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 math as tl_math
import torch.nn as nn
import torch.distributed
import torch.optim.lr_scheduler
import torch.utils.data
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_eq_0(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__softmax_div_masked_fill_1(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
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp12 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp17 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = -1000000000.0
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp8 = tmp7 * tmp2
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp13 = tmp12 * tmp2
    tmp14 = tl.where(tmp11, tmp4, tmp13)
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp18 = tmp17 * tmp2
    tmp19 = tl.where(tmp16, tmp4, tmp18)
    tmp20 = triton_helpers.maximum(tmp15, tmp19)
    tmp21 = tmp5 - tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp9 - tmp20
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tmp14 - tmp20
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp19 - tmp20
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tl.store(out_ptr0 + x2, tmp20, xmask)
    tl.store(out_ptr1 + x2, tmp31, xmask)


@triton.jit
def triton_poi_fused__softmax_div_masked_fill_2(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
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    x4 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp6 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = -1000000000.0
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(in_out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (8, 4), (4, 1))
    assert_size_stride(primals_8, (8,), (1,))
    assert_size_stride(primals_9, (4, 8), (8, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_4
        del primals_5
        buf1 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(primals_6, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 8), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_7
        del primals_8
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 
            0), reinterpret_tensor(buf1, (4, 4, 4), (32, 1, 8), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_eq_0[grid(16)](primals_2, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_2
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused__softmax_div_masked_fill_1[grid(16)](buf3, buf2,
            buf4, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf6 = buf2
        del buf2
        triton_poi_fused__softmax_div_masked_fill_2[grid(64)](buf6, buf3,
            buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf4
        del buf5
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf6, reinterpret_tensor(buf1, (4, 4, 4), (32, 8,
            1), 4), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_3[grid(128)](primals_3, buf7, buf8, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0)
        del buf7
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 8), (8, 1), 0),
            reinterpret_tensor(primals_9, (8, 4), (1, 8), 0), out=buf9)
    return reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(buf6, (4, 1, 4, 4), (16, 16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0
        ), buf3, buf6, reinterpret_tensor(buf8, (16, 8), (8, 1), 0
        ), primals_9, reinterpret_tensor(buf1, (4, 4, 4), (32, 1, 8), 4
        ), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf1, (4, 4, 4), (32, 8, 1), 0)


class MutiLevelEnhanceNew(nn.Module):

    def __init__(self, idim, odim, nhead=1, use_bias=True):
        super(MutiLevelEnhanceNew, self).__init__()
        self.idim = idim
        self.odim = odim
        self.nheads = nhead
        self.use_bias = use_bias
        self.c_lin = nn.Linear(self.idim, self.odim * 2, bias=self.use_bias)
        self.v_lin = nn.Linear(self.idim, self.odim, bias=self.use_bias)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        self.drop = nn.Dropout(0)
        self.out_lin = nn.Linear(2 * self.odim, self.odim, bias=False)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_7 = self.c_lin.weight
        primals_8 = self.c_lin.bias
        primals_1 = self.v_lin.weight
        primals_5 = self.v_lin.bias
        primals_9 = self.out_lin.weight
        primals_3 = input_0
        primals_6 = input_1
        primals_2 = input_2
        primals_4 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1]


Measured latency_ms: 0.069142