# 2022.06.08-Changed for implementation of TokenFusion
#            Huawei Technologies Co., Ltd. <foss@huawei.com>
# ---------------------------------------------------------------
# Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# ---------------------------------------------------------------
import math
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 .modules import ModuleParallel, LayerNormParallel, num_parallel, LinearFuse


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 = ModuleParallel(nn.Linear(in_features, hidden_features))
        self.dwconv = DWConv(hidden_features)
        self.act = ModuleParallel(act_layer())
        self.fc2 = ModuleParallel(nn.Linear(hidden_features, out_features))
        self.drop = ModuleParallel(nn.Dropout(drop))

        self.apply(self._init_weights)

    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)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x, H, W):
        x = self.fc1(x)
        x = [self.dwconv(x[0], H, W), self.dwconv(x[1], H, W)]
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):
    def __init__(self, dim, ratio, 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 = ModuleParallel(nn.Linear(dim, dim, bias=qkv_bias))
        self.kv = ModuleParallel(nn.Linear(dim, dim * 2, bias=qkv_bias))
        self.attn_drop = ModuleParallel(nn.Dropout(attn_drop))
        self.proj = ModuleParallel(nn.Linear(dim, dim))
        self.proj_drop = ModuleParallel(nn.Dropout(proj_drop))

        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = ModuleParallel(nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio))
            self.norm = LayerNormParallel(dim)
        self.exchange = LinearFuse(ratio)

        self.apply(self._init_weights)

    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)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

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

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

        attn = [(q_ @ k_.transpose(-2, -1)) * self.scale for (q_, k_) in zip(q, k)]
        attn = [attn_.softmax(dim=-1) for attn_ in attn]
        attn = self.attn_drop(attn)

        x = [(attn_ @ v_).transpose(1, 2).reshape(B, N, C) for (attn_, v_) in zip(attn, v)]
        x = self.proj(x)
        x = self.proj_drop(x)

        # x = [x_ * mask_.unsqueeze(2) for (x_, mask_) in zip(x, mask)]
        x = self.exchange(x)

        return x


# class PredictorLG(nn.Module):
#     """ Image to Patch Embedding from DydamicVit
#     """
#     def __init__(self, embed_dim=384):
#         super().__init__()
#         self.score_nets = nn.ModuleList([nn.Sequential(
#             nn.LayerNorm(embed_dim),
#             nn.Linear(embed_dim, embed_dim),
#             nn.GELU(),
#             nn.Linear(embed_dim, embed_dim // 2),
#             nn.GELU(),
#             nn.Linear(embed_dim // 2, embed_dim // 4),
#             nn.GELU(),
#             nn.Linear(embed_dim // 4, 2),
#             nn.LogSoftmax(dim=-1)
#         ) for _ in range(num_parallel)])

#     def forward(self, x):
#         x = [self.score_nets[i](x[i]) for i in range(num_parallel)]
#         return x


class Block(nn.Module):

    def __init__(self, dim, ratio, 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=LayerNormParallel, sr_ratio=1):
        super().__init__()
        self.norm1 = norm_layer(dim)
        # self.score = PredictorLG(dim)

        self.attn = Attention(
            dim, ratio,
            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 = ModuleParallel(DropPath(drop_path)) if drop_path > 0. else ModuleParallel(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.exchange = TokenExchange()

        self.apply(self._init_weights)

    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)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x, H, W, mask=None):
        B = x[0].shape[0]
        # norm1 = self.norm1(x)
        # score = self.score(norm1)
        # mask = [F.gumbel_softmax(score_.reshape(B, -1, 2), hard=True)[:, :, 0] for score_ in score]
        # if mask is not None:
        #     norm = [norm_ * mask_.unsqueeze(2) for (norm_, mask_) in zip(norm, mask)]
        f = self.drop_path(self.attn(self.norm1(x), H, W))
        x = [x_ + f_ for (x_, f_) in zip (x, f)]
        f = self.drop_path(self.mlp(self.norm2(x), H, W))
        x = [x_ + f_ for (x_, f_) in zip (x, f)]
        # if mask is not None:
        #     x = self.exchange(x, mask, mask_threshold=0.02)
        return x


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

    def __init__(self, masking_ratio = 0.25, img_size=224, patch_size=7, stride=4, 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
        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 = ModuleParallel(nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
                              padding=(patch_size[0] // 2, patch_size[1] // 2)))
        self.norm = LayerNormParallel(embed_dim)
        self.masking_ratio = masking_ratio
        self.embed_dim = embed_dim
        self.mask_token = nn.parameter.Parameter(torch.randn(self.embed_dim), requires_grad = True)#None #When training in the SupOnly loop, unused params raise error in DDP. Hence instantiating mask_token only when masked training begins
        self.apply(self._init_weights)

    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)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def mask_with_mean(self, x):
        # print(x.shape)
        avg = torch.mean(x, dim = 1)
        avg = avg.clone().detach().requires_grad_(False)
        N, L, D = x.shape  # batch, length, dim
        len_keep = int(L * (1 - self.masking_ratio))
        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]
        
        # sort noise for each sample
        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove

        # keep the first subset
        ids_mask = ids_shuffle[:, len_keep:]
        # for i in range(N):
        #     x[i][ids_mask[i]] = avg[i]
        # return x
        avg = avg.unsqueeze(dim = 1)
        avg = avg.repeat(1, L, 1)
        mask = ids_mask.unsqueeze(dim = 2)
        mask = mask.repeat(1, 1, D)
        masked = torch.scatter(x, dim = 1, index = mask, src = avg) # self[i] [index[i][j][k]] [k] = src[i][j][k]  # if dim == 1
        # self.printcheck(x[0], masked[0], avg[0])
        return masked
    
    def mask_with_learnt_mask(self, x, masking_branch):
        # if self.mask_token is None: #When training in the SupOnly loop, unused params raise error in DDP. Hence instantiating mask_token only when masked training begins
        #     self.mask_token = nn.parameter.Parameter(torch.randn(self.embed_dim, device=x.device), requires_grad = True)
        #     print(self.mask_token[:10], x.device, "token")
        _, N, L, D = x.shape  # modality, batch, length, dim
        N = torch.sum( torch.tensor(masking_branch) != -1)
        indicies = torch.FloatTensor(N, L).uniform_() <= self.masking_ratio
        # x[indicies] = self.mask_token
        masking_branch = torch.tensor(masking_branch).to(x.device)
        index = torch.stack([torch.tensor(masking_branch == 0), torch.tensor(masking_branch == 1)]).to(x.device)
        xtemp = x[index]
        xtemp[indicies] = self.mask_token
        x[index] = xtemp
        return x
        

    def printcheck(self, x, masked, avg):
        L, D = x.shape
        same = 0
        avgsame = 0
        for i in range(L):
            if (x[i] == masked[i]).all():
                same += 1
            # else:
            #     print(i, x[i])
            if (masked[i].data == avg[i].data).all():
                avgsame += 1
        print(same, avgsame)
        return

    def forward(self, x, mask, masking_branch = None, range_batches_to_mask = None):
        sum_mask = torch.sum(self.mask_token)
        x = self.proj(x)
        _, _, H, W = x[0].shape
        x = [x_.flatten(2).transpose(1, 2) for x_ in x]
        x = self.norm(x)
        if mask:
            assert masking_branch is not None and range_batches_to_mask is not None, "expected the range of batches to mask to not mask the labeled images"
            # x[masking_branch][range_batches_to_mask[0]:range_batches_to_mask[1]] = self.mask_with_learnt_mask(x[masking_branch][range_batches_to_mask[0]:range_batches_to_mask[1]])
            xstacked = torch.stack(x)
            xstacked[:, range_batches_to_mask[0]:range_batches_to_mask[1]]  = self.mask_with_learnt_mask(xstacked[:, range_batches_to_mask[0]:range_batches_to_mask[1]], masking_branch)
            x = [xstacked[i] for i in range(xstacked.shape[0])]
        else:
            x[0] = x[0] + 0*sum_mask #So that when training with SupOnly (and not using any masking), DDP doesn't raise an error that you have unused parameters. 
        return x, H, W


class MixVisionTransformer(nn.Module):
    def __init__(self, ratio, masking_ratio, 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=LayerNormParallel,
                 depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
        super().__init__()
        self.num_classes = num_classes
        self.depths = depths
        self.embed_dims = embed_dims

        # patch_embed
        self.patch_embed1 = OverlapPatchEmbedAndMask(masking_ratio = masking_ratio, img_size=img_size, patch_size=7, stride=4, in_chans=in_chans,
                                              embed_dim=embed_dims[0])
        self.patch_embed2 = OverlapPatchEmbedAndMask(masking_ratio = masking_ratio, img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0],
                                              embed_dim=embed_dims[1])
        self.patch_embed3 = OverlapPatchEmbedAndMask(masking_ratio = masking_ratio, img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1],
                                              embed_dim=embed_dims[2])
        self.patch_embed4 = OverlapPatchEmbedAndMask(masking_ratio = masking_ratio, img_size=img_size // 16, patch_size=3, stride=2, in_chans=embed_dims[2],
                                              embed_dim=embed_dims[3])

        # predictor_list = [PredictorLG(embed_dims[i]) for i in range(len(depths))]
        # self.score_predictor = nn.ModuleList(predictor_list)

        # 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], ratio = ratio, 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])])
        self.norm1 = norm_layer(embed_dims[0])

        cur += depths[0]
        self.block2 = nn.ModuleList([Block(
            dim=embed_dims[1], ratio = ratio, 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])])
        self.norm2 = norm_layer(embed_dims[1])

        cur += depths[1]
        self.block3 = nn.ModuleList([Block(
            dim=embed_dims[2], ratio = ratio, 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])])
        self.norm3 = norm_layer(embed_dims[2])

        cur += depths[2]
        self.block4 = nn.ModuleList([Block(
            dim=embed_dims[3], ratio = ratio, 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.norm4 = norm_layer(embed_dims[3])

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

        self.apply(self._init_weights)

    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)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()
    '''
    def init_weights(self, pretrained=None):
        if isinstance(pretrained, str):
            logger = get_root_logger()
            load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
    '''
    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 freeze_patch_emb(self):
        self.patch_embed1.requires_grad = False

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'}  # has pos_embed may be better

    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 forward_features(self, x, mask, masking_branch, range_batches_to_mask):
        B = x[0].shape[0]
        outs0, outs1 = [], []
        # masks = []
        # stage 1

        x, H, W = self.patch_embed1(x, mask = mask, masking_branch = masking_branch, range_batches_to_mask = range_batches_to_mask)
        for i, blk in enumerate(self.block1):
            # score = self.score_predictor[0](x)
            # mask = [F.softmax(score_.reshape(B, -1, 2), dim=2)[:, :, 0] for score_ in score]  # mask_: [B, N]
            # masks.append(mask)
            x = blk(x, H, W)
        x = self.norm1(x)
        x = [x_.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() for x_ in x]
        outs0.append(x[0])
        outs1.append(x[1])

        # stage 2
        x, H, W = self.patch_embed2(x, mask = False)
        # x, H, W = self.patch_embed2(x, masking_branch = masking_branch, range_batches_to_mask = range_batches_to_mask)
        for i, blk in enumerate(self.block2):
            # score = self.score_predictor[1](x)
            # mask = [F.softmax(score_.reshape(B, -1, 2), dim=2)[:, :, 0] for score_ in score]  # mask_: [B, N]
            # masks.append(mask)
            x = blk(x, H, W)
        x = self.norm2(x)
        x = [x_.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() for x_ in x]
        outs0.append(x[0])
        outs1.append(x[1])

        # stage 3
        x, H, W = self.patch_embed3(x, mask = False)
        # x, H, W = self.patch_embed3(x, masking_branch = masking_branch, range_batches_to_mask = range_batches_to_mask)
        for i, blk in enumerate(self.block3):
            # score = self.score_predictor[2](x)
            # mask = [F.softmax(score_.reshape(B, -1, 2), dim=2)[:, :, 0] for score_ in score]  # mask_: [B, N]
            # masks.append(mask)
            x = blk(x, H, W)
        x = self.norm3(x)
        x = [x_.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() for x_ in x]
        outs0.append(x[0])
        outs1.append(x[1])

        # stage 4
        x, H, W = self.patch_embed4(x, mask = False)
        # x, H, W = self.patch_embed4(x, masking_branch = masking_branch, range_batches_to_mask = range_batches_to_mask)
        for i, blk in enumerate(self.block4):
            # score = self.score_predictor[3](x)
            # mask = [F.softmax(score_.reshape(B, -1, 2), dim=2)[:, :, 0] for score_ in score]  # mask_: [B, N]
            # masks.append(mask)
            x = blk(x, H, W)
        x = self.norm4(x)
        x = [x_.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() for x_ in x]
        outs0.append(x[0])
        outs1.append(x[1])

        return [outs0, outs1]

    def forward(self, x, mask, masking_branch = None, range_batches_to_mask = None):
        x = self.forward_features(x, mask, masking_branch, range_batches_to_mask)
        return x


class DWConv(nn.Module):
    def __init__(self, dim=768):
        super(DWConv, self).__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)

    def forward(self, x, H, W):
        B, N, C = x.shape
        x = x.transpose(1, 2).contiguous().view(B, C, H, W)
        x = self.dwconv(x)
        x = x.flatten(2).transpose(1, 2).contiguous()

        return x

class mit_b0(MixVisionTransformer):
    def __init__(self, ratio, masking_ratio, **kwargs):
        super(mit_b0, self).__init__(ratio = ratio, masking_ratio = masking_ratio, 
            patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=LayerNormParallel, depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b1(MixVisionTransformer):
    def __init__(self, ratio, masking_ratio, **kwargs):
        super(mit_b1, self).__init__(ratio = ratio, masking_ratio = masking_ratio, 
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=LayerNormParallel, depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b2(MixVisionTransformer):
    def __init__(self, ratio, masking_ratio, **kwargs):
        super(mit_b2, self).__init__(ratio = ratio, masking_ratio = masking_ratio, 
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=LayerNormParallel, depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b3(MixVisionTransformer):
    def __init__(self, ratio, masking_ratio, **kwargs):
        super(mit_b3, self).__init__(ratio = ratio, masking_ratio = masking_ratio, 
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=LayerNormParallel, depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b4(MixVisionTransformer):
    def __init__(self, ratio, masking_ratio, **kwargs):
        super(mit_b4, self).__init__(ratio = ratio, masking_ratio = masking_ratio, 
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=LayerNormParallel, depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b5(MixVisionTransformer):
    def __init__(self, ratio, masking_ratio, **kwargs):
        super(mit_b5, self).__init__(ratio = ratio, masking_ratio = masking_ratio, 
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=LayerNormParallel, depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)