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import json
import logging
import os
import random
from abc import ABC, abstractmethod
from distutils.dir_util import copy_tree
from glob import glob
from typing import Dict, List, Union
import matplotlib.pyplot as plt
import numpy as np
import torch
from numpy.polynomial.polynomial import polyfit
from sklearn.metrics import mean_squared_error
import lmms_eval
from lmms_eval.api.registry import ALL_TASKS
from lmms_eval.evaluator import evaluate
from lmms_eval.tasks import (
ConfigurableTask,
get_task_dict,
include_path,
initialize_tasks,
)
from lmms_eval.utils import simple_parse_args_string
eval_logger = logging.getLogger("lmms-eval")
class BaseShrinker(ABC):
def __init__(self, task: str, num_items: Union[int, float], name: str, push_to_hub: bool = True) -> None:
super().__init__()
self.name = name
self.task = task
self.num_items = float(num_items)
self.push_to_hub = push_to_hub
@abstractmethod
def shrink(self):
return
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