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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_add_convolution_sigmoid_1(in_ptr0, in_ptr1, in_ptr2,
    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
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp6 = tl.sigmoid(tmp5)
    tl.store(out_ptr0 + x3, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (64, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (1, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_9, (1,), (1,))
    assert_size_stride(primals_10, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(4096)](buf1, primals_2, 
            4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_0[grid(4096)](buf3, primals_5, 
            4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_0[grid(4096)](buf5, primals_7, 
            4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 1, 4, 4), (16, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_convolution_sigmoid_1[grid(256)](primals_10,
            buf6, primals_9, buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf6
        del primals_10
        del primals_9
    return (buf7, primals_1, primals_3, primals_4, primals_6, primals_8,
        buf1, buf3, buf5, buf7)


class PlainRefinerNew(nn.Module):
    """Simple refiner from Deep Image Matting.

    Args:
        conv_channels (int): Number of channels produced by the three main
            convolutional layer.
        loss_refine (dict): Config of the loss of the refiner. Default: None.
        pretrained (str): Name of pretrained model. Default: None.
    """

    def __init__(self, conv_channels=64, pretrained=None):
        super(PlainRefinerNew, self).__init__()
        self.refine_conv1 = nn.Conv2d(4, conv_channels, kernel_size=3,
            padding=1)
        self.refine_conv2 = nn.Conv2d(conv_channels, conv_channels,
            kernel_size=3, padding=1)
        self.refine_conv3 = nn.Conv2d(conv_channels, conv_channels,
            kernel_size=3, padding=1)
        self.refine_pred = nn.Conv2d(conv_channels, 1, kernel_size=3, padding=1
            )
        self.relu = nn.ReLU(inplace=True)

    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                xavier_init(m)

    def forward(self, input_0, input_1):
        primals_1 = self.refine_conv1.weight
        primals_2 = self.refine_conv1.bias
        primals_4 = self.refine_conv2.weight
        primals_5 = self.refine_conv2.bias
        primals_6 = self.refine_conv3.weight
        primals_7 = self.refine_conv3.bias
        primals_8 = self.refine_pred.weight
        primals_9 = self.refine_pred.bias
        primals_3 = input_0
        primals_10 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]


Measured latency_ms: 0.016944