import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial

from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg

__all__ = [
    'pvt_tiny', 'pvt_small', 'pvt_medium', 'pvt_large',
    'pvt_small_sk2ffn'
]


class SK2(nn.Module):

    def __init__(self, dim, reduce_ratio=4.):
        super().__init__()
        hidden_dim = max(int(dim // reduce_ratio), 32)
        print('hidden_dim = ', hidden_dim)
        self.mlp = nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.BatchNorm1d(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, dim),
            nn.BatchNorm1d(dim),
        )
        # Mlp(in_features=dim, hidden_features=hidden_dim)
        self.alpha = nn.Parameter(torch.zeros(1, 1))

    def forward(self, x0, x1):
        sig = self.alpha.sigmoid()
        attn = (1.0 - sig) * x0.mean(dim=1, keepdim=False) + sig * x1.mean(dim=1, keepdim=False)
        attn = self.mlp(attn).sigmoid().unsqueeze(1)
        x = x0 + attn * (x1 - x0)
        return x


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 SK2Mlp(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)
        self.sk2 = SK2(hidden_features)

    def forward(self, x):
        y = self.fc1(x)
        y = self.act(y)
        x = self.sk2(x, y)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Block_sk2mlp(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
            attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = SK2Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward(self, x, H, W):
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        # x = self.sk2(x, self.drop_path(self.attn(self.norm1(x), H, W)))
        # x = self.sk2(x, self.attn(self.norm1(x), H, W))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        # x = self.sk2ln(x, self.mlp(self.norm2(x)))

        return x



class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
            self.norm = nn.LayerNorm(dim)

    def forward(self, x, H, W):
        B, N, C = x.shape
        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)

        if self.sr_ratio > 1:
            x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
            x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
            x_ = self.norm(x_)
            kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        else:
            kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        k, v = kv[0], kv[1]

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class SK2LN(nn.Module):

    def __init__(self, dim, reduce_ratio=4.):
        super().__init__()
        hidden_dim = max(int(dim // reduce_ratio), 32)
        print('hidden_dim = ', hidden_dim)
        self.mlp = nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, dim),
            nn.LayerNorm(dim),
            #nn.BatchNorm1d(dim),
        )
        # Mlp(in_features=dim, hidden_features=hidden_dim)
        self.alpha = nn.Parameter(torch.zeros(1, 1))

    def forward(self, x0, x1):
        sig = self.alpha.sigmoid()
        attn = (1.0 - sig) * x0.mean(dim=1, keepdim=False) + sig * x1.mean(dim=1, keepdim=False)
        attn = self.mlp(attn).sigmoid().unsqueeze(1)
        x = x0 + attn * (x1 - x0)
        return x


class Block_sk2lnffn(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
            attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else 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)
        self.sk2ln = SK2LN(dim)
        print('using ffn sk2ln')

    def forward(self, x, H, W):
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        # x = self.sk2(x, self.drop_path(self.attn(self.norm1(x), H, W)))
        # x = self.sk2(x, self.attn(self.norm1(x), H, W))
        # x = x + self.drop_path(self.mlp(self.norm2(x)))
        x = self.sk2ln(x, self.mlp(self.norm2(x)))

        return x


class Block(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
            attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else 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)
        #self.sk2 = SK2(dim)
        #print('using ffn sk2')

    def forward(self, x, H, W):
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        # x = self.sk2(x, self.drop_path(self.attn(self.norm1(x), H, W)))
        # x = self.sk2(x, self.attn(self.norm1(x), H, W))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        # x = self.sk2(x, self.mlp(self.norm2(x)))

        return x


class Block_sk2ffn(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
            attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else 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)
        self.sk2 = SK2(dim)
        print('using ffn sk2')

    def forward(self, x, H, W):
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        # x = self.sk2(x, self.drop_path(self.attn(self.norm1(x), H, W)))
        # x = self.sk2(x, self.attn(self.norm1(x), H, W))
        # x = x + self.drop_path(self.mlp(self.norm2(x)))
        x = self.sk2(x, self.mlp(self.norm2(x)))

        return x


class Block_sk2ffnx(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
            attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else 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)
        self.sk2 = SK2(dim)
        print('using ffn sk2 x, x+mlp(x)')

    def forward(self, x, H, W):
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        # x = self.sk2(x, self.drop_path(self.attn(self.norm1(x), H, W)))
        # x = self.sk2(x, self.attn(self.norm1(x), H, W))
        # x = x + self.drop_path(self.mlp(self.norm2(x)))
        x = self.sk2(x, x + self.mlp(self.norm2(x)))

        return x


class Block_sk2(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
            attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else 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)
        self.sk2 = SK2(dim)
        print('using sk2')

    def forward(self, x, H, W):
        # x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        # x = self.sk2(x, self.drop_path(self.attn(self.norm1(x), H, W)))
        x = self.sk2(x, self.attn(self.norm1(x), H, W))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        # x = self.sk2(x, self.mlp(self.norm2(x)))

        return x


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

    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)

        self.img_size = img_size
        self.patch_size = patch_size
        assert img_size[0] % patch_size[0] == 0 and img_size[1] % patch_size[1] == 0, \
            f"img_size {img_size} should be divided by patch_size {patch_size}."
        self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
        self.num_patches = self.H * self.W
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.norm = nn.LayerNorm(embed_dim)

    def forward(self, x):
        B, C, H, W = x.shape

        x = self.proj(x).flatten(2).transpose(1, 2)
        x = self.norm(x)
        H, W = H // self.patch_size[0], W // self.patch_size[1]

        return x, (H, W)


class PyramidVisionTransformer(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
                 num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
                 depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], Block=None):
        super().__init__()
        self.num_classes = num_classes
        self.depths = depths

        # patch_embed
        self.patch_embed1 = PatchEmbed(img_size=img_size, patch_size=patch_size, in_chans=in_chans,
                                       embed_dim=embed_dims[0])
        self.patch_embed2 = PatchEmbed(img_size=img_size // 4, patch_size=2, in_chans=embed_dims[0],
                                       embed_dim=embed_dims[1])
        self.patch_embed3 = PatchEmbed(img_size=img_size // 8, patch_size=2, in_chans=embed_dims[1],
                                       embed_dim=embed_dims[2])
        self.patch_embed4 = PatchEmbed(img_size=img_size // 16, patch_size=2, in_chans=embed_dims[2],
                                       embed_dim=embed_dims[3])

        # pos_embed
        self.pos_embed1 = nn.Parameter(torch.zeros(1, self.patch_embed1.num_patches, embed_dims[0]))
        self.pos_drop1 = nn.Dropout(p=drop_rate)
        self.pos_embed2 = nn.Parameter(torch.zeros(1, self.patch_embed2.num_patches, embed_dims[1]))
        self.pos_drop2 = nn.Dropout(p=drop_rate)
        self.pos_embed3 = nn.Parameter(torch.zeros(1, self.patch_embed3.num_patches, embed_dims[2]))
        self.pos_drop3 = nn.Dropout(p=drop_rate)
        self.pos_embed4 = nn.Parameter(torch.zeros(1, self.patch_embed4.num_patches + 1, embed_dims[3]))
        self.pos_drop4 = nn.Dropout(p=drop_rate)

        # transformer encoder
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
        cur = 0
        self.block1 = nn.ModuleList([Block(
            dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[0])
            for i in range(depths[0])])

        cur += depths[0]
        self.block2 = nn.ModuleList([Block(
            dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[1])
            for i in range(depths[1])])

        cur += depths[1]
        self.block3 = nn.ModuleList([Block(
            dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[2])
            for i in range(depths[2])])

        cur += depths[2]
        self.block4 = nn.ModuleList([Block(
            dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[3])
            for i in range(depths[3])])
        self.norm = norm_layer(embed_dims[3])

        # cls_token
        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims[3]))

        # classification head
        self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()

        # init weights
        trunc_normal_(self.pos_embed1, std=.02)
        trunc_normal_(self.pos_embed2, std=.02)
        trunc_normal_(self.pos_embed3, std=.02)
        trunc_normal_(self.pos_embed4, std=.02)
        trunc_normal_(self.cls_token, std=.02)
        self.apply(self._init_weights)

    def reset_drop_path(self, drop_path_rate):
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
        cur = 0
        for i in range(self.depths[0]):
            self.block1[i].drop_path.drop_prob = dpr[cur + i]

        cur += self.depths[0]
        for i in range(self.depths[1]):
            self.block2[i].drop_path.drop_prob = dpr[cur + i]

        cur += self.depths[1]
        for i in range(self.depths[2]):
            self.block3[i].drop_path.drop_prob = dpr[cur + i]

        cur += self.depths[2]
        for i in range(self.depths[3]):
            self.block4[i].drop_path.drop_prob = dpr[cur + i]

    def _init_weights(self, 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)

    @torch.jit.ignore
    def no_weight_decay(self):
        # return {'pos_embed', 'cls_token'} # has pos_embed may be better
        return {'cls_token'}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    # def _get_pos_embed(self, pos_embed, patch_embed, H, W):
    #     if H * W == self.patch_embed1.num_patches:
    #         return pos_embed
    #     else:
    #         return F.interpolate(
    #             pos_embed.reshape(1, patch_embed.H, patch_embed.W, -1).permute(0, 3, 1, 2),
    #             size=(H, W), mode="bilinear").reshape(1, -1, H * W).permute(0, 2, 1)

    def forward_features(self, x):
        B = x.shape[0]

        # stage 1
        x, (H, W) = self.patch_embed1(x)
        x = x + self.pos_embed1
        x = self.pos_drop1(x)
        for blk in self.block1:
            x = blk(x, H, W)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()

        # stage 2
        x, (H, W) = self.patch_embed2(x)
        x = x + self.pos_embed2
        x = self.pos_drop2(x)
        for blk in self.block2:
            x = blk(x, H, W)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()

        # stage 3
        x, (H, W) = self.patch_embed3(x)
        x = x + self.pos_embed3
        x = self.pos_drop3(x)
        for blk in self.block3:
            x = blk(x, H, W)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()

        # stage 4
        x, (H, W) = self.patch_embed4(x)
        cls_tokens = self.cls_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x = x + self.pos_embed4
        x = self.pos_drop4(x)
        for blk in self.block4:
            x = blk(x, H, W)

        x = self.norm(x)

        return x[:, 0]

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

        return x


def _conv_filter(state_dict, patch_size=16):
    """ convert patch embedding weight from manual patchify + linear proj to conv"""
    out_dict = {}
    for k, v in state_dict.items():
        if 'patch_embed.proj.weight' in k:
            v = v.reshape((v.shape[0], 3, patch_size, patch_size))
        out_dict[k] = v

    return out_dict


@register_model
def pvt_tiny(pretrained=False, **kwargs):
    model = PyramidVisionTransformer(
        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
        **kwargs)
    model.default_cfg = _cfg()

    return model


@register_model
def pvt_small(pretrained=False, **kwargs):
    model = PyramidVisionTransformer(
        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], Block=Block, **kwargs)
    model.default_cfg = _cfg()

    return model


@register_model
def pvt_small_sk2ffn(pretrained=False, **kwargs):
    model = PyramidVisionTransformer(
        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
        Block=Block_sk2ffn, **kwargs)
    model.default_cfg = _cfg()

    return model


@register_model
def pvt_small_sk2ffnx(pretrained=False, **kwargs):
    model = PyramidVisionTransformer(
        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
        Block=Block_sk2ffnx, **kwargs)
    model.default_cfg = _cfg()

    return model


@register_model
def pvt_small_sk2(pretrained=False, **kwargs):
    model = PyramidVisionTransformer(
        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
        Block=Block_sk2, **kwargs)
    model.default_cfg = _cfg()

    return model


@register_model
def pvt_small_sk2lnffn(pretrained=False, **kwargs):
    model = PyramidVisionTransformer(
        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
        Block=Block_sk2lnffn, **kwargs)
    model.default_cfg = _cfg()

    return model


@register_model
def pvt_medium(pretrained=False, **kwargs):
    model = PyramidVisionTransformer(
        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
        **kwargs)
    model.default_cfg = _cfg()

    return model


@register_model
def pvt_large(pretrained=False, **kwargs):
    model = PyramidVisionTransformer(
        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
        **kwargs)
    model.default_cfg = _cfg()

    return model


@register_model
def pvt_huge_v2(pretrained=False, **kwargs):
    model = PyramidVisionTransformer(
        patch_size=4, embed_dims=[128, 256, 512, 768], num_heads=[2, 4, 8, 12], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 10, 60, 3], sr_ratios=[8, 4, 2, 1],
        # drop_rate=0.0, drop_path_rate=0.02)
        **kwargs)
    model.default_cfg = _cfg()

    return model

torch.nn.Linear torch.zeros torch.cat torch.nn.Dropout torch.nn.LayerNorm torch.nn.Identity torch.nn.init.constant_ torch.nn.ReLU torch.nn.Conv2d torch.nn.BatchNorm1d