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
import torch.optim
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_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tmp1 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp3 / tmp3
    tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp4, None)


@triton.jit
def triton_poi_fused_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (1,), (1,))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1,), (1,), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(1)](primals_1, buf0, 1, XBLOCK=1,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_sum_1[grid(16)](primals_2, buf0, buf1, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
    return buf1, buf0, primals_2, buf0


class WeightedViewNew(nn.Module):
    """Calculate weighted view
    
    Args:
        num_groups: int, number of groups (views)
        reduce_dimension: bool, default False. If True, reduce dimension dim
        dim: default -1. Only used when reduce_dimension is True
        
    Shape: 
        - Input: if dim is None, (N, num_features*num_groups)
        - Output: (N, num_features)
        
    Attributes:
        weight: (num_groups)
        
    Examples:
    
        >>> model = WeightedView(3)
        >>> x = Variable(torch.randn(1, 6))
        >>> print(model(x))
        >>> model = WeightedView(3, True, 1)
        >>> model(x.view(1,3,2))
    """

    def __init__(self, num_groups, reduce_dimension=False, dim=-1):
        super(WeightedViewNew, self).__init__()
        self.num_groups = num_groups
        self.reduce_dimension = reduce_dimension
        self.dim = dim
        self.weight = nn.Parameter(torch.Tensor(num_groups))
        self.weight.data.uniform_(-1.0 / num_groups, 1.0 / num_groups)

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


Measured latency_ms: 0.027785