# 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_6/inductor_cache/nt/cntluvaffbysyz5pl7cfv7grirm2neo7szmil32jxohuw5bvsqgm.py
# Topologically Sorted Source Nodes: [scales_0, scales_unf_2], Original ATen: [aten.mean, aten.sub]
# Source node to ATen node mapping:
#   scales_0 => mean
#   scales_unf_2 => sub
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%slice_9, [2], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%permute_3, %mean), kwargs = {})
triton_poi_fused_mean_sub_0 = async_compile.triton('triton_poi_fused_mean_sub_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: '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_mean_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_mean_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x6 = xindex
    x3 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x6), xmask)
    tmp1 = tl.load(in_ptr0 + (10 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (26 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (42 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (58 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tl.store(out_ptr0 + (x6), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/pd/cpdt4kam7ixiwhrkhjlxo6qqxlostsz2utlvs2wvamplmmctoc3l.py
# Topologically Sorted Source Nodes: [mul, mul_1, scales_unf_3, scales_unf_4, inp_unf_3], Original ATen: [aten.mul, aten.exp, aten.mean]
# Source node to ATen node mapping:
#   inp_unf_3 => mul_2
#   mul => mul
#   mul_1 => mul_1
#   scales_unf_3 => exp
#   scales_unf_4 => mean_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute_5, -0.5), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %permute_5), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul_1,), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%exp, [2], True), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute_1, %mean_1), kwargs = {})
triton_poi_fused_exp_mean_mul_1 = async_compile.triton('triton_poi_fused_exp_mean_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=[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_exp_mean_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_exp_mean_mul_1(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
    x6 = xindex
    x3 = (xindex // 64)
    x4 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (x6), xmask)
    tmp1 = tl.load(in_ptr1 + (x4 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (16 + x4 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (32 + x4 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (48 + x4 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp2 = -0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3 * tmp1
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7 * tmp6
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp5 + tmp9
    tmp12 = tmp11 * tmp2
    tmp13 = tmp12 * tmp11
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp10 + tmp14
    tmp17 = tmp16 * tmp2
    tmp18 = tmp17 * tmp16
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp15 + tmp19
    tmp21 = 4.0
    tmp22 = tmp20 / tmp21
    tmp23 = tmp0 * tmp22
    tl.store(out_ptr0 + (x6), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/jv/cjvei5rjsycnymo6gngdmxeretv7fd55mbdxjtxprhxn2qrqghyj.py
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.view]
# Source node to ATen node mapping:
#   matmul => view_15
# Graph fragment:
#   %view_15 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%permute_13, [4, 64]), kwargs = {})
triton_poi_fused_view_2 = async_compile.triton('triton_poi_fused_view_2', '''
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_view_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_view_2(in_ptr0, 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 % 64
    x1 = (xindex // 64)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*((x0 % 4) // 4)) + (16*(((4*((x0 // 4) % 4)) + (x0 % 4)) // 16)) + (64*x1)), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ty/ctylqooc7v3esbmsccgqkdfyd5bxgdufxgg6aw63h62dmrgldcfz.py
# Topologically Sorted Source Nodes: [out_unf_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   out_unf_1 => add_4
# Graph fragment:
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%permute_14, %view_17), kwargs = {})
triton_poi_fused_add_3 = async_compile.triton('triton_poi_fused_add_3', '''
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: '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_3', '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_3(in_out_ptr0, in_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_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', 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, (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, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 256, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [scales_0, scales_unf_2], Original ATen: [aten.mean, aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mean_sub_0.run(primals_3, buf0, 256, grid=grid(256), stream=stream0)
        del primals_3
        buf1 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 1, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mul_1, scales_unf_3, scales_unf_4, inp_unf_3], Original ATen: [aten.mul, aten.exp, aten.mean]
        triton_poi_fused_exp_mean_mul_1.run(primals_1, buf0, buf1, 256, grid=grid(256), stream=stream0)
        del primals_1
        buf2 = reinterpret_tensor(buf0, (4, 64), (64, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.view]
        triton_poi_fused_view_2.run(buf1, buf2, 256, grid=grid(256), stream=stream0)
        del buf1
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(buf2, reinterpret_tensor(primals_2, (64, 4), (1, 64), 0), out=buf3)
        del primals_2
        buf4 = reinterpret_tensor(buf3, (4, 4, 1), (4, 1, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [out_unf_1], Original ATen: [aten.add]
        triton_poi_fused_add_3.run(buf4, primals_4, 16, grid=grid(16), stream=stream0)
        del primals_4
    return (reinterpret_tensor(buf4, (4, 4, 1, 1), (4, 1, 4, 4), 0), 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)
    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 math
import torch
from torch import Tensor
import torch.nn as nn
from torch.nn import Parameter


class adaConv2d(nn.Module):

    def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
        'int', stride: 'int'=1, padding: 'int'=0, dilation: 'int'=1, bias:
        'bool'=True):
        super(adaConv2d, self).__init__()
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.bias = bias
        self.weight = Parameter(torch.Tensor(out_channels, in_channels,
            kernel_size, kernel_size))
        if bias:
            self.bias = Parameter(torch.Tensor(out_channels))
        self.reset_parameters()

    def manual_conv(self, inp, kernel_size, stride, padding, dilation, scales):
        """
        :param inp: input feature map
        :param scales: scales for patches
        :return: new feature map
        """
        unfold = nn.Unfold(kernel_size=kernel_size, dilation=dilation,
            padding=padding, stride=stride)
        Hin = inp.shape[2]
        Win = inp.shape[3]
        w = self.weight
        Hout = math.floor((Hin + 2 * padding - dilation * (kernel_size - 1) -
            1) / stride + 1)
        Wout = math.floor((Win + 2 * padding - dilation * (kernel_size - 1) -
            1) / stride + 1)
        inp_unf = unfold(inp)
        n_boxes = inp_unf.shape[-1]
        inp_unf = inp_unf.view(inp.shape[0], inp.shape[1], kernel_size,
            kernel_size, n_boxes)
        inp_unf = inp_unf.permute(0, 4, 1, 2, 3)
        scales_unf = unfold(scales)
        scales_unf = scales_unf.view(scales.shape[0], scales.shape[1],
            kernel_size, kernel_size, scales_unf.shape[-1]).permute(0, 4, 1,
            2, 3)
        center_y, center_x = kernel_size // 2, kernel_size // 2
        scales_0 = torch.mean(scales_unf[:, :, :, center_y:center_y + 1,
            center_x:center_x + 1], axis=2, keepdim=True)
        scales_unf -= scales_0
        scales_unf = torch.exp(-0.5 * scales_unf * scales_unf)
        scales_unf = torch.mean(scales_unf, axis=2, keepdim=True)
        inp_unf *= scales_unf
        inp_unf = inp_unf.permute(0, 2, 3, 4, 1).view(inp.shape[0], inp.
            shape[1] * kernel_size * kernel_size, n_boxes)
        out_unf = inp_unf.transpose(1, 2).matmul(w.view(w.size(0), -1).t()
            ).transpose(1, 2)
        out_unf += self.bias.view(1, self.bias.shape[0], 1)
        output = out_unf.view(inp.shape[0], self.weight.shape[0], Hout, Wout)
        return output

    def reset_parameters(self) ->None:
        """
        init weight and bias
        :return:
        """
        nn.init.xavier_uniform(self.weight)
        if self.bias is not None:
            nn.init.constant_(self.bias.data, 0)

    def forward(self, input, scales=1):
        return self.manual_conv(input, self.kernel_size, self.stride, self.
            padding, self.dilation, scales=scales)


class adaModule(nn.Module):
    """
    paper module
    """

    def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
        'int', stride: 'int'=1, padding: 'int'=0, dilation: 'int'=1):
        super(adaModule, self).__init__()
        self.conv = adaConv2d(in_channels, out_channels, kernel_size=
            kernel_size, dilation=dilation, padding=padding, stride=stride)

    def forward(self, input: 'Tensor', scales: 'Tensor') ->Tensor:
        return self.conv(input, scales=scales)


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
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import torch.nn as nn
from torch.nn import Parameter
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_mean_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x6 = xindex
    x3 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x6, xmask)
    tmp1 = tl.load(in_ptr0 + (10 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (26 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (42 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (58 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tl.store(out_ptr0 + x6, tmp10, xmask)


@triton.jit
def triton_poi_fused_exp_mean_mul_1(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
    x6 = xindex
    x3 = xindex // 64
    x4 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x6, xmask)
    tmp1 = tl.load(in_ptr1 + (x4 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr1 + (16 + x4 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr1 + (32 + x4 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr1 + (48 + x4 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = -0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3 * tmp1
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7 * tmp6
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp5 + tmp9
    tmp12 = tmp11 * tmp2
    tmp13 = tmp12 * tmp11
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp10 + tmp14
    tmp17 = tmp16 * tmp2
    tmp18 = tmp17 * tmp16
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp15 + tmp19
    tmp21 = 4.0
    tmp22 = tmp20 / tmp21
    tmp23 = tmp0 * tmp22
    tl.store(out_ptr0 + x6, tmp23, xmask)


@triton.jit
def triton_poi_fused_view_2(in_ptr0, 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 % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * (x0 % 4 // 4) + 16 * ((4 * (x0 // 4 %
        4) + x0 % 4) // 16) + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_3(in_out_ptr0, in_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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = 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,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 256, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_sub_0[grid(256)](primals_3, buf0, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_3
        buf1 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 1, 16, 4, 1), torch
            .float32)
        triton_poi_fused_exp_mean_mul_1[grid(256)](primals_1, buf0, buf1, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf2 = reinterpret_tensor(buf0, (4, 64), (64, 1), 0)
        del buf0
        triton_poi_fused_view_2[grid(256)](buf1, buf2, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf1
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_2, (64, 4), (1, 
            64), 0), out=buf3)
        del primals_2
        buf4 = reinterpret_tensor(buf3, (4, 4, 1), (4, 1, 1), 0)
        del buf3
        triton_poi_fused_add_3[grid(16)](buf4, primals_4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_4
    return reinterpret_tensor(buf4, (4, 4, 1, 1), (4, 1, 4, 4), 0), buf2


class adaConv2d(nn.Module):

    def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
        'int', stride: 'int'=1, padding: 'int'=0, dilation: 'int'=1, bias:
        'bool'=True):
        super(adaConv2d, self).__init__()
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.bias = bias
        self.weight = Parameter(torch.Tensor(out_channels, in_channels,
            kernel_size, kernel_size))
        if bias:
            self.bias = Parameter(torch.Tensor(out_channels))
        self.reset_parameters()

    def manual_conv(self, inp, kernel_size, stride, padding, dilation, scales):
        """
        :param inp: input feature map
        :param scales: scales for patches
        :return: new feature map
        """
        unfold = nn.Unfold(kernel_size=kernel_size, dilation=dilation,
            padding=padding, stride=stride)
        Hin = inp.shape[2]
        Win = inp.shape[3]
        w = self.weight
        Hout = math.floor((Hin + 2 * padding - dilation * (kernel_size - 1) -
            1) / stride + 1)
        Wout = math.floor((Win + 2 * padding - dilation * (kernel_size - 1) -
            1) / stride + 1)
        inp_unf = unfold(inp)
        n_boxes = inp_unf.shape[-1]
        inp_unf = inp_unf.view(inp.shape[0], inp.shape[1], kernel_size,
            kernel_size, n_boxes)
        inp_unf = inp_unf.permute(0, 4, 1, 2, 3)
        scales_unf = unfold(scales)
        scales_unf = scales_unf.view(scales.shape[0], scales.shape[1],
            kernel_size, kernel_size, scales_unf.shape[-1]).permute(0, 4, 1,
            2, 3)
        center_y, center_x = kernel_size // 2, kernel_size // 2
        scales_0 = torch.mean(scales_unf[:, :, :, center_y:center_y + 1,
            center_x:center_x + 1], axis=2, keepdim=True)
        scales_unf -= scales_0
        scales_unf = torch.exp(-0.5 * scales_unf * scales_unf)
        scales_unf = torch.mean(scales_unf, axis=2, keepdim=True)
        inp_unf *= scales_unf
        inp_unf = inp_unf.permute(0, 2, 3, 4, 1).view(inp.shape[0], inp.
            shape[1] * kernel_size * kernel_size, n_boxes)
        out_unf = inp_unf.transpose(1, 2).matmul(w.view(w.size(0), -1).t()
            ).transpose(1, 2)
        out_unf += self.bias.view(1, self.bias.shape[0], 1)
        output = out_unf.view(inp.shape[0], self.weight.shape[0], Hout, Wout)
        return output

    def reset_parameters(self) ->None:
        """
        init weight and bias
        :return:
        """
        nn.init.xavier_uniform(self.weight)
        if self.bias is not None:
            nn.init.constant_(self.bias.data, 0)

    def forward(self, input, scales=1):
        return self.manual_conv(input, self.kernel_size, self.stride, self.
            padding, self.dilation, scales=scales)


class adaModuleNew(nn.Module):
    """
    paper module
    """

    def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
        'int', stride: 'int'=1, padding: 'int'=0, dilation: 'int'=1):
        super(adaModuleNew, self).__init__()
        self.conv = adaConv2d(in_channels, out_channels, kernel_size=
            kernel_size, dilation=dilation, padding=padding, stride=stride)

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