LWC 51:683. K Empty Slots

本文介绍了一种算法解决方案,用于在一个连续的花园槽位中找出两个已开花植物间恰好相隔k个未开花植物的最早天数。通过维护有序集合,实现了高效查找。

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LWC 51:683. K Empty Slots

传送门:683. K Empty Slots

Problem:

There is a garden with N slots. In each slot, there is a flower. The N flowers will bloom one by one in N days. In each day, there will be exactly one flower blooming and it will be in the status of blooming since then.

Given an array flowers consists of number from 1 to N. Each number in the array represents the place where the flower will open in that day.

For example, flowers[i] = x means that the unique flower that blooms at day i will be at position x, where i and x will be in the range from 1 to N.

Also given an integer k, you need to output in which day there exists two flowers in the status of blooming, and also the number of flowers between them is k and these flowers are not blooming.

If there isn’t such day, output -1.

Example 1:

Input:
flowers: [1,3,2]
k: 1
Output: 2
Explanation: In the second day, the first and the third flower have become blooming.

Example 2:

Input:
flowers: [1,2,3]
k: 1
Output: -1

Note:

  • The given array will be in the range [1, 20000].

题意:

题目要求满足开花的两个slot之间的间隙恰好为k个的天数。

很暴力,遍历每个位置i,因为在位置i的左侧一定都是开花的,而在位置i的右侧则都是还没开过的花。所以在左侧的位置中,找到两个相邻位置之差为k个即可,为了快速比较相邻slot之间的间隙,i在不断累加的同时,始终保持i左侧位置的有序性,在插入的同时,去比较它的左侧位置,和右侧位置,看是否符合间隙之差为k+1。

当然上述操作需要维护位置的有序性,于是采用了插入排序算法,并且输出插入对应的位置。这样就能方便的找寻当前位置的左侧位置和右侧位置。

代码如下:

    public int kEmptySlots(int[] flowers, int k) {
        int n = flowers.length;
        if (n == 1 && k == 0) return 1;
        sort = new int[n + 16];
        for (int i = 0; i < n; ++i) {
            int index = add(flowers[i]);
            int min = index - 1 < 0 ? -11111111 : sort[index - 1];
            int max = index + 1 >= tot ? -11111111 : sort[index + 1];
            if (valid(flowers[i], k, min, max)) return i + 1;
        }
        return -1;
    }

    boolean valid(int x, int k, int min, int max) {
        if (max - x == k + 1) return true;
        if (x - min == k + 1) return true;
        return false;
    }

    int[] sort;
    int tot = 0;
    public int add(int x) {
        int j = 0;
        while (j < tot && sort[j] < x) {
            ++j;
        }
        for (int i = tot - 1; i >= j; --i) {
            sort[i + 1] = sort[i];
        }
        sort[j] = x;
        tot++;
        return j;
    }

当然,你也可以使用JAVA自带的数据结构,TreeSet来实现,代码精简很多。

代码如下:

    public int kEmptySlots(int[] flowers, int k) {
        int n = flowers.length;
        if (n == 1 && k == 0) return 1;
        TreeSet<Integer> sort = new TreeSet<>();
        for (int i = 0; i < n; ++i) {
            sort.add(flowers[i]);
            Integer min = sort.lower(flowers[i]);
            Integer max = sort.higher(flowers[i]);
            int mi = min == null ? -1111111 : min;
            int ma = max == null ? -1111111 : max;
            if (valid(flowers[i], k, mi, ma)) return i + 1;
        }
        return -1;
    }

    boolean valid(int x, int k, int min, int max) {
        if (max - x == k + 1) return true;
        if (x - min == k + 1) return true;
        return false;
    }
class UniformAffineQuantizer(nn.Module): def __init__( self, n_bits: int = 8, symmetric: bool = False, per_channel_axes=[], metric="minmax", dynamic=False, dynamic_method="per_cluster", group_size=None, shape=None, lwc=False, disable_zero_point=False, ): """ support cluster quantize dynamic_method support per_token and per_cluster """ super().__init__() self.symmetric = symmetric self.disable_zero_point = disable_zero_point assert 2 <= n_bits <= 16, "bitwidth not supported" self.n_bits = n_bits if self.disable_zero_point: self.qmin = -(2 ** (n_bits - 1)) self.qmax = 2 ** (n_bits - 1) - 1 else: self.qmin = 0 self.qmax = 2 ** (n_bits) - 1 self.per_channel_axes = per_channel_axes self.metric = metric self.cluster_counts = None self.cluster_dim = None self.scale = None self.zero_point = None self.round_zero_point = None self.cached_xmin = None self.cached_xmax = None self.dynamic = dynamic self.dynamic_method = dynamic_method self.deficiency = 0 self.lwc = lwc init_value = 4. # inti value of learnable weight clipping if lwc: if group_size: dim1 = int(shape[0]*math.ceil(shape[1]/group_size)) self.deficiency = shape[-1]%group_size if self.deficiency > 0: self.deficiency = group_size - self.deficiency assert self.symmetric # support for mlc-llm symmetric quantization else: dim1 = shape[0] self.upbound_factor = nn.Parameter(torch.ones((dim1,1))*init_value) self.lowbound_factor = nn.Parameter(torch.ones((dim1,1))*init_value) self.sigmoid = nn.Sigmoid() self.enable = True self.group_size = group_size def change_n_bits(self, n_bits): self.n_bits = n_bits if self.disable_zero_point: self.qmin = -(2 ** (n_bits - 1)) self.qmax = 2 ** (n_bits - 1) - 1 else: self.qmin = 0 self.qmax = 2 ** (n_bits) - 1 def fake_quant(self, x, scale, round_zero_point): if self.deficiency > 0: pad_zeros = torch.zeros((x.shape[0],self.deficiency),dtype=x.dtype,device=x.device) x = torch.cat((x,pad_zeros),dim=1) if self.group_size: assert len(x.shape)==2, "only support linear layer now" dim1, dim2 = x.shape x = x.reshape(-1, self.group_size) x_int = round_ste(x / scale) if round_zero_point is not None: x_int = x_int.add(round_zero_point) x_int = x_int.clamp(self.qmin, self.qmax) x_dequant = x_int if round_zero_point is not None: x_dequant = x_dequant.sub(round_zero_point) x_dequant = x_dequant.mul(scale) if self.group_size: x_dequant = x_dequant.reshape(dim1, dim2) if self.deficiency > 0: x_dequant = x_dequant[:,:-self.deficiency] return x_dequant def forward(self, x: torch.Tensor): if self.n_bits >= 16 or not self.enable: return x if self.metric == "fix0to1": return x.mul_(2**self.n_bits-1).round_().div_(2**self.n_bits-1) if self.dynamic_method == "per_token" or self.dynamic_method == "per_channel": self.per_token_dynamic_calibration(x) else: raise NotImplementedError() x_dequant = self.fake_quant(x, self.scale, self.round_zero_point) return x_dequant def per_token_dynamic_calibration(self, x): if self.group_size: if self.deficiency == 0: x = x.reshape(-1,self.group_size) else: pad_zeros = torch.zeros((x.shape[0],self.deficiency),dtype=x.dtype,device=x.device) x = torch.cat((x,pad_zeros),dim=1) x = x.reshape(-1,self.group_size) reduce_shape = [-1] xmin = x.amin(reduce_shape, keepdim=True) xmax = x.amax(reduce_shape, keepdim=True) if self.lwc: xmax = self.sigmoid(self.upbound_factor)*xmax xmin = self.sigmoid(self.lowbound_factor)*xmin if self.symmetric: abs_max = torch.max(xmax.abs(),xmin.abs()) scale = abs_max / (2**(self.n_bits-1)-1) self.scale = scale.clamp(min=CLIPMIN, max=1e4) zero_point = (2**(self.n_bits-1)-1)*torch.ones_like(self.scale) else: range = xmax - xmin scale = range / (2**self.n_bits-1) self.scale = scale.clamp(min=CLIPMIN, max=1e4) zero_point = -(xmin) / (self.scale) if self.disable_zero_point: self.round_zero_point = None else: self.round_zero_point = zero_point.clamp(min=-1e4, max=1e4).round() def register_scales_and_zeros(self): self.register_buffer('scales', self.scale) self.register_buffer('zeros', self.round_zero_point) del self.scale del self.round_zero_point
最新发布
07-24
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