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from peft.peft_model import BaseTunerLayer, PeftModel
from peft.utils.other import ModulesToSaveWrapper
def adapter_is_disabled(model: PeftModel) -> bool:
    """Given a peft model work out is adapters are enabled or disabled"""
    for module in model.model.modules():
        if isinstance(module, (BaseTunerLayer, ModulesToSaveWrapper)):
            # print(help(module.enable_adapters))
            return module._disable_adapters

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import sympy as sp
from typing import Dict, Any
from IPython.display import display
from sympy import init_printing
init_printing() 


def shypothesis(hypothesis: str, variables: Dict[str, Any] = None, round=3, verbose=False):
    """
    Evaluate a hypothesis using SymPy, showing simplified equation and result.

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"""
craftax = "1.4.1"
jax = "^0.4.28"
jax = 
https://gist.github.com/wassname/9f410d11f33cec75393b64d62286dd41
"""
import numpy as np
import numpy as np
from craftax.craftax.craftax_state import EnvState, Inventory, Mobs
from craftax.craftax.constants import MAX_OBS_DIM, OBS_DIM, BlockType, ItemType, MONSTERS_KILLED_TO_CLEAR_LEVEL

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{
    // Use IntelliSense to learn about possible attributes.
    // Hover to view descriptions of existing attributes.
    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
    "version": "0.2.0",
    "configurations": [
        {
            "name": "test",
            "type": "debugpy",
            "request": "launch",

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from jaxtyping import Float, Int
import torch
from torch.nn import functional as F
from torch import Tensor
from typing import List, Callable, Tuple, Dict, Optional
import pandas as pd
from transformers import AutoModelForCausalLM, AutoTokenizer


def get_valid_next_choices(choices_tokens, current_tokens):

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# Directly taken from https://huggingface.co/spaces/evaluate-measurement/perplexity/blob/main/perplexity.py
# TODO replace with a strided version https://github.com/huggingface/transformers/issues/9648#issuecomment-812981524
import numpy as np
import torch
import itertools
from torch.nn import CrossEntropyLoss
from tqdm.auto import tqdm
import torch.nn.functional as F
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM

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What is two-hot encoding?

Description

Two hot encoding was introduced in 2017 in "Marc G Bellemare et all "A distributional perspective on reinforcement learning" but the clearest description is in the 2020 paper "Dreamer-v3" by Danijar Hafner et al.) where it is used for reward and value distributions.

two-hot encoding is a generalization of onehot encoding to continuous values. It produces a vector of length |B| where all elements are 0 except for the two entries closest to the encoded continuous number, at positions k and k + 1. These two entries sum up to 1, with more weight given to the entry that is closer to the encoded number

Code samples

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"""
how to wrap a scikit-learn scalar like RobustScaler for pytorch
"""
import torch
import numpy as np
from einops import rearrange
from sklearn.preprocessing import StandardScaler, RobustScaler

class TorchRobustScaler(RobustScaler):

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"""
you cannot display, you need to specify html
- see also https://pandas.pydata.org/docs/user_guide/style.html#Builtin-Styles
"""
import pandas as pd
from IPython.display import display, HTML

df = pd.DataFrame({
    "strings": ["Adam", "Mike"],
    "ints": [1, 3],

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# see https://cheatography.com/linux-china/cheat-sheets/justfile/

# we can set sehll
set shell := ["zsh", "-cu"]

# settings
set dotenv-load

# Export all just variables as environment variables.
set export