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 dsu_cuda(torch::Tensor x);
        """

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

        __device__ __forceinline__ float dsu_op(float x) {
            const float PI = 3.14159265358979323846f;
            const float y = x - PI;

            if (fabsf(y) < 1e-6f) {
                return PI;
            } else {

                return sinf(PI * y) / y;
            }
        }

        __global__ void dsu_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;

            for (int i = tid; i < n_elements; i += stride) {
                output[i] = dsu_op(x[i]);
            }
        }

        torch::Tensor dsu_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 blocks = std::min((n_elements + threads - 1) / threads, 65535);

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

            return output;
        }
        """

        self.op = load_inline(
            name="dsu_v1",
            cpp_sources=cpp_source,
            cuda_sources=cuda_source,
            functions=["dsu_cuda"],
            extra_cuda_cflags=["-O3"],
            verbose=False
        )

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