"""Learning rate scheduler implementations with factory pattern."""

import math
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Type

from torch.optim.lr_scheduler import LRScheduler

from astrai.core.factory import BaseFactory


class BaseScheduler(LRScheduler, ABC):
    """Base scheduler class for all other schedulers."""

    def __init__(self, optimizer, last_epoch: int = -1):
        super().__init__(optimizer, last_epoch)

    @abstractmethod
    def get_lr(self) -> List[float]:
        """Calculate the current learning rate."""
        raise NotImplementedError

    def state_dict(self) -> Dict[str, Any]:
        return super().state_dict()

    def load_state_dict(self, state_dict: Dict[str, Any]):
        super().load_state_dict(state_dict)


class SchedulerFactory(BaseFactory["BaseScheduler"]):
    """Factory class for creating learning rate schedulers.

    Supports decorator-based registration for extensible scheduler types.
    Also supports creation from ScheduleConfig objects.

    Example usage:
        @SchedulerFactory.register("custom")
        class CustomScheduler(BaseScheduler):
            ...

        scheduler = SchedulerFactory.create("custom", optimizer, **kwargs)
    """

    _registry: Dict[str, Type[BaseScheduler]] = {}

    @classmethod
    def _validate_component(cls, scheduler_cls: Type[BaseScheduler]) -> None:
        """Validate that the scheduler class inherits from BaseScheduler."""
        if not issubclass(scheduler_cls, BaseScheduler):
            raise TypeError(f"{scheduler_cls.__name__} must inherit from BaseScheduler")

    @classmethod
    def create(
        cls, optimizer, schedule_type: str = "none", **kwargs
    ) -> "BaseScheduler":
        """Create a scheduler instance by type name.

        Args:
            optimizer: PyTorch optimizer
            schedule_type: Type of scheduler ("cosine", "sgdr")
            **kwargs: Arguments passed to the scheduler constructor

        Returns:
            Scheduler instance
        """
        return super().create(schedule_type, optimizer, **kwargs)

    @classmethod
    def available_types(cls) -> list:
        """Return list of registered scheduler type names."""
        return cls.list_registered()


# ----------- Scheduler implementations -----------


@SchedulerFactory.register("cosine")
class CosineScheduler(BaseScheduler):
    """Cosine decay scheduler with warmup, implemented as PyTorch LRScheduler."""

    def __init__(
        self,
        optimizer,
        warmup_steps: int,
        lr_decay_steps: int,
        min_rate: float = 0.05,
        last_epoch: int = -1,
    ):
        self.warmup_steps = warmup_steps
        self.lr_decay_steps = lr_decay_steps
        self.min_rate = min_rate
        self.total_steps = warmup_steps + lr_decay_steps
        super().__init__(optimizer, last_epoch)

    def get_lr(self) -> List[float]:
        # warmup
        if self.last_epoch < self.warmup_steps:
            warmup_factor = max(self.min_rate, self.last_epoch / self.warmup_steps)
            return [base_lr * warmup_factor for base_lr in self.base_lrs]

        # cosine decay
        decay_progress = (self.last_epoch - self.warmup_steps) / self.lr_decay_steps
        decay_progress = min(decay_progress, 1.0)
        cosine_decay = 0.5 * (1.0 + math.cos(math.pi * decay_progress))
        decay_factor = max(self.min_rate, cosine_decay)
        return [base_lr * decay_factor for base_lr in self.base_lrs]

    def state_dict(self):
        state = super().state_dict()
        state.update(
            {
                "warmup_steps": self.warmup_steps,
                "lr_decay_steps": self.lr_decay_steps,
                "min_rate": self.min_rate,
                "total_steps": self.total_steps,
            }
        )
        return state

    def load_state_dict(self, state_dict):
        self.warmup_steps = state_dict.pop("warmup_steps")
        self.lr_decay_steps = state_dict.pop("lr_decay_steps")
        self.min_rate = state_dict.pop("min_rate")
        self.total_steps = state_dict.pop("total_steps")
        super().load_state_dict(state_dict)


@SchedulerFactory.register("sgdr")
class SGDRScheduler(BaseScheduler):
    """SGDR (Stochastic Gradient Descent with Warm Restarts) scheduler."""

    def __init__(
        self,
        optimizer,
        warmup_steps: int,
        cycle_length: int,
        min_rate: float = 0.05,
        t_mult: int = 2,
        last_epoch: int = -1,
    ):
        self.warmup_steps = warmup_steps
        self.cycle_length = cycle_length
        self.min_rate = min_rate
        self.t_mult = t_mult

        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        # warmup
        if self.last_epoch < self.warmup_steps:
            warmup_factor = max(self.min_rate, self.last_epoch / self.warmup_steps)
            return [base_lr * warmup_factor for base_lr in self.base_lrs]

        # SGDR
        steps_since_warmup = self.last_epoch - self.warmup_steps

        # 1. Calculate current cycle and position within cycle
        current_cycle_length = self.cycle_length
        total_cycles_length = 0
        cycle_num = 0

        while total_cycles_length + current_cycle_length <= steps_since_warmup:
            total_cycles_length += current_cycle_length
            current_cycle_length *= self.t_mult
            cycle_num += 1

        steps_in_cycle = steps_since_warmup - total_cycles_length

        # 2. Cosine annealing within the current cycle
        cosine_factor = 0.5 * (
            1 + math.cos(math.pi * steps_in_cycle / current_cycle_length)
        )
        learning_rate_factor = self.min_rate + (1 - self.min_rate) * cosine_factor

        return [base_lr * learning_rate_factor for base_lr in self.base_lrs]

    def state_dict(self):
        """Returns the state of the scheduler as a dict."""
        state = super().state_dict()
        state.update(
            {
                "warmup_steps": self.warmup_steps,
                "cycle_length": self.cycle_length,
                "min_rate": self.min_rate,
                "t_mult": self.t_mult,
            }
        )
        return state

    def load_state_dict(self, state_dict):
        """Loads the scheduler's state."""
        self.warmup_steps = state_dict.pop("warmup_steps")
        self.cycle_length = state_dict.pop("cycle_length")
        self.min_rate = state_dict.pop("min_rate")
        self.t_mult = state_dict.pop("t_mult")
        super().load_state_dict(state_dict)