import torch
import torch.nn as nn


class OcclusionAwareSimilarity(nn.Module):

    def __init__(self, threshold):
        super(OcclusionAwareSimilarity, self).__init__()
        self.threshold = threshold

    def forward(self, similarity_matrix):
        indicator_zero = similarity_matrix <= self.threshold
        similarity_matrix[indicator_zero] = 0
        return similarity_matrix


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


def get_init_inputs():
    return [[], {'threshold': 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_poi_fused_index_put_lift_fresh_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 4.0
    tmp2 = tmp0 <= tmp1
    tmp3 = 0.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tl.store(out_ptr0 + x0, tmp4, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        get_raw_stream(0)
        triton_poi_fused_index_put_lift_fresh_0[grid(256)](arg0_1, arg0_1, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
    return arg0_1,


class OcclusionAwareSimilarityNew(nn.Module):

    def __init__(self, threshold):
        super(OcclusionAwareSimilarityNew, self).__init__()
        self.threshold = threshold

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
