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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_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
    x2 = xindex
    x0 = xindex % 256
    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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (256, 8), (8, 1))
    assert_size_stride(primals_4, (256,), (1,))
    assert_size_stride(primals_5, (256, 256), (256, 1))
    assert_size_stride(primals_6, (256,), (1,))
    assert_size_stride(primals_7, (1, 256), (256, 1))
    assert_size_stride(primals_8, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 256), (1,
            8), 0), out=buf1)
        del primals_3
        buf2 = buf1
        del buf1
        triton_poi_fused_relu_1[grid(1024)](buf2, primals_4, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (256, 256), (
            1, 256), 0), out=buf3)
        buf4 = buf3
        del buf3
        triton_poi_fused_relu_1[grid(1024)](buf4, primals_6, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_6
        buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7,
            (256, 1), (1, 256), 0), alpha=1, beta=1, out=buf6)
        del primals_8
    return buf6, buf0, buf2, buf4, primals_7, primals_5


def weight_init(m):
    """Custom weight init for Conv2D and Linear layers."""
    if isinstance(m, nn.Linear):
        nn.init.orthogonal_(m.weight.data)
        m.bias.data.fill_(0.0)
    elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
        assert m.weight.size(2) == m.weight.size(3)
        m.weight.data.fill_(0.0)
        m.bias.data.fill_(0.0)
        mid = m.weight.size(2) // 2
        gain = nn.init.calculate_gain('relu')
        nn.init.orthogonal_(m.weight.data[:, :, mid, mid], gain)


class CriticNew(nn.Module):

    def __init__(self, state_dim, action_dim, hidden_dim=256):
        super(CriticNew, self).__init__()
        self.l1 = nn.Linear(state_dim + action_dim, hidden_dim)
        self.l2 = nn.Linear(hidden_dim, hidden_dim)
        self.l3 = nn.Linear(hidden_dim, 1)
        self.apply(weight_init)

    def forward(self, input_0, input_1):
        primals_3 = self.l1.weight
        primals_4 = self.l1.bias
        primals_5 = self.l2.weight
        primals_6 = self.l2.bias
        primals_7 = self.l3.weight
        primals_8 = self.l3.bias
        primals_1 = input_0
        primals_2 = 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: 0.065053