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

class ModelNew(nn.Module):
    def __init__(self, in_features, alpha=0.0):
        super().__init__()
        self.alpha = nn.Parameter(torch.full((in_features,), alpha))
        self._compile_cuda_kernel()

    def _compile_cuda_kernel(self):
        cpp_source = """
        #include <torch/extension.h>
        torch::Tensor soft_exponential_cuda(torch::Tensor x, torch::Tensor alpha);
        """

        cuda_source = """
        #include <cuda_runtime.h>

        __device__ __forceinline__ float compute_se(float x, float a) {
            if (fabsf(a) < 1e-6f) {
                return x;
            } else if (a > 0.0f) {
                return (expf(a * x) - 1.0f) / a + a;
            } else {
                float val = 1.0f - a * (x + a);
                return (val > 0.0f) ? (-logf(val) / a) : 0.0f;
            }
        }

        __global__ void soft_exponential_vec4(
            const float* __restrict__ x,
            float* __restrict__ y,
            const float* __restrict__ alpha,
            int total_vecs,
            int spatial_vecs,
            int channels) 
        {
            int idx = blockIdx.x * blockDim.x + threadIdx.x;
            int stride = gridDim.x * blockDim.x;

            const float4* x_vec = reinterpret_cast<const float4*>(x);
            float4* y_vec = reinterpret_cast<float4*>(y);

            for (int i = idx; i < total_vecs; i += stride) {
                float4 v = x_vec[i];
                float4 out;

                int c = (i / spatial_vecs) % channels;
                float a = alpha[c];

                out.x = compute_se(v.x, a);
                out.y = compute_se(v.y, a);
                out.z = compute_se(v.z, a);
                out.w = compute_se(v.w, a);

                y_vec[i] = out;
            }
        }

        __global__ void soft_exponential_scalar(
            const float* __restrict__ x,
            float* __restrict__ y,
            const float* __restrict__ alpha,
            int total_elements,
            int spatial_size,
            int channels) 
        {
            int idx = blockIdx.x * blockDim.x + threadIdx.x;
            int stride = gridDim.x * blockDim.x;

            for (int i = idx; i < total_elements; i += stride) {
                int c = (i / spatial_size) % channels;
                float a = alpha[c];
                y[i] = compute_se(x[i], a);
            }
        }

        torch::Tensor soft_exponential_cuda(torch::Tensor x, torch::Tensor alpha) {
            auto x_c = x.contiguous();
            auto alpha_c = alpha.contiguous();
            auto output = torch::empty_like(x_c);

            int total_elements = x_c.numel();
            int N = x_c.size(0);
            int C = x_c.size(1);
            int S = total_elements / (N * C);

            if (S % 4 == 0) {
                int total_vecs = total_elements / 4;
                int spatial_vecs = S / 4;
                int threads = 256;
                int blocks = (total_vecs + threads - 1) / threads;
                if (blocks > 65535) blocks = 65535;

                soft_exponential_vec4<<<blocks, threads>>>(
                    x_c.data_ptr<float>(),
                    output.data_ptr<float>(),
                    alpha_c.data_ptr<float>(),
                    total_vecs,
                    spatial_vecs,
                    C
                );
            } else {
                int threads = 256;
                int blocks = (total_elements + threads - 1) / threads;
                if (blocks > 65535) blocks = 65535;

                soft_exponential_scalar<<<blocks, threads>>>(
                    x_c.data_ptr<float>(),
                    output.data_ptr<float>(),
                    alpha_c.data_ptr<float>(),
                    total_elements,
                    S,
                    C
                );
            }

            return output;
        }
        """

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

    def forward(self, x):
        return self.op.soft_exponential_cuda(x, self.alpha)