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 = 156800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 1225 % 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_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 // 256 % 64
    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_convolution_relu_threshold_backward_2(in_out_ptr0,
    in_ptr0, out_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 // 196 % 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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_3(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)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, None)


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, primals_14, primals_15) = args
    args.clear()
    assert_size_stride(primals_1, (32, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 4, 144, 144), (82944, 20736, 144, 1))
    assert_size_stride(primals_4, (64, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (512, 3136), (3136, 1))
    assert_size_stride(primals_9, (512,), (1,))
    assert_size_stride(primals_10, (512, 3136), (3136, 1))
    assert_size_stride(primals_11, (512,), (1,))
    assert_size_stride(primals_12, (4, 512), (512, 1))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4, 512), (512, 1))
    assert_size_stride(primals_15, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 
            4), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 35, 35), (39200, 1225, 35, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(156800)](buf1, primals_2, 
            156800, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(65536)](buf3, primals_5, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 14, 14), (12544, 196, 14, 1))
        buf5 = buf4
        del buf4
        buf9 = empty_strided_cuda((4, 64, 14, 14), (12544, 196, 14, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_2[grid(50176)](
            buf5, primals_7, buf9, 50176, XBLOCK=256, num_warps=4, num_stages=1
            )
        del primals_7
        buf6 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (16, 3136), (3136, 1), 0
            ), reinterpret_tensor(primals_8, (3136, 512), (1, 3136), 0),
            out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_relu_3[grid(8192)](buf7, primals_9, 8192, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_15, buf7, reinterpret_tensor(
            primals_14, (512, 4), (1, 512), 0), alpha=1, beta=1, out=buf8)
        del primals_15
    return (buf8, primals_1, primals_3, primals_4, primals_6, buf1, buf3,
        reinterpret_tensor(buf5, (16, 3136), (3136, 1), 0), buf7,
        primals_14, primals_8, buf9)


class Conv_QNew(nn.Module):

    def __init__(self, frames, num_actions):
        super(Conv_QNew, self).__init__()
        self.c1 = nn.Conv2d(frames, 32, kernel_size=8, stride=4)
        self.c2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
        self.c3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
        self.q1 = nn.Linear(3136, 512)
        self.q2 = nn.Linear(512, num_actions)
        self.i1 = nn.Linear(3136, 512)
        self.i2 = nn.Linear(512, num_actions)

    def forward(self, input_0):
        primals_1 = self.c1.weight
        primals_2 = self.c1.bias
        primals_4 = self.c2.weight
        primals_5 = self.c2.bias
        primals_6 = self.c3.weight
        primals_7 = self.c3.bias
        primals_8 = self.q1.weight
        primals_9 = self.q1.bias
        primals_12 = self.q2.weight
        primals_13 = self.q2.bias
        primals_10 = self.i1.weight
        primals_11 = self.i1.bias
        primals_14 = self.i2.weight
        primals_15 = self.i2.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, primals_12, primals_13, primals_14,
            primals_15])
        return output[0]


Measured latency_ms: 0.176902