import torch
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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0


@triton.jit
def triton_per_fused__to_copy_cumsum_ne_0(in_ptr0, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 16
    x1 = xindex // 16
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * r2 + 64 * x1), xmask, other=0.0)
    tmp1 = 4.0
    tmp2 = tmp0 != tmp1
    tmp3 = tmp2.to(tl.int32)
    tmp4 = tmp3.to(tl.int64)
    tmp5 = tmp4.to(tl.int64)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp7, = tl.associative_scan((tmp6,), 1, _triton_helper_fn_add0)
    tl.store(out_ptr0 + (x0 + 16 * r2 + 64 * x1), tmp7, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_mul_ne_1(in_out_ptr0, in_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_out_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0.to(tl.int32)
    tmp3 = 4.0
    tmp4 = tmp2 != tmp3
    tmp5 = tmp4.to(tl.int32)
    tmp6 = tmp1 * tmp5
    tmp7 = tmp6.to(tl.int64)
    tmp8 = tl.full([1], 4, tl.int64)
    tmp9 = tmp7 + tmp8
    tl.store(in_out_ptr0 + x0, tmp9, xmask)


@triton.jit
def triton_poi_fused_embedding_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 9, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 9) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 9')
    tmp6 = tl.load(in_ptr1 + (x0 + 4 * tmp4), xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (9, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.int64)
        get_raw_stream(0)
        triton_per_fused__to_copy_cumsum_ne_0[grid(64)](primals_1, buf0, 64,
            4, XBLOCK=1, num_warps=2, num_stages=1)
        buf1 = buf0
        del buf0
        triton_poi_fused__to_copy_add_mul_ne_1[grid(256)](buf1, primals_1, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_embedding_2[grid(1024)](buf1, primals_2, buf2, 
            1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
    return buf2, buf1, buf1


def create_position_ids_from_input_ids(input_ids, padding_idx):
    """ Replace non-padding symbols with their position numbers. Position numbers begin at
    padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
    `utils.make_positions`.

    :param torch.Tensor x:
    :return torch.Tensor:
    """
    mask = input_ids.ne(padding_idx).int()
    incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
    return incremental_indices.long() + padding_idx


class LearnedPositionalEmbeddingNew(nn.Embedding):
    """
    This module learns positional embeddings up to a fixed maximum size.
    Padding ids are ignored by either offsetting based on padding_idx
    or by setting padding_idx to None and ensuring that the appropriate
    position ids are passed to the forward function.
    """

    def __init__(self, num_embeddings: 'int', embedding_dim: 'int',
        padding_idx: 'int'):
        assert padding_idx is not None
        num_embeddings += padding_idx + 1
        super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx
            )

    def forward(self, input_0):
        primals_2 = self.weight
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0], output[1]


Measured latency_ms: 0.03898