export CORES=`grep -c ^processor /proc/cpuinfo`
export XLA_PYTHON_CLIENT_PREALLOCATE=false

export HF_PROJECT="byt5-small-dutch-english"
export DATASET="yhavinga/mc4_nl_cleaned"
export DATASET_CONFIG="tiny_en_nl" # Config of the dataset in the Huggingface Hub
export DATASET_SPLIT="train" # Split to use for training tokenizer and model
export CONFIG_NAME="google/byt5-small"
export TOKENIZER_NAME="google/byt5-small"
export MODEL_PATH="${HOME}/data/${HF_PROJECT}" # Path to the model

mkdir -p "${MODEL_PATH}"

python ../train/run_t5_mlm_flax_pmap.py \
    --output_dir="${MODEL_PATH}" \
    --model_type="t5" \
    --config_name="${CONFIG_NAME}" \
    --tokenizer_name="${TOKENIZER_NAME}" \
    --preprocessing_num_workers="${CORES}" \
    --do_train --do_eval \
    --dataset_name="${DATASET}" \
    --dataset_config_name="${DATASET_CONFIG}" \
    --max_seq_length="1024" \
    --per_device_train_batch_size="16" \
    --per_device_eval_batch_size="16" \
    --gradient_accumulation_steps="8" \
    --mean_noise_span_length="20" \
    --dtype="bfloat16" \
    --z_loss="1e-4" \
    --optim="adafactor" \
    --learning_rate="0.0034" \
    --lr_decay="linear" \
    --overwrite_output_dir \
    --num_train_epochs="2" \
    --logging_steps="20" \
    --save_steps="1000" \
    --eval_steps="1000" \
    --warmup_steps="300" \
    --validation_split_count="15000" \
    --wandb_project="byt5-small-dutch-english" \
    --wandb_job_type="pmap"

#     \
#    --max_train_samples="160000" \
#    --max_eval_samples="1000" \

#    --model_name_or_path="${MODEL_PATH}" \
#    --resume_from_checkpoint="${MODEL_PATH}" \

#     \

#    --lr_decay="exponential" \
#    --lr_transition_steps="400000" \
#    --lr_decay_rate="0.7" \
#    --lr_staircase="false" \
#    --auth_token="$(cat ~/.huggingface/token)" \