# todo combine benchmarks of scorers into common code base
import timeit

import numpy as np
import pandas


def benchmark(name, func, setup, lengths, count):
    print(f"starting {name}")
    start = timeit.default_timer()
    results = []
    from tqdm import tqdm

    for length in tqdm(lengths):
        test = timeit.Timer(func, setup=setup.format(length, count))
        results.append(min(test.timeit(number=1) for _ in range(7)) / count)
    stop = timeit.default_timer()
    print(f"finished {name}, Runtime: ", stop - start)
    return results


setup = """
from rapidfuzz.distance import JaroWinkler
import jellyfish
import string
import random
random.seed(18)
characters = string.ascii_letters + string.digits + string.whitespace + string.punctuation
a      = ''.join(random.choice(characters) for _ in range({0}))
b_list = [''.join(random.choice(characters) for _ in range({0})) for _ in range({1})]
"""

lengths = list(range(1, 256, 4))
count = 4000

time_rapidfuzz = benchmark(
    "rapidfuzz", "[JaroWinkler.similarity(a, b) for b in b_list]", setup, lengths, count
)

# this gets very slow, so only benchmark it for smaller values
time_jellyfish = (
    benchmark(
        "jellyfish",
        "[jellyfish.jaro_winkler_similarity(a, b) for b in b_list]",
        setup,
        list(range(1, 128, 4)),
        count,
    )
    + [np.NaN] * 32
)

df = pandas.DataFrame(
    data={
        "length": lengths,
        "rapidfuzz": time_rapidfuzz,
        "jellyfish": time_jellyfish,
    }
)

df.to_csv("results/jaro_winkler.csv", sep=",", index=False)