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 import nn
from torch.nn import Parameter
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_add_copy_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16 % 4
    x1 = xindex // 4 % 4
    x3 = xindex // 64
    x4 = xindex // 4
    x6 = xindex % 16
    x7 = xindex
    tmp3 = tl.load(in_ptr0 + (x1 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x6, xmask, eviction_policy='evict_last')
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp6 + tmp4
    tmp9 = tmp7 + tmp8
    tmp10 = tl.where(tmp2, tmp5, tmp9)
    tl.store(out_ptr0 + x7, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (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((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            primals_3, out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_copy_0[grid(256)](buf0, primals_4, primals_1,
            buf1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_1
        del primals_4
    return buf1, reinterpret_tensor(primals_2, (4, 16), (1, 4), 0)


class LinearPotentialNew(torch.nn.Module):

    def __init__(self, n_features, n_states, init_idx=None, eos_idx=None):
        super(LinearPotentialNew, self).__init__()
        self.transition = Parameter(torch.zeros((n_states, n_states)))
        self.init_idx = init_idx
        self.eos_idx = eos_idx
        self.weight = Parameter(torch.zeros((n_features, n_states)))
        self.bias = Parameter(torch.zeros(n_states))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.xavier_uniform_(self.weight)
        nn.init.xavier_uniform_(self.transition)
        self.bias.data.fill_(0.0)

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


Measured latency_ms: 0.013784