# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/dk/cdk72ukckc7pl742ptr7uq6co2phfp2vo22pyues7whuukhyywf4.py
# Topologically Sorted Source Nodes: [x_1, mul, out], Original ATen: [aten.sin, aten.mul, aten.add]
# Source node to ATen node mapping:
#   mul => mul
#   out => add
#   x_1 => sin
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_5), kwargs = {})
#   %sin : [num_users=1] = call_function[target=torch.ops.aten.sin.default](args = (%addmm_2,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%addmm, %sin), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %add_tensor), kwargs = {})
triton_poi_fused_add_mul_sin_0 = async_compile.triton('triton_poi_fused_add_mul_sin_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sin_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [gain], Original ATen: [aten.addmm]
        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)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.addmm]
        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  # reuse
        # Topologically Sorted Source Nodes: [x_1, mul, out], Original ATen: [aten.sin, aten.mul, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_sin_0.run(buf3, buf0, buf2, primals_5, 16, grid=grid(16), stream=stream0)
        del primals_5
    return (buf3, primals_3, primals_6, buf0, buf2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import numpy as np
import torch.nn as nn


class FiLMLayer_PreSin(nn.Module):

    def __init__(self, in_dim, out_dim, style_dim, use_style_fc=True,
        which_linear=nn.Linear, **kwargs):
        super(FiLMLayer_PreSin, 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 forward(self, x, style):
        """

    :param x: (b, c) or (b, n, c)
    :param style: (b, c)
    :return:
    """
        if self.use_style_fc:
            gain = self.gain_fc(style)
            bias = self.bias_fc(style)
        else:
            style = rearrange(style, 'b (n c) -> b n c', n=2)
            gain, bias = style.unbind(dim=1)
        if x.dim() == 3:
            gain = rearrange(gain, 'b c -> b 1 c')
            bias = rearrange(bias, 'b c -> b 1 c')
        elif x.dim() == 2:
            pass
        else:
            assert 0
        x = self.linear(x)
        x = torch.sin(x)
        out = gain * x + bias
        return out

    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 get_inputs():
    return [torch.rand([4, 4]), torch.rand([4, 4])]


def get_init_inputs():
    return [[], {'in_dim': 4, 'out_dim': 4, 'style_dim': 4}]

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
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_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]
