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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@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 // 1024 % 6
    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):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 256 % 9
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 3072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 64 % 12
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 16 * x1), xmask, eviction_policy
        ='evict_last')
    tmp7 = tl.load(in_ptr0 + (8 + 2 * x0 + 16 * x1), xmask, eviction_policy
        ='evict_last')
    tmp12 = tl.load(in_ptr0 + (9 + 2 * x0 + 16 * x1), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + x2, tmp15, xmask)
    tl.store(out_ptr1 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 96
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, 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 + x2, tmp4, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_2, (6, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_3, (6,), (1,))
    assert_size_stride(primals_4, (9, 6, 3, 3), (54, 9, 3, 1))
    assert_size_stride(primals_5, (9,), (1,))
    assert_size_stride(primals_6, (12, 9, 3, 3), (81, 9, 3, 1))
    assert_size_stride(primals_7, (12,), (1,))
    assert_size_stride(primals_8, (96, 192), (192, 1))
    assert_size_stride(primals_9, (96,), (1,))
    assert_size_stride(primals_10, (16, 96), (96, 1))
    assert_size_stride(primals_11, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(2, 
            2), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 6, 32, 32), (6144, 1024, 32, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(24576)](buf1, primals_3, 
            24576, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 9, 16, 16), (2304, 256, 16, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(9216)](buf3, primals_5, 
            9216, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 12, 8, 8), (768, 64, 8, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_2[grid(3072)](buf5, primals_7, 
            3072, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.int8)
        buf7 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32
            )
        triton_poi_fused_max_pool2d_with_indices_3[grid(768)](buf5, buf6,
            buf7, 768, XBLOCK=128, num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((4, 96), (96, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (4, 192), (192, 1), 0),
            reinterpret_tensor(primals_8, (192, 96), (1, 192), 0), out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_relu_4[grid(384)](buf9, primals_9, 384, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_9
        buf10 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf9, reinterpret_tensor(
            primals_10, (96, 16), (1, 96), 0), alpha=1, beta=1, out=buf10)
        del primals_11
    return (buf10, primals_2, primals_4, primals_6, primals_1, buf1, buf3,
        buf5, buf6, reinterpret_tensor(buf7, (4, 192), (192, 1), 0), buf9,
        primals_10, primals_8)


class AnimalStudentNetNew(nn.Module):

    def __init__(self, num_classes=16):
        super(AnimalStudentNetNew, self).__init__()
        self.pool = nn.MaxPool2d(2, 2)
        self.dropout = nn.Dropout2d(p=0.1)
        self.conv1 = nn.Conv2d(3, 6, kernel_size=3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(6, 9, kernel_size=3, stride=2, padding=1)
        self.conv3 = nn.Conv2d(9, 12, kernel_size=3, stride=2, padding=1)
        self.fc = nn.Linear(12 * 4 * 4, 96)
        self.cls = nn.Linear(96, num_classes)

    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.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.fc.weight
        primals_9 = self.fc.bias
        primals_10 = self.cls.weight
        primals_11 = self.cls.bias
        primals_1 = 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])
        return output[0]


Measured latency_ms: 0.103269