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 math
from itertools import product as product
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_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_convolution_div_eq_masked_fill_ones_like_sub_1(
    in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_out_ptr0 + x2, xmask)
    tmp4 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp5 - tmp4
    tmp7 = 1.0
    tmp8 = tl.where(tmp2, tmp7, tmp0)
    tmp9 = tmp6 / tmp8
    tmp10 = tmp9 + tmp4
    tmp11 = tl.where(tmp2, tmp1, tmp10)
    tmp12 = tl.where(tmp2, tmp1, tmp7)
    tl.store(in_out_ptr0 + x2, tmp11, xmask)
    tl.store(out_ptr0 + x2, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 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_mul_0[grid(256)](primals_1, primals_2, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_3, 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, 1, 1), (4, 1, 1, 1))
        buf2 = 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(buf2, (4, 4, 1, 1), (4, 1, 1, 1))
        del primals_2
        del primals_5
        buf3 = buf1
        del buf1
        buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_add_convolution_div_eq_masked_fill_ones_like_sub_1[
            grid(16)](buf3, buf2, primals_4, buf4, 16, XBLOCK=16, num_warps
            =1, num_stages=1)
        del primals_4
    return buf3, buf4, primals_3, buf0, buf2


def weights_init(init_type='gaussian'):

    def init_fun(m):
        classname = m.__class__.__name__
        if (classname.find('Conv') == 0 or classname.find('Linear') == 0
            ) and hasattr(m, 'weight'):
            if init_type == 'gaussian':
                nn.init.normal_(m.weight, 0.0, 0.02)
            elif init_type == 'xavier':
                nn.init.xavier_normal_(m.weight, gain=math.sqrt(2))
            elif init_type == 'kaiming':
                nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in')
            elif init_type == 'orthogonal':
                nn.init.orthogonal_(m.weight, gain=math.sqrt(2))
            elif init_type == 'default':
                pass
            else:
                assert 0, 'Unsupported initialization: {}'.format(init_type)
            if hasattr(m, 'bias') and m.bias is not None:
                nn.init.constant_(m.bias, 0.0)
    return init_fun


class PartialConvNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=True):
        super().__init__()
        self.input_conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding, dilation, groups, bias)
        self.mask_conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding, dilation, groups, False)
        self.input_conv.apply(weights_init('kaiming'))
        torch.nn.init.constant_(self.mask_conv.weight, 1.0)
        for param in self.mask_conv.parameters():
            param.requires_grad = False

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


Measured latency_ms: 0.045129