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
from torch.nn.init import kaiming_uniform_
from torch.nn.init import uniform_
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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 19456
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 304
    x1 = xindex // 304
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 300, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (300 * x1 + x0), tmp4 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 304, tl.int64)
    tmp15 = tl.load(in_ptr2 + (4 * x1 + (-300 + x0)), tmp12 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.where(tmp4, tmp11, tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 25600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 400
    x2 = xindex % 1600
    x3 = xindex // 1600
    tmp0 = tl.load(in_out_ptr0 + x4, 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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x4, tmp4, xmask)
    tl.store(out_ptr0 + (x2 + 1664 * x3), tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 19200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 300
    x2 = xindex // 1200
    x4 = xindex % 1200
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x0, 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(out_ptr0 + (x4 + 1280 * x2), tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (300, 4), (4, 1))
    assert_size_stride(primals_2, (300,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (400, 304), (304, 1))
    assert_size_stride(primals_6, (400,), (1,))
    assert_size_stride(primals_7, (1, 400), (400, 1))
    assert_size_stride(primals_8, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 300), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 304), (4864, 1216, 304, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(19456)](buf0, primals_2, primals_4,
            buf1, 19456, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
        buf2 = empty_strided_cuda((64, 400), (400, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 304), (304, 1), 0),
            reinterpret_tensor(primals_5, (304, 400), (1, 304), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 400), (6400, 1600, 400, 1), 0
            )
        del buf2
        buf6 = empty_strided_cuda((4, 4, 4, 400), (6656, 1664, 400, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(25600)](buf3,
            primals_6, buf6, 25600, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_6
        buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf3, (64, 400),
            (400, 1), 0), reinterpret_tensor(primals_7, (400, 1), (1, 400),
            0), alpha=1, beta=1, out=buf5)
        del primals_8
        buf7 = empty_strided_cuda((4, 4, 4, 300), (5120, 1280, 300, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(19200)](buf0,
            primals_2, buf7, 19200, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
    return reinterpret_tensor(buf5, (4, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 304), (304, 1), 0
        ), reinterpret_tensor(buf3, (64, 400), (400, 1), 0
        ), primals_7, buf6, primals_5, buf7


def mini_weight_init(m):
    if m.__class__.__name__ == 'Linear':
        m.weight.data.copy_(uniform_(m.weight.data, -0.003, 0.003))
        m.bias.data.fill_(0)


def weight_init(m):
    if m.__class__.__name__ == 'Linear':
        m.weight.data.copy_(kaiming_uniform_(m.weight.data))
        m.bias.data.fill_(0)


class QNetNew(nn.Module):

    def __init__(self, ob_space, ac_space, h1=300, h2=400):
        super(QNetNew, self).__init__()
        self.fc1 = nn.Linear(ob_space.shape[0], h1)
        self.fc2 = nn.Linear(ac_space.shape[0] + h1, h2)
        self.output_layer = nn.Linear(h2, 1)
        self.fc1.apply(weight_init)
        self.fc2.apply(weight_init)
        self.output_layer.apply(mini_weight_init)

    def forward(self, input_0, input_1):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_5 = self.fc2.weight
        primals_6 = self.fc2.bias
        primals_7 = self.output_layer.weight
        primals_8 = self.output_layer.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]


Measured latency_ms: 0.098184