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 numpy as np
from torch import 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, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_clamp_div_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp5 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (256 + x0), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (384 + x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 1.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp5, tmp1)
    tmp7 = triton_helpers.minimum(tmp6, tmp3)
    tmp8 = tmp7 * tmp7
    tmp10 = triton_helpers.maximum(tmp9, tmp1)
    tmp11 = triton_helpers.minimum(tmp10, tmp3)
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp15 = triton_helpers.maximum(tmp14, tmp1)
    tmp16 = triton_helpers.minimum(tmp15, tmp3)
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp20 = triton_helpers.maximum(tmp19, tmp1)
    tmp21 = triton_helpers.minimum(tmp20, tmp3)
    tmp22 = tmp21 * tmp21
    tmp23 = tmp18 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = 1e-12
    tmp26 = triton_helpers.maximum(tmp24, tmp25)
    tmp27 = tmp4 / tmp26
    tl.store(out_ptr0 + x2, tmp27, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = 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, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((128, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (128, 4),
            (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        triton_poi_fused_clamp_div_1[grid(512)](buf1, buf2, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
    return buf2, reinterpret_tensor(buf0, (128, 4), (4, 1), 0), buf1


class ActorNew(nn.Module):

    def __init__(self, input_dim, output_dim):
        super(ActorNew, self).__init__()
        self.layer1 = nn.Linear(input_dim, output_dim, bias=True)

    def _format(self, state):
        x = state
        if not isinstance(x, torch.Tensor):
            x = torch.tensor(x, dtype=torch.float32)
            x = x.unsqueeze(0)
        return x

    def full_pass(self, a, p):
        logits = self.forward(a, p)
        dist = torch.distributions.Categorical(logits=logits)
        action = dist.sample()
        logpa = dist.log_prob(action).unsqueeze(-1)
        entropy = dist.entropy().unsqueeze(-1)
        is_exploratory = action != np.argmax(logits.detach().numpy())
        return action.item(), is_exploratory.item(), logpa, entropy

    def select_action(self, a, p):
        logits = self.forward(a, p)
        dist = torch.distributions.Categorical(logits=logits)
        action = dist.sample()
        return action.item()

    def select_greedy_action(self, a, p):
        logits = self.forward(a, p)
        return np.argmax(logits.detach().numpy())

    def forward(self, input_0, input_1):
        primals_3 = self.layer1.weight
        primals_4 = self.layer1.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.038049