# /// script
# requires-python = ">=3.10,<4.0"
# dependencies = [
#     "datasets==3.5.0",
#     "huggingface-hub==0.24.0",
#     "pandas==2.2.0",
#     "requests==2.32.3",
# ]
# ///

"""Create the Allocine-mini sentiment dataset and upload it to the HF Hub."""

import pandas as pd
from constants import MAX_NUM_CHARS_IN_DOCUMENT, MIN_NUM_CHARS_IN_DOCUMENT  # noqa
from datasets import Dataset, DatasetDict, Split, load_dataset
from huggingface_hub import HfApi


def main() -> None:
    """Create the Allocine-mini sentiment dataset and upload it to the HF Hub."""
    # Define the base download URL
    repo_id = "tblard/allocine"

    # Download the dataset
    dataset = load_dataset(path=repo_id, token=True)
    assert isinstance(dataset, DatasetDict)

    # Convert the dataset to a dataframe
    train_df = dataset["train"].to_pandas()
    val_df = dataset["validation"].to_pandas()
    test_df = dataset["test"].to_pandas()
    assert isinstance(train_df, pd.DataFrame)
    assert isinstance(val_df, pd.DataFrame)
    assert isinstance(test_df, pd.DataFrame)

    # Rename `review` to `text`
    train_df.rename(columns={"review": "text"}, inplace=True)
    val_df.rename(columns={"review": "text"}, inplace=True)
    test_df.rename(columns={"review": "text"}, inplace=True)

    # Convert labels to text
    label_mapping = {0: "negative", 1: "positive"}
    train_df["label"] = train_df["label"].map(lambda x: label_mapping[int(x)])
    val_df["label"] = val_df["label"].map(lambda x: label_mapping[int(x)])
    test_df["label"] = test_df["label"].map(lambda x: label_mapping[int(x)])

    # Create validation split
    val_size = 256
    val_df = val_df.sample(n=val_size, random_state=4242)

    # Create test split
    test_size = 2048
    test_df = test_df.sample(n=test_size, random_state=4242)

    # Create train split
    train_size = 1024
    train_df = train_df.sample(n=train_size, random_state=4242)

    # Reset the index
    train_df = train_df.reset_index(drop=True)
    val_df = val_df.reset_index(drop=True)
    test_df = test_df.reset_index(drop=True)

    # Only work with samples where the document is not very large or small
    # We do it after we have made the splits to ensure that the dataset is minimally
    # affected.
    new_train_df = train_df.copy()
    new_train_df["text_len"] = new_train_df.text.str.len()
    new_train_df = new_train_df.query("text_len >= @MIN_NUM_CHARS_IN_DOCUMENT").query(
        "text_len <= @MAX_NUM_CHARS_IN_DOCUMENT"
    )
    new_val_df = val_df.copy()
    new_val_df["text_len"] = new_val_df.text.str.len()
    new_val_df = new_val_df.query("text_len >= @MIN_NUM_CHARS_IN_DOCUMENT").query(
        "text_len <= @MAX_NUM_CHARS_IN_DOCUMENT"
    )
    new_test_df = test_df.copy()
    new_test_df["text_len"] = new_test_df.text.str.len()
    new_test_df = new_test_df.query("text_len >= @MIN_NUM_CHARS_IN_DOCUMENT").query(
        "text_len <= @MAX_NUM_CHARS_IN_DOCUMENT"
    )

    # Collect datasets in a dataset dictionary
    dataset = DatasetDict(
        {
            "train": Dataset.from_pandas(new_train_df, split=Split.TRAIN),
            "val": Dataset.from_pandas(new_val_df, split=Split.VALIDATION),
            "test": Dataset.from_pandas(new_test_df, split=Split.TEST),
        }
    )

    # Create dataset ID
    dataset_id = "EuroEval/allocine-mini"

    # Remove the dataset from Hugging Face Hub if it already exists
    HfApi().delete_repo(dataset_id, repo_type="dataset", missing_ok=True)

    # Push the dataset to the Hugging Face Hub
    dataset.push_to_hub(dataset_id, private=True)


if __name__ == "__main__":
    main()
