"""
Shared DAG definition for XGBoost Complete End-to-End Pipeline with Wiki Generation

This module provides the shared DAG definition for a complete XGBoost workflow
that includes training, calibration, packaging, registration, evaluation with inference,
metrics computation, and wiki generation.

The DAG includes:
1) Data Loading (training)
2) Preprocessing (training)
3) XGBoost Model Training
4) Model Calibration
5) Package Model
6) Payload Generation
7) Model Registration
8) Data Loading (calibration)
9) Preprocessing (calibration)
10) Model Inference (calibration)
11) Model Metrics Computation
12) Model Wiki Generation
"""

import logging
from typing import Dict, Any

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

logger = logging.getLogger(__name__)


def create_xgboost_complete_e2e_with_wiki_dag() -> PipelineDAG:
    """
    Create a DAG for complete XGBoost E2E pipeline with wiki generation.

    This DAG represents a complete end-to-end workflow including training,
    calibration, packaging, registration, evaluation with inference, metrics
    computation, and wiki generation of an XGBoost model.

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

    # Add all 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_calibration"
    )  # Model calibration step with calibration variant
    dag.add_node("Package")  # Package step
    dag.add_node("Registration")  # MIMS registration step
    dag.add_node("Payload")  # Payload step
    dag.add_node("CradleDataLoading_calibration")  # Data load for calibration
    dag.add_node(
        "TabularPreprocessing_calibration"
    )  # Tabular preprocessing for calibration
    dag.add_node("XGBoostModelInference_calibration")  # Model inference step
    dag.add_node(
        "ModelMetricsComputation_calibration"
    )  # Model metrics computation step
    dag.add_node("ModelWikiGenerator")  # Model wiki generator step

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

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

    # Inference and evaluation flow
    dag.add_edge("XGBoostTraining", "XGBoostModelInference")
    dag.add_edge("TabularPreprocessing_calibration", "XGBoostModelInference")
    dag.add_edge(
        "XGBoostModelInference_calibration", "ModelMetricsComputation_calibration"
    )
    dag.add_edge("ModelMetricsComputation_calibration", "ModelWikiGenerator")

    # Model calibration flow - depends on inference
    dag.add_edge("XGBoostModelInference_calibration", "ModelCalibration_calibration")

    # Output flow
    dag.add_edge("ModelCalibration_calibration", "Package")
    dag.add_edge("XGBoostTraining", "Package")  # Raw model is also input to packaging
    dag.add_edge("XGBoostTraining", "Payload")  # Payload test uses the raw model
    dag.add_edge("Package", "Registration")
    dag.add_edge("Payload", "Registration")

    logger.info(
        f"Created XGBoost complete E2E with wiki DAG with {len(dag.nodes)} nodes and {len(dag.edges)} edges"
    )
    return dag


def get_dag_metadata() -> DAGMetadata:
    """
    Get metadata for the XGBoost complete end-to-end DAG with wiki generation.

    Returns:
        DAGMetadata: Metadata describing the DAG structure and purpose
    """
    return DAGMetadata(
        description="Complete XGBoost end-to-end pipeline with training, calibration, packaging, registration, inference, metrics computation, and wiki generation",
        complexity="comprehensive",
        features=[
            "training",
            "calibration",
            "packaging",
            "registration",
            "inference",
            "metrics",
            "wiki_generation",
        ],
        framework="xgboost",
        node_count=12,
        edge_count=13,
        extra_metadata={
            "name": "xgboost_complete_e2e_with_wiki",
            "task_type": "end_to_end",
            "entry_points": [
                "CradleDataLoading_training",
                "CradleDataLoading_calibration",
            ],
            "exit_points": ["Registration", "ModelWikiGenerator"],
            "required_configs": [
                "CradleDataLoading_training",
                "CradleDataLoading_calibration",
                "TabularPreprocessing_training",
                "TabularPreprocessing_calibration",
                "XGBoostTraining",
                "XGBoostModelInference",
                "ModelMetricsComputation",
                "ModelWikiGenerator",
                "ModelCalibration_calibration",
                "Package",
                "Payload",
                "Registration",
            ],
        },
    )


def validate_dag_structure(dag: PipelineDAG) -> Dict[str, Any]:
    """
    Validate the structure of the XGBoost complete end-to-end DAG with wiki generation.

    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
