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 math as tl_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


@triton.jit
def triton_poi_fused_abs_mean_sub_0(in_ptr0, 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 // 64
    x4 = xindex % 16
    x0 = xindex % 4
    x2 = xindex // 16 % 4
    x1 = xindex // 4 % 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x4 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (16 + x4 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (32 + x4 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (48 + x4 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = 1 + x0 + x2
    tl.full([1], 0, tl.int64)
    tmp4 = tl.full([1], 4, tl.int64)
    tmp5 = tmp1 < tmp4
    tmp6 = tl.load(in_ptr0 + (4 * x1 + 64 * x3 + (1 + x0 + x2)), tmp5 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp1 >= tmp4
    tl.full([1], 8, tl.int64)
    tmp10 = tl.load(in_ptr0 + (4 * x1 + 64 * x3 + (-3 + x0 + x2)), tmp7 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tl.where(tmp5, tmp6, tmp10)
    tmp12 = tmp0 - tmp11
    tmp13 = tl_math.abs(tmp12)
    tmp15 = tl.load(in_ptr0 + (16 + 4 * x1 + 16 * ((x0 + 4 * x1) // 16) + 
        64 * x3 + (1 + x0 + x2)), tmp5 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tl.load(in_ptr0 + (16 + 4 * x1 + 16 * ((x0 + 4 * x1) // 16) + 
        64 * x3 + (-3 + x0 + x2)), tmp7 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tl.where(tmp5, tmp15, tmp16)
    tmp18 = tmp14 - tmp17
    tmp19 = tl_math.abs(tmp18)
    tmp20 = tmp13 + tmp19
    tmp22 = tl.load(in_ptr0 + (32 + 4 * x1 + 16 * ((x0 + 4 * x1) // 16) + 
        64 * x3 + (1 + x0 + x2)), tmp5 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp23 = tl.load(in_ptr0 + (32 + 4 * x1 + 16 * ((x0 + 4 * x1) // 16) + 
        64 * x3 + (-3 + x0 + x2)), tmp7 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp24 = tl.where(tmp5, tmp22, tmp23)
    tmp25 = tmp21 - tmp24
    tmp26 = tl_math.abs(tmp25)
    tmp27 = tmp20 + tmp26
    tmp29 = tl.load(in_ptr0 + (48 + 4 * x1 + 16 * ((x0 + 4 * x1) // 16) + 
        64 * x3 + (1 + x0 + x2)), tmp5 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp30 = tl.load(in_ptr0 + (48 + 4 * x1 + 16 * ((x0 + 4 * x1) // 16) + 
        64 * x3 + (-3 + x0 + x2)), tmp7 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp31 = tl.where(tmp5, tmp29, tmp30)
    tmp32 = tmp28 - tmp31
    tmp33 = tl_math.abs(tmp32)
    tmp34 = tmp27 + tmp33
    tmp35 = 4.0
    tmp36 = tmp34 / tmp35
    tl.store(out_ptr0 + x5, tmp36, 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, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_abs_mean_sub_0[grid(256)](primals_1, buf0, 256,
            XBLOCK=256, 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 DisparityConvNew(nn.Module):

    def __init__(self, max_shift, output_nc):
        super().__init__()
        self.max_shift = int(max_shift)
        self.conv = nn.Conv2d(self.max_shift, output_nc, kernel_size=3,
            stride=1, padding=1, bias=True)

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


Measured latency_ms: 0.033167