"""
Utility functions shared across graph algorithms.

This module provides common functionality used by multiple graph algorithms
including backend selection, parameter validation, and result formatting.
"""

from typing import Any, Literal

import pandas as pd


def select_backend(
    graph: Any,  # GraphFrame type hint will be added after implementation
    backend: Literal["auto", "pandas", "dask"] | None = "auto",
    algorithm: str = "unknown",
) -> Literal["pandas", "dask"]:
    """
    Select the optimal backend for graph algorithm execution.

    Makes intelligent backend selection based on graph size, current data backend,
    user preference, and algorithm capabilities.

    Args:
        graph: GraphFrame object
        backend: User backend preference ('auto', 'pandas', 'dask')
        algorithm: Algorithm name for backend capability checking

    Returns:
        Selected backend ('pandas' or 'dask')

    Raises:
        NotImplementedError: If requested backend is not available for algorithm

    Examples:
        Automatic selection:
            >>> backend = select_backend(graph, 'auto', 'bfs')
            >>> print(f"Selected {backend} backend for BFS")
    """
    # TODO: Phase 1.2 - Implement backend selection logic
    # 1. If backend explicitly specified, validate and return
    # 2. Check algorithm capabilities (e.g., DFS not available on Dask)
    # 3. Consider current graph data backend (vertices.islazy, edges.islazy)
    # 4. Consider graph size and system memory
    # 5. Return optimal backend with appropriate warnings
    raise NotImplementedError("Backend selection implementation pending - Phase 1.2")


def validate_sources(
    graph: Any,  # GraphFrame type hint will be added after implementation
    sources: int | list[int] | None,
) -> list[int]:
    """
    Validate and normalize source vertex specification.

    Ensures source vertices exist in the graph and returns a consistent
    list format for algorithm processing.

    Args:
        graph: GraphFrame object
        sources: Source vertex specification (int, list, or None)

    Returns:
        List of validated source vertex IDs

    Raises:
        ValueError: If sources contain invalid vertex IDs or graph is empty

    Examples:
        Validate single source:
            >>> sources = validate_sources(graph, 42)
            >>> print(sources)  # [42]

        Validate multiple sources:
            >>> sources = validate_sources(graph, [1, 10, 100])
            >>> print(f"Validated {len(sources)} source vertices")
    """
    # TODO: Phase 1.2 - Implement source validation
    # 1. Handle None case (default to vertex 0 or first available)
    # 2. Convert single int to list
    # 3. Validate all source IDs exist in graph
    # 4. Remove duplicates while preserving order
    # 5. Ensure at least one valid source
    raise NotImplementedError("Source validation implementation pending - Phase 1.2")


def create_result_dataframe(
    data: dict[str, list],
    columns: list[str],
    dtypes: dict[str, str] | None = None,
) -> pd.DataFrame:
    """
    Create a standardized result DataFrame with proper column types.

    Ensures consistent column naming and data types across all algorithm results.

    Args:
        data: Dictionary mapping column names to value lists
        columns: Expected column order for the result
        dtypes: Optional dtype specifications for columns

    Returns:
        Formatted pandas DataFrame with correct types

    Examples:
        Create BFS result DataFrame:
            >>> data = {
            ...     'vertex': [0, 1, 2],
            ...     'distance': [0, 1, 2],
            ...     'predecessor': [None, 0, 1]
            ... }
            >>> result = create_result_dataframe(data, ['vertex', 'distance', 'predecessor'])
    """
    # TODO: Phase 1.2 - Implement result DataFrame creation
    # 1. Create DataFrame from data dict
    # 2. Reorder columns according to expected order
    # 3. Apply proper dtypes (int64, float64, nullable types)
    # 4. Handle None/nullable columns appropriately
    # 5. Validate result consistency
    raise NotImplementedError(
        "Result DataFrame creation implementation pending - Phase 1.2"
    )


def symmetrize_edges(
    graph: Any,  # GraphFrame type hint will be added after implementation
    directed: bool | None = None,
) -> Any:  # Return type will be EdgeSet or similar
    """
    Create symmetrized edge set for undirected graph algorithms.

    For directed graphs that need to be treated as undirected (e.g., weak components),
    adds reverse edges to make the graph symmetric.

    Args:
        graph: GraphFrame object
        directed: Whether to treat graph as directed (None = use graph.is_directed)

    Returns:
        EdgeSet with potentially symmetrized edges

    Examples:
        Symmetrize directed graph:
            >>> undirected_edges = symmetrize_edges(graph, directed=False)
            >>> print(f"Original: {len(graph.edges)} edges")
            >>> print(f"Symmetrized: {len(undirected_edges)} edges")
    """
    # TODO: Phase 1.2 - Implement edge symmetrization
    # 1. Check if symmetrization is needed
    # 2. Get original edges DataFrame
    # 3. Create reverse edges (swap src/dst columns)
    # 4. Concatenate original and reverse edges
    # 5. Remove duplicate edges if any
    # 6. Return new EdgeSet with symmetrized data
    raise NotImplementedError("Edge symmetrization implementation pending - Phase 1.2")


def check_convergence(
    old_values: pd.Series | Any,  # Could be Dask Series
    new_values: pd.Series | Any,  # Could be Dask Series
    tol: float,
    metric: Literal["l1", "l2", "max"] = "l1",
) -> bool:
    """
    Check convergence between old and new algorithm values.

    Computes difference metric between iterations to determine if
    algorithm has converged within tolerance.

    Args:
        old_values: Previous iteration values
        new_values: Current iteration values
        tol: Convergence tolerance threshold
        metric: Distance metric ('l1', 'l2', 'max')

    Returns:
        True if converged (difference < tolerance)

    Examples:
        Check PageRank convergence:
            >>> converged = check_convergence(old_ranks, new_ranks, tol=1e-6)
            >>> if converged:
            ...     print("Algorithm converged!")
    """
    # TODO: Phase 1.2 - Implement convergence checking
    # 1. Handle pandas vs Dask Series appropriately
    # 2. Compute difference based on specified metric
    # 3. Return boolean convergence status
    # 4. Handle edge cases (empty series, NaN values)
    raise NotImplementedError("Convergence checking implementation pending - Phase 1.2")
