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


class ModelNew(nn.Module):
    def __init__(self, alpha=1.0, t=1.5):
        super().__init__()
        self.alpha = alpha
        self.t = t
        self._compile_cuda_kernel()

    def _compile_cuda_kernel(self):
        cpp_source = """
        torch::Tensor pdelu_cuda(torch::Tensor x, float alpha, float t);
        """

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

        __global__ void pdelu_kernel(
            const float* __restrict__ x,
            float* __restrict__ output,
            const int n_elements,
            const float alpha,
            const float t) 
        {
            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);

            const float one_minus_t = 1.0f - t;
            const float inv_one_minus_t = 1.0f / one_minus_t;

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

                if (v.x > 0.0f) {
                    r.x = v.x;
                } else {
                    float base = 1.0f + one_minus_t * v.x;
                    r.x = alpha * (powf(base, inv_one_minus_t) - 1.0f);
                }

                if (v.y > 0.0f) {
                    r.y = v.y;
                } else {
                    float base = 1.0f + one_minus_t * v.y;
                    r.y = alpha * (powf(base, inv_one_minus_t) - 1.0f);
                }

                if (v.z > 0.0f) {
                    r.z = v.z;
                } else {
                    float base = 1.0f + one_minus_t * v.z;
                    r.z = alpha * (powf(base, inv_one_minus_t) - 1.0f);
                }

                if (v.w > 0.0f) {
                    r.w = v.w;
                } else {
                    float base = 1.0f + one_minus_t * v.w;
                    r.w = alpha * (powf(base, inv_one_minus_t) - 1.0f);
                }

                out_vec[i] = r;
            }

            const int tail_start = vec_loops << 2;
            for (int i = tail_start + tid; i < n_elements; i += stride) {
                float v = x[i];
                if (v > 0.0f) {
                    output[i] = v;
                } else {
                    float base = 1.0f + one_minus_t * v;
                    output[i] = alpha * (powf(base, inv_one_minus_t) - 1.0f);
                }
            }
        }

        torch::Tensor pdelu_cuda(torch::Tensor x, float alpha, float t) {
            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);

            pdelu_kernel<<<blocks, threads>>>(
                x_c.data_ptr<float>(),
                output.data_ptr<float>(),
                n_elements,
                alpha,
                t
            );

            return output;
        }
        """

        self.op = load_inline(
            name="pdelu_v1",
            cpp_sources=cpp_source,
            cuda_sources=cuda_source,
            functions=["pdelu_cuda"],
            extra_cuda_cflags=["-O3", "--use_fast_math"],
            verbose=False
        )

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