# 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/p3/cp32zuxrp2cknaaat4l46gcxlkrjzggsmqqhfyznul7wqfb4ebec.py
# Topologically Sorted Source Nodes: [xx], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   xx => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 4), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[1024], 
    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_0', '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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 16
    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')


# kernel path: runs/run_shard_6/inductor_cache/yw/cyw4t5424bprgroqie5pxshpi27l7d63l2rxaqwlzxu63uoqmxlu.py
# Topologically Sorted Source Nodes: [pow_1, neg, xx_2, xx_3], Original ATen: [aten.pow, aten.neg, aten.exp, aten.div]
# Source node to ATen node mapping:
#   neg => neg
#   pow_1 => pow_1
#   xx_2 => exp
#   xx_3 => div
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%convolution_1, 2), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%pow_1,), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %view_1), kwargs = {})
triton_poi_fused_div_exp_neg_pow_1 = async_compile.triton('triton_poi_fused_div_exp_neg_pow_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=[1024], 
    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_div_exp_neg_pow_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_div_exp_neg_pow_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x1 = (xindex // 16) % 16
    x2 = (xindex // 256)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp4 = tl.load(in_ptr0 + (x0 + (64*(x1 // 4)) + (256*x2)), xmask)
    tmp8 = tl.load(in_ptr0 + (16 + x0 + (64*(x1 // 4)) + (256*x2)), xmask)
    tmp13 = tl.load(in_ptr0 + (32 + x0 + (64*(x1 // 4)) + (256*x2)), xmask)
    tmp18 = tl.load(in_ptr0 + (48 + x0 + (64*(x1 // 4)) + (256*x2)), xmask)
    tmp1 = tmp0 * tmp0
    tmp2 = -tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp4 * tmp4
    tmp6 = -tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp8 * tmp8
    tmp10 = -tmp9
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp7 + tmp11
    tmp14 = tmp13 * tmp13
    tmp15 = -tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp12 + tmp16
    tmp19 = tmp18 * tmp18
    tmp20 = -tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp17 + tmp21
    tmp23 = 9.999999747378752e-06
    tmp24 = tmp22 + tmp23
    tmp25 = tmp3 / tmp24
    tl.store(out_ptr0 + (x3), tmp25, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/35/c35cbqrbqo4v4atcenugxqrmtp6yss7r5x474kr35mnw7tigbh5z.py
# Topologically Sorted Source Nodes: [xx_4], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   xx_4 => avg_pool2d
# Graph fragment:
#   %avg_pool2d : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%div, [4, 4], [4, 4], [0, 0], False, False), kwargs = {})
triton_poi_fused_avg_pool2d_2 = async_compile.triton('triton_poi_fused_avg_pool2d_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=[64], 
    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_avg_pool2d_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, '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_avg_pool2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (16*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp8 = tmp7 + tmp6
    tmp10 = tmp9 + tmp8
    tmp12 = tmp11 + tmp10
    tmp14 = tmp13 + tmp12
    tmp16 = tmp15 + tmp14
    tmp18 = tmp17 + tmp16
    tmp20 = tmp19 + tmp18
    tmp22 = tmp21 + tmp20
    tmp24 = tmp23 + tmp22
    tmp26 = tmp25 + tmp24
    tmp28 = tmp27 + tmp26
    tmp30 = tmp29 + tmp28
    tmp31 = 0.0625
    tmp32 = tmp30 * tmp31
    tl.store(out_ptr0 + (x0), tmp32, 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, (16, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (16, 1, 1, 1), (1, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [xx], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf0, (4, 16, 4, 4), (256, 16, 4, 1))
        del primals_1
        del primals_3
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [xx], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 1024, grid=grid(1024), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [xx_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=16, bias=None)
        assert_size_stride(buf2, (4, 16, 4, 4), (256, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, neg, xx_2, xx_3], Original ATen: [aten.pow, aten.neg, aten.exp, aten.div]
        triton_poi_fused_div_exp_neg_pow_1.run(buf2, buf3, 1024, grid=grid(1024), stream=stream0)
        buf4 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [xx_4], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_2.run(buf3, buf4, 64, grid=grid(64), stream=stream0)
    return (buf4, primals_4, buf1, buf2, buf3, )


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((16, 1, 1, 1), (1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, ), (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((16, 1, 1, 1), (1, 1, 1, 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
import numpy as np
import torch.nn as nn


class HistogramLayerUNET(nn.Module):

    def __init__(self, in_channels, kernel_size, dim=2, num_bins=4, stride=
        None, padding=0, normalize_count=True, normalize_bins=True,
        count_include_pad=False, ceil_mode=False, skip_connection=False):
        super(HistogramLayerUNET, self).__init__()
        self.in_channels = in_channels
        self.numBins = num_bins
        if stride is None:
            self.stride = kernel_size
        else:
            self.stride = stride
        self.kernel_size = kernel_size
        self.dim = dim
        self.padding = padding
        self.normalize_count = normalize_count
        self.normalize_bins = normalize_bins
        self.count_include_pad = count_include_pad
        self.ceil_mode = ceil_mode
        self.skip_connection = skip_connection
        if self.dim == 1:
            self.bin_centers_conv = nn.Conv1d(self.in_channels, self.
                numBins * self.in_channels, 1, groups=self.in_channels,
                bias=True)
            self.bin_centers_conv.weight.data.fill_(1)
            self.bin_centers_conv.weight.requires_grad = False
            self.bin_widths_conv = nn.Conv1d(self.numBins * self.
                in_channels, self.numBins * self.in_channels, 1, groups=
                self.numBins * self.in_channels, bias=False)
            self.hist_pool = nn.AvgPool1d(self.filt_dim, stride=self.stride,
                padding=self.padding, ceil_mode=self.ceil_mode,
                count_include_pad=self.count_include_pad)
            self.centers = self.bin_centers_conv.bias
            self.widths = self.bin_widths_conv.weight
        elif self.dim == 2:
            self.bin_centers_conv = nn.Conv2d(self.in_channels, self.
                numBins * self.in_channels, 1, groups=self.in_channels,
                bias=True)
            self.bin_centers_conv.weight.data.fill_(1)
            self.bin_centers_conv.weight.requires_grad = False
            self.bin_widths_conv = nn.Conv2d(self.numBins * self.
                in_channels, self.numBins * self.in_channels, 1, groups=
                self.numBins * self.in_channels, bias=False)
            self.hist_pool = nn.AvgPool2d(self.kernel_size, stride=self.
                stride, padding=self.padding, ceil_mode=self.ceil_mode,
                count_include_pad=self.count_include_pad)
            self.centers = self.bin_centers_conv.bias
            self.widths = self.bin_widths_conv.weight
        elif self.dim == 3:
            self.bin_centers_conv = nn.Conv3d(self.in_channels, self.
                numBins * self.in_channels, 1, groups=self.in_channels,
                bias=True)
            self.bin_centers_conv.weight.data.fill_(1)
            self.bin_centers_conv.weight.requires_grad = False
            self.bin_widths_conv = nn.Conv3d(self.numBins * self.
                in_channels, self.numBins * self.in_channels, 1, groups=
                self.numBins * self.in_channels, bias=False)
            self.hist_pool = nn.AvgPool3d(self.filt_dim, stride=self.stride,
                padding=self.padding, ceil_mode=self.ceil_mode,
                count_include_pad=self.count_include_pad)
            self.centers = self.bin_centers_conv.bias
            self.widths = self.bin_widths_conv.weight
        else:
            raise RuntimeError('Invalid dimension for histogram layer')

    def forward(self, xx):
        xx = self.bin_centers_conv(xx)
        xx = self.bin_widths_conv(xx)
        xx = torch.exp(-xx ** 2)
        if self.normalize_bins:
            xx = self.constrain_bins(xx)
        if not self.skip_connection:
            if self.normalize_count:
                xx = self.hist_pool(xx)
            else:
                xx = np.prod(np.asarray(self.hist_pool.kernel_size)
                    ) * self.hist_pool(xx)
        else:
            pass
        return xx

    def constrain_bins(self, xx):
        if self.dim == 1:
            n, c, l = xx.size()
            xx_sum = xx.reshape(n, c // self.numBins, self.numBins, l).sum(2
                ) + torch.tensor(1e-05)
            xx_sum = torch.repeat_interleave(xx_sum, self.numBins, dim=1)
            xx = xx / xx_sum
        elif self.dim == 2:
            n, c, h, w = xx.size()
            xx_sum = xx.reshape(n, c // self.numBins, self.numBins, h, w).sum(2
                ) + torch.tensor(1e-05)
            xx_sum = torch.repeat_interleave(xx_sum, self.numBins, dim=1)
            xx = xx / xx_sum
        elif self.dim == 3:
            n, c, d, h, w = xx.size()
            xx_sum = xx.reshape(n, c // self.numBins, self.numBins, d, h, w
                ).sum(2) + torch.tensor(1e-05)
            xx_sum = torch.repeat_interleave(xx_sum, self.numBins, dim=1)
            xx = xx / xx_sum
        else:
            raise RuntimeError('Invalid dimension for histogram layer')
        return xx


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


def get_init_inputs():
    return [[], {'in_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 torch.nn as nn
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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 16
    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)


@triton.jit
def triton_poi_fused_div_exp_neg_pow_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x1 = xindex // 16 % 16
    x2 = xindex // 256
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp4 = tl.load(in_ptr0 + (x0 + 64 * (x1 // 4) + 256 * x2), xmask)
    tmp8 = tl.load(in_ptr0 + (16 + x0 + 64 * (x1 // 4) + 256 * x2), xmask)
    tmp13 = tl.load(in_ptr0 + (32 + x0 + 64 * (x1 // 4) + 256 * x2), xmask)
    tmp18 = tl.load(in_ptr0 + (48 + x0 + 64 * (x1 // 4) + 256 * x2), xmask)
    tmp1 = tmp0 * tmp0
    tmp2 = -tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp4 * tmp4
    tmp6 = -tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp8 * tmp8
    tmp10 = -tmp9
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp7 + tmp11
    tmp14 = tmp13 * tmp13
    tmp15 = -tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp12 + tmp16
    tmp19 = tmp18 * tmp18
    tmp20 = -tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp17 + tmp21
    tmp23 = 9.999999747378752e-06
    tmp24 = tmp22 + tmp23
    tmp25 = tmp3 / tmp24
    tl.store(out_ptr0 + x3, tmp25, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp7 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp29 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp8 = tmp7 + tmp6
    tmp10 = tmp9 + tmp8
    tmp12 = tmp11 + tmp10
    tmp14 = tmp13 + tmp12
    tmp16 = tmp15 + tmp14
    tmp18 = tmp17 + tmp16
    tmp20 = tmp19 + tmp18
    tmp22 = tmp21 + tmp20
    tmp24 = tmp23 + tmp22
    tmp26 = tmp25 + tmp24
    tmp28 = tmp27 + tmp26
    tmp30 = tmp29 + tmp28
    tmp31 = 0.0625
    tmp32 = tmp30 * tmp31
    tl.store(out_ptr0 + x0, tmp32, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (16, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (16, 1, 1, 1), (1, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf0, (4, 16, 4, 4), (256, 16, 4, 1))
        del primals_1
        del primals_3
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(1024)](buf1, primals_2, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=16, bias=None)
        assert_size_stride(buf2, (4, 16, 4, 4), (256, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32
            )
        triton_poi_fused_div_exp_neg_pow_1[grid(1024)](buf2, buf3, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        triton_poi_fused_avg_pool2d_2[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
    return buf4, primals_4, buf1, buf2, buf3


class HistogramLayerUNETNew(nn.Module):

    def __init__(self, in_channels, kernel_size, dim=2, num_bins=4, stride=
        None, padding=0, normalize_count=True, normalize_bins=True,
        count_include_pad=False, ceil_mode=False, skip_connection=False):
        super(HistogramLayerUNETNew, self).__init__()
        self.in_channels = in_channels
        self.numBins = num_bins
        if stride is None:
            self.stride = kernel_size
        else:
            self.stride = stride
        self.kernel_size = kernel_size
        self.dim = dim
        self.padding = padding
        self.normalize_count = normalize_count
        self.normalize_bins = normalize_bins
        self.count_include_pad = count_include_pad
        self.ceil_mode = ceil_mode
        self.skip_connection = skip_connection
        if self.dim == 1:
            self.bin_centers_conv = nn.Conv1d(self.in_channels, self.
                numBins * self.in_channels, 1, groups=self.in_channels,
                bias=True)
            self.bin_centers_conv.weight.data.fill_(1)
            self.bin_centers_conv.weight.requires_grad = False
            self.bin_widths_conv = nn.Conv1d(self.numBins * self.
                in_channels, self.numBins * self.in_channels, 1, groups=
                self.numBins * self.in_channels, bias=False)
            self.hist_pool = nn.AvgPool1d(self.filt_dim, stride=self.stride,
                padding=self.padding, ceil_mode=self.ceil_mode,
                count_include_pad=self.count_include_pad)
            self.centers = self.bin_centers_conv.bias
            self.widths = self.bin_widths_conv.weight
        elif self.dim == 2:
            self.bin_centers_conv = nn.Conv2d(self.in_channels, self.
                numBins * self.in_channels, 1, groups=self.in_channels,
                bias=True)
            self.bin_centers_conv.weight.data.fill_(1)
            self.bin_centers_conv.weight.requires_grad = False
            self.bin_widths_conv = nn.Conv2d(self.numBins * self.
                in_channels, self.numBins * self.in_channels, 1, groups=
                self.numBins * self.in_channels, bias=False)
            self.hist_pool = nn.AvgPool2d(self.kernel_size, stride=self.
                stride, padding=self.padding, ceil_mode=self.ceil_mode,
                count_include_pad=self.count_include_pad)
            self.centers = self.bin_centers_conv.bias
            self.widths = self.bin_widths_conv.weight
        elif self.dim == 3:
            self.bin_centers_conv = nn.Conv3d(self.in_channels, self.
                numBins * self.in_channels, 1, groups=self.in_channels,
                bias=True)
            self.bin_centers_conv.weight.data.fill_(1)
            self.bin_centers_conv.weight.requires_grad = False
            self.bin_widths_conv = nn.Conv3d(self.numBins * self.
                in_channels, self.numBins * self.in_channels, 1, groups=
                self.numBins * self.in_channels, bias=False)
            self.hist_pool = nn.AvgPool3d(self.filt_dim, stride=self.stride,
                padding=self.padding, ceil_mode=self.ceil_mode,
                count_include_pad=self.count_include_pad)
            self.centers = self.bin_centers_conv.bias
            self.widths = self.bin_widths_conv.weight
        else:
            raise RuntimeError('Invalid dimension for histogram layer')

    def constrain_bins(self, xx):
        if self.dim == 1:
            n, c, l = xx.size()
            xx_sum = xx.reshape(n, c // self.numBins, self.numBins, l).sum(2
                ) + torch.tensor(1e-05)
            xx_sum = torch.repeat_interleave(xx_sum, self.numBins, dim=1)
            xx = xx / xx_sum
        elif self.dim == 2:
            n, c, h, w = xx.size()
            xx_sum = xx.reshape(n, c // self.numBins, self.numBins, h, w).sum(2
                ) + torch.tensor(1e-05)
            xx_sum = torch.repeat_interleave(xx_sum, self.numBins, dim=1)
            xx = xx / xx_sum
        elif self.dim == 3:
            n, c, d, h, w = xx.size()
            xx_sum = xx.reshape(n, c // self.numBins, self.numBins, d, h, w
                ).sum(2) + torch.tensor(1e-05)
            xx_sum = torch.repeat_interleave(xx_sum, self.numBins, dim=1)
            xx = xx / xx_sum
        else:
            raise RuntimeError('Invalid dimension for histogram layer')
        return xx

    def forward(self, input_0):
        primals_2 = self.centers
        primals_1 = self.widths
        primals_4 = self.bin_centers_conv.weight
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
