chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import tarfile
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from typing import TYPE_CHECKING, Literal, overload
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import numpy as np
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from paddle.dataset.common import _check_exists_and_download
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from paddle.io import Dataset
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if TYPE_CHECKING:
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import numpy.typing as npt
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_Wmt14DataSetMode = Literal["train", "test", "gen"]
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__all__ = []
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URL_DEV_TEST = (
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'http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz'
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)
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MD5_DEV_TEST = '7d7897317ddd8ba0ae5c5fa7248d3ff5'
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# this is a small set of data for test. The original data is too large and
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# will be add later.
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URL_TRAIN = 'http://paddlemodels.bj.bcebos.com/wmt/wmt14.tgz'
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MD5_TRAIN = '0791583d57d5beb693b9414c5b36798c'
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START = "<s>"
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END = "<e>"
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UNK = "<unk>"
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UNK_IDX = 2
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class WMT14(Dataset):
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"""
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Implementation of `WMT14 <http://www.statmt.org/wmt14/>`_ test dataset.
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The original WMT14 dataset is too large and a small set of data for set is
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provided. This module will download dataset from
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http://paddlemodels.bj.bcebos.com/wmt/wmt14.tgz .
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Args:
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data_file(str|None): path to data tar file, can be set None if
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:attr:`download` is True. Default None.
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mode(str): 'train', 'test' or 'gen'. Default 'train'.
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dict_size(int): word dictionary size. Default -1.
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download(bool): whether to download dataset automatically if
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:attr:`data_file` is not set. Default True.
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Returns:
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Dataset: Instance of WMT14 dataset
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- src_ids (np.array) - The sequence of token ids of source language.
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- trg_ids (np.array) - The sequence of token ids of target language.
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- trg_ids_next (np.array) - The next sequence of token ids of target language.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.text.datasets import WMT14
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>>> class SimpleNet(paddle.nn.Layer):
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... def __init__(self):
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... super().__init__()
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...
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... def forward(self, src_ids, trg_ids, trg_ids_next):
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... return paddle.sum(src_ids), paddle.sum(trg_ids), paddle.sum(trg_ids_next)
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>>> wmt14 = WMT14(mode='train', dict_size=50)
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>>> for i in range(10):
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... src_ids, trg_ids, trg_ids_next = wmt14[i]
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... src_ids = paddle.to_tensor(src_ids)
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... trg_ids = paddle.to_tensor(trg_ids)
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... trg_ids_next = paddle.to_tensor(trg_ids_next)
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...
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... model = SimpleNet()
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... src_ids, trg_ids, trg_ids_next = model(src_ids, trg_ids, trg_ids_next)
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... print(src_ids.item(), trg_ids.item(), trg_ids_next.item())
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91 38 39
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123 81 82
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556 229 230
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182 26 27
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447 242 243
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116 110 111
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403 288 289
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258 221 222
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136 34 35
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281 136 137
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"""
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mode: _Wmt14DataSetMode
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data_file: str | None
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dict_size: int
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src_ids: list[list[int]]
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trg_ids: list[list[int]]
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trg_ids_next: list[list[int]]
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src_dict: dict[str, int]
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trg_dict: dict[str, int]
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def __init__(
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self,
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data_file: str | None = None,
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mode: _Wmt14DataSetMode = 'train',
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dict_size: int = -1,
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download: bool = True,
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) -> None:
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assert mode.lower() in [
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'train',
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'test',
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'gen',
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], f"mode should be 'train', 'test' or 'gen', but got {mode}"
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self.mode = mode.lower()
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self.data_file = data_file
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if self.data_file is None:
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assert download, (
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"data_file is not set and downloading automatically is disabled"
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)
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self.data_file = _check_exists_and_download(
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data_file, URL_TRAIN, MD5_TRAIN, 'wmt14', download
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)
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# read dataset into memory
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assert dict_size > 0, "dict_size should be set as positive number"
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self.dict_size = dict_size
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self._load_data()
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def _load_data(self) -> None:
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def __to_dict(fd, size: int) -> dict[str, int]:
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out_dict = {}
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for line_count, line in enumerate(fd):
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if line_count < size:
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out_dict[line.strip().decode()] = line_count
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else:
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break
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return out_dict
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self.src_ids = []
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self.trg_ids = []
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self.trg_ids_next = []
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with tarfile.open(self.data_file, mode='r') as f:
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names = [
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each_item.name
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for each_item in f
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if each_item.name.endswith("src.dict")
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]
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assert len(names) == 1
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self.src_dict = __to_dict(f.extractfile(names[0]), self.dict_size)
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names = [
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each_item.name
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for each_item in f
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if each_item.name.endswith("trg.dict")
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]
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assert len(names) == 1
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self.trg_dict = __to_dict(f.extractfile(names[0]), self.dict_size)
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file_name = f"{self.mode}/{self.mode}"
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names = [
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each_item.name
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for each_item in f
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if each_item.name.endswith(file_name)
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]
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for name in names:
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for line in f.extractfile(name):
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line = line.decode()
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line_split = line.strip().split('\t')
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if len(line_split) != 2:
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continue
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src_seq = line_split[0] # one source sequence
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src_words = src_seq.split()
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src_ids = [
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self.src_dict.get(w, UNK_IDX)
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for w in [START, *src_words, END]
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]
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trg_seq = line_split[1] # one target sequence
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trg_words = trg_seq.split()
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trg_ids = [self.trg_dict.get(w, UNK_IDX) for w in trg_words]
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# remove sequence whose length > 80 in training mode
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if len(src_ids) > 80 or len(trg_ids) > 80:
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continue
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trg_ids_next = [*trg_ids, self.trg_dict[END]]
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trg_ids = [self.trg_dict[START], *trg_ids]
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self.src_ids.append(src_ids)
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self.trg_ids.append(trg_ids)
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self.trg_ids_next.append(trg_ids_next)
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def __getitem__(
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self, idx: int
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) -> tuple[
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npt.NDArray[np.int_],
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npt.NDArray[np.int_],
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npt.NDArray[np.int_],
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]:
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return (
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np.array(self.src_ids[idx]),
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np.array(self.trg_ids[idx]),
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np.array(self.trg_ids_next[idx]),
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)
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def __len__(self) -> int:
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return len(self.src_ids)
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@overload
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def get_dict(
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self, reverse: Literal[True] = ...
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) -> tuple[dict[int, str], dict[int, str]]: ...
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@overload
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def get_dict(
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self, reverse: Literal[False] = ...
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) -> tuple[dict[str, int], dict[str, int]]: ...
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@overload
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def get_dict(
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self, reverse: bool = ...
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) -> (
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tuple[dict[str, int], dict[str, int]]
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| tuple[dict[int, str], dict[int, str]]
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): ...
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def get_dict(self, reverse=False):
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"""
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Get the source and target dictionary.
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Args:
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reverse (bool): whether to reverse key and value in dictionary,
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i.e. key: value to value: key.
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Returns:
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Two dictionaries, the source and target dictionary.
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Examples:
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.. code-block:: pycon
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>>> from paddle.text.datasets import WMT14
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>>> wmt14 = WMT14(mode='train', dict_size=50)
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>>> src_dict, trg_dict = wmt14.get_dict()
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"""
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src_dict, trg_dict = self.src_dict, self.trg_dict
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if reverse:
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src_dict = {v: k for k, v in src_dict.items()}
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trg_dict = {v: k for k, v in trg_dict.items()}
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return src_dict, trg_dict
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