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
import torch.nn as nn
import torch.fx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 46128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3844 % 3
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_1(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 86400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = xindex // 3600 % 6
    x0 = xindex % 3600
    x3 = xindex // 3600
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x4, tmp4, xmask)
    tl.store(out_ptr0 + (x0 + 3712 * x3), tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (3, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (3,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (6, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_5, (6,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 3, 62, 62), (11532, 3844, 62, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(46128)](buf1, primals_2, 
            46128, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 6, 60, 60), (21600, 3600, 60, 1))
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 6, 60, 60), (22272, 3712, 60, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_1[grid(86400)](
            buf3, primals_5, buf4, 86400, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_5
    return buf3, primals_1, primals_3, primals_4, buf1, buf4


class ModuleFallbackSub(nn.Module):

    def __init__(self):
        super(ModuleFallbackSub, self).__init__()
        self.conv = nn.Conv2d(1, 3, 3)
        self.relu = nn.ReLU()

    def forward(self, x):
        return self.relu(self.conv(x))


class ModuleFallbackMainNew(nn.Module):

    def __init__(self):
        super(ModuleFallbackMainNew, self).__init__()
        self.layer1 = ModuleFallbackSub()
        self.conv = nn.Conv2d(3, 6, 3)
        self.relu = nn.ReLU()

    def forward(self, input_0):
        primals_1 = self.layer1.conv.weight
        primals_2 = self.layer1.conv.bias
        primals_4 = self.conv.weight
        primals_5 = self.conv.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.014324