using System;
using System.Buffers;
using System.Runtime.InteropServices;

using llama_token = System.Int32;

namespace LLama.Native
{
    /// <summary>
    /// Contains an array of LLamaTokenData, potentially sorted.
    /// </summary>
    public struct LLamaTokenDataArray
    {
        /// <summary>
        /// The LLamaTokenData
        /// </summary>
        public readonly Memory<LLamaTokenData> data;

        /// <summary>
        /// Indicates if `data` is sorted by logits in descending order. If this is false the token data is in _no particular order_.
        /// </summary>
        public bool sorted;

        /// <summary>
        /// Create a new LLamaTokenDataArray
        /// </summary>
        /// <param name="tokens"></param>
        /// <param name="isSorted"></param>
        public LLamaTokenDataArray(Memory<LLamaTokenData> tokens, bool isSorted = false)
        {
            data = tokens;
            sorted = isSorted;
        }

        /// <summary>
        /// Create a new LLamaTokenDataArray, copying the data from the given logits
        /// </summary>
        /// <param name="logits"></param>
        /// <returns></returns>
        public static LLamaTokenDataArray Create(ReadOnlySpan<float> logits)
        {
            var candidates = new LLamaTokenData[logits.Length];
            for (var token_id = 0; token_id < logits.Length; token_id++)
                candidates[token_id] = new LLamaTokenData(token_id, logits[token_id], 0.0f);

            return new LLamaTokenDataArray(candidates);
        }

        #region sampling
        /// <summary>
        /// Apply grammar rules to candidate tokens
        /// </summary>
        /// <param name="ctx"></param>
        /// <param name="grammar"></param>
        public void ApplyGrammar(SafeLLamaContextHandle ctx, SafeLLamaGrammarHandle grammar)
        {
            using (LLamaTokenDataArrayNative.Create(this, out var st))
            {
                NativeApi.llama_sample_grammar(ctx, ref st, grammar);
                sorted = st.sorted;
            }
        }

        /// <summary>
        /// Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
        /// </summary>
        /// <param name="context"></param>
        /// <param name="k">Number of tokens to keep</param>
        /// <param name="minKeep">Minimum number to keep</param>
        public void TopK(SafeLLamaContextHandle context, int k, ulong minKeep = 1)
        {
            using (LLamaTokenDataArrayNative.Create(this, out var st))
            {
                NativeApi.llama_sample_top_k(context, ref st, k, minKeep);
                sorted = st.sorted;
            }
        }

        /// <summary>
        /// Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
        /// </summary>
        /// <param name="context"></param>
        /// <param name="p"></param>
        /// <param name="minKeep"></param>
        public void TopP(SafeLLamaContextHandle context, float p, ulong minKeep = 1)
        {
            using (LLamaTokenDataArrayNative.Create(this, out var st))
            {
                NativeApi.llama_sample_top_p(context, ref st, p, minKeep);
                sorted = st.sorted;
            }
        }

        /// <summary>
        /// Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
        /// </summary>
        /// <param name="context"></param>
        /// <param name="z"></param>
        /// <param name="min_keep"></param>
        public void TailFree(SafeLLamaContextHandle context, float z, ulong min_keep = 1)
        {
            using (LLamaTokenDataArrayNative.Create(this, out var st))
            {
                NativeApi.llama_sample_tail_free(context, ref st, z, min_keep);
                sorted = st.sorted;
            }
        }

        /// <summary>
        /// Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
        /// </summary>
        /// <param name="context"></param>
        /// <param name="p"></param>
        /// <param name="min_keep"></param>
        public void LocallyTypical(SafeLLamaContextHandle context, float p, ulong min_keep = 1)
        {
            using (LLamaTokenDataArrayNative.Create(this, out var st))
            {
                NativeApi.llama_sample_typical(context, ref st, p, min_keep);
                sorted = st.sorted;
            }
        }

        /// <summary>
        /// Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
        /// Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
        /// </summary>
        /// <param name="context"></param>
        /// <param name="last_tokens"></param>
        /// <param name="penalty_repeat"></param>
        /// <param name="penalty_freq"></param>
        /// <param name="penalty_present"></param>
        public void RepetitionPenalty(SafeLLamaContextHandle context, Memory<llama_token> last_tokens, float penalty_repeat, float penalty_freq, float penalty_present)
        {
            unsafe
            {
                using (LLamaTokenDataArrayNative.Create(this, out var st))
                using (var last_tokens_handle = last_tokens.Pin())
                {
                    NativeApi.llama_sample_repetition_penalties(context, ref st, (int*)last_tokens_handle.Pointer, (ulong)last_tokens.Length, penalty_repeat, penalty_freq, penalty_present);
                    sorted = st.sorted;
                }
            }
        }

        /// <summary>
        /// Sample with temperature.
        /// As temperature increases, the prediction becomes more diverse but also vulnerable to hallucinations -- generating tokens that are sensible but not factual
        /// </summary>
        /// <param name="context"></param>
        /// <param name="temp"></param>
        public void Temperature(SafeLLamaContextHandle context, float temp)
        {
            using (LLamaTokenDataArrayNative.Create(this, out var st))
            {
                NativeApi.llama_sample_temperature(context, ref st, temp);
                sorted = st.sorted;
            }
        }

        /// <summary>
        /// Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
        /// </summary>
        /// <param name="context"></param>
        public void Softmax(SafeLLamaContextHandle context)
        {
            using (LLamaTokenDataArrayNative.Create(this, out var st))
            {
                NativeApi.llama_sample_softmax(context, ref st);
                sorted = st.sorted;
            }
        }

        /// <summary>
        /// Randomly selects a token from the candidates based on their probabilities.
        /// </summary>
        /// <param name="context"></param>
        /// <returns></returns>
        public int SampleToken(SafeLLamaContextHandle context)
        {
            using (LLamaTokenDataArrayNative.Create(this, out var st))
            {
                var token = NativeApi.llama_sample_token(context, ref st);
                sorted = st.sorted;
                return token;
            }
        }

        /// <summary>
        /// Selects the token with the highest probability.
        /// </summary>
        /// <param name="context"></param>
        /// <returns></returns>
        public int SampleTokenGreedy(SafeLLamaContextHandle context)
        {
            using (LLamaTokenDataArrayNative.Create(this, out var st))
            {
                var token = NativeApi.llama_sample_token_greedy(context, ref st);
                sorted = st.sorted;
                return token;
            }
        }

        /// <summary>
        /// Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
        /// </summary>
        /// <param name="context"></param>
        /// <param name="tau">The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.</param>
        /// <param name="eta">The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.</param>
        /// <param name="m">The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.</param>
        /// <param name="mu">Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.</param>
        /// <returns></returns>
        public int SampleTokenMirostat(SafeLLamaContextHandle context, float tau, float eta, int m, ref float mu)
        {
            using (LLamaTokenDataArrayNative.Create(this, out var st))
            {
                var token = NativeApi.llama_sample_token_mirostat(context, ref st, tau, eta, m, ref mu);
                sorted = st.sorted;
                return token;
            }
        }

        /// <summary>
        /// Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
        /// </summary>
        /// <param name="context"></param>
        /// <param name="tau">The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.</param>
        /// <param name="eta">The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.</param>
        /// <param name="mu">Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.</param>
        /// <returns></returns>
        public int SampleTokenMirostat2(SafeLLamaContextHandle context, float tau, float eta, ref float mu)
        {
            using (LLamaTokenDataArrayNative.Create(this, out var st))
            {
                var token = NativeApi.llama_sample_token_mirostat_v2(context, ref st, tau, eta, ref mu);
                sorted = st.sorted;
                return token;
            }
        }
        #endregion
    }

    /// <summary>
    /// Contains a pointer to an array of LLamaTokenData which is pinned in memory.
    /// </summary>
    [StructLayout(LayoutKind.Sequential)]
    public struct LLamaTokenDataArrayNative
    {
        /// <summary>
        /// A pointer to an array of LlamaTokenData
        /// </summary>
        /// <remarks>Memory must be pinned in place for all the time this LLamaTokenDataArrayNative is in use</remarks>
        public IntPtr data;

        /// <summary>
        /// Number of LLamaTokenData in the array
        /// </summary>
        public ulong size;

        /// <summary>
        /// Indicates if the items in the array are sorted
        /// </summary>
        public bool sorted
        {
            get => Convert.ToBoolean(_sorted);
            set => _sorted = Convert.ToSByte(value);
        }
        private sbyte _sorted;

        /// <summary>
        /// Create a new LLamaTokenDataArrayNative around the data in the LLamaTokenDataArray 
        /// </summary>
        /// <param name="array">Data source</param>
        /// <param name="native">Created native array</param>
        /// <returns>A memory handle, pinning the data in place until disposed</returns>
        public static MemoryHandle Create(LLamaTokenDataArray array, out LLamaTokenDataArrayNative native)
        {
            var handle = array.data.Pin();

            unsafe
            {
                native = new LLamaTokenDataArrayNative
                {
                    data = new IntPtr(handle.Pointer),
                    size = (ulong)array.data.Length,
                    sorted = array.sorted
                };
            }

            return handle;
        }
    }
}