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.autograd
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_add_convolution_relu_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex // 16
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = tmp0 + tmp5
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_1(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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, 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(out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4,), (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, 4, 1, 1), (4, 1, 1, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_convolution_relu_0[grid(256)](primals_3, buf0,
            primals_2, buf1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        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, 4, 1, 1), (4, 1, 1, 1))
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_convolution_relu_0[grid(256)](buf1, buf2,
            primals_5, buf3, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_1[grid(16)](buf2,
            primals_5, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf2
        del primals_5
        buf5 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_1[grid(16)](buf0,
            primals_2, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        del primals_2
    return buf3, primals_1, primals_3, primals_4, buf1, buf4, buf5


class MatrixConv2dMultiResblockNew(nn.Module):

    def __init__(self, weight_shape, stride=1, padding=0, with_batchnorm=
        False, act_func='ReLU'):
        super(MatrixConv2dMultiResblockNew, self).__init__()
        self.conv1 = nn.Conv2d(weight_shape[3], weight_shape[0],
            weight_shape[1], stride=stride, padding=padding, bias=not
            with_batchnorm)
        if with_batchnorm:
            self.bn1 = nn.BatchNorm2d(weight_shape[0])
        else:
            self.bn1 = None
        if act_func is not None:
            self.f1 = getattr(nn, act_func)()
        else:
            self.f1 = None
        self.conv2 = nn.Conv2d(weight_shape[3], weight_shape[0],
            weight_shape[1], stride=stride, padding=padding, bias=not
            with_batchnorm)
        if with_batchnorm:
            self.bn2 = nn.BatchNorm2d(weight_shape[0])
        else:
            self.bn2 = None
        if act_func is not None:
            self.f2 = getattr(nn, act_func)()
        else:
            self.f2 = None

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_3 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.014972