"""
Shared DAG definition for XGBoost Training with Calibration Pipeline

This module provides the shared DAG definition for XGBoost training workflows
that include model calibration. This DAG can be used by both regular and MODS
pipeline variants to ensure consistency.

The DAG includes:
1) Data Loading (training)
2) Preprocessing (training) 
3) XGBoost Model Training
4) Model Calibration
5) Data Loading (calibration)
6) Preprocessing (calibration)
"""

import logging
from typing import Dict, Any

from ....api.dag.base_dag import PipelineDAG
from .. import DAGMetadata

logger = logging.getLogger(__name__)


def create_xgboost_training_with_calibration_dag() -> PipelineDAG:
    """
    Create a DAG for training and calibrating an XGBoost model.

    This DAG represents a workflow that includes training an XGBoost model
    and then calibrating it with a separate calibration dataset.

    Returns:
        PipelineDAG: The directed acyclic graph for the pipeline
    """
    dag = PipelineDAG()

    # Add nodes
    dag.add_node("CradleDataLoading_training")  # Data load for training
    dag.add_node("TabularPreprocessing_training")  # Tabular preprocessing for training
    dag.add_node("XGBoostTraining")  # XGBoost training step
    dag.add_node(
        "ModelCalibration_training"
    )  # Model calibration step with training variant
    dag.add_node("CradleDataLoading_calibration")  # Data load for calibration
    dag.add_node(
        "TabularPreprocessing_calibration"
    )  # Tabular preprocessing for calibration

    # Training flow
    dag.add_edge("CradleDataLoading_training", "TabularPreprocessing_training")
    dag.add_edge("TabularPreprocessing_training", "XGBoostTraining")
    dag.add_edge("XGBoostTraining", "ModelCalibration_training")

    # Calibration flow
    dag.add_edge("CradleDataLoading_calibration", "TabularPreprocessing_calibration")

    # Connect calibration data to model calibration
    dag.add_edge("TabularPreprocessing_calibration", "ModelCalibration_training")

    logger.info(
        f"Created XGBoost training with calibration DAG with {len(dag.nodes)} nodes and {len(dag.edges)} edges"
    )
    return dag


def get_dag_metadata() -> DAGMetadata:
    """
    Get metadata for the XGBoost training with calibration DAG.

    Returns:
        DAGMetadata: Metadata describing the DAG structure and purpose
    """
    return DAGMetadata(
        description="XGBoost training pipeline with model calibration",
        complexity="medium",
        features=["training", "calibration", "data_loading", "preprocessing"],
        framework="xgboost",
        node_count=6,
        edge_count=5,
        extra_metadata={
            "name": "xgboost_training_with_calibration",
            "task_type": "training",
            "entry_points": [
                "CradleDataLoading_training",
                "CradleDataLoading_calibration",
            ],
            "exit_points": ["ModelCalibration_training"],
            "required_configs": [
                "CradleDataLoading_training",
                "CradleDataLoading_calibration",
                "TabularPreprocessing_training",
                "TabularPreprocessing_calibration",
                "XGBoostTraining",
                "ModelCalibration_training",
            ],
        },
    )


def validate_dag_structure(dag: PipelineDAG) -> Dict[str, Any]:
    """
    Validate the structure of the XGBoost training with calibration DAG.

    Args:
        dag: The DAG to validate

    Returns:
        Dict containing validation results
    """
    metadata = get_dag_metadata()

    validation_result = {"is_valid": True, "errors": [], "warnings": []}

    # Check node count
    if len(dag.nodes) != metadata.node_count:
        validation_result["errors"].append(
            f"Expected {metadata.node_count} nodes, found {len(dag.nodes)}"
        )
        validation_result["is_valid"] = False

    # Check edge count
    if len(dag.edges) != metadata.edge_count:
        validation_result["errors"].append(
            f"Expected {metadata.edge_count} edges, found {len(dag.edges)}"
        )
        validation_result["is_valid"] = False

    # Check required nodes exist
    required_configs = metadata.extra_metadata.get("required_configs", [])
    missing_nodes = set(required_configs) - set(dag.nodes)
    if missing_nodes:
        validation_result["errors"].append(f"Missing required nodes: {missing_nodes}")
        validation_result["is_valid"] = False

    # Check entry points exist
    entry_points = metadata.extra_metadata.get("entry_points", [])
    missing_entry_points = set(entry_points) - set(dag.nodes)
    if missing_entry_points:
        validation_result["errors"].append(
            f"Missing entry points: {missing_entry_points}"
        )
        validation_result["is_valid"] = False

    # Check exit points exist
    exit_points = metadata.extra_metadata.get("exit_points", [])
    missing_exit_points = set(exit_points) - set(dag.nodes)
    if missing_exit_points:
        validation_result["errors"].append(
            f"Missing exit points: {missing_exit_points}"
        )
        validation_result["is_valid"] = False

    return validation_result
