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 = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, 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
    x2 = xindex
    x0 = xindex % 4
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + 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, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((128, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (128, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf1, (4, 8, 4, 4), (128, 16, 4, 1), 0)
        del buf1
        buf7 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(512)](buf2,
            primals_4, buf7, 512, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((128, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (128, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 8, 4, 4), (128, 16, 4, 1), 0)
        del buf3
        buf6 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(512)](buf4,
            primals_6, buf6, 512, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_6
        buf5 = empty_strided_cuda((128, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf4, (128, 4),
            (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf5)
        del primals_8
    return reinterpret_tensor(buf5, (4, 8, 4, 4), (128, 16, 4, 1), 0
        ), reinterpret_tensor(buf0, (128, 4), (4, 1), 0), reinterpret_tensor(
        buf2, (128, 4), (4, 1), 0), reinterpret_tensor(buf4, (128, 4), (4, 
        1), 0), primals_7, buf6, primals_5, buf7


class SACCriticNetworkNew(nn.Module):

    def __init__(self, input_shape, output_shape, n_features, **kwargs):
        super().__init__()
        n_input = input_shape[-1]
        n_output = output_shape[0]
        self._h1 = nn.Linear(n_input, n_features)
        self._h2 = nn.Linear(n_features, n_features)
        self._h3 = nn.Linear(n_features, n_output)
        nn.init.xavier_uniform_(self._h1.weight, gain=nn.init.
            calculate_gain('relu'))
        nn.init.xavier_uniform_(self._h2.weight, gain=nn.init.
            calculate_gain('relu'))
        nn.init.xavier_uniform_(self._h3.weight, gain=nn.init.
            calculate_gain('linear'))

    def forward(self, input_0, input_1):
        primals_3 = self._h1.weight
        primals_4 = self._h1.bias
        primals_5 = self._h2.weight
        primals_6 = self._h2.bias
        primals_7 = self._h3.weight
        primals_8 = self._h3.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: 0.050673