# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.


from typing import Any, Callable, Dict, Optional, Union

from torchtune.data import InputOutputToMessages
from torchtune.datasets._packed import PackedDataset
from torchtune.datasets._sft import SFTDataset
from torchtune.modules.transforms.tokenizers import ModelTokenizer


def grammar_dataset(
    tokenizer: ModelTokenizer,
    *,
    source: str = "liweili/c4_200m",
    column_map: Optional[Dict[str, str]] = None,
    train_on_input: bool = False,
    new_system_prompt: Optional[str] = None,
    packed: bool = False,
    filter_fn: Optional[Callable] = None,
    split: str = "train",
    **load_dataset_kwargs: Dict[str, Any],
) -> Union[SFTDataset, PackedDataset]:
    """
    Support for grammar correction datasets and their variants from Hugging Face Datasets.
    Here is an `example <https://huggingface.co/datasets/liweili/c4_200m>`_ of a grammar correction dataset.

    It is recommended to configure the tokenizer with the :class:`~torchtune.data.GrammarErrorCorrectionTemplate`
    in conjunction with this dataset.

    Masking of the prompt during training is controlled by the ``train_on_input`` flag, which is
    set to ``False`` by default
    - If ``train_on_input`` is True, the prompt is used during training and
    contributes to the loss.
    - If ``train_on_input`` is False, the prompt is masked out (tokens replaced with -100)

    Args:
        tokenizer (ModelTokenizer): Tokenizer used by the model that implements the ``tokenize_messages`` method.
        source (str): path to dataset repository on Hugging Face. For local datasets,
            define source as the data file type (e.g. "json", "csv", "text"), pass
            in the filepath in ``data_files``, and set ``split="train"``. See `Hugging Face's
            <https://huggingface.co/docs/datasets/en/package_reference/loading_methods#datasets.load_dataset.path>`_
            ``load_dataset`` for more details. Default is ``liweili/c4_200m``.
        column_map (Optional[Dict[str, str]]): a mapping from the expected columns in the message transform
            :class:`~torchtune.data.InputOutputToMessages` to the new column names in the dataset. Keys should be
            "input" and "output" and values should be the actual column names. If None, use
            the default column names ``"input"`` and ``"output"``in ``liweili/c4_200m``.
        train_on_input (bool): Whether the model is trained on the prompt or not. Default is False.
        new_system_prompt (Optional[str]): if specified, prepend a system message to every sample. This can
            serve as instructions to guide the model response. Setting this will OVERRIDE any system
            messages already present in the dataset. Default is None.
        packed (bool): Whether or not to pack the dataset to tokenizer's ``max_seq_len`` prior to training. Default is False.
        filter_fn (Optional[Callable]): callable used to filter the dataset prior to any pre-processing. See
            the Hugging Face `docs <https://huggingface.co/docs/datasets/v2.20.0/process#select-and-filter>`_ for more
            details.
        split (str): ``split`` argument for ``datasets.load_dataset``. You can use this argument to load a subset
            of a given split, e.g. ``split="train[:10%]"``. Default is "train".
        **load_dataset_kwargs (Dict[str, Any]): additional keyword arguments to pass to ``load_dataset``.

    Returns:
        Union[SFTDataset, PackedDataset]: dataset configured with source data and template

    Raises:
        ValueError: If ``packed=True`` and ``tokenizer.max_seq_len`` is not set.

    Example:
        >>> grammar_ds = grammar_dataset(model_transform=tokenizer)
        >>> for batch in Dataloader(grammar_ds, batch_size=8):
        >>>     print(f"Batch size: {len(batch)}")
        >>> Batch size: 8
    """

    message_transform = InputOutputToMessages(
        train_on_input=train_on_input,
        column_map=column_map,
        new_system_prompt=new_system_prompt,
    )
    ds = SFTDataset(
        source=source,
        message_transform=message_transform,
        model_transform=tokenizer,
        filter_fn=filter_fn,
        split=split,
        **load_dataset_kwargs,
    )
    if packed:
        if tokenizer.max_seq_len is None:
            raise ValueError(
                "PackedDataset requires a max_seq_len to be set on the tokenizer."
            )
        return PackedDataset(ds, max_seq_len=tokenizer.max_seq_len)
    return ds
