# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
from dataclasses import dataclass


@dataclass
class SamplingParams:
    """Inference parameters sent along with the prompts.
    This class contains request-level attributes that control the sampling techniques used when
    generating text. This is distinct from megatron.core.inference.contexts.BaseInferenceContext,
        which is sets model-level
    inference attributes such as the maximum sequence length, and contains the KV cache.

    For an explanation of these parameters refer to this blog
    https://ivibudh.medium.com/a-guide-to-controlling-llm-model-output-exploring-top-k-top-p-and-
    temperature-parameters-ed6a31313910
    """

    temperature: float = 1.0
    top_k: int = 0
    top_p: float = 0.0
    return_log_probs: bool = False
    return_segments: bool = False  # Whether to return individually detokenized tokens
    num_tokens_to_generate: int = 30
    top_n_logprobs: int = 0
    return_prompt_top_n_logprobs: bool = False

    def add_attributes(self, attribute_value_pair: dict):
        """Utility to add more attributes to sampling params

        Use this method to pass in a custom dictionary to add more sampling parameter attributes.
        c = SamplingParams
        c.add_attributes({'min_length':4, 'eod_id':153})

        Args:
            attribute_value_pair (dict): A dictionary containing attributes as the key names and
            their values as the values.
        """
        for key, value in attribute_value_pair.items():
            setattr(self, key, value)
