def top_p_sampling(logits: mx.array, top_p: float, temperature: float) -> mx.array:
    """
    Apply top-p (nucleus) sampling to logits.

    Args:
        logits: The logits from the model's output.
        top_p: The cumulative probability threshold for top-p filtering.
        temperature: Temperature parameter for softmax distribution reshaping.

    Returns:
        token selected based on the top-p criterion.

    """
    # referenced implementation from https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py#L449-L460
    probs = mx.softmax(logits / temperature, axis=-1)

    # sort probs in ascending order
    sorted_indices = mx.argsort(probs, axis=-1)
    sorted_probs = probs[..., sorted_indices.squeeze(0)]

    cumulative_probs = mx.cumsum(sorted_probs, axis=-1)

    # select tokens with cumulative probs below threshold
    top_probs = mx.where(
        cumulative_probs > 1 - top_p,
        sorted_probs,
        mx.zeros_like(sorted_probs),
    )

    sorted_token = mx.random.categorical(mx.log(top_probs))

    return sorted_indices.squeeze(0)[sorted_token]

    Apply top-p (nucleus) sampling to logits.

    Args:
        logits: The logits from the model's output.
        top_p: The cumulative probability threshold for top-p filtering.
        temperature: Temperature parameter for softmax distribution reshaping.

    Returns:
        token selected based on the top-p criterion.

    
llama-index-integrations/llms/llama-index-llms-mlx/llama_index/llms/mlx/utils.py