"""
EfficientFormer_v2
Paper: https://arxiv.org/abs/2212.08059
Source code: https://github.com/snap-research/EfficientFormer
"""
import os
import copy
import torch
import torch.nn as nn
# import torch.nn.functional as F
import math
# from typing import Dict
import itertools

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, trunc_normal_
from timm.models.registry import register_model
from timm.models.layers.helpers import to_2tuple

EfficientFormer_width = {
    'L': [40, 80, 192, 384],  # 26m 83.3% 6attn
    'S2': [32, 64, 144, 288],  # 12m 81.6% 4attn dp0.02
    'S1': [32, 48, 120, 224],  # 6.1m 79.0
    'S0': [32, 48, 96, 176],  # 75.0 75.7
}

EfficientFormer_depth = {
    'L': [5, 5, 15, 10],  # 26m 83.3%
    'S2': [4, 4, 12, 8],  # 12m
    'S1': [3, 3, 9, 6],  # 79.0
    'S0': [2, 2, 6, 4],  # 75.7
}

# 26m
expansion_ratios_L = {
    '0': [4, 4, 4, 4, 4],
    '1': [4, 4, 4, 4, 4],
    '2': [4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4],
    '3': [4, 4, 4, 3, 3, 3, 3, 4, 4, 4],
}

# 12m
expansion_ratios_S2 = {
    '0': [4, 4, 4, 4],
    '1': [4, 4, 4, 4],
    '2': [4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4],
    '3': [4, 4, 3, 3, 3, 3, 4, 4],
}

# 6.1m
expansion_ratios_S1 = {
    '0': [4, 4, 4],
    '1': [4, 4, 4],
    '2': [4, 4, 3, 3, 3, 3, 4, 4, 4],
    '3': [4, 4, 3, 3, 4, 4],
}

# 3.5m
expansion_ratios_S0 = {
    '0': [4, 4],
    '1': [4, 4],
    '2': [4, 3, 3, 3, 4, 4],
    '3': [4, 3, 3, 4],
}

# in this study, use mean0 and std0 as calculated on ColoPola dataset
mean0 = (0.2679, 0.2679, 0.2679)
std0 = (0.2679, 0.2679, 0.2679)


class Attention4D(torch.nn.Module):
    def __init__(self, dim=384, key_dim=32, num_heads=8,
                 attn_ratio=4,
                 resolution=7,
                 act_layer=nn.ReLU,
                 stride=None):
        super().__init__()
        self.num_heads = num_heads
        self.scale = key_dim ** -0.5
        self.key_dim = key_dim
        self.nh_kd = nh_kd = key_dim * num_heads

        if stride is not None:
            self.resolution = math.ceil(resolution / stride)
            self.stride_conv = nn.Sequential(nn.Conv2d(dim, dim, kernel_size=3, stride=stride, padding=1, groups=dim),
                                             nn.BatchNorm2d(dim), )
            self.upsample = nn.Upsample(scale_factor=stride, mode='bilinear')
        else:
            self.resolution = resolution
            self.stride_conv = None
            self.upsample = None

        self.N = self.resolution ** 2
        self.N2 = self.N
        self.d = int(attn_ratio * key_dim)
        self.dh = int(attn_ratio * key_dim) * num_heads
        self.attn_ratio = attn_ratio
        h = self.dh + nh_kd * 2
        self.q = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.key_dim, 1),
                               nn.BatchNorm2d(self.num_heads * self.key_dim), )
        self.k = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.key_dim, 1),
                               nn.BatchNorm2d(self.num_heads * self.key_dim), )
        self.v = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.d, 1),
                               nn.BatchNorm2d(self.num_heads * self.d),
                               )
        self.v_local = nn.Sequential(nn.Conv2d(self.num_heads * self.d, self.num_heads * self.d,
                                               kernel_size=3, stride=1, padding=1, groups=self.num_heads * self.d),
                                     nn.BatchNorm2d(self.num_heads * self.d), )
        self.talking_head1 = nn.Conv2d(self.num_heads, self.num_heads, kernel_size=1, stride=1, padding=0)
        self.talking_head2 = nn.Conv2d(self.num_heads, self.num_heads, kernel_size=1, stride=1, padding=0)

        self.proj = nn.Sequential(act_layer(),
                                  nn.Conv2d(self.dh, dim, 1),
                                  nn.BatchNorm2d(dim), )

        points = list(itertools.product(range(self.resolution), range(self.resolution)))
        N = len(points)
        attention_offsets = {}
        idxs = []
        for p1 in points:
            for p2 in points:
                offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
                if offset not in attention_offsets:
                    attention_offsets[offset] = len(attention_offsets)
                idxs.append(attention_offsets[offset])
        self.attention_biases = torch.nn.Parameter(
            torch.zeros(num_heads, len(attention_offsets)))
        self.register_buffer('attention_bias_idxs',
                             torch.LongTensor(idxs).view(N, N))

    @torch.no_grad()
    def train(self, mode=True):
        super().train(mode)
        if mode and hasattr(self, 'ab'):
            del self.ab
        else:
            self.ab = self.attention_biases[:, self.attention_bias_idxs]

    def forward(self, x):  # x (B,N,C)
        B, C, H, W = x.shape
        if self.stride_conv is not None:
            x = self.stride_conv(x)

        q = self.q(x).flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 3, 2)
        k = self.k(x).flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 2, 3)
        v = self.v(x)
        v_local = self.v_local(v)
        v = v.flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 3, 2)

        attn = (
                (q @ k) * self.scale
                +
                (self.attention_biases[:, self.attention_bias_idxs]
                 if self.training else self.ab)
        )
        # attn = (q @ k) * self.scale
        attn = self.talking_head1(attn)
        attn = attn.softmax(dim=-1)
        attn = self.talking_head2(attn)

        x = (attn @ v)

        out = x.transpose(2, 3).reshape(B, self.dh, self.resolution, self.resolution) + v_local
        if self.upsample is not None:
            out = self.upsample(out)

        out = self.proj(out)
        return out


def stem(in_chs, out_chs, act_layer=nn.ReLU):
    return nn.Sequential(
        nn.Conv2d(in_chs, out_chs // 2, kernel_size=3, stride=2, padding=1),
        nn.BatchNorm2d(out_chs // 2),
        act_layer(),
        nn.Conv2d(out_chs // 2, out_chs, kernel_size=3, stride=2, padding=1),
        nn.BatchNorm2d(out_chs),
        act_layer(),
    )


class LGQuery(torch.nn.Module):
    def __init__(self, in_dim, out_dim, resolution1, resolution2):
        super().__init__()
        self.resolution1 = resolution1
        self.resolution2 = resolution2
        self.pool = nn.AvgPool2d(1, 2, 0)
        self.local = nn.Sequential(nn.Conv2d(in_dim, in_dim, kernel_size=3, stride=2, padding=1, groups=in_dim),
                                   )
        self.proj = nn.Sequential(nn.Conv2d(in_dim, out_dim, 1),
                                  nn.BatchNorm2d(out_dim), )

    def forward(self, x):
        local_q = self.local(x)
        pool_q = self.pool(x)
        q = local_q + pool_q
        q = self.proj(q)
        return q


class Attention4DDownsample(torch.nn.Module):
    def __init__(self, dim=384, key_dim=16, num_heads=8,
                 attn_ratio=4,
                 resolution=7,
                 out_dim=None,
                 act_layer=None,
                 ):
        super().__init__()

        self.num_heads = num_heads
        self.scale = key_dim ** -0.5
        self.key_dim = key_dim
        self.nh_kd = nh_kd = key_dim * num_heads

        self.resolution = resolution

        self.d = int(attn_ratio * key_dim)
        self.dh = int(attn_ratio * key_dim) * num_heads
        self.attn_ratio = attn_ratio
        h = self.dh + nh_kd * 2

        if out_dim is not None:
            self.out_dim = out_dim
        else:
            self.out_dim = dim
        self.resolution2 = math.ceil(self.resolution / 2)
        self.q = LGQuery(dim, self.num_heads * self.key_dim, self.resolution, self.resolution2)

        self.N = self.resolution ** 2
        self.N2 = self.resolution2 ** 2

        self.k = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.key_dim, 1),
                               nn.BatchNorm2d(self.num_heads * self.key_dim), )
        self.v = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.d, 1),
                               nn.BatchNorm2d(self.num_heads * self.d),
                               )
        self.v_local = nn.Sequential(nn.Conv2d(self.num_heads * self.d, self.num_heads * self.d,
                                               kernel_size=3, stride=2, padding=1, groups=self.num_heads * self.d),
                                     nn.BatchNorm2d(self.num_heads * self.d), )

        self.proj = nn.Sequential(
            act_layer(),
            nn.Conv2d(self.dh, self.out_dim, 1),
            nn.BatchNorm2d(self.out_dim), )

        points = list(itertools.product(range(self.resolution), range(self.resolution)))
        points_ = list(itertools.product(
            range(self.resolution2), range(self.resolution2)))
        N = len(points)
        N_ = len(points_)
        attention_offsets = {}
        idxs = []
        for p1 in points_:
            for p2 in points:
                size = 1
                offset = (
                    abs(p1[0] * math.ceil(self.resolution / self.resolution2) - p2[0] + (size - 1) / 2),
                    abs(p1[1] * math.ceil(self.resolution / self.resolution2) - p2[1] + (size - 1) / 2))
                if offset not in attention_offsets:
                    attention_offsets[offset] = len(attention_offsets)
                idxs.append(attention_offsets[offset])
        self.attention_biases = torch.nn.Parameter(
            torch.zeros(num_heads, len(attention_offsets)))
        self.register_buffer('attention_bias_idxs',
                             torch.LongTensor(idxs).view(N_, N))

    @torch.no_grad()
    def train(self, mode=True):
        super().train(mode)
        if mode and hasattr(self, 'ab'):
            del self.ab
        else:
            self.ab = self.attention_biases[:, self.attention_bias_idxs]

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

        q = self.q(x).flatten(2).reshape(B, self.num_heads, -1, self.N2).permute(0, 1, 3, 2)
        k = self.k(x).flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 2, 3)
        v = self.v(x)
        v_local = self.v_local(v)
        v = v.flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 3, 2)

        # @ symbol is for matrix multiplication since Python 3.5
        attn = (
                (q @ k) * self.scale
                +
                (self.attention_biases[:, self.attention_bias_idxs]
                 if self.training else self.ab)
        )

        # attn = (q @ k) * self.scale
        attn = attn.softmax(dim=-1)
        x = (attn @ v).transpose(2, 3)
        out = x.reshape(B, self.dh, self.resolution2, self.resolution2) + v_local

        out = self.proj(out)
        return out


class Embedding(nn.Module):
    def __init__(self, patch_size=3, stride=2, padding=1,
                 in_chans=3, embed_dim=768, norm_layer=nn.BatchNorm2d,
                 light=False, asub=False, resolution=None, act_layer=nn.ReLU, attn_block=Attention4DDownsample):
        super().__init__()
        self.light = light
        self.asub = asub

        if self.light:
            self.new_proj = nn.Sequential(
                nn.Conv2d(in_chans, in_chans, kernel_size=3, stride=2, padding=1, groups=in_chans),
                nn.BatchNorm2d(in_chans),
                nn.Hardswish(),
                nn.Conv2d(in_chans, embed_dim, kernel_size=1, stride=1, padding=0),
                nn.BatchNorm2d(embed_dim),
            )
            self.skip = nn.Sequential(
                nn.Conv2d(in_chans, embed_dim, kernel_size=1, stride=2, padding=0),
                nn.BatchNorm2d(embed_dim)
            )
        elif self.asub:
            self.attn = attn_block(dim=in_chans, out_dim=embed_dim,
                                   resolution=resolution, act_layer=act_layer)
            patch_size = to_2tuple(patch_size)
            stride = to_2tuple(stride)
            padding = to_2tuple(padding)
            self.conv = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size,
                                  stride=stride, padding=padding)
            self.bn = norm_layer(embed_dim) if norm_layer else nn.Identity()
        else:
            patch_size = to_2tuple(patch_size)
            stride = to_2tuple(stride)
            padding = to_2tuple(padding)
            self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size,
                                  stride=stride, padding=padding)
            self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        if self.light:
            out = self.new_proj(x) + self.skip(x)
        elif self.asub:
            out_conv = self.conv(x)
            out_conv = self.bn(out_conv)
            out = self.attn(x) + out_conv
        else:
            x = self.proj(x)
            out = self.norm(x)
        return out


class Mlp(nn.Module):
    """
    Implementation of MLP with 1*1 convolutions.
    Input: tensor with shape [B, C, H, W]
    """

    def __init__(self, in_features, hidden_features=None,
                 out_features=None, act_layer=nn.GELU, drop=0., mid_conv=False):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.mid_conv = mid_conv
        self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
        self.act = act_layer()
        self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
        self.drop = nn.Dropout(drop)
        self.apply(self._init_weights)

        if self.mid_conv:
            self.mid = nn.Conv2d(hidden_features, hidden_features, kernel_size=3, stride=1, padding=1,
                                 groups=hidden_features)
            self.mid_norm = nn.BatchNorm2d(hidden_features)

        self.norm1 = nn.BatchNorm2d(hidden_features)
        self.norm2 = nn.BatchNorm2d(out_features)

    def _init_weights(self, m):
        if isinstance(m, nn.Conv2d):
            trunc_normal_(m.weight, std=.02)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        x = self.fc1(x)
        x = self.norm1(x)
        x = self.act(x)

        if self.mid_conv:
            x_mid = self.mid(x)
            x_mid = self.mid_norm(x_mid)
            x = self.act(x_mid)
        x = self.drop(x)

        x = self.fc2(x)
        x = self.norm2(x)

        x = self.drop(x)
        return x


class AttnFFN(nn.Module):
    def __init__(self, dim, mlp_ratio=4.,
                 act_layer=nn.ReLU, norm_layer=nn.LayerNorm,
                 drop=0., drop_path=0.,
                 use_layer_scale=True, layer_scale_init_value=1e-5,
                 resolution=7, stride=None):

        super().__init__()

        self.token_mixer = Attention4D(dim, resolution=resolution, act_layer=act_layer, stride=stride)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
                       act_layer=act_layer, drop=drop, mid_conv=True)

        self.drop_path = DropPath(drop_path) if drop_path > 0. \
            else nn.Identity()
        self.use_layer_scale = use_layer_scale
        if use_layer_scale:
            self.layer_scale_1 = nn.Parameter(
                layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)
            self.layer_scale_2 = nn.Parameter(
                layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)

    def forward(self, x):
        if self.use_layer_scale:
            x = x + self.drop_path(self.layer_scale_1 * self.token_mixer(x))
            x = x + self.drop_path(self.layer_scale_2 * self.mlp(x))

        else:
            x = x + self.drop_path(self.token_mixer(x))
            x = x + self.drop_path(self.mlp(x))
        return x


class FFN(nn.Module):
    def __init__(self, dim, pool_size=3, mlp_ratio=4.,
                 act_layer=nn.GELU,
                 drop=0., drop_path=0.,
                 use_layer_scale=True, layer_scale_init_value=1e-5):
        super().__init__()

        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
                       act_layer=act_layer, drop=drop, mid_conv=True)

        self.drop_path = DropPath(drop_path) if drop_path > 0. \
            else nn.Identity()
        self.use_layer_scale = use_layer_scale
        if use_layer_scale:
            self.layer_scale_2 = nn.Parameter(
                layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)

    def forward(self, x):
        if self.use_layer_scale:
            x = x + self.drop_path(self.layer_scale_2 * self.mlp(x))
        else:
            x = x + self.drop_path(self.mlp(x))
        return x


def eformer_block(dim, index, layers,
                  pool_size=3, mlp_ratio=4.,
                  act_layer=nn.GELU, norm_layer=nn.LayerNorm,
                  drop_rate=.0, drop_path_rate=0.,
                  use_layer_scale=True, layer_scale_init_value=1e-5, vit_num=1, resolution=7, e_ratios=None):
    blocks = []
    for block_idx in range(layers[index]):
        block_dpr = drop_path_rate * (
                block_idx + sum(layers[:index])) / (sum(layers) - 1)
        mlp_ratio = e_ratios[str(index)][block_idx]
        if index >= 2 and block_idx > layers[index] - 1 - vit_num:
            if index == 2:
                stride = 2
            else:
                stride = None
            blocks.append(AttnFFN(
                dim, mlp_ratio=mlp_ratio,
                act_layer=act_layer, norm_layer=norm_layer,
                drop=drop_rate, drop_path=block_dpr,
                use_layer_scale=use_layer_scale,
                layer_scale_init_value=layer_scale_init_value,
                resolution=resolution,
                stride=stride,
            ))
        else:
            blocks.append(FFN(
                dim, pool_size=pool_size, mlp_ratio=mlp_ratio,
                act_layer=act_layer,
                drop=drop_rate, drop_path=block_dpr,
                use_layer_scale=use_layer_scale,
                layer_scale_init_value=layer_scale_init_value,
            ))
    blocks = nn.Sequential(*blocks)
    return blocks


class EfficientFormerV2(nn.Module):
    def __init__(self, layers, embed_dims=None,
                 mlp_ratios=4, downsamples=None,
                 pool_size=3,
                 norm_layer=nn.BatchNorm2d, act_layer=nn.GELU,
                 num_classes=1000, in_channels=3,
                 down_patch_size=3, down_stride=2, down_pad=1,
                 drop_rate=0., drop_path_rate=0.,
                 use_layer_scale=True, layer_scale_init_value=1e-5,
                 fork_feat=False,
                 init_cfg=None,
                 pretrained=None,
                 vit_num=0,
                 distillation=False,  # True
                 resolution=224,
                 e_ratios=expansion_ratios_L,
                 **kwargs):
        super().__init__()

        if not fork_feat:
            self.num_classes = num_classes
        self.fork_feat = fork_feat

        self.patch_embed = stem(in_channels, embed_dims[0], act_layer=act_layer)

        network = []
        for i in range(len(layers)):
            stage = eformer_block(embed_dims[i], i, layers,
                                  pool_size=pool_size, mlp_ratio=mlp_ratios,
                                  act_layer=act_layer, norm_layer=norm_layer,
                                  drop_rate=drop_rate,
                                  drop_path_rate=drop_path_rate,
                                  use_layer_scale=use_layer_scale,
                                  layer_scale_init_value=layer_scale_init_value,
                                  resolution=math.ceil(resolution / (2 ** (i + 2))),
                                  vit_num=vit_num,
                                  e_ratios=e_ratios)
            network.append(stage)
            if i >= len(layers) - 1:
                break
            if downsamples[i] or embed_dims[i] != embed_dims[i + 1]:
                # downsampling between two stages
                if i >= 2:
                    asub = True
                else:
                    asub = False
                network.append(
                    Embedding(
                        patch_size=down_patch_size, stride=down_stride,
                        padding=down_pad,
                        in_chans=embed_dims[i], embed_dim=embed_dims[i + 1],
                        resolution=math.ceil(resolution / (2 ** (i + 2))),
                        asub=asub,
                        act_layer=act_layer, norm_layer=norm_layer,
                    )
                )

        self.network = nn.ModuleList(network)

        if self.fork_feat:
            # add a norm layer for each output
            self.out_indices = [0, 2, 4, 6]
            for i_emb, i_layer in enumerate(self.out_indices):
                if i_emb == 0 and os.environ.get('FORK_LAST3', None):
                    layer = nn.Identity()
                else:
                    layer = norm_layer(embed_dims[i_emb])
                layer_name = f'norm{i_layer}'
                self.add_module(layer_name, layer)
        else:
            # Classifier head
            self.norm = norm_layer(embed_dims[-1])
            self.head = nn.Linear(
                embed_dims[-1], num_classes) if num_classes > 0 \
                else nn.Identity()
            self.dist = distillation
            if self.dist:
                self.dist_head = nn.Linear(
                    embed_dims[-1], num_classes) if num_classes > 0 \
                    else nn.Identity()

        self.apply(self.cls_init_weights)

        self.init_cfg = copy.deepcopy(init_cfg)
        # load pre-trained model
        if self.fork_feat and (
                self.init_cfg is not None or pretrained is not None):
            self.init_weights()

    # init for classification
    def cls_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)

    # init for mmdetection or mmsegmentation by loading
    # imagenet pre-trained weights
    def init_weights(self, pretrained=None):
        """
        logger = get_root_logger()
        if self.init_cfg is None and pretrained is None:
            logger.warn(f'No pre-trained weights for '
                        f'{self.__class__.__name__}, '
                        f'training start from scratch')
            pass
        else:
            assert 'checkpoint' in self.init_cfg, f'Only support ' \
                                                  f'specify `Pretrained` in ' \
                                                  f'`init_cfg` in ' \
                                                  f'{self.__class__.__name__} '
            if self.init_cfg is not None:
                ckpt_path = self.init_cfg['checkpoint']
            elif pretrained is not None:
                ckpt_path = pretrained

            ckpt = _load_checkpoint(
                ckpt_path, logger=logger, map_location='cpu')
            if 'state_dict' in ckpt:
                _state_dict = ckpt['state_dict']
            elif 'model' in ckpt:
                _state_dict = ckpt['model']
            else:
                _state_dict = ckpt

            state_dict = _state_dict
            missing_keys, unexpected_keys = \
                self.load_state_dict(state_dict, False)
        """
        pass  # training start from scratch

    def forward_tokens(self, x):
        outs = []
        for idx, block in enumerate(self.network):
            x = block(x)
            if self.fork_feat and idx in self.out_indices:
                norm_layer = getattr(self, f'norm{idx}')
                x_out = norm_layer(x)
                outs.append(x_out)
        if self.fork_feat:
            return outs
        return x

    def forward(self, x):
        x = self.patch_embed(x)
        x = self.forward_tokens(x)
        if self.fork_feat:
            # otuput features of four stages for dense prediction
            return x
        # print(x.size())
        x = self.norm(x)
        if self.dist:
            cls_out = self.head(x.flatten(2).mean(-1)), self.dist_head(x.flatten(2).mean(-1))
            if not self.training:
                cls_out = (cls_out[0] + cls_out[1]) / 2
        else:
            cls_out = self.head(x.flatten(2).mean(-1))
        # for image classification
        return cls_out


def _cfg(url='', **kwargs):
    return {
        'url': url,
        # 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
        'num_classes': 1, 'input_size': (36, 224, 224), 'pool_size': None,
        'crop_pct': .95, 'interpolation': 'bicubic',
        # 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'mean': mean0, 'std': std0,
        'classifier': 'head',
        **kwargs
    }


@register_model
def efficientformerv2_s0(pretrained=False, **kwargs):
    model = EfficientFormerV2(
        layers=EfficientFormer_depth['S0'],
        embed_dims=EfficientFormer_width['S0'],
        downsamples=[True, True, True, True, True],
        vit_num=2,
        drop_path_rate=0.0,
        e_ratios=expansion_ratios_S0,
        **kwargs)
    model.default_cfg = _cfg(crop_pct=0.9)
    return model


@register_model
def efficientformerv2_s1(pretrained=False, **kwargs):
    model = EfficientFormerV2(
        layers=EfficientFormer_depth['S1'],
        embed_dims=EfficientFormer_width['S1'],
        downsamples=[True, True, True, True],
        vit_num=2,
        drop_path_rate=0.0,
        e_ratios=expansion_ratios_S1,
        **kwargs)
    model.default_cfg = _cfg(crop_pct=0.9)
    return model


@register_model
def efficientformerv2_s2(pretrained=False, **kwargs):
    model = EfficientFormerV2(
        layers=EfficientFormer_depth['S2'],
        embed_dims=EfficientFormer_width['S2'],
        downsamples=[True, True, True, True],
        vit_num=4,
        drop_path_rate=0.02,
        e_ratios=expansion_ratios_S2,
        **kwargs)
    model.default_cfg = _cfg(crop_pct=0.9)
    return model


@register_model
def efficientformerv2_l(pretrained=False, **kwargs):
    model = EfficientFormerV2(
        layers=EfficientFormer_depth['L'],
        embed_dims=EfficientFormer_width['L'],
        downsamples=[True, True, True, True],
        vit_num=6,
        drop_path_rate=0.1,
        e_ratios=expansion_ratios_L,
        **kwargs)
    model.default_cfg = _cfg(crop_pct=0.9)
    return model
