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# --------------------------------------------------------------------------------------------------
# cwm3/baselines_dms/vendor/videomaev2_model.py — VENDORED COPY of models/modeling_finetune.py from
#   https://github.com/OpenGVLab/VideoMAEv2  (pinned commit 29eab1e8a588d1b3ec0cdec7b03a86cca491b74b, master, fetched 2026-09-14)
# Upstream license: MIT (Copyright (c) 2023 OpenGVLab) — see https://github.com/OpenGVLab/VideoMAEv2/blob/29eab1e8a588d1b3ec0cdec7b03a86cca491b74b/LICENSE
# The pretrained weight `vit_s_k710_dl_from_giant.pth` is distributed separately at https://huggingface.co/OpenGVLab/VideoMAE2 (model-card license: apache-2.0).
#
# Local modifications (nothing else changed; the network definition is byte-identical to upstream):
#   1. `from timm.models.layers import ...`  -> `from timm.layers import ...` (timm >= 1.0 moved the module; behaviour identical)
#   2. `from timm.models.registry import register_model` -> a local no-op decorator (we never use timm.create_model for this file)
# Used by baselines_dms as a FROZEN feature extractor (forward_features -> fc_norm(mean-pool) -> 384-d for ViT-S); no fine-tuning.
# --------------------------------------------------------------------------------------------------
# MIT License
#
# Copyright (c) 2023 OpenGVLab
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# --------------------------------------------------------------------------------------------------
# --------------------------------------------------------
# Based on BEiT, timm, DINO and DeiT code bases
# https://github.com/microsoft/unilm/tree/master/beit
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit
# https://github.com/facebookresearch/dino
# --------------------------------------------------------'
from functools import partial

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from timm.layers import drop_path, to_2tuple, trunc_normal_  # vendored: timm>=1.0 path


def register_model(fn):  # vendored: no-op stand-in for timm.models.registry.register_model
    return fn


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 400,
        'input_size': (3, 224, 224),
        'pool_size': None,
        'crop_pct': .9,
        'interpolation': 'bicubic',
        'mean': (0.5, 0.5, 0.5),
        'std': (0.5, 0.5, 0.5),
        **kwargs
    }


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 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,
                 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)
        # commit this for the orignal BERT implement
        x = self.fc2(x)
        x = self.drop(x)
        return x


class CosAttention(nn.Module):

    def __init__(self,
                 dim,
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop=0.,
                 proj_drop=0.,
                 attn_head_dim=None):
        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
        # DO NOT RENAME [self.scale] (for no weight decay)
        if qk_scale is None:
            self.scale = nn.Parameter(
                torch.log(10 * torch.ones((num_heads, 1, 1))),
                requires_grad=True)
        else:
            self.scale = qk_scale

        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

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(all_head_dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        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)
        q, k, v = qkv[0], qkv[1], qkv[
            2]  # make torchscript happy (cannot use tensor as tuple)

        attn = (
            F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))

        # torch.log(torch.tensor(1. / 0.01)) = 4.6052
        logit_scale = torch.clamp(self.scale, max=4.6052).exp()

        attn = attn * logit_scale

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

        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)

        x = self.proj(x)
        x = self.proj_drop(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.,
                 attn_head_dim=None):
        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.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

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(all_head_dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        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)
        q, k, v = qkv[0], qkv[1], qkv[
            2]  # make torchscript happy (cannot use tensor as tuple)

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

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

        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)

        x = self.proj(x)
        x = self.proj_drop(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.,
                 init_values=None,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 attn_head_dim=None,
                 cos_attn=False):
        super().__init__()
        self.norm1 = norm_layer(dim)
        if cos_attn:
            self.attn = CosAttention(
                dim,
                num_heads=num_heads,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                attn_drop=attn_drop,
                proj_drop=drop,
                attn_head_dim=attn_head_dim)
        else:
            self.attn = Attention(
                dim,
                num_heads=num_heads,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                attn_drop=attn_drop,
                proj_drop=drop,
                attn_head_dim=attn_head_dim)
        # 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)

        if 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

    def forward(self, x):
        if self.gamma_1 is None:
            x = x + self.drop_path(self.attn(self.norm1(x)))
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        else:
            x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
            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,
                 num_frames=16,
                 tubelet_size=2):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_spatial_patches = (img_size[0] // patch_size[0]) * (
            img_size[1] // patch_size[1])
        num_patches = num_spatial_patches * (num_frames // tubelet_size)

        self.img_size = img_size
        self.tubelet_size = tubelet_size
        self.patch_size = patch_size
        self.num_patches = num_patches
        self.proj = nn.Conv3d(
            in_channels=in_chans,
            out_channels=embed_dim,
            kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]),
            stride=(self.tubelet_size, patch_size[0], patch_size[1]))

    def forward(self, x, **kwargs):
        B, C, T, H, W = x.shape
        assert H == self.img_size[0] and W == self.img_size[
            1], f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        # b, c, l -> b, l, c
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


# sin-cos position encoding
# https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31
def get_sinusoid_encoding_table(n_position, d_hid):
    ''' Sinusoid position encoding table '''

    # TODO: make it with torch instead of numpy
    def get_position_angle_vec(position):
        return [
            position / np.power(10000, 2 * (hid_j // 2) / d_hid)
            for hid_j in range(d_hid)
        ]

    sinusoid_table = np.array(
        [get_position_angle_vec(pos_i) for pos_i in range(n_position)])
    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1

    return torch.tensor(
        sinusoid_table, dtype=torch.float, requires_grad=False).unsqueeze(0)


class VisionTransformer(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=1000,
                 embed_dim=768,
                 depth=12,
                 num_heads=12,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 head_drop_rate=0.,
                 norm_layer=nn.LayerNorm,
                 init_values=0.,
                 use_learnable_pos_emb=False,
                 init_scale=0.,
                 all_frames=16,
                 tubelet_size=2,
                 use_mean_pooling=True,
                 with_cp=False,
                 cos_attn=False):
        super().__init__()
        self.num_classes = num_classes
        # num_features for consistency with other models
        self.num_features = self.embed_dim = embed_dim
        self.tubelet_size = tubelet_size
        self.patch_embed = PatchEmbed(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            num_frames=all_frames,
            tubelet_size=tubelet_size)
        num_patches = self.patch_embed.num_patches
        self.with_cp = with_cp

        if use_learnable_pos_emb:
            self.pos_embed = nn.Parameter(
                torch.zeros(1, num_patches, embed_dim))
        else:
            # sine-cosine positional embeddings is on the way
            self.pos_embed = get_sinusoid_encoding_table(
                num_patches, embed_dim)

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

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)
               ]  # stochastic depth decay rule
        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,
                cos_attn=cos_attn) 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
        self.head_dropout = nn.Dropout(head_drop_rate)
        self.head = nn.Linear(
            embed_dim, num_classes) if num_classes > 0 else nn.Identity()

        if use_learnable_pos_emb:
            trunc_normal_(self.pos_embed, std=.02)

        self.apply(self._init_weights)

        self.head.weight.data.mul_(init_scale)
        self.head.bias.data.mul_(init_scale)

    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)

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

    @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, 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):
        B = x.size(0)

        x = self.patch_embed(x)

        if self.pos_embed is not None:
            x = x + self.pos_embed.expand(B, -1, -1).type_as(x).to(
                x.device).clone().detach()
        x = self.pos_drop(x)

        for blk in self.blocks:
            if self.with_cp:
                x = cp.checkpoint(blk, x)
            else:
                x = blk(x)

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

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


@register_model
def vit_small_patch16_224(pretrained=False, **kwargs):
    model = VisionTransformer(
        patch_size=16,
        embed_dim=384,
        depth=12,
        num_heads=6,
        mlp_ratio=4,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        **kwargs)
    model.default_cfg = _cfg()
    return model


@register_model
def vit_base_patch16_224(pretrained=False, **kwargs):
    model = VisionTransformer(
        patch_size=16,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        **kwargs)
    model.default_cfg = _cfg()
    return model


@register_model
def vit_large_patch16_224(pretrained=False, **kwargs):
    model = VisionTransformer(
        patch_size=16,
        embed_dim=1024,
        depth=24,
        num_heads=16,
        mlp_ratio=4,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        **kwargs)
    model.default_cfg = _cfg()
    return model


@register_model
def vit_huge_patch16_224(pretrained=False, **kwargs):
    model = VisionTransformer(
        patch_size=16,
        embed_dim=1280,
        depth=32,
        num_heads=16,
        mlp_ratio=4,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        **kwargs)
    model.default_cfg = _cfg()
    return model


@register_model
def vit_giant_patch14_224(pretrained=False, **kwargs):
    model = VisionTransformer(
        patch_size=14,
        embed_dim=1408,
        depth=40,
        num_heads=16,
        mlp_ratio=48 / 11,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        **kwargs)
    model.default_cfg = _cfg()
    return model

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	# --------------------------------------------------------------------------------------------------
	# cwm3/baselines_dms/vendor/videomaev2_model.py — VENDORED COPY of models/modeling_finetune.py from
	# https://github.com/OpenGVLab/VideoMAEv2 (pinned commit 29eab1e8a588d1b3ec0cdec7b03a86cca491b74b, master, fetched 2026-09-14)
	# Upstream license: MIT (Copyright (c) 2023 OpenGVLab) — see https://github.com/OpenGVLab/VideoMAEv2/blob/29eab1e8a588d1b3ec0cdec7b03a86cca491b74b/LICENSE
	# The pretrained weight `vit_s_k710_dl_from_giant.pth` is distributed separately at https://huggingface.co/OpenGVLab/VideoMAE2 (model-card license: apache-2.0).
	#
	# Local modifications (nothing else changed; the network definition is byte-identical to upstream):
	# 1. `from timm.models.layers import ...` -> `from timm.layers import ...` (timm >= 1.0 moved the module; behaviour identical)
	# 2. `from timm.models.registry import register_model` -> a local no-op decorator (we never use timm.create_model for this file)
	# Used by baselines_dms as a FROZEN feature extractor (forward_features -> fc_norm(mean-pool) -> 384-d for ViT-S); no fine-tuning.
	# --------------------------------------------------------------------------------------------------
	# MIT License
	#
	# Copyright (c) 2023 OpenGVLab
	#
	# Permission is hereby granted, free of charge, to any person obtaining a copy
	# of this software and associated documentation files (the "Software"), to deal
	# in the Software without restriction, including without limitation the rights
	# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	# copies of the Software, and to permit persons to whom the Software is
	# furnished to do so, subject to the following conditions:
	#
	# The above copyright notice and this permission notice shall be included in all
	# copies or substantial portions of the Software.
	#
	# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	# SOFTWARE.
	# --------------------------------------------------------------------------------------------------
	# --------------------------------------------------------
	# Based on BEiT, timm, DINO and DeiT code bases
	# https://github.com/microsoft/unilm/tree/master/beit
	# https://github.com/rwightman/pytorch-image-models/tree/master/timm
	# https://github.com/facebookresearch/deit
	# https://github.com/facebookresearch/dino
	# --------------------------------------------------------'
	from functools import partial

	import numpy as np
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import torch.utils.checkpoint as cp
	from timm.layers import drop_path, to_2tuple, trunc_normal_ # vendored: timm>=1.0 path


	def register_model(fn): # vendored: no-op stand-in for timm.models.registry.register_model
	return fn


	def _cfg(url='', **kwargs):
	return {
	'url': url,
	'num_classes': 400,
	'input_size': (3, 224, 224),
	'pool_size': None,
	'crop_pct': .9,
	'interpolation': 'bicubic',
	'mean': (0.5, 0.5, 0.5),
	'std': (0.5, 0.5, 0.5),
	**kwargs
	}


	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 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,
	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)
	# commit this for the orignal BERT implement
	x = self.fc2(x)
	x = self.drop(x)
	return x


	class CosAttention(nn.Module):

	def __init__(self,
	dim,
	num_heads=8,
	qkv_bias=False,
	qk_scale=None,
	attn_drop=0.,
	proj_drop=0.,
	attn_head_dim=None):
	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
	# DO NOT RENAME [self.scale] (for no weight decay)
	if qk_scale is None:
	self.scale = nn.Parameter(
	torch.log(10 * torch.ones((num_heads, 1, 1))),
	requires_grad=True)
	else:
	self.scale = qk_scale

	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

	self.attn_drop = nn.Dropout(attn_drop)
	self.proj = nn.Linear(all_head_dim, dim)
	self.proj_drop = nn.Dropout(proj_drop)

	def forward(self, x):
	B, N, C = x.shape
	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)
	q, k, v = qkv[0], qkv[1], qkv[
	2] # make torchscript happy (cannot use tensor as tuple)

	attn = (
	F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))

	# torch.log(torch.tensor(1. / 0.01)) = 4.6052
	logit_scale = torch.clamp(self.scale, max=4.6052).exp()

	attn = attn * logit_scale

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

	x = (attn @ v).transpose(1, 2).reshape(B, N, -1)

	x = self.proj(x)
	x = self.proj_drop(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.,
	attn_head_dim=None):
	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.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

	self.attn_drop = nn.Dropout(attn_drop)
	self.proj = nn.Linear(all_head_dim, dim)
	self.proj_drop = nn.Dropout(proj_drop)

	def forward(self, x):
	B, N, C = x.shape
	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)
	q, k, v = qkv[0], qkv[1], qkv[
	2] # make torchscript happy (cannot use tensor as tuple)

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

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

	x = (attn @ v).transpose(1, 2).reshape(B, N, -1)

	x = self.proj(x)
	x = self.proj_drop(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.,
	init_values=None,
	act_layer=nn.GELU,
	norm_layer=nn.LayerNorm,
	attn_head_dim=None,
	cos_attn=False):
	super().__init__()
	self.norm1 = norm_layer(dim)
	if cos_attn:
	self.attn = CosAttention(
	dim,
	num_heads=num_heads,
	qkv_bias=qkv_bias,
	qk_scale=qk_scale,
	attn_drop=attn_drop,
	proj_drop=drop,
	attn_head_dim=attn_head_dim)
	else:
	self.attn = Attention(
	dim,
	num_heads=num_heads,
	qkv_bias=qkv_bias,
	qk_scale=qk_scale,
	attn_drop=attn_drop,
	proj_drop=drop,
	attn_head_dim=attn_head_dim)
	# 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)

	if 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

	def forward(self, x):
	if self.gamma_1 is None:
	x = x + self.drop_path(self.attn(self.norm1(x)))
	x = x + self.drop_path(self.mlp(self.norm2(x)))
	else:
	x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
	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,
	num_frames=16,
	tubelet_size=2):
	super().__init__()
	img_size = to_2tuple(img_size)
	patch_size = to_2tuple(patch_size)
	num_spatial_patches = (img_size[0] // patch_size[0]) * (
	img_size[1] // patch_size[1])
	num_patches = num_spatial_patches * (num_frames // tubelet_size)

	self.img_size = img_size
	self.tubelet_size = tubelet_size
	self.patch_size = patch_size
	self.num_patches = num_patches
	self.proj = nn.Conv3d(
	in_channels=in_chans,
	out_channels=embed_dim,
	kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]),
	stride=(self.tubelet_size, patch_size[0], patch_size[1]))

	def forward(self, x, **kwargs):
	B, C, T, H, W = x.shape
	assert H == self.img_size[0] and W == self.img_size[
	1], f"Input image size ({H}{W}) doesn't match model ({self.img_size[0]}{self.img_size[1]})."
	# b, c, l -> b, l, c
	x = self.proj(x).flatten(2).transpose(1, 2)
	return x


	# sin-cos position encoding
	# https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31
	def get_sinusoid_encoding_table(n_position, d_hid):
	''' Sinusoid position encoding table '''

	# TODO: make it with torch instead of numpy
	def get_position_angle_vec(position):
	return [
	position / np.power(10000, 2 * (hid_j // 2) / d_hid)
	for hid_j in range(d_hid)
	]

	sinusoid_table = np.array(
	[get_position_angle_vec(pos_i) for pos_i in range(n_position)])
	sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
	sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1

	return torch.tensor(
	sinusoid_table, dtype=torch.float, requires_grad=False).unsqueeze(0)


	class VisionTransformer(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=1000,
	embed_dim=768,
	depth=12,
	num_heads=12,
	mlp_ratio=4.,
	qkv_bias=False,
	qk_scale=None,
	drop_rate=0.,
	attn_drop_rate=0.,
	drop_path_rate=0.,
	head_drop_rate=0.,
	norm_layer=nn.LayerNorm,
	init_values=0.,
	use_learnable_pos_emb=False,
	init_scale=0.,
	all_frames=16,
	tubelet_size=2,
	use_mean_pooling=True,
	with_cp=False,
	cos_attn=False):
	super().__init__()
	self.num_classes = num_classes
	# num_features for consistency with other models
	self.num_features = self.embed_dim = embed_dim
	self.tubelet_size = tubelet_size
	self.patch_embed = PatchEmbed(
	img_size=img_size,
	patch_size=patch_size,
	in_chans=in_chans,
	embed_dim=embed_dim,
	num_frames=all_frames,
	tubelet_size=tubelet_size)
	num_patches = self.patch_embed.num_patches
	self.with_cp = with_cp

	if use_learnable_pos_emb:
	self.pos_embed = nn.Parameter(
	torch.zeros(1, num_patches, embed_dim))
	else:
	# sine-cosine positional embeddings is on the way
	self.pos_embed = get_sinusoid_encoding_table(
	num_patches, embed_dim)

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

	dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)
	] # stochastic depth decay rule
	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,
	cos_attn=cos_attn) 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
	self.head_dropout = nn.Dropout(head_drop_rate)
	self.head = nn.Linear(
	embed_dim, num_classes) if num_classes > 0 else nn.Identity()

	if use_learnable_pos_emb:
	trunc_normal_(self.pos_embed, std=.02)

	self.apply(self._init_weights)

	self.head.weight.data.mul_(init_scale)
	self.head.bias.data.mul_(init_scale)

	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)

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

	@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, 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):
	B = x.size(0)

	x = self.patch_embed(x)

	if self.pos_embed is not None:
	x = x + self.pos_embed.expand(B, -1, -1).type_as(x).to(
	x.device).clone().detach()
	x = self.pos_drop(x)

	for blk in self.blocks:
	if self.with_cp:
	x = cp.checkpoint(blk, x)
	else:
	x = blk(x)

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

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


	@register_model
	def vit_small_patch16_224(pretrained=False, **kwargs):
	model = VisionTransformer(
	patch_size=16,
	embed_dim=384,
	depth=12,
	num_heads=6,
	mlp_ratio=4,
	qkv_bias=True,
	norm_layer=partial(nn.LayerNorm, eps=1e-6),
	**kwargs)
	model.default_cfg = _cfg()
	return model


	@register_model
	def vit_base_patch16_224(pretrained=False, **kwargs):
	model = VisionTransformer(
	patch_size=16,
	embed_dim=768,
	depth=12,
	num_heads=12,
	mlp_ratio=4,
	qkv_bias=True,
	norm_layer=partial(nn.LayerNorm, eps=1e-6),
	**kwargs)
	model.default_cfg = _cfg()
	return model


	@register_model
	def vit_large_patch16_224(pretrained=False, **kwargs):
	model = VisionTransformer(
	patch_size=16,
	embed_dim=1024,
	depth=24,
	num_heads=16,
	mlp_ratio=4,
	qkv_bias=True,
	norm_layer=partial(nn.LayerNorm, eps=1e-6),
	**kwargs)
	model.default_cfg = _cfg()
	return model


	@register_model
	def vit_huge_patch16_224(pretrained=False, **kwargs):
	model = VisionTransformer(
	patch_size=16,
	embed_dim=1280,
	depth=32,
	num_heads=16,
	mlp_ratio=4,
	qkv_bias=True,
	norm_layer=partial(nn.LayerNorm, eps=1e-6),
	**kwargs)
	model.default_cfg = _cfg()
	return model


	@register_model
	def vit_giant_patch14_224(pretrained=False, **kwargs):
	model = VisionTransformer(
	patch_size=14,
	embed_dim=1408,
	depth=40,
	num_heads=16,
	mlp_ratio=48 / 11,
	qkv_bias=True,
	norm_layer=partial(nn.LayerNorm, eps=1e-6),
	**kwargs)
	model.default_cfg = _cfg()
	return model