import torch
import torch.nn as tnn


class Net(tnn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = tnn.Conv2d(3, 6, 5)
        self.pool = tnn.MaxPool2d(2, 2)
        self.conv2 = tnn.Conv2d(6, 16, 5)
        self.fc1 = tnn.Linear(16 * 5 * 5, 120)
        self.fc2 = tnn.Linear(120, 84)
        self.fc3 = tnn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(self.conv1(x))
        x = self.pool(self.conv2(x))
        x = x.view(-1, 16 * 5 * 5)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x


def get_inputs():
    return [torch.rand([4, 3, 32, 32])]


def get_init_inputs():
    return [[], {}]

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 tnn
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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 18816
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 784 % 6
    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)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4704
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 14
    x3 = xindex // 14
    x2 = xindex // 1176
    x4 = xindex % 1176
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 56 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 56 * x3), xmask, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (28 + 2 * x0 + 56 * x3), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (29 + 2 * x0 + 56 * x3), xmask,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x4 + 1184 * x2), tmp6, xmask)
    tl.store(out_ptr1 + (x4 + 1280 * x2), tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 6400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 100 % 16
    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)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = xindex // 5
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 20 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 20 * x1), xmask, eviction_policy
        ='evict_last')
    tmp7 = tl.load(in_ptr0 + (10 + 2 * x0 + 20 * x1), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (11 + 2 * x0 + 20 * 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)


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, (6, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_2, (6,), (1,))
    assert_size_stride(primals_3, (4, 3, 32, 32), (3072, 1024, 32, 1))
    assert_size_stride(primals_4, (16, 6, 5, 5), (150, 25, 5, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (120, 400), (400, 1))
    assert_size_stride(primals_7, (120,), (1,))
    assert_size_stride(primals_8, (84, 120), (120, 1))
    assert_size_stride(primals_9, (84,), (1,))
    assert_size_stride(primals_10, (10, 84), (84, 1))
    assert_size_stride(primals_11, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 6, 28, 28), (4704, 784, 28, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(18816)](buf1, primals_2, 18816,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 6, 14, 14), (1184, 196, 14, 1), torch
            .float32)
        buf3 = empty_strided_cuda((4, 6, 14, 14), (1280, 196, 14, 1), torch
            .int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(4704)](buf1, buf2,
            buf3, 4704, XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 16, 10, 10), (1600, 100, 10, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_2[grid(6400)](buf5, primals_5, 6400,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 16, 5, 5), (400, 25, 5, 1), torch.int8)
        buf7 = empty_strided_cuda((4, 16, 5, 5), (400, 25, 5, 1), torch.float32
            )
        triton_poi_fused_max_pool2d_with_indices_3[grid(1600)](buf5, buf6,
            buf7, 1600, XBLOCK=128, num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((4, 120), (120, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf7, (4, 400),
            (400, 1), 0), reinterpret_tensor(primals_6, (400, 120), (1, 400
            ), 0), alpha=1, beta=1, out=buf8)
        del primals_7
        buf9 = empty_strided_cuda((4, 84), (84, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf8, reinterpret_tensor(primals_8,
            (120, 84), (1, 120), 0), alpha=1, beta=1, out=buf9)
        del primals_9
        buf10 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf9, reinterpret_tensor(
            primals_10, (84, 10), (1, 84), 0), alpha=1, beta=1, out=buf10)
        del primals_11
    return (buf10, primals_1, primals_3, primals_4, buf1, buf2, buf3, buf5,
        buf6, reinterpret_tensor(buf7, (4, 400), (400, 1), 0), buf8, buf9,
        primals_10, primals_8, primals_6)


class NetNew(tnn.Module):

    def __init__(self):
        super(NetNew, self).__init__()
        self.conv1 = tnn.Conv2d(3, 6, 5)
        self.pool = tnn.MaxPool2d(2, 2)
        self.conv2 = tnn.Conv2d(6, 16, 5)
        self.fc1 = tnn.Linear(16 * 5 * 5, 120)
        self.fc2 = tnn.Linear(120, 84)
        self.fc3 = tnn.Linear(84, 10)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.fc1.weight
        primals_7 = self.fc1.bias
        primals_8 = self.fc2.weight
        primals_9 = self.fc2.bias
        primals_10 = self.fc3.weight
        primals_11 = self.fc3.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])
        return output[0]
