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
import torch.nn as nn
import torch.nn.functional as F
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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    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 % 64
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


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, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (64, 4), (4, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (64, 64), (64, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (4, 64), (64, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 64), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool
            )
        buf2 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(4096)](buf0, primals_3, buf1,
            buf2, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = buf0
        del buf0
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 64), (64, 1), 0),
            reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool
            )
        buf5 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_0[grid(4096)](buf3, primals_5, buf4,
            buf5, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        del primals_5
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf5, (64, 64),
            (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0),
            alpha=1, beta=1, out=buf6)
        del primals_7
    return reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), buf1, reinterpret_tensor(buf2, (64, 64), (64, 1), 0
        ), buf4, reinterpret_tensor(buf5, (64, 64), (64, 1), 0
        ), primals_6, primals_4


class BasePolicyNew(nn.Module):
    """
    Base policy network
    """

    def __init__(self, input_dim, out_dim, hidden_dim=64, nonlin=F.
        leaky_relu, norm_in=False, onehot_dim=0):
        """
        Inputs:
            input_dim (int): Number of dimensions in input
            out_dim (int): Number of dimensions in output
            hidden_dim (int): Number of hidden dimensions
            nonlin (PyTorch function): Nonlinearity to apply to hidden layers
        """
        super(BasePolicyNew, self).__init__()
        if norm_in:
            self.in_fn = nn.BatchNorm1d(input_dim, affine=False)
        else:
            self.in_fn = lambda x: x
        self.fc1 = nn.Linear(input_dim + onehot_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
        self.fc3 = nn.Linear(hidden_dim, out_dim)
        self.nonlin = nonlin
        return

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.056363