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


@triton.jit
def triton_poi_fused__to_copy_0(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 = 1.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp5 - tmp2
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp8.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_clamp_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 = 1.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp5 - tmp2
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp8.to(tl.int32)
    tmp10 = tl.full([1], 1, tl.int64)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_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 = 1.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp5 - tmp2
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp8.to(tl.int32)
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tmp8 - tmp10
    tmp12 = triton_helpers.maximum(tmp11, tmp7)
    tmp13 = triton_helpers.minimum(tmp12, tmp4)
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_3(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    in_ptr8, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x5 = xindex // 16
    x2 = xindex // 16 % 4
    x6 = 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')
    tmp36 = tl.load(in_out_ptr0 + x6, xmask)
    tmp37 = tl.load(in_ptr8 + x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, 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 + 4 * tmp4 + 16 * x5), 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 + 4 * tmp4 + 16 * x5), 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 + 4 * tmp25 + 16 * x5), xmask,
        eviction_policy='evict_last')
    tmp27 = tmp26 + tmp10
    tmp28 = tl.load(in_ptr2 + (tmp15 + 4 * tmp25 + 16 * x5), xmask,
        eviction_policy='evict_last')
    tmp29 = tmp28 + tmp10
    tmp30 = tmp29 - tmp27
    tmp31 = tmp30 * tmp19
    tmp32 = tmp27 + tmp31
    tmp33 = tmp32 - tmp21
    tmp35 = tmp33 * tmp34
    tmp38 = tmp36 + tmp37
    tmp39 = tmp21 + tmp35
    tmp40 = tmp38 + tmp39
    tmp41 = tl.full([1], 0, tl.int32)
    tmp42 = triton_helpers.maximum(tmp41, tmp40)
    tl.store(in_out_ptr0 + x6, tmp42, xmask)


@triton.jit
def triton_poi_fused_convolution_sigmoid_4(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = 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, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_8, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_3, 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, 4, 4), (64, 16, 4, 1))
        buf1 = extern_kernels.convolution(primals_2, primals_5, stride=(1, 
            1), padding=(0, 0), 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 = empty_strided_cuda((4, 1), (1, 1), torch.int64)
        get_raw_stream(0)
        triton_poi_fused__to_copy_0[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_1[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_0[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_1[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_2[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_2[grid(4)](buf8,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf10 = buf0
        del buf0
        triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_3[grid(256)
            ](buf10, buf2, buf4, buf1, primals_6, buf5, buf6, buf3, buf8,
            primals_4, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del primals_4
        del primals_6
        buf11 = extern_kernels.convolution(buf10, primals_7, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 1, 4, 4), (16, 16, 4, 1))
        buf12 = buf11
        del buf11
        triton_poi_fused_convolution_sigmoid_4[grid(64)](buf12, primals_8, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_8
    return (buf12, primals_1, primals_2, primals_3, primals_5, primals_7,
        buf2, buf3, buf4, buf5, buf6, buf8, buf10, buf12)


class GridAttentionBlockNew(nn.Module):

    def __init__(self, in_channels):
        super(GridAttentionBlockNew, self).__init__()
        self.inter_channels = in_channels
        self.in_channels = in_channels
        self.gating_channels = in_channels
        self.theta = nn.Conv2d(in_channels=self.in_channels, out_channels=
            self.inter_channels, kernel_size=1)
        self.phi = nn.Conv2d(in_channels=self.gating_channels, out_channels
            =self.inter_channels, kernel_size=1, stride=1, padding=0, bias=True
            )
        self.psi = nn.Conv2d(in_channels=self.inter_channels, out_channels=
            1, kernel_size=1, stride=1, padding=0, bias=True)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, input_0, input_1):
        primals_3 = self.theta.weight
        primals_4 = self.theta.bias
        primals_5 = self.phi.weight
        primals_6 = self.phi.bias
        primals_7 = self.psi.weight
        primals_8 = self.psi.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]


Measured latency_ms: 0.031879