import torch
import torch.nn as nn


class Model(nn.Module):
    """
    RBF (Squared Exponential) covariance matrix computation for Gaussian
    Processes.  Given two sets of input points, computes the full
    kernel matrix K[i,j] = sigma^2 * exp(-||x_i - x_j||^2 / (2 * l^2)).
    The dominant cost in GP inference and a natural target for fusion.
    """

    def __init__(self, input_dim, lengthscale=1.0, variance=1.0):
        super().__init__()
        self.log_lengthscale = nn.Parameter(torch.tensor(lengthscale).log())
        self.log_variance = nn.Parameter(torch.tensor(variance).log())

    def forward(self, X1: torch.Tensor, X2: torch.Tensor) -> torch.Tensor:
        """
        Args:
            X1: (B, N, D) – first set of inputs
            X2: (B, M, D) – second set of inputs
        Returns:
            K: (B, N, M) – covariance matrix
        """
        l2 = self.log_lengthscale.exp() ** 2
        var = self.log_variance.exp()

        # Squared distances via expansion
        X1_sq = (X1 ** 2).sum(-1, keepdim=True)  # (B, N, 1)
        X2_sq = (X2 ** 2).sum(-1, keepdim=True)  # (B, M, 1)
        dist_sq = X1_sq - 2.0 * X1 @ X2.transpose(-1, -2) + X2_sq.transpose(-1, -2)
        dist_sq = dist_sq.clamp(min=0.0)

        return var * torch.exp(-0.5 * dist_sq / l2)


input_dim = 8
num_train = 512
num_test = 128
batch_size = 16


def get_inputs():
    X1 = torch.randn(popcorn_pri.jitter_int(batch_size), popcorn_pri.jitter_int(num_train), input_dim)
    X2 = torch.randn(popcorn_pri.jitter_int(batch_size), popcorn_pri.jitter_int(num_test), input_dim)
    return [X1, X2]


def get_init_inputs():
    return [input_dim]

import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

cuda_source = '''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

__global__ void rbf_kernel(
    const float* __restrict__ X1,
    const float* __restrict__ X2,
    float* __restrict__ K,
    const float l2_inv,
    const float var,
    int B, int N, int M, int D
) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int total = B * N * M;
    if (idx >= total) return;

    int b = idx / (N * M);
    int rem = idx % (N * M);
    int i = rem / M;
    int j = rem % M;

    float dist_sq = 0.0f;
    const float* x1_ptr = X1 + (b * N + i) * D;
    const float* x2_ptr = X2 + (b * M + j) * D;
    for (int d = 0; d < D; d++) {
        float diff = x1_ptr[d] - x2_ptr[d];
        dist_sq += diff * diff;
    }

    float val = var * expf(-0.5f * dist_sq * l2_inv);
    K[idx] = val;
}

torch::Tensor rbf_cuda(torch::Tensor X1, torch::Tensor X2, float log_lengthscale, float log_variance) {
    int B = X1.size(0);
    int N = X1.size(1);
    int D = X1.size(2);
    int M = X2.size(1);

    auto K = torch::empty({B, N, M}, X1.options());

    float l2 = expf(log_lengthscale);
    l2 = l2 * l2;
    float l2_inv = 1.0f / l2;
    float var = expf(log_variance);

    int total = B * N * M;
    int threads = 256;
    int blocks = (total + threads - 1) / threads;

    rbf_kernel<<<blocks, threads>>>(
        X1.data_ptr<float>(),
        X2.data_ptr<float>(),
        K.data_ptr<float>(),
        l2_inv,
        var,
        B, N, M, D
    );

    return K;
}
'''

cpp_source = '''
torch::Tensor rbf_cuda(torch::Tensor X1, torch::Tensor X2, float log_lengthscale, float log_variance);
'''

module = load_inline(
    name='rbf_module',
    cpp_sources=[cpp_source],
    cuda_sources=[cuda_source],
    functions=['rbf_cuda'],
    verbose=False
)

class ModelNew(nn.Module):
    def __init__(self, input_dim, lengthscale=1.0, variance=1.0):
        super().__init__()
        self.log_lengthscale = nn.Parameter(torch.tensor(lengthscale).log());
        self.log_variance = nn.Parameter(torch.tensor(variance).log());

    def forward(self, X1: torch.Tensor, X2: torch.Tensor) -> torch.Tensor:
        return module.rbf_cuda(X1, X2, self.log_lengthscale.item(), self.log_variance.item())