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._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
from torch import 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_sin_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_out_ptr0 + x2, xmask)
    tmp5 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl_math.sin(tmp1)
    tmp3 = tmp0 * tmp2
    tmp6 = tmp4 + tmp5
    tmp7 = tmp3 + tmp6
    tl.store(in_out_ptr0 + x2, tmp7, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(
            primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_4, (4, 4),
            (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, primals_6, reinterpret_tensor(
            primals_7, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_7
        del primals_8
        buf3 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_mul_sin_0[grid(16)](buf3, buf0, buf2,
            primals_5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_5
    return buf3, primals_3, primals_6, buf0, buf2


class FiLMLayer_PreSinNew(nn.Module):

    def __init__(self, in_dim, out_dim, style_dim, use_style_fc=True,
        which_linear=nn.Linear, **kwargs):
        super(FiLMLayer_PreSinNew, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.style_dim = style_dim
        self.use_style_fc = use_style_fc
        self.linear = which_linear(in_dim, out_dim)
        nn.init.uniform_(self.linear.weight, -np.sqrt(9 / in_dim), np.sqrt(
            9 / in_dim))
        if use_style_fc:
            self.gain_fc = which_linear(style_dim, out_dim)
            self.bias_fc = which_linear(style_dim, out_dim)
            self.gain_fc.weight.data.mul_(0.25)
            self.gain_fc.bias.data.fill_(1)
            self.bias_fc.weight.data.mul_(0.25)
        else:
            self.style_dim = out_dim * 2
        pass

    def __repr__(self):
        s = (
            f'{self.__class__.__name__}(in_dim={self.in_dim}, out_dim={self.out_dim}, style_dim={self.style_dim}, use_style_fc={self.use_style_fc}, )'
            )
        return s

    def forward(self, input_0, input_1):
        primals_1 = self.linear.weight
        primals_2 = self.linear.bias
        primals_3 = self.gain_fc.weight
        primals_5 = self.gain_fc.bias
        primals_4 = self.bias_fc.weight
        primals_8 = self.bias_fc.bias
        primals_6 = input_0
        primals_7 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]


Measured latency_ms: 0.049773