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import org.apache.commons.math3.distribution.TDistribution;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression;
import org.apache.commons.math3.util.FastMath;

import java.util.Arrays;

public class MyOLSMultipleLinearRegression extends OLSMultipleLinearRegression {
    /**
     * <p>Calculates the two-tailed p-values for the t-statistics for regression parameters.

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import collections
import uuid
from typing import Dict

import numpy as np
import tritonclient
import tritonclient.grpc as grpcclient
import tritonclient.utils.shared_memory as shm

ShmHandle = collections.namedtuple(

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import functools
import logging
import time

logger = logging.getLogger(__name__)


def pretty_time(t):
    if t < 1:
        return f'{t * 1000:.1f} ms'

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from typing import Union, Tuple, List, Dict

import numpy as np
import onnx
import onnxruntime as ort
import torch
import torch.nn as nn


def torch2onnx(

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import torch
import torch.nn as nn

class Classifier(nn.Module):
    def __init__(self, pretrained_linear: nn.Linear):
        super().__init__()
        assert pretrained_linear.bias is not None  # changeable
        self.linear = nn.Linear(
            in_features=pretrained_linear.in_features,
            out_features=pretrained_linear.out_features,

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"""
Copied from sentence-transformers / sentence_transformers/evaluation/BinaryClassificationEvaluator.py
"""
def find_best_f1_and_threshold(scores, labels, high_score_more_similar: bool):
    assert len(scores) == len(labels)

    scores = np.asarray(scores)
    labels = np.asarray(labels)

    rows = list(zip(scores, labels))

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#!/bin/bash

# Parse command line arguments using getopts
while getopts ":s:t:" opt; do
  case $opt in
    s)
      source_dir="$OPTARG"
      ;;
    t)
      target_path="$OPTARG"

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import collections
import itertools

Distribution = dict[tuple[int, int], int]  # {(#wins, #loss): #players}
State = tuple[float, Distribution]  # (prob, dist)


def sum_counter(counters):
    s = collections.Counter()
    for c in counters:

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"""
Very verbose implementation for finding topk with minimum comparison
https://stackoverflow.com/questions/4956593/optimal-algorithm-for-returning-top-k-values-from-an-array-of-length-n
"""

from __future__ import annotations
import dataclasses
import enum