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
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


@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 5 % 5
    x0 = xindex % 5
    x2 = xindex // 25
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(400)](primals_1, buf0, 400,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(256)](buf2, primals_3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return buf2, primals_2, buf0


class SamePadConv2dNew(nn.Conv2d):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        dilation=1, groups=1, bias=True):
        super(SamePadConv2dNew, self).__init__(in_channels, out_channels,
            kernel_size, stride, 0, dilation, groups, bias)

    def get_pad_odd(self, in_, weight, stride, dilation):
        effective_filter_size_rows = (weight - 1) * dilation + 1
        out_rows = (in_ + stride - 1) // stride
        max(0, (out_rows - 1) * stride + effective_filter_size_rows - in_)
        padding_rows = max(0, (out_rows - 1) * stride + (weight - 1) *
            dilation + 1 - in_)
        rows_odd = padding_rows % 2 != 0
        return padding_rows, rows_odd

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


Measured latency_ms: 0.033849