# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
Hugging Face Ptb_text_only load function
"""
# pylint: disable=C0103
import os
from typing import Union, Tuple
from datasets import load_dataset as hf_load
from mindspore.dataset import GeneratorDataset
from mindnlp.dataset.utils import make_bucket
from mindnlp.transforms import BasicTokenizer, PadTransform, Truncate
from mindnlp.dataset.register import load_dataset, process
from mindnlp.dataset.process import common_process
from mindnlp.configs import DEFAULT_ROOT
[docs]class HFptb_text_only:
"""
Hugging Face Ptb_text_only dataset source
"""
def __init__(self, dataset_list) -> None:
self.dataset_list = dataset_list
self._sentence = []
self._load()
def _load(self):
for every_dict in self.dataset_list:
self._sentence.append(every_dict['sentence'])
def __getitem__(self, index):
return self._sentence[index]
def __len__(self):
return len(self._sentence)
[docs]@load_dataset.register
def HF_Ptb_text_only(
root: str = DEFAULT_ROOT,
split: Union[Tuple[str], str] = ("train", "validation", "test"),
shuffle=True,
):
r"""
Load the huggingface Ptb_text_only dataset.
Args:
name (str):Task name
root (str): Directory where the datasets are saved.
Default:~/.mindnlp
split (str|Tuple[str]): Split or splits to be returned.
Default:('train', 'validation', 'test').
shuffle (bool): Whether to shuffle the dataset.
Default:True.
Returns:
- **datasets_list** (list) -A list of loaded datasets.
If only one type of dataset is specified,such as 'trian',
this dataset is returned instead of a list of datasets.
Examples:
>>> from mindnlp.dataset import HF_Ptb_text_only
>>> split = ('train', 'test')
>>> dataset_train, dataset_test = HF_Ptb_text_only(split=split)
>>> train_iter = dataset_train.create_tuple_iterator()
>>> print(next(train_iter))
"""
if root == DEFAULT_ROOT:
cache_dir = os.path.join(root, "datasets", "hf_datasets", "Ptb_text_only")
else:
cache_dir = root
column_names = ['sentence']
datasets_list = []
mode_list = []
if isinstance(split, str):
mode_list.append(split)
else:
for s in split:
mode_list.append(s)
ds_list = hf_load('ptb_text_only', split=mode_list, cache_dir=cache_dir)
for every_ds in ds_list:
datasets_list.append(GeneratorDataset(
source=HFptb_text_only(every_ds),
column_names=column_names, shuffle=shuffle)
)
if len(mode_list) == 1:
return datasets_list[0]
return datasets_list
[docs]@process.register
def HF_Ptb_text_only_Process(dataset, column="sentence", tokenizer=BasicTokenizer(), vocab=None,
batch_size=64, max_len=500, bucket_boundaries=None, drop_remainder=False):
"""
the process of the Ptb_text_only dataset
Args:
dataset (GeneratorDataset): Ptb_text_only dataset.
column (str): the column needed to be transpormed of the Ptb_text_only dataset.
tokenizer (TextTensorOperation): tokenizer you choose to tokenize the text dataset.
vocab (Vocab): vocabulary object, used to store the mapping of token and index.
batch_size (int): size of the batch.
max_len (int): max length of the sentence.
bucket_boundaries (list[int]): A list consisting of the upper boundaries of the buckets.
drop_remainder (bool): If True, will drop the last batch for each bucket if it is not a full batch
Returns:
- **dataset** (MapDataset) - dataset after transforms.
Raises:
TypeError: If `input_column` is not a string.
Examples:
>>> from mindnlp.dataset import HF_Ptb_text_only, HF_Ptb_text_only_Process
>>> dataset_train, dataset_test = HF_Ptb_text_only()
>>> dataset_train = HF_Ptb_text_only_Process(dataset_train)
>>> train_iter = dataset_train.create_tuple_iterator()
>>> print(next(train_iter))
"""
if vocab is None:
dataset, vocab = common_process(dataset, column, tokenizer, vocab)
else:
dataset = common_process(dataset, column, tokenizer, vocab)
pad_value = vocab.tokens_to_ids("<pad>")
trancate_op = Truncate(max_len)
dataset = dataset.map([trancate_op], column)
if bucket_boundaries is not None:
if not isinstance(bucket_boundaries, list):
raise ValueError(
f"'bucket_boundaries' must be a list of int, but get {type(bucket_boundaries)}")
if bucket_boundaries[-1] < max_len + 1:
bucket_boundaries.append(max_len + 1)
bucket_batch_sizes = [batch_size] * (len(bucket_boundaries) + 1)
dataset = make_bucket(dataset, column, pad_value,
bucket_boundaries, bucket_batch_sizes, drop_remainder)
else:
pad_op = PadTransform(max_len, pad_value)
dataset = dataset.map([pad_op], column)
dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
return dataset, vocab