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
from torch import Tensor
from torch import 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_native_layer_norm_0(in_ptr0, 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
    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_1(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
    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__softmax_mul_sum_2(in_ptr0, 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
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 48 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 48 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (16 + x0 + 48 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (28 + x0 + 48 * x1), xmask)
    tmp13 = tl.load(in_ptr0 + (40 + x0 + 48 * x1), xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp0 * tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = triton_helpers.maximum(tmp4, tmp7)
    tmp10 = tmp0 * tmp9
    tmp11 = tmp10 * tmp3
    tmp12 = triton_helpers.maximum(tmp8, tmp11)
    tmp14 = tmp0 * tmp13
    tmp15 = tmp14 * tmp3
    tmp16 = triton_helpers.maximum(tmp12, tmp15)
    tmp17 = tmp4 - tmp16
    tmp18 = tmp17 * tmp3
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp7 - tmp16
    tmp21 = tmp20 * tmp3
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp19 + tmp22
    tmp24 = tmp11 - tmp16
    tmp25 = tmp24 * tmp3
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp23 + tmp26
    tmp28 = tmp15 - tmp16
    tmp29 = tmp28 * tmp3
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp27 + tmp30
    tl.store(out_ptr0 + x2, tmp16, xmask)
    tl.store(out_ptr1 + x2, tmp31, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_sum_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    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
    y0 = yindex % 4
    y1 = yindex // 4
    x2 = xindex
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 48 * y1), ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (4 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + y3, ymask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + y3, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp3
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp10, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    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 + (8 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 + x0 + 4 * (-1 + 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_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + 1)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp9 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + 2)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp16 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + 3)
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK])
    tmp23 = tl.load(in_ptr2 + (3 + 4 * x0), 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 + x0, tmp28, xmask)
    tl.store(out_ptr1 + x0, 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
    x2 = xindex
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x2, xmask)
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, 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 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_add_gelu_8(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp5 = 0.7071067811865476
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.erf(tmp6)
    tmp8 = 1.0
    tmp9 = tmp7 + tmp8
    tmp10 = tmp4 * tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_cat_9(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    tmp15 = tl.load(in_ptr2 + x3, xmask)
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0 + 4 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tmp5 * tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp13 = tl.load(in_ptr2 + (4 + x0 + 4 * (-1 + x1) + 16 * x2), tmp10 &
        xmask, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tmp16 = tmp14 + tmp15
    tl.store(out_ptr0 + x3, tmp16, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (12, 4), (4, 1))
    assert_size_stride(primals_6, (12,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (16, 4), (4, 1))
    assert_size_stride(primals_13, (16,), (1,))
    assert_size_stride(primals_14, (4, 16), (16, 1))
    assert_size_stride(primals_15, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(16)](primals_4, buf0,
            buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(64)](primals_4, buf0,
            buf1, primals_2, primals_3, buf2, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_2
        del primals_3
        buf3 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf2, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_5, (4, 12), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_6
        buf4 = buf1
        del buf1
        buf5 = buf0
        del buf0
        triton_poi_fused__softmax_mul_sum_2[grid(16)](buf3, buf4, buf5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_mul_sum_3[grid(16, 4)](buf3, buf4, buf5,
            buf6, 16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_4[grid(16, 4)](buf3, buf7, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf8 = reinterpret_tensor(buf5, (16, 1, 1), (1, 1, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 1, 4), (4, 4, 1), 
            0), reinterpret_tensor(buf7, (16, 4, 1), (4, 1, 0), 0), out=buf8)
        buf9 = reinterpret_tensor(buf4, (4, 4), (4, 1), 0)
        del buf4
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf8, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf9)
        del primals_8
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_cat_5[grid(64)](buf9, buf2, buf10, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf11 = reinterpret_tensor(buf9, (4, 4, 1), (4, 1, 16), 0)
        del buf9
        buf12 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_mul_native_layer_norm_6[grid(16)](primals_4,
            primals_1, buf10, buf11, buf12, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf13 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_native_layer_norm_7[grid(64)](primals_4,
            primals_1, buf10, buf11, buf12, primals_9, primals_10, buf13, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf11
        del primals_10
        buf14 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf13, (4, 4), (16, 1), 0),
            reinterpret_tensor(primals_12, (4, 16), (1, 4), 0), out=buf14)
        buf15 = empty_strided_cuda((4, 1, 16), (16, 16, 1), torch.float32)
        triton_poi_fused_add_gelu_8[grid(64)](buf14, primals_13, buf15, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf16 = reinterpret_tensor(buf12, (4, 4), (4, 1), 0)
        del buf12
        extern_kernels.addmm(primals_15, reinterpret_tensor(buf15, (4, 16),
            (16, 1), 0), reinterpret_tensor(primals_14, (16, 4), (1, 16), 0
            ), alpha=1, beta=1, out=buf16)
        del primals_15
        buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_cat_9[grid(64)](primals_11, buf16, buf13,
            buf17, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return (buf17, primals_1, primals_4, primals_9, primals_11, primals_13,
        reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(
        buf3, (4, 4, 4, 1), (48, 1, 12, 1), 4), reinterpret_tensor(buf3, (4,
        4, 1, 1), (48, 1, 12, 1), 0), buf6, reinterpret_tensor(buf8, (4, 4),
        (4, 1), 0), buf10, reinterpret_tensor(buf13, (4, 4), (16, 1), 0),
        buf14, reinterpret_tensor(buf15, (4, 16), (16, 1), 0), buf16,
        primals_14, primals_12, primals_7, reinterpret_tensor(buf7, (16, 1,
        4), (4, 1, 1), 0), primals_5)


class MLP(nn.Module):

    def __init__(self, dim, hidden_dim, out_dim=None) ->None:
        super().__init__()
        out_dim = out_dim or dim
        self.fc1 = nn.Linear(dim, hidden_dim)
        self.act = nn.GELU()
        self.fc2 = nn.Linear(hidden_dim, out_dim)

    def forward(self, x: 'Tensor') ->Tensor:
        return self.fc2(self.act(self.fc1(x)))


class ClassAttention(nn.Module):
    """ClassAttention as in CaiT
    """

    def __init__(self, dim: 'int', heads: 'int'):
        super().__init__()
        self.num_heads = heads
        self.scale = (dim // heads) ** -0.5
        self.qkv = nn.Linear(dim, dim * 3)
        self.proj = nn.Linear(dim, dim)

    def forward(self, x: 'Tensor') ->Tensor:
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads
            ).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        qc = q[:, :, 0:1]
        attn_cls = (qc * k).sum(dim=-1) * self.scale
        attn_cls = attn_cls.softmax(dim=-1)
        cls_token = (attn_cls.unsqueeze(2) @ v).transpose(1, 2).reshape(B, 1, C
            )
        cls_token = self.proj(cls_token)
        x = torch.cat([cls_token, x[:, 1:]], dim=1)
        return x


class ClassAttentionBlockNew(nn.Module):

    def __init__(self, dim, heads, eta=1e-05):
        super().__init__()
        self.norm1 = nn.LayerNorm(dim)
        self.attn = ClassAttention(dim, heads)
        self.norm2 = nn.LayerNorm(dim)
        self.mlp = MLP(dim, int(dim * 4))
        self.gamma1 = nn.Parameter(eta * torch.ones(dim))
        self.gamma2 = nn.Parameter(eta * torch.ones(dim))

    def forward(self, input_0):
        primals_1 = self.gamma1
        primals_2 = self.gamma2
        primals_3 = self.norm1.weight
        primals_8 = self.norm1.bias
        primals_5 = self.attn.qkv.weight
        primals_6 = self.attn.qkv.bias
        primals_7 = self.attn.proj.weight
        primals_9 = self.attn.proj.bias
        primals_10 = self.norm2.weight
        primals_11 = self.norm2.bias
        primals_12 = self.mlp.fc1.weight
        primals_13 = self.mlp.fc1.bias
        primals_14 = self.mlp.fc2.weight
        primals_15 = self.mlp.fc2.bias
        primals_4 = input_0
        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])
        return output[0]


Measured latency_ms: 0.059754