"""
Sequential Loaders Base Class
Complete independent implementation - No dependency on FreeVRAM
"""

import time
from typing import Any, Dict, List, Tuple, Optional

# Type compatibility with FreeVRAM node
ANY_SOCKET_TYPES = "IMAGE,LATENT,MODEL,CONDITIONING,CLIP,VAE,MASK,INT,FLOAT,STRING,BOOLEAN"


class SequentialLoaderBase:
    """
    Base class for all Sequential loaders

    Features:
    - trigger input/output management
    - Memory tracking (independent implementation)
    - Unified log output
    - Error handling
    """

    CATEGORY = "u5"

    # Class variable: Memory tracker
    _memory_tracker: List[Dict[str, Any]] = []

    @classmethod
    def add_trigger_input(cls, input_types: Dict) -> Dict:
        """
        Add trigger input to INPUT_TYPES

        Args:
            input_types: Existing input definition

        Returns:
            Input definition with trigger input added
        """
        if "optional" not in input_types:
            input_types["optional"] = {}

        input_types["optional"]["trigger"] = (ANY_SOCKET_TYPES, {
            "tooltip": "Trigger(Any): Connect from previous node to control execution order"
        })
        return input_types

    def with_trigger_passthrough(
        self,
        results: Any,
        trigger: Optional[Any] = None
    ) -> Tuple:
        """
        Add trigger to results for passthrough

        Args:
            results: Loader execution results
            trigger: Input trigger

        Returns:
            Tuple of (results..., trigger)
        """
        if isinstance(results, tuple):
            return (*results, trigger)
        return (results, trigger)

    def log_execution(self, loader_type: str, params: Dict[str, Any]):
        """
        Unified log output

        Args:
            loader_type: Loader type (e.g., "Checkpoint", "VAE")
            params: Parameters to log
        """
        print(f"[Sequential {loader_type}] Executing: {params}")

    @classmethod
    def track_memory(cls, node_name: str, memory_gb: float):
        """
        Record memory usage

        Args:
            node_name: Node identifier
            memory_gb: Memory usage in GB
        """
        cls._memory_tracker.append({
            "node": node_name,
            "memory_gb": memory_gb,
            "timestamp": time.time()
        })

    @classmethod
    def get_memory_report(cls) -> Dict[str, Any]:
        """
        Generate memory usage report

        Returns:
            Memory report dictionary
        """
        total = sum(item["memory_gb"] for item in cls._memory_tracker)
        return {
            "total_gb": total,
            "count": len(cls._memory_tracker),
            "entries": cls._memory_tracker
        }

    @classmethod
    def reset_memory_tracker(cls):
        """Reset memory tracker"""
        cls._memory_tracker.clear()