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.triton_helpers import libdevice
import math
import torch.nn as 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_zeros_0(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 = 0.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused__to_copy_eq_index_put_lift_fresh_mul_reciprocal_sum_1(
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 2.0
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp4 == tmp1
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp3 + tmp6
    tmp9 = tmp8 == tmp1
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tmp7 + tmp10
    tmp13 = tmp12 == tmp1
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp11 + tmp14
    tmp16 = tl.full([1], 1, tl.int32)
    tmp17 = tmp16 / tmp15
    tmp18 = 1.0
    tmp19 = tmp17 * tmp18
    tmp20 = libdevice.isinf(tmp19).to(tl.int1)
    tmp21 = 0.0
    tmp22 = tl.where(tmp20, tmp21, tmp19)
    tl.store(out_ptr0 + x0, tmp22, xmask)


@triton.jit
def triton_poi_fused__to_copy_eq_mul_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp4 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = 2.0
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp3 * tmp4
    tl.store(in_out_ptr0 + x2, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_zeros_0[grid(16)](buf0, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf1 = torch.ops.aten._sparse_addmm.default(reinterpret_tensor(buf0,
            (4, 4), (1, 4), 0), primals_2, primals_1, beta=0)
        del primals_1
        del primals_2
        buf2 = buf1
        del buf1
        buf3 = torch.ops.aten._sparse_addmm.default(reinterpret_tensor(buf0,
            (4, 4), (1, 4), 0), buf2, primals_3, beta=0)
        del primals_3
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused__to_copy_eq_index_put_lift_fresh_mul_reciprocal_sum_1[
            grid(4)](buf4, buf5, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused__to_copy_eq_mul_2[grid(16)](buf6, buf5, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del buf5
        buf7 = buf2
        del buf2
        extern_kernels.addmm(primals_6, primals_5, primals_4, alpha=1, beta
            =1, out=buf7)
        del primals_4
        del primals_6
        buf8 = buf0
        del buf0
        extern_kernels.mm(buf6, buf7, out=buf8)
        del buf7
    return buf8, reinterpret_tensor(buf6, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0)


class PatchToPatchEdgeConvolutionNew(nn.Module):

    def __init__(self, in_features, out_features):
        super(PatchToPatchEdgeConvolutionNew, self).__init__()
        self.weight = nn.parameter.Parameter(torch.FloatTensor(in_features,
            out_features))
        self.bias = nn.parameter.Parameter(torch.FloatTensor(out_features))
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_1 = self.weight
        primals_6 = self.bias
        primals_2 = input_0
        primals_3 = input_1
        primals_4 = input_2
        primals_5 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.069011