# 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/q7/cq7zmg72e6s2ic35ycnxko7oug6l7uzhfsviwwill3oxbcrcxr4f.py
# Topologically Sorted Source Nodes: [simi], Original ATen: [aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   simi => clamp_min, clamp_min_1, div, div_1, mul_2, pow_1, pow_2, pow_3, pow_4, sum_1, sum_2, sum_3
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%expand_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [4], True), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%expand, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, [4], True), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_2, 1e-08), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_4, 1e-08), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%expand, %clamp_min_1), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%expand_1, %clamp_min), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_1, %div), kwargs = {})
#   %sum_3 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [4]), kwargs = {})
triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_sum_0 = async_compile.triton('triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_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_clamp_min_div_linalg_vector_norm_mul_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 12, '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_clamp_min_div_linalg_vector_norm_mul_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 4
    x3 = (xindex // 64)
    x0 = xindex % 4
    x6 = xindex
    x4 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + ((4*x1) + (16*x3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (4*x1) + (16*x3)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + (4*x1) + (16*x3)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + (4*x1) + (16*x3)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr2 + (4*x4), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr2 + (1 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr2 + (2 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr2 + (3 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 * tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 * tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 * tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = libdevice.sqrt(tmp18)
    tmp20 = 1e-08
    tmp21 = triton_helpers.maximum(tmp19, tmp20)
    tmp23 = tmp22 * tmp1
    tmp24 = tmp23 * tmp23
    tmp26 = tmp25 * tmp5
    tmp27 = tmp26 * tmp26
    tmp28 = tmp24 + tmp27
    tmp30 = tmp29 * tmp10
    tmp31 = tmp30 * tmp30
    tmp32 = tmp28 + tmp31
    tmp34 = tmp33 * tmp15
    tmp35 = tmp34 * tmp34
    tmp36 = tmp32 + tmp35
    tmp37 = libdevice.sqrt(tmp36)
    tmp38 = triton_helpers.maximum(tmp37, tmp20)
    tmp39 = tmp23 / tmp38
    tmp40 = tmp2 / tmp21
    tmp41 = tmp39 * tmp40
    tmp42 = tmp26 / tmp38
    tmp43 = tmp6 / tmp21
    tmp44 = tmp42 * tmp43
    tmp45 = tmp41 + tmp44
    tmp46 = tmp30 / tmp38
    tmp47 = tmp11 / tmp21
    tmp48 = tmp46 * tmp47
    tmp49 = tmp45 + tmp48
    tmp50 = tmp34 / tmp38
    tmp51 = tmp16 / tmp21
    tmp52 = tmp50 * tmp51
    tmp53 = tmp49 + tmp52
    tl.store(in_out_ptr0 + (x6), tmp53, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/it/citzkeschktwjljiduztmmsvdx5pz74a4lxb4lsc6lm7baxzrphg.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%getitem, %mean], 2), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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=[128], 
    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_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((16*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (4 + (16*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tl.load(in_ptr0 + (8 + (16*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp9 = triton_helpers.maximum(tmp7, tmp8)
    tmp10 = tl.load(in_ptr0 + (12 + (16*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tmp15 = tl.full([1], 8, tl.int64)
    tmp16 = tmp0 < tmp15
    tmp17 = tl.load(in_ptr0 + ((16*x1) + ((-4) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp18 = tl.load(in_ptr0 + (4 + (16*x1) + ((-4) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tmp17 + tmp18
    tmp20 = tl.load(in_ptr0 + (8 + (16*x1) + ((-4) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.load(in_ptr0 + (12 + (16*x1) + ((-4) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp23 = tmp21 + tmp22
    tmp24 = 4.0
    tmp25 = tmp23 / tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp14, tmp25, tmp26)
    tmp28 = tl.where(tmp4, tmp13, tmp27)
    tl.store(out_ptr0 + (x2), tmp28, 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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256), torch.float32)
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [simi], Original ATen: [aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_sum_0.run(buf2, primals_2, primals_3, primals_1, 256, grid=grid(256), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf2, buf3, 128, grid=grid(128), 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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (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 as nn
from torch.nn.functional import cosine_similarity


def multi_perspective_expand_for_2D(in_tensor, decompose_params):
    """
    Return: [batch_size, decompse_dim, dim]
    """
    in_tensor = in_tensor.unsqueeze(1)
    decompose_params = decompose_params.unsqueeze(0)
    return torch.mul(in_tensor, decompose_params)


class MaxpoolMatchLay(nn.Module):

    def __init__(self, mp_dim, cont_dim):
        super().__init__()
        self.cont_dim = cont_dim
        self.mp_dim = mp_dim
        self.register_parameter('weight', nn.Parameter(torch.Tensor(mp_dim,
            cont_dim)))
        self.weight.data.uniform_(-1.0, 1.0)

    def forward(self, cont_repres, other_cont_repres):
        """
        Args:
            cont_repres - [batch_size, this_len, context_lstm_dim]
            other_cont_repres - [batch_size, other_len, context_lstm_dim]
        Return:
            size - [bsz, this_len, mp_dim*2]
        """
        bsz = cont_repres.size(0)
        this_len = cont_repres.size(1)
        other_len = other_cont_repres.size(1)
        cont_repres = cont_repres.view(-1, self.cont_dim)
        other_cont_repres = other_cont_repres.view(-1, self.cont_dim)
        cont_repres = multi_perspective_expand_for_2D(cont_repres, self.weight)
        other_cont_repres = multi_perspective_expand_for_2D(other_cont_repres,
            self.weight)
        cont_repres = cont_repres.view(bsz, this_len, self.mp_dim, self.
            cont_dim)
        other_cont_repres = other_cont_repres.view(bsz, other_len, self.
            mp_dim, self.cont_dim)
        cont_repres = cont_repres.unsqueeze(2)
        other_cont_repres = other_cont_repres.unsqueeze(1)
        simi = cosine_similarity(cont_repres, other_cont_repres, 
            cont_repres.dim() - 1)
        t_max, _ = simi.max(2)
        t_mean = simi.mean(2)
        return torch.cat((t_max, t_mean), 2)


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


def get_init_inputs():
    return [[], {'mp_dim': 4, 'cont_dim': 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 libdevice
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x3 = xindex // 64
    x0 = xindex % 4
    x6 = xindex
    x4 = xindex // 16
    tmp0 = tl.load(in_ptr0 + (4 * x1 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x1 + 16 * x3), xmask, eviction_policy
        ='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + 4 * x1 + 16 * x3), xmask, eviction_policy
        ='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x1 + 16 * x3), xmask,
        eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr2 + 4 * x4, xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr2 + (1 + 4 * x4), xmask, eviction_policy='evict_last'
        )
    tmp29 = tl.load(in_ptr2 + (2 + 4 * x4), xmask, eviction_policy='evict_last'
        )
    tmp33 = tl.load(in_ptr2 + (3 + 4 * x4), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 * tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 * tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 * tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 * tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = libdevice.sqrt(tmp18)
    tmp20 = 1e-08
    tmp21 = triton_helpers.maximum(tmp19, tmp20)
    tmp23 = tmp22 * tmp1
    tmp24 = tmp23 * tmp23
    tmp26 = tmp25 * tmp5
    tmp27 = tmp26 * tmp26
    tmp28 = tmp24 + tmp27
    tmp30 = tmp29 * tmp10
    tmp31 = tmp30 * tmp30
    tmp32 = tmp28 + tmp31
    tmp34 = tmp33 * tmp15
    tmp35 = tmp34 * tmp34
    tmp36 = tmp32 + tmp35
    tmp37 = libdevice.sqrt(tmp36)
    tmp38 = triton_helpers.maximum(tmp37, tmp20)
    tmp39 = tmp23 / tmp38
    tmp40 = tmp2 / tmp21
    tmp41 = tmp39 * tmp40
    tmp42 = tmp26 / tmp38
    tmp43 = tmp6 / tmp21
    tmp44 = tmp42 * tmp43
    tmp45 = tmp41 + tmp44
    tmp46 = tmp30 / tmp38
    tmp47 = tmp11 / tmp21
    tmp48 = tmp46 * tmp47
    tmp49 = tmp45 + tmp48
    tmp50 = tmp34 / tmp38
    tmp51 = tmp16 / tmp21
    tmp52 = tmp50 * tmp51
    tmp53 = tmp49 + tmp52
    tl.store(in_out_ptr0 + x6, tmp53, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (16 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (4 + 16 * x1 + x0), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tl.load(in_ptr0 + (8 + 16 * x1 + x0), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp9 = triton_helpers.maximum(tmp7, tmp8)
    tmp10 = tl.load(in_ptr0 + (12 + 16 * x1 + x0), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp17 = tl.load(in_ptr0 + (16 * x1 + (-4 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp18 = tl.load(in_ptr0 + (4 + 16 * x1 + (-4 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp19 = tmp17 + tmp18
    tmp20 = tl.load(in_ptr0 + (8 + 16 * x1 + (-4 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.load(in_ptr0 + (12 + 16 * x1 + (-4 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp23 = tmp21 + tmp22
    tmp24 = 4.0
    tmp25 = tmp23 / tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp14, tmp25, tmp26)
    tmp28 = tl.where(tmp4, tmp13, tmp27)
    tl.store(out_ptr0 + x2, tmp28, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256),
            torch.float32)
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_sum_0[grid(256)](
            buf2, primals_2, primals_3, primals_1, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(128)](buf2, buf3, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf2
    return buf3, primals_1, primals_2, primals_3


def multi_perspective_expand_for_2D(in_tensor, decompose_params):
    """
    Return: [batch_size, decompse_dim, dim]
    """
    in_tensor = in_tensor.unsqueeze(1)
    decompose_params = decompose_params.unsqueeze(0)
    return torch.mul(in_tensor, decompose_params)


class MaxpoolMatchLayNew(nn.Module):

    def __init__(self, mp_dim, cont_dim):
        super().__init__()
        self.cont_dim = cont_dim
        self.mp_dim = mp_dim
        self.register_parameter('weight', nn.Parameter(torch.Tensor(mp_dim,
            cont_dim)))
        self.weight.data.uniform_(-1.0, 1.0)

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