"""Simple comparison of holder performances, relative to unique_ptr holder."""

import collections
import sys
import time
from typing import Any, Callable, Dict, List

import pybind11_ubench_holder_comparison as m  # type: ignore

number_bucket_pc = None


def pflush(*args: Any, **kwargs: Any) -> None:
    print(*args, **kwargs)
    # Using "file" here because it is the name of the built-in keyword argument.
    file = kwargs.get("file", sys.stdout)  # pylint: disable=redefined-builtin
    file.flush()  # file object must have a flush method.


def run(args: List[str]) -> None:
    if not args:
        size_exponent_min = 0
        size_exponent_max = 16
        size_exponent_step = 4
        call_repetitions_first_pass = 100
        call_repetitions_target_elapsed_secs = 0.1
        num_samples = 10
        selected_holder_type = "all"
    else:
        assert len(args) == 7, (
            "size_exponent_min size_exponent_max size_exponent_step"
            " call_repetitions_first_pass call_repetitions_target_elapsed_secs"
            " num_samples selected_holder_type"
        )
        size_exponent_min = int(args[0])
        size_exponent_max = int(args[1])
        size_exponent_step = int(args[2])
        call_repetitions_first_pass = int(args[3])
        call_repetitions_target_elapsed_secs = float(args[4])
        num_samples = int(args[5])
        selected_holder_type = args[6]
    pflush(
        "command-line arguments:",
        size_exponent_min,
        size_exponent_max,
        size_exponent_step,
        call_repetitions_first_pass,
        "%.3f" % call_repetitions_target_elapsed_secs,
        num_samples,
        selected_holder_type,
    )
    pflush("sizeof_smart_holder:", m.sizeof_smart_holder())

    def find_call_repetitions(
        callable: Callable[[int], float],
        time_delta_floor: float = 1.0e-6,
        target_elapsed_secs_multiplier: float = 1.05,  # Empirical.
        target_elapsed_secs_tolerance: float = 0.05,
        max_iterations: int = 100,
    ) -> int:
        td_target = (
            call_repetitions_target_elapsed_secs * target_elapsed_secs_multiplier
        )
        crd = call_repetitions_first_pass
        for _ in range(max_iterations):
            td = callable(crd)
            crd = max(1, int(td_target * crd / max(td, time_delta_floor)))
            if abs(td - td_target) / td_target < target_elapsed_secs_tolerance:
                return crd
        raise RuntimeError("find_call_repetitions failure: max_iterations exceeded.")

    for size_exponent in range(
        size_exponent_min, size_exponent_max + 1, size_exponent_step
    ):
        data_size = 2**size_exponent
        pflush(data_size, "data_size")
        ratios: Dict[str, List[float]] = collections.defaultdict(list)
        call_repetitions = None
        for _ in range(num_samples):
            row_0 = None
            for nb_label, nb_type in [
                ("up", m.number_bucket_up),
                ("sp", m.number_bucket_sp),
                ("pu", m.number_bucket_pu),
                ("sh", m.number_bucket_sh),
                ("pc", number_bucket_pc),
            ]:
                if nb_label == "pc" and nb_type is None:
                    continue
                if selected_holder_type != "all" and nb_label != selected_holder_type:
                    continue
                nb1 = nb_type(data_size)  # type: ignore
                nb2 = nb_type(data_size)  # type: ignore

                def many_sum(call_repetitions: int) -> float:
                    assert int(round(nb1.sum())) == data_size
                    t0 = time.time()
                    for _ in range(call_repetitions):
                        nb1.sum()
                    return time.time() - t0

                def many_add(call_repetitions: int) -> float:
                    assert nb1.add(nb2) == data_size
                    t0 = time.time()
                    for _ in range(call_repetitions):
                        nb1.add(nb2)
                    return time.time() - t0

                if call_repetitions is None:
                    call_repetitions = find_call_repetitions(many_sum)
                    pflush(call_repetitions, "call_repetitions")

                td_sum = many_sum(call_repetitions)
                td_add = many_add(call_repetitions)
                row = [td_sum, td_add]
                if row_0 is None:
                    pflush("     Sum   Add  ratS  ratA")
                    row_0 = row
                else:
                    for curr, prev in zip(row, row_0):
                        if prev:
                            rat = curr / prev
                        else:
                            rat = -1
                        row.append(curr / prev)
                    ratios[nb_label + "_ratS"].append(row[-2])
                    ratios[nb_label + "_ratA"].append(row[-1])
                pflush(nb_label, " ".join(["%.3f" % v for v in row]))
        pflush("          Min  Mean   Max")
        for key, rat in ratios.items():
            print(
                key,
                "{:5.3f} {:5.3f} {:5.3f}".format(
                    min(rat), sum(rat) / len(rat), max(rat)
                ),
            )


if __name__ == "__main__":
    run(args=sys.argv[1:])