# Filename: Validate_sudokuCSV_V2.1.py
#
# Description:
# This script validates the 'solutions' column of a sudoku.csv file generated by
# Generate_sudokuCSV_V2.1.py or a similar script. It mathematically checks if each
# solved grid adheres to the rules of Sudoku.
#
# Key Logic:
# 1.  **Loads Data:** Reads the 'sudoku.csv' file into a pandas DataFrame.
# 2.  **Validates Each Solution:** For every row, it checks the 'solutions' grid to ensure:
#     - Each row (1-9) contains exactly one of each digit from 1 to 9.
#     - Each column (1-9) contains exactly one of each digit from 1 to 9.
#     - Each 3x3 box (1-9) contains exactly one of each digit from 1 to 9.
# 3.  **Reports Findings:** Prints a clear summary of how many grids are valid and invalid.
# 4.  **Handles Invalid Grids (Colab-Friendly):**
#     - If invalid grids are found, it saves their row indices to a file named
#       `invalid_sudoku_indices.txt`.
#     - It then instructs the user on how to clean the master CSV by changing a
#       single configuration flag (`PERFORM_CLEANUP`) and re-running the script.
# 5.  **Performs Cleanup (If Enabled):** If the `PERFORM_CLEANUP` flag is set to True
#     and the index file exists, it will:
#     - Move the invalid rows from 'sudoku.csv' to a new 'invalid_sudokus.csv' file.
#     - Overwrite 'sudoku.csv' with a new, clean version.

import pandas as pd
import numpy as np
import os
import sys

# ==============================================================================
# === CONFIGURATION ===
# ==============================================================================

# --- Primary Action ---
# Set this to True AFTER running the script once and seeing an 'invalid' report.
# This will trigger the cleanup process on the next run.
PERFORM_CLEANUP = False

# --- File Paths ---
INPUT_CSV_PATH = 'sudoku.csv'
INVALID_INDICES_FILE = 'invalid_sudoku_indices.txt'
INVALID_CSV_EXPORT_PATH = 'invalid_sudokus.csv'

# ==============================================================================
# === VALIDATION LOGIC ===
# ==============================================================================

def validate_solution_grid(grid_1d: np.ndarray) -> bool:
    """
    Mathematically validates a 1D numpy array representing a 9x9 Sudoku solution.

    Args:
        grid_1d: A numpy array of 81 integers.

    Returns:
        True if the grid is a valid Sudoku solution, False otherwise.
    """
    if grid_1d.shape[0] != 81 or np.any(grid_1d == 0):
        # Must be a fully filled 81-cell grid
        return False

    grid = grid_1d.reshape(9, 9)
    
    # The set of digits {1, 2, 3, 4, 5, 6, 7, 8, 9}
    required_set = set(range(1, 10))

    # 1. Check all rows
    for i in range(9):
        if set(grid[i, :]) != required_set:
            return False

    # 2. Check all columns
    for j in range(9):
        if set(grid[:, j]) != required_set:
            return False

    # 3. Check all 3x3 boxes
    for box_row_start in range(0, 9, 3):
        for box_col_start in range(0, 9, 3):
            box = grid[box_row_start:box_row_start+3, box_col_start:box_col_start+3]
            if set(box.flatten()) != required_set:
                return False

    # If all checks pass, the grid is valid
    return True

# ==============================================================================
# === MAIN EXECUTION SCRIPT ===
# ==============================================================================

def run_validation():
    """Main function to run the validation and reporting process."""
    print("--- Sudoku CSV Validator V2.1 ---")

    if not os.path.exists(INPUT_CSV_PATH):
        print(f"FATAL ERROR: The file '{INPUT_CSV_PATH}' was not found.")
        print("Please make sure you have generated it using 'Generate_sudokuCSV_V2.1.py'.")
        sys.exit(1)

    print(f"Loading data from '{INPUT_CSV_PATH}'...")
    try:
        df = pd.read_csv(INPUT_CSV_PATH)
        # Ensure columns are treated as strings to preserve leading zeros, then convert to numbers
        df['solutions_str'] = df['solutions'].astype(str).str.zfill(81)
    except Exception as e:
        print(f"Error reading CSV file: {e}")
        sys.exit(1)

    print("Validating all solution grids...")
    valid_indices = []
    invalid_indices = []

    for index, row in df.iterrows():
        try:
            # Convert the string of digits into a numpy array of integers
            solution_grid_1d = np.array(list(map(int, row['solutions_str'])))
            if validate_solution_grid(solution_grid_1d):
                valid_indices.append(index)
            else:
                invalid_indices.append(index)
        except (ValueError, TypeError):
            # Handle cases where a row might be malformed
            invalid_indices.append(index)

    # --- Report Findings ---
    num_valid = len(valid_indices)
    num_invalid = len(invalid_indices)
    total_grids = len(df)

    print("\n--- VALIDATION REPORT ---")
    print(f"Total grids scanned: {total_grids}")
    print(f"  => Valid solutions:   {num_valid}")
    print(f"  => Invalid solutions: {num_invalid}")
    print("-------------------------\n")

    if num_invalid > 0:
        print(f"Found {num_invalid} invalid grids. Saving their indices to '{INVALID_INDICES_FILE}'.")
        # Save indices to the text file, one index per line
        with open(INVALID_INDICES_FILE, 'w') as f:
            for index in invalid_indices:
                f.write(f"{index}\n")

        print("\n*** ACTION REQUIRED ***")
        print(f"To clean your '{INPUT_CSV_PATH}', please follow these steps:")
        print("1. Open this script ('Validate_sudokuCSV_V2.1.py') in the editor.")
        print("2. Change the configuration flag at the top from 'PERFORM_CLEANUP = False' to 'PERFORM_CLEANUP = True'.")
        print("3. Re-run this script.")
        print("This will move the invalid entries to 'invalid_sudokus.csv' and create a clean 'sudoku.csv'.")
    else:
        print("Congratulations! All solution grids in the CSV are valid.")
        # If no invalid grids were found, remove the old index file if it exists
        if os.path.exists(INVALID_INDICES_FILE):
            os.remove(INVALID_INDICES_FILE)


def run_cleanup():
    """Main function to perform the cleanup process."""
    print("--- Sudoku CSV Cleanup Utility ---")
    print(f"PERFORM_CLEANUP is set to True. Attempting to clean '{INPUT_CSV_PATH}'.")

    if not os.path.exists(INVALID_INDICES_FILE):
        print(f"ERROR: The file '{INVALID_INDICES_FILE}' was not found.")
        print("Please run the script with 'PERFORM_CLEANUP = False' first to generate the list of invalid indices.")
        sys.exit(1)

    print(f"Loading master data from '{INPUT_CSV_PATH}'...")
    df = pd.read_csv(INPUT_CSV_PATH)

    print(f"Loading invalid indices from '{INVALID_INDICES_FILE}'...")
    with open(INVALID_INDICES_FILE, 'r') as f:
        invalid_indices = [int(line.strip()) for line in f]

    print(f"Found {len(invalid_indices)} indices to remove.")

    # Separate the DataFrame into invalid and valid parts
    df_invalid = df.loc[invalid_indices]
    df_valid = df.drop(invalid_indices)

    # --- Perform the file operations ---
    # 1. Export invalid puzzles and their solutions
    print(f"Saving {len(df_invalid)} invalid entries to '{INVALID_CSV_EXPORT_PATH}'...")
    df_invalid.to_csv(INVALID_CSV_EXPORT_PATH, index=False)

    # 2. Overwrite the original file with only the valid data
    print(f"Overwriting '{INPUT_CSV_PATH}' with {len(df_valid)} valid entries...")
    df_valid.to_csv(INPUT_CSV_PATH, index=False)

    # 3. Clean up the index file
    os.remove(INVALID_INDICES_FILE)

    print("\n--- Cleanup Complete! ---")
    print(f"Your '{INPUT_CSV_PATH}' is now clean.")
    print(f"The invalid entries have been moved to '{INVALID_CSV_EXPORT_PATH}'.")
    print("Set 'PERFORM_CLEANUP = False' before running validation again.")


if __name__ == '__main__':
    if PERFORM_CLEANUP:
        run_cleanup()
    else:
        run_validation()