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import pandas as pd |
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from datasets import load_dataset |
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import gradio as gr |
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import hashlib |
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from typing import Iterable, Union |
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from constants import RESULTS_REPO, ASSAY_RENAME, LEADERBOARD_RESULTS_COLUMNS |
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pd.set_option("display.max_columns", None) |
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def show_output_box(message): |
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return gr.update(value=message, visible=True) |
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def anonymize_user(username: str) -> str: |
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return hashlib.sha256(username.encode()).hexdigest()[:8] |
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def fetch_hf_results(): |
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df = load_dataset( |
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RESULTS_REPO, |
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data_files="auto_submissions/metrics_all.csv", |
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)["train"].to_pandas() |
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assert all( |
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col in df.columns for col in LEADERBOARD_RESULTS_COLUMNS |
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), f"Expected columns {LEADERBOARD_RESULTS_COLUMNS} not found in {df.columns}. Missing columns: {set(LEADERBOARD_RESULTS_COLUMNS) - set(df.columns)}" |
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df = df.sort_values("submission_time", ascending=False).drop_duplicates( |
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subset=["model", "assay", "user"], keep="first" |
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) |
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df["property"] = df["assay"].map(ASSAY_RENAME) |
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df.loc[df["anonymous"] != False, "user"] = "anon-" + df.loc[df["anonymous"] != False, "user"].apply(readable_hash) |
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return df |
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ADJECTIVES = [ |
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"ancient","brave","calm","clever","crimson","curious","dapper","eager", |
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"fuzzy","gentle","glowing","golden","happy","icy","jolly","lucky", |
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"magical","mellow","nimble","peachy","quick","royal","shiny","silent", |
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"sly","sparkly","spicy","spry","sturdy","sunny","swift","tiny","vivid", |
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"witty" |
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] |
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ANIMALS = [ |
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"ant","bat","bear","bee","bison","boar","bug","cat","crab","crow", |
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"deer","dog","duck","eel","elk","fox","frog","goat","gull","hare", |
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"hawk","hen","horse","ibis","kid","kiwi","koala","lamb","lark","lemur", |
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"lion","llama","loon","lynx","mole","moose","mouse","newt","otter","owl", |
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"ox","panda","pig","prawn","puma","quail","quokka","rabbit","rat","ray", |
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"robin","seal","shark","sheep","shrew","skunk","slug","snail","snake", |
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"swan","toad","trout","turtle","vole","walrus","wasp","whale","wolf", |
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"worm","yak","zebra" |
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] |
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NOUNS = [ |
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"rock","sand","star","tree","leaf","seed","stone","cloud","rain","snow", |
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"wind","fire","ash","dirt","mud","ice","wave","shell","dust","sun", |
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"moon","hill","lake","pond","reef","root","twig","wood" |
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] |
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def readable_hash( |
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data: Union[str, bytes, Iterable[int]], |
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*, |
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salt: Union[str, bytes, None] = None, |
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words: tuple[list[str], list[str]] = (ADJECTIVES, ANIMALS + NOUNS), |
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sep: str = "-", |
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checksum_len: int = 2, |
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case: str = "lower", |
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) -> str: |
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""" |
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Deterministically map input data to 'adjective-animal[-checksum]'. Generated using ChatGPT. |
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Examples |
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-------- |
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>>> readable_hash("hello world") |
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'magical-panda-6h' |
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>>> readable_hash("hello world", salt="my-app-v1", checksum_len=3) |
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'royal-otter-1pz' |
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>>> readable_hash(b"\x00\x01\x02\x03", case="title", checksum_len=0) |
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'Fuzzy-Tiger' |
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Vocabulary |
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---------- |
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ADJECTIVES: ~160 safe, descriptive words (e.g. "ancient", "brave", "silent", "swift") |
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ANIMALS: ~80 short, common animals (e.g. "dog", "owl", "whale", "tiger") |
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NOUNS: optional set of ~30 neutral nouns (e.g. "rock", "star", "tree", "cloud") |
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Combinations |
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------------ |
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- adjective + animal: ~13,000 unique names |
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- adjective + noun: ~5,000 unique names |
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- adjective + animal + noun: ~390,000 unique names |
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Checksum |
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-------- |
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An optional short base-36 suffix (e.g. "-6h" or "-1pz"). The checksum |
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acts as a disambiguator in case two different inputs map to the same |
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word combination. With 2-3 characters, collisions become vanishingly rare. |
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If you only need fun, human-readable names, you can disable it by setting |
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``checksum_len=0``. If you need unique, stable identifiers, keep it enabled. |
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""" |
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if isinstance(data, str): |
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data = data.encode() |
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elif isinstance(data, Iterable) and not isinstance(data, (bytes, bytearray)): |
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data = bytes(data) |
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h = hashlib.blake2b(digest_size=8) |
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if salt: |
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h.update(salt.encode() if isinstance(salt, str) else salt) |
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h.update(b"\x00") |
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h.update(data) |
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digest = h.digest() |
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n1 = int.from_bytes(digest[0:3], "big") |
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n2 = int.from_bytes(digest[3:6], "big") |
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adj = words[0][n1 % len(words[0])] |
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noun = words[1][n2 % len(words[1])] |
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phrase = f"{adj}{sep}{noun}" |
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if checksum_len > 0: |
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cs = int.from_bytes(digest[6:], "big") |
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base36 = "" |
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alphabet = "0123456789abcdefghijklmnopqrstuvwxyz" |
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while cs: |
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cs, r = divmod(cs, 36) |
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base36 = alphabet[r] + base36 |
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base36 = (base36 or "0")[:checksum_len] |
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phrase = f"{phrase}{sep}{base36}" |
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if case == "title": |
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phrase = sep.join(p.capitalize() for p in phrase.split(sep)) |
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elif case == "upper": |
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phrase = phrase.upper() |
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return phrase |
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