Fast TLSH Similarity Search with Approximate Nearest Neighbors For CyberSecurity

README.md

Fast TLSH Similarity Search with Approximate Nearest Neighbors

Contributors:

• Susmit Yenkar
• Jonathan Oliver
• John Holowczak
• Debanjan Bhattacharya

TLSH + PyNNDescent Integration

1. Open sourced the component for making TLSH vectors compatible with PyNNDescent.

   • 1(a). Provided code for using PyNNDescent (no modification to PyNNDescent required).
   • 1(b). Provided documentation of how to use 1(a).
   • 1(c). Provided comparison benchmarks to show significant speedup achieved.

2. TLSH GitHub Version 5.0.0

   • Merged a pull request into the official TLSH GitHub for efficiently converting TLSH vectors into PyNNDescent vectors.

3. py-tlsh 5.0.0

   • Published a new version of py-tlsh that enables this functionality. Added ByteArray generation for getting TLSH vectors.
   • Currently this is hosted on test.pypi and we intend to publish this version with BlackHat Arsenal dates.

Published artifacts: run_full_comparison.py, buildANNnPredict.py, tool_script.py, final_bit_pairs.py

What each script does

Script	Purpose
run_full_comparison.py	End-to-end benchmark: loads a TLSH CSV, runs all four search methods (linear + three ANN variants), measures timing/recall/QPS, writes a results CSV and a 3×3 chart PNG, and appends a row to experiment_log.csv.
buildANNnPredict.py	Reusable library: TLSH-to-numpy preprocessing (132-byte and 36-byte), Numba JIT distance metrics, PyNNDescent index build/query, and batch linear search. Import this in your own pipelines.
tool_script.py	Minimal usage template showing how to import and call buildANNnPredict. Replace "your_file.csv" with your actual data path before running.
final_bit_pairs.py	Data file: contains merged_diff_list, a precomputed 65 536-element lookup table of bucket-pair distances used by the 36-byte TLSH distance metric. Required by buildANNnPredict.py at import time — must be present in the same directory.

---

Description

Features

• Provides ANN & TLSH compatible "glue": We transform and extract TLSH vectors to enable ANN search. Giving a min ~1000x boost over linear search.
• Four methods compared: Linear search (ground truth) plus three ANN variants: Python 132-byte, Python 36-byte, and native C++ 36-byte preprocessing, each with optional two-stage (ANN + linear refinement) search.
• Ground-truth caching: Exact k-NN and linear search time are computed once per (data, split, k) and reused; Method 4 does not re-run the full linear scan on later runs with the same cache.
• Experiment log: Each run appends one row to experiment_log.csv (config + per-method metrics) for comparing runs across different parameters.
• Visualization: 3×3 grid of charts (search time vs linear baseline, speedup, QPS, preprocessing, memory, recall, top-1 accuracy, build time, total time).
• Reusable library: buildANNnPredict.py provides TLSH preprocessing (132- or 36-byte), Numba JIT distance metrics, PyNNDescent index build/query, and batch linear search for use in your own pipelines.

License

This project uses TLSH and PyNNDescent. This code is subject to the licenses of those dependencies:

• TLSH: Trend Micro TLSH — see the TLSH license for terms.
• PyNNDescent: PyNNDescent — see the PyNNDescent license for terms.

Compliance with those licenses is required when distributing or using this project.

Changelog

• Current: Full comparison script (run_full_comparison.py) with ground-truth caching, linear search time reuse, experiment log CSV, and 3×3 visualization; buildANNnPredict.py with 132/36-byte preprocessing, Numba metrics, and batch linear search. README structured as Description → Installation → Usage.
• TLSH - Change History:

4.13.1

06/02/2026
	Change default behaviour to output T1 at the start of the digest

4.13.2

06/02/2026
	Refactor lsh_bin_struct to provide easy access to byte vector

4.13.3

06/02/2026
	Add function WriteVector(unsigned char *vec) to get the byte array of a TLSH

4.13.4

17/02/2026
	Resolve #162 Reverse out T1 change made in 4.13.1

5.0.0

17/02/2026
	Change default behaviour to output T1 at the start of the digest
	A T1 digest requires that the number of buckets = 128 and the checksum is 1 byte

---

Installation

System requirements

• Python: 3.8 or later.
• OS: Linux, macOS, or Windows (any platform that supports the listed dependencies).
• Hardware: Benchmarks in this project were run on a machine with an AMD EPYC 7763 64-core CPU, 512 GB RAM. A modern multi-core CPU is recommended; buildANNnPredict.py uses Numba parallel JIT and PyNNDescent's n_jobs parameter to exploit all available cores. The scripts will run on less powerful hardware but build/search times will differ.

Dependencies

Install with:

python -m venv check-ann
source check-ann/bin/activate  # On Windows: check-ann\Scripts\activate
pip install pandas numpy numba pynndescent scikit-learn matplotlib seaborn tqdm
pip install --no-build-isolation -i https://test.pypi.org/simple/ py-tlsh

Note on the py-tlsh install from test.pypi:

• py-tlsh 5.0.0 is currently hosted on test.pypi and will be published to the main PyPI index around the BlackHat Arsenal dates.
• The --no-build-isolation flag is required because test.pypi does not host all build dependencies (e.g. setuptools/wheel); without it the install may fail with a No matching distribution error.
• All other dependencies (pandas, numpy, etc.) must be installed from the main PyPI index before running the py-tlsh install command, as test.pypi cannot resolve them.
• If the install still fails, try: pip install --no-build-isolation --extra-index-url https://pypi.org/simple/ -i https://test.pypi.org/simple/ py-tlsh

Required files

• Input data: A CSV with a column named tlsh containing valid TLSH hash strings (e.g. T1A2B3...).
• For buildANNnPredict.py: final_bit_pairs.py must be present in the same directory — it defines merged_diff_list, which buildANNnPredict.py imports at module level. run_full_comparison.py imports from buildANNnPredict, which in turn imports from final_bit_pairs.

Supported platforms / languages

• Platforms: Linux, macOS, Windows (anywhere Python 3.8+ and the listed dependencies run).
• Languages: Python 3.8+. Core distance and linear-search routines in buildANNnPredict.py use Numba JIT; the TLSH library uses C/C++ extensions. No other languages required for the published scripts.

---

Usage

Parameters

run_full_comparison.py accepts:

Argument	Type	Default	Description
--data-path	str		Path to CSV with tlsh column
--num-samples	int	None	Number of rows to use (None = all)
--test-size	float	0.1	Test fraction (0–1)
--random-state	int	42	Random seed for reproducibility
--k-neighbors	int	5	k for k-NN
--test-batch-size	int	100	Number of queries per timing batch
--timing-iterations	int	3	Number of timing runs per method
--force-regenerate	flag	false	Regenerate ground truth even if cached
--ground-truth-file	str	None	Path to precomputed ground truth (auto-detect if not set)
--ground-truth-dir	str	ground_truth	Directory for ground truth cache
--n-neighbors-build	int	20	ANN index: neighbors during build
--n-trees	int	5	ANN index: random projection trees
--n-iters	int	5	ANN index: refinement iterations
--n-jobs	int	-1	Parallel jobs (-1 = all cores)
--max-candidates	int	60	ANN search: max candidates

Usage examples

Run full comparison (default data path):

python run_full_comparison.py

Custom data path, k, and test size:

python run_full_comparison.py --data-path my_hashes.csv --k-neighbors 10 --test-size 0.2

Subset of data and force new ground truth:

python run_full_comparison.py --num-samples 50000 --force-regenerate

Outputs produced:

• Console: Per-method timing, QPS, recall, top-1 accuracy, speedups, and a short summary.
• Files:
  • full_comparison_results_<timestamp>.csv — One row per method (timing, QPS, recall, etc.).
  • full_comparison_plot_<timestamp>.png — 3×3 chart grid (first chart includes linear search time for comparison).
  • experiment_log.csv — One row per run (config + all method metrics); new runs are appended.
  • ground_truth/ — Cached ground truth and linear search time; reused for same (data_path, num_samples, test_size, random_state, k_neighbors).

Using buildANNnPredict.py in your own code:

import pandas as pd
from buildANNnPredict import buildANNnPredict, merged_diff_list_np
from buildANNnPredict import tlsh_distance_metric_36, tlsh_distance_metric_132, linear_search_batch

# merged_diff_list_np is defined in buildANNnPredict (imported from final_bit_pairs)
bmc = buildANNnPredict(merged_diff_list_np)

df = pd.read_csv("your_file.csv")  # must have 'tlsh' column

# method options: "132" (Python 128-bucket), "36" (Python packed), "native36" (C++ toByteArray)
tlsh_list = bmc.preprocess(df, method="132")
nn_model, build_time = bmc.fit(tlsh_list, metric_type=132)

results_df = bmc.predict(
    nn_model, tlsh_list, df, df,
    k_val=5,
    threshold=100
)

---

References

• TLSH – Trend Micro Locality Sensitive Hash
• PyNNDescent
• Numba
• Oliver, Jonathan, Jue Mo, Susmit Yenkar, Raghav Batta, and Sekhar Josyoula. "On the Role of Similarity in Detecting Masquerading Files." In International Conference on Machine Learning for Cyber Security, pp. 44-55. Singapore: Springer Nature Singapore, 2023.

buildANNnPredict.py

"""
Build and Predict with an ANN model for Fast TLSH search.
"""

# Imports
import time

import numpy as np
import pandas as pd
import numba
import tlsh
from pynndescent import NNDescent

from final_bit_pairs import merged_diff_list

merged_diff_list_np = np.array(merged_diff_list, dtype=np.int64)

@numba.njit(fastmath=True)
def mod_diff(x, y, R):
    """Calculate modular distance (circular difference)."""
    if y > x:
        return min(y-x, x+R-y)
    else:
        return min(x-y, y+R-x)


@numba.njit(fastmath=True)
def h_distance_36(x_narr, y_narr, bit_pairs_list):
    """Calculate Hamming-like distance between bucket values."""
    diff = 0
    for i in range(4, 36):
        x = x_narr[i]
        y = y_narr[i]
        diff += bit_pairs_list[int(256*x + y)]
    return diff


@numba.njit(fastmath=True)
def h_distance_132(x_narr, y_narr) -> int:
    """Calculate bucket distance for 132-byte representation (one bucket value per byte)."""
    diff = 0
    for idx in range(4, 132):
        x = int(x_narr[idx])
        y = int(y_narr[idx])
        if x > y:
            this_diff = np.subtract(x, y)
        else:
            this_diff = np.subtract(y, x)
        if this_diff == 3:
            this_diff = 6
        diff += this_diff
    return diff


@numba.njit(fastmath=True)
def totalDiff(this_narr, other_narr, bit_pairs_list, config=36):
    """
    Calculate total TLSH distance between two hashes.
    Works for both 132-byte and 36-byte representations.
    """
    diff = 0
    ldiff = mod_diff(this_narr[1], other_narr[1], 256)
    if ldiff == 0:
        diff = 0
    elif ldiff == 1:
        diff = 1
    else:
        diff += ldiff * 12

    q1diff = mod_diff(this_narr[2], other_narr[2], 16)
    if q1diff <= 1:
        diff += q1diff
    else:
        diff += (q1diff - 1) * 12

    q2diff = mod_diff(this_narr[3], other_narr[3], 16)
    if q2diff <= 1:
        diff += q2diff
    else:
        diff += (q2diff - 1) * 12

    if this_narr[0] != other_narr[0]:
        diff += 1

    if config == 36:
        diff += h_distance_36(this_narr, other_narr, bit_pairs_list)
    elif config == 132:
        diff += h_distance_132(this_narr, other_narr)
    else:
        raise ValueError(f"Invalid configuration: {config}")
    return diff


@numba.njit(fastmath=True)
def tlsh_distance_metric_36(x, y):
    """Wrapper function for PyNNDescent compatibility. Uses global merged_diff_list_np."""
    return totalDiff(x, y, merged_diff_list_np, config=36)


@numba.njit(fastmath=True)
def tlsh_distance_metric_132(x, y):
    """Wrapper function for PyNNDescent compatibility. Uses global merged_diff_list_np."""
    return totalDiff(x, y, merged_diff_list_np, config=132)


@numba.njit(parallel=True, fastmath=True)
def linear_search_batch(queries, database, k, bit_pairs_list):
    """
    Perform linear search for k nearest neighbors.

    Args:
        queries: Array of query vectors
        database: Array of database vectors
        k: Number of neighbors to return
        bit_pairs_list: Precomputed bit pair differences

    Returns:
        indices: Array of neighbor indices (n_queries, k)
        distances: Array of distances (n_queries, k)
    """
    n_queries = queries.shape[0]
    n_database = database.shape[0]

    indices = np.zeros((n_queries, k), dtype=np.int64)
    distances = np.zeros((n_queries, k), dtype=np.float64)

    for i in numba.prange(n_queries):
        dists = np.zeros(n_database, dtype=np.float64)
        for j in range(n_database):
            dists[j] = totalDiff(queries[i], database[j], bit_pairs_list)

        idx = np.argsort(dists)[:k]
        indices[i] = idx
        distances[i] = dists[idx]

    return indices, distances


class buildANNnPredict(object):

    totalDiff = staticmethod(totalDiff)
    h_distance_36 = staticmethod(h_distance_36)
    h_distance_132 = staticmethod(h_distance_132)
    mod_diff = staticmethod(mod_diff)

    def __init__(self, merged_diff_list) -> None:
        self.bit_pairs_list = merged_diff_list
    
    def valid_tlsh(self, h1):
        if len(h1) == 72:
            if h1[0:2] == "T1":
                return 1
        if len(h1) == 70:
            return 1
        return 0

    def transform_36(self, h1):
        narr1 = np.zeros(36, dtype=np.uint8)
        narr1[0] = h1.checksum(0)
        narr1[1] = h1.lvalue
        narr1[2] = h1.q1ratio
        narr1[3] = h1.q2ratio
        for byte_idx in range(32):
            b0 = byte_idx * 4
            byte_val = 64 * h1.bucket_value(b0) + 16 * h1.bucket_value(b0+1) + 4 * h1.bucket_value(b0+2) + h1.bucket_value(b0+3)
            narr1[4 + byte_idx] = byte_val
        return(narr1)
    
    def transform_132(self, h1):
        narr1 = np.zeros(132, dtype=np.uint8)
        narr1[0] = h1.checksum(0)
        narr1[1] = h1.lvalue
        narr1[2] = h1.q1ratio
        narr1[3] = h1.q2ratio
        for bucket_idx in range(128):
            narr1[4 + bucket_idx] = h1.bucket_value(bucket_idx)
        return narr1

    def tlsh_obj_generator(self, the_tlsh):
        final_tlsh = tlsh.Tlsh()
        final_tlsh.fromTlshStr(the_tlsh)
        return final_tlsh

    def tlsh_bytearray_generator(self, the_tlsh):
        final_tlsh = tlsh.Tlsh()
        final_tlsh.fromTlshStr(the_tlsh)
        byte_data = bytearray(36)
        final_tlsh.toByteArray(byte_data)
        numpy_array = np.asarray(memoryview(byte_data), dtype=np.uint8)
        return numpy_array

    def preprocess(self, the_data_frame, method="132"):
        """
        Input:
        A dataframe with `tlsh` column.
        method: "132" or "36"
        Returns:
        A list of preprocessed and ready for model train or predict
        """
        try:
            df = the_data_frame[the_data_frame["tlsh"].apply(self.valid_tlsh) == 1].reset_index(drop=True)
            if len(df) < len(the_data_frame):
                print(f"[preprocess] Dropped {len(the_data_frame) - len(df)} row(s) with invalid/null TLSH values.")
            if method == "132":
                res = df["tlsh"].apply(self.tlsh_obj_generator).apply(self.transform_132).to_list()
            elif method == "36":
                res = df["tlsh"].apply(self.tlsh_obj_generator).apply(self.transform_36).to_list()
            elif method == "native36":
                res = df["tlsh"].apply(self.tlsh_bytearray_generator).to_list()
            else:
                raise ValueError(f"Invalid method: {method}")
            return res
        except KeyError as err:
            print(f"{err}\nPlease provide a DataFrame with a 'tlsh' column.")
        except Exception as err:
            print(f"Can't resolve {err}")

    def fit(self, tlsh_list, metric_type=36):
        """
        Input:
        The preprocessed list of `tlsh` values

        Return:
        Returns a pynnmodel and build time.
        """
        if metric_type == 36:
            metric = tlsh_distance_metric_36
        else:
            metric = tlsh_distance_metric_132
        start_time = time.time()
        pynn_narr = np.asarray(tlsh_list)
        nn_model = NNDescent(
            pynn_narr,
            metric=metric,
            n_neighbors=15,
            n_trees=5,
            n_iters=5,
            n_jobs=-1,
            max_candidates=60,
            verbose=False
        )
        build_time = time.time() - start_time
        return nn_model, build_time

    def predict(self, nn_model, test_tlsh_list, centroids_df, test_tlsh_df, k_val, threshold):
        """
        Input:
        nn_model: pynn_model object
        test_tlsh_list: preprocessed numpy arrays or bytearrays for the query set
        centroids_df: DataFrame containing the database (must have 'tlsh' column)
        test_tlsh_df: DataFrame for the query set (results are added as new columns)
        k_val: number of nearest neighbors to retrieve
        threshold: maximum distance to consider a match; returns "Not Found" if exceeded

        Return:
        List of indices which lie within the threshold distance,
        if dist does not lie between threshold return "Not Found"
        This list can be then merged to the actual test data frame or used as needed.
        """
        test_tlsh_df_copy = test_tlsh_df.copy() # copy the test_tlsh_df to avoid modifying the original dataframe
        nn_model.prepare()
        neighbors = nn_model.query(test_tlsh_list, k=k_val)
        top_index = [each[0] for each in neighbors[0]]
        top_index_dist = [each[0] for each in neighbors[1]]
        top_index_tlsh = [centroids_df["tlsh"][the_index] for the_index in top_index]
        select_index = [
            the_index if neighbors[1][count_ind][0] <= threshold else "Not Found"
            for count_ind, the_index in enumerate(top_index)
        ]
        test_tlsh_df_copy["top_index"] = top_index
        test_tlsh_df_copy["select_index"] = select_index
        test_tlsh_df_copy["top_index_tlsh"] = top_index_tlsh
        test_tlsh_df_copy["top_index_dist"] = top_index_dist
        return test_tlsh_df_copy


if __name__ == "__main__":
    bmc_obj = buildANNnPredict(merged_diff_list_np)
    tlsh_df = pd.read_csv("<your_file>.csv").head(100)
    tlsh_list = bmc_obj.preprocess(tlsh_df, method="132")
    nn_model = bmc_obj.fit(tlsh_list)
    test_tlsh_list = bmc_obj.preprocess(tlsh_df, method="132")
    test_tlsh_df = bmc_obj.predict(nn_model, test_tlsh_list, tlsh_df, tlsh_df, 5, 10)
    print(test_tlsh_df)

final_bit_pairs.py

# This is used for comparision benchmarks
merged_diff_list = [0, 1, 2, 6, 1, 2, 3, 7, 2, 3, 4, 8, 6, 7, 8, 12, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 9, 13, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 6, 7, 8, 12, 7, 8, 9, 13, 8, 9, 10, 14, 12, 13, 14, 18, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 9, 13, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 7, 8, 9, 13, 8, 9, 10, 14, 9, 10, 11, 15, 13, 14, 15, 19, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 4, 5, 6, 10, 5, 6, 7, 11, 6, 7, 8, 12, 10, 11, 12, 16, 8, 9, 10, 14, 9, 10, 11, 15, 10, 11, 12, 16, 14, 15, 16, 20, 6, 7, 8, 12, 7, 8, 9, 13, 8, 9, 10, 14, 12, 13, 14, 18, 7, 8, 9, 13, 8, 9, 10, 14, 9, 10, 11, 15, 13, 14, 15, 19, 8, 9, 10, 14, 9, 10, 11, 15, 10, 11, 12, 16, 14, 15, 16, 20, 12, 13, 14, 18, 13, 14, 15, 19, 14, 15, 16, 20, 18, 19, 20, 24, 1, 0, 1, 2, 2, 1, 2, 3, 3, 2, 3, 4, 7, 6, 7, 8, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 8, 7, 8, 9, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 7, 6, 7, 8, 8, 7, 8, 9, 9, 8, 9, 10, 13, 12, 13, 14, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 8, 7, 8, 9, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 8, 7, 8, 9, 9, 8, 9, 10, 10, 9, 10, 11, 14, 13, 14, 15, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 5, 4, 5, 6, 6, 5, 6, 7, 7, 6, 7, 8, 11, 10, 11, 12, 9, 8, 9, 10, 10, 9, 10, 11, 11, 10, 11, 12, 15, 14, 15, 16, 7, 6, 7, 8, 8, 7, 8, 9, 9, 8, 9, 10, 13, 12, 13, 14, 8, 7, 8, 9, 9, 8, 9, 10, 10, 9, 10, 11, 14, 13, 14, 15, 9, 8, 9, 10, 10, 9, 10, 11, 11, 10, 11, 12, 15, 14, 15, 16, 13, 12, 13, 14, 14, 13, 14, 15, 15, 14, 15, 16, 19, 18, 19, 20, 2, 1, 0, 1, 3, 2, 1, 2, 4, 3, 2, 3, 8, 7, 6, 7, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 9, 8, 7, 8, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 8, 7, 6, 7, 9, 8, 7, 8, 10, 9, 8, 9, 14, 13, 12, 13, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 9, 8, 7, 8, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 9, 8, 7, 8, 10, 9, 8, 9, 11, 10, 9, 10, 15, 14, 13, 14, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 6, 5, 4, 5, 7, 6, 5, 6, 8, 7, 6, 7, 12, 11, 10, 11, 10, 9, 8, 9, 11, 10, 9, 10, 12, 11, 10, 11, 16, 15, 14, 15, 8, 7, 6, 7, 9, 8, 7, 8, 10, 9, 8, 9, 14, 13, 12, 13, 9, 8, 7, 8, 10, 9, 8, 9, 11, 10, 9, 10, 15, 14, 13, 14, 10, 9, 8, 9, 11, 10, 9, 10, 12, 11, 10, 11, 16, 15, 14, 15, 14, 13, 12, 13, 15, 14, 13, 14, 16, 15, 14, 15, 20, 19, 18, 19, 6, 2, 1, 0, 7, 3, 2, 1, 8, 4, 3, 2, 12, 8, 7, 6, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 12, 8, 7, 6, 13, 9, 8, 7, 14, 10, 9, 8, 18, 14, 13, 12, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 15, 11, 10, 9, 13, 9, 8, 7, 14, 10, 9, 8, 15, 11, 10, 9, 19, 15, 14, 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9, 8, 7, 8, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 8, 7, 6, 7, 4, 3, 2, 3, 3, 2, 1, 2, 2, 1, 0, 1, 9, 8, 7, 8, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 9, 8, 7, 8, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 11, 10, 9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 11, 10, 9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 11, 10, 9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 12, 11, 10, 11, 8, 7, 6, 7, 7, 6, 5, 6, 6, 5, 4, 5, 15, 14, 13, 14, 11, 10, 9, 10, 10, 9, 8, 9, 9, 8, 7, 8, 14, 13, 12, 13, 10, 9, 8, 9, 9, 8, 7, 8, 8, 7, 6, 7, 15, 14, 13, 14, 11, 10, 9, 10, 10, 9, 8, 9, 9, 8, 7, 8, 16, 15, 14, 15, 12, 11, 10, 11, 11, 10, 9, 10, 10, 9, 8, 9, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 12, 8, 7, 6, 8, 4, 3, 2, 7, 3, 2, 1, 6, 2, 1, 0, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 16, 12, 11, 10, 12, 8, 7, 6, 11, 7, 6, 5, 10, 6, 5, 4, 19, 15, 14, 13, 15, 11, 10, 9, 14, 10, 9, 8, 13, 9, 8, 7, 18, 14, 13, 12, 14, 10, 9, 8, 13, 9, 8, 7, 12, 8, 7, 6, 19, 15, 14, 13, 15, 11, 10, 9, 14, 10, 9, 8, 13, 9, 8, 7, 20, 16, 15, 14, 16, 12, 11, 10, 15, 11, 10, 9, 14, 10, 9, 8, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 9, 13, 0, 1, 2, 6, 1, 2, 3, 7, 2, 3, 4, 8, 6, 7, 8, 12, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 9, 13, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 9, 13, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 4, 5, 6, 10, 5, 6, 7, 11, 6, 7, 8, 12, 10, 11, 12, 16, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 8, 9, 10, 14, 9, 10, 11, 15, 10, 11, 12, 16, 14, 15, 16, 20, 7, 8, 9, 13, 8, 9, 10, 14, 9, 10, 11, 15, 13, 14, 15, 19, 6, 7, 8, 12, 7, 8, 9, 13, 8, 9, 10, 14, 12, 13, 14, 18, 7, 8, 9, 13, 8, 9, 10, 14, 9, 10, 11, 15, 13, 14, 15, 19, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 8, 7, 8, 9, 1, 0, 1, 2, 2, 1, 2, 3, 3, 2, 3, 4, 7, 6, 7, 8, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 8, 7, 8, 9, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 8, 7, 8, 9, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 5, 4, 5, 6, 6, 5, 6, 7, 7, 6, 7, 8, 11, 10, 11, 12, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 9, 8, 9, 10, 10, 9, 10, 11, 11, 10, 11, 12, 15, 14, 15, 16, 8, 7, 8, 9, 9, 8, 9, 10, 10, 9, 10, 11, 14, 13, 14, 15, 7, 6, 7, 8, 8, 7, 8, 9, 9, 8, 9, 10, 13, 12, 13, 14, 8, 7, 8, 9, 9, 8, 9, 10, 10, 9, 10, 11, 14, 13, 14, 15, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 9, 8, 7, 8, 2, 1, 0, 1, 3, 2, 1, 2, 4, 3, 2, 3, 8, 7, 6, 7, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 9, 8, 7, 8, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 9, 8, 7, 8, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 6, 5, 4, 5, 7, 6, 5, 6, 8, 7, 6, 7, 12, 11, 10, 11, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 10, 9, 8, 9, 11, 10, 9, 10, 12, 11, 10, 11, 16, 15, 14, 15, 9, 8, 7, 8, 10, 9, 8, 9, 11, 10, 9, 10, 15, 14, 13, 14, 8, 7, 6, 7, 9, 8, 7, 8, 10, 9, 8, 9, 14, 13, 12, 13, 9, 8, 7, 8, 10, 9, 8, 9, 11, 10, 9, 10, 15, 14, 13, 14, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 6, 2, 1, 0, 7, 3, 2, 1, 8, 4, 3, 2, 12, 8, 7, 6, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 15, 11, 10, 9, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 10, 6, 5, 4, 11, 7, 6, 5, 12, 8, 7, 6, 16, 12, 11, 10, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 15, 11, 10, 9, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 15, 11, 10, 9, 14, 10, 9, 8, 15, 11, 10, 9, 16, 12, 11, 10, 20, 16, 15, 14, 13, 9, 8, 7, 14, 10, 9, 8, 15, 11, 10, 9, 19, 15, 14, 13, 12, 8, 7, 6, 13, 9, 8, 7, 14, 10, 9, 8, 18, 14, 13, 12, 13, 9, 8, 7, 14, 10, 9, 8, 15, 11, 10, 9, 19, 15, 14, 13, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 1, 2, 3, 7, 0, 1, 2, 6, 1, 2, 3, 7, 2, 3, 4, 8, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 4, 5, 6, 10, 5, 6, 7, 11, 6, 7, 8, 12, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 8, 9, 10, 14, 9, 10, 11, 15, 10, 11, 12, 16, 8, 9, 10, 14, 7, 8, 9, 13, 8, 9, 10, 14, 9, 10, 11, 15, 7, 8, 9, 13, 6, 7, 8, 12, 7, 8, 9, 13, 8, 9, 10, 14, 8, 9, 10, 14, 7, 8, 9, 13, 8, 9, 10, 14, 9, 10, 11, 15, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 3, 2, 3, 4, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 2, 1, 2, 3, 1, 0, 1, 2, 2, 1, 2, 3, 3, 2, 3, 4, 3, 2, 3, 4, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 3, 2, 3, 4, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 5, 4, 5, 6, 6, 5, 6, 7, 7, 6, 7, 8, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 9, 8, 9, 10, 10, 9, 10, 11, 11, 10, 11, 12, 9, 8, 9, 10, 8, 7, 8, 9, 9, 8, 9, 10, 10, 9, 10, 11, 8, 7, 8, 9, 7, 6, 7, 8, 8, 7, 8, 9, 9, 8, 9, 10, 9, 8, 9, 10, 8, 7, 8, 9, 9, 8, 9, 10, 10, 9, 10, 11, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 3, 2, 1, 2, 2, 1, 0, 1, 3, 2, 1, 2, 4, 3, 2, 3, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 6, 5, 4, 5, 7, 6, 5, 6, 8, 7, 6, 7, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 10, 9, 8, 9, 11, 10, 9, 10, 12, 11, 10, 11, 10, 9, 8, 9, 9, 8, 7, 8, 10, 9, 8, 9, 11, 10, 9, 10, 9, 8, 7, 8, 8, 7, 6, 7, 9, 8, 7, 8, 10, 9, 8, 9, 10, 9, 8, 9, 9, 8, 7, 8, 10, 9, 8, 9, 11, 10, 9, 10, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 7, 3, 2, 1, 6, 2, 1, 0, 7, 3, 2, 1, 8, 4, 3, 2, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 10, 6, 5, 4, 11, 7, 6, 5, 12, 8, 7, 6, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 15, 11, 10, 9, 14, 10, 9, 8, 15, 11, 10, 9, 16, 12, 11, 10, 14, 10, 9, 8, 13, 9, 8, 7, 14, 10, 9, 8, 15, 11, 10, 9, 13, 9, 8, 7, 12, 8, 7, 6, 13, 9, 8, 7, 14, 10, 9, 8, 14, 10, 9, 8, 13, 9, 8, 7, 14, 10, 9, 8, 15, 11, 10, 9, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 2, 3, 4, 8, 1, 2, 3, 7, 0, 1, 2, 6, 1, 2, 3, 7, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 6, 7, 8, 12, 5, 6, 7, 11, 4, 5, 6, 10, 5, 6, 7, 11, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 10, 11, 12, 16, 9, 10, 11, 15, 8, 9, 10, 14, 9, 10, 11, 15, 9, 10, 11, 15, 8, 9, 10, 14, 7, 8, 9, 13, 8, 9, 10, 14, 8, 9, 10, 14, 7, 8, 9, 13, 6, 7, 8, 12, 7, 8, 9, 13, 9, 10, 11, 15, 8, 9, 10, 14, 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8, 7, 8, 9, 9, 8, 9, 10, 10, 9, 10, 11, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 6, 5, 6, 7, 5, 4, 5, 6, 6, 5, 6, 7, 7, 6, 7, 8, 10, 9, 10, 11, 9, 8, 9, 10, 10, 9, 10, 11, 11, 10, 11, 12, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 10, 9, 8, 9, 9, 8, 7, 8, 10, 9, 8, 9, 11, 10, 9, 10, 3, 2, 1, 2, 2, 1, 0, 1, 3, 2, 1, 2, 4, 3, 2, 3, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 9, 8, 7, 8, 8, 7, 6, 7, 9, 8, 7, 8, 10, 9, 8, 9, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 10, 9, 8, 9, 9, 8, 7, 8, 10, 9, 8, 9, 11, 10, 9, 10, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 7, 6, 5, 6, 6, 5, 4, 5, 7, 6, 5, 6, 8, 7, 6, 7, 11, 10, 9, 10, 10, 9, 8, 9, 11, 10, 9, 10, 12, 11, 10, 11, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 14, 10, 9, 8, 13, 9, 8, 7, 14, 10, 9, 8, 15, 11, 10, 9, 7, 3, 2, 1, 6, 2, 1, 0, 7, 3, 2, 1, 8, 4, 3, 2, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 13, 9, 8, 7, 12, 8, 7, 6, 13, 9, 8, 7, 14, 10, 9, 8, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 14, 10, 9, 8, 13, 9, 8, 7, 14, 10, 9, 8, 15, 11, 10, 9, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 11, 7, 6, 5, 10, 6, 5, 4, 11, 7, 6, 5, 12, 8, 7, 6, 15, 11, 10, 9, 14, 10, 9, 8, 15, 11, 10, 9, 16, 12, 11, 10, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 9, 10, 11, 15, 8, 9, 10, 14, 7, 8, 9, 13, 8, 9, 10, 14, 2, 3, 4, 8, 1, 2, 3, 7, 0, 1, 2, 6, 1, 2, 3, 7, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 8, 9, 10, 14, 7, 8, 9, 13, 6, 7, 8, 12, 7, 8, 9, 13, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 9, 10, 11, 15, 8, 9, 10, 14, 7, 8, 9, 13, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 6, 7, 8, 12, 5, 6, 7, 11, 4, 5, 6, 10, 5, 6, 7, 11, 10, 11, 12, 16, 9, 10, 11, 15, 8, 9, 10, 14, 9, 10, 11, 15, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 3, 2, 3, 4, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 10, 9, 10, 11, 9, 8, 9, 10, 8, 7, 8, 9, 9, 8, 9, 10, 3, 2, 3, 4, 2, 1, 2, 3, 1, 0, 1, 2, 2, 1, 2, 3, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 3, 2, 3, 4, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 9, 8, 9, 10, 8, 7, 8, 9, 7, 6, 7, 8, 8, 7, 8, 9, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 3, 2, 3, 4, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 10, 9, 10, 11, 9, 8, 9, 10, 8, 7, 8, 9, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 7, 6, 7, 8, 6, 5, 6, 7, 5, 4, 5, 6, 6, 5, 6, 7, 11, 10, 11, 12, 10, 9, 10, 11, 9, 8, 9, 10, 10, 9, 10, 11, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 11, 10, 9, 10, 10, 9, 8, 9, 9, 8, 7, 8, 10, 9, 8, 9, 4, 3, 2, 3, 3, 2, 1, 2, 2, 1, 0, 1, 3, 2, 1, 2, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 10, 9, 8, 9, 9, 8, 7, 8, 8, 7, 6, 7, 9, 8, 7, 8, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 11, 10, 9, 10, 10, 9, 8, 9, 9, 8, 7, 8, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 8, 7, 6, 7, 7, 6, 5, 6, 6, 5, 4, 5, 7, 6, 5, 6, 12, 11, 10, 11, 11, 10, 9, 10, 10, 9, 8, 9, 11, 10, 9, 10, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 15, 11, 10, 9, 14, 10, 9, 8, 13, 9, 8, 7, 14, 10, 9, 8, 8, 4, 3, 2, 7, 3, 2, 1, 6, 2, 1, 0, 7, 3, 2, 1, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 14, 10, 9, 8, 13, 9, 8, 7, 12, 8, 7, 6, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 15, 11, 10, 9, 14, 10, 9, 8, 13, 9, 8, 7, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 12, 8, 7, 6, 11, 7, 6, 5, 10, 6, 5, 4, 11, 7, 6, 5, 16, 12, 11, 10, 15, 11, 10, 9, 14, 10, 9, 8, 15, 11, 10, 9, 7, 8, 9, 13, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 9, 10, 11, 15, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 13, 14, 15, 19, 9, 10, 11, 15, 8, 9, 10, 14, 7, 8, 9, 13, 6, 7, 8, 12, 2, 3, 4, 8, 1, 2, 3, 7, 0, 1, 2, 6, 7, 8, 9, 13, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 12, 13, 14, 18, 8, 9, 10, 14, 7, 8, 9, 13, 6, 7, 8, 12, 7, 8, 9, 13, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 9, 10, 11, 15, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 13, 14, 15, 19, 9, 10, 11, 15, 8, 9, 10, 14, 7, 8, 9, 13, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 9, 10, 11, 15, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 10, 11, 12, 16, 6, 7, 8, 12, 5, 6, 7, 11, 4, 5, 6, 10, 14, 15, 16, 20, 10, 11, 12, 16, 9, 10, 11, 15, 8, 9, 10, 14, 8, 7, 8, 9, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 10, 9, 10, 11, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 14, 13, 14, 15, 10, 9, 10, 11, 9, 8, 9, 10, 8, 7, 8, 9, 7, 6, 7, 8, 3, 2, 3, 4, 2, 1, 2, 3, 1, 0, 1, 2, 8, 7, 8, 9, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 13, 12, 13, 14, 9, 8, 9, 10, 8, 7, 8, 9, 7, 6, 7, 8, 8, 7, 8, 9, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 10, 9, 10, 11, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 14, 13, 14, 15, 10, 9, 10, 11, 9, 8, 9, 10, 8, 7, 8, 9, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 10, 9, 10, 11, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 11, 10, 11, 12, 7, 6, 7, 8, 6, 5, 6, 7, 5, 4, 5, 6, 15, 14, 15, 16, 11, 10, 11, 12, 10, 9, 10, 11, 9, 8, 9, 10, 9, 8, 7, 8, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 11, 10, 9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 15, 14, 13, 14, 11, 10, 9, 10, 10, 9, 8, 9, 9, 8, 7, 8, 8, 7, 6, 7, 4, 3, 2, 3, 3, 2, 1, 2, 2, 1, 0, 1, 9, 8, 7, 8, 5, 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9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 11, 10, 9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 11, 10, 9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 12, 11, 10, 11, 8, 7, 6, 7, 7, 6, 5, 6, 6, 5, 4, 5, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 12, 8, 7, 6, 8, 4, 3, 2, 7, 3, 2, 1, 6, 2, 1, 0, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 16, 12, 11, 10, 12, 8, 7, 6, 11, 7, 6, 5, 10, 6, 5, 4, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 9, 13, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 9, 13, 0, 1, 2, 6, 1, 2, 3, 7, 2, 3, 4, 8, 6, 7, 8, 12, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 9, 13, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 9, 13, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 4, 5, 6, 10, 5, 6, 7, 11, 6, 7, 8, 12, 10, 11, 12, 16, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 8, 7, 8, 9, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 8, 7, 8, 9, 1, 0, 1, 2, 2, 1, 2, 3, 3, 2, 3, 4, 7, 6, 7, 8, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 8, 7, 8, 9, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 8, 7, 8, 9, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 5, 4, 5, 6, 6, 5, 6, 7, 7, 6, 7, 8, 11, 10, 11, 12, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 9, 8, 7, 8, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 9, 8, 7, 8, 2, 1, 0, 1, 3, 2, 1, 2, 4, 3, 2, 3, 8, 7, 6, 7, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 9, 8, 7, 8, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 9, 8, 7, 8, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 6, 5, 4, 5, 7, 6, 5, 6, 8, 7, 6, 7, 12, 11, 10, 11, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 15, 11, 10, 9, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 6, 2, 1, 0, 7, 3, 2, 1, 8, 4, 3, 2, 12, 8, 7, 6, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 15, 11, 10, 9, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 10, 6, 5, 4, 11, 7, 6, 5, 12, 8, 7, 6, 16, 12, 11, 10, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 15, 11, 10, 9, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 15, 11, 10, 9, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 1, 2, 3, 7, 0, 1, 2, 6, 1, 2, 3, 7, 2, 3, 4, 8, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 4, 5, 6, 10, 5, 6, 7, 11, 6, 7, 8, 12, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 3, 2, 3, 4, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 3, 2, 3, 4, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 2, 1, 2, 3, 1, 0, 1, 2, 2, 1, 2, 3, 3, 2, 3, 4, 3, 2, 3, 4, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 3, 2, 3, 4, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 5, 4, 5, 6, 6, 5, 6, 7, 7, 6, 7, 8, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 3, 2, 1, 2, 2, 1, 0, 1, 3, 2, 1, 2, 4, 3, 2, 3, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 6, 5, 4, 5, 7, 6, 5, 6, 8, 7, 6, 7, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 7, 3, 2, 1, 6, 2, 1, 0, 7, 3, 2, 1, 8, 4, 3, 2, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 10, 6, 5, 4, 11, 7, 6, 5, 12, 8, 7, 6, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 2, 3, 4, 8, 1, 2, 3, 7, 0, 1, 2, 6, 1, 2, 3, 7, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 6, 7, 8, 12, 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5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 12, 8, 7, 6, 11, 7, 6, 5, 10, 6, 5, 4, 11, 7, 6, 5, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 8, 4, 3, 2, 7, 3, 2, 1, 6, 2, 1, 0, 7, 3, 2, 1, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 11, 12, 16, 6, 7, 8, 12, 5, 6, 7, 11, 4, 5, 6, 10, 9, 10, 11, 15, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 9, 10, 11, 15, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 9, 10, 11, 15, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 7, 8, 9, 13, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 7, 8, 9, 13, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 6, 7, 8, 12, 2, 3, 4, 8, 1, 2, 3, 7, 0, 1, 2, 6, 7, 8, 9, 13, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 9, 10, 11, 15, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 7, 8, 9, 13, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 11, 10, 11, 12, 7, 6, 7, 8, 6, 5, 6, 7, 5, 4, 5, 6, 10, 9, 10, 11, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 10, 9, 10, 11, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 10, 9, 10, 11, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 8, 7, 8, 9, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 8, 7, 8, 9, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 7, 6, 7, 8, 3, 2, 3, 4, 2, 1, 2, 3, 1, 0, 1, 2, 8, 7, 8, 9, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 10, 9, 10, 11, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 8, 7, 8, 9, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 12, 11, 10, 11, 8, 7, 6, 7, 7, 6, 5, 6, 6, 5, 4, 5, 11, 10, 9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 11, 10, 9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 11, 10, 9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 9, 8, 7, 8, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 9, 8, 7, 8, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 8, 7, 6, 7, 4, 3, 2, 3, 3, 2, 1, 2, 2, 1, 0, 1, 9, 8, 7, 8, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 11, 10, 9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 9, 8, 7, 8, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 16, 12, 11, 10, 12, 8, 7, 6, 11, 7, 6, 5, 10, 6, 5, 4, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 12, 8, 7, 6, 8, 4, 3, 2, 7, 3, 2, 1, 6, 2, 1, 0, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 8, 9, 10, 14, 9, 10, 11, 15, 10, 11, 12, 16, 14, 15, 16, 20, 4, 5, 6, 10, 5, 6, 7, 11, 6, 7, 8, 12, 10, 11, 12, 16, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 7, 8, 9, 13, 8, 9, 10, 14, 9, 10, 11, 15, 13, 14, 15, 19, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 9, 13, 6, 7, 8, 12, 7, 8, 9, 13, 8, 9, 10, 14, 12, 13, 14, 18, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 9, 13, 0, 1, 2, 6, 1, 2, 3, 7, 2, 3, 4, 8, 6, 7, 8, 12, 7, 8, 9, 13, 8, 9, 10, 14, 9, 10, 11, 15, 13, 14, 15, 19, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 9, 10, 11, 15, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 9, 13, 9, 8, 9, 10, 10, 9, 10, 11, 11, 10, 11, 12, 15, 14, 15, 16, 5, 4, 5, 6, 6, 5, 6, 7, 7, 6, 7, 8, 11, 10, 11, 12, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 8, 7, 8, 9, 9, 8, 9, 10, 10, 9, 10, 11, 14, 13, 14, 15, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 8, 7, 8, 9, 7, 6, 7, 8, 8, 7, 8, 9, 9, 8, 9, 10, 13, 12, 13, 14, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 8, 7, 8, 9, 1, 0, 1, 2, 2, 1, 2, 3, 3, 2, 3, 4, 7, 6, 7, 8, 8, 7, 8, 9, 9, 8, 9, 10, 10, 9, 10, 11, 14, 13, 14, 15, 4, 3, 4, 5, 5, 4, 5, 6, 6, 5, 6, 7, 10, 9, 10, 11, 3, 2, 3, 4, 4, 3, 4, 5, 5, 4, 5, 6, 9, 8, 9, 10, 2, 1, 2, 3, 3, 2, 3, 4, 4, 3, 4, 5, 8, 7, 8, 9, 10, 9, 8, 9, 11, 10, 9, 10, 12, 11, 10, 11, 16, 15, 14, 15, 6, 5, 4, 5, 7, 6, 5, 6, 8, 7, 6, 7, 12, 11, 10, 11, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 9, 8, 7, 8, 10, 9, 8, 9, 11, 10, 9, 10, 15, 14, 13, 14, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 9, 8, 7, 8, 8, 7, 6, 7, 9, 8, 7, 8, 10, 9, 8, 9, 14, 13, 12, 13, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 9, 8, 7, 8, 2, 1, 0, 1, 3, 2, 1, 2, 4, 3, 2, 3, 8, 7, 6, 7, 9, 8, 7, 8, 10, 9, 8, 9, 11, 10, 9, 10, 15, 14, 13, 14, 5, 4, 3, 4, 6, 5, 4, 5, 7, 6, 5, 6, 11, 10, 9, 10, 4, 3, 2, 3, 5, 4, 3, 4, 6, 5, 4, 5, 10, 9, 8, 9, 3, 2, 1, 2, 4, 3, 2, 3, 5, 4, 3, 4, 9, 8, 7, 8, 14, 10, 9, 8, 15, 11, 10, 9, 16, 12, 11, 10, 20, 16, 15, 14, 10, 6, 5, 4, 11, 7, 6, 5, 12, 8, 7, 6, 16, 12, 11, 10, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 15, 11, 10, 9, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 13, 9, 8, 7, 14, 10, 9, 8, 15, 11, 10, 9, 19, 15, 14, 13, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 15, 11, 10, 9, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 12, 8, 7, 6, 13, 9, 8, 7, 14, 10, 9, 8, 18, 14, 13, 12, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 6, 2, 1, 0, 7, 3, 2, 1, 8, 4, 3, 2, 12, 8, 7, 6, 13, 9, 8, 7, 14, 10, 9, 8, 15, 11, 10, 9, 19, 15, 14, 13, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 15, 11, 10, 9, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 9, 10, 11, 15, 8, 9, 10, 14, 9, 10, 11, 15, 10, 11, 12, 16, 5, 6, 7, 11, 4, 5, 6, 10, 5, 6, 7, 11, 6, 7, 8, 12, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 8, 9, 10, 14, 7, 8, 9, 13, 8, 9, 10, 14, 9, 10, 11, 15, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 5, 6, 7, 11, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 4, 5, 6, 10, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 3, 4, 5, 9, 7, 8, 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3, 19, 15, 14, 13, 18, 14, 13, 12, 19, 15, 14, 13, 20, 16, 15, 14, 15, 11, 10, 9, 14, 10, 9, 8, 15, 11, 10, 9, 16, 12, 11, 10, 14, 10, 9, 8, 13, 9, 8, 7, 14, 10, 9, 8, 15, 11, 10, 9, 13, 9, 8, 7, 12, 8, 7, 6, 13, 9, 8, 7, 14, 10, 9, 8, 15, 11, 10, 9, 14, 10, 9, 8, 15, 11, 10, 9, 16, 12, 11, 10, 11, 7, 6, 5, 10, 6, 5, 4, 11, 7, 6, 5, 12, 8, 7, 6, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 14, 10, 9, 8, 13, 9, 8, 7, 14, 10, 9, 8, 15, 11, 10, 9, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 11, 7, 6, 5, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 13, 9, 8, 7, 12, 8, 7, 6, 13, 9, 8, 7, 14, 10, 9, 8, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 10, 6, 5, 4, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 9, 5, 4, 3, 7, 3, 2, 1, 6, 2, 1, 0, 7, 3, 2, 1, 8, 4, 3, 2, 14, 15, 16, 20, 13, 14, 15, 19, 12, 13, 14, 18, 13, 14, 15, 19, 10, 11, 12, 16, 9, 10, 11, 15, 8, 9, 10, 14, 9, 10, 11, 15, 9, 10, 11, 15, 8, 9, 10, 14, 7, 8, 9, 13, 8, 9, 10, 14, 8, 9, 10, 14, 7, 8, 9, 13, 6, 7, 8, 12, 7, 8, 9, 13, 10, 11, 12, 16, 9, 10, 11, 15, 8, 9, 10, 14, 9, 10, 11, 15, 6, 7, 8, 12, 5, 6, 7, 11, 4, 5, 6, 10, 5, 6, 7, 11, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 9, 10, 11, 15, 8, 9, 10, 14, 7, 8, 9, 13, 8, 9, 10, 14, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 4, 5, 6, 10, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 8, 9, 10, 14, 7, 8, 9, 13, 6, 7, 8, 12, 7, 8, 9, 13, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 3, 4, 5, 9, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 2, 3, 4, 8, 2, 3, 4, 8, 1, 2, 3, 7, 0, 1, 2, 6, 1, 2, 3, 7, 15, 14, 15, 16, 14, 13, 14, 15, 13, 12, 13, 14, 14, 13, 14, 15, 11, 10, 11, 12, 10, 9, 10, 11, 9, 8, 9, 10, 10, 9, 10, 11, 10, 9, 10, 11, 9, 8, 9, 10, 8, 7, 8, 9, 9, 8, 9, 10, 9, 8, 9, 10, 8, 7, 8, 9, 7, 6, 7, 8, 8, 7, 8, 9, 11, 10, 11, 12, 10, 9, 10, 11, 9, 8, 9, 10, 10, 9, 10, 11, 7, 6, 7, 8, 6, 5, 6, 7, 5, 4, 5, 6, 6, 5, 6, 7, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 10, 9, 10, 11, 9, 8, 9, 10, 8, 7, 8, 9, 9, 8, 9, 10, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 5, 4, 5, 6, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 3, 2, 3, 4, 9, 8, 9, 10, 8, 7, 8, 9, 7, 6, 7, 8, 8, 7, 8, 9, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 4, 3, 4, 5, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 3, 2, 3, 4, 3, 2, 3, 4, 2, 1, 2, 3, 1, 0, 1, 2, 2, 1, 2, 3, 16, 15, 14, 15, 15, 14, 13, 14, 14, 13, 12, 13, 15, 14, 13, 14, 12, 11, 10, 11, 11, 10, 9, 10, 10, 9, 8, 9, 11, 10, 9, 10, 11, 10, 9, 10, 10, 9, 8, 9, 9, 8, 7, 8, 10, 9, 8, 9, 10, 9, 8, 9, 9, 8, 7, 8, 8, 7, 6, 7, 9, 8, 7, 8, 12, 11, 10, 11, 11, 10, 9, 10, 10, 9, 8, 9, 11, 10, 9, 10, 8, 7, 6, 7, 7, 6, 5, 6, 6, 5, 4, 5, 7, 6, 5, 6, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 11, 10, 9, 10, 10, 9, 8, 9, 9, 8, 7, 8, 10, 9, 8, 9, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 6, 5, 4, 5, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 10, 9, 8, 9, 9, 8, 7, 8, 8, 7, 6, 7, 9, 8, 7, 8, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 5, 4, 3, 4, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 4, 3, 2, 3, 4, 3, 2, 3, 3, 2, 1, 2, 2, 1, 0, 1, 3, 2, 1, 2, 20, 16, 15, 14, 19, 15, 14, 13, 18, 14, 13, 12, 19, 15, 14, 13, 16, 12, 11, 10, 15, 11, 10, 9, 14, 10, 9, 8, 15, 11, 10, 9, 15, 11, 10, 9, 14, 10, 9, 8, 13, 9, 8, 7, 14, 10, 9, 8, 14, 10, 9, 8, 13, 9, 8, 7, 12, 8, 7, 6, 13, 9, 8, 7, 16, 12, 11, 10, 15, 11, 10, 9, 14, 10, 9, 8, 15, 11, 10, 9, 12, 8, 7, 6, 11, 7, 6, 5, 10, 6, 5, 4, 11, 7, 6, 5, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 15, 11, 10, 9, 14, 10, 9, 8, 13, 9, 8, 7, 14, 10, 9, 8, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 10, 6, 5, 4, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 14, 10, 9, 8, 13, 9, 8, 7, 12, 8, 7, 6, 13, 9, 8, 7, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 9, 5, 4, 3, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 8, 4, 3, 2, 8, 4, 3, 2, 7, 3, 2, 1, 6, 2, 1, 0, 7, 3, 2, 1, 18, 19, 20, 24, 14, 15, 16, 20, 13, 14, 15, 19, 12, 13, 14, 18, 14, 15, 16, 20, 10, 11, 12, 16, 9, 10, 11, 15, 8, 9, 10, 14, 13, 14, 15, 19, 9, 10, 11, 15, 8, 9, 10, 14, 7, 8, 9, 13, 12, 13, 14, 18, 8, 9, 10, 14, 7, 8, 9, 13, 6, 7, 8, 12, 14, 15, 16, 20, 10, 11, 12, 16, 9, 10, 11, 15, 8, 9, 10, 14, 10, 11, 12, 16, 6, 7, 8, 12, 5, 6, 7, 11, 4, 5, 6, 10, 9, 10, 11, 15, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 13, 14, 15, 19, 9, 10, 11, 15, 8, 9, 10, 14, 7, 8, 9, 13, 9, 10, 11, 15, 5, 6, 7, 11, 4, 5, 6, 10, 3, 4, 5, 9, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 7, 8, 9, 13, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 12, 13, 14, 18, 8, 9, 10, 14, 7, 8, 9, 13, 6, 7, 8, 12, 8, 9, 10, 14, 4, 5, 6, 10, 3, 4, 5, 9, 2, 3, 4, 8, 7, 8, 9, 13, 3, 4, 5, 9, 2, 3, 4, 8, 1, 2, 3, 7, 6, 7, 8, 12, 2, 3, 4, 8, 1, 2, 3, 7, 0, 1, 2, 6, 19, 18, 19, 20, 15, 14, 15, 16, 14, 13, 14, 15, 13, 12, 13, 14, 15, 14, 15, 16, 11, 10, 11, 12, 10, 9, 10, 11, 9, 8, 9, 10, 14, 13, 14, 15, 10, 9, 10, 11, 9, 8, 9, 10, 8, 7, 8, 9, 13, 12, 13, 14, 9, 8, 9, 10, 8, 7, 8, 9, 7, 6, 7, 8, 15, 14, 15, 16, 11, 10, 11, 12, 10, 9, 10, 11, 9, 8, 9, 10, 11, 10, 11, 12, 7, 6, 7, 8, 6, 5, 6, 7, 5, 4, 5, 6, 10, 9, 10, 11, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 14, 13, 14, 15, 10, 9, 10, 11, 9, 8, 9, 10, 8, 7, 8, 9, 10, 9, 10, 11, 6, 5, 6, 7, 5, 4, 5, 6, 4, 3, 4, 5, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 8, 7, 8, 9, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 13, 12, 13, 14, 9, 8, 9, 10, 8, 7, 8, 9, 7, 6, 7, 8, 9, 8, 9, 10, 5, 4, 5, 6, 4, 3, 4, 5, 3, 2, 3, 4, 8, 7, 8, 9, 4, 3, 4, 5, 3, 2, 3, 4, 2, 1, 2, 3, 7, 6, 7, 8, 3, 2, 3, 4, 2, 1, 2, 3, 1, 0, 1, 2, 20, 19, 18, 19, 16, 15, 14, 15, 15, 14, 13, 14, 14, 13, 12, 13, 16, 15, 14, 15, 12, 11, 10, 11, 11, 10, 9, 10, 10, 9, 8, 9, 15, 14, 13, 14, 11, 10, 9, 10, 10, 9, 8, 9, 9, 8, 7, 8, 14, 13, 12, 13, 10, 9, 8, 9, 9, 8, 7, 8, 8, 7, 6, 7, 16, 15, 14, 15, 12, 11, 10, 11, 11, 10, 9, 10, 10, 9, 8, 9, 12, 11, 10, 11, 8, 7, 6, 7, 7, 6, 5, 6, 6, 5, 4, 5, 11, 10, 9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 15, 14, 13, 14, 11, 10, 9, 10, 10, 9, 8, 9, 9, 8, 7, 8, 11, 10, 9, 10, 7, 6, 5, 6, 6, 5, 4, 5, 5, 4, 3, 4, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 9, 8, 7, 8, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 14, 13, 12, 13, 10, 9, 8, 9, 9, 8, 7, 8, 8, 7, 6, 7, 10, 9, 8, 9, 6, 5, 4, 5, 5, 4, 3, 4, 4, 3, 2, 3, 9, 8, 7, 8, 5, 4, 3, 4, 4, 3, 2, 3, 3, 2, 1, 2, 8, 7, 6, 7, 4, 3, 2, 3, 3, 2, 1, 2, 2, 1, 0, 1, 24, 20, 19, 18, 20, 16, 15, 14, 19, 15, 14, 13, 18, 14, 13, 12, 20, 16, 15, 14, 16, 12, 11, 10, 15, 11, 10, 9, 14, 10, 9, 8, 19, 15, 14, 13, 15, 11, 10, 9, 14, 10, 9, 8, 13, 9, 8, 7, 18, 14, 13, 12, 14, 10, 9, 8, 13, 9, 8, 7, 12, 8, 7, 6, 20, 16, 15, 14, 16, 12, 11, 10, 15, 11, 10, 9, 14, 10, 9, 8, 16, 12, 11, 10, 12, 8, 7, 6, 11, 7, 6, 5, 10, 6, 5, 4, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 19, 15, 14, 13, 15, 11, 10, 9, 14, 10, 9, 8, 13, 9, 8, 7, 15, 11, 10, 9, 11, 7, 6, 5, 10, 6, 5, 4, 9, 5, 4, 3, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 18, 14, 13, 12, 14, 10, 9, 8, 13, 9, 8, 7, 12, 8, 7, 6, 14, 10, 9, 8, 10, 6, 5, 4, 9, 5, 4, 3, 8, 4, 3, 2, 13, 9, 8, 7, 9, 5, 4, 3, 8, 4, 3, 2, 7, 3, 2, 1, 12, 8, 7, 6, 8, 4, 3, 2, 7, 3, 2, 1, 6, 2, 1, 0]

run_full_comparison.py

#!/usr/bin/env python3
"""
TLSH Similarity Search: Comprehensive 4-Method Comparison with Accuracy Metrics

Compares four approaches:
1. Linear Search (Baseline - Ground Truth)
2. Python Preprocessing 132-byte + ANN
3. Python Preprocessing 36-byte + ANN
4. Native C++ Preprocessing 36-byte + ANN

Includes accuracy/recall metrics and configurable ANN parameters.
Supports ground truth caching for faster repeated experiments.
"""

import os
import sys
import time
import argparse
import pickle
import heapq
from pathlib import Path
from tqdm import tqdm
import numpy as np
import pandas as pd
import tlsh
from pynndescent import NNDescent
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
from buildANNnPredict import buildANNnPredict, merged_diff_list_np, linear_search_batch
from buildANNnPredict import totalDiff
from buildANNnPredict import tlsh_distance_metric_36, tlsh_distance_metric_132

sns.set_style("whitegrid")

# ============================================================================
# Configuration Class
# ============================================================================


class Config:
    """Configurable parameters for the comparison"""
    def __init__(self, args=None):
        if args:
            self.DATA_PATH = args.data_path
            self.NUM_SAMPLES = args.num_samples
            self.TEST_SIZE = args.test_size
            self.RANDOM_STATE = args.random_state
            self.K_NEIGHBORS = args.k_neighbors
            self.TEST_BATCH_SIZE = args.test_batch_size
            self.TIMING_ITERATIONS = args.timing_iterations
            self.FORCE_REGENERATE = getattr(args, 'force_regenerate', False)
            self.GROUND_TRUTH_FILE = getattr(args, 'ground_truth_file', None)
            self.GROUND_TRUTH_DIR = getattr(args, 'ground_truth_dir', 'ground_truth')
            # ANN parameters
            self.N_NEIGHBORS_BUILD = args.n_neighbors_build
            self.N_TREES = args.n_trees
            self.N_ITERS = args.n_iters
            self.N_JOBS = args.n_jobs
            self.MAX_CANDIDATES = args.max_candidates
        else:
            # Default values
            self.DATA_PATH = "vt_mb_centers.csv"
            self.NUM_SAMPLES = None
            self.TEST_SIZE = 0.0001
            self.RANDOM_STATE = 42
            self.K_NEIGHBORS = 15
            self.TEST_BATCH_SIZE = 1000
            self.TIMING_ITERATIONS = 3
            self.FORCE_REGENERATE = False
            self.GROUND_TRUTH_FILE = None
            self.GROUND_TRUTH_DIR = 'ground_truth'
            # ANN parameters
            self.N_NEIGHBORS_BUILD = 15
            self.N_TREES = 5
            self.N_ITERS = 5
            self.N_JOBS = None
            self.MAX_CANDIDATES = 60

    def __str__(self):
        return f"""Configuration:
            Data: {self.DATA_PATH}
            Samples: {self.NUM_SAMPLES if self.NUM_SAMPLES else 'All'}
            Test Size: {self.TEST_SIZE * 100:.0f}%
            K Neighbors: {self.K_NEIGHBORS}
            Test Batch: {self.TEST_BATCH_SIZE}
            Timing Iterations: {self.TIMING_ITERATIONS}

            ANN Parameters:
            - n_neighbors_build: {self.N_NEIGHBORS_BUILD}
            - n_trees: {self.N_TREES}
            - n_iters: {self.N_ITERS}
            - n_jobs: {self.N_JOBS}
            - max_candidates: {self.MAX_CANDIDATES}"""

# ============================================================================
# Preprocessing Functions
# ============================================================================


def preprocess_python_36byte(df):
    """
    Python preprocessing to 36-byte arrays.
    Packs 4 bucket values per byte using Python logic.
    """
    checksum = 0
    lvalue = 1
    q1value = 2
    q2value = 3

    result = []
    for tlsh_str in df['tlsh']:
        h = tlsh.Tlsh()
        h.fromTlshStr(tlsh_str)

        narr1 = np.zeros(36, dtype=np.uint8)
        narr1[checksum] = h.checksum(0)
        narr1[lvalue] = h.lvalue
        narr1[q1value] = h.q1ratio
        narr1[q2value] = h.q2ratio

        for byte_idx in range(32):
            b0 = byte_idx * 4
            byte_val = (
                64 * h.bucket_value(b0)
                + 16 * h.bucket_value(b0 + 1)
                + 4 * h.bucket_value(b0 + 2)
                + h.bucket_value(b0 + 3)
            )
            narr1[4 + byte_idx] = byte_val

        result.append(narr1)
    return result


def preprocess_native_36byte(df):
    """
    Native C++ preprocessing to 36-byte arrays.
    Uses optimized toByteArray() method.
    """
    result = []
    for tlsh_str in df['tlsh']:
        h = tlsh.Tlsh()
        h.fromTlshStr(tlsh_str)
        byte_data = bytearray(36)
        h.toByteArray(byte_data)
        numpy_array = np.asarray(memoryview(byte_data), dtype=np.uint8)
        result.append(numpy_array)
    return result

# ============================================================================
# Two-Stage Search: ANN + Linear Refinement
# ============================================================================


def two_stage_search(ann_model, queries, database, k_final, k_candidates, bit_pairs_list,
                     query_tlsh_list=None, db_tlsh_list=None, verbose=False):
    """
    Two-stage search: ANN to get candidates, then linear search within candidates.

    Args:
        ann_model: Trained ANN model
        queries: Query vectors (numpy arrays)
        database: Full database vectors (numpy arrays)
        k_final: Number of final neighbors to return
        k_candidates: Number of candidates to get from ANN (should be >= k_final)
        bit_pairs_list: Bit pairs for distance calculation (not used if TLSH strings provided)
        query_tlsh_list: Optional list of TLSH strings for queries
        db_tlsh_list: Optional list of TLSH strings for database
        verbose: If True, print when two-stage improves top-1 result

    Returns:
        refined_indices: Best k_final indices after linear refinement
        refined_distances: Corresponding distances
    """

    # Stage 1: Get top k_candidates from ANN
    ann_model.prepare()
    ann_indices, ann_distances = ann_model.query(queries, k=k_candidates)

    n_queries = queries.shape[0]
    refined_indices = np.zeros((n_queries, k_final), dtype=np.int64)
    refined_distances = np.zeros((n_queries, k_final), dtype=np.float64)

    improvements = 0

    # Stage 2: Linear search within candidates for each query
    for i in range(n_queries):
        candidate_indices = ann_indices[i]

        exact_distances = np.zeros(k_candidates, dtype=np.float64)

        if query_tlsh_list is not None and db_tlsh_list is not None:
            query_tlsh = query_tlsh_list[i]
            for j, candidate_idx in enumerate(candidate_indices):
                db_tlsh = db_tlsh_list[candidate_idx]
                exact_distances[j] = tlsh.diff(query_tlsh, db_tlsh)
        else:
            for j, candidate_idx in enumerate(candidate_indices):
                exact_distances[j] = totalDiff(
                    queries[i],
                    database[candidate_idx],
                    bit_pairs_list
                )

        sorted_positions = np.argsort(exact_distances)[:k_final]
        refined_indices[i] = candidate_indices[sorted_positions]
        refined_distances[i] = exact_distances[sorted_positions]

        ann_top1_idx = candidate_indices[0]
        refined_top1_idx = refined_indices[i][0]

        if ann_top1_idx != refined_top1_idx:
            improvements += 1
            if verbose:
                ann_top1_dist = exact_distances[0]
                refined_top1_dist = refined_distances[i][0]
                print(f"  Query {i}: Two-stage improved top-1!")
                print(f"    ANN top-1: idx={ann_top1_idx}, dist={ann_top1_dist:.0f}")
                print(f"    Refined top-1: idx={refined_top1_idx}, dist={refined_top1_dist:.0f}")
                print(f"    Improvement: {ann_top1_dist - refined_top1_dist:.0f} distance units")

    if improvements > 0:
        improvement_rate = (improvements / n_queries) * 100
        print(f"\nTwo-stage improved top-1 for {improvements}/{n_queries} queries ({improvement_rate:.1f}%)")

    return refined_indices, refined_distances

# ============================================================================
# Linear Search Function
# ============================================================================


def find_top_k_closest(target_batch, reference_batch, k=10, show_progress=False):
    """
    Linearly finds the Top K closest TLSH matches.
    Complexity: O(N * M * log K)
    """
    all_results = []
    print(f"Linear search: {len(target_batch):,} queries × {len(reference_batch):,} references = {len(target_batch) * len(reference_batch):,} distance computations")

    iterator = tqdm(target_batch, desc="Computing ground truth", unit="query") if show_progress else target_batch

    for target in iterator:
        indices = []
        distances = []
        hashes = []
        top_k_heap = []

        for i, ref in enumerate(reference_batch):
            distance = tlsh.diff(target, ref)

            if len(top_k_heap) < k:
                heapq.heappush(top_k_heap, (-distance, i, ref))
            else:
                if -distance > top_k_heap[0][0]:
                    heapq.heapreplace(top_k_heap, (-distance, i, ref))

        while top_k_heap:
            neg_dist, idx, ref_hash = heapq.heappop(top_k_heap)
            indices.append(idx)
            distances.append(-neg_dist)
            hashes.append(ref_hash)

        all_results.append({
            "target": target,
            "indices": indices,
            "distances": distances,
            "hashes": hashes
        })

    return all_results

# ============================================================================
# Helper Functions
# ============================================================================


def batch_ranges(n_total, batch_size):
    """Yield (start, end) indices to iterate over n_total items in batches of batch_size."""
    for start in range(0, n_total, batch_size):
        end = min(start + batch_size, n_total)
        yield start, end


def config_to_dict(config):
    """Flatten Config into a dict suitable for CSV (one row per experiment)."""
    return {
        'data_path': config.DATA_PATH,
        'num_samples': config.NUM_SAMPLES if config.NUM_SAMPLES is not None else '',
        'test_size': config.TEST_SIZE,
        'random_state': config.RANDOM_STATE,
        'k_neighbors': config.K_NEIGHBORS,
        'test_batch_size': config.TEST_BATCH_SIZE,
        'timing_iterations': config.TIMING_ITERATIONS,
        'force_regenerate': config.FORCE_REGENERATE,
        'n_neighbors_build': config.N_NEIGHBORS_BUILD,
        'n_trees': config.N_TREES,
        'n_iters': config.N_ITERS,
        'n_jobs': config.N_JOBS if config.N_JOBS is not None else '',
        'max_candidates': config.MAX_CANDIDATES,
    }


EXPERIMENT_LOG_PATH = "experiment_log.csv"


def get_model_size_mb(model):
    """Calculate the size of a PyNNDescent model in MB"""
    try:
        total_size = 0
        # Get size of main data structures
        if hasattr(model, '_raw_data'):
            total_size += sys.getsizeof(model._raw_data)
            if hasattr(model._raw_data, 'nbytes'):
                total_size += model._raw_data.nbytes
        if hasattr(model, 'neighbor_graph'):
            total_size += sys.getsizeof(model.neighbor_graph)
            if isinstance(model.neighbor_graph, tuple) and len(model.neighbor_graph) == 2:
                for arr in model.neighbor_graph:
                    if hasattr(arr, 'nbytes'):
                        total_size += arr.nbytes
        if hasattr(model, '_rp_forest'):
            total_size += sys.getsizeof(model._rp_forest)
        return total_size / (1024 * 1024)
    except Exception:
        return 0.0

# ============================================================================
# Accuracy Calculation
# ============================================================================


def calculate_accuracy_metrics(true_indices, true_distances, approx_indices, approx_distances, k):
    """
    Calculate comprehensive accuracy metrics.

    Returns:
        dict with recall, precision, distance_error, and distance_correlation
    """
    n_queries = len(true_indices)

    # Recall: How many true neighbors were found
    total_correct = 0
    total_possible = n_queries * k

    for i in range(n_queries):
        true_neighbors = set(true_indices[i])
        approx_neighbors = set(approx_indices[i])
        correct = len(true_neighbors.intersection(approx_neighbors))
        total_correct += correct

    recall = (total_correct / total_possible) * 100

    # Precision: Of the neighbors found, how many were correct
    precision = recall  # Same as recall for k-NN

    distance_error = np.mean(np.abs(approx_distances - true_distances))

    distance_correlation = np.corrcoef(
        true_distances.flatten(),
        approx_distances.flatten()
    )[0, 1]

    top1_correct = 0
    for i in range(n_queries):
        if true_indices[i][0] in approx_indices[i]:
            top1_correct += 1
    top1_accuracy = (top1_correct / n_queries) * 100

    return {
        'recall': recall,
        'precision': precision,
        'distance_error': distance_error,
        'distance_correlation': distance_correlation,
        'top1_accuracy': top1_accuracy
    }

# ============================================================================
# Main Comparison Function
# ============================================================================


def run_comparison(config):
    """Run the complete 4-method comparison with ground truth caching"""

    print("=" * 80)
    print("TLSH SIMILARITY SEARCH: 4-METHOD COMPARISON WITH ACCURACY METRICS")
    print("=" * 80)
    print(f"\n{config}\n")

    # ========================================================================
    # GROUND TRUTH: Check cache or generate
    # ========================================================================
    print("=" * 80)
    print("GROUND TRUTH MANAGEMENT")
    print("=" * 80)

    # Create ground truth directory if it doesn't exist
    gt_dir = Path(config.GROUND_TRUTH_DIR)
    gt_dir.mkdir(exist_ok=True)

    # Determine ground truth filepath: explicit file overrides auto-generated name
    if config.GROUND_TRUTH_FILE:
        gt_filepath = Path(config.GROUND_TRUTH_FILE)
    else:
        df_temp = pd.read_csv(config.DATA_PATH)
        if config.NUM_SAMPLES and config.NUM_SAMPLES < len(df_temp):
            total_samples = config.NUM_SAMPLES
        else:
            total_samples = len(df_temp)
        gt_filename = f"ground_truth_n{total_samples}_k{config.K_NEIGHBORS}_seed{config.RANDOM_STATE}.pkl"
        gt_filepath = gt_dir / gt_filename

    # Check if ground truth exists and if we should use it
    force_regen = config.FORCE_REGENERATE
    cached_linear_search_time = None  # reused in Method 4 to skip re-timing linear search

    if force_regen and gt_filepath.exists():
        print("Force regeneration requested - ignoring cached ground truth")

    if gt_filepath.exists() and not force_regen:
        print(f"✓ Found cached ground truth: {gt_filepath}")
        print("  Loading from cache...")

        with open(gt_filepath, 'rb') as f:
            gt_cache = pickle.load(f)

        print(f"  Generated: {gt_cache['metadata']['generated_at']}")
        print(f"  Train size: {gt_cache['metadata']['train_size']:,}")
        print(f"  Test size: {gt_cache['metadata']['test_size']:,}")
        print(f"  K neighbors: {gt_cache['metadata']['k_neighbors']}")

        # Reuse linear search time from when GT was computed (no re-timing in Method 4)
        cached_linear_search_time = gt_cache['metadata']['generation_time_seconds']
        print(f"  Linear search time (cached): {cached_linear_search_time:.2f}s")

        # Extract from cache
        train_df = gt_cache['train_df']
        test_df = gt_cache['test_df']
        train_tlsh_list = gt_cache['train_tlsh']
        test_tlsh_list = gt_cache['test_tlsh']
        gt_results = gt_cache['ground_truth']['full_results']
        gt_indices = gt_cache['ground_truth']['indices']
        gt_distances = gt_cache['ground_truth']['distances']

        print("Ground truth loaded successfully")

    else:
        print(f"✗ Ground truth not found: {gt_filepath}")
        print("  Generating ground truth now...")

        # Load data
        print("\nLoading data...")
        df = pd.read_csv(config.DATA_PATH)
        print(f"   Loaded {len(df):,} records")

        if config.NUM_SAMPLES and config.NUM_SAMPLES < len(df):
            df = df.sample(n=config.NUM_SAMPLES, random_state=config.RANDOM_STATE)
            print(f"   Sampled {config.NUM_SAMPLES:,} records")

        df = df.dropna(subset=['tlsh'])

        # Split data
        train_df, test_df = train_test_split(
            df, test_size=config.TEST_SIZE, random_state=config.RANDOM_STATE
        )
        print(f"   Training: {len(train_df):,} | Test: {len(test_df):,}")

        # Generate ground truth using find_top_k_closest
        print("\nComputing ground truth (linear search)...")
        train_tlsh_list = train_df['tlsh'].tolist()
        test_tlsh_list = test_df['tlsh'].tolist()

        start_time = time.time()
        gt_results = find_top_k_closest(test_tlsh_list, train_tlsh_list, k=config.K_NEIGHBORS, show_progress=True)
        elapsed_time = time.time() - start_time

        print(f"Ground truth computed in {elapsed_time:.2f}s")
        print(f"  Rate: {len(test_tlsh_list)/elapsed_time:.2f} queries/second")
        cached_linear_search_time = elapsed_time  # reuse in Method 4 (no re-timing)

        gt_indices = np.array([result['indices'] for result in gt_results])
        gt_distances = np.array([result['distances'] for result in gt_results])
        gt_hashes = [result['hashes'] for result in gt_results]

        print("\nSaving ground truth to cache...")
        gt_cache = {
            'metadata': {
                'data_path': config.DATA_PATH,
                'num_samples': config.NUM_SAMPLES,
                'total_records': len(df),
                'train_size': len(train_df),
                'test_size': len(test_df),
                'test_fraction': config.TEST_SIZE,
                'k_neighbors': config.K_NEIGHBORS,
                'random_state': config.RANDOM_STATE,
                'generated_at': time.strftime('%Y-%m-%d %H:%M:%S'),
                'generation_time_seconds': elapsed_time
            },
            'train_tlsh': train_tlsh_list,
            'test_tlsh': test_tlsh_list,
            'train_df': train_df,
            'test_df': test_df,
            'ground_truth': {
                'full_results': gt_results,
                'indices': gt_indices,
                'distances': gt_distances,
                'hashes': gt_hashes
            }
        }

        with open(gt_filepath, 'wb') as f:
            pickle.dump(gt_cache, f, protocol=pickle.HIGHEST_PROTOCOL)

        file_size_mb = os.path.getsize(gt_filepath) / (1024 * 1024)
        print(f"Ground truth saved: {gt_filepath}")
        print(f"  File size: {file_size_mb:.2f} MB")
        print("\nThis ground truth will be reused for future runs with same parameters!")

    # ========================================================================
    # Continue with ANN comparisons using cached ground truth
    # ========================================================================
    print("\n" + "=" * 80)
    print("RUNNING ANN COMPARISONS")
    print("=" * 80)
    print(f"Training: {len(train_df):,} | Test: {len(test_df):,}")

    n_test = len(test_tlsh_list)
    batch_size = config.TEST_BATCH_SIZE

    bmc = buildANNnPredict(merged_diff_list_np)

    # ========================================================================
    # METHOD 1: Python Preprocessing 132-byte + ANN
    # ========================================================================
    print("\n" + "=" * 80)
    print("METHOD 1: Python Preprocessing (132-byte) + ANN")
    print("=" * 80)

    print("\n1. Preprocessing...")
    start = time.time()
    train_python_132 = bmc.preprocess(train_df, method="132")
    train_python_132_arr = np.asarray(train_python_132)
    preprocess_python_132_time = time.time() - start
    print(f"   Time: {preprocess_python_132_time:.2f}s ({len(train_df)/preprocess_python_132_time:.0f} records/s)")
    print(f"   Shape: {train_python_132_arr.shape}")
    print(f"   Memory: {train_python_132_arr.nbytes / 1024 / 1024:.2f} MB")

    test_python_132 = bmc.preprocess(test_df, method="132")
    test_python_132_arr = np.asarray(test_python_132)

    print("\n2. Building ANN index...")

    start = time.time()
    nn_model_132 = NNDescent(
        train_python_132_arr,
        metric=tlsh_distance_metric_132,
        n_neighbors=config.N_NEIGHBORS_BUILD,
        n_trees=config.N_TREES,
        n_iters=config.N_ITERS,
        n_jobs=config.N_JOBS,
        max_candidates=config.MAX_CANDIDATES,
        random_state=config.RANDOM_STATE,
        verbose=False
    )
    build_python_132_time = time.time() - start
    print(f"   Build time: {build_python_132_time:.2f}s")

    nn_model_132.prepare()

    print("\n3. Benchmarking ANN search (full test set in batches)...")
    ann_132_times = []
    for i in range(config.TIMING_ITERATIONS):
        start = time.time()
        all_idx, all_dist = [], []
        for start_i, end_i in batch_ranges(n_test, batch_size):
            batch = test_python_132_arr[start_i:end_i]
            idx, dist = nn_model_132.query(batch, k=config.K_NEIGHBORS)
            all_idx.append(idx)
            all_dist.append(dist)
        elapsed = time.time() - start
        ann_132_times.append(elapsed)
        print(f"   Iteration {i+1}: {elapsed:.4f}s")
        if i == config.TIMING_ITERATIONS - 1:
            ann_132_indices = np.vstack(all_idx)
            ann_132_distances = np.vstack(all_dist)

    ann_132_avg = np.mean(ann_132_times)
    ann_132_qps = n_test / ann_132_avg
    print(f"   Average: {ann_132_avg:.4f}s ({ann_132_qps:.2f} QPS, {n_test:,} queries)")

    print("\n4. Two-stage search (ANN + Linear refinement, full test set in batches)...")
    k_candidates = config.K_NEIGHBORS * 2
    two_stage_times = []
    for i in range(config.TIMING_ITERATIONS):
        start = time.time()
        all_idx, all_dist = [], []
        for start_i, end_i in batch_ranges(n_test, batch_size):
            batch = test_python_132_arr[start_i:end_i]
            batch_tlsh = test_tlsh_list[start_i:end_i]
            idx, dist = two_stage_search(
                nn_model_132, batch, train_python_132_arr,
                config.K_NEIGHBORS, k_candidates, merged_diff_list_np,
                query_tlsh_list=batch_tlsh,
                db_tlsh_list=train_tlsh_list,
                verbose=False
            )
            all_idx.append(idx)
            all_dist.append(dist)
        elapsed = time.time() - start
        two_stage_times.append(elapsed)
        print(f"   Iteration {i+1}: {elapsed:.4f}s")
        if i == config.TIMING_ITERATIONS - 1:
            refined_132_indices = np.vstack(all_idx)
            refined_132_distances = np.vstack(all_dist)

    two_stage_132_avg = np.mean(two_stage_times)
    two_stage_132_qps = n_test / two_stage_132_avg
    print(f"   Average: {two_stage_132_avg:.4f}s ({two_stage_132_qps:.2f} QPS)")

    model_size_132 = get_model_size_mb(nn_model_132)
    print(f"   Model size: {model_size_132:.2f} MB")

    method1_data = {
        'indices': ann_132_indices,
        'distances': ann_132_distances,
        'refined_indices': refined_132_indices,
        'refined_distances': refined_132_distances,
        'preprocess_time': preprocess_python_132_time,
        'build_time': build_python_132_time,
        'search_time': ann_132_avg,
        'two_stage_time': two_stage_132_avg,
        'qps': ann_132_qps,
        'two_stage_qps': two_stage_132_qps,
        'memory_mb': train_python_132_arr.nbytes / 1024 / 1024,
        'model_size_mb': model_size_132
    }

    # ========================================================================
    # METHOD 2: Python Preprocessing 36-byte + ANN
    # ========================================================================
    print("\n" + "=" * 80)
    print("METHOD 2: Python Preprocessing (36-byte) + ANN")
    print("=" * 80)

    print("\n1. Preprocessing...")
    start = time.time()
    train_python_36 = bmc.preprocess(train_df, method="36")
    train_python_36_arr = np.asarray(train_python_36)
    preprocess_python_36_time = time.time() - start
    print(f"   Time: {preprocess_python_36_time:.2f}s ({len(train_df)/preprocess_python_36_time:.0f} records/s)")
    print(f"   Shape: {train_python_36_arr.shape}")
    print(f"   Memory: {train_python_36_arr.nbytes / 1024 / 1024:.2f} MB")

    test_python_36 = preprocess_python_36byte(test_df)
    test_python_36_arr = np.asarray(test_python_36)

    print("\n2. Building ANN index...")
    start = time.time()
    nn_model_py36 = NNDescent(
        train_python_36_arr,
        metric=tlsh_distance_metric_36,
        n_neighbors=config.N_NEIGHBORS_BUILD,
        n_trees=config.N_TREES,
        n_iters=config.N_ITERS,
        n_jobs=config.N_JOBS,
        max_candidates=config.MAX_CANDIDATES,
        random_state=config.RANDOM_STATE,
        verbose=False
    )
    build_python_36_time = time.time() - start
    print(f"   Build time: {build_python_36_time:.2f}s")

    nn_model_py36.prepare()

    print("\n3. Benchmarking ANN search (full test set in batches)...")
    ann_py36_times = []
    for i in range(config.TIMING_ITERATIONS):
        start = time.time()
        all_idx, all_dist = [], []
        for start_i, end_i in batch_ranges(n_test, batch_size):
            batch = test_python_36_arr[start_i:end_i]
            idx, dist = nn_model_py36.query(batch, k=config.K_NEIGHBORS)
            all_idx.append(idx)
            all_dist.append(dist)
        elapsed = time.time() - start
        ann_py36_times.append(elapsed)
        print(f"   Iteration {i+1}: {elapsed:.4f}s")
        if i == config.TIMING_ITERATIONS - 1:
            ann_py36_indices = np.vstack(all_idx)
            ann_py36_distances = np.vstack(all_dist)

    ann_py36_avg = np.mean(ann_py36_times)
    ann_py36_qps = n_test / ann_py36_avg
    print(f"   Average: {ann_py36_avg:.4f}s ({ann_py36_qps:.2f} QPS, {n_test:,} queries)")

    # Two-stage search: ANN + Linear refinement (full test set in batches)
    print("\n4. Two-stage search (ANN + Linear refinement, full test set in batches)...")
    two_stage_py36_times = []
    for i in range(config.TIMING_ITERATIONS):
        start = time.time()
        all_idx, all_dist = [], []
        for start_i, end_i in batch_ranges(n_test, batch_size):
            batch = test_python_36_arr[start_i:end_i]
            batch_tlsh = test_tlsh_list[start_i:end_i]
            idx, dist = two_stage_search(
                nn_model_py36, batch, train_python_36_arr,
                config.K_NEIGHBORS, k_candidates, merged_diff_list_np,
                query_tlsh_list=batch_tlsh,
                db_tlsh_list=train_tlsh_list,
                verbose=False
            )
            all_idx.append(idx)
            all_dist.append(dist)
        elapsed = time.time() - start
        two_stage_py36_times.append(elapsed)
        print(f"   Iteration {i+1}: {elapsed:.4f}s")
        if i == config.TIMING_ITERATIONS - 1:
            refined_py36_indices = np.vstack(all_idx)
            refined_py36_distances = np.vstack(all_dist)

    two_stage_py36_avg = np.mean(two_stage_py36_times)
    two_stage_py36_qps = n_test / two_stage_py36_avg
    print(f"   Average: {two_stage_py36_avg:.4f}s ({two_stage_py36_qps:.2f} QPS)")

    model_size_py36 = get_model_size_mb(nn_model_py36)
    print(f"   Model size: {model_size_py36:.2f} MB")

    method2_data = {
        'indices': ann_py36_indices,
        'distances': ann_py36_distances,
        'refined_indices': refined_py36_indices,
        'refined_distances': refined_py36_distances,
        'preprocess_time': preprocess_python_36_time,
        'build_time': build_python_36_time,
        'search_time': ann_py36_avg,
        'two_stage_time': two_stage_py36_avg,
        'qps': ann_py36_qps,
        'two_stage_qps': two_stage_py36_qps,
        'memory_mb': train_python_36_arr.nbytes / 1024 / 1024,
        'model_size_mb': model_size_py36
    }

    # ========================================================================
    # METHOD 3: Native C++ Preprocessing 36-byte + ANN
    # ========================================================================
    print("\n" + "=" * 80)
    print("METHOD 3: Native C++ Preprocessing (36-byte) + ANN")
    print("=" * 80)

    print("\n1. Preprocessing...")
    start = time.time()
    train_native_36 = preprocess_native_36byte(train_df)
    train_native_36_arr = np.asarray(train_native_36)
    preprocess_native_36_time = time.time() - start
    print(f"   Time: {preprocess_native_36_time:.2f}s ({len(train_df)/preprocess_native_36_time:.0f} records/s)")
    print(f"   Shape: {train_native_36_arr.shape}")
    print(f"   Memory: {train_native_36_arr.nbytes / 1024 / 1024:.2f} MB")

    test_native_36 = preprocess_native_36byte(test_df)
    test_native_36_arr = np.asarray(test_native_36)

    print("\n2. Building ANN index...")
    start = time.time()
    nn_model_native = NNDescent(
        train_native_36_arr,
        metric=tlsh_distance_metric_36,
        n_neighbors=config.N_NEIGHBORS_BUILD,
        n_trees=config.N_TREES,
        n_iters=config.N_ITERS,
        n_jobs=config.N_JOBS,
        max_candidates=config.MAX_CANDIDATES,
        random_state=config.RANDOM_STATE,
        verbose=False
    )
    build_native_36_time = time.time() - start
    print(f"   Build time: {build_native_36_time:.2f}s")

    nn_model_native.prepare()

    print("\n3. Benchmarking ANN search (full test set in batches)...")
    ann_native_times = []
    for i in range(config.TIMING_ITERATIONS):
        start = time.time()
        all_idx, all_dist = [], []
        for start_i, end_i in batch_ranges(n_test, batch_size):
            batch = test_native_36_arr[start_i:end_i]
            idx, dist = nn_model_native.query(batch, k=config.K_NEIGHBORS)
            all_idx.append(idx)
            all_dist.append(dist)
        elapsed = time.time() - start
        ann_native_times.append(elapsed)
        print(f"   Iteration {i+1}: {elapsed:.4f}s")
        if i == config.TIMING_ITERATIONS - 1:
            ann_native_indices = np.vstack(all_idx)
            ann_native_distances = np.vstack(all_dist)

    ann_native_avg = np.mean(ann_native_times)
    ann_native_qps = n_test / ann_native_avg
    print(f"   Average: {ann_native_avg:.4f}s ({ann_native_qps:.2f} QPS, {n_test:,} queries)")

    # Two-stage search: ANN + Linear refinement (full test set in batches)
    print("\n4. Two-stage search (ANN + Linear refinement, full test set in batches)...")
    two_stage_native_times = []
    for i in range(config.TIMING_ITERATIONS):
        start = time.time()
        all_idx, all_dist = [], []
        for start_i, end_i in batch_ranges(n_test, batch_size):
            batch = test_native_36_arr[start_i:end_i]
            batch_tlsh = test_tlsh_list[start_i:end_i]
            idx, dist = two_stage_search(
                nn_model_native, batch, train_native_36_arr,
                config.K_NEIGHBORS, k_candidates, merged_diff_list_np,
                query_tlsh_list=batch_tlsh,
                db_tlsh_list=train_tlsh_list,
                verbose=False
            )
            all_idx.append(idx)
            all_dist.append(dist)
        elapsed = time.time() - start
        two_stage_native_times.append(elapsed)
        print(f"   Iteration {i+1}: {elapsed:.4f}s")
        if i == config.TIMING_ITERATIONS - 1:
            refined_native_indices = np.vstack(all_idx)
            refined_native_distances = np.vstack(all_dist)

    two_stage_native_avg = np.mean(two_stage_native_times)
    two_stage_native_qps = n_test / two_stage_native_avg
    print(f"   Average: {two_stage_native_avg:.4f}s ({two_stage_native_qps:.2f} QPS)")

    model_size_native = get_model_size_mb(nn_model_native)
    print(f"   Model size: {model_size_native:.2f} MB")

    method3_data = {
        'indices': ann_native_indices,
        'distances': ann_native_distances,
        'refined_indices': refined_native_indices,
        'refined_distances': refined_native_distances,
        'preprocess_time': preprocess_native_36_time,
        'build_time': build_native_36_time,
        'search_time': ann_native_avg,
        'two_stage_time': two_stage_native_avg,
        'qps': ann_native_qps,
        'two_stage_qps': two_stage_native_qps,
        'memory_mb': train_native_36_arr.nbytes / 1024 / 1024,
        'model_size_mb': model_size_native
    }

    # ========================================================================
    # METHOD 4: Linear Search (Ground Truth) - Using Cached Results
    # ========================================================================
    print("\n" + "=" * 80)
    print("METHOD 4: Linear Search (Ground Truth)")
    print("=" * 80)

    print("\n✓ Using cached ground truth results (reused for all iterations)")
    print(f"  Already computed for {len(test_tlsh_list):,} queries")

    # Use cached linear search time when available (same GT set); otherwise time once
    if cached_linear_search_time is not None:
        linear_avg = cached_linear_search_time
        linear_qps = n_test / linear_avg
        print(f"\n✓ Using cached linear search time (no re-run): {linear_avg:.2f}s")
        print(f"   QPS: {linear_qps:.2f} ({n_test:,} queries)")
    else:
        print("\nBenchmarking linear search (full test set in batches)...")
        linear_times = []
        for i in range(config.TIMING_ITERATIONS):
            start = time.time()
            for start_i, end_i in batch_ranges(n_test, batch_size):
                batch_tlsh = test_tlsh_list[start_i:end_i]
                _ = find_top_k_closest(batch_tlsh, train_tlsh_list, config.K_NEIGHBORS)
            elapsed = time.time() - start
            linear_times.append(elapsed)
            print(f"   Iteration {i+1}: {elapsed:.4f}s")
        linear_avg = np.mean(linear_times)
        linear_qps = n_test / linear_avg
        print(f"   Average: {linear_avg:.4f}s ({linear_qps:.2f} QPS, {n_test:,} queries)")

    # Reuse full ground truth for accuracy (no per-iteration recompute)
    linear_indices = gt_indices
    linear_distances = gt_distances

    method4_data = {
        'indices': linear_indices,
        'distances': linear_distances,
        'preprocess_time': 0.0,
        'build_time': 0.0,
        'search_time': linear_avg,
        'qps': linear_qps,
        'memory_mb': 0.0,
        'model_size_mb': 0.0
    }

    # ========================================================================
    # ACCURACY METRICS
    # ========================================================================
    print("\n" + "=" * 80)
    print("CALCULATING ACCURACY METRICS")
    print("=" * 80)

    print("\nComparing against Linear Search (Ground Truth)...")

    # Method 1 accuracy
    print("\n  Method 1 (Python 132-byte + ANN):")
    print("    Standard ANN:")
    acc1 = calculate_accuracy_metrics(
        linear_indices, linear_distances,
        ann_132_indices, ann_132_distances,
        config.K_NEIGHBORS
    )
    print(f"      Recall: {acc1['recall']:.2f}%")
    print(f"      Top-1 Accuracy: {acc1['top1_accuracy']:.2f}%")
    print(f"      Distance Error: {acc1['distance_error']:.2f}")

    print("    Two-Stage (ANN + Linear):")
    acc1_refined = calculate_accuracy_metrics(
        linear_indices, linear_distances,
        refined_132_indices, refined_132_distances,
        config.K_NEIGHBORS
    )
    print(f"      Recall: {acc1_refined['recall']:.2f}%")
    print(f"      Top-1 Accuracy: {acc1_refined['top1_accuracy']:.2f}%")
    print(f"      Distance Error: {acc1_refined['distance_error']:.2f}")

    method1_data.update({
        'recall': acc1['recall'],
        'top1_accuracy': acc1['top1_accuracy'],
        'distance_error': acc1['distance_error'],
        'distance_correlation': acc1['distance_correlation'],
        'recall_refined': acc1_refined['recall'],
        'top1_accuracy_refined': acc1_refined['top1_accuracy'],
        'distance_error_refined': acc1_refined['distance_error']
    })

    # Method 2 accuracy (need ground truth for 36-byte, full test set in batches, once)
    print("\n  Method 2 (Python 36-byte + ANN):")
    print("    Computing ground truth for 36-byte arrays (full test set, once)...")
    all_36_idx, all_36_dist = [], []
    for start_i, end_i in batch_ranges(n_test, batch_size):
        batch = test_python_36_arr[start_i:end_i]
        idx, dist = linear_search_batch(
            batch, train_python_36_arr, config.K_NEIGHBORS, merged_diff_list_np
        )
        all_36_idx.append(idx)
        all_36_dist.append(dist)
    linear_36_indices = np.vstack(all_36_idx)
    linear_36_distances = np.vstack(all_36_dist)
    print("    Standard ANN:")
    acc2 = calculate_accuracy_metrics(
        linear_36_indices, linear_36_distances,
        ann_py36_indices, ann_py36_distances,
        config.K_NEIGHBORS
    )
    print(f"      Recall: {acc2['recall']:.2f}%")
    print(f"      Top-1 Accuracy: {acc2['top1_accuracy']:.2f}%")
    print(f"      Distance Error: {acc2['distance_error']:.2f}")

    print("    Two-Stage (ANN + Linear):")
    acc2_refined = calculate_accuracy_metrics(
        linear_36_indices, linear_36_distances,
        refined_py36_indices, refined_py36_distances,
        config.K_NEIGHBORS
    )
    print(f"      Recall: {acc2_refined['recall']:.2f}%")
    print(f"      Top-1 Accuracy: {acc2_refined['top1_accuracy']:.2f}%")
    print(f"      Distance Error: {acc2_refined['distance_error']:.2f}")

    method2_data.update({
        'recall': acc2['recall'],
        'top1_accuracy': acc2['top1_accuracy'],
        'distance_error': acc2['distance_error'],
        'distance_correlation': acc2['distance_correlation'],
        'recall_refined': acc2_refined['recall'],
        'top1_accuracy_refined': acc2_refined['top1_accuracy'],
        'distance_error_refined': acc2_refined['distance_error']
    })

    # Method 3 accuracy (ground truth for native 36-byte, full test set in batches, once)
    print("\n  Method 3 (Native C++ 36-byte + ANN):")
    print("    Computing ground truth for native 36-byte arrays (full test set, once)...")
    all_native_idx, all_native_dist = [], []
    for start_i, end_i in batch_ranges(n_test, batch_size):
        batch = test_native_36_arr[start_i:end_i]
        idx, dist = linear_search_batch(
            batch, train_native_36_arr, config.K_NEIGHBORS, merged_diff_list_np
        )
        all_native_idx.append(idx)
        all_native_dist.append(dist)
    linear_native_indices = np.vstack(all_native_idx)
    linear_native_distances = np.vstack(all_native_dist)
    print("    Standard ANN:")
    acc3 = calculate_accuracy_metrics(
        linear_native_indices, linear_native_distances,
        ann_native_indices, ann_native_distances,
        config.K_NEIGHBORS
    )
    print(f"      Recall: {acc3['recall']:.2f}%")
    print(f"      Top-1 Accuracy: {acc3['top1_accuracy']:.2f}%")
    print(f"      Distance Error: {acc3['distance_error']:.2f}")

    print("    Two-Stage (ANN + Linear):")
    acc3_refined = calculate_accuracy_metrics(
        linear_native_indices, linear_native_distances,
        refined_native_indices, refined_native_distances,
        config.K_NEIGHBORS
    )
    print(f"      Recall: {acc3_refined['recall']:.2f}%")
    print(f"      Top-1 Accuracy: {acc3_refined['top1_accuracy']:.2f}%")
    print(f"      Distance Error: {acc3_refined['distance_error']:.2f}")

    method3_data.update({
        'recall': acc3['recall'],
        'top1_accuracy': acc3['top1_accuracy'],
        'distance_error': acc3['distance_error'],
        'distance_correlation': acc3['distance_correlation'],
        'recall_refined': acc3_refined['recall'],
        'top1_accuracy_refined': acc3_refined['top1_accuracy'],
        'distance_error_refined': acc3_refined['distance_error']
    })

    # Method 4 is ground truth
    method4_data.update({
        'recall': 100.0,
        'precision': 100.0,
        'top1_accuracy': 100.0,
        'distance_error': 0.0,
        'distance_correlation': 1.0,
        'recall_refined': 100.0,
        'top1_accuracy_refined': 100.0,
        'distance_error_refined': 0.0,
        'two_stage_time': linear_avg,
        'two_stage_qps': linear_qps
    })

    # ========================================================================
    # RESULTS SUMMARY
    # ========================================================================
    print("\n" + "=" * 80)
    print("COMPREHENSIVE RESULTS SUMMARY")
    print("=" * 80)

    results_data = [
        {
            'Method': 'Python 132-byte + ANN',
            'Array Size': 132,
            **method1_data
        },
        {
            'Method': 'Python 36-byte + ANN',
            'Array Size': 36,
            **method2_data
        },
        {
            'Method': 'Native C++ 36-byte + ANN',
            'Array Size': 36,
            **method3_data
        },
        {
            'Method': 'Linear Search',
            'Array Size': 132,
            **method4_data
        }
    ]

    for r in results_data:
        r.pop('indices', None)
        r.pop('distances', None)
        r.pop('refined_indices', None)
        r.pop('refined_distances', None)

    results_df = pd.DataFrame(results_data)

    # Add computed columns
    results_df['Preprocess (s)'] = results_df['preprocess_time']
    results_df['Build (s)'] = results_df['build_time']
    results_df['Search (s)'] = results_df['search_time']
    results_df['Total Time (s)'] = results_df['preprocess_time'] + results_df['build_time'] + results_df['search_time']
    results_df['QPS'] = results_df['qps']
    results_df['Memory (MB)'] = results_df['memory_mb']
    results_df['Model Size (MB)'] = results_df['model_size_mb']
    results_df['Speedup vs Linear'] = linear_avg / results_df['search_time']
    results_df['Recall (%)'] = results_df['recall']
    results_df['Dist Error'] = results_df['distance_error']
    results_df['Dist Corr'] = results_df['distance_correlation']

    # Display standard ANN results
    print("\nSTANDARD ANN RESULTS:")
    display_cols = [
        'Method', 'Array Size', 'Preprocess (s)', 'Build (s)', 'Search (s)',
        'QPS', 'Speedup vs Linear', 'Recall (%)', 'Top-1 Acc (%)', 'Memory (MB)', 'Model Size (MB)'
    ]
    results_df['Top-1 Acc (%)'] = results_df['top1_accuracy']
    print("\n" + results_df[display_cols].to_string(index=False))

    # Display two-stage results
    print("\n\nTWO-STAGE (ANN + LINEAR REFINEMENT) RESULTS:")
    results_df['Two-Stage (s)'] = results_df['two_stage_time']
    results_df['Two-Stage QPS'] = results_df['two_stage_qps']
    results_df['Refined Recall (%)'] = results_df['recall_refined']
    results_df['Refined Top-1 (%)'] = results_df['top1_accuracy_refined']
    results_df['Speedup (2-Stage)'] = linear_avg / results_df['two_stage_time']

    display_cols_2stage = [
        'Method', 'Two-Stage (s)', 'Two-Stage QPS', 'Speedup (2-Stage)',
        'Refined Recall (%)', 'Refined Top-1 (%)', 'Dist Error'
    ]
    results_df['Dist Error'] = results_df['distance_error_refined']
    print("\n" + results_df[display_cols_2stage].to_string(index=False))

    # Save results
    timestamp = int(time.time())
    output_file = f"full_comparison_results_{timestamp}.csv"
    results_df.to_csv(output_file, index=False)
    print(f"\n✓ Results saved to: {output_file}")

    # Append one row to experiment log (config + results for comparison across runs)
    log_row = dict(config_to_dict(config))
    log_row['run_id'] = timestamp
    log_row['n_train'] = len(train_df)
    log_row['n_test'] = n_test
    method_names = [
        'Python 132-byte + ANN',
        'Python 36-byte + ANN',
        'Native C++ 36-byte + ANN',
        'Linear Search',
    ]
    for i, method in enumerate(method_names):
        prefix = f"M{i+1}_"
        row = results_df[results_df['Method'] == method].iloc[0]
        log_row[prefix + 'Preprocess_s'] = row['Preprocess (s)']
        log_row[prefix + 'Build_s'] = row['Build (s)']
        log_row[prefix + 'Search_s'] = row['Search (s)']
        log_row[prefix + 'QPS'] = row['QPS']
        log_row[prefix + 'Recall_pct'] = row['Recall (%)']
        log_row[prefix + 'Top1_pct'] = row['Top-1 Acc (%)']
        log_row[prefix + 'TwoStage_s'] = row['Two-Stage (s)']
        log_row[prefix + 'TwoStage_QPS'] = row['Two-Stage QPS']
        log_row[prefix + 'Speedup_vs_Linear'] = row['Speedup vs Linear'] if method != 'Linear Search' else 1.0
        log_row[prefix + 'Speedup_2Stage'] = (
            row['Speedup (2-Stage)'] if method != 'Linear Search' else row['Speedup vs Linear']
        )
        log_row[prefix + 'Refined_Recall_pct'] = row['Refined Recall (%)'] if method != 'Linear Search' else 100.0
        log_row[prefix + 'Refined_Top1_pct'] = row['Refined Top-1 (%)'] if method != 'Linear Search' else 100.0
        log_row[prefix + 'Memory_MB'] = row['Memory (MB)']
        log_row[prefix + 'Model_Size_MB'] = row['Model Size (MB)']
    log_df = pd.DataFrame([log_row])
    write_header = not Path(EXPERIMENT_LOG_PATH).exists()
    log_df.to_csv(EXPERIMENT_LOG_PATH, mode='a', header=write_header, index=False)
    print(f"✓ Experiment log appended to: {EXPERIMENT_LOG_PATH}")

    # ========================================================================
    # KEY FINDINGS
    # ========================================================================
    print("\n" + "=" * 80)
    print("KEY FINDINGS")
    print("=" * 80)

    ann_methods = results_df[results_df['Method'] != 'Linear Search']

    fastest_preprocess = results_df.loc[results_df['Preprocess (s)'].idxmin()]
    fastest_build = ann_methods.loc[ann_methods['Build (s)'].idxmin()]
    fastest_search = ann_methods.loc[ann_methods['Search (s)'].idxmin()]
    most_accurate = ann_methods.loc[ann_methods['Recall (%)'].idxmax()]
    most_memory_efficient = results_df.loc[results_df['Memory (MB)'].idxmin()]

    print(f"\nFastest Preprocessing: {fastest_preprocess['Method']}")
    print(f"   Time: {fastest_preprocess['Preprocess (s)']:.2f}s")
    print(f"   Rate: {len(train_df)/fastest_preprocess['Preprocess (s)']:.0f} records/s")

    print(f"\nFastest Index Build: {fastest_build['Method']}")
    print(f"   Time: {fastest_build['Build (s)']:.2f}s")

    print(f"\nFastest Search: {fastest_search['Method']}")
    print(f"   Time: {fastest_search['Search (s)']:.4f}s")
    print(f"   QPS: {fastest_search['QPS']:.2f}")
    print(f"   Speedup: {fastest_search['Speedup vs Linear']:.2f}x vs linear")
    print(f"   Linear time: {linear_avg:.4f}s")

    print(f"\nMost Accurate (ANN): {most_accurate['Method']}")
    print(f"   Recall: {most_accurate['Recall (%)']:.2f}%")
    print(f"   Distance Error: {most_accurate['Dist Error']:.2f}")
    print(f"   Distance Correlation: {most_accurate['Dist Corr']:.4f}")

    print(f"\nMost Memory Efficient: {most_memory_efficient['Method']}")
    print(f"   Memory: {most_memory_efficient['Memory (MB)']:.2f} MB")
    print(f"   Array Size: {most_memory_efficient['Array Size']} bytes/record")

    print("\n" + "=" * 80)
    print("TIMING BREAKDOWN")
    print("=" * 80)

    for _, row in results_df.iterrows():
        print(f"\n{row['Method']}:")
        print(f"  Preprocess: {row['Preprocess (s)']:.2f}s")
        print(f"  Build:      {row['Build (s)']:.2f}s")
        print(f"  Search:     {row['Search (s)']:.4f}s")
        print(f"  Total:      {row['Total Time (s)']:.2f}s")
        if row['Method'] != 'Linear Search':
            print(f"  Speedup:    {row['Speedup vs Linear']:.2f}x")

    print("\n" + "=" * 80)
    print("TWO-STAGE SEARCH IMPROVEMENTS")
    print("=" * 80)

    print("\nAccuracy Improvements with Two-Stage Search:")
    for _, row in results_df[results_df['Method'] != 'Linear Search'].iterrows():
        improvement = row['Refined Recall (%)'] - row['Recall (%)']
        top1_improvement = row['Refined Top-1 (%)'] - row['Top-1 Acc (%)']
        print(f"\n  {row['Method']}:")
        print(f"    Recall: {row['Recall (%)']:.1f}% → {row['Refined Recall (%)']:.1f}% (+{improvement:.1f}%)")
        print(f"    Top-1: {row['Top-1 Acc (%)']:.1f}% → {row['Refined Top-1 (%)']:.1f}% (+{top1_improvement:.1f}%)")
        print(f"    Time: {row['Search (s)']:.4f}s → {row['Two-Stage (s)']:.4f}s")
        print(f"    Still {row['Speedup (2-Stage)']:.0f}x faster than pure linear!")

    # Visualization
    print("\nGenerating visualizations...")
    fig, axes = plt.subplots(3, 3, figsize=(20, 15))

    methods = results_df['Method']
    colors = ['#2E86AB', '#F18F01', '#A6206A', '#A23B72']
    ann_df = results_df[results_df['Method'] != 'Linear Search']

    # 1. Search Time Comparison (Standard vs Two-Stage) + Linear baseline
    ax = axes[0, 0]
    x = np.arange(len(ann_df))
    width = 0.35
    ax.bar(x - width/2, ann_df['Search (s)'], width, label='Standard ANN', color='#2E86AB')
    ax.bar(x + width/2, ann_df['Two-Stage (s)'], width, label='Two-Stage', color='#F18F01')
    ax.axhline(y=linear_avg, color='#333333', linestyle='--', linewidth=2, label=f'Linear Search ({linear_avg:.1f}s)')
    ax.set_xticks(x)
    ax.set_xticklabels(ann_df['Method'], rotation=20, ha='right', fontsize=8)
    ax.set_ylabel('Time (seconds)', fontweight='bold')
    ax.set_title('Search Time: Standard vs Two-Stage vs Linear', fontweight='bold', fontsize=10)
    ax.legend()
    ax.grid(axis='y', alpha=0.3)

    # 2. Speedup Comparison - speedup at start
    ax = axes[0, 1]
    x = np.arange(len(ann_df))
    width = 0.35
    ax.bar(x - width/2, ann_df['Speedup vs Linear'], width, label='Standard ANN', color='#2E86AB')
    ax.bar(x + width/2, ann_df['Speedup (2-Stage)'], width, label='Two-Stage', color='#F18F01')
    ax.set_xticks(x)
    ax.set_xticklabels(ann_df['Method'], rotation=20, ha='right', fontsize=8)
    ax.set_ylabel('Speedup Factor', fontweight='bold')
    ax.set_title('Speedup vs Linear: Standard vs Two-Stage', fontweight='bold', fontsize=10)
    ax.legend()
    ax.grid(axis='y', alpha=0.3)
    ax.axhline(y=1, color='red', linestyle='--', alpha=0.5)

    # 3. QPS / Throughput - speed chart
    ax = axes[0, 2]
    x = np.arange(len(ann_df))
    width = 0.35
    ax.bar(x - width/2, ann_df['QPS'], width, label='Standard ANN', color='#2E86AB')
    ax.bar(x + width/2, ann_df['Two-Stage QPS'], width, label='Two-Stage', color='#F18F01')
    ax.set_xticks(x)
    ax.set_xticklabels(ann_df['Method'], rotation=20, ha='right', fontsize=8)
    ax.set_ylabel('Queries Per Second', fontweight='bold')
    ax.set_title('Throughput: Standard vs Two-Stage', fontweight='bold', fontsize=10)
    ax.legend()
    ax.grid(axis='y', alpha=0.3)

    # 4. Preprocessing Time
    ax = axes[1, 0]
    ax.bar(range(len(methods)), results_df['Preprocess (s)'], color=colors)
    ax.set_xticks(range(len(methods)))
    ax.set_xticklabels(methods, rotation=20, ha='right', fontsize=8)
    ax.set_ylabel('Time (seconds)', fontweight='bold')
    ax.set_title('Preprocessing Time', fontweight='bold', fontsize=10)
    ax.grid(axis='y', alpha=0.3)

    # 5. Memory Usage (Data + Model)
    ax = axes[1, 1]
    x = np.arange(len(methods))
    width = 0.35
    ax.bar(x - width/2, results_df['Memory (MB)'], width, label='Data Memory', color='#2E86AB')
    ax.bar(x + width/2, results_df['Model Size (MB)'], width, label='Model Size', color='#F18F01')
    ax.set_xticks(x)
    ax.set_xticklabels(methods, rotation=20, ha='right', fontsize=8)
    ax.set_ylabel('Memory (MB)', fontweight='bold')
    ax.set_title('Memory Usage: Data + Model', fontweight='bold', fontsize=10)
    ax.legend()
    ax.grid(axis='y', alpha=0.3)

    # 6. Recall Comparison
    ax = axes[1, 2]
    x = np.arange(len(ann_df))
    width = 0.35
    ax.bar(x - width/2, ann_df['Recall (%)'], width, label='Standard ANN', color='#2E86AB')
    ax.bar(x + width/2, ann_df['Refined Recall (%)'], width, label='Two-Stage', color='#F18F01')
    ax.set_xticks(x)
    ax.set_xticklabels(ann_df['Method'], rotation=20, ha='right', fontsize=8)
    ax.set_ylabel('Recall (%)', fontweight='bold')
    ax.set_title('Recall: Standard vs Two-Stage', fontweight='bold', fontsize=10)
    ax.legend()
    ax.grid(axis='y', alpha=0.3)
    ax.set_ylim([min(ann_df['Recall (%)'].min(), ann_df['Refined Recall (%)'].min()) - 5, 101])

    # 7. Top-1 Accuracy Comparison
    ax = axes[2, 0]
    x = np.arange(len(ann_df))
    width = 0.35
    ax.bar(x - width/2, ann_df['Top-1 Acc (%)'], width, label='Standard ANN', color='#2E86AB')
    ax.bar(x + width/2, ann_df['Refined Top-1 (%)'], width, label='Two-Stage', color='#F18F01')
    ax.set_xticks(x)
    ax.set_xticklabels(ann_df['Method'], rotation=20, ha='right', fontsize=8)
    ax.set_ylabel('Top-1 Accuracy (%)', fontweight='bold')
    ax.set_title('Top-1 Accuracy: Standard vs Two-Stage', fontweight='bold', fontsize=10)
    ax.legend()
    ax.grid(axis='y', alpha=0.3)
    ax.set_ylim([min(ann_df['Top-1 Acc (%)'].min(), ann_df['Refined Top-1 (%)'].min()) - 5, 101])

    # 8. Build Time
    ax = axes[2, 1]
    ax.bar(range(len(methods)), results_df['Build (s)'], color=colors)
    ax.set_xticks(range(len(methods)))
    ax.set_xticklabels(methods, rotation=20, ha='right', fontsize=8)
    ax.set_ylabel('Time (seconds)', fontweight='bold')
    ax.set_title('Index Build Time', fontweight='bold', fontsize=10)
    ax.grid(axis='y', alpha=0.3)

    # 9. Total Time
    ax = axes[2, 2]
    ax.bar(range(len(methods)), results_df['Total Time (s)'], color=colors)
    ax.set_xticks(range(len(methods)))
    ax.set_xticklabels(methods, rotation=20, ha='right', fontsize=8)
    ax.set_ylabel('Time (seconds)', fontweight='bold')
    ax.set_title('Total Time (Preprocess + Build + Search)', fontweight='bold', fontsize=10)
    ax.grid(axis='y', alpha=0.3)
    for i, v in enumerate(results_df['Total Time (s)']):
        ax.text(i, v, f'{v:.2f}', ha='center', va='bottom', fontsize=8)

    plt.tight_layout()
    plot_file = f"full_comparison_plot_{timestamp}.png"
    plt.savefig(plot_file, dpi=300, bbox_inches='tight')
    print(f"Visualization saved to: {plot_file}")
    plt.close()

    print("\nFull comparison complete!")
    return results_df


# ============================================================================
# Command Line Interface
# ============================================================================


if __name__ == "__main__":

    parser = argparse.ArgumentParser(
        description='TLSH Similarity Search: 4-Method Comparison with Configurable Parameters',
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    # Data parameters
    parser.add_argument(
        '--data-path', type=str, default='vt_mb_centers.csv',
        help='Path to TLSH dataset CSV file'
    )
    parser.add_argument(
        '--num-samples', type=int, default=None,
        help='Number of samples to use (None = all)'
    )
    parser.add_argument(
        '--test-size', type=float, default=0.1,
        help='Fraction of data to use for testing'
    )
    parser.add_argument(
        '--random-state', type=int, default=42,
        help='Random seed for reproducibility'
    )

    # Ground truth parameters
    parser.add_argument(
        '--ground-truth-file', type=str, default=None,
        help='Path to pre-computed ground truth file (auto-detect if not specified)'
    )
    parser.add_argument(
        '--ground-truth-dir', type=str, default='ground_truth',
        help='Directory containing ground truth files'
    )
    parser.add_argument(
        '--force-regenerate', action='store_true',
        help='Force regeneration of ground truth even if cached version exists'
    )

    # Query parameters
    parser.add_argument(
        '--k-neighbors', type=int, default=5,
        help='Number of nearest neighbors to find'
    )
    parser.add_argument(
        '--test-batch-size', type=int, default=100,
        help='Number of queries to test'
    )
    parser.add_argument(
        '--timing-iterations', type=int, default=3,
        help='Number of timing iterations'
    )

    # ANN parameters
    parser.add_argument(
        '--n-neighbors-build', type=int, default=20,
        help='Number of neighbors during index build'
    )
    parser.add_argument(
        '--n-trees', type=int, default=5,
        help='Number of random projection trees'
    )
    parser.add_argument(
        '--n-iters', type=int, default=5,
        help='Number of refinement iterations'
    )
    parser.add_argument(
        '--n-jobs', type=int, default=-1,
        help='Number of parallel jobs (-1 = all cores)'
    )
    parser.add_argument(
        '--max-candidates', type=int, default=60,
        help='Max candidates during search'
    )

    args = parser.parse_args()
    config = Config(args)

    run_comparison(config)

tool_script.py

"""
This is the simplest sample script to use the tool. Has endless possibilities.

This script is a starting-point example — not a runnable demo out of the box.
Before running, replace "your_file.csv" / "your_train_file.csv" / "your_test_file.csv"
with paths to your actual CSV files (each must contain a column named 'tlsh').

Required files in the same directory:
  - buildANNnPredict.py
  - final_bit_pairs.py
"""

import sys
import time
import logging

import pandas as pd

from buildANNnPredict import buildANNnPredict, merged_diff_list_np

# ---------------------------------------------------------------------------
# Update this portion
# ---------------------------------------------------------------------------

TRAIN_CSV   = "your_train_file.csv"   # CSV with 'tlsh' column — training / database set
TEST_CSV    = "your_test_file.csv"    # CSV with 'tlsh' column — query set
METHOD      = "132"                   # "132", "36", or "native36"
METRIC_TYPE = 132                     # must match METHOD (132 or 36)
K_VAL       = 10                      # number of nearest neighbours to retrieve
THRESHOLD   = 30                      # max TLSH distance to count as a match
BATCH_SIZE  = 1000                    # rows per prediction batch (e.g. 100, 1000, 5000)

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s  %(levelname)-8s  %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
)
log = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# Script
# ---------------------------------------------------------------------------

def load_csv(path: str) -> pd.DataFrame:
    """Load a CSV and validate that it contains a 'tlsh' column."""
    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        log.error("CSV file not found: %s", path)
        sys.exit(1)
    except Exception as exc:
        log.error("Failed to read '%s': %s", path, exc)
        sys.exit(1)

    if "tlsh" not in df.columns:
        log.error("'tlsh' column missing in '%s'. Found columns: %s", path, list(df.columns))
        sys.exit(1)

    log.info("Loaded '%s'  (%d rows)", path, len(df))
    return df


def preprocess(bmc: buildANNnPredict, df: pd.DataFrame, method: str, label: str) -> list:
    """Preprocess a dataframe and exit on failure."""
    result = bmc.preprocess(df, method=method)
    if result is None or len(result) == 0:
        log.error("Preprocessing returned no valid TLSH vectors for '%s'. Aborting.", label)
        sys.exit(1)
    log.info("Preprocessed '%s'  (%d valid vectors, method=%s)", label, len(result), method)
    return result


def main() -> None:
    train_df = load_csv(TRAIN_CSV)
    test_df  = load_csv(TEST_CSV)

    bap = buildANNnPredict(merged_diff_list_np)

    train_tlsh_list = preprocess(bap, train_df, METHOD, TRAIN_CSV)
    test_tlsh_list  = preprocess(bap, test_df,  METHOD, TEST_CSV)

    try:
        nn_model, build_time = bap.fit(train_tlsh_list, metric_type=METRIC_TYPE)
    except Exception as exc:
        log.error("Failed to build ANN index: %s", exc)
        sys.exit(1)
    log.info("ANN index built in %.3fs", build_time)

    train_df_reset = train_df.reset_index(drop=True)
    test_df_reset  = test_df.reset_index(drop=True)

    total = len(test_tlsh_list)
    batch_results = []
    overall_start = time.time()

    for batch_start in range(0, total, BATCH_SIZE):
        batch_end = min(batch_start + BATCH_SIZE, total)
        batch_queries = test_tlsh_list[batch_start:batch_end]
        batch_test_df = test_df_reset.iloc[batch_start:batch_end].reset_index(drop=True)

        try:
            batch_start_time = time.time()
            batch_df = bap.predict(
                nn_model,
                batch_queries,
                train_df_reset,
                batch_test_df,
                k_val=K_VAL,
                threshold=THRESHOLD,
            )
            batch_elapsed = time.time() - batch_start_time
        except Exception as exc:
            log.error("Prediction failed for batch %d–%d: %s", batch_start, batch_end - 1, exc)
            sys.exit(1)

        batch_results.append(batch_df)

    overall_elapsed = time.time() - overall_start

    if not batch_results:
        log.error("No batch results produced. Aborting.")
        sys.exit(1)

    try:
        results_df = pd.concat(batch_results, ignore_index=True)
    except Exception as exc:
        log.error("Failed to concatenate batch results: %s", exc)
        sys.exit(1)

    log.info("Done. Total results: %d  |  total time: %.3fs", len(results_df), overall_elapsed)
    print(results_df.head())


if __name__ == "__main__":
    main()