from transformers import AutoTokenizer, FlaxBertForSequenceClassification
import datasets
import jax
import jax.numpy as jnp
import time
from flax.training.common_utils import shard
from jax import pmap


def pred_fn(inputs):
    outputs = model(**inputs)
    return jax.nn.sigmoid(outputs.logits)


def get_toxicity(batch, batch_size):
    num_examples = len(batch["text"])
    inputs = tokenizer(
        batch["text"],
        return_tensors="np",
        truncation=True,
        padding="max_length",
        max_length=512,
    )

    inputs = shard(
        {
            k: jnp.pad(jnp.array(v), ((0, batch_size - num_examples), (0, 0)))
            for k, v in inputs.items()
        }
    )
    preds = p_pred(inputs)
    preds = preds.reshape(-1, preds.shape[-1])[:num_examples]
    for k, v in model.config.id2label.items():
        batch[v] = preds[:, k].tolist()
    return batch


p_pred = pmap(pred_fn, "inputs")

tokenizer = AutoTokenizer.from_pretrained("TurkuNLP/bert-large-finnish-cased-toxicity")
model = FlaxBertForSequenceClassification.from_pretrained(
    "TurkuNLP/bert-large-finnish-cased-toxicity", from_pt=True, dtype=jnp.bfloat16
)


dataset = datasets.load_from_disk("/researchdisk/mc4_3.1.0_fi_cleaned")

BATCH_SIZE = 8192
dataset = dataset.map(
    get_toxicity,
    num_proc=1,
    batched=True,
    batch_size=BATCH_SIZE,
    fn_kwargs={"batch_size": BATCH_SIZE},
)
print(dataset)

# SAVE DATASET
dataset.save_to_disk(
    "/researchdisk/mc4_3.1.0_fi_cleaned_dataset_toxicity_labels", num_proc=32
)