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
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_mul_tanh_3(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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (1024, 4), (4, 1))
    assert_size_stride(primals_2, (1024,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (512, 1024), (1024, 1))
    assert_size_stride(primals_5, (512,), (1,))
    assert_size_stride(primals_6, (256, 512), (512, 1))
    assert_size_stride(primals_7, (256,), (1,))
    assert_size_stride(primals_8, (4, 256), (256, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1024), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 1024), (16384, 4096, 1024,
            1), 0)
        del buf0
        buf10 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(65536)](buf1,
            primals_2, buf10, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 1024), (1024, 1), 0
            ), reinterpret_tensor(primals_4, (1024, 512), (1, 1024), 0),
            out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 512), (8192, 2048, 512, 1), 0
            )
        del buf2
        buf9 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(32768)](buf3,
            primals_5, buf9, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 512), (512, 1), 0),
            reinterpret_tensor(primals_6, (512, 256), (1, 512), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 256), (4096, 1024, 256, 1), 0
            )
        del buf4
        buf8 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(16384)](buf5,
            primals_7, buf8, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (64, 256),
            (256, 1), 0), reinterpret_tensor(primals_8, (256, 4), (1, 256),
            0), alpha=1, beta=1, out=buf6)
        del primals_9
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_tanh_3[grid(256)](buf6, buf7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
    return buf7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 1024), (1024, 1), 0
        ), reinterpret_tensor(buf3, (64, 512), (512, 1), 0
        ), reinterpret_tensor(buf5, (64, 256), (256, 1), 0
        ), buf6, primals_8, buf8, primals_6, buf9, primals_4, buf10


class ActorNew(nn.Module):

    def __init__(self, state_dim, action_dim, max_action):
        super(ActorNew, self).__init__()
        self.l1 = nn.Linear(state_dim, 1024)
        self.l2 = nn.Linear(1024, 512)
        self.l3 = nn.Linear(512, 256)
        self.l4 = nn.Linear(256, action_dim)
        self.max_action = max_action

    def forward(self, input_0):
        primals_1 = self.l1.weight
        primals_2 = self.l1.bias
        primals_4 = self.l2.weight
        primals_5 = self.l2.bias
        primals_6 = self.l3.weight
        primals_7 = self.l3.bias
        primals_8 = self.l4.weight
        primals_9 = self.l4.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.156576