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
from torch import nn
import torch.nn.functional as F
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_relu_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
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + (x0 + 8 * x1), tmp10, xmask)


@triton.jit
def triton_poi_fused_gt_2(in_ptr0, 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_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_masked_fill_max_3(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp1 = tl.load(in_ptr1 + (x0 + 32 * x1), xmask)
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp5 = tl.load(in_ptr1 + (8 + x0 + 32 * x1), xmask)
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp9 = tl.load(in_ptr1 + (16 + x0 + 32 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp13 = tl.load(in_ptr1 + (24 + x0 + 32 * x1), xmask)
    tmp2 = float('-inf')
    tmp3 = tl.where(tmp0, tmp2, tmp1)
    tmp6 = tl.where(tmp4, tmp2, tmp5)
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp10 = tl.where(tmp8, tmp2, tmp9)
    tmp11 = triton_helpers.maximum(tmp7, tmp10)
    tmp14 = tl.where(tmp12, tmp2, tmp13)
    tmp15 = triton_helpers.maximum(tmp11, tmp14)
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_masked_fill_max_repeat_4(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 % 4
    x2 = xindex // 16
    x3 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (x0 + 8 * x3), tmp0, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 8), (8, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(16)](buf0, buf1, buf2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        buf3 = reinterpret_tensor(buf7, (4, 4, 4), (32, 8, 1), 0)
        triton_poi_fused_native_layer_norm_relu_1[grid(64)](buf0, buf1,
            buf2, primals_3, primals_4, buf3, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf1
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.bool)
        triton_poi_fused_gt_2[grid(16)](primals_5, buf4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_5
        buf5 = reinterpret_tensor(buf2, (4, 1, 4), (4, 16, 1), 0)
        del buf2
        triton_poi_fused_masked_fill_max_3[grid(16)](buf4, buf3, buf5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf7, (4, 4, 4), (32, 8, 1), 4)
        triton_poi_fused_masked_fill_max_repeat_4[grid(64)](buf5, buf6, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf5
        buf8 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf7, (16, 8), (
            8, 1), 0), reinterpret_tensor(primals_6, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf8)
        del primals_7
    return reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0
        ), primals_3, primals_4, reinterpret_tensor(primals_1, (16, 4), (4,
        1), 0), buf0, buf4, reinterpret_tensor(buf7, (16, 8), (8, 1), 0
        ), primals_6


class LocalMLP(nn.Module):

    def __init__(self, dim_in: 'int', use_norm: 'bool'=True):
        """a Local 1 layer MLP

        :param dim_in: feat in size
        :type dim_in: int
        :param use_norm: if to apply layer norm, defaults to True
        :type use_norm: bool, optional
        """
        super().__init__()
        self.linear = nn.Linear(dim_in, dim_in, bias=not use_norm)
        self.use_norm = use_norm
        if use_norm:
            self.norm = nn.LayerNorm(dim_in)

    def forward(self, x: 'torch.Tensor') ->torch.Tensor:
        """forward of the module

        :param x: input tensor (..., dim_in)
        :type x: torch.Tensor
        :return: output tensor (..., dim_in)
        :rtype: torch.Tensor
        """
        x = self.linear(x)
        if hasattr(self, 'norm'):
            x = self.norm(x)
        x = F.relu(x, inplace=True)
        return x


class LocalSubGraphLayerNew(nn.Module):

    def __init__(self, dim_in: 'int', dim_out: 'int') ->None:
        """Local subgraph layer

        :param dim_in: input feat size
        :type dim_in: int
        :param dim_out: output feat size
        :type dim_out: int
        """
        super(LocalSubGraphLayerNew, self).__init__()
        self.mlp = LocalMLP(dim_in)
        self.linear_remap = nn.Linear(dim_in * 2, dim_out)

    def forward(self, input_0, input_1):
        primals_2 = self.mlp.linear.weight
        primals_3 = self.mlp.norm.weight
        primals_4 = self.mlp.norm.bias
        primals_6 = self.linear_remap.weight
        primals_7 = self.linear_remap.bias
        primals_1 = input_0
        primals_5 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.041488