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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 6 % 6
    x0 = xindex % 6
    x2 = xindex // 36
    x4 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask,
        other=0.0)
    tl.store(out_ptr0 + x4, tmp11, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(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 // 16 % 32
    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_max_pool2d_with_indices_2(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * 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)
    triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_3(in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex % 16
    x5 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-10 + 2 * x4 + 16 * x2), tmp10,
        eviction_policy='evict_last', other=0.0)
    tmp12 = tl.load(in_ptr0 + (-9 + 2 * x4 + 16 * x2), tmp10,
        eviction_policy='evict_last', other=0.0)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tl.load(in_ptr0 + (-6 + 2 * x4 + 16 * x2), tmp10,
        eviction_policy='evict_last', other=0.0)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = tl.load(in_ptr0 + (-5 + 2 * x4 + 16 * x2), tmp10,
        eviction_policy='evict_last', other=0.0)
    tmp17 = triton_helpers.maximum(tmp16, tmp15)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp10, tmp17, tmp18)
    tl.store(out_ptr0 + x5, tmp19, None)


@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 64
    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_5(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + 4 * x0), 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 + x0, tmp15, xmask)
    tl.store(out_ptr1 + x0, tmp16, xmask)


@triton.jit
def triton_poi_fused_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    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_7(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 336
    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, (4, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_2, (32, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (128, 64), (64, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (84, 128), (128, 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 = empty_strided_cuda((4, 3, 6, 6), (108, 36, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(432)](primals_1, buf0, 432,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 32, 4, 4), (512, 16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_relu_1[grid(2048)](buf2, primals_3, 
            2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((4, 32, 2, 2), (128, 4, 2, 1), torch.int8)
        triton_poi_fused_max_pool2d_with_indices_2[grid(512)](buf2, buf3, 
            512, XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.float32
            )
        triton_poi_fused_constant_pad_nd_max_pool2d_with_indices_3[grid(2048)](
            buf2, buf4, 2048, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 64, 2, 2), (256, 4, 2, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_relu_4[grid(1024)](buf6, primals_5, 
            1024, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf7 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 1, 1), torch.int8)
        buf8 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 256, 256), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_5[grid(256)](buf6, buf7,
            buf8, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (4, 64), (64, 1), 0),
            reinterpret_tensor(primals_6, (64, 128), (1, 64), 0), out=buf9)
        buf10 = buf9
        del buf9
        triton_poi_fused_relu_6[grid(512)](buf10, primals_7, 512, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_7
        buf11 = empty_strided_cuda((4, 84), (84, 1), torch.float32)
        extern_kernels.mm(buf10, reinterpret_tensor(primals_8, (128, 84), (
            1, 128), 0), out=buf11)
        buf12 = buf11
        del buf11
        triton_poi_fused_relu_7[grid(336)](buf12, primals_9, 336, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
        buf13 = empty_strided_cuda((4, 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_2, primals_4, buf0, buf2, buf3, buf4, buf6, buf7,
        reinterpret_tensor(buf8, (4, 64), (64, 1), 0), buf10, buf12,
        primals_10, primals_8, primals_6)


class ConvNetNew(nn.Module):

    def __init__(self, img_size):
        super(ConvNetNew, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3)
        self.conv2 = nn.Conv2d(32, 64, 3)
        self.relu = nn.ReLU()
        self.padding = nn.ZeroPad2d(1)
        self.fc1 = nn.Linear(4 * img_size * img_size, 128)
        self.fc2 = nn.Linear(128, 84)
        self.fc3 = nn.Linear(84, 4)
        self.flat = nn.Flatten(1)
        self.max_pool = nn.MaxPool2d(2)

    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_10 = self.fc3.weight
        primals_11 = self.fc3.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: 0.152471