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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as 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_add_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp7 = tl.load(in_ptr2 + x0, xmask)
    tmp4 = tmp3 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 / tmp5
    tmp8 = tmp6 * tmp7
    tmp9 = 0.0
    tmp10 = tmp8 + tmp9
    tmp11 = tmp1 * tmp10
    tl.store(out_ptr0 + x0, tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (1,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_0[grid(64)](primals_3, primals_1,
            primals_2, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return buf0, primals_1, primals_3, reinterpret_tensor(primals_2, (4, 4,
        4), (16, 4, 1), 0)


class ScalarMixNew(nn.Module):
    """
    Computes a parameterized scalar mixture of :math:`N` tensors, :math:`mixture = \\gamma * \\sum_{k}(s_k * tensor_k)`
    where :math:`s = \\mathrm{softmax}(w)`, with :math:`w` and :math:`\\gamma` scalar parameters.

    Args:
        n_layers (int):
            The number of layers to be mixed, i.e., :math:`N`.
        dropout (float):
            The dropout ratio of the layer weights.
            If dropout > 0, then for each scalar weight, adjusts its softmax weight mass to 0
            with the dropout probability (i.e., setting the unnormalized weight to -inf).
            This effectively redistributes the dropped probability mass to all other weights.
            Default: 0.
    """

    def __init__(self, n_layers, dropout=0):
        super().__init__()
        self.n_layers = n_layers
        self.weights = nn.Parameter(torch.zeros(n_layers))
        self.gamma = nn.Parameter(torch.tensor([1.0]))
        self.dropout = nn.Dropout(dropout)

    def __repr__(self):
        s = f'n_layers={self.n_layers}'
        if self.dropout.p > 0:
            s += f', dropout={self.dropout.p}'
        return f'{self.__class__.__name__}({s})'

    def forward(self, input_0):
        primals_1 = self.weights
        primals_3 = self.gamma
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.024431