#include "infinicore/nn/rope.hpp"
#include "../../utils.h"
#include "../utils.hpp"
#include "infinicore/ops.hpp"
#include <algorithm>
#include <cmath>
#include <functional>
#include <stdexcept>
#include <utility>
#include <vector>

namespace infinicore::nn {

RoPE::RoPE(size_t head_dim,
           size_t max_seq_len,
           double theta,
           Algo algo,
           const DataType &dtype,
           const Device &device)
    : head_dim_(head_dim),
      max_seq_len_(max_seq_len),
      theta_(theta),
      algo_(algo),
      dtype_(dtype) {
    if (head_dim % 2 != 0) {
        throw std::invalid_argument("head_dim must be even for RoPE, got " + std::to_string(head_dim));
    }

    device_ = device;

    // Initialize cache tables
    initialize_cache();
}

void RoPE::initialize_cache() {
    size_t cache_dim = head_dim_ / 2;

    // Create sin and cos cache tables: [max_seq_len, cache_dim]
    INFINICORE_NN_BUFFER_INIT(sin_cache, ({max_seq_len_, cache_dim}, dtype_, device_));
    INFINICORE_NN_BUFFER_INIT(cos_cache, ({max_seq_len_, cache_dim}, dtype_, device_));

    // Pre-compute sin and cos values
    // Frequency generation always uses GPT-J style (theta^(-2j/head_dim)).
    // The rotation algorithm (algo_) controls how dimensions are paired in the kernel.

    // Compute on CPU first, then copy to device
    auto cpu_device = Device(Device::Type::CPU, 0);

    // Allocate CPU buffers
    std::vector<float> sin_data(max_seq_len_ * cache_dim);
    std::vector<float> cos_data(max_seq_len_ * cache_dim);

    for (size_t pos = 0; pos < max_seq_len_; pos++) {
        for (size_t j = 0; j < cache_dim; j++) {
            // GPT-J style inverse frequency: theta^(-2j/head_dim)
            // Compute directly in float to avoid double->float casting
            float inv_freq = 1.0f / std::pow(static_cast<float>(theta_), 2.0f * static_cast<float>(j) / static_cast<float>(head_dim_));

            // Compute angle: position * inverse_frequency
            float angle = static_cast<float>(pos) * inv_freq;

            // Compute sin and cos directly on float
            sin_data[pos * cache_dim + j] = std::sin(angle);
            cos_data[pos * cache_dim + j] = std::cos(angle);
        }
    }

    // Convert to target dtype on CPU (matching Python's numpy astype conversion pattern)
    // Python: np_array.astype(ml_dtypes.bfloat16, copy=True) converts F32 -> BF16
    if (dtype_ == DataType::F32) {
        // Direct use of F32 data
        auto sin_f32_cpu = Tensor::from_blob(sin_data.data(), {max_seq_len_, cache_dim}, DataType::F32, cpu_device);
        auto cos_f32_cpu = Tensor::from_blob(cos_data.data(), {max_seq_len_, cache_dim}, DataType::F32, cpu_device);
        sin_cache_->copy_from(sin_f32_cpu);
        cos_cache_->copy_from(cos_f32_cpu);
    } else if (dtype_ == DataType::BF16) {
        // Convert F32 to BF16 using the same conversion as Python's ml_dtypes.bfloat16
        // This uses round-to-nearest-even (matching _f32_to_bf16 implementation)
        std::vector<bf16_t> sin_bf16_data(max_seq_len_ * cache_dim);
        std::vector<bf16_t> cos_bf16_data(max_seq_len_ * cache_dim);

        for (size_t i = 0; i < sin_data.size(); i++) {
            sin_bf16_data[i] = utils::cast<bf16_t, float>(sin_data[i]);
            cos_bf16_data[i] = utils::cast<bf16_t, float>(cos_data[i]);
        }

        auto sin_bf16_cpu = Tensor::from_blob(sin_bf16_data.data(), {max_seq_len_, cache_dim}, DataType::BF16, cpu_device);
        auto cos_bf16_cpu = Tensor::from_blob(cos_bf16_data.data(), {max_seq_len_, cache_dim}, DataType::BF16, cpu_device);

        // copy_from handles cross-device copying to target device
        sin_cache_->copy_from(sin_bf16_cpu);
        cos_cache_->copy_from(cos_bf16_cpu);
    } else if (dtype_ == DataType::F16) {
        // Convert F32 to F16
        std::vector<fp16_t> sin_f16_data(max_seq_len_ * cache_dim);
        std::vector<fp16_t> cos_f16_data(max_seq_len_ * cache_dim);

        for (size_t i = 0; i < sin_data.size(); i++) {
            sin_f16_data[i] = utils::cast<fp16_t, float>(sin_data[i]);
            cos_f16_data[i] = utils::cast<fp16_t, float>(cos_data[i]);
        }

        auto sin_f16_cpu = Tensor::from_blob(sin_f16_data.data(), {max_seq_len_, cache_dim}, DataType::F16, cpu_device);
        auto cos_f16_cpu = Tensor::from_blob(cos_f16_data.data(), {max_seq_len_, cache_dim}, DataType::F16, cpu_device);

        sin_cache_->copy_from(sin_f16_cpu);
        cos_cache_->copy_from(cos_f16_cpu);
    } else {
        throw std::runtime_error(
            "RoPE cache dtype conversion not yet supported for dtype: "
            + std::to_string(static_cast<int>(dtype_)));
    }
}

Tensor RoPE::forward(const Tensor &x, const Tensor &pos) const {
    // Delegate to InfiniCore op (backed by InfiniRT/InfiniOP)
    // Validation is handled by the op layer
    return op::rope(x, pos, sin_cache_, cos_cache_, algo_);
}

std::string RoPE::extra_repr() const {
    std::string algo_str = (algo_ == Algo::GPT_J) ? "GPT_J" : "GPT_NEOX";
    return "RoPE(head_dim=" + std::to_string(head_dim_) + ", max_seq_len=" + std::to_string(max_seq_len_) + ", theta=" + std::to_string(theta_) + ", algo=" + algo_str + ", dtype=" + std::to_string(static_cast<int>(dtype_)) + ")";
}

} // namespace infinicore::nn
