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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x4 = xindex
    x0 = xindex % 4096
    tmp0 = tl.load(in_out_ptr0 + x4, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x4, tmp4, None)
    tl.store(out_ptr0 + x4, tmp6, None)


@triton.jit
def triton_poi_fused_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4096
    x1 = xindex // 4096
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 16384 * (x1 % 4 // 4) + 
        65536 * ((4 * (x1 // 4 % 4) + x1 % 4) // 16)), None)
    tl.store(out_ptr0 + x2, tmp0, None)


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, (4096, 4), (4, 1))
    assert_size_stride(primals_2, (4096,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4096, 4096), (4096, 1))
    assert_size_stride(primals_5, (4096,), (1,))
    assert_size_stride(primals_6, (4, 4096), (4096, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4096), (4096, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4096), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4096), (65536, 16384, 
            4096, 1), 0)
        del buf0
        buf8 = empty_strided_cuda((4, 4, 4, 4096), (65536, 16384, 4096, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(262144)](buf1,
            primals_2, buf8, 262144, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4096), (4096, 1), torch.float32)
        triton_poi_fused_view_1[grid(262144)](buf1, buf2, 262144, XBLOCK=
            512, num_warps=8, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (64, 4096), (4096, 1), 0)
        del buf1
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4096, 4096),
            (1, 4096), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 4096), (65536, 16384, 
            4096, 1), 0)
        del buf3
        buf7 = empty_strided_cuda((4, 4, 4, 4096), (65536, 16384, 4096, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(262144)](buf4,
            primals_5, buf7, 262144, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((64, 4096), (4096, 1), torch.float32)
        triton_poi_fused_view_1[grid(262144)](buf4, buf5, 262144, XBLOCK=
            512, num_warps=8, num_stages=1)
        del buf4
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf5, reinterpret_tensor(primals_6,
            (4096, 4), (1, 4096), 0), alpha=1, beta=1, out=buf6)
        del primals_7
    return reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf2, buf5, primals_6, buf7, primals_4, buf8


class AlexDense(nn.Module):
    """
    AlexNet specific dense block.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    """

    def __init__(self, in_channels, out_channels):
        super(AlexDense, self).__init__()
        self.fc = nn.Linear(in_features=in_channels, out_features=out_channels)
        self.activ = nn.ReLU(inplace=True)
        self.dropout = nn.Dropout(p=0.5)

    def forward(self, x):
        x = self.fc(x)
        x = self.activ(x)
        x = self.dropout(x)
        return x


class AlexOutputBlockNew(nn.Module):
    """
    AlexNet specific output block.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    classes : int
        Number of classification classes.
    """

    def __init__(self, in_channels, classes):
        super(AlexOutputBlockNew, self).__init__()
        mid_channels = 4096
        self.fc1 = AlexDense(in_channels=in_channels, out_channels=mid_channels
            )
        self.fc2 = AlexDense(in_channels=mid_channels, out_channels=
            mid_channels)
        self.fc3 = nn.Linear(in_features=mid_channels, out_features=classes)

    def forward(self, input_0):
        primals_1 = self.fc1.fc.weight
        primals_2 = self.fc1.fc.bias
        primals_4 = self.fc2.fc.weight
        primals_5 = self.fc2.fc.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.013635