#!/bin/bash

# Source the bash profile to load Conda properly
source ~/.bashrc

export PYTHONPATH=$PYTHONPATH:/home/mvm/arc

# Activate your Conda environment
conda activate ditto_env

# Run the experiments
# python src/evaluate_checkpoint.py \
#     -w TheThinker/ARC/upbeat-valley-3073--checkpoint:v3 \
#     -jc json/arc-agi_training_challenges.json \
#     -js json/arc-agi_training_solutions.json \
#     -i mean \
#     --num-permutations 1 \
#     --use-wandb False \
#     --dataset-seed 0 \
#     --random-search-seed 0 \
#     --only-n-tasks 20 \



python src/evaluate_checkpoint.py \
    -w TheThinker/ARC/upbeat-valley-3073--checkpoint:v3 \
    -jc json/arc-agi_evaluation_challenges.json \
    -js json/arc-agi_evaluation_solutions.json \
    -i gradient_ascent \
    --num-permutations 256 \
    --num-steps 100 \
    --lr 1.0 \
    --lr-schedule true \
    --optimizer adam \
    --optimizer-kwargs '{"b2": 0.9}' \
    --use-wandb False \
    --dataset-seed 0 \
    --random-search-seed 0 \
    --only-n-tasks 40 \











# python src/evaluate_checkpoint.py \
#     -w TheThinker/ARC/upbeat-valley-3073--checkpoint:v3 \
#     -jc json/arc-agi_evaluation_challenges.json \
#     -js json/arc-agi_evaluation_solutions.json \
#     -i gradient_ascent \
#     --num-steps 1 \
#     --lr 0.1 \
#     --lr-schedule true \
#     --optimizer adam \
#     --optimizer-kwargs '{"b2": 0.9}' \
#     --use-wandb False \
#     --dataset-seed 0 \
#     --random-search-seed 0 \

# python src/evaluate_checkpoint.py \
#     -w TheThinker/ARC/upbeat-valley-3073--checkpoint:v3 \
#     -jc json/arc-agi_evaluation_challenges.json \
#     -js json/arc-agi_evaluation_solutions.json \
#     -i gradient_ascent \
#     --num-steps 5 \
#     --lr 0.1 \
#     --lr-schedule true \
#     --optimizer adam \
#     --optimizer-kwargs '{"b2": 0.9}' \
#     --use-wandb False \
#     --dataset-seed 0 \
#     --random-search-seed 0 \


# python src/evaluate_checkpoint.py \
#     -w TheThinker/ARC/upbeat-valley-3073--checkpoint:v3 \
#     -jc json/arc-agi_evaluation_challenges.json \
#     -js json/arc-agi_evaluation_solutions.json \
#     -i gradient_ascent \
#     --num-steps 25 \
#     --lr 0.1 \
#     --lr-schedule true \
#     --optimizer adam \
#     --optimizer-kwargs '{"b2": 0.9}' \
#     --use-wandb False \
#     --dataset-seed 0 \
#     --random-search-seed 0 \

# python src/evaluate_checkpoint.py \
#     -w TheThinker/ARC/upbeat-valley-3073--checkpoint:v3 \
#     -jc json/arc-agi_evaluation_challenges.json \
#     -js json/arc-agi_evaluation_solutions.json \
#     -i gradient_ascent \
#     --num-steps 100 \
#     --lr 0.1 \
#     --lr-schedule true \
#     --optimizer adam \
#     --optimizer-kwargs '{"b2": 0.9}' \
#     --use-wandb False \
#     --dataset-seed 0 \
#     --random-search-seed 0 \

# python src/evaluate_checkpoint.py \
#     -w TheThinker/ARC/upbeat-valley-3073--checkpoint:v3 \
#     -jc json/arc-agi_evaluation_challenges.json \
#     -js json/arc-agi_evaluation_solutions.json \
#     -i gradient_ascent \
#     --num-steps 200 \
#     --lr 0.1 \
#     --lr-schedule true \
#     --optimizer adam \
#     --optimizer-kwargs '{"b2": 0.9}' \
#     --use-wandb False \
#     --dataset-seed 0 \
#     --random-search-seed 0 \

# python src/evaluate_checkpoint.py \
#     -w TheThinker/ARC/upbeat-valley-3073--checkpoint:v3 \
#     -jc json/arc-agi_evaluation_challenges.json \
#     -js json/arc-agi_evaluation_solutions.json \
#     -i gradient_ascent \
#     --num-steps 300 \
#     --lr 0.1 \
#     --lr-schedule true \
#     --optimizer adam \
#     --optimizer-kwargs '{"b2": 0.9}' \
#     --use-wandb False \
#     --dataset-seed 0 \
#     --random-search-seed 0 \

# python src/evaluate_checkpoint.py \
#     -w TheThinker/ARC/upbeat-valley-3073--checkpoint:v3 \
#     -jc json/arc-agi_evaluation_challenges.json \
#     -js json/arc-agi_evaluation_solutions.json \
#     -i gradient_ascent \
#     --num-steps 400 \
#     --lr 0.1 \
#     --lr-schedule true \
#     --optimizer adam \
#     --optimizer-kwargs '{"b2": 0.9}' \
#     --use-wandb False \
#     --dataset-seed 0 \
#     --random-search-seed 0 \

echo "All experiments completed"
