# Copyright (c) 2026 LightSeek Foundation
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# of this software and associated documentation files (the "Software"), to deal
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# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# SOFTWARE.

import json
from abc import ABC, abstractmethod
from functools import cache
from typing import Any, Self

import dill
import torch


@cache
def _cache_from_str(json_str: str) -> "CustomLogitProcessor":
    """Deserialize a json string to a Callable object.
    This function is cached to avoid redundant deserialization.
    """
    data = json.loads(json_str)
    return dill.loads(bytes.fromhex(data["callable"]))


class CustomLogitProcessor(ABC):
    """Abstract base class for callable functions."""

    @abstractmethod
    def __call__(
        self,
        logits: torch.Tensor,
        custom_param_list: list[dict[str, Any]] | None = None,
    ) -> torch.Tensor:
        """Define the callable behavior."""
        raise NotImplementedError

    def to_str(self) -> str:
        """Serialize the callable function to a JSON-compatible string."""
        return json.dumps({"callable": dill.dumps(self).hex()})

    @classmethod
    def from_str(cls, json_str: str) -> Self:
        """Deserialize a callable function from a JSON string."""
        return _cache_from_str(json_str)