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 numpy as np
import torch.nn as nn
from torch.distributions import Normal
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 = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 36
    x1 = xindex // 36
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (32 * 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], 36, tl.int64)
    tmp15 = tl.load(in_ptr2 + (4 * x1 + (-32 + 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 = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    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_clamp_ge_le_logical_and_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = -20.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 2.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp2 >= tmp3
    tmp8 = tmp2 <= tmp5
    tmp9 = tmp7 & tmp8
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(out_ptr1 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    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, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (32, 4), (4, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (32, 36), (36, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (32, 36), (36, 1))
    assert_size_stride(primals_7, (32,), (1,))
    assert_size_stride(primals_8, (32, 36), (36, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (4, 32), (32, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4, 32), (32, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 32),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 36), (36, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(144)](buf0, primals_2, primals_3, buf1,
            144, XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (36, 32), (1,
            36), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 36), (36, 1), torch.float32)
        triton_poi_fused_cat_0[grid(144)](buf2, primals_5, primals_3, buf3,
            144, XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_6, (36, 32), (1,
            36), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 36), (36, 1), torch.float32)
        triton_poi_fused_cat_0[grid(144)](buf4, primals_7, primals_3, buf5,
            144, XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_8, (36, 32), (1,
            36), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_relu_1[grid(128)](buf7, primals_9, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf7, reinterpret_tensor(
            primals_10, (32, 4), (1, 32), 0), alpha=1, beta=1, out=buf8)
        del primals_11
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf7, reinterpret_tensor(primals_12, (32, 4), (1,
            32), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_clamp_ge_le_logical_and_2[grid(16)](buf9,
            primals_13, buf10, buf11, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf9
        del primals_13
        buf12 = empty_strided_cuda((4, 32), (32, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(128)](buf4,
            primals_7, buf12, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del buf4
        del primals_7
        buf13 = empty_strided_cuda((4, 32), (32, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(128)](buf2,
            primals_5, buf13, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del buf2
        del primals_5
        buf14 = empty_strided_cuda((4, 32), (32, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(128)](buf0,
            primals_2, buf14, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del primals_2
    return (buf8, buf10, primals_3, buf1, buf3, buf5, buf7, buf11,
        primals_12, primals_10, primals_8, buf12, primals_6, buf13,
        primals_4, buf14)


def hidden_init(layer):
    fan_in = layer.weight.data.size()[0]
    lim = 1.0 / np.sqrt(fan_in)
    return -lim, lim


class DeepActorNew(nn.Module):
    """Actor (Policy) Model."""

    def __init__(self, state_size, action_size, seed, device, hidden_size=
        32, init_w=0.003, log_std_min=-20, log_std_max=2):
        """Initialize parameters and build model.
        Params
        ======
            state_size (int): Dimension of each state
            action_size (int): Dimension of each action
            seed (int): Random seed
            fc1_units (int): Number of nodes in first hidden layer
            fc2_units (int): Number of nodes in second hidden layer
        """
        super(DeepActorNew, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.device = device
        self.log_std_min = log_std_min
        self.log_std_max = log_std_max
        in_dim = hidden_size + state_size
        self.fc1 = nn.Linear(state_size, hidden_size)
        self.fc2 = nn.Linear(in_dim, hidden_size)
        self.fc3 = nn.Linear(in_dim, hidden_size)
        self.fc4 = nn.Linear(in_dim, hidden_size)
        self.mu = nn.Linear(hidden_size, action_size)
        self.log_std_linear = nn.Linear(hidden_size, action_size)

    def reset_parameters(self, init_w=0.003):
        self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
        self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
        self.fc3.weight.data.uniform_(*hidden_init(self.fc3))
        self.fc4.weight.data.uniform_(*hidden_init(self.fc4))
        self.mu.weight.data.uniform_(-init_w, init_w)
        self.log_std_linear.weight.data.uniform_(-init_w, init_w)

    def evaluate(self, state, epsilon=1e-06):
        mu, log_std = self.forward(state)
        std = log_std.exp()
        dist = Normal(mu, std)
        e = dist.rsample()
        action = torch.tanh(e)
        log_prob = (dist.log_prob(e) - torch.log(1 - action.pow(2) + epsilon)
            ).sum(1, keepdim=True)
        return action, log_prob

    def get_action(self, state):
        """
        returns the action based on a squashed gaussian policy. That means the samples are obtained according to:
        a(s,e)= tanh(mu(s)+sigma(s)+e)
        """
        mu, log_std = self.forward(state)
        std = log_std.exp()
        dist = Normal(mu, std)
        e = dist.rsample()
        action = torch.tanh(e)
        return action.detach().cpu()

    def get_det_action(self, state):
        mu, _log_std = self.forward(state)
        return torch.tanh(mu).detach().cpu()

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_8 = self.fc4.weight
        primals_9 = self.fc4.bias
        primals_10 = self.mu.weight
        primals_11 = self.mu.bias
        primals_12 = self.log_std_linear.weight
        primals_13 = self.log_std_linear.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,
            primals_10, primals_11, primals_12, primals_13])
        return output[0], output[1]


Measured latency_ms: 0.150351