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import numpy as np


def stable_softmax(x, axis=None):
    """taken from scipy.special.softmax"""
    x_max = np.amax(x, axis=axis, keepdims=True)
    exp_x_shifted = np.exp(x - x_max)
    return exp_x_shifted / np.sum(exp_x_shifted, axis=axis, keepdims=True)

def get_prob(arr: np.ndarray, temp: float) -> np.ndarray:

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# Copyright 2024 AllenAI. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

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from collections import deque
import queue
import time
import numpy as np
import ray
from vllm import SamplingParams, LLM
import wandb
from open_instruct.dataset_transformation import TokenizerConfig, get_cached_dataset_rlvr
from open_instruct.vllm_utils3 import create_vllm_engines
from transformers import HfArgumentParser

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from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import torch.nn.functional as F

model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-135M")
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M")
tokenizer.add_special_tokens({"pad_token": "<PAD>"})
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
device = torch.device("cpu")
model.to(device)

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import argparse
import numpy as np


p = 100 # padding token id
o = 1 # observation (prompt / input ids)
a = 2 # action (response ids)

queries = [
    [p, p, o, o, o],

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import torch
import torch.nn as nn
import torch.optim as optim

# Create target distribution (fixed)
target_logits = torch.randn(10)
target_log_probs = torch.log_softmax(target_logits, dim=0)

# Create learnable distribution
learnable_logits = nn.Parameter(torch.rand_like(target_logits))  # Initialize randomly

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{
  "name": "material-ui-nextjs-ts",
  "version": "5.0.0",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "material-ui-nextjs-ts",
      "version": "5.0.0",
      "dependencies": {

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# Taken and modified from https://github.com/huggingface/trl
# Copyright 2024 The AllenAI Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

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import json
import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "0"

import gradio as gr
import numpy as np
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import HfApi, snapshot_download

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# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/models/olmo/modeling_olmo.py
# Copyright 2024 The vLLM team.
# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.