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):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 784 % 16
    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_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 12544
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 14
    x1 = xindex // 14
    x4 = xindex
    x3 = xindex // 3136
    x5 = xindex % 3136
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 56 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 56 * x1), xmask, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (28 + 2 * x0 + 56 * x1), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (29 + 2 * x0 + 56 * x1), 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, tmp6, xmask)
    tl.store(out_ptr1 + (x5 + 3200 * x3), tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 25088
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 196 % 32
    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 = 6272
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 7
    x3 = xindex // 7
    x2 = xindex // 1568
    x4 = xindex % 1568
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 28 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 28 * x3), xmask, eviction_policy
        ='evict_last')
    tmp7 = tl.load(in_ptr0 + (14 + 2 * x0 + 28 * x3), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (15 + 2 * x0 + 28 * x3), 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 + (x4 + 1664 * x2), tmp15, xmask)
    tl.store(out_ptr1 + x5, tmp16, xmask)


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, (4, 1, 28, 28), (784, 784, 28, 1))
    assert_size_stride(primals_2, (16, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(primals_3, (16,), (1,))
    assert_size_stride(primals_4, (32, 16, 5, 5), (400, 25, 5, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (10, 1568), (1568, 1))
    assert_size_stride(primals_7, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 28, 28), (12544, 784, 28, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(50176)](buf1, primals_3, 
            50176, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 16, 14, 14), (3136, 196, 14, 1),
            torch.float32)
        buf3 = empty_strided_cuda((4, 16, 14, 14), (3200, 196, 14, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(12544)](buf1, buf2,
            buf3, 12544, XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 32, 14, 14), (6272, 196, 14, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_2[grid(25088)](buf5, primals_5, 
            25088, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 32, 7, 7), (1664, 49, 7, 1), torch.int8)
        buf7 = empty_strided_cuda((4, 32, 7, 7), (1568, 49, 7, 1), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_3[grid(6272)](buf5, buf6,
            buf7, 6272, XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf7, (4, 1568),
            (1568, 1), 0), reinterpret_tensor(primals_6, (1568, 10), (1, 
            1568), 0), alpha=1, beta=1, out=buf8)
        del primals_7
    return (buf8, primals_2, primals_4, primals_1, buf1, buf2, buf3, buf5,
        buf6, reinterpret_tensor(buf7, (4, 1568), (1568, 1), 0), primals_6)


class CnnViewModelNew(nn.Module):

    def __init__(self, out_dim=10):
        super(CnnViewModelNew, self).__init__()
        self.cnn1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5,
            stride=1, padding=2)
        self.relu1 = nn.ReLU()
        self.maxpool1 = nn.MaxPool2d(kernel_size=2)
        self.cnn2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=
            5, stride=1, padding=2)
        self.relu2 = nn.ReLU()
        self.maxpool2 = nn.MaxPool2d(kernel_size=2)
        self.fc1 = nn.Linear(32 * 7 * 7, out_dim)

    def forward(self, input_0):
        primals_2 = self.cnn1.weight
        primals_3 = self.cnn1.bias
        primals_4 = self.cnn2.weight
        primals_5 = self.cnn2.bias
        primals_6 = self.fc1.weight
        primals_7 = self.fc1.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.093547