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.utils.data
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 // 4096 % 128
    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_convolution_relu_1(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 // 1024 % 128
    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_convolution_relu_2(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 // 256 % 128
    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_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 132096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 33024
    x1 = xindex // 33024
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 24576, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (24576 * x1 + x0 % 24576), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0 // 4096 % 6, tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 30720, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr2 + (6144 * x1 + (-24576 + x0) % 6144), tmp13 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.load(in_ptr3 + (-24576 + x0) // 1024 % 6, tmp13 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp14 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp13, tmp16, tmp17)
    tmp19 = tmp0 >= tmp11
    tl.full([1], 33024, tl.int64)
    tmp22 = tl.load(in_ptr4 + (2304 * x1 + (-30720 + x0) % 2304), tmp19 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp23 = tl.load(in_ptr5 + (-30720 + x0) // 256 % 9, tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp24 = tmp22 + tmp23
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp19, tmp24, tmp25)
    tmp27 = tl.where(tmp13, tmp18, tmp26)
    tmp28 = tl.where(tmp4, tmp9, tmp27)
    tl.store(out_ptr0 + x2, tmp28, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (128, 2048, 3, 3), (18432, 9, 3, 1))
    assert_size_stride(primals_2, (128,), (1,))
    assert_size_stride(primals_3, (4, 2048, 64, 64), (8388608, 4096, 64, 1))
    assert_size_stride(primals_4, (6, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_5, (6,), (1,))
    assert_size_stride(primals_6, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (6, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_9, (6,), (1,))
    assert_size_stride(primals_10, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_11, (128,), (1,))
    assert_size_stride(primals_12, (9, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_13, (9,), (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, 128, 64, 64), (524288, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(2097152)](buf1, primals_2,
            2097152, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 6, 64, 64), (24576, 4096, 64, 1))
        buf3 = extern_kernels.convolution(buf1, primals_6, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 128, 32, 32), (131072, 1024, 32, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_relu_1[grid(524288)](buf4, primals_7, 
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_7
        buf5 = extern_kernels.convolution(buf4, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 6, 32, 32), (6144, 1024, 32, 1))
        buf6 = extern_kernels.convolution(buf4, primals_10, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_2[grid(131072)](buf7, primals_11,
            131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_11
        buf8 = extern_kernels.convolution(buf7, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 9, 16, 16), (2304, 256, 16, 1))
        buf9 = empty_strided_cuda((4, 33024), (33024, 1), torch.float32)
        triton_poi_fused_cat_3[grid(132096)](buf2, primals_5, buf5,
            primals_9, buf8, primals_13, buf9, 132096, XBLOCK=512,
            num_warps=8, num_stages=1)
        del buf2
        del buf5
        del buf8
        del primals_13
        del primals_5
        del primals_9
    return (buf9, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, buf1, buf4, buf7)


def conv1x1(in_channels, out_channels, stride=1, groups=1, bias=False):
    """
    Convolution 1x1 layer.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    stride : int or tuple/list of 2 int, default 1
        Strides of the convolution.
    groups : int, default 1
        Number of groups.
    bias : bool, default False
        Whether the layer uses a bias vector.
    """
    return nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
        kernel_size=1, stride=stride, groups=groups, bias=bias)


def conv3x3(in_channels, out_channels, stride=1, padding=1, dilation=1,
    groups=1, bias=False):
    """
    Convolution 3x3 layer.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    stride : int or tuple/list of 2 int, default 1
        Strides of the convolution.
    padding : int or tuple/list of 2 int, default 1
        Padding value for convolution layer.
    groups : int, default 1
        Number of groups.
    bias : bool, default False
        Whether the layer uses a bias vector.
    """
    return nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
        kernel_size=3, stride=stride, padding=padding, dilation=dilation,
        groups=groups, bias=bias)


class Flatten(nn.Module):
    """
    Simple flatten module.
    """

    def forward(self, x):
        return x.view(x.size(0), -1)


class NavigatorBranch(nn.Module):
    """
    Navigator branch block for Navigator unit.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    stride : int or tuple/list of 2 int
        Strides of the convolution.
    """

    def __init__(self, in_channels, out_channels, stride):
        super(NavigatorBranch, self).__init__()
        mid_channels = 128
        self.down_conv = conv3x3(in_channels=in_channels, out_channels=
            mid_channels, stride=stride, bias=True)
        self.activ = nn.ReLU(inplace=False)
        self.tidy_conv = conv1x1(in_channels=mid_channels, out_channels=
            out_channels, bias=True)
        self.flatten = Flatten()

    def forward(self, x):
        y = self.down_conv(x)
        y = self.activ(y)
        z = self.tidy_conv(y)
        z = self.flatten(z)
        return z, y


class NavigatorUnitNew(nn.Module):
    """
    Navigator init.
    """

    def __init__(self):
        super(NavigatorUnitNew, self).__init__()
        self.branch1 = NavigatorBranch(in_channels=2048, out_channels=6,
            stride=1)
        self.branch2 = NavigatorBranch(in_channels=128, out_channels=6,
            stride=2)
        self.branch3 = NavigatorBranch(in_channels=128, out_channels=9,
            stride=2)

    def forward(self, input_0):
        primals_1 = self.branch1.down_conv.weight
        primals_2 = self.branch1.down_conv.bias
        primals_4 = self.branch1.tidy_conv.weight
        primals_5 = self.branch1.tidy_conv.bias
        primals_6 = self.branch2.down_conv.weight
        primals_7 = self.branch2.down_conv.bias
        primals_8 = self.branch2.tidy_conv.weight
        primals_9 = self.branch2.tidy_conv.bias
        primals_10 = self.branch3.down_conv.weight
        primals_11 = self.branch3.down_conv.bias
        primals_12 = self.branch3.tidy_conv.weight
        primals_13 = self.branch3.tidy_conv.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]


Measured latency_ms: 0.014225