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


class ModelNew(nn.Module):
    def __init__(self):
        super().__init__()
        self._compile_cuda_kernel()

    def _compile_cuda_kernel(self):
        cpp_source = """
        torch::Tensor sq_rbf_cuda(torch::Tensor x);
        """

        cuda_source = """
        #include <torch/extension.h>
        #include <cuda_runtime.h>
        #include <math.h>

        __device__ __forceinline__ float sq_rbf_op(float x) {
            float x_abs = fabsf(x);

            // Region 1: |x| <= 1
            if (x_abs <= 1.0f) {
                return 1.0f - x * x * 0.5f;
            }

            // Region 3: 2 <= |x|
            if (x_abs >= 2.0f) {
                return 0.0f;
            }

            // Region 2: 1 < |x| < 2
            // 0.5 * (2 - |x|)^2
            float diff = 2.0f - x_abs;
            return 0.5f * diff * diff;
        }

        __global__ void sq_rbf_kernel(
            const float* __restrict__ x,
            float* __restrict__ output,
            const int n_elements) 
        {
            const int tid = blockIdx.x * blockDim.x + threadIdx.x;
            const int stride = blockDim.x * gridDim.x;

            const int vec_loops = n_elements >> 2;
            const float4* x_vec = reinterpret_cast<const float4*>(x);
            float4* out_vec = reinterpret_cast<float4*>(output);

            for (int i = tid; i < vec_loops; i += stride) {
                float4 v = __ldg(&x_vec[i]);
                float4 r;

                r.x = sq_rbf_op(v.x);
                r.y = sq_rbf_op(v.y);
                r.z = sq_rbf_op(v.z);
                r.w = sq_rbf_op(v.w);

                out_vec[i] = r;
            }

            const int tail_start = vec_loops << 2;
            for (int i = tail_start + tid; i < n_elements; i += stride) {
                output[i] = sq_rbf_op(x[i]);
            }
        }

        torch::Tensor sq_rbf_cuda(torch::Tensor x) {
            auto x_c = x.contiguous();
            const int n_elements = x_c.numel();
            auto output = torch::empty_like(x_c);

            const int threads = 256;
            const int max_blocks = 65535;
            const int blocks = std::min((n_elements + threads * 4 - 1) / (threads * 4), max_blocks);

            sq_rbf_kernel<<<blocks, threads>>>(
                x_c.data_ptr<float>(),
                output.data_ptr<float>(),
                n_elements
            );

            return output;
        }
        """

        self.op = load_inline(
            name="sq_rbf_v1",
            cpp_sources=cpp_source,
            cuda_sources=cuda_source,
            functions=["sq_rbf_cuda"],
            extra_cuda_cflags=["-O3", "--use_fast_math"],
            verbose=False
        )

    def forward(self, x):
        return self.op.sq_rbf_cuda(x)