data_adapter.py

import json
import os
import re
from pathlib import Path
from typing import Any, Dict, Iterable, Optional, Tuple

from datasets import Dataset, DatasetDict, load_dataset, load_from_disk

from utils import SYSTEM_PROMPT


_LOCAL_DATASET_LOADERS = {
    ".json": "json",
    ".jsonl": "json",
    ".csv": "csv",
    ".parquet": "parquet",
}


def load_sft_dataset(
    dataset_name: str,
    dataset_sub_name: str = "",
    split: str = "train",
    default_system_prompt: str = SYSTEM_PROMPT,
) -> Dataset:
    dataset = load_dataset_split(dataset_name, dataset_sub_name=dataset_sub_name, split=split)
    return dataset.map(
        lambda example: normalize_sft_example(example, default_system_prompt=default_system_prompt),
        remove_columns=dataset.column_names,
    )


def load_preference_dataset(
    dataset_name: str,
    dataset_sub_name: str = "",
    split: str = "train",
    default_system_prompt: str = SYSTEM_PROMPT,
) -> Dataset:
    dataset = load_dataset_split(dataset_name, dataset_sub_name=dataset_sub_name, split=split)
    return dataset.map(
        lambda example: normalize_preference_example(example, default_system_prompt=default_system_prompt),
        remove_columns=dataset.column_names,
    )


def load_prompt_dataset(
    dataset_name: str,
    dataset_sub_name: str = "",
    split: str = "train",
    default_system_prompt: str = SYSTEM_PROMPT,
) -> Dataset:
    dataset = load_dataset_split(dataset_name, dataset_sub_name=dataset_sub_name, split=split)
    return dataset.map(
        lambda example: normalize_prompt_example(example, default_system_prompt=default_system_prompt),
        remove_columns=dataset.column_names,
    )


def load_dataset_split(dataset_name: str, dataset_sub_name: str = "", split: str = "train") -> Dataset:
    expanded_name = os.path.expanduser(dataset_name)
    if os.path.exists(expanded_name):
        dataset = _load_local_dataset(Path(expanded_name), split=split)
        return _select_split(dataset, split)

    if dataset_sub_name:
        return load_dataset(dataset_name, dataset_sub_name, split=split)
    return load_dataset(dataset_name, split=split)


def normalize_sft_example(example: Dict[str, Any], default_system_prompt: str = SYSTEM_PROMPT) -> Dict[str, str]:
    if "messages" in example or "conversations" in example:
        messages = _coerce_messages(example.get("messages", example.get("conversations")))
        if messages:
            system_prompt, prompt, response = _messages_to_sft_fields(messages, default_system_prompt)
            return {
                "system": system_prompt,
                "prompt": prompt,
                "response": response,
            }

    prompt = _get_first_value(example, ("prompt", "question"))
    response = _get_first_value(example, ("response", "answer", "output", "completion"))

    if prompt is None and "instruction" in example and "output" in example:
        prompt = " ".join(
            part for part in (_stringify_text(example["instruction"]), _stringify_text(example.get("input"))) if part
        )
        response = _stringify_text(example["output"])

    if prompt is None or response is None:
        raise ValueError(
            "Unsupported SFT dataset schema. Expected prompt/response, question/answer, instruction/output, "
            f"or messages. Found columns: {sorted(example.keys())}"
        )

    return {
        "system": _extract_system_prompt(example, default_system_prompt),
        "prompt": _stringify_text(prompt),
        "response": _stringify_text(response),
    }


def normalize_preference_example(
    example: Dict[str, Any],
    default_system_prompt: str = SYSTEM_PROMPT,
) -> Dict[str, str]:
    if {"chosen", "rejected"}.issubset(example.keys()) and "prompt" not in example and "question" not in example:
        system_prompt, prompt, chosen = _extract_hh_prompt_and_response(
            _stringify_text(example["chosen"]),
            default_system_prompt=default_system_prompt,
        )
        _, rejected_prompt, rejected = _extract_hh_prompt_and_response(
            _stringify_text(example["rejected"]),
            default_system_prompt=default_system_prompt,
        )
        prompt = prompt or rejected_prompt
        return {
            "system": system_prompt,
            "prompt": prompt,
            "chosen": chosen,
            "rejected": rejected,
        }

    if {
        "prompt",
        "response_0",
        "response_1",
        "better_response_id",
    }.issubset(example.keys()):
        chosen, rejected = _select_pairwise_responses(example)
        return {
            "system": _extract_system_prompt(example, default_system_prompt),
            "prompt": _stringify_text(example["prompt"]),
            "chosen": chosen,
            "rejected": rejected,
        }

    prompt = _get_first_value(example, ("prompt", "question"))
    chosen = _get_first_value(example, ("chosen", "response_chosen", "preferred"))
    rejected = _get_first_value(example, ("rejected", "response_rejected", "dispreferred"))

    if prompt is None or chosen is None or rejected is None:
        raise ValueError(
            "Unsupported preference dataset schema. Expected prompt/chosen/rejected, "
            "question/response_chosen/response_rejected, SafeRLHF columns, or HH-RLHF chosen/rejected. "
            f"Found columns: {sorted(example.keys())}"
        )

    return {
        "system": _extract_system_prompt(example, default_system_prompt),
        "prompt": _stringify_text(prompt),
        "chosen": _stringify_text(chosen),
        "rejected": _stringify_text(rejected),
    }


def normalize_prompt_example(example: Dict[str, Any], default_system_prompt: str = SYSTEM_PROMPT) -> Dict[str, str]:
    if "messages" in example or "conversations" in example:
        messages = _coerce_messages(example.get("messages", example.get("conversations")))
        if messages:
            system_prompt, prompt, _ = _messages_to_sft_fields(messages, default_system_prompt)
            return {
                "system": system_prompt,
                "prompt": prompt,
            }

    if {
        "chosen",
        "rejected",
    }.issubset(example.keys()) and "prompt" not in example and "question" not in example:
        system_prompt, prompt, _ = _extract_hh_prompt_and_response(
            _stringify_text(example["chosen"]),
            default_system_prompt=default_system_prompt,
        )
        return {
            "system": system_prompt,
            "prompt": prompt,
        }

    prompt = _get_first_value(example, ("prompt", "question"))
    if prompt is None and "instruction" in example:
        prompt = " ".join(
            part for part in (_stringify_text(example["instruction"]), _stringify_text(example.get("input"))) if part
        )

    if prompt is None:
        raise ValueError(
            "Unsupported prompt dataset schema. Expected prompt/question/instruction, messages, or HH-RLHF chosen. "
            f"Found columns: {sorted(example.keys())}"
        )

    return {
        "system": _extract_system_prompt(example, default_system_prompt),
        "prompt": _stringify_text(prompt),
    }


def _load_local_dataset(dataset_path: Path, split: str):
    if dataset_path.is_file():
        loader_name = _loader_name_from_suffix(dataset_path.suffix)
        return load_dataset(loader_name, data_files={split: str(dataset_path)})

    try:
        return load_from_disk(str(dataset_path))
    except (FileNotFoundError, ValueError):
        data_file = _discover_local_data_file(dataset_path, split=split)
        loader_name = _loader_name_from_suffix(data_file.suffix)
        return load_dataset(loader_name, data_files={split: str(data_file)})


def _select_split(dataset, split: str) -> Dataset:
    if isinstance(dataset, DatasetDict):
        if split in dataset:
            return dataset[split]
        first_split = next(iter(dataset.keys()))
        return dataset[first_split]
    return dataset


def _discover_local_data_file(dataset_dir: Path, split: str) -> Path:
    for suffix in _LOCAL_DATASET_LOADERS:
        candidate = dataset_dir / f"{split}{suffix}"
        if candidate.exists():
            return candidate

    candidates = []
    for suffix in _LOCAL_DATASET_LOADERS:
        candidates.extend(sorted(dataset_dir.glob(f"*{suffix}")))

    if len(candidates) == 1:
        return candidates[0]

    raise ValueError(
        f"Could not infer dataset file under {dataset_dir}. "
        f"Expected {split}.jsonl/.json/.csv/.parquet or exactly one supported file."
    )


def _loader_name_from_suffix(suffix: str) -> str:
    if suffix not in _LOCAL_DATASET_LOADERS:
        raise ValueError(f"Unsupported local dataset format: {suffix}")
    return _LOCAL_DATASET_LOADERS[suffix]


def _extract_system_prompt(example: Dict[str, Any], default_system_prompt: str) -> str:
    system_prompt = _get_first_value(example, ("system", "system_prompt", "system_message"))
    system_prompt = _stringify_text(system_prompt)
    return system_prompt or default_system_prompt


def _get_first_value(example: Dict[str, Any], keys: Iterable[str]):
    for key in keys:
        if key in example and example[key] is not None:
            return example[key]
    return None


def _stringify_text(value: Any) -> str:
    if value is None:
        return ""
    if isinstance(value, str):
        return value.strip()
    if isinstance(value, list):
        parts = [_stringify_text(item) for item in value]
        return "\n".join(part for part in parts if part).strip()
    if isinstance(value, dict):
        text_value = value.get("text")
        if text_value is not None:
            return _stringify_text(text_value)
        content_value = value.get("content")
        if content_value is not None:
            return _stringify_text(content_value)
        return json.dumps(value, ensure_ascii=False, sort_keys=True)
    return str(value).strip()


def _coerce_messages(raw_messages: Any):
    if raw_messages is None:
        return None
    if isinstance(raw_messages, str):
        raw_messages = json.loads(raw_messages)
    if not isinstance(raw_messages, list):
        raise ValueError(f"Unsupported messages payload: {type(raw_messages)}")
    return raw_messages


def _messages_to_sft_fields(messages, default_system_prompt: str) -> Tuple[str, str, str]:
    if not messages:
        raise ValueError("Empty messages payload.")

    assistant_indexes = [
        index for index, message in enumerate(messages) if _normalize_role(message.get("role", message.get("from"))) == "assistant"
    ]
    if not assistant_indexes:
        raise ValueError("messages must contain at least one assistant turn.")

    final_assistant_index = assistant_indexes[-1]
    system_parts = []
    prompt_lines = []
    response = ""

    for index, message in enumerate(messages):
        role = _normalize_role(message.get("role", message.get("from", message.get("speaker"))))
        content = _stringify_text(message.get("content", message.get("value", message.get("text"))))
        if not content:
            continue
        if role == "system":
            system_parts.append(content)
            continue
        if index == final_assistant_index:
            response = content
            continue
        prompt_lines.append(f"{_render_role(role)}: {content}")

    if not response:
        raise ValueError("The final assistant turn is empty.")

    system_prompt = "\n".join(system_parts).strip() or default_system_prompt
    prompt = "\n".join(prompt_lines).strip()
    return system_prompt, prompt, response


def _normalize_role(role: Optional[str]) -> str:
    normalized_role = (role or "").strip().lower()
    role_map = {
        "human": "user",
        "user": "user",
        "assistant": "assistant",
        "gpt": "assistant",
        "bot": "assistant",
        "system": "system",
        "tool": "tool",
        "function": "tool",
    }
    return role_map.get(normalized_role, normalized_role or "user")


def _render_role(role: str) -> str:
    label_map = {
        "user": "User",
        "assistant": "Assistant",
        "tool": "Tool",
    }
    return label_map.get(role, role.title())


def _extract_hh_prompt_and_response(text: str, default_system_prompt: str) -> Tuple[str, str, str]:
    cleaned_text = text.lstrip()
    chunks = re.split(r"\n\nAssistant:", cleaned_text)
    if len(chunks) < 2:
        raise ValueError("Invalid HH-RLHF transcript: missing assistant response.")

    prompt_part = "\n\nAssistant:".join(chunks[:-1]).strip()
    response = chunks[-1].strip()
    prompt_lines = []
    for block in re.split(r"\n\n", prompt_part):
        current_block = block.strip()
        if current_block.startswith("Human:"):
            prompt_lines.append(f"User: {current_block[len('Human:'):].strip()}")
        elif current_block.startswith("Assistant:"):
            prompt_lines.append(f"Assistant: {current_block[len('Assistant:'):].strip()}")

    return default_system_prompt, "\n".join(prompt_lines).strip(), response


def _select_pairwise_responses(example: Dict[str, Any]) -> Tuple[str, str]:
    response_0 = _stringify_text(example["response_0"])
    response_1 = _stringify_text(example["response_1"])
    better_id = int(example["better_response_id"])

    if {
        "is_response_0_safe",
        "is_response_1_safe",
    }.issubset(example.keys()):
        response_0_safe = bool(example["is_response_0_safe"])
        response_1_safe = bool(example["is_response_1_safe"])
        if response_0_safe and not response_1_safe:
            return response_0, response_1
        if response_1_safe and not response_0_safe:
            return response_1, response_0

    if better_id == 0:
        return response_0, response_1
    return response_1, response_0