# 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/3f/c3fzeyeopdwc2ltlmlivpocuknebcj6ej2cz3ueq4yldr3lwgorg.py
# Topologically Sorted Source Nodes: [modulation, x], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   modulation => mul
#   x => mul_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, 0.5), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_4), 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=[256], 
    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_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = (xindex // 16)
    x1 = (xindex // 16) % 4
    x0 = xindex % 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tl.store(out_ptr0 + (x4), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/at/catyivxdqgliwunkrygqmudtr3fumc4lgfzxwaogch2z5s2rnn7a.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   out => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution, %primals_6), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, 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 = 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, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_6, (1, 4, 1, 1), (4, 1, 1, 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: [], Original ATen: []
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [modulation, x], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(buf0, primals_2, primals_4, buf1, 256, grid=grid(256), stream=stream0)
        del buf0
        del primals_2
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.add]
        triton_poi_fused_add_1.run(buf3, primals_6, 256, grid=grid(256), stream=stream0)
        del primals_6
    return (buf3, primals_3, primals_4, primals_5, buf1, )


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, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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 math
import torch
import torch.nn as nn
import torch.nn.functional as F


class ModulatedConv2d(nn.Module):

    def __init__(self, channels_in, channels_out, style_dim, kernel_size,
        demodulate=True):
        super().__init__()
        self.weight = nn.Parameter(torch.randn(channels_out, channels_in,
            kernel_size, kernel_size))
        self.modulation = nn.Linear(style_dim, channels_in, bias=True)
        self.modulation.bias.data.fill_(1.0)
        self.demodulate = demodulate
        if self.demodulate:
            self.register_buffer('style_inv', torch.randn(1, 1, channels_in,
                1, 1))
        self.scale = 1.0 / math.sqrt(channels_in * kernel_size ** 2)
        self.padding = kernel_size // 2

    def forward(self, x, style):
        modulation = self.get_modulation(style)
        x = modulation * x
        x = F.conv2d(x, self.weight, padding=self.padding)
        if self.demodulate:
            demodulation = self.get_demodulation(style)
            x = demodulation * x
        return x

    def get_modulation(self, style):
        style = self.modulation(style).view(style.size(0), -1, 1, 1)
        modulation = self.scale * style
        return modulation

    def get_demodulation(self, style):
        w = self.weight.unsqueeze(0)
        norm = torch.rsqrt((self.scale * self.style_inv * w).pow(2).sum([2,
            3, 4]) + 1e-08)
        demodulation = norm
        return demodulation.view(*demodulation.size(), 1, 1)


class MultichannelIamge(nn.Module):

    def __init__(self, channels_in, channels_out, style_dim, kernel_size=1):
        super().__init__()
        self.conv = ModulatedConv2d(channels_in, channels_out, style_dim,
            kernel_size, demodulate=False)
        self.bias = nn.Parameter(torch.zeros(1, channels_out, 1, 1))

    def forward(self, hidden, style):
        out = self.conv(hidden, style)
        out = out + self.bias
        return out


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


def get_init_inputs():
    return [[], {'channels_in': 4, 'channels_out': 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
import math
import torch.nn as 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, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 16
    x1 = xindex // 16 % 4
    x0 = xindex % 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tl.store(out_ptr0 + x4, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_6, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(256)](buf0, primals_2, primals_4, buf1,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_add_1[grid(256)](buf3, primals_6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_6
    return buf3, primals_3, primals_4, primals_5, buf1


class ModulatedConv2d(nn.Module):

    def __init__(self, channels_in, channels_out, style_dim, kernel_size,
        demodulate=True):
        super().__init__()
        self.weight = nn.Parameter(torch.randn(channels_out, channels_in,
            kernel_size, kernel_size))
        self.modulation = nn.Linear(style_dim, channels_in, bias=True)
        self.modulation.bias.data.fill_(1.0)
        self.demodulate = demodulate
        if self.demodulate:
            self.register_buffer('style_inv', torch.randn(1, 1, channels_in,
                1, 1))
        self.scale = 1.0 / math.sqrt(channels_in * kernel_size ** 2)
        self.padding = kernel_size // 2

    def forward(self, x, style):
        modulation = self.get_modulation(style)
        x = modulation * x
        x = F.conv2d(x, self.weight, padding=self.padding)
        if self.demodulate:
            demodulation = self.get_demodulation(style)
            x = demodulation * x
        return x

    def get_modulation(self, style):
        style = self.modulation(style).view(style.size(0), -1, 1, 1)
        modulation = self.scale * style
        return modulation

    def get_demodulation(self, style):
        w = self.weight.unsqueeze(0)
        norm = torch.rsqrt((self.scale * self.style_inv * w).pow(2).sum([2,
            3, 4]) + 1e-08)
        demodulation = norm
        return demodulation.view(*demodulation.size(), 1, 1)


class MultichannelIamgeNew(nn.Module):

    def __init__(self, channels_in, channels_out, style_dim, kernel_size=1):
        super().__init__()
        self.conv = ModulatedConv2d(channels_in, channels_out, style_dim,
            kernel_size, demodulate=False)
        self.bias = nn.Parameter(torch.zeros(1, channels_out, 1, 1))

    def forward(self, input_0, input_1):
        primals_6 = self.bias
        primals_5 = self.conv.weight
        primals_1 = self.conv.modulation.weight
        primals_2 = self.conv.modulation.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
