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 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__unsafe_index_replication_pad2d_0(in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 10 % 10
    x0 = xindex % 10
    x2 = xindex // 100
    x5 = xindex
    tmp0 = 7 * (7 <= 0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) + (0 *
        (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) * (0 * (0 >= -1 + x1) +
        (-1 + x1) * (-1 + x1 > 0) < 7)
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = 7 * (7 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) + (0 *
        (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -1 + x0) +
        (-1 + x0) * (-1 + x0 > 0) < 7)
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * tmp4 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x5, tmp9, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 64 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_replication_pad2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 10
    x1 = xindex // 10 % 10
    x2 = xindex // 100
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (8 * (7 * (7 <= 0 * (0 >= -1 + x1) + (-1 + x1) *
        (-1 + x1 > 0)) + (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) *
        (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0) < 7)) + 64 * x2 + (
        7 * (7 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) + (0 * (0 >=
        -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -1 + x0) + (-1 +
        x0) * (-1 + x0 > 0) < 7))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 64 % 3
    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,
        primals_7) = 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,))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (3, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 10, 10), (400, 100, 10, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_index_replication_pad2d_0[grid(1600)](
            primals_1, buf0, 1600, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, 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, 8, 8), (256, 64, 8, 1))
        buf2 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        triton_poi_fused_convolution_leaky_relu_1[grid(1024)](buf1,
            primals_3, buf2, buf3, 1024, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 4, 10, 10), (400, 100, 10, 1), torch.
            float32)
        triton_poi_fused_replication_pad2d_2[grid(1600)](buf3, buf4, 1600,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 8, 8), (256, 64, 8, 1))
        buf6 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
        buf7 = buf1
        del buf1
        triton_poi_fused_convolution_leaky_relu_1[grid(1024)](buf5,
            primals_5, buf6, buf7, 1024, XBLOCK=256, num_warps=4, num_stages=1)
        del buf5
        del primals_5
        buf8 = extern_kernels.convolution(buf7, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 3, 8, 8), (192, 64, 8, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_3[grid(768)](buf9, primals_7, 768,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
    return (buf7, buf9, primals_2, primals_4, primals_6, buf0, buf2, buf3,
        buf4, buf6, buf7)


def leaky_relu(p=0.2):
    return nn.LeakyReLU(p)


class GeneratorBlockNew(nn.Module):

    def __init__(self, input_channels, latent_channels, output_channels,
        upsample=True):
        super(GeneratorBlockNew, self).__init__()
        if upsample:
            self.upsample = nn.Upsample(scale_factor=2)
        else:
            self.upsample = nn.Identity()
        self.conv1 = nn.Conv2d(input_channels, latent_channels, 3, 1, 1,
            padding_mode='replicate')
        self.conv1.bias.data = torch.zeros(*self.conv1.bias.data.shape)
        self.act1 = leaky_relu()
        self.conv2 = nn.Conv2d(latent_channels, output_channels, 3, 1, 1,
            padding_mode='replicate')
        self.conv2.bias.data = torch.zeros(*self.conv2.bias.data.shape)
        self.act2 = leaky_relu()
        self.to_rgb = nn.Conv2d(output_channels, 3, 1, 1, 0)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.to_rgb.weight
        primals_7 = self.to_rgb.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]


Measured latency_ms: 0.094273