# 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_1/inductor_cache/mx/cmx6dfvbwwboegeo6cdkzoh6dehxfv227gvzvph6d5f2okoxf6us.py
# Topologically Sorted Source Nodes: [mul_1], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul_1 => mul_1
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, 0.5), kwargs = {})
triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    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 = 0.5
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ff/cffjfugu73gwgosaw5dogfgr2rkm6o5hkmlbc3clibdbj6tbyhvb.py
# Topologically Sorted Source Nodes: [add, mul_2, x], Original ATen: [aten.add, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   mul_2 => mul_2
#   x => add_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select, 1.0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_4, %add), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %select_1), kwargs = {})
triton_poi_fused_add_mul_1 = async_compile.triton('triton_poi_fused_add_mul_1', '''
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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 4
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1 + (8*x2)), None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x1), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (4 + x1 + (8*x2)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + x1), None, eviction_policy='evict_last')
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp5 = tmp1 + tmp4
    tmp6 = tmp5 + tmp3
    tmp7 = tmp0 * tmp6
    tmp10 = tmp9 * tmp3
    tmp11 = tmp8 + tmp10
    tmp12 = tmp7 + tmp11
    tl.store(out_ptr0 + (x3), tmp12, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (8, ), (1, ))
    assert_size_stride(primals_2, (8, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (64, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((8, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_1], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(primals_2, buf0, 32, grid=grid(32), stream=stream0)
        del primals_2
        buf1 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf0, (4, 8), (1, 4), 0), out=buf1)
        del buf0
        buf2 = empty_strided_cuda((64, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, mul_2, x], Original ATen: [aten.add, aten.mul]
        triton_poi_fused_add_mul_1.run(primals_4, buf1, primals_1, buf2, 4096, grid=grid(4096), stream=stream0)
        del buf1
        del primals_1
    return (buf2, primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), )


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((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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
from torch import nn
import torch.nn
import torch.nn.functional as F


class MyLinear(nn.Module):
    """Linear layer with equalized learning rate and custom learning rate multiplier."""

    def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
        False, lrmul=1, bias=True):
        super().__init__()
        he_std = gain * input_size ** -0.5
        if use_wscale:
            init_std = 1.0 / lrmul
            self.w_mul = he_std * lrmul
        else:
            init_std = he_std / lrmul
            self.w_mul = lrmul
        self.weight = torch.nn.Parameter(torch.randn(output_size,
            input_size) * init_std)
        if bias:
            self.bias = torch.nn.Parameter(torch.zeros(output_size))
            self.b_mul = lrmul
        else:
            self.bias = None

    def forward(self, x):
        bias = self.bias
        if bias is not None:
            bias = bias * self.b_mul
        return F.linear(x, self.weight * self.w_mul, bias)


class StyleMod(nn.Module):

    def __init__(self, latent_size, channels, use_wscale):
        super(StyleMod, self).__init__()
        self.lin = MyLinear(latent_size, channels * 2, gain=1.0, use_wscale
            =use_wscale)

    def forward(self, x, latent):
        style = self.lin(latent)
        shape = [-1, 2, x.size(1)] + (x.dim() - 2) * [1]
        style = style.view(shape)
        x = x * (style[:, 0] + 1.0) + style[:, 1]
        return x


def get_inputs():
    return [torch.rand([64, 4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'latent_size': 4, 'channels': 4, 'use_wscale': 1.0}]

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 import nn
import torch.nn
import torch.nn.functional as F
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_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    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 = 0.5
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_mul_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 4
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (x1 + 8 * x2), None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (4 + x1 + 8 * x2), None, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + x1), None, eviction_policy='evict_last')
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp5 = tmp1 + tmp4
    tmp6 = tmp5 + tmp3
    tmp7 = tmp0 * tmp6
    tmp10 = tmp9 * tmp3
    tmp11 = tmp8 + tmp10
    tmp12 = tmp7 + tmp11
    tl.store(out_ptr0 + x3, tmp12, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (8,), (1,))
    assert_size_stride(primals_2, (8, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (64, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((8, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(32)](primals_2, buf0, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(buf0, (4, 8), (1, 4), 0), out=buf1)
        del buf0
        buf2 = empty_strided_cuda((64, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_1[grid(4096)](primals_4, buf1, primals_1,
            buf2, 4096, XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del primals_1
    return buf2, primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0)


class MyLinear(nn.Module):
    """Linear layer with equalized learning rate and custom learning rate multiplier."""

    def __init__(self, input_size, output_size, gain=2 ** 0.5, use_wscale=
        False, lrmul=1, bias=True):
        super().__init__()
        he_std = gain * input_size ** -0.5
        if use_wscale:
            init_std = 1.0 / lrmul
            self.w_mul = he_std * lrmul
        else:
            init_std = he_std / lrmul
            self.w_mul = lrmul
        self.weight = torch.nn.Parameter(torch.randn(output_size,
            input_size) * init_std)
        if bias:
            self.bias = torch.nn.Parameter(torch.zeros(output_size))
            self.b_mul = lrmul
        else:
            self.bias = None

    def forward(self, x):
        bias = self.bias
        if bias is not None:
            bias = bias * self.b_mul
        return F.linear(x, self.weight * self.w_mul, bias)


class StyleModNew(nn.Module):

    def __init__(self, latent_size, channels, use_wscale):
        super(StyleModNew, self).__init__()
        self.lin = MyLinear(latent_size, channels * 2, gain=1.0, use_wscale
            =use_wscale)

    def forward(self, input_0, input_1):
        primals_2 = self.lin.weight
        primals_1 = self.lin.bias
        primals_4 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
