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 math
import uuid
from torch import Tensor
import torch.nn as nn
from typing import Tuple
import torch.nn.functional as F
from typing import Optional
from typing import Dict
from torch.nn import Parameter
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_mean_sub_0(in_ptr0, 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
    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 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_mul_pow_sqrt_1(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
    x0 = xindex % 4
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp2 * tmp2
    tmp5 = tmp4 * tmp4
    tmp6 = tmp3 + tmp5
    tmp8 = tmp7 * tmp7
    tmp9 = tmp6 + tmp8
    tmp11 = tmp10 * tmp10
    tmp12 = tmp9 + tmp11
    tmp13 = 4.0
    tmp14 = tmp12 / tmp13
    tmp15 = 1e-12
    tmp16 = tmp14 + tmp15
    tmp17 = libdevice.sqrt(tmp16)
    tmp18 = tmp1 / tmp17
    tmp19 = tmp0 * tmp18
    tmp21 = tmp19 + tmp20
    tl.store(out_ptr0 + x2, tmp21, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 12
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (-4 + x0), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp14 = tl.load(in_ptr2 + (-8 + x0), tmp11 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x0, tmp16, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x0 = xindex % 4
    x4 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x3 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 5, tl.int64)
    tmp9 = tl.load(in_ptr1 + x0, tmp6 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x4, tmp10, xmask)


@triton.jit
def triton_poi_fused_mul_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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_out_ptr0 + x2, xmask)
    tmp1 = x2 % 4
    tl.full([1], 0, tl.int64)
    tmp4 = tl.full([1], 4, tl.int64)
    tmp5 = tmp1 < tmp4
    tmp6 = tl.load(in_ptr0 + x0 % 4, tmp5 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tmp1 >= tmp4
    tmp8 = tl.full([1], 8, tl.int64)
    tmp9 = tmp1 < tmp8
    tmp10 = tmp7 & tmp9
    tmp11 = tl.load(in_ptr1 + (-4 + x0 % 4), tmp10 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp12 = tmp1 >= tmp8
    tl.full([1], 12, tl.int64)
    tmp15 = tl.load(in_ptr2 + (-8 + x0 % 4), tmp12 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp16 = tl.where(tmp10, tmp11, tmp15)
    tmp17 = tl.where(tmp5, tmp6, tmp16)
    tmp18 = tmp0 + tmp17
    tmp19 = 1.0
    tmp20 = tmp18 * tmp19
    tl.store(in_out_ptr0 + x2, tmp20, xmask)


@triton.jit
def triton_poi_fused__softmax_5(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 5 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + 5 * x0), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp0 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp1 - tmp8
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp7 - tmp8
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp22, xmask)


@triton.jit
def triton_poi_fused__softmax_6(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 5
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 / tmp4
    tl.store(in_out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_clone_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 16
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (x1 + 16 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_mean_8(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 20
    x1 = xindex // 20
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 80 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (20 + x0 + 80 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (40 + x0 + 80 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (60 + x0 + 80 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_mean_pow_sub_9(in_ptr0, in_ptr1, 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')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + x0, tmp16, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_mul_sqrt_sub_10(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x2, xmask)
    tmp4 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 - tmp4
    tmp7 = 1e-12
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tmp10 = tmp5 / tmp9
    tmp11 = tmp0 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_add_div_erf_mul_11(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    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 = 0.7071067811865475
    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_12(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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)
    tmp3 = tl.load(in_out_ptr0 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x2, tmp6, 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,
        primals_18, primals_19) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (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,), (1,))
    assert_size_stride(primals_7, (1, 1, 4), (4, 4, 1))
    assert_size_stride(primals_8, (1, 1, 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, 4), (4, 1))
    assert_size_stride(primals_12, (4, 4), (4, 1))
    assert_size_stride(primals_13, (4, 4), (4, 1))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (4,), (1,))
    assert_size_stride(primals_16, (4, 4), (4, 1))
    assert_size_stride(primals_17, (4,), (1,))
    assert_size_stride(primals_18, (4, 4), (4, 1))
    assert_size_stride(primals_19, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_sub_0[grid(64)](primals_1, buf0, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_pow_sqrt_1[grid(64)](primals_2,
            buf0, primals_3, buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
        del primals_3
        buf2 = reinterpret_tensor(buf0, (16, 4), (4, 1), 0)
        del buf0
        extern_kernels.mm(reinterpret_tensor(buf1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((12,), (1,), torch.float32)
        triton_poi_fused_cat_2[grid(12)](primals_4, primals_5, primals_6,
            buf3, 12, XBLOCK=16, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(buf3, (4,), (1,), 4),
            reinterpret_tensor(buf1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_12, (4, 4), (1, 4), 0), alpha=1,
            beta=1, out=buf4)
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(buf3, (4,), (1,), 8),
            reinterpret_tensor(buf1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_13, (4, 4), (1, 4), 0), alpha=1,
            beta=1, out=buf5)
        del buf3
        buf6 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_cat_3[grid(80)](buf5, primals_8, buf6, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_8
        buf7 = reinterpret_tensor(buf2, (16, 4, 1), (1, 16, 64), 0)
        del buf2
        triton_poi_fused_mul_4[grid(64)](buf7, primals_4, primals_5,
            primals_6, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_4
        del primals_5
        del primals_6
        buf8 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_cat_3[grid(80)](buf4, primals_7, buf8, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_7
        buf9 = empty_strided_cuda((16, 4, 5), (20, 5, 1), torch.float32)
        extern_kernels.bmm(buf7, reinterpret_tensor(buf8, (16, 1, 5), (1, 0,
            16), 0), out=buf9)
        buf10 = reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 64), 0)
        del buf4
        buf11 = reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 64), 0)
        del buf5
        triton_poi_fused__softmax_5[grid(64)](buf9, buf10, buf11, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf12 = buf9
        del buf9
        triton_poi_fused__softmax_6[grid(320)](buf12, buf10, buf11, 320,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf13 = reinterpret_tensor(buf11, (16, 4, 1), (4, 1, 1), 0)
        del buf11
        extern_kernels.bmm(buf12, reinterpret_tensor(buf6, (16, 5, 1), (1, 
            16, 0), 0), out=buf13)
        buf14 = reinterpret_tensor(buf10, (4, 16, 1), (16, 1, 1), 0)
        del buf10
        triton_poi_fused_clone_7[grid(4, 16)](buf13, buf14, 4, 16, XBLOCK=
            16, YBLOCK=4, num_warps=1, num_stages=1)
        buf15 = reinterpret_tensor(buf13, (16, 4), (4, 1), 0)
        del buf13
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf14, (16, 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, 5), (20, 5, 1), torch.float32)
        triton_poi_fused_mean_8[grid(80)](buf12, buf16, 80, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf17 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf18 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_mean_pow_sub_9[grid(16)](primals_1, buf15,
            buf17, buf18, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf19 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_sqrt_sub_10[grid(64)](primals_14,
            primals_1, buf15, buf17, buf18, primals_15, buf19, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del buf17
        del buf18
        del primals_15
        buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_17, reinterpret_tensor(buf19, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_16, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf20)
        del primals_17
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_erf_mul_11[grid(64)](buf20, buf21, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf22 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf21, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_18, (4, 4), (1, 4), 0), out=buf22)
        buf23 = reinterpret_tensor(buf22, (4, 4, 4), (16, 4, 1), 0)
        del buf22
        triton_poi_fused_add_12[grid(64)](buf23, primals_1, buf15,
            primals_19, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_19
    return buf23, buf16, primals_1, primals_14, reinterpret_tensor(buf1, (
        16, 4), (4, 1), 0), buf12, reinterpret_tensor(buf14, (16, 4), (4, 1), 0
        ), buf15, reinterpret_tensor(buf19, (16, 4), (4, 1), 0
        ), buf20, reinterpret_tensor(buf21, (16, 4), (4, 1), 0
        ), primals_18, primals_16, primals_9, reinterpret_tensor(buf6, (16,
        1, 5), (1, 1, 16), 0), reinterpret_tensor(buf7, (16, 1, 4), (1, 1, 
        16), 0), reinterpret_tensor(buf8, (16, 5, 1), (1, 16, 1), 0
        ), primals_13, primals_12, primals_11


def gelu(x):
    """Implementation of the gelu activation function.

    For information: OpenAI GPT's gelu is slightly different
    (and gives slightly different results):
    0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
    """
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))


def utils_softmax(x, dim: 'int', onnx_trace: 'bool'=False):
    if onnx_trace:
        return F.softmax(x.float(), dim=dim)
    else:
        return F.softmax(x, dim=dim, dtype=torch.float32)


def with_incremental_state(cls):
    cls.__bases__ = (FairseqIncrementalState,) + tuple(b for b in cls.
        __bases__ if b != FairseqIncrementalState)
    return cls


class ESM1LayerNorm(nn.Module):

    def __init__(self, hidden_size, eps=1e-12, affine=True):
        """Construct a layernorm layer in the TF style (eps inside the sqrt)."""
        super().__init__()
        self.hidden_size = (hidden_size,) if isinstance(hidden_size, int
            ) else tuple(hidden_size)
        self.eps = eps
        self.affine = bool(affine)
        if self.affine:
            self.weight = nn.Parameter(torch.ones(hidden_size))
            self.bias = nn.Parameter(torch.zeros(hidden_size))
        else:
            self.weight, self.bias = None, None

    def forward(self, x):
        dims = tuple(-(i + 1) for i in range(len(self.hidden_size)))
        means = x.mean(dims, keepdim=True)
        x_zeromean = x - means
        variances = x_zeromean.pow(2).mean(dims, keepdim=True)
        x = x_zeromean / torch.sqrt(variances + self.eps)
        if self.affine:
            x = self.weight * x + self.bias
        return x


class FairseqIncrementalState(object):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.init_incremental_state()

    def init_incremental_state(self):
        self._incremental_state_id = str(uuid.uuid4())

    def _get_full_incremental_state_key(self, key: 'str') ->str:
        return '{}.{}'.format(self._incremental_state_id, key)

    def get_incremental_state(self, incremental_state:
        'Optional[Dict[str, Dict[str, Optional[Tensor]]]]', key: 'str'
        ) ->Optional[Dict[str, Optional[Tensor]]]:
        """Helper for getting incremental state for an nn.Module."""
        full_key = self._get_full_incremental_state_key(key)
        if incremental_state is None or full_key not in incremental_state:
            return None
        return incremental_state[full_key]

    def set_incremental_state(self, incremental_state:
        'Optional[Dict[str, Dict[str, Optional[Tensor]]]]', key: 'str',
        value: 'Dict[str, Optional[Tensor]]') ->Optional[Dict[str, Dict[str,
        Optional[Tensor]]]]:
        """Helper for setting incremental state for an nn.Module."""
        if incremental_state is not None:
            full_key = self._get_full_incremental_state_key(key)
            incremental_state[full_key] = value
        return incremental_state


@with_incremental_state
class MultiheadAttention(nn.Module):
    """Multi-headed attention.

    See "Attention Is All You Need" for more details.
    """

    def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=
        0.0, bias=True, add_bias_kv=False, add_zero_attn=False,
        self_attention=False, encoder_decoder_attention=False):
        super().__init__()
        self.embed_dim = embed_dim
        self.kdim = kdim if kdim is not None else embed_dim
        self.vdim = vdim if vdim is not None else embed_dim
        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, 'embed_dim must be divisible by num_heads'
        self.scaling = self.head_dim ** -0.5
        self.self_attention = self_attention
        self.encoder_decoder_attention = encoder_decoder_attention
        assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and value to be of the same size'
        self.k_proj = nn.Linear(self.kdim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(self.vdim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        if add_bias_kv:
            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
        else:
            self.bias_k = self.bias_v = None
        self.add_zero_attn = add_zero_attn
        self.reset_parameters()
        self.onnx_trace = False
        self.enable_torch_version = False
        if hasattr(F, 'multi_head_attention_forward'):
            self.enable_torch_version = True
        else:
            self.enable_torch_version = False

    def prepare_for_onnx_export_(self):
        self.onnx_trace = True

    def reset_parameters(self):
        if self.qkv_same_dim:
            nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
            nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
            nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
        else:
            nn.init.xavier_uniform_(self.k_proj.weight)
            nn.init.xavier_uniform_(self.v_proj.weight)
            nn.init.xavier_uniform_(self.q_proj.weight)
        nn.init.xavier_uniform_(self.out_proj.weight)
        if self.out_proj.bias is not None:
            nn.init.constant_(self.out_proj.bias, 0.0)
        if self.bias_k is not None:
            nn.init.xavier_normal_(self.bias_k)
        if self.bias_v is not None:
            nn.init.xavier_normal_(self.bias_v)

    def forward(self, query, key: 'Optional[Tensor]', value:
        'Optional[Tensor]', key_padding_mask: 'Optional[Tensor]'=None,
        incremental_state:
        'Optional[Dict[str, Dict[str, Optional[Tensor]]]]'=None,
        need_weights: 'bool'=True, static_kv: 'bool'=False, attn_mask:
        'Optional[Tensor]'=None, before_softmax: 'bool'=False,
        need_head_weights: 'bool'=False) ->Tuple[Tensor, Optional[Tensor]]:
        """Input shape: Time x Batch x Channel

        Args:
            key_padding_mask (ByteTensor, optional): mask to exclude
                keys that are pads, of shape `(batch, src_len)`, where
                padding elements are indicated by 1s.
            need_weights (bool, optional): return the attention weights,
                averaged over heads (default: False).
            attn_mask (ByteTensor, optional): typically used to
                implement causal attention, where the mask prevents the
                attention from looking forward in time (default: None).
            before_softmax (bool, optional): return the raw attention
                weights and values before the attention softmax.
            need_head_weights (bool, optional): return the attention
                weights for each head. Implies *need_weights*. Default:
                return the average attention weights over all heads.
        """
        if need_head_weights:
            need_weights = True
        tgt_len, bsz, embed_dim = query.size()
        assert embed_dim == self.embed_dim
        assert list(query.size()) == [tgt_len, bsz, embed_dim]
        if (self.enable_torch_version and not self.onnx_trace and 
            incremental_state is None and not static_kv and not torch.jit.
            is_scripting() and not need_head_weights):
            assert key is not None and value is not None
            return F.multi_head_attention_forward(query, key, value, self.
                embed_dim, self.num_heads, torch.empty([0]), torch.cat((
                self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
                self.bias_k, self.bias_v, self.add_zero_attn, self.dropout,
                self.out_proj.weight, self.out_proj.bias, self.training,
                key_padding_mask, need_weights, attn_mask,
                use_separate_proj_weight=True, q_proj_weight=self.q_proj.
                weight, k_proj_weight=self.k_proj.weight, v_proj_weight=
                self.v_proj.weight)
        if incremental_state is not None:
            saved_state = self._get_input_buffer(incremental_state)
            if saved_state is not None and 'prev_key' in saved_state:
                if static_kv:
                    assert self.encoder_decoder_attention and not self.self_attention
                    key = value = None
        else:
            saved_state = None
        if self.self_attention:
            q = self.q_proj(query)
            k = self.k_proj(query)
            v = self.v_proj(query)
        elif self.encoder_decoder_attention:
            q = self.q_proj(query)
            if key is None:
                assert value is None
                k = v = None
            else:
                k = self.k_proj(key)
                v = self.v_proj(key)
        else:
            assert key is not None and value is not None
            q = self.q_proj(query)
            k = self.k_proj(key)
            v = self.v_proj(value)
        q *= self.scaling
        if self.bias_k is not None:
            assert self.bias_v is not None
            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
            if attn_mask is not None:
                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(
                    attn_mask.size(0), 1)], dim=1)
            if key_padding_mask is not None:
                key_padding_mask = torch.cat([key_padding_mask,
                    key_padding_mask.new_zeros(key_padding_mask.size(0), 1)
                    ], dim=1)
        q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim
            ).transpose(0, 1)
        if k is not None:
            k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim
                ).transpose(0, 1)
        if v is not None:
            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim
                ).transpose(0, 1)
        if saved_state is not None:
            if 'prev_key' in saved_state:
                _prev_key = saved_state['prev_key']
                assert _prev_key is not None
                prev_key = _prev_key.view(bsz * self.num_heads, -1, self.
                    head_dim)
                if static_kv:
                    k = prev_key
                else:
                    assert k is not None
                    k = torch.cat([prev_key, k], dim=1)
            if 'prev_value' in saved_state:
                _prev_value = saved_state['prev_value']
                assert _prev_value is not None
                prev_value = _prev_value.view(bsz * self.num_heads, -1,
                    self.head_dim)
                if static_kv:
                    v = prev_value
                else:
                    assert v is not None
                    v = torch.cat([prev_value, v], dim=1)
            prev_key_padding_mask: 'Optional[Tensor]' = None
            if 'prev_key_padding_mask' in saved_state:
                prev_key_padding_mask = saved_state['prev_key_padding_mask']
            assert k is not None and v is not None
            key_padding_mask = (MultiheadAttention.
                _append_prev_key_padding_mask(key_padding_mask=
                key_padding_mask, prev_key_padding_mask=
                prev_key_padding_mask, batch_size=bsz, src_len=k.size(1),
                static_kv=static_kv))
            saved_state['prev_key'] = k.view(bsz, self.num_heads, -1, self.
                head_dim)
            saved_state['prev_value'] = v.view(bsz, self.num_heads, -1,
                self.head_dim)
            saved_state['prev_key_padding_mask'] = key_padding_mask
            assert incremental_state is not None
            incremental_state = self._set_input_buffer(incremental_state,
                saved_state)
        assert k is not None
        src_len = k.size(1)
        if key_padding_mask is not None and key_padding_mask.dim() == 0:
            key_padding_mask = None
        if key_padding_mask is not None:
            assert key_padding_mask.size(0) == bsz
            assert key_padding_mask.size(1) == src_len
        if self.add_zero_attn:
            assert v is not None
            src_len += 1
            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])],
                dim=1)
            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])],
                dim=1)
            if attn_mask is not None:
                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(
                    attn_mask.size(0), 1)], dim=1)
            if key_padding_mask is not None:
                key_padding_mask = torch.cat([key_padding_mask, torch.zeros
                    (key_padding_mask.size(0), 1).type_as(key_padding_mask)
                    ], dim=1)
        attn_weights = torch.bmm(q, k.transpose(1, 2))
        attn_weights = MultiheadAttention.apply_sparse_mask(attn_weights,
            tgt_len, src_len, bsz)
        assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len,
            src_len]
        if attn_mask is not None:
            attn_mask = attn_mask.unsqueeze(0)
            if self.onnx_trace:
                attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1)
            attn_weights += attn_mask
        if key_padding_mask is not None:
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len,
                src_len)
            attn_weights = attn_weights.masked_fill(key_padding_mask.
                unsqueeze(1).unsqueeze(2), float('-inf'))
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len,
                src_len)
        if before_softmax:
            return attn_weights, v
        attn_weights_float = utils_softmax(attn_weights, dim=-1, onnx_trace
            =self.onnx_trace)
        attn_weights = attn_weights_float.type_as(attn_weights)
        attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=
            self.dropout, training=self.training)
        assert v is not None
        attn = torch.bmm(attn_probs, v)
        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.
            head_dim]
        if self.onnx_trace and attn.size(1) == 1:
            attn = attn.contiguous().view(tgt_len, bsz, embed_dim)
        else:
            attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz,
                embed_dim)
        attn = self.out_proj(attn)
        attn_weights: 'Optional[Tensor]' = None
        if need_weights:
            attn_weights = attn_weights_float.view(bsz, self.num_heads,
                tgt_len, src_len).transpose(1, 0)
            if not need_head_weights:
                attn_weights = attn_weights.mean(dim=0)
        return attn, attn_weights

    @staticmethod
    def _append_prev_key_padding_mask(key_padding_mask: 'Optional[Tensor]',
        prev_key_padding_mask: 'Optional[Tensor]', batch_size: 'int',
        src_len: 'int', static_kv: 'bool') ->Optional[Tensor]:
        if prev_key_padding_mask is not None and static_kv:
            new_key_padding_mask = prev_key_padding_mask
        elif prev_key_padding_mask is not None and key_padding_mask is not None:
            new_key_padding_mask = torch.cat([prev_key_padding_mask.float(),
                key_padding_mask.float()], dim=1)
        elif prev_key_padding_mask is not None:
            filler = torch.zeros((batch_size, src_len -
                prev_key_padding_mask.size(1)), device=
                prev_key_padding_mask.device)
            new_key_padding_mask = torch.cat([prev_key_padding_mask.float(),
                filler.float()], dim=1)
        elif key_padding_mask is not None:
            filler = torch.zeros((batch_size, src_len - key_padding_mask.
                size(1)), device=key_padding_mask.device)
            new_key_padding_mask = torch.cat([filler.float(),
                key_padding_mask.float()], dim=1)
        else:
            new_key_padding_mask = prev_key_padding_mask
        return new_key_padding_mask

    @torch.jit.export
    def reorder_incremental_state(self, incremental_state:
        'Dict[str, Dict[str, Optional[Tensor]]]', new_order: 'Tensor'):
        """Reorder buffered internal state (for incremental generation)."""
        input_buffer = self._get_input_buffer(incremental_state)
        if input_buffer is not None:
            for k in input_buffer.keys():
                input_buffer_k = input_buffer[k]
                if input_buffer_k is not None:
                    if self.encoder_decoder_attention and input_buffer_k.size(0
                        ) == new_order.size(0):
                        break
                    input_buffer[k] = input_buffer_k.index_select(0, new_order)
            incremental_state = self._set_input_buffer(incremental_state,
                input_buffer)
        return incremental_state

    def _get_input_buffer(self, incremental_state:
        'Optional[Dict[str, Dict[str, Optional[Tensor]]]]') ->Dict[str,
        Optional[Tensor]]:
        result = self.get_incremental_state(incremental_state, 'attn_state')
        if result is not None:
            return result
        else:
            empty_result: 'Dict[str, Optional[Tensor]]' = {}
            return empty_result

    def _set_input_buffer(self, incremental_state:
        'Dict[str, Dict[str, Optional[Tensor]]]', buffer:
        'Dict[str, Optional[Tensor]]'):
        return self.set_incremental_state(incremental_state, 'attn_state',
            buffer)

    def apply_sparse_mask(attn_weights, tgt_len: 'int', src_len: 'int', bsz:
        'int'):
        return attn_weights

    def upgrade_state_dict_named(self, state_dict, name):
        prefix = name + '.' if name != '' else ''
        items_to_add = {}
        keys_to_remove = []
        for k in state_dict.keys():
            if k.endswith(prefix + 'in_proj_weight'):
                dim = int(state_dict[k].shape[0] / 3)
                items_to_add[prefix + 'q_proj.weight'] = state_dict[k][:dim]
                items_to_add[prefix + 'k_proj.weight'] = state_dict[k][dim:
                    2 * dim]
                items_to_add[prefix + 'v_proj.weight'] = state_dict[k][2 * dim:
                    ]
                keys_to_remove.append(k)
                k_bias = prefix + 'in_proj_bias'
                if k_bias in state_dict.keys():
                    dim = int(state_dict[k].shape[0] / 3)
                    items_to_add[prefix + 'q_proj.bias'] = state_dict[k_bias][:
                        dim]
                    items_to_add[prefix + 'k_proj.bias'] = state_dict[k_bias][
                        dim:2 * dim]
                    items_to_add[prefix + 'v_proj.bias'] = state_dict[k_bias][
                        2 * dim:]
                    keys_to_remove.append(prefix + 'in_proj_bias')
        for k in keys_to_remove:
            del state_dict[k]
        for key, value in items_to_add.items():
            state_dict[key] = value


class TransformerLayerNew(nn.Module):
    """Transformer layer block."""

    def __init__(self, embed_dim, ffn_embed_dim, attention_heads,
        add_bias_kv=True, use_esm1b_layer_norm=False):
        super().__init__()
        self.embed_dim = embed_dim
        self.ffn_embed_dim = ffn_embed_dim
        self.attention_heads = attention_heads
        self._init_submodules(add_bias_kv, use_esm1b_layer_norm)

    def _init_submodules(self, add_bias_kv, use_esm1b_layer_norm):
        BertLayerNorm = (ESM1bLayerNorm if use_esm1b_layer_norm else
            ESM1LayerNorm)
        self.self_attn = MultiheadAttention(self.embed_dim, self.
            attention_heads, add_bias_kv=add_bias_kv, add_zero_attn=False)
        self.self_attn_layer_norm = BertLayerNorm(self.embed_dim)
        self.fc1 = nn.Linear(self.embed_dim, self.ffn_embed_dim)
        self.fc2 = nn.Linear(self.ffn_embed_dim, self.embed_dim)
        self.final_layer_norm = BertLayerNorm(self.embed_dim)

    def forward(self, input_0):
        primals_7 = self.self_attn.bias_k
        primals_8 = self.self_attn.bias_v
        primals_9 = self.self_attn.k_proj.weight
        primals_2 = self.self_attn.k_proj.bias
        primals_11 = self.self_attn.v_proj.weight
        primals_3 = self.self_attn.v_proj.bias
        primals_12 = self.self_attn.q_proj.weight
        primals_4 = self.self_attn.q_proj.bias
        primals_13 = self.self_attn.out_proj.weight
        primals_5 = self.self_attn.out_proj.bias
        primals_6 = self.self_attn_layer_norm.weight
        primals_10 = self.self_attn_layer_norm.bias
        primals_16 = self.fc1.weight
        primals_14 = self.fc1.bias
        primals_18 = self.fc2.weight
        primals_15 = self.fc2.bias
        primals_17 = self.final_layer_norm.weight
        primals_19 = self.final_layer_norm.bias
        primals_1 = 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, primals_16, primals_17, primals_18, primals_19])
        return output[0], output[1]


Measured latency_ms: 0.038624