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
import torch.utils.data
import torch.nn.parallel
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 = 1616
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 404
    x1 = xindex // 404
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 400, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (400 * 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], 404, tl.int64)
    tmp15 = tl.load(in_ptr2 + (4 * x1 + (-400 + 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_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 300
    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_threshold_backward_2(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 400
    tmp0 = tl.load(in_ptr0 + x2, 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 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (400, 4), (4, 1))
    assert_size_stride(primals_2, (400,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (300, 404), (404, 1))
    assert_size_stride(primals_6, (300,), (1,))
    assert_size_stride(primals_7, (300, 300), (300, 1))
    assert_size_stride(primals_8, (300,), (1,))
    assert_size_stride(primals_9, (1, 300), (300, 1))
    assert_size_stride(primals_10, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 400),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 404), (404, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(1616)](buf0, primals_2, primals_4, buf1,
            1616, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf2 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_5, (404, 300), (
            1, 404), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(1200)](buf3, primals_6, 1200, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf3, reinterpret_tensor(primals_7,
            (300, 300), (1, 300), 0), alpha=1, beta=1, out=buf4)
        del primals_8
        buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_10, buf4, reinterpret_tensor(primals_9,
            (300, 1), (1, 300), 0), alpha=1, beta=1, out=buf6)
        del primals_10
        buf7 = empty_strided_cuda((4, 400), (400, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(1600)](buf0,
            primals_2, buf7, 1600, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del primals_2
    return (buf6, primals_3, buf1, buf3, buf4, primals_9, primals_7,
        primals_5, buf7)


class CriticNetNew(nn.Module):

    def __init__(self, args):
        super(CriticNetNew, self).__init__()
        state_dim = args.state_dim
        action_dim = args.z_dim
        self.l1 = nn.Linear(state_dim, 400)
        self.l2 = nn.Linear(400 + action_dim, 300)
        self.l3_additional = nn.Linear(300, 300)
        self.l3 = nn.Linear(300, 1)

    def forward(self, input_0, input_1):
        primals_1 = self.l1.weight
        primals_2 = self.l1.bias
        primals_5 = self.l2.weight
        primals_6 = self.l2.bias
        primals_7 = self.l3_additional.weight
        primals_8 = self.l3_additional.bias
        primals_9 = self.l3.weight
        primals_10 = self.l3.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, primals_9, primals_10])
        return output[0]


Measured latency_ms: 0.028197