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
import torch.nn.parallel
import torch.utils.data
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_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 160
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 10
    x2 = xindex // 40
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= 0 * (0 >= -3 + x1) + (-3 + x1) *
        (-3 + x1 > 0)) + (0 * (0 >= -3 + x1) + (-3 + x1) * (-3 + x1 > 0)) *
        (0 * (0 >= -3 + x1) + (-3 + x1) * (-3 + x1 > 0) < 3)) + 64 * x2 + (
        3 * (3 <= x0) + x0 * (x0 < 3))), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_replication_pad2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 160
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 10
    x2 = xindex // 40
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (16 + 4 * (3 * (3 <= 0 * (0 >= -3 + x1) + (-3 +
        x1) * (-3 + x1 > 0)) + (0 * (0 >= -3 + x1) + (-3 + x1) * (-3 + x1 >
        0)) * (0 * (0 >= -3 + x1) + (-3 + x1) * (-3 + x1 > 0) < 3)) + 64 *
        x2 + (3 * (3 <= x0) + x0 * (x0 < 3))), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_replication_pad2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 160
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 10
    x2 = xindex // 40
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (32 + 4 * (3 * (3 <= 0 * (0 >= -3 + x1) + (-3 +
        x1) * (-3 + x1 > 0)) + (0 * (0 >= -3 + x1) + (-3 + x1) * (-3 + x1 >
        0)) * (0 * (0 >= -3 + x1) + (-3 + x1) * (-3 + x1 > 0) < 3)) + 64 *
        x2 + (3 * (3 <= x0) + x0 * (x0 < 3))), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_replication_pad2d_3(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 160
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 10
    x2 = xindex // 40
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (48 + 4 * (3 * (3 <= 0 * (0 >= -3 + x1) + (-3 +
        x1) * (-3 + x1 > 0)) + (0 * (0 >= -3 + x1) + (-3 + x1) * (-3 + x1 >
        0)) * (0 * (0 >= -3 + x1) + (-3 + x1) * (-3 + x1 > 0) < 3)) + 64 *
        x2 + (3 * (3 <= x0) + x0 * (x0 < 3))), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_cat_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 4
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 3, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 2, tl.int64)
    tmp6 = tmp0 < tmp5
    tmp7 = tmp6 & tmp4
    tmp8 = tl.full([1], 1, tl.int64)
    tmp9 = tmp0 < tmp8
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (x0 + 16 * x2), tmp10 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp12 = tmp0 >= tmp8
    tmp13 = tmp12 & tmp7
    tmp14 = tl.load(in_ptr1 + (x0 + 16 * x2), tmp13 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp11, tmp14)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp7, tmp15, tmp16)
    tmp18 = tmp0 >= tmp5
    tmp19 = tmp18 & tmp4
    tmp20 = tl.load(in_ptr2 + (x0 + 16 * x2), tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp21 = tl.where(tmp6, tmp17, tmp20)
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp4, tmp21, tmp22)
    tmp24 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp27 = tl.load(in_ptr3 + (x0 + 16 * x2), tmp24 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp28 = tl.where(tmp4, tmp23, tmp27)
    tl.store(out_ptr0 + x3, tmp28, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 1, 7, 1), (7, 7, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 10, 4), (40, 40, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_replication_pad2d_0[grid(160)](primals_1, buf0, 
            160, XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 4, 4), (16, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 1, 10, 4), (40, 40, 4, 1), torch.float32)
        triton_poi_fused_replication_pad2d_1[grid(160)](primals_1, buf2, 
            160, XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = extern_kernels.convolution(buf2, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 1, 10, 4), (40, 40, 4, 1), torch.float32)
        triton_poi_fused_replication_pad2d_2[grid(160)](primals_1, buf4, 
            160, XBLOCK=256, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 1, 4, 4), (16, 16, 4, 1))
        buf6 = empty_strided_cuda((4, 1, 10, 4), (40, 40, 4, 1), torch.float32)
        triton_poi_fused_replication_pad2d_3[grid(160)](primals_1, buf6, 
            160, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf7 = extern_kernels.convolution(buf6, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 1, 4, 4), (16, 16, 4, 1))
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_cat_4[grid(256)](buf1, buf3, buf5, buf7, buf8, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del buf3
        del buf5
        del buf7
    return buf8, primals_2, buf0, buf2, buf4, buf6


class forfilterNew(nn.Module):

    def __init__(self, inplanes):
        super(forfilterNew, self).__init__()
        self.forfilter1 = nn.Conv2d(1, 1, (7, 1), 1, (0, 0), bias=False)
        self.inplanes = inplanes

    def forward(self, input_0):
        primals_2 = self.forfilter1.weight
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.100842