"""Hierarchical Taxonomy Clustering - Complete Pipeline

This script demonstrates the complete 4-step pipeline for building
a unified hierarchical taxonomy from multiple e-commerce data sources.

Pipeline Steps:
1. Load and Filter Data
2. Generate Weighted Embeddings
3. Recursive Clustering with Word-Based Naming
4. Apply Assignments and Export Results
"""


import pandas as pd
from step1_preprocessing_and_merge import standardize_and_filter_sources
from step2_weighted_embedding_generation import generate_embeddings
from step3_recursive_clustering_naming import recursive_taxonomy_clustering
from step4_result_assignments import apply_assignments, export_results, print_quality_metrics


def run_pipeline(source_dfs, output_dir, target_depth=5,
                 min_clusters_l1=10, max_clusters=20, min_clusters_other=3):
    """
    Run the complete hierarchical taxonomy clustering pipeline.

    Args:
        source_dfs: List of tuples [(df1, 'source1'), (df2, 'source2'), ...]
                    Each df must have 'category_path' column
        output_dir: Directory to save output files
        target_depth: Maximum depth to consider (default 5)
        min_clusters_l1: Min clusters for level 1 (default 10)
        max_clusters: Max clusters per cut (default 20)
        min_clusters_other: Min clusters for levels 2-5 (default 3)

    Returns:
        Tuple of (result_df, hierarchy_df)
    """
    print("=" * 80)
    print("HIERARCHICAL TAXONOMY CLUSTERING PIPELINE")
    print("=" * 80)

    # Step 1: Load, Standardize, Filter and Merge
    print("\n" + "=" * 80)
    print("STEP 1: LOAD, STANDARDIZE, FILTER AND MERGE")
    print("=" * 80)

    df = standardize_and_filter_sources(source_dfs, target_depth=target_depth)

    print("\nMerged data:")
    print(f"  Records: {len(df)}")
    print(f"  Depth distribution: {df['depth'].value_counts().sort_index().to_dict()}")

    # Step 2: Generate Weighted Embeddings
    print("\n" + "=" * 80)
    print("STEP 2: GENERATE WEIGHTED EMBEDDINGS")
    print("=" * 80)

    weights = {1: 1.0, 2: 0.6, 3: 0.36, 4: 0.216, 5: 0.1296}
    embeddings = generate_embeddings(df, target_depth=target_depth, weights=weights)

    # Step 3: Recursive Clustering with Word-Based Naming
    print("\n" + "=" * 80)
    print("STEP 3: RECURSIVE CLUSTERING WITH WORD-BASED NAMING")
    print("=" * 80)

    all_indices = list(range(len(df)))
    assignments = recursive_taxonomy_clustering(
        df=df,
        embeddings=embeddings,
        indices=all_indices,
        current_level=1,
        max_level=target_depth,
        parent_words=set(),
        parent_label='ROOT',
        global_parent_categories=set(),
        min_clusters_l1=min_clusters_l1,
        max_clusters=max_clusters,
        min_clusters_other=min_clusters_other
    )

    print("\n✅ Clustering complete!")
    print(f"   Assigned {len(assignments)} records")

    # Step 4: Apply Assignments and Export Results
    print("\n" + "=" * 80)
    print("STEP 4: APPLY ASSIGNMENTS AND EXPORT RESULTS")
    print("=" * 80)

    result_df = apply_assignments(df, assignments, max_level=target_depth)
    full_df, hierarchy_df = export_results(result_df, output_dir, max_level=target_depth)

    # Print quality metrics
    print_quality_metrics(result_df, max_level=target_depth)

    print("\n" + "=" * 80)
    print("PIPELINE COMPLETE!")
    print("=" * 80)
    print(f"\nOutputs saved to: {output_dir}")
    print(f"  1. unified_taxonomy_full.csv - Full mapping ({len(full_df)} records)")
    print(f"  2. unified_taxonomy_hierarchy.csv - Taxonomy structure ({len(hierarchy_df)} paths)")

    return result_df, hierarchy_df


if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser(
        description='Build unified hierarchical taxonomy from e-commerce categories'
    )
    parser.add_argument('--sources', nargs='+', required=True,
                       help='Source CSV files (format: path:name path:name ...)')
    parser.add_argument('--output', required=True, help='Output directory path')
    parser.add_argument('--depth', type=int, default=5, help='Target depth (default: 5)')
    parser.add_argument('--min-l1', type=int, default=10, help='Min level 1 clusters (default: 10)')
    parser.add_argument('--max-clusters', type=int, default=20, help='Max clusters (default: 20)')
    parser.add_argument('--min-others', type=int, default=3, help='Min clusters for levels 2-5 (default: 3)')

    args = parser.parse_args()

    # Load source data
    source_dfs = []
    for source_spec in args.sources:
        path, name = source_spec.split(':')
        print(f"Loading {name} from: {path}")
        df = pd.read_csv(path)
        source_dfs.append((df, name))

    # Run pipeline
    result_df, hierarchy_df = run_pipeline(
        source_dfs=source_dfs,
        output_dir=args.output,
        target_depth=args.depth,
        min_clusters_l1=args.min_l1,
        max_clusters=args.max_clusters,
        min_clusters_other=args.min_others
    )

    print("\n✅ Done!")