# 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/ho/cho65iisnaf25ldqwazqthm4dk6kkvugfqryyb5hcwumhgthhuzm.py
# Topologically Sorted Source Nodes: [add, add_1, add_2, weight_1], Original ATen: [aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   weight_1 => div
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_4, %slice_8), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %slice_12), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %slice_16), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_2, 4), kwargs = {})
triton_poi_fused_add_div_0 = async_compile.triton('triton_poi_fused_add_div_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=[512], 
    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_div_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_div_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 5) % 5
    x0 = xindex % 5
    x2 = (xindex // 25)
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (x0 + (4*x1) + (16*x2)), tmp10 & xmask, other=0.0)
    tmp12 = 0.1767766952966369
    tmp13 = tmp11 * tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp10, tmp13, tmp14)
    tmp16 = (-1) + x1
    tmp17 = tmp16 >= tmp1
    tmp18 = tmp16 < tmp3
    tmp19 = tmp17 & tmp18
    tmp20 = tmp19 & tmp6
    tmp21 = tmp20 & tmp7
    tmp22 = tl.load(in_ptr0 + ((-4) + x0 + (4*x1) + (16*x2)), tmp21 & xmask, other=0.0)
    tmp23 = tmp22 * tmp12
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp21, tmp23, tmp24)
    tmp26 = tmp15 + tmp25
    tmp27 = (-1) + x0
    tmp28 = tmp27 >= tmp1
    tmp29 = tmp27 < tmp3
    tmp30 = tmp8 & tmp28
    tmp31 = tmp30 & tmp29
    tmp32 = tl.load(in_ptr0 + ((-1) + x0 + (4*x1) + (16*x2)), tmp31 & xmask, other=0.0)
    tmp33 = tmp32 * tmp12
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp31, tmp33, tmp34)
    tmp36 = tmp26 + tmp35
    tmp37 = tmp19 & tmp28
    tmp38 = tmp37 & tmp29
    tmp39 = tl.load(in_ptr0 + ((-5) + x0 + (4*x1) + (16*x2)), tmp38 & xmask, other=0.0)
    tmp40 = tmp39 * tmp12
    tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype)
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp36 + tmp42
    tmp44 = 0.25
    tmp45 = tmp43 * tmp44
    tl.store(in_out_ptr0 + (x4), tmp45, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2q/c2qxl3444r7faal6wdwqwnbo4yy446moujhj4vpwvty2afomxxzq.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   out => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %div, %primals_2, [2, 2], [0, 0], [1, 1], True, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_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=[2048], 
    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_convolution_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_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 121) % 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [add, add_1, add_2, weight_1], Original ATen: [aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_0.run(buf1, primals_1, 400, grid=grid(400), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_3, buf1, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 11, 11), (484, 121, 11, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf3, primals_2, 1936, grid=grid(1936), stream=stream0)
        del primals_2
    return (buf3, primals_3, 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, 4), (64, 16, 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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
from torch.nn import functional as F
from math import sqrt


class FusedUpsample(nn.Module):

    def __init__(self, in_channel, out_channel, kernel_size, padding=0):
        super().__init__()
        weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
        bias = torch.zeros(out_channel)
        fan_in = in_channel * kernel_size * kernel_size
        self.multiplier = sqrt(2 / fan_in)
        self.weight = nn.Parameter(weight)
        self.bias = nn.Parameter(bias)
        self.pad = padding

    def forward(self, input):
        weight = F.pad(self.weight * self.multiplier, [1, 1, 1, 1])
        weight = (weight[:, :, 1:, 1:] + weight[:, :, :-1, 1:] + weight[:,
            :, 1:, :-1] + weight[:, :, :-1, :-1]) / 4
        out = F.conv_transpose2d(input, weight, self.bias, stride=2,
            padding=self.pad)
        return out


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


def get_init_inputs():
    return [[], {'in_channel': 4, 'out_channel': 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
from torch import nn
from math import sqrt
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_add_div_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 5 % 5
    x0 = xindex % 5
    x2 = xindex // 25
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp10 & xmask, other=0.0
        )
    tmp12 = 0.1767766952966369
    tmp13 = tmp11 * tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp10, tmp13, tmp14)
    tmp16 = -1 + x1
    tmp17 = tmp16 >= tmp1
    tmp18 = tmp16 < tmp3
    tmp19 = tmp17 & tmp18
    tmp20 = tmp19 & tmp6
    tmp21 = tmp20 & tmp7
    tmp22 = tl.load(in_ptr0 + (-4 + x0 + 4 * x1 + 16 * x2), tmp21 & xmask,
        other=0.0)
    tmp23 = tmp22 * tmp12
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp21, tmp23, tmp24)
    tmp26 = tmp15 + tmp25
    tmp27 = -1 + x0
    tmp28 = tmp27 >= tmp1
    tmp29 = tmp27 < tmp3
    tmp30 = tmp8 & tmp28
    tmp31 = tmp30 & tmp29
    tmp32 = tl.load(in_ptr0 + (-1 + x0 + 4 * x1 + 16 * x2), tmp31 & xmask,
        other=0.0)
    tmp33 = tmp32 * tmp12
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp31, tmp33, tmp34)
    tmp36 = tmp26 + tmp35
    tmp37 = tmp19 & tmp28
    tmp38 = tmp37 & tmp29
    tmp39 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x2), tmp38 & xmask,
        other=0.0)
    tmp40 = tmp39 * tmp12
    tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype)
    tmp42 = tl.where(tmp38, tmp40, tmp41)
    tmp43 = tmp36 + tmp42
    tmp44 = 0.25
    tmp45 = tmp43 * tmp44
    tl.store(in_out_ptr0 + x4, tmp45, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 1936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 121 % 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_div_0[grid(400)](buf1, primals_1, 400, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_1
        buf2 = extern_kernels.convolution(primals_3, buf1, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 11, 11), (484, 121, 11, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_1[grid(1936)](buf3, primals_2, 1936,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
    return buf3, primals_3, buf1


class FusedUpsampleNew(nn.Module):

    def __init__(self, in_channel, out_channel, kernel_size, padding=0):
        super().__init__()
        weight = torch.randn(in_channel, out_channel, kernel_size, kernel_size)
        bias = torch.zeros(out_channel)
        fan_in = in_channel * kernel_size * kernel_size
        self.multiplier = sqrt(2 / fan_in)
        self.weight = nn.Parameter(weight)
        self.bias = nn.Parameter(bias)
        self.pad = padding

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