danesherbs

import csv
from datetime import datetime, timedelta

START_DATE = datetime.today()
WEEKS = 15
INITIAL_DISTANCE = 35
MAX_INCREASE_RATE = 0.10
PARK_RUN_DISTANCE = 8
INCREASE_RATE = 0.05

danesherbs

from flask import Flask, render_template, request, redirect
import jsonlines
import os

app = Flask(__name__)


@app.route("/")
def home():
    default_fname = "tmp/evallogs/230807123401PG6IQKIY_gpt-3.5-turbo-16k,gpt-3.5-turbo-16k_make-me-say.jsonl"

danesherbs

"""A teeny tiny compiler!"""

import re
from typing import List, Tuple
from dataclasses import dataclass

TOKEN_TYPES = [
    ("def", r"\bdef\b"),
    ("end", r"\bend\b"),
    ("identifier", r"\b[a-zA-Z]+\b"),

danesherbs

def sample(seq: Sequence, k: int, include: Union[None, set] = None, exclude: Union[None, set] = None) -> Sequence:
    """Samples `k` items from `seq` without replacement, optionally including or excluding items."""

    # pre-condition
    assert k >= 0, "Number of items to sample must be non-negative"
    assert k <= len(seq), f"Cannot sample {k} items without replacement from a sequence with {len(seq)} items"

    if include is None:
        include = set()
    

danesherbs

class DictDataset(torch.utils.data.Dataset):
    """Makes a dataset from a dictionary of tensors"""

    def __init__(self, inputs: Dict[str, Tensor]):
        assert len(inputs) > 0, "inputs must be non-empty"
        
        keys = list(inputs.keys())
        key = keys[0]
        self._length = inputs[key].shape[0]
        

danesherbs

# This hook is particularly useful when ablating layers

class ContextHook:
    def __init__(self, layer):
        self.layer = layer

    def __enter__(self):
        self.handle = self.layer.register_forward_hook(self.hook)
        return self

danesherbs

class StatefulHook:
    def __init__(self):
        self.module = None
        self.input = None
        self.output = None

    def __call__(self, module, input, output):
        self.module = module
        self.input = input
        self.output = output