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
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.nn.functional as F
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
    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 = libdevice.tanh(tmp5)
    tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
    tmp8 = tl.where(tmp4, tmp6, tmp7)
    tmp9 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp12 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp13 = libdevice.tanh(tmp12)
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp9, tmp13, tmp14)
    tmp16 = tl.where(tmp4, tmp8, 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,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 19200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 300
    x2 = xindex // 1200
    x3 = xindex % 1200
    tmp0 = tl.load(in_ptr0 + x4, 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 + (x3 + 1216 * x2), tmp4, xmask)
    tl.store(out_ptr1 + (x3 + 1280 * x2), tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_view_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 19200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 300
    x1 = xindex // 300
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 300 * (x1 % 4) + 1216 * (x1 // 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_mul_tanh_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_per_fused_div_linalg_vector_norm_5(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = 1e-12
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp0 / tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp9, xmask)


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) = 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, (400, 8), (8, 1))
    assert_size_stride(primals_4, (400,), (1,))
    assert_size_stride(primals_5, (300, 400), (400, 1))
    assert_size_stride(primals_6, (300,), (1,))
    assert_size_stride(primals_7, (4, 300), (300, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (400, 8), (8, 1))
    assert_size_stride(primals_10, (400,), (1,))
    assert_size_stride(primals_11, (300, 400), (400, 1))
    assert_size_stride(primals_12, (300,), (1,))
    assert_size_stride(primals_13, (16, 300), (300, 1))
    assert_size_stride(primals_14, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 400), (400, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_3, (8, 400), (1, 8), 0), out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 400), (6400, 1600, 400, 1), 0
            )
        del buf1
        buf20 = empty_strided_cuda((4, 4, 4, 400), (6656, 1664, 400, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(25600)](buf2,
            primals_4, buf20, 25600, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 400), (400, 1), 0),
            reinterpret_tensor(primals_5, (400, 300), (1, 400), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 300), (4864, 1216, 300, 1),
            torch.float32)
        buf19 = empty_strided_cuda((4, 4, 4, 300), (5120, 1280, 300, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(19200)](buf3,
            primals_6, buf4, buf19, 19200, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_6
        buf5 = buf3
        del buf3
        triton_poi_fused_relu_view_3[grid(19200)](buf4, buf5, 19200, XBLOCK
            =128, num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf5, reinterpret_tensor(primals_7,
            (300, 4), (1, 300), 0), alpha=1, beta=1, out=buf6)
        del primals_8
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_tanh_4[grid(256)](buf6, buf7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((64, 400), (400, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_9, (8, 400), (1, 8), 0), out=buf8)
        del primals_9
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 400), (6400, 1600, 400, 1), 0
            )
        del buf8
        buf18 = empty_strided_cuda((4, 4, 4, 400), (6656, 1664, 400, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(25600)](buf9,
            primals_10, buf18, 25600, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_10
        buf10 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf9, (64, 400), (400, 1), 0),
            reinterpret_tensor(primals_11, (400, 300), (1, 400), 0), out=buf10)
        buf11 = buf4
        del buf4
        buf17 = empty_strided_cuda((4, 4, 4, 300), (5120, 1280, 300, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(19200)](buf10,
            primals_12, buf11, buf17, 19200, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_12
        buf12 = buf10
        del buf10
        triton_poi_fused_relu_view_3[grid(19200)](buf11, buf12, 19200,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf11
        buf13 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_14, buf12, reinterpret_tensor(
            primals_13, (300, 16), (1, 300), 0), alpha=1, beta=1, out=buf13)
        del primals_14
        buf14 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf15 = reinterpret_tensor(buf14, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf14
        buf16 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
            float32)
        triton_per_fused_div_linalg_vector_norm_5[grid(64)](buf15, buf13,
            buf16, 64, 16, XBLOCK=32, num_warps=4, num_stages=1)
    return (buf7, buf16, reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
        reinterpret_tensor(buf2, (64, 400), (400, 1), 0), buf5, buf6,
        reinterpret_tensor(buf9, (64, 400), (400, 1), 0), buf12, buf13,
        buf15, primals_13, buf17, primals_11, buf18, primals_7, buf19,
        primals_5, buf20)


class MLPBase(nn.Module):

    def __init__(self, num_inputs, num_outputs):
        super(MLPBase, self).__init__()
        self.l1 = nn.Linear(num_inputs, 400)
        self.l2 = nn.Linear(400, 300)
        self.l3 = nn.Linear(300, num_outputs)

    def forward(self, inputs):
        x = F.relu(self.l1(inputs))
        x = F.relu(self.l2(x))
        x = self.l3(x)
        return x


class ActorDownActionNew(nn.Module):
    """a top-down module used in bothway message passing that passes messages to children and outputs action"""

    def __init__(self, self_input_dim, action_dim, msg_dim, max_action,
        max_children):
        super(ActorDownActionNew, self).__init__()
        self.max_action = max_action
        self.action_base = MLPBase(self_input_dim + msg_dim, action_dim)
        self.msg_base = MLPBase(self_input_dim + msg_dim, msg_dim *
            max_children)

    def forward(self, input_0, input_1):
        primals_3 = self.action_base.l1.weight
        primals_4 = self.action_base.l1.bias
        primals_5 = self.action_base.l2.weight
        primals_6 = self.action_base.l2.bias
        primals_7 = self.action_base.l3.weight
        primals_8 = self.action_base.l3.bias
        primals_9 = self.msg_base.l1.weight
        primals_10 = self.msg_base.l1.bias
        primals_11 = self.msg_base.l2.weight
        primals_12 = self.msg_base.l2.bias
        primals_13 = self.msg_base.l3.weight
        primals_14 = self.msg_base.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, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14])
        return output[0], output[1]


Measured latency_ms: 0.182653