# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/yx/cyx5u6kg47bcb2a4mvrlkw5ynd42h4mj76hk2j6tveptehbkmic4.py
# Topologically Sorted Source Nodes: [sub, pow_1, sum_1, scaled_l2_norm], Original ATen: [aten.sub, aten.pow, aten.sum, aten.mul]
# Source node to ATen node mapping:
#   pow_1 => pow_1
#   scaled_l2_norm => mul
#   sub => sub
#   sum_1 => sum_1
# Graph fragment:
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%expand, %view_2), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [3]), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %sum_1), kwargs = {})
triton_poi_fused_mul_pow_sub_sum_0 = async_compile.triton('triton_poi_fused_mul_pow_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_pow_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_pow_sub_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 16
    x2 = (xindex // 64)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (4*x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (16 + x1 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (32 + x1 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (48 + x1 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp4 = tmp3 * tmp3
    tmp7 = tmp5 - tmp6
    tmp8 = tmp7 * tmp7
    tmp9 = tmp4 + tmp8
    tmp12 = tmp10 - tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tmp9 + tmp13
    tmp17 = tmp15 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp14 + tmp18
    tmp20 = tmp0 * tmp19
    tl.store(out_ptr0 + (x4), tmp20, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hz/chz2sqsqk26mwhf2dxhgh44jfpu2er5yqjftwkzfav5ctqtx5e7f.py
# Topologically Sorted Source Nodes: [assigment_weights], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   assigment_weights => amax, exp, sub_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul, [2], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_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__softmax_1(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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3f/c3fx6bzkalkw7u7askqdnz4rzlcoyqiec4r434sjc5x3axxgkrmr.py
# Topologically Sorted Source Nodes: [assigment_weights], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   assigment_weights => div, sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_2), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_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__softmax_2', '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__softmax_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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/36/c36bunrpokrzs5svt4e6kwmfyyitmjh7nivu5rc6sidx55znumrf.py
# Topologically Sorted Source Nodes: [sub, mul_1, encoded_feat], Original ATen: [aten.sub, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   encoded_feat => sum_3
#   mul_1 => mul_1
#   sub => sub
# Graph fragment:
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%expand, %view_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze_2, %sub), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [1]), kwargs = {})
triton_per_fused_mul_sub_sum_3 = async_compile.triton('triton_per_fused_mul_sub_sum_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.persistent_reduction(
    size_hints=[64, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mul_sub_sum_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_mul_sub_sum_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r3 = rindex
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16)
    x0 = xindex % 4
    x4 = xindex % 16
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + (4*r3) + (64*x2)), xmask, eviction_policy='evict_last', other=0.0)
    tmp1 = tl.load(in_ptr1 + (r3 + (16*x0) + (64*x2)), xmask, eviction_policy='evict_last', other=0.0)
    tmp2 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp5, 0)
    tmp8 = tl.sum(tmp7, 1)[:, None]
    tl.store(out_ptr0 + (x5), tmp8, 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, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, pow_1, sum_1, scaled_l2_norm], Original ATen: [aten.sub, aten.pow, aten.sum, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_pow_sub_sum_0.run(primals_3, primals_1, primals_2, buf0, 256, grid=grid(256), stream=stream0)
        buf1 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [assigment_weights], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf0, buf1, 256, grid=grid(256), stream=stream0)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [assigment_weights], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf1, buf2, 256, grid=grid(256), stream=stream0)
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, mul_1, encoded_feat], Original ATen: [aten.sub, aten.mul, aten.sum]
        triton_per_fused_mul_sub_sum_3.run(buf2, primals_1, primals_2, buf3, 64, 16, grid=grid(64), stream=stream0)
        del buf2
    return (buf3, primals_1, primals_2, primals_3, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((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
import torch.nn.functional as F
import torch.nn as nn
import torch._C
import torch.serialization


class Encoding(nn.Module):
    """Encoding Layer: a learnable residual encoder.

    Input is of shape  (batch_size, channels, height, width).
    Output is of shape (batch_size, num_codes, channels).

    Args:
        channels: dimension of the features or feature channels
        num_codes: number of code words
    """

    def __init__(self, channels, num_codes):
        super(Encoding, self).__init__()
        self.channels, self.num_codes = channels, num_codes
        std = 1.0 / (num_codes * channels) ** 0.5
        self.codewords = nn.Parameter(torch.empty(num_codes, channels,
            dtype=torch.float).uniform_(-std, std), requires_grad=True)
        self.scale = nn.Parameter(torch.empty(num_codes, dtype=torch.float)
            .uniform_(-1, 0), requires_grad=True)

    @staticmethod
    def scaled_l2(x, codewords, scale):
        num_codes, channels = codewords.size()
        batch_size = x.size(0)
        reshaped_scale = scale.view((1, 1, num_codes))
        expanded_x = x.unsqueeze(2).expand((batch_size, x.size(1),
            num_codes, channels))
        reshaped_codewords = codewords.view((1, 1, num_codes, channels))
        scaled_l2_norm = reshaped_scale * (expanded_x - reshaped_codewords
            ).pow(2).sum(dim=3)
        return scaled_l2_norm

    @staticmethod
    def aggregate(assigment_weights, x, codewords):
        num_codes, channels = codewords.size()
        reshaped_codewords = codewords.view((1, 1, num_codes, channels))
        batch_size = x.size(0)
        expanded_x = x.unsqueeze(2).expand((batch_size, x.size(1),
            num_codes, channels))
        encoded_feat = (assigment_weights.unsqueeze(3) * (expanded_x -
            reshaped_codewords)).sum(dim=1)
        return encoded_feat

    def forward(self, x):
        assert x.dim() == 4 and x.size(1) == self.channels
        batch_size = x.size(0)
        x = x.view(batch_size, self.channels, -1).transpose(1, 2).contiguous()
        assigment_weights = F.softmax(self.scaled_l2(x, self.codewords,
            self.scale), dim=2)
        encoded_feat = self.aggregate(assigment_weights, x, self.codewords)
        return encoded_feat

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += (
            f'(Nx{self.channels}xHxW =>Nx{self.num_codes}x{self.channels})')
        return repr_str


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


def get_init_inputs():
    return [[], {'channels': 4, 'num_codes': 4}]

import torch
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch._C
import torch.serialization
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_pow_sub_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 16
    x2 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (16 + x1 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (32 + x1 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr1 + (48 + x1 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp1 - tmp2
    tmp4 = tmp3 * tmp3
    tmp7 = tmp5 - tmp6
    tmp8 = tmp7 * tmp7
    tmp9 = tmp4 + tmp8
    tmp12 = tmp10 - tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tmp9 + tmp13
    tmp17 = tmp15 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp14 + tmp18
    tmp20 = tmp0 * tmp19
    tl.store(out_ptr0 + x4, tmp20, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused_mul_sub_sum_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r3 = rindex
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x0 = xindex % 4
    x4 = xindex % 16
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + 4 * r3 + 64 * x2), xmask,
        eviction_policy='evict_last', other=0.0)
    tmp1 = tl.load(in_ptr1 + (r3 + 16 * x0 + 64 * x2), xmask,
        eviction_policy='evict_last', other=0.0)
    tmp2 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp5, 0)
    tmp8 = tl.sum(tmp7, 1)[:, None]
    tl.store(out_ptr0 + x5, tmp8, 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, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_pow_sub_sum_0[grid(256)](primals_3, primals_1,
            primals_2, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf0, buf1, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf2 = buf0
        del buf0
        triton_poi_fused__softmax_2[grid(256)](buf1, buf2, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_per_fused_mul_sub_sum_3[grid(64)](buf2, primals_1, primals_2,
            buf3, 64, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
    return buf3, primals_1, primals_2, primals_3


class EncodingNew(nn.Module):
    """Encoding Layer: a learnable residual encoder.

    Input is of shape  (batch_size, channels, height, width).
    Output is of shape (batch_size, num_codes, channels).

    Args:
        channels: dimension of the features or feature channels
        num_codes: number of code words
    """

    def __init__(self, channels, num_codes):
        super(EncodingNew, self).__init__()
        self.channels, self.num_codes = channels, num_codes
        std = 1.0 / (num_codes * channels) ** 0.5
        self.codewords = nn.Parameter(torch.empty(num_codes, channels,
            dtype=torch.float).uniform_(-std, std), requires_grad=True)
        self.scale = nn.Parameter(torch.empty(num_codes, dtype=torch.float)
            .uniform_(-1, 0), requires_grad=True)

    @staticmethod
    def scaled_l2(x, codewords, scale):
        num_codes, channels = codewords.size()
        batch_size = x.size(0)
        reshaped_scale = scale.view((1, 1, num_codes))
        expanded_x = x.unsqueeze(2).expand((batch_size, x.size(1),
            num_codes, channels))
        reshaped_codewords = codewords.view((1, 1, num_codes, channels))
        scaled_l2_norm = reshaped_scale * (expanded_x - reshaped_codewords
            ).pow(2).sum(dim=3)
        return scaled_l2_norm

    @staticmethod
    def aggregate(assigment_weights, x, codewords):
        num_codes, channels = codewords.size()
        reshaped_codewords = codewords.view((1, 1, num_codes, channels))
        batch_size = x.size(0)
        expanded_x = x.unsqueeze(2).expand((batch_size, x.size(1),
            num_codes, channels))
        encoded_feat = (assigment_weights.unsqueeze(3) * (expanded_x -
            reshaped_codewords)).sum(dim=1)
        return encoded_feat

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += (
            f'(Nx{self.channels}xHxW =>Nx{self.num_codes}x{self.channels})')
        return repr_str

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