q-qy*ws; int iy=wy*ws+qy-pad; int ix=wx*ws+qx-pad; if((unsigned)iy<(unsigned)H && (unsigned)ix<(unsigned)W) acc += ww[q]*x[(((long long)bb*H+iy)*W+ix)*C+c]; }
        out[idx]=acc; }}
__global__ void patch_merge_norm_kernel(const float* __restrict__ x,const float* __restrict__ gamma,const float* __restrict__ beta,float* __restrict__ out,int B,int H,int W,int C){
    extern __shared__ float sm[]; float* ssum=sm; float* ssq=sm+blockDim.x;
    int row=blockIdx.x; int tid=threadIdx.x; int H2=H>>1; int W2=W>>1; int D=C*4; int b=row/(H2*W2); int r=row%(H2*W2); int oh=r/W2; int ow=r-oh*W2;
    float sum=0.f, sq=0.f;
    for(int d=tid; d<D; d+=blockDim.x){ int part=d/C; int cc=d-part*C; int yy=2*oh + ((part==1)||(part==3)); int xx=2*ow + (part>=2); float v=x[(((long long)b*H+yy)*W+xx)*C+cc]; sum+=v; sq+=v*v; }
    ssum[tid]=sum; ssq[tid]=sq; __syncthreads();
    for(int off=blockDim.x/2; off>0; off>>=1){ if(tid<off){ ssum[tid]+=ssum[tid+off]; ssq[tid]+=ssq[tid+off]; } __syncthreads(); }
    float mean=ssum[0]/D; float var=ssq[0]/D - mean*mean; float inv=rsqrtf(var + 1.0e-5f);
    for(int d=tid; d<D; d+=blockDim.x){ int part=d/C; int cc=d-part*C; int yy=2*oh + ((part==1)||(part==3)); int xx=2*ow + (part>=2); float v=x[(((long long)b*H+yy)*W+xx)*C+cc]; out[(long long)row*D+d]=(v-mean)*inv*gamma[d]+beta[d]; }
}
torch::Tensor spatial_mlp_forward_cuda(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t H64,int64_t W64,int64_t heads64,int64_t ws64,int64_t shift64){int B=(int)x.size(0),C=(int)x.size(2); int H=(int)H64,W=(int)W64,heads=(int)heads64,ws=(int)ws64,shift=(int)shift64; auto out=torch::empty_like(x); long long total=(long long)B*H*W*C; int threads=256; int blocks=(int)((total+threads-1)/threads); if(blocks>65535) blocks=65535; spatial_mlp_kernel<<<blocks,threads>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),out.data_ptr<float>(),B,H,W,C,heads,ws,shift); return out;}
torch::Tensor patch_merge_norm_forward_cuda(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t H64, int64_t W64){int B=(int)x.size(0), C=(int)x.size(2), H=(int)H64, W=(int)W64; auto out=torch::empty({B,(H/2)*(W/2),4*C}, x.options()); int rows=B*(H/2)*(W/2); int threads=256; patch_merge_norm_kernel<<<rows,threads,threads*2*sizeof(float)>>>(x.data_ptr<float>(),gamma.data_ptr<float>(),beta.data_ptr<float>(),out.data_ptr<float>(),B,H,W,C); return out;}
'''
_ext = load_inline(name='swin_mlp_spatial_patch_ext_final', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['spatial_mlp_forward','patch_merge_norm_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3'], verbose=False)
class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__(); out_features=out_features or in_features; hidden_features=hidden_features or in_features; self.fc1=nn.Linear(in_features,hidden_features); self.act=act_layer(); self.fc2=nn.Linear(hidden_features,out_features); self.drop=nn.Dropout(drop)
    def forward(self,x): x=self.fc1(x); x=self.act(x); x=self.drop(x); x=self.fc2(x); x=self.drop(x); return x
class SwinMLPBlock(nn.Module):
    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4., drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__(); self.dim=dim; self.input_resolution=input_resolution; self.num_heads=num_heads; self.window_size=window_size; self.shift_size=shift_size; self.mlp_ratio=mlp_ratio
        if min(self.input_resolution)<=self.window_size: self.shift_size=0; self.window_size=min(self.input_resolution)
        self.padding=[self.window_size-self.shift_size,self.shift_size,self.window_size-self.shift_size,self.shift_size]; self.norm1=norm_layer(dim); self.spatial_mlp=nn.Conv1d(self.num_heads*self.window_size**2,self.num_heads*self.window_size**2,kernel_size=1,groups=self.num_heads); self.drop_path=nn.Identity(); self.norm2=norm_layer(dim); self.mlp=Mlp(in_features=dim,hidden_features=int(dim*mlp_ratio),act_layer=act_layer,drop=drop)
    def forward(self,x):
        H,W=self.input_resolution; y=self.norm1(x).contiguous(); y=_ext.spatial_mlp_forward(y,self.spatial_mlp.weight.contiguous(),self.spatial_mlp.bias.contiguous(),H,W,self.num_heads,self.window_size,self.shift_size); x=x+y; return x+self.mlp(self.norm2(x))
class PatchMerging(nn.Module):
    def __init__(self,input_resolution,dim,norm_layer=nn.LayerNorm): super().__init__(); self.input_resolution=input_resolution; self.dim=dim; self.reduction=nn.Linear(4*dim,2*dim,bias=False); self.norm=norm_layer(4*dim)
    def forward(self,x):
        H,W=self.input_resolution; y=_ext.patch_merge_norm_forward(x.contiguous(), self.norm.weight.contiguous(), self.norm.bias.contiguous(), H, W); return self.reduction(y)
class BasicLayer(nn.Module):
    def __init__(self,dim,input_resolution,depth,num_heads,window_size,mlp_ratio=4.,drop=0.,drop_path=0.,norm_layer=nn.LayerNorm,downsample=None,use_checkpoint=False): super().__init__(); self.dim=dim; self.input_resolution=input_resolution; self.depth=depth; self.use_checkpoint=use_checkpoint; self.blocks=nn.ModuleList([SwinMLPBlock(dim=dim,input_resolution=input_resolution,num_heads=num_heads,window_size=window_size,shift_size=0 if (i%2==0) else window_size//2,mlp_ratio=mlp_ratio,drop=drop,drop_path=drop_path[i] if isinstance(drop_path,list) else drop_path,norm_layer=norm_layer) for i in range(depth)]); self.downsample=downsample(input_resolution,dim=dim,norm_layer=norm_layer) if downsample is not None else None
    def forward(self,x):
        for blk in self.blocks: x=blk(x)
        if self.downsample is not None: x=self.downsample(x)
        return x
def _ntuple(n):
    def parse(x): return tuple(x) if isinstance(x,collections.abc.Iterable) and not isinstance(x,str) else tuple(repeat(x,n))
    return parse
to_2tuple=_ntuple(2)
class PatchEmbed(nn.Module):
    def __init__(self,img_size=224,patch_size=4,in_chans=3,embed_dim=96,norm_layer=None): super().__init__(); img_size=to_2tuple(img_size); patch_size=to_2tuple(patch_size); self.img_size=img_size; self.patch_size=patch_size; self.patches_resolution=[img_size[0]//patch_size[0],img_size[1]//patch_size[1]]; self.num_patches=self.patches_resolution[0]*self.patches_resolution[1]; self.in_chans=in_chans; self.embed_dim=embed_dim; self.proj=nn.Conv2d(in_chans,embed_dim,kernel_size=patch_size,stride=patch_size); self.norm=norm_layer(embed_dim) if norm_layer is not None else None
    def forward(self,x): x=self.proj(x).flatten(2).transpose(1,2); return self.norm(x) if self.norm is not None else x
class ModelNew(nn.Module):
    def __init__(self,img_size=224,patch_size=4,in_chans=3,num_classes=1000,embed_dim=96,depths=[2,2,6,2],num_heads=[3,6,12,24],window_size=7,mlp_ratio=4.,drop_rate=0.,drop_path_rate=0.1,norm_layer=nn.LayerNorm,patch_norm=True,use_checkpoint=False,**kwargs):
        super().__init__(); self.num_classes=num_classes; self.num_layers=len(depths); self.embed_dim=embed_dim; self.patch_norm=patch_norm; self.num_features=int(embed_dim*2**(self.num_layers-1)); self.mlp_ratio=mlp_ratio; self.patch_embed=PatchEmbed(img_size=img_size,patch_size=patch_size,in_chans=in_chans,embed_dim=embed_dim,norm_layer=norm_layer if self.patch_norm else None); pr=self.patch_embed.patches_resolution; self.patches_resolution=pr; self.pos_drop=nn.Dropout(p=drop_rate); dpr=[x.item() for x in torch.linspace(0,drop_path_rate,sum(depths))]; self.layers=nn.ModuleList()
        for i in range(self.num_layers): self.layers.append(BasicLayer(dim=int(embed_dim*2**i),input_resolution=(pr[0]//(2**i),pr[1]//(2**i)),depth=depths[i],num_heads=num_heads[i],window_size=window_size,mlp_ratio=self.mlp_ratio,drop=drop_rate,drop_path=dpr[sum(depths[:i]):sum(depths[:i+1])],norm_layer=norm_layer,downsample=PatchMerging if i<self.num_layers-1 else None,use_checkpoint=use_checkpoint))
        self.norm=norm_layer(self.num_features); self.avgpool=nn.AdaptiveAvgPool1d(1); self.head=nn.Linear(self.num_features,num_classes) if num_classes>0 else nn.Identity()
    def forward_features(self,x):
        x=self.patch_embed(x); x=self.pos_drop(x)
        for layer in self.layers: x=layer(x)
        x=self.norm(x); x=self.avgpool(x.transpose(1,2)); return torch.flatten(x,1)
    def forward(self,x): return self.head(self.forward_features(x))