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):
    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_1(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_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_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) = 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (300, 400), (400, 1))
    assert_size_stride(primals_5, (300,), (1,))
    assert_size_stride(primals_6, (4, 300), (300, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 400), (400, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 400), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 400), (6400, 1600, 400, 1), 0
            )
        del buf0
        buf8 = empty_strided_cuda((4, 4, 4, 400), (6656, 1664, 400, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(25600)](buf1,
            primals_2, buf8, 25600, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 300), (300, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 400), (400, 1), 0),
            reinterpret_tensor(primals_4, (400, 300), (1, 400), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4, 300), (4864, 1216, 300, 1),
            torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 300), (5120, 1280, 300, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(19200)](buf2,
            primals_5, buf3, buf7, 19200, XBLOCK=128, num_warps=4, num_stages=1
            )
        del primals_5
        buf4 = buf2
        del buf2
        triton_poi_fused_relu_view_2[grid(19200)](buf3, buf4, 19200, XBLOCK
            =256, num_warps=4, num_stages=1)
        del buf3
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf4, reinterpret_tensor(primals_6,
            (300, 4), (1, 300), 0), alpha=1, beta=1, out=buf5)
        del primals_7
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_tanh_3[grid(256)](buf5, buf6, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
    return buf6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 400), (400, 1), 0
        ), buf4, buf5, primals_6, buf7, primals_4, buf8


class ActorNew(nn.Module):
    """ Neural Network for the Actor Model """

    def __init__(self, state_size, action_size, max_action, seed=0,
        layer1_units=400, layer2_units=300):
        """Initialize parameters and build model.
				Params
				======
					state_size (int): Dimension of each state
					action_size (int): Dimension of each action
					seed (int): Random seed
					layer1_units (int): Number of nodes in first hidden layer
					layer2_units (int): Number of nodes in second hidden layer
		"""
        super(ActorNew, self).__init__()
        self.layer_1 = nn.Linear(state_size, layer1_units)
        self.layer_2 = nn.Linear(layer1_units, layer2_units)
        self.layer_3 = nn.Linear(layer2_units, action_size)
        self.max_action = max_action

    def forward(self, input_0):
        primals_1 = self.layer_1.weight
        primals_2 = self.layer_1.bias
        primals_4 = self.layer_2.weight
        primals_5 = self.layer_2.bias
        primals_6 = self.layer_3.weight
        primals_7 = self.layer_3.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.114336