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

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

__global__ void imitation_kernel(const float* pred_actions, const float* expert_actions, float* output, int size) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < size) {
        float diff = pred_actions[idx] - expert_actions[idx];
        output[idx] = diff * diff;
    }
}

torch::Tensor imitation_cuda(torch::Tensor pred_actions, torch::Tensor expert_actions) {
    auto size = pred_actions.numel();
    auto output = torch::empty_like(pred_actions);
    const int block_size = 256;
    int num_blocks = (size + block_size - 1) / block_size;
    imitation_kernel<<<num_blocks, block_size>>>(pred_actions.data_ptr<float>(), expert_actions.data_ptr<float>(), output.data_ptr<float>(), size);
    return output.mean();
}
"""

cpp_source = """
torch::Tensor imitation_cuda(torch::Tensor pred_actions, torch::Tensor expert_actions);
"""

il_module = load_inline(
    name="imitation_loss",
    cpp_sources=cpp_source,
    cuda_sources=cuda_source,
    functions=["imitation_cuda"],
    verbose=True
)

class ModelNew(torch.nn.Module):
    def __init__(self):
        super(ModelNew, self).__init__()
        self.il_module = il_module

    def forward(self, pred_actions, expert_actions):
        return self.il_module.imitation_cuda(pred_actions, expert_actions)