import torch
import torch.nn as nn


def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
    if drop_prob == 0.0 or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.
        device)
    random_tensor.floor_()
    output = x.div(keep_prob) * random_tensor
    return output


class 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):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5
        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.k = nn.Linear(dim, dim, bias=qkv_bias)
        self.v = nn.Linear(dim, dim, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        q = self.q(x[:, 0]).unsqueeze(1).reshape(B, 1, self.num_heads, C //
            self.num_heads).permute(0, 2, 1, 3)
        k = self.k(x).reshape(B, N, self.num_heads, C // self.num_heads
            ).permute(0, 2, 1, 3)
        q = q * self.scale
        v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads
            ).permute(0, 2, 1, 3)
        attn = q @ k.transpose(-2, -1)
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        x_cls = (attn @ v).transpose(1, 2).reshape(B, 1, C)
        x_cls = self.proj(x_cls)
        x_cls = self.proj_drop(x_cls)
        return x_cls


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)


class Mlp(nn.Module):

    def __init__(self, in_features, hidden_features=None, out_features=None,
        act_layer=nn.GELU, drop=0.0):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

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


class LayerScale_Block_CA(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, act_layer=nn
        .GELU, norm_layer=nn.LayerNorm, Attention_block=Class_Attention,
        Mlp_block=Mlp, init_values=0.0001):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention_block(dim, num_heads=num_heads, qkv_bias=
            qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        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)
        self.mlp = Mlp_block(in_features=dim, hidden_features=
            mlp_hidden_dim, act_layer=act_layer, drop=drop)
        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)

    def forward(self, x, x_cls):
        u = torch.cat((x_cls, x), dim=1)
        x_cls = x_cls + self.drop_path(self.gamma_1 * self.attn(self.norm1(u)))
        x_cls = x_cls + self.drop_path(self.gamma_2 * self.mlp(self.norm2(
            x_cls)))
        return x_cls


def get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'dim': 4, 'num_heads': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 8
    x0 = xindex % 4
    x2 = xindex // 32
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1) + 16 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_mul_3(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 8
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 32 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 8 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_per_fused__softmax_5(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 8 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 8 * x0), tmp11, xmask)


@triton.jit
def triton_poi_fused_add_mul_native_layer_norm_6(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tl.load(in_ptr2 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + 1)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp9 = tl.load(in_ptr2 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + 2)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp16 = tl.load(in_ptr2 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + 3)
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK])
    tmp23 = tl.load(in_ptr2 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp4 = tmp2 * tmp3
    tmp5 = tmp0 + tmp4
    tmp10 = tmp8 * tmp9
    tmp11 = tmp6 + tmp10
    tmp12 = tmp5 + tmp11
    tmp17 = tmp15 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp12 + tmp18
    tmp24 = tmp22 * tmp23
    tmp25 = tmp20 + tmp24
    tmp26 = tmp19 + tmp25
    tmp27 = 4.0
    tmp28 = tmp26 / tmp27
    tmp29 = tmp5 - tmp28
    tmp30 = tmp29 * tmp29
    tmp31 = tmp11 - tmp28
    tmp32 = tmp31 * tmp31
    tmp33 = tmp30 + tmp32
    tmp34 = tmp18 - tmp28
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp25 - tmp28
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp39 / tmp27
    tl.store(out_ptr0 + x2, tmp28, xmask)
    tl.store(out_ptr1 + x2, tmp40, xmask)


@triton.jit
def triton_poi_fused_add_mul_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x4, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 1e-05
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.rsqrt(tmp9)
    tmp11 = tmp6 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + x3, tmp15, xmask)


@triton.jit
def triton_poi_fused_gelu_8(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_mul_9(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + x3, xmask)
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp7 = tmp5 * tmp6
    tmp8 = tmp4 + tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (16, 4), (4, 1))
    assert_size_stride(primals_15, (16,), (1,))
    assert_size_stride(primals_16, (4, 16), (16, 1))
    assert_size_stride(primals_17, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](primals_1, primals_2, buf0, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4, 8, 1), (8, 1, 32), torch.float32)
        buf2 = empty_strided_cuda((4, 8, 1), (8, 1, 32), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(32)](buf0, buf1, buf2, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_2[grid(128)](buf0, buf1, buf2,
            primals_4, primals_5, buf3, 128, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf1
        del buf2
        del primals_4
        del primals_5
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (4, 4), (32, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((32, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (32, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf5)
        buf6 = empty_strided_cuda((32, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (32, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf6)
        buf7 = reinterpret_tensor(buf4, (4, 4, 1, 1), (4, 1, 16, 16), 0)
        del buf4
        triton_poi_fused_mul_3[grid(16)](buf7, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf8 = empty_strided_cuda((4, 4, 1, 8), (32, 8, 8, 1), torch.float32)
        triton_poi_fused_clone_4[grid(16, 8)](buf5, buf8, 16, 8, XBLOCK=8,
            YBLOCK=16, num_warps=4, num_stages=1)
        buf9 = reinterpret_tensor(buf5, (16, 1, 8), (8, 8, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 1, 1), (1, 0, 0), 
            0), reinterpret_tensor(buf8, (16, 1, 8), (8, 0, 1), 0), out=buf9)
        buf12 = empty_strided_cuda((4, 4, 1, 8), (32, 8, 8, 1), torch.float32)
        triton_per_fused__softmax_5[grid(16)](buf9, buf12, 16, 8, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf13 = reinterpret_tensor(buf9, (4, 4, 8, 1), (32, 8, 1, 1), 0)
        del buf9
        triton_poi_fused_clone_4[grid(16, 8)](buf6, buf13, 16, 8, XBLOCK=8,
            YBLOCK=16, num_warps=4, num_stages=1)
        del buf6
        buf14 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf12, (16, 1, 8), (8, 8, 1),
            0), reinterpret_tensor(buf13, (16, 8, 1), (8, 1, 0), 0), out=buf14)
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf14, (4, 4),
            (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf15)
        del primals_10
        buf16 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf17 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_mul_native_layer_norm_6[grid(16)](primals_1,
            primals_3, buf15, buf16, buf17, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_native_layer_norm_7[grid(64)](primals_1,
            primals_3, buf15, buf16, buf17, primals_12, primals_13, buf18, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf16
        del buf17
        del primals_13
        buf19 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_15, reinterpret_tensor(buf18, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_14, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf19)
        del primals_15
        buf20 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_gelu_8[grid(256)](buf19, buf20, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf21 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_17, reinterpret_tensor(buf20, (16, 16),
            (16, 1), 0), reinterpret_tensor(primals_16, (16, 4), (1, 16), 0
            ), alpha=1, beta=1, out=buf21)
        del primals_17
        buf22 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_9[grid(64)](primals_1, primals_3, buf15,
            primals_11, buf21, buf22, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return (buf22, primals_1, primals_3, primals_11, primals_12, buf0,
        reinterpret_tensor(buf3, (4, 4), (32, 1), 0), reinterpret_tensor(
        buf3, (32, 4), (4, 1), 0), buf12, reinterpret_tensor(buf14, (4, 4),
        (4, 1), 0), buf15, reinterpret_tensor(buf18, (16, 4), (4, 1), 0),
        buf19, reinterpret_tensor(buf20, (16, 16), (16, 1), 0), buf21,
        primals_16, primals_14, primals_9, reinterpret_tensor(buf13, (16, 1,
        8), (8, 1, 1), 0), reinterpret_tensor(buf7, (16, 1, 1), (1, 1, 4), 
        0), reinterpret_tensor(buf8, (16, 8, 1), (8, 1, 8), 0), primals_8,
        primals_7, primals_6)


def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
    if drop_prob == 0.0 or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.
        device)
    random_tensor.floor_()
    output = x.div(keep_prob) * random_tensor
    return output


class 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):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5
        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.k = nn.Linear(dim, dim, bias=qkv_bias)
        self.v = nn.Linear(dim, dim, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        q = self.q(x[:, 0]).unsqueeze(1).reshape(B, 1, self.num_heads, C //
            self.num_heads).permute(0, 2, 1, 3)
        k = self.k(x).reshape(B, N, self.num_heads, C // self.num_heads
            ).permute(0, 2, 1, 3)
        q = q * self.scale
        v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads
            ).permute(0, 2, 1, 3)
        attn = q @ k.transpose(-2, -1)
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        x_cls = (attn @ v).transpose(1, 2).reshape(B, 1, C)
        x_cls = self.proj(x_cls)
        x_cls = self.proj_drop(x_cls)
        return x_cls


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)


class Mlp(nn.Module):

    def __init__(self, in_features, hidden_features=None, out_features=None,
        act_layer=nn.GELU, drop=0.0):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

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


class LayerScale_Block_CANew(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, act_layer=nn
        .GELU, norm_layer=nn.LayerNorm, Attention_block=Class_Attention,
        Mlp_block=Mlp, init_values=0.0001):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention_block(dim, num_heads=num_heads, qkv_bias=
            qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        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)
        self.mlp = Mlp_block(in_features=dim, hidden_features=
            mlp_hidden_dim, act_layer=act_layer, drop=drop)
        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)

    def forward(self, input_0, input_1):
        primals_3 = self.gamma_1
        primals_4 = self.gamma_2
        primals_5 = self.norm1.weight
        primals_10 = self.norm1.bias
        primals_6 = self.attn.q.weight
        primals_7 = self.attn.k.weight
        primals_8 = self.attn.v.weight
        primals_9 = self.attn.proj.weight
        primals_11 = self.attn.proj.bias
        primals_12 = self.norm2.weight
        primals_13 = self.norm2.bias
        primals_14 = self.mlp.fc1.weight
        primals_15 = self.mlp.fc1.bias
        primals_16 = self.mlp.fc2.weight
        primals_17 = self.mlp.fc2.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17])
        return output[0]
