#!/usr/bin/env python3
"""
Consolidated script to diagnose and fix h5ad files for transcriptformer.

This script performs a series of checks to validate an AnnData object and
automatically applies fixes for common issues, preparing the data for
inference with transcriptformer.

Usage:
    python preprocess_adata.py <input_h5ad_file> <output_h5ad_file>
"""

import sys
import os
import numpy as np
import anndata as ad
import scanpy as sc
from pathlib import Path

def preprocess_adata(input_path, output_path):
    """
    Diagnose and fix an h5ad file for transcriptformer compatibility.
    """
    print(f"🚀 Starting preprocessing for: {input_path}")
    print("=" * 70)

    # 1. Load Data
    print("📖 1. Loading AnnData object...")
    if not os.path.exists(input_path):
        print(f"❌ ERROR: Input file not found: {input_path}")
        return False
    
    try:
        adata = ad.read_h5ad(input_path)
        print(f"✅ Loaded: {adata.shape[0]} cells × {adata.shape[1]} genes")
    except Exception as e:
        print(f"❌ ERROR: Could not load AnnData file. Reason: {e}")
        return False

    original_shape = adata.shape

    # 2. Run Diagnostics
    print("\n🔬 2. Running Diagnostics...")
    issues_found = []

    # Check for NaN/Inf values
    has_nan = np.isnan(adata.X.data).any() if hasattr(adata.X, 'data') else np.isnan(adata.X).any()
    has_inf = np.isinf(adata.X.data).any() if hasattr(adata.X, 'data') else np.isinf(adata.X).any()
    if has_nan: issues_found.append("NaN values found in data matrix.")
    if has_inf: issues_found.append("Infinite values found in data matrix.")
    print(f"   - NaN/Inf values: {'❌ Found' if has_nan or has_inf else '✅ None'}")

    # Check for unique gene indices
    if adata.var.index.nunique() < len(adata.var.index):
        issues_found.append("Duplicate gene indices (var_names) found.")
        print("   - Duplicate gene indices: ❌ Found")
    else:
        print("   - Duplicate gene indices: ✅ Unique")

    # Check for ensembl_id column
    if 'ensembl_id' not in adata.var.columns:
        issues_found.append("'ensembl_id' column missing in var.")
        print("   - 'ensembl_id' column: ❌ Missing")
    else:
        print("   - 'ensembl_id' column: ✅ Present")

    # Check for zero-expression genes
    genes_before_filter = adata.n_vars
    sc.pp.filter_genes(adata, min_cells=1)
    if adata.n_vars < genes_before_filter:
        num_removed = genes_before_filter - adata.n_vars
        issues_found.append(f"{num_removed} genes with zero expression found.")
        print(f"   - Zero-expression genes: ❌ Found ({num_removed} genes)")
    else:
        print("   - Zero-expression genes: ✅ None")
    
    # Restore original object for fixing step
    adata = ad.read_h5ad(input_path)

    # 3. Apply Fixes
    print("\n🔧 3. Applying Fixes...")
    fixes_applied = []

    # Fix: Ensure var_names are unique
    if adata.var.index.nunique() < len(adata.var.index):
        adata.var_names_make_unique()
        fixes_applied.append("Made var_names unique using .var_names_make_unique()")
        print("   - ✅ Made gene indices (var_names) unique.")
    else:
        print("   - ✅ Gene indices are already unique.")

    # Fix: Add ensembl_id column if it's missing
    if 'ensembl_id' not in adata.var.columns:
        print("   - Adding 'ensembl_id' column from var.index.")
        adata.var['ensembl_id'] = adata.var.index
        fixes_applied.append("Added 'ensembl_id' column from var.index.")
    else:
        print("   - ✅ 'ensembl_id' column already exists.")

    # Fix: Filter out genes with zero expression
    genes_before_filter = adata.n_vars
    sc.pp.filter_genes(adata, min_cells=1)
    if adata.n_vars < genes_before_filter:
        num_removed = genes_before_filter - adata.n_vars
        fixes_applied.append(f"Removed {num_removed} genes with no expression.")
        print(f"   - ✅ Removed {num_removed} zero-expression genes.")
    else:
        print("   - ✅ No zero-expression genes to remove.")

    # 4. Save Processed File
    print("\n💾 4. Saving Processed File...")
    try:
        adata.write(output_path)
        print(f"   - ✅ Successfully saved to: {output_path}")
    except Exception as e:
        print(f"❌ ERROR: Could not save file. Reason: {e}")
        return False

    # 5. Final Summary
    print("\n📋 5. Summary")
    print("-" * 70)
    print(f"   - Original shape: {original_shape[0]} cells × {original_shape[1]} genes")
    print(f"   - Final shape:    {adata.shape[0]} cells × {adata.shape[1]} genes")
    print("\n   - Issues Found:")
    if issues_found:
        for issue in issues_found:
            print(f"     - {issue}")
    else:
        print("     - None")
    
    print("\n   - Fixes Applied:")
    if fixes_applied:
        for fix in fixes_applied:
            print(f"     - {fix}")
    else:
        print("     - None")
    
    print("\n🎉 Preprocessing complete!")
    return True

def main():
    if len(sys.argv) != 3:
        print("Usage: python preprocess_adata.py <input_h5ad_file> <output_h5ad_file>")
        sys.exit(1)
    
    input_path = sys.argv[1]
    output_path = sys.argv[2]
    
    if os.path.abspath(input_path) == os.path.abspath(output_path):
        print("❌ ERROR: Input and output paths cannot be the same.")
        sys.exit(1)

    if os.path.exists(output_path):
        response = input(f"⚠️  Output file already exists: {output_path}\nOverwrite? (y/N): ")
        if response.lower() != 'y':
            print("Operation cancelled.")
            sys.exit(1)

    success = preprocess_adata(input_path, output_path)
    
    if not success:
        sys.exit(1)

if __name__ == "__main__":
    main()