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.triton_helpers import libdevice
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, in_ptr2, out_ptr0, out_ptr1,
    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 = 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)
    tmp11 = tl.load(in_ptr2 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = libdevice.tanh(tmp11)
    tmp13 = tmp5 + tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp4, tmp13, tmp14)
    tmp16 = tl.where(tmp4, tmp15, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)
    tl.store(out_ptr1 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_tanh_1(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 = libdevice.tanh(tmp2)
    tl.store(out_ptr0 + (x4 + 1216 * x2), tmp3, xmask)


@triton.jit
def triton_poi_fused_addmm_2(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_add_tanh_3(in_ptr0, in_ptr1, in_ptr2, 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 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = libdevice.tanh(tmp1)
    tmp3 = tmp0 + tmp2
    tmp5 = libdevice.tanh(tmp4)
    tmp6 = tmp3 + tmp5
    tl.store(out_ptr0 + x0, 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, (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, (300, 8), (8, 1))
    assert_size_stride(primals_4, (300,), (1,))
    assert_size_stride(primals_5, (1, 300), (300, 1))
    assert_size_stride(primals_6, (1,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 8), (8, 1))
    assert_size_stride(primals_10, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf6)
        del primals_7
        del primals_8
        buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        buf7 = 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, buf6, buf0,
            buf7, 512, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_3, (8, 300), (1, 8), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 300), (4864, 1216, 300, 1),
            torch.float32)
        triton_poi_fused_tanh_1[grid(19200)](buf1, primals_4, buf2, 19200,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
        buf3 = buf1
        del buf1
        triton_poi_fused_addmm_2[grid(19200)](buf2, buf3, 19200, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_6, buf3, reinterpret_tensor(primals_5,
            (300, 1), (1, 300), 0), alpha=1, beta=1, out=buf5)
        del buf3
        del primals_6
        buf8 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf7, (64, 8),
            (8, 1), 0), reinterpret_tensor(primals_9, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf8)
        del primals_10
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_tanh_3[grid(256)](primals_1, buf6, buf8, buf9,
            256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf9, reinterpret_tensor(buf5, (4, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(buf0, (64, 8), (8, 1), 0
        ), buf2, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), buf6, reinterpret_tensor(buf7, (64, 8), (8, 1), 0
        ), buf8, primals_9, primals_5


def layer_init(layer, w_scale=1.0):
    nn.init.orthogonal_(layer.weight.data)
    layer.weight.data.mul_(w_scale)
    nn.init.constant_(layer.bias.data, 0)
    return layer


class EnvModelNew(nn.Module):

    def __init__(self, phi_dim, action_dim):
        super(EnvModelNew, self).__init__()
        self.hidden_dim = 300
        self.fc_r1 = layer_init(nn.Linear(phi_dim + action_dim, self.
            hidden_dim))
        self.fc_r2 = layer_init(nn.Linear(self.hidden_dim, 1))
        self.fc_t1 = layer_init(nn.Linear(phi_dim, phi_dim))
        self.fc_t2 = layer_init(nn.Linear(phi_dim + action_dim, phi_dim))

    def forward(self, input_0, input_1):
        primals_3 = self.fc_r1.weight
        primals_4 = self.fc_r1.bias
        primals_5 = self.fc_r2.weight
        primals_6 = self.fc_r2.bias
        primals_7 = self.fc_t1.weight
        primals_8 = self.fc_t1.bias
        primals_9 = self.fc_t2.weight
        primals_10 = self.fc_t2.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])
        return output[0], output[1]


Measured latency_ms: 0.102385