# --------------------------------------------------------
# Adapted from EVA CLIP
# https://github.com/baaivision/EVA/tree/master/EVA-CLIP/rei/eva_clip
# --------------------------------------------------------

import math
import os
from functools import partial

import torch
import torch.nn as nn
import torch.nn.functional as f

try:
    from timm.models.layers import drop_path as timm_drop_path
    from timm.models.layers import to_2tuple, trunc_normal_
except ImportError or ModuleNotFoundError:
    from timm.layers import drop_path as timm_drop_path, to_2tuple, trunc_normal_

from .rope_embeddings import VisionRotaryEmbeddingFast

if os.getenv('ENV_TYPE') == 'deepspeed':
    try:
        from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint
    except ImportError or ModuleNotFoundError:
        from torch.utils.checkpoint import checkpoint
else:
    from torch.utils.checkpoint import checkpoint

try:
    import xformers.ops as xops
except ImportError:
    xops = None


class PatchDropout(nn.Module):
    """
    https://arxiv.org/abs/2212.00794
    """

    def __init__(self, prob, exclude_first_token=True):
        super().__init__()
        assert 0 <= prob < 1.0
        self.prob = prob
        self.exclude_first_token = exclude_first_token  # exclude CLS token

    def forward(self, x):
        if not self.training or self.prob == 0.0:
            return x

        if self.exclude_first_token:
            cls_tokens, x = x[:, :1], x[:, 1:]
        else:
            cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1])

        batch = x.size()[0]
        num_tokens = x.size()[1]

        batch_indices = torch.arange(batch)
        batch_indices = batch_indices[..., None]

        keep_prob = 1 - self.prob
        num_patches_keep = max(1, int(num_tokens * keep_prob))

        rand = torch.randn(batch, num_tokens)
        patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices

        x = x[batch_indices, patch_indices_keep]

        if self.exclude_first_token:
            x = torch.cat((cls_tokens, x), dim=1)

        return x, patch_indices_keep


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of
    residual blocks)."""

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return timm_drop_path(x, self.drop_prob, self.training)

    def extra_repr(self) -> str:
        return 'p={}'.format(self.drop_prob)


class Mlp(nn.Module):
    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        drop=0.0,
        subln=False,
    ):
        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.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()

        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)
        # commit this for the orignal BERT implement
        x = self.ffn_ln(x)

        x = self.fc2(x)
        x = self.drop(x)
        return x


class SwiGLU(nn.Module):
    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.SiLU,
        drop=0.0,
        norm_layer=nn.LayerNorm,
        subln=False,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features

        self.w1 = nn.Linear(in_features, hidden_features)
        self.w2 = nn.Linear(in_features, hidden_features)

        self.act = act_layer()
        self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()
        self.w3 = nn.Linear(hidden_features, out_features)

        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x1 = self.w1(x)
        x2 = self.w2(x)
        hidden = self.act(x1) * x2
        x = self.ffn_ln(hidden)
        x = self.w3(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
        window_size=None,
        attn_head_dim=None,
        xattn=False,
        rope=None,
        subln=False,
        norm_layer=nn.LayerNorm,
    ):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        if attn_head_dim is not None:
            head_dim = attn_head_dim
        all_head_dim = head_dim * self.num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.subln = subln
        if self.subln:
            self.q_proj = nn.Linear(dim, all_head_dim, bias=False)
            self.k_proj = nn.Linear(dim, all_head_dim, bias=False)
            self.v_proj = nn.Linear(dim, all_head_dim, bias=False)
        else:
            self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)

        if qkv_bias:
            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
        else:
            self.q_bias = None
            self.v_bias = None

        if window_size:
            self.window_size = window_size
            self.num_relative_distance = (2 * window_size[0] - 1) * (
                2 * window_size[1] - 1
            ) + 3
            self.relative_position_bias_table = nn.Parameter(
                torch.zeros(self.num_relative_distance, num_heads)
            )  # 2*Wh-1 * 2*Ww-1, nH
            # cls to token & token 2 cls & cls to cls

            # get pair-wise relative position index for each token inside the window
            coords_h = torch.arange(window_size[0])
            coords_w = torch.arange(window_size[1])
            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
            coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
            relative_coords = (
                coords_flatten[:, :, None] - coords_flatten[:, None, :]
            )  # 2, Wh*Ww, Wh*Ww
            relative_coords = relative_coords.permute(
                1, 2, 0
            ).contiguous()  # Wh*Ww, Wh*Ww, 2
            relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
            relative_coords[:, :, 1] += window_size[1] - 1
            relative_coords[:, :, 0] *= 2 * window_size[1] - 1
            relative_position_index = torch.zeros(
                size=(window_size[0] * window_size[1] + 1,) * 2,
                dtype=relative_coords.dtype,
            )
            relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
            relative_position_index[0, 0:] = self.num_relative_distance - 3
            relative_position_index[0:, 0] = self.num_relative_distance - 2
            relative_position_index[0, 0] = self.num_relative_distance - 1

            self.register_buffer('relative_position_index', relative_position_index)
        else:
            self.window_size = None
            self.relative_position_bias_table = None
            self.relative_position_index = None

        self.attn_drop = nn.Dropout(attn_drop)
        self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity()
        # self.proj = nn.Linear(all_head_dim, all_head_dim)
        self.proj = nn.Linear(all_head_dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.xattn = xattn
        self.xattn_drop = attn_drop

        self.rope = rope

    def forward(self, x, rel_pos_bias=None, attn_mask=None):
        b, n, _ = x.shape
        if self.subln:
            q = f.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias)
            k = f.linear(input=x, weight=self.k_proj.weight, bias=None)
            v = f.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias)

            q = q.reshape(b, n, self.num_heads, -1).permute(
                0, 2, 1, 3
            )  # B, num_heads, N, C
            k = k.reshape(b, n, self.num_heads, -1).permute(0, 2, 1, 3)
            v = v.reshape(b, n, self.num_heads, -1).permute(0, 2, 1, 3)
        else:
            qkv_bias = None
            if self.q_bias is not None:
                qkv_bias = torch.cat(
                    (
                        self.q_bias,
                        torch.zeros_like(self.v_bias, requires_grad=False),
                        self.v_bias,
                    )
                )

            qkv = f.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
            qkv = qkv.reshape(b, n, 3, self.num_heads, -1).permute(
                2, 0, 3, 1, 4
            )  # 3, B, num_heads, N, C
            q, k, v = qkv[0], qkv[1], qkv[2]

        if self.rope:
            # slightly fast impl
            q_t = q[:, :, 1:, :]
            ro_q_t = self.rope(q_t)
            q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v)

            k_t = k[:, :, 1:, :]
            ro_k_t = self.rope(k_t)
            k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v)

        if self.xattn:
            if xops is None:
                raise ValueError(
                    "Can't use xattn without xformers. Please 'pip install xformers'"
                )
            q = q.permute(0, 2, 1, 3)  # B, num_heads, N, C -> B, N, num_heads, C
            k = k.permute(0, 2, 1, 3)
            v = v.permute(0, 2, 1, 3)

            x = xops.memory_efficient_attention(
                q,
                k,
                v,
                p=self.xattn_drop,
                scale=self.scale,
            )
            x = x.reshape(b, n, -1)
            x = self.inner_attn_ln(x)
            x = self.proj(x)
            x = self.proj_drop(x)
        else:
            q = q * self.scale
            attn = q @ k.transpose(-2, -1)

            if self.relative_position_bias_table is not None:
                relative_position_bias = self.relative_position_bias_table[
                    self.relative_position_index.view(-1)
                ].view(
                    self.window_size[0] * self.window_size[1] + 1,
                    self.window_size[0] * self.window_size[1] + 1,
                    -1,
                )  # Wh*Ww,Wh*Ww,nH
                relative_position_bias = relative_position_bias.permute(
                    2, 0, 1
                ).contiguous()  # nH, Wh*Ww, Wh*Ww
                attn = attn + relative_position_bias.unsqueeze(0).type_as(attn)

            if rel_pos_bias is not None:
                attn = attn + rel_pos_bias.type_as(attn)

            if attn_mask is not None:
                attn_mask = attn_mask.bool()
                attn = attn.masked_fill(~attn_mask[:, None, None, :], float('-inf'))

            attn = attn.softmax(dim=-1)
            attn = self.attn_drop(attn)

            x = (attn @ v).transpose(1, 2).reshape(b, n, -1)
            x = self.inner_attn_ln(x)
            x = self.proj(x)
            x = self.proj_drop(x)
        return x


class Block(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        init_values=None,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        window_size=None,
        attn_head_dim=None,
        xattn=False,
        rope=None,
        postnorm=False,
        subln=False,
        naiveswiglu=False,
    ):
        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,
            window_size=window_size,
            attn_head_dim=attn_head_dim,
            xattn=xattn,
            rope=rope,
            subln=subln,
            norm_layer=norm_layer,
        )
        # 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 > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)

        if naiveswiglu:
            self.mlp = SwiGLU(
                in_features=dim,
                hidden_features=mlp_hidden_dim,
                subln=subln,
                norm_layer=norm_layer,
            )
        else:
            self.mlp = Mlp(
                in_features=dim,
                hidden_features=mlp_hidden_dim,
                act_layer=act_layer,
                subln=subln,
                drop=drop,
            )

        if init_values is not None and init_values > 0:
            self.gamma_1 = nn.Parameter(
                init_values * torch.ones((dim,)), requires_grad=True
            )
            self.gamma_2 = nn.Parameter(
                init_values * torch.ones((dim,)), requires_grad=True
            )
        else:
            self.gamma_1, self.gamma_2 = None, None

        self.postnorm = postnorm

    def forward(self, x, rel_pos_bias=None, attn_mask=None):
        if self.gamma_1 is None:
            if self.postnorm:
                x = x + self.drop_path(
                    self.norm1(
                        self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)
                    )
                )
                x = x + self.drop_path(self.norm2(self.mlp(x)))
            else:
                x = x + self.drop_path(
                    self.attn(
                        self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask
                    )
                )
                x = x + self.drop_path(self.mlp(self.norm2(x)))
        else:
            if self.postnorm:
                x = x + self.drop_path(
                    self.gamma_1
                    * self.norm1(
                        self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)
                    )
                )
                x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x)))
            else:
                x = x + self.drop_path(
                    self.gamma_1
                    * self.attn(
                        self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask
                    )
                )
                x = x + self.drop_path(self.gamma_2 * 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)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.proj = nn.Conv2d(
            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
        )

    def forward(self, x, **_):
        target_dtype = self.proj.weight.dtype
        _, __, 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 "
            f'({self.img_size[0]}*{self.img_size[1]}).'
        )
        x = self.proj(x.to(dtype=target_dtype)).flatten(2).transpose(1, 2)
        return x


class RelativePositionBias(nn.Module):
    def __init__(self, window_size, num_heads):
        super().__init__()
        self.window_size = window_size
        self.num_relative_distance = (2 * window_size[0] - 1) * (
            2 * window_size[1] - 1
        ) + 3
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros(self.num_relative_distance, num_heads)
        )  # 2*Wh-1 * 2*Ww-1, nH
        # cls to token & token 2 cls & cls to cls

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(window_size[0])
        coords_w = torch.arange(window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = (
            coords_flatten[:, :, None] - coords_flatten[:, None, :]
        )  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(
            1, 2, 0
        ).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * window_size[1] - 1
        relative_position_index = torch.zeros(
            size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype
        )
        relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        relative_position_index[0, 0:] = self.num_relative_distance - 3
        relative_position_index[0:, 0] = self.num_relative_distance - 2
        relative_position_index[0, 0] = self.num_relative_distance - 1

        self.register_buffer('relative_position_index', relative_position_index)

    def forward(self):
        relative_position_bias = self.relative_position_bias_table[
            self.relative_position_index.view(-1)
        ].view(
            self.window_size[0] * self.window_size[1] + 1,
            self.window_size[0] * self.window_size[1] + 1,
            -1,
        )  # Wh*Ww,Wh*Ww,nH
        return relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww


class EVAVisionTransformer(nn.Module):
    """Vision Transformer with support for patch or hybrid CNN input stage"""

    def __init__(
        self,
        img_size=224,
        patch_size=16,
        in_chans=3,
        num_classes=0,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
        norm_layer=nn.LayerNorm,
        init_values=None,
        patch_dropout=0.0,
        use_abs_pos_emb=True,
        use_rel_pos_bias=False,
        use_shared_rel_pos_bias=False,
        rope=False,
        use_mean_pooling=True,
        init_scale=0.001,
        grad_checkpointing=False,
        xattn=False,
        postnorm=False,
        pt_hw_seq_len=16,
        intp_freq=False,
        naiveswiglu=False,
        subln=False,
        proj_type=None,
    ):
        super().__init__()
        self.image_size = img_size
        self.num_classes = num_classes
        # num_features for consistency with other models
        self.num_features = self.embed_dim = embed_dim

        self.patch_embed = PatchEmbed(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
        )
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        # self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        if use_abs_pos_emb:
            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        else:
            self.pos_embed = None
        self.pos_drop = nn.Dropout(p=drop_rate)

        if use_shared_rel_pos_bias:
            self.rel_pos_bias = RelativePositionBias(
                window_size=self.patch_embed.patch_shape, num_heads=num_heads
            )
        else:
            self.rel_pos_bias = None

        if rope:
            half_head_dim = embed_dim // num_heads // 2
            hw_seq_len = img_size // patch_size
            self.rope = VisionRotaryEmbeddingFast(
                dim=half_head_dim,
                pt_seq_len=pt_hw_seq_len,
                ft_seq_len=hw_seq_len if intp_freq else None,
                patch_dropout=patch_dropout,
            )
        else:
            self.rope = None

        self.naiveswiglu = naiveswiglu

        dpr = [
            x.item() for x in torch.linspace(0, drop_path_rate, depth)
        ]  # stochastic depth decay rule
        self.use_rel_pos_bias = use_rel_pos_bias
        self.blocks = nn.ModuleList(
            [
                Block(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[i],
                    norm_layer=norm_layer,
                    init_values=init_values,
                    window_size=self.patch_embed.patch_shape
                    if use_rel_pos_bias
                    else None,
                    xattn=xattn,
                    rope=self.rope,
                    postnorm=postnorm,
                    subln=subln,
                    naiveswiglu=naiveswiglu,
                )
                for i in range(depth)
            ]
        )
        self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
        self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
        if (num_classes == embed_dim) and (proj_type is None):
            self.head = nn.Identity()
        elif proj_type == 'linear':
            self.head = nn.Linear(embed_dim, num_classes, bias=qkv_bias)
        elif proj_type == 'mlp':
            hidden_size = (embed_dim + num_classes) // 2
            self.proj = nn.Sequential(
                nn.Linear(embed_dim, hidden_size, bias=qkv_bias),
                nn.GELU(),
                nn.Linear(hidden_size, num_classes, bias=qkv_bias),
            )

        if self.pos_embed is not None:
            trunc_normal_(self.pos_embed, std=0.02)

        trunc_normal_(self.cls_token, std=0.02)

        self.apply(self._init_weights)
        self.fix_init_weight()

        if isinstance(self.head, nn.Linear):
            trunc_normal_(self.head.weight, std=0.02)
            self.head.weight.data.mul_(init_scale)
            if qkv_bias:
                self.head.bias.data.mul_(init_scale)

        # setting a patch_dropout of 0. would mean it is disabled and this function
        # would be the identity fn
        self.patch_dropout = (
            PatchDropout(patch_dropout) if patch_dropout > 0.0 else nn.Identity()
        )

        self.grad_checkpointing = grad_checkpointing

    def fix_init_weight(self):
        def rescale(param, _layer_id):
            param.div_(math.sqrt(2.0 * _layer_id))

        for layer_id, layer in enumerate(self.blocks):
            rescale(layer.attn.proj.weight.data, layer_id + 1)
            if self.naiveswiglu:
                rescale(layer.mlp.w3.weight.data, layer_id + 1)
            else:
                rescale(layer.mlp.fc2.weight.data, layer_id + 1)

    def get_cast_dtype(self) -> torch.dtype:
        return self.blocks[0].mlp.fc2.weight.dtype

    @staticmethod
    def _init_weights(m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if 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)

    def get_num_layers(self):
        return len(self.blocks)

    def lock(self, unlocked_groups=0, *_, **__):
        assert (
            unlocked_groups == 0
        ), 'partial locking not currently supported for this model'
        for param in self.parameters():
            param.requires_grad = False

    @torch.jit.ignore
    def set_grad_checkpointing(self, enable=True):
        self.grad_checkpointing = enable

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed', 'cls_token'}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, *_, **__):
        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, return_all_features=False):
        x = self.patch_embed(x)
        batch_size, seq_len, _ = x.size()

        cls_tokens = self.cls_token.expand(
            batch_size, -1, -1
        )  # stole cls_tokens impl from Phil Wang, thanks
        x = torch.cat((cls_tokens, x), dim=1)
        if self.pos_embed is not None:
            x = x + self.pos_embed
        x = self.pos_drop(x)

        # a patch_dropout of 0. would mean it is disabled and this function would do
        # nothing but return what was passed in
        if self.rope is not None:
            if self.training and not isinstance(self.patch_dropout, nn.Identity):
                x, patch_indices_keep = self.patch_dropout(x)
                self.rope.forward = partial(
                    self.rope.forward, patch_indices_keep=patch_indices_keep
                )
            else:
                self.rope.forward = partial(self.rope.forward, patch_indices_keep=None)
                x = self.patch_dropout(x)
        else:
            x = self.patch_dropout(x)

        rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
        for blk in self.blocks:
            if self.grad_checkpointing:
                x = checkpoint(blk, x, (rel_pos_bias,))
            else:
                x = blk(x, rel_pos_bias=rel_pos_bias)

        if not return_all_features:
            x = self.norm(x)
            if self.fc_norm is not None:
                return self.fc_norm(x.mean(1))
            else:
                return x[:, 0]
        return x

    def forward(self, x, return_all_features=False):
        if return_all_features:
            return self.forward_features(x, return_all_features)
        x = self.forward_features(x)
        x = self.head(x)
        return x