// Copyright © 2024 Apple Inc.

import AsyncAlgorithms
import Foundation
import MLX
import MLXRandom

private func topPSampling(logits: MLXArray, topP: Float, temp: Float) -> MLXArray {
    var logits = logits
    if logits.dtype == .bfloat16 {
        logits = logits.asType(.float32)
    }

    let probs = softMax(logits / temp, axis: -1)
    let sortedIndices = argSort(probs, axis: -1)

    // probs shape is [B,V] and after take it will be [1, B, V], so we squeeze it back to [B, V]
    let sortedProbs = take(probs, sortedIndices, axis: -1).squeezed(axis: 0)

    let cumulativeProbs = cumsum(sortedProbs, axis: -1)

    let topProbs = MLX.where(cumulativeProbs .> (1 - topP), sortedProbs, zeros(like: sortedProbs))

    let sortedToken = categorical(log(topProbs))
    return sortedIndices.squeezed(axis: 0)[sortedToken]
}

private func sample(logits: MLXArray, temp: Float, topP: Float = 1.0) -> MLXArray {
    if temp == 0 {
        return argMax(logits, axis: -1)
    } else {
        if topP > 0 && topP < 1 {
            return topPSampling(logits: logits, topP: topP, temp: temp)
        }
        return categorical(logits * (1 / temp))
    }
}

/// Synchronous generator of tokens.
///
/// Port of `generate_step()` from https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/utils.py
public struct TokenIterator: Sequence, IteratorProtocol {
    let model: LLMModel
    let temp: Float
    let topP: Float
    var y: MLXArray
    var cache: [(MLXArray, MLXArray)]

    var first = true

    public init(prompt: MLXArray, model: LLMModel, temp: Float = 0.0, topP: Float = 1.0) {
        self.model = model
        self.temp = temp
        self.topP = topP
        self.y = prompt
        self.cache = []
    }

    mutating public func next() -> MLXArray? {
        var logits: MLXArray
        (logits, cache) = model(expandedDimensions(y, axis: 0), cache: cache.isEmpty ? nil : cache)
        y = sample(logits: logits[-1, axis: 1], temp: temp, topP: topP)

        return y
    }
}

/// Async generator of tokens.
///
/// Port of `generate_step()` from https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/utils.py.
///
/// Note that because MLXArray is not thread safe this eval's the result and sends the TokenId back
/// to the caller.
public func generate(prompt: MLXArray, model: LLMModel, temp: Float = 0.0, topP: Float = 1.0) -> (
    Task<Void, Never>, AsyncBufferSequence<AsyncChannel<Int>>
) {
    let channel = AsyncChannel<Int>()
    let buffer = channel.buffer(policy: .bounded(10))

    let task = Task {
        var y = prompt
        var cache = [(MLXArray, MLXArray)]()

        while !Task.isCancelled {
            var logits: MLXArray
            (logits, cache) = model(
                expandedDimensions(y, axis: 0), cache: cache.isEmpty ? nil : cache)
            y = sample(logits: logits[-1, axis: 1], temp: temp, topP: topP)
            eval(y)

            await channel.send(y.item(Int.self))
        }
    }

    return (task, buffer)
}