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# Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from dataclasses import dataclass
from typing import Any, Literal, Optional, Union
from datasets import load_dataset
from ...config.data_args import DataArguments
from ...extras.types import DatasetInfo, HFDataset
@dataclass
class DataLoaderPlugin:
"""Plugin for loading dataset."""
args: DataArguments
"""Data arguments."""
def _get_builder_name(self, path: str) -> Literal["arrow", "csv", "json", "parquet", "text"]:
"""Get dataset builder name.
Args:
path (str): Dataset path.
Returns:
Literal["arrow", "csv", "json", "parquet", "text"]: Dataset builder name.
"""
return os.path.splitext(path)[-1][1:].replace("jsonl", "json").replace("txt", "text")
def auto_load_data(self, dataset_info: DatasetInfo) -> HFDataset:
dataset_dir = dataset_info.get("dataset_dir", self.args.dataset_dir)
split = dataset_info.get("split", "train")
streaming = dataset_info.get("streaming", False)
if "file_name" in dataset_info:
filepath = os.path.join(dataset_dir, dataset_info["file_name"])
return self.load_data_from_file(filepath, split, streaming)
else:
raise NotImplementedError()
def load_data_from_file(self, filepath: str, split: str, streaming: bool) -> HFDataset:
if os.path.isdir(filepath):
filetype = self._get_builder_name(os.listdir(filepath)[0])
dataset = load_dataset(filetype, data_dir=filepath, split=split)
elif os.path.isfile(filepath):
filetype = self._get_builder_name(filepath)
dataset = load_dataset(filetype, data_files=filepath, split=split)
else:
raise ValueError(f"Can not load dataset from {filepath}.")
if streaming:
dataset = dataset.to_iterable_dataset()
return dataset
@dataclass
class DataIndexPlugin:
"""Plugin for adjusting dataset index."""
def adjust_data_index(
self, data_index: list[tuple[str, int]], size: Optional[int], weight: Optional[float]
) -> list[tuple[str, int]]:
"""Adjust dataset index by size and weight.
Args:
data_index (list[tuple[str, int]]): List of (dataset_name, sample_index).
size (Optional[int]): Desired dataset size.
weight (Optional[float]): Desired dataset weight.
Returns:
list[tuple[str, int]]: Adjusted dataset index.
"""
if size is not None:
data_index = self.adjust_by_size(data_index, size)
if weight is not None:
data_index = self.adjust_by_weight(data_index, weight)
return data_index
def adjust_by_size(self, data_index: list[tuple[str, int]], size: int) -> list[tuple[str, int]]:
raise NotImplementedError()
def adjust_by_weight(self, data_index: list[tuple[str, int]], weight: float) -> list[tuple[str, int]]:
raise NotImplementedError()
@dataclass
class DataSelectorPlugin:
"""Plugin for selecting dataset samples."""
data_index: list[tuple[str, int]]
"""List of (dataset_name, sample_index)"""
def select(self, index: Union[slice, list[int], Any]) -> Union[tuple[str, int], list[tuple[str, int]]]:
"""Select dataset samples.
Args:
index (Union[slice, list[int], Any]): Index of dataset samples.
Returns:
Union[tuple[str, int], list[tuple[str, int]]]: Selected dataset samples.
"""
if isinstance(index, slice):
return [self.data_index[i] for i in range(*index.indices(len(self.data_index)))]
elif isinstance(index, list):
return [self.data_index[i] for i in index]
else:
raise ValueError(f"Invalid index type {type(index)}.")
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