from abc import ABC, abstractmethod
from collections.abc import Iterable
from typing import Any

import numpy as np
import torch
from torch import Tensor
from torch.optim import Adam
from torch.optim.lr_scheduler import LambdaLR, LRScheduler

from tianshou.highlevel.experiment import RLSamplingConfig

TParams = Iterable[Tensor] | Iterable[dict[str, Any]]


class OptimizerFactory(ABC):
    @abstractmethod
    def create_optimizer(self, module: torch.nn.Module, lr: float) -> torch.optim.Optimizer:
        pass


class TorchOptimizerFactory(OptimizerFactory):
    def __init__(self, optim_class: Any, **kwargs):
        self.optim_class = optim_class
        self.kwargs = kwargs

    def create_optimizer(self, module: torch.nn.Module, lr: float) -> torch.optim.Optimizer:
        return self.optim_class(module.parameters(), lr=lr, **self.kwargs)


class AdamOptimizerFactory(OptimizerFactory):
    def create_optimizer(self, module: torch.nn.Module, lr: float) -> Adam:
        return Adam(module.parameters(), lr=lr)


class LRSchedulerFactory(ABC):
    @abstractmethod
    def create_scheduler(self, optim: torch.optim.Optimizer) -> LRScheduler:
        pass


class LinearLRSchedulerFactory(LRSchedulerFactory):
    def __init__(self, sampling_config: RLSamplingConfig):
        self.sampling_config = sampling_config

    def create_scheduler(self, optim: torch.optim.Optimizer) -> LRScheduler:
        max_update_num = (
            np.ceil(self.sampling_config.step_per_epoch / self.sampling_config.step_per_collect)
            * self.sampling_config.num_epochs
        )
        return LambdaLR(optim, lr_lambda=lambda epoch: 1 - epoch / max_update_num)