# SPDX-License-Identifier: Apache-2.0

from abc import ABC, abstractmethod
from typing import Optional, Tuple, Union

import torch
from diffusers.utils import BaseOutput


class BaseScheduler(ABC):
    timesteps: torch.Tensor
    order: int

    def __init__(self, *args, **kwargs) -> None:
        # Check if subclass has defined all required properties
        required_attributes = ['timesteps', 'order']

        for attr in required_attributes:
            if not hasattr(self, attr):
                raise AttributeError(
                    f"Subclasses of BaseScheduler must define '{attr}' property"
                )

    @abstractmethod
    def set_shift(self, shift: float) -> None:
        pass

    @abstractmethod
    def set_timesteps(self, *args, **kwargs) -> None:
        pass

    @abstractmethod
    def scale_model_input(self,
                          sample: torch.Tensor,
                          timestep: Optional[int] = None) -> torch.Tensor:
        pass

    @abstractmethod
    def step(
        self,
        model_output: torch.Tensor,
        timestep: Union[int, torch.Tensor],
        sample: torch.Tensor,
        return_dict: bool = True,
    ) -> Union[BaseOutput, Tuple]:
        pass