import torch
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 94 * 94, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 4)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 94 * 94)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


def get_inputs():
    return [torch.rand([4, 3, 576, 576])]


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 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_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 7852416
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 327184 % 6
    x0 = xindex % 327184
    x4 = xindex // 327184
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x0 + 327200 * x4), tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1963104
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 286
    x1 = xindex // 286 % 286
    x2 = xindex // 81796
    x3 = xindex % 81796
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 1144 * x1 + 327200 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 1144 * x1 + 327200 * x2), xmask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (572 + 2 * x0 + 1144 * x1 + 327200 * x2),
        xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (573 + 2 * x0 + 1144 * x1 + 327200 * x2),
        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 + (x3 + 81824 * x2), tmp6, xmask)
    tl.store(out_ptr1 + (x3 + 81920 * x2), tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 5089536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 79524 % 16
    x0 = xindex % 79524
    x4 = xindex // 79524
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x0 + 79552 * x4), tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1272384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 141
    x1 = xindex // 141 % 141
    x2 = xindex // 19881
    x3 = xindex % 19881
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 564 * x1 + 79552 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 564 * x1 + 79552 * x2), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (282 + 2 * x0 + 564 * x1 + 79552 * x2), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (283 + 2 * x0 + 564 * x1 + 79552 * x2), 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 + (x3 + 19968 * x2), tmp15, xmask)
    tl.store(out_ptr1 + (x3 + 19904 * x2), tmp16, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_view_4(in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1272384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 141376
    x1 = xindex // 141376
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (19904 * ((x0 + 141376 * x1) // 19881) + (x0 +
        141376 * x1) % 19881), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1080
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 120
    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)


@triton.jit
def triton_poi_fused_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 756
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 84
    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, (6, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_2, (6,), (1,))
    assert_size_stride(primals_3, (4, 3, 576, 576), (995328, 331776, 576, 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, 141376), (141376, 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, (4, 84), (84, 1))
    assert_size_stride(primals_11, (4,), (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, 572, 572), (1963104, 327184, 572, 1))
        buf1 = empty_strided_cuda((4, 6, 572, 572), (1963200, 327200, 572, 
            1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(7852416)](buf0, primals_2,
            buf1, 7852416, XBLOCK=512, num_warps=8, num_stages=1)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((4, 6, 286, 286), (490944, 81824, 286, 1),
            torch.float32)
        buf3 = empty_strided_cuda((4, 6, 286, 286), (491520, 81920, 286, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(1963104)](buf1,
            buf2, buf3, 1963104, XBLOCK=512, num_warps=8, 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, 282, 282), (1272384, 79524, 282, 1))
        buf5 = empty_strided_cuda((4, 16, 282, 282), (1272832, 79552, 282, 
            1), torch.float32)
        triton_poi_fused_convolution_relu_2[grid(5089536)](buf4, primals_5,
            buf5, 5089536, XBLOCK=512, num_warps=8, num_stages=1)
        del buf4
        del primals_5
        buf6 = empty_strided_cuda((4, 16, 141, 141), (319488, 19968, 141, 1
            ), torch.int8)
        buf7 = empty_strided_cuda((4, 16, 141, 141), (318464, 19904, 141, 1
            ), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_3[grid(1272384)](buf5,
            buf6, buf7, 1272384, XBLOCK=512, num_warps=8, num_stages=1)
        buf8 = empty_strided_cuda((9, 141376), (141376, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_view_4[grid(1272384)](buf7,
            buf8, 1272384, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf7
        buf9 = empty_strided_cuda((9, 120), (120, 1), torch.float32)
        extern_kernels.mm(buf8, reinterpret_tensor(primals_6, (141376, 120),
            (1, 141376), 0), out=buf9)
        buf10 = buf9
        del buf9
        triton_poi_fused_relu_5[grid(1080)](buf10, primals_7, 1080, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_7
        buf11 = empty_strided_cuda((9, 84), (84, 1), torch.float32)
        extern_kernels.mm(buf10, reinterpret_tensor(primals_8, (120, 84), (
            1, 120), 0), out=buf11)
        buf12 = buf11
        del buf11
        triton_poi_fused_relu_6[grid(756)](buf12, primals_9, 756, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
        buf13 = empty_strided_cuda((9, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf12, reinterpret_tensor(
            primals_10, (84, 4), (1, 84), 0), alpha=1, beta=1, out=buf13)
        del primals_11
    return (buf13, primals_1, primals_3, primals_4, buf1, buf2, buf3, buf5,
        buf6, buf8, buf10, buf12, primals_10, primals_8, primals_6)


class NetNew(nn.Module):

    def __init__(self):
        super(NetNew, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 94 * 94, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 4)

    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]
