#!/bin/bash
echo $PATH
cd /path/to/your/project
conda activate ./env
cd language_modeling

models=("meta-llama/Llama-2-70b-chat-hf"
       "meta-llama/Llama-2-13b-chat-hf"
       "meta-llama/Llama-2-13b-hf"
       "meta-llama/Llama-2-70b-hf"
       "meta-llama/Meta-Llama-3-8B"
        "meta-llama/Meta-Llama-3-8B-Instruct"
        "meta-llama/Meta-Llama-3-70B-Instruct"
        "meta-llama/Meta-Llama-3-70B"
        "ALL"
        )
#top_ps=(0.9 0.95)
top_ps=(0.9)
num_top_ps=${#top_ps[@]}
model_index=$((SLURM_ARRAY_TASK_ID / num_top_ps))
top_p_index=$((SLURM_ARRAY_TASK_ID % num_top_ps))
model=${models[model_index]}
top_p=${top_ps[top_p_index]}
# for latest_eval/bbc_news_all
#source_dir="response_news"
for source_dir in "response_news"
do
  python ../uncertainty_quantification/loglik_analysis.py \
    --source_dir ${source_dir} \
    --top_p ${top_p} \
    --force_recompute \
    --max_tokens 512 \
    --model "${model}"
done
source_dir="response_news_1024_max_tokens_2048"
python ../uncertainty_quantification/loglik_analysis.py \
  --source_dir ${source_dir} \
  --top_p ${top_p} \
  --constraints 0,3,5 \
  --force_recompute \
  --max_tokens 2048 \
  --model "${model}"
