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 math as tl_math
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 = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 8
    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_convolution_relu_1(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 // 16 % 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_convolution_2(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
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_relu_3(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 = 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_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 100
    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__softmax_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 12
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 3
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 3 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 3 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 3 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp0 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp1 - tmp5
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp2 - tmp5
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp9 + tmp11
    tmp13 = tmp4 - tmp5
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp12 + tmp14
    tmp16 = tmp7 / tmp15
    tl.store(out_ptr0 + 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, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (8, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (8,), (1,))
    assert_size_stride(primals_4, (16, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (32, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_7, (32,), (1,))
    assert_size_stride(primals_8, (200, 512), (512, 1))
    assert_size_stride(primals_9, (200,), (1,))
    assert_size_stride(primals_10, (100, 200), (200, 1))
    assert_size_stride(primals_11, (100,), (1,))
    assert_size_stride(primals_12, (3, 100), (100, 1))
    assert_size_stride(primals_13, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(512)](buf1, primals_3, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 16, 4, 4), (256, 16, 4, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(1024)](buf3, primals_5, 
            1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 32, 4, 4), (512, 16, 4, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_2[grid(2048)](buf5, primals_7, 2048,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((4, 200), (200, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (4, 512), (512, 1), 0),
            reinterpret_tensor(primals_8, (512, 200), (1, 512), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_relu_3[grid(800)](buf7, primals_9, 800, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((4, 100), (100, 1), torch.float32)
        extern_kernels.mm(buf7, reinterpret_tensor(primals_10, (200, 100),
            (1, 200), 0), out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_relu_4[grid(400)](buf9, primals_11, 400, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_11
        buf10 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        extern_kernels.addmm(primals_13, buf9, reinterpret_tensor(
            primals_12, (100, 3), (1, 100), 0), alpha=1, beta=1, out=buf10)
        del primals_13
        buf11 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        triton_poi_fused__softmax_5[grid(12)](buf10, buf11, 12, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf10
    return (buf11, primals_2, primals_4, primals_6, primals_1, buf1, buf3,
        reinterpret_tensor(buf5, (4, 512), (512, 1), 0), buf7, buf9, buf11,
        primals_12, primals_10, primals_8)


class SimulatorRewardNew(torch.nn.Module):

    def __init__(self):
        super(SimulatorRewardNew, self).__init__()
        self.conv1 = torch.nn.Conv2d(4, 8, kernel_size=3, padding=1)
        self.conv2 = torch.nn.Conv2d(8, 16, kernel_size=3, padding=1)
        self.conv3 = torch.nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.fc1 = torch.nn.Linear(512, 200)
        self.fc2 = torch.nn.Linear(200, 100)
        self.fc3 = torch.nn.Linear(100, 3)

    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.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.fc1.weight
        primals_9 = self.fc1.bias
        primals_10 = self.fc2.weight
        primals_11 = self.fc2.bias
        primals_12 = self.fc3.weight
        primals_13 = 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, primals_12, primals_13])
        return output[0]


Measured latency_ms: 0.130085