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import dataclasses
import fcntl
import json
import pickle
import shutil
import tempfile
from collections.abc import Mapping
from contextlib import contextmanager
from io import BytesIO
from os import PathLike

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from typing import Generic, Hashable, Iterable, Mapping, Optional, Sequence, TypeVar

import numpy
from sklearn.base import TransformerMixin

T_Token = TypeVar("T_Token", bound=Hashable)
Self = TypeVar("Self", bound="SwemVectorizer")


class SwemVectorizer(Generic[T_Token], TransformerMixin):  # type: ignore[misc]

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import abc
import functools
from os import PathLike
from typing import Any, Callable, Dict, List, NamedTuple, Optional, Union

import fugashi
import minato


class Token(NamedTuple):

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import functools
import itertools
import sys
import threading
import time
from collections.abc import Callable, Iterable
from concurrent.futures import ThreadPoolExecutor
from typing import Any, TextIO, TypeVar

T = TypeVar("T")

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import time
from collections.abc import Iterable, Iterator
from multiprocessing import Process, Queue
from types import TracebackType
from typing import Generic, Type, TypeVar

T = TypeVar("T")


class NumberIterator:

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import dataclasses
import datetime
import re
import socket
import socketserver
import time


class config:
    SOCKET_TIMEOUT = 10

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from typing import TypeVar

import numpy
from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.cluster import KMeans

Self = TypeVar("Self", bound="HierarchicalKmeans")


class HierarchicalKmeans(BaseEstimator, ClusterMixin):

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from __future__ import annotations

import os
import shutil
import subprocess


class Selector:
    SUPPORTED_SELECTOR_COMMANDS = {"fzf", "peco"}
    ENVKEY_SELECTOR_COMMAND = "SELECTOR_COMMAND"

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from typing import Optional, cast

import torch


class MAB(torch.nn.Module):
    def __init__(
        self,
        input_dim: int,
        num_heads: int,