# 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_7/inductor_cache/p3/cp3qleddjiuuytozrtebx5pzf2ycpwtw4mkq2jsx7qqswymv2bm6.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul
# Graph fragment:
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %primals_2), 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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': [], '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_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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 + (x0), xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mv/cmvn76bzl7ybw2kvfc2lhvwgawc45yfp7nogxqi7x5o2n5qsklcs.py
# Topologically Sorted Source Nodes: [output, no_update_holes, mask_sum, sub, truediv, output_pre, output_1, new_mask, new_mask_1], Original ATen: [aten.convolution, aten.eq, aten.masked_fill, aten.sub, aten.div, aten.add, aten.ones_like]
# Source node to ATen node mapping:
#   mask_sum => full_default, where
#   new_mask => full_default_2
#   new_mask_1 => where_2
#   no_update_holes => eq
#   output => convolution
#   output_1 => full_default_1, where_1
#   output_pre => add
#   sub => sub
#   truediv => div
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mul, %primals_3, %primals_4, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %eq : [num_users=3] = call_function[target=torch.ops.aten.eq.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %convolution_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution, %expand), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %where), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %expand), kwargs = {})
#   %full_default_1 : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default_1, %add), kwargs = {})
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 1, 1], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default_1, %full_default_2), kwargs = {})
triton_poi_fused_add_convolution_div_eq_masked_fill_ones_like_sub_1 = async_compile.triton('triton_poi_fused_add_convolution_div_eq_masked_fill_ones_like_sub_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=[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_convolution_div_eq_masked_fill_ones_like_sub_1', 'mutated_arg_names': ['in_out_ptr0'], '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_add_convolution_div_eq_masked_fill_ones_like_sub_1(in_out_ptr0, 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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp4 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp5 - tmp4
    tmp7 = 1.0
    tmp8 = tl.where(tmp2, tmp7, tmp0)
    tmp9 = tmp6 / tmp8
    tmp10 = tmp9 + tmp4
    tmp11 = tl.where(tmp2, tmp1, tmp10)
    tmp12 = tl.where(tmp2, tmp1, tmp7)
    tl.store(in_out_ptr0 + (x2), tmp11, xmask)
    tl.store(out_ptr0 + (x2), tmp12, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(primals_1, primals_2, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1, 1), (4, 1, 1, 1))
        # Topologically Sorted Source Nodes: [output_mask], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_2, 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, 1, 1), (4, 1, 1, 1))
        del primals_2
        del primals_5
        buf3 = buf1; del buf1  # reuse
        buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output, no_update_holes, mask_sum, sub, truediv, output_pre, output_1, new_mask, new_mask_1], Original ATen: [aten.convolution, aten.eq, aten.masked_fill, aten.sub, aten.div, aten.add, aten.ones_like]
        triton_poi_fused_add_convolution_div_eq_masked_fill_ones_like_sub_1.run(buf3, buf2, primals_4, buf4, 16, grid=grid(16), stream=stream0)
        del primals_4
    return (buf3, buf4, primals_3, 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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 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((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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.parallel
import torch.utils.data


def weights_init(init_type='gaussian'):

    def init_fun(m):
        classname = m.__class__.__name__
        if (classname.find('Conv') == 0 or classname.find('Linear') == 0
            ) and hasattr(m, 'weight'):
            if init_type == 'gaussian':
                nn.init.normal_(m.weight, 0.0, 0.02)
            elif init_type == 'xavier':
                nn.init.xavier_normal_(m.weight, gain=math.sqrt(2))
            elif init_type == 'kaiming':
                nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in')
            elif init_type == 'orthogonal':
                nn.init.orthogonal_(m.weight, gain=math.sqrt(2))
            elif init_type == 'default':
                pass
            else:
                assert 0, 'Unsupported initialization: {}'.format(init_type)
            if hasattr(m, 'bias') and m.bias is not None:
                nn.init.constant_(m.bias, 0.0)
    return init_fun


class PartialConv(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=True):
        super().__init__()
        self.input_conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding, dilation, groups, bias)
        self.mask_conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding, dilation, groups, False)
        self.input_conv.apply(weights_init('kaiming'))
        torch.nn.init.constant_(self.mask_conv.weight, 1.0)
        for param in self.mask_conv.parameters():
            param.requires_grad = False

    def forward(self, input, mask):
        output = self.input_conv(input * mask)
        if self.input_conv.bias is not None:
            output_bias = self.input_conv.bias.view(1, -1, 1, 1).expand_as(
                output)
        else:
            output_bias = torch.zeros_like(output)
        with torch.no_grad():
            output_mask = self.mask_conv(mask)
        no_update_holes = output_mask == 0
        mask_sum = output_mask.masked_fill_(no_update_holes, 1.0)
        output_pre = (output - output_bias) / mask_sum + output_bias
        output = output_pre.masked_fill_(no_update_holes, 0.0)
        new_mask = torch.ones_like(output)
        new_mask = new_mask.masked_fill_(no_update_holes, 0.0)
        return output, new_mask


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 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.parallel
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_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    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 + x0, xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_convolution_div_eq_masked_fill_ones_like_sub_1(
    in_out_ptr0, 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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_out_ptr0 + x2, xmask)
    tmp4 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp5 - tmp4
    tmp7 = 1.0
    tmp8 = tl.where(tmp2, tmp7, tmp0)
    tmp9 = tmp6 / tmp8
    tmp10 = tmp9 + tmp4
    tmp11 = tl.where(tmp2, tmp1, tmp10)
    tmp12 = tl.where(tmp2, tmp1, tmp7)
    tl.store(in_out_ptr0 + x2, tmp11, xmask)
    tl.store(out_ptr0 + x2, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(256)](primals_1, primals_2, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1, 1), (4, 1, 1, 1))
        buf2 = extern_kernels.convolution(primals_2, 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, 1, 1), (4, 1, 1, 1))
        del primals_2
        del primals_5
        buf3 = buf1
        del buf1
        buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_add_convolution_div_eq_masked_fill_ones_like_sub_1[
            grid(16)](buf3, buf2, primals_4, buf4, 16, XBLOCK=16, num_warps
            =1, num_stages=1)
        del primals_4
    return buf3, buf4, primals_3, buf0, buf2


def weights_init(init_type='gaussian'):

    def init_fun(m):
        classname = m.__class__.__name__
        if (classname.find('Conv') == 0 or classname.find('Linear') == 0
            ) and hasattr(m, 'weight'):
            if init_type == 'gaussian':
                nn.init.normal_(m.weight, 0.0, 0.02)
            elif init_type == 'xavier':
                nn.init.xavier_normal_(m.weight, gain=math.sqrt(2))
            elif init_type == 'kaiming':
                nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in')
            elif init_type == 'orthogonal':
                nn.init.orthogonal_(m.weight, gain=math.sqrt(2))
            elif init_type == 'default':
                pass
            else:
                assert 0, 'Unsupported initialization: {}'.format(init_type)
            if hasattr(m, 'bias') and m.bias is not None:
                nn.init.constant_(m.bias, 0.0)
    return init_fun


class PartialConvNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=True):
        super().__init__()
        self.input_conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding, dilation, groups, bias)
        self.mask_conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding, dilation, groups, False)
        self.input_conv.apply(weights_init('kaiming'))
        torch.nn.init.constant_(self.mask_conv.weight, 1.0)
        for param in self.mask_conv.parameters():
            param.requires_grad = False

    def forward(self, input_0, input_1):
        primals_1 = self.input_conv.weight
        primals_4 = self.input_conv.bias
        primals_2 = self.mask_conv.weight
        primals_3 = input_0
        primals_5 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]
