#!/usr/bin/env python3

"""
Run this script as ./conversion_script.py to convert the Causal News Corpus
(CNC) "V1" (2022) DIRECTLY from its original repository -- ``UniCausal2HF``
already reads a CSV given any path/URL pandas' own ``read_csv`` accepts, so
no manual download/caching step is needed; point it straight at the raw
GitHub URLs. Previously this dataset had 0 parquet files on disk (the old
script expected a manually-placed ``cnc_train.csv`` that was never
provided) -- this had never once been run.

Citation / original source
---------------------------
Tan, F. A., Ng, S.-K., & Ong, A. R. (2022). "The Causal News Corpus:
Annotating Causal Relations in Event Sentences from News." LREC 2022.
https://aclanthology.org/2022.lrec-1.246/
Repo (verified live, public, no login): github.com/tanfiona/CausalNewsCorpus
License: CC0-1.0 (verified via GitHub API) -- public domain, no restrictions.

This is the original 2022 release -- see ../CNCv2/conversion_script.py for
"V2" (published as RECESS), the actively-maintained version the
maintainers themselves now recommend ("For 2023 Shared Task, please use
V2" -- repo README): V1's span annotations (subtask 2, Cause/Effect/Signal)
are much sparser, only 183 causal relations total vs. V2's 2257 (verified:
`train_subtask2_grouped.csv` + `dev_subtask2_grouped.csv` row counts). Kept
as its own separate dataset rather than silently overwritten, so both
remain available for comparison.

Format: the ``_grouped`` CSVs already match causalatee's ``UniCausal2HF``
grouped format exactly (one row per sentence; ``causal_text_w_pairs`` is a
Python-repr'd list of 0+ independently <ARG0>/<ARG1>/<SIGn>-tagged copies
of that row's ``text``, one per causal relation -- ARG0=cause, ARG1=effect,
confirmed against real "because"/"due to" examples) -- see
causalatee/data/conversion/_unicausal2hf.py for the shared parsing logic
(also used by BECauSEv2, AltLex).

No usable test split: the real held-out test set (``test_subtask2_text.csv``)
has NO gold labels at all and never will -- confirmed directly from
data/V1's upstream README: labels are withheld for a recurring shared
task. Mapped here as: upstream train -> causalatee train, upstream dev ->
causalatee dev.parquet -- written as ``dev.parquet``, NOT ``test.parquet``
(fixed 2026-07-20; previously written as test.parquet, which claimed a
real held-out test set exists when it's actually the upstream dev split).
`with_validation_split` in the evaluation harness treats a dataset with
dev but no test as: promote this dev to serve as the final eval target,
and carve a FRESH validation split out of train for early stopping
instead, so the promoted dev is never touched during training either way
-- see its docstring.
"""

from pathlib import Path

from causalatee.data.constants import Task
from causalatee.data.conversion import UniCausal2HF

_BASE_URL = "https://raw.githubusercontent.com/tanfiona/CausalNewsCorpus/master/data/V1"

converter = UniCausal2HF(
    {
        "train": f"{_BASE_URL}/train_subtask2_grouped.csv",
        "dev": f"{_BASE_URL}/dev_subtask2_grouped.csv",
    },
    Path.cwd(),
)

for split in ["train", "dev"]:
    converter.convert(Task.CausalityDetection, split)
    converter.convert(Task.CausalCandidateExtraction, split)
    converter.convert(Task.CausalityIdentification, split)