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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 2
    x0 = xindex % 16
    x2 = xindex // 32
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp9 = tmp7 + tmp8
    tmp10 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp4, tmp13, tmp14)
    tmp16 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp19 = tl.load(in_ptr1 + (x0 + 64 * x2), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.load(in_ptr1 + (48 + x0 + 64 * x2), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tmp25 / tmp12
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp16, tmp26, tmp27)
    tmp29 = tl.where(tmp4, tmp15, tmp28)
    tl.store(out_ptr0 + x3, tmp29, xmask)


@triton.jit
def triton_poi_fused__to_copy_1(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_clamp_2(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = triton_helpers.minimum(tmp10, tmp9)
    tl.store(out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_3(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 - tmp9
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tmp12 = 1.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_sub_4(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x6 = xindex // 16
    x2 = xindex // 16 % 2
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr6 + x1, xmask, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr7 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 2, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr2 + (tmp8 + 2 * tmp4 + 4 * x6), xmask,
        eviction_policy='evict_last')
    tmp11 = tmp9 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp12 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp12)
    tmp16 = tl.load(in_ptr2 + (tmp15 + 2 * tmp4 + 4 * x6), xmask,
        eviction_policy='evict_last')
    tmp17 = tmp16 + tmp10
    tmp18 = tmp17 - tmp11
    tmp20 = tmp18 * tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 + tmp1
    tmp24 = tmp22 < 0
    tmp25 = tl.where(tmp24, tmp23, tmp22)
    tmp26 = tl.load(in_ptr2 + (tmp8 + 2 * tmp25 + 4 * x6), xmask,
        eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr2 + (tmp15 + 2 * tmp25 + 4 * x6), xmask,
        eviction_policy='evict_last')
    tmp29 = tmp28 + tmp10
    tmp30 = tmp29 - tmp27
    tmp31 = tmp30 * tmp19
    tmp32 = tmp27 + tmp31
    tmp33 = tmp32 - tmp21
    tmp35 = tmp33 * tmp34
    tmp36 = tmp21 + tmp35
    tl.store(in_out_ptr0 + x4, tmp36, xmask)


@triton.jit
def triton_poi_fused_convolution_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 2
    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, primals_4, primals_5, primals_6 = 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, (2, 2, 3, 3), (18, 9, 3, 1))
    assert_size_stride(primals_4, (2,), (1,))
    assert_size_stride(primals_5, (2, 2, 1, 1), (2, 1, 1, 1))
    assert_size_stride(primals_6, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](primals_1, primals_2, buf0, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 2, 2, 2), (8, 4, 2, 1))
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
        triton_poi_fused__to_copy_1[grid(4)](buf2, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
        buf3 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
        triton_poi_fused_add_clamp_2[grid(4)](buf3, 4, XBLOCK=4, num_warps=
            1, num_stages=1)
        buf4 = empty_strided_cuda((4,), (1,), torch.int64)
        triton_poi_fused__to_copy_1[grid(4)](buf4, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
        buf5 = empty_strided_cuda((4,), (1,), torch.int64)
        triton_poi_fused_add_clamp_2[grid(4)](buf5, 4, XBLOCK=4, num_warps=
            1, num_stages=1)
        buf6 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_3[grid(4)](buf6,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf8 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        triton_poi_fused__to_copy_add_arange_clamp_mul_sub_3[grid(4)](buf8,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf9 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        buf10 = buf9
        del buf9
        triton_poi_fused__unsafe_index_add_convolution_mul_sub_4[grid(128)](
            buf10, buf2, buf4, buf1, primals_4, buf5, buf6, buf3, buf8, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_4
        buf11 = extern_kernels.convolution(buf10, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 2, 4, 4), (32, 16, 4, 1))
        buf12 = buf11
        del buf11
        triton_poi_fused_convolution_5[grid(128)](buf12, primals_6, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_6
    return reinterpret_tensor(buf12, (4, 1, 4, 4), (32, 16, 4, 1), 0
        ), reinterpret_tensor(buf12, (4, 1, 4, 4), (32, 16, 4, 1), 16
        ), primals_3, primals_5, buf0, buf2, buf3, buf4, buf5, buf6, buf8, buf10


class Spatial_AttentionNew(nn.Module):

    def __init__(self, channels, length):
        super(Spatial_AttentionNew, self).__init__()
        self.conv_3x3 = nn.Conv2d(in_channels=2, out_channels=2,
            kernel_size=3, stride=2, padding=3 // 2)
        self.resize_bilinear = nn.Upsample([length, length], mode='bilinear')
        self.sigmoid = nn.Sigmoid()
        self.conv_1x1 = nn.Conv2d(2, 2, 1, 1, 0)

    def forward(self, input_0, input_1):
        primals_3 = self.conv_3x3.weight
        primals_4 = self.conv_3x3.bias
        primals_5 = self.conv_1x1.weight
        primals_6 = self.conv_1x1.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]


Measured latency_ms: 0.138389