Jonathan Chang changjonathanc

Let me think about this problem. Codeforces contest 2237 problem H — I don't know this specific problem, but let me derive.

Slime occupies a connected set of m vertices in a tree. A move: remove one occupied vertex and add an unoccupied vertex, keeping connectivity and size m. Food appears sequentially at vertices; slime must occupy the vertex to eat. f = min moves to eat all foods in order, starting from initial set. Queries are online: each query appends a food vertex and asks total minimal moves.

Key: online with q up to 1e5, sum n 1e5. We need incremental computation.

Classic result: a connected set of size m in a tree is like a "subtree of size m". Moving it toward a target vertex v: cost to reach v = distance from v to the set (dist to nearest occupied vertex). Actually each move can shift the slime one step: remove a leaf of the occupied subtree, add adjacent vertex toward v. So cost to make v occupied = dist(v, S) where S is current set. But then subsequent foods depend on the resulting shape of S,

Below is a consolidated reference to the current Model‑Context Protocol (MCP) features that govern how I think, reason, and format replies—excluding the tool catalog itself.

1. Instruction‑layer hierarchy

System → Developer → User: Every response is shaped by this ordered stack. Higher‑layer rules always override lower‑layer rules.
Guarantees that user‑level preferences are honored unless they conflict with developer or system‑level safety or policy constraints.

2. Multi‑channel response model

Run huggingface transformers on M1 Macs, on GPU

Requirement: macOS 12 Monterey
First, install a conda env with M1 support

wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh
sh ~/Downloads/Miniforge3-MacOSX-arm64.sh

	#!/usr/bin/env python3

	import argparse
	import base64
	import hashlib
	import json
	import os
	import secrets
	import sys
	import time

	import asyncio
	import logging
	import os
	import pty
	import pwd
	import subprocess
	from contextlib import asynccontextmanager

	import uvicorn
	from fastapi import FastAPI, HTTPException, Request, Response

	import torch

	def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, dtype=torch.float32, mod=True):
	freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
	t = torch.arange(end, device=freqs.device) # type: ignore
	angles = torch.outer(t, freqs) # type: ignore
	if mod:
	angles = angles % (2 * torch.pi)
	# Now convert angles to the desired lower precision
	angles = angles.to(dtype)

	"""Generates a document causal attention mask based on a document ID tensor"""

	from typing import List, Union

	import torch
	from torch import Tensor
	from torch.nn.attention.flex_attention import _mask_mod_signature, or_masks
	from attn_gym.masks import causal_mask

	from transformers import AutoTokenizer, AutoModelWithLMHead

	tokenizer = AutoTokenizer.from_pretrained("gpt2")

	# model = AutoModelWithLMHead.from_pretrained("gpt2")

	import re

	nums = []

Jonathan Chang changjonathanc

1. Instruction‑layer hierarchy

2. Multi‑channel response model

Run huggingface transformers on M1 Macs, on GPU

1. Instruction‑layer hierarchy

2. Multi‑channel response model