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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import 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_tanh_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 230400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 900 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x3, tmp3, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 57600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 15
    x1 = xindex // 15
    x4 = xindex
    x3 = xindex // 14400
    x5 = xindex % 14400
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 60 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 60 * x1), xmask, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (30 + 2 * x0 + 60 * x1), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (31 + 2 * x0 + 60 * 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 + 14464 * x3), tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_tanh_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 5184
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 81 % 16
    x2 = xindex // 1296
    x4 = xindex % 1296
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(out_ptr0 + (x4 + 1312 * x2), tmp3, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 16
    x3 = xindex // 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 18 * x1 + 81 * x2 + 1312 * x3),
        xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 18 * x1 + 81 * x2 + 1312 * x3),
        xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (9 + 2 * x0 + 18 * x1 + 81 * x2 + 1312 * x3),
        xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (10 + 2 * x0 + 18 * x1 + 81 * x2 + 1312 * 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, tmp15, xmask)
    tl.store(out_ptr1 + x4, tmp16, xmask)


@triton.jit
def triton_poi_fused_tanh_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 200
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_per_fused__log_softmax_5(in_ptr0, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    rnumel = 10
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 10 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask & xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(rmask & xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp5 - tmp11
    tl.store(out_ptr2 + (r1 + 10 * x0), tmp12, rmask & xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 32, 32), (1024, 1024, 32, 1))
    assert_size_stride(primals_2, (64, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (16, 64, 7, 7), (3136, 49, 7, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (200, 256), (256, 1))
    assert_size_stride(primals_7, (200,), (1,))
    assert_size_stride(primals_8, (10, 200), (200, 1))
    assert_size_stride(primals_9, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 30, 30), (57600, 900, 30, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_tanh_0[grid(230400)](buf1, primals_3, 
            230400, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 64, 15, 15), (14400, 225, 15, 1),
            torch.float32)
        buf3 = empty_strided_cuda((4, 64, 15, 15), (14464, 225, 15, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(57600)](buf1, buf2,
            buf3, 57600, XBLOCK=256, 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, 9, 9), (1296, 81, 9, 1))
        buf5 = empty_strided_cuda((4, 16, 9, 9), (1312, 81, 9, 1), torch.
            float32)
        triton_poi_fused_convolution_tanh_2[grid(5184)](buf4, primals_5,
            buf5, 5184, XBLOCK=128, num_warps=4, num_stages=1)
        del buf4
        del primals_5
        buf6 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.int8)
        buf7 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32
            )
        triton_poi_fused_max_pool2d_with_indices_3[grid(1024)](buf5, buf6,
            buf7, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((4, 200), (200, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (4, 256), (256, 1), 0),
            reinterpret_tensor(primals_6, (256, 200), (1, 256), 0), out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_tanh_4[grid(800)](buf9, primals_7, 800, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_7
        buf10 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf9, reinterpret_tensor(primals_8,
            (200, 10), (1, 200), 0), alpha=1, beta=1, out=buf10)
        del primals_9
        buf13 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        triton_per_fused__log_softmax_5[grid(4)](buf10, buf13, 4, 10,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf10
    return (buf13, primals_2, primals_4, primals_1, buf1, buf2, buf3, buf5,
        buf6, reinterpret_tensor(buf7, (4, 256), (256, 1), 0), buf9, buf13,
        primals_8, primals_6)


class CNN_NetNew(nn.Module):

    def __init__(self, device=None):
        super(CNN_NetNew, self).__init__()
        self.conv1 = nn.Conv2d(1, 64, 3, 1)
        self.conv2 = nn.Conv2d(64, 16, 7, 1)
        self.fc1 = nn.Linear(4 * 4 * 16, 200)
        self.fc2 = nn.Linear(200, 10)

    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.fc1.weight
        primals_7 = self.fc1.bias
        primals_8 = self.fc2.weight
        primals_9 = self.fc2.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])
        return output[0]


Measured latency_ms: 0.13294