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
from typing import Callable
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_2(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 + (y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_3(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 + (4 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_4(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr1 + x2, xmask)
    tmp26 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp29 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp31 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = float('-inf')
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2 == 0
    tmp4 = tmp3.to(tl.int64)
    tmp5 = tmp4 != 0
    tmp7 = tmp6 == tmp1
    tmp8 = tmp7 == 0
    tmp9 = tmp8.to(tl.int64)
    tmp10 = tmp9 != 0
    tmp11 = tmp5 | tmp10
    tmp13 = tmp12 == tmp1
    tmp14 = tmp13 == 0
    tmp15 = tmp14.to(tl.int64)
    tmp16 = tmp15 != 0
    tmp17 = tmp11 | tmp16
    tmp19 = tmp18 == tmp1
    tmp20 = tmp19 == 0
    tmp21 = tmp20.to(tl.int64)
    tmp22 = tmp21 != 0
    tmp23 = tmp17 | tmp22
    tmp24 = tmp23 == 0
    tmp28 = tmp26 + tmp27
    tmp30 = tmp28 + tmp29
    tmp32 = tmp30 + tmp31
    tmp33 = tmp25 / tmp32
    tmp34 = 0.0
    tmp35 = tl.where(tmp24, tmp34, tmp33)
    tl.store(out_ptr0 + x2, tmp35, xmask)


@triton.jit
def triton_poi_fused_clone_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 48
    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
    x3 = xindex
    y0 = yindex % 4
    y1 = yindex // 4 % 4
    y2 = yindex // 16
    y4 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * y2 + 12 * x3 + 48 * y1), xmask &
        ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3 + 4 * y4), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_7(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 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_8(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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + 1)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr2 + 2)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr2 + 3)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp10 = tmp7 + tmp9
    tmp11 = tmp6 + tmp10
    tmp12 = tmp5 + tmp11
    tmp17 = tmp14 + tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp12 + tmp18
    tmp24 = tmp21 + 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_native_layer_norm_9(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 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    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_gelu_10(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_11(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    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
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr0 + x2, xmask)
    tmp6 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 + tmp7
    tl.store(in_out_ptr0 + x2, 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
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (12, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (16, 4), (4, 1))
    assert_size_stride(primals_10, (16,), (1,))
    assert_size_stride(primals_11, (4, 16), (16, 1))
    assert_size_stride(primals_12, (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_3, 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_3, buf0,
            buf1, primals_1, primals_2, buf2, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 12), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_2[grid(16, 4)](buf3, buf4, 16, 4, XBLOCK=4, YBLOCK
            =16, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        triton_poi_fused_3[grid(16, 4)](buf3, buf5, 16, 4, XBLOCK=4, YBLOCK
            =16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf5, (16, 1, 4), (4, 0, 1), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_4[grid(256)](buf6, buf7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_5[grid(256)](buf6, buf7, buf8, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((3, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        triton_poi_fused_clone_6[grid(48, 4)](buf3, buf9, 48, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        del buf3
        buf10 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf9, (16, 4, 1), (4, 1, 0), 128), out=buf10
            )
        buf11 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_7[grid(16, 4)](buf10, buf11, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf12 = reinterpret_tensor(buf10, (16, 4), (4, 1), 0)
        del buf10
        extern_kernels.mm(reinterpret_tensor(buf11, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf12)
        buf13 = buf1
        del buf1
        buf14 = buf0
        del buf0
        triton_poi_fused_add_native_layer_norm_8[grid(16)](primals_3, buf12,
            primals_6, buf13, buf14, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf15 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_9[grid(64)](primals_3, buf12,
            primals_6, buf13, buf14, primals_7, primals_8, buf15, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf13
        del buf14
        del primals_8
        buf16 = reinterpret_tensor(buf7, (16, 16), (16, 1), 0)
        del buf7
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf15, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_9, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf16)
        del primals_10
        buf17 = reinterpret_tensor(buf6, (4, 4, 16), (64, 16, 1), 0)
        del buf6
        triton_poi_fused_gelu_10[grid(256)](buf16, buf17, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf18 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf17, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_11, (16, 4), (1, 16), 0), out=buf18)
        buf19 = reinterpret_tensor(buf18, (4, 4, 4), (16, 4, 1), 0)
        del buf18
        triton_poi_fused_add_11[grid(64)](buf19, primals_3, buf12,
            primals_6, primals_12, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_12
    return buf19, primals_3, primals_6, primals_7, reinterpret_tensor(buf2,
        (16, 4), (4, 1), 0), buf8, reinterpret_tensor(buf9, (16, 1, 4), (4,
        1, 1), 128), reinterpret_tensor(buf4, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 4), 0
        ), reinterpret_tensor(buf11, (16, 4), (4, 1), 0
        ), buf12, reinterpret_tensor(buf15, (16, 4), (4, 1), 0
        ), buf16, reinterpret_tensor(buf17, (16, 16), (16, 1), 0
        ), primals_11, primals_9, primals_5, primals_4


class MLP(nn.Module):
    """Multi Layer Perceptron class"""

    def __init__(self, in_feats: 'int', hidden_feats: 'int'=None, out_feats:
        'int'=None, act_layer: 'Callable[[torch.Tensor], torch.Tensor]'=nn.
        GELU, drop_rate: 'float'=0.0):
        """Initialize MLP

        Args:
            in_feats (int): input number of features
            hidden_feats (int, optional): hidden dimension. Defaults to None.
            out_feats (int, optional): output dimension. Defaults to None.
            act_layer (Callable[[torch.Tensor], torch.Tensor], optional): activation function.
                                                                          Defaults to nn.GELU.
            drop_rate (float, optional): dropout. Defaults to 0.0.
        """
        super().__init__()
        hidden_feats = hidden_feats or in_feats
        out_feats = out_feats or in_feats
        self.fc1 = nn.Linear(in_feats, hidden_feats)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_feats, out_feats)
        self.drop = nn.Dropout(drop_rate)

    def forward(self, x: 'torch.Tensor') ->torch.Tensor:
        """Forward"""
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):
    """Attention class"""

    def __init__(self, dim: 'int', num_heads: 'int'=8, qkv_bias: 'bool'=
        False, qk_scale: 'float'=None, attn_drop: 'float'=0.0, proj_drop:
        'float'=0.0):
        """Initialize module

        Args:
            dim (int): input dimension
            num_heads (int, optional): number of heads. Defaults to 8.
            qkv_bias (bool, optional): Apply bias. Defaults to False.
            qk_scale (float, optional): scale factor to query-key. Defaults to None.
            attn_drop (float, optional): dropout for attention. Defaults to 0.0.
            proj_drop (float, optional): dropout. Defaults to 0.0.
        """
        super().__init__()
        self._num_heads = num_heads
        head_dim = dim // num_heads
        self._scale = head_dim ** -0.5
        self._qkv = nn.Linear(dim, dim * 3, 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: 'torch.Tensor') ->torch.Tensor:
        """Forward"""
        B, N, C = x.shape
        qkv_out = self._qkv(x).view(B, N, 3, self._num_heads, C // self.
            _num_heads)
        qkv = qkv_out.permute(2, 0, 3, 1, 4).contiguous()
        q, k, v = qkv[0], qkv[1], qkv[2]
        attn = q @ k.transpose(-2, -1) * self._scale
        attn = attn.softmax(dim=-1)
        attn = self._attn_drop(attn)
        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self._proj(x)
        x = self._proj_drop(x)
        return x


class MultiHeadedAttentionBlockNew(nn.Module):
    """Multi-headed attention block"""

    def __init__(self, dim: 'int', num_heads: 'int', mlp_ratio: 'float'=4.0,
        qkv_bias: 'bool'=False, qk_scale: 'float'=None, drop: 'float'=0.0,
        attn_drop: 'float'=0.0, dropout_ratio=0.0, act_layer:
        'Callable[[torch.Tensor], torch.Tensor]'=nn.GELU, norm_layer:
        'Callable[[torch.Tensor], torch.Tensor]'=nn.LayerNorm):
        """Initialize class

        Args:
            dim (int): dimension
            num_heads (int): number of heads
            mlp_ratio (float, optional): How much it changes the input. Defaults to 4..
            qkv_bias (bool, optional): Apply bias. Defaults to False.
            qk_scale (float, optional): scale factor. Defaults to None.
            drop (float, optional): dropout for MLP. Defaults to 0.0.
            attn_drop (float, optional): dropout on attention layer. Defaults to 0.0.
            dropout_ratio (float, optional): drop-out for positional embedding.
                                             Defaults to 0.0.
            act_layer (Callable[[torch.Tensor], torch.Tensor], optional): activation layer.
                                                                          Defaults to nn.GELU.
            norm_layer (Callable[[torch.Tensor], torch.Tensor], optional): normalization layer.
                                                                           Defaults to nn.LayerNorm.
        """
        super().__init__()
        self._attn_norm = norm_layer(dim)
        self._mlp_norm = 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)
        if dropout_ratio > 0.0:
            self._drop_path = nn.Dropout(p=dropout_ratio, inplace=True)
        else:
            self._drop_path = nn.Identity()
        mlp_hidden_dim = int(dim * mlp_ratio)
        self._mlp = MLP(in_feats=dim, hidden_feats=mlp_hidden_dim,
            act_layer=act_layer, drop_rate=drop)

    def forward(self, input_0):
        primals_1 = self._attn_norm.weight
        primals_2 = self._attn_norm.bias
        primals_6 = self._mlp_norm.weight
        primals_7 = self._mlp_norm.bias
        primals_4 = self._attn._qkv.weight
        primals_5 = self._attn._proj.weight
        primals_8 = self._attn._proj.bias
        primals_9 = self._mlp.fc1.weight
        primals_10 = self._mlp.fc1.bias
        primals_11 = self._mlp.fc2.weight
        primals_12 = self._mlp.fc2.bias
        primals_3 = 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])
        return output[0]


Measured latency_ms: 0.016072