# Copyright 2020 The TensorFlow Datasets Authors.
#
# 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.
# Lint as: python3
"""Download manager interface."""
import enum
import io
import multiprocessing
import os
import posixpath
import warnings
from datetime import datetime
from functools import partial
from typing import Dict, List, Optional, Union
import fsspec
from fsspec.core import url_to_fs
from tqdm.contrib.concurrent import thread_map
from .. import config
from ..utils import tqdm as hf_tqdm
from ..utils.deprecation_utils import DeprecatedEnum, deprecated
from ..utils.file_utils import (
ArchiveIterable,
FilesIterable,
cached_path,
get_from_cache,
hash_url_to_filename,
is_relative_path,
stack_multiprocessing_download_progress_bars,
url_or_path_join,
)
from ..utils.info_utils import get_size_checksum_dict
from ..utils.logging import get_logger, tqdm
from ..utils.py_utils import NestedDataStructure, map_nested, size_str
from ..utils.track import tracked_str
from .download_config import DownloadConfig
logger = get_logger(__name__)
class DownloadMode(enum.Enum):
"""`Enum` for how to treat pre-existing downloads and data.
The default mode is `REUSE_DATASET_IF_EXISTS`, which will reuse both
raw downloads and the prepared dataset if they exist.
The generations modes:
| | Downloads | Dataset |
|-------------------------------------|-----------|---------|
| `REUSE_DATASET_IF_EXISTS` (default) | Reuse | Reuse |
| `REUSE_CACHE_IF_EXISTS` | Reuse | Fresh |
| `FORCE_REDOWNLOAD` | Fresh | Fresh |
"""
REUSE_DATASET_IF_EXISTS = "reuse_dataset_if_exists"
REUSE_CACHE_IF_EXISTS = "reuse_cache_if_exists"
FORCE_REDOWNLOAD = "force_redownload"
class GenerateMode(DeprecatedEnum):
REUSE_DATASET_IF_EXISTS = "reuse_dataset_if_exists"
REUSE_CACHE_IF_EXISTS = "reuse_cache_if_exists"
FORCE_REDOWNLOAD = "force_redownload"
@property
def help_message(self):
return "Use 'DownloadMode' instead."
class DownloadManager:
is_streaming = False
def __init__(
self,
dataset_name: Optional[str] = None,
data_dir: Optional[str] = None,
download_config: Optional[DownloadConfig] = None,
base_path: Optional[str] = None,
record_checksums=True,
):
"""Download manager constructor.
Args:
data_dir:
can be used to specify a manual directory to get the files from.
dataset_name (`str`):
name of dataset this instance will be used for. If
provided, downloads will contain which datasets they were used for.
download_config (`DownloadConfig`):
to specify the cache directory and other
download options
base_path (`str`):
base path that is used when relative paths are used to
download files. This can be a remote url.
record_checksums (`bool`, defaults to `True`):
Whether to record the checksums of the downloaded files. If None, the value is inferred from the builder.
"""
self._dataset_name = dataset_name
self._data_dir = data_dir
self._base_path = base_path or os.path.abspath(".")
# To record what is being used: {url: {num_bytes: int, checksum: str}}
self._recorded_sizes_checksums: Dict[str, Dict[str, Optional[Union[int, str]]]] = {}
self.record_checksums = record_checksums
self.download_config = download_config or DownloadConfig()
self.downloaded_paths = {}
self.extracted_paths = {}
@property
def manual_dir(self):
return self._data_dir
@property
def downloaded_size(self):
"""Returns the total size of downloaded files."""
return sum(checksums_dict["num_bytes"] for checksums_dict in self._recorded_sizes_checksums.values())
@staticmethod
def ship_files_with_pipeline(downloaded_path_or_paths, pipeline):
"""Ship the files using Beam FileSystems to the pipeline temp dir.
Args:
downloaded_path_or_paths (`str` or `list[str]` or `dict[str, str]`):
Nested structure containing the
downloaded path(s).
pipeline ([`utils.beam_utils.BeamPipeline`]):
Apache Beam Pipeline.
Returns:
`str` or `list[str]` or `dict[str, str]`
"""
from ..utils.beam_utils import upload_local_to_remote
remote_dir = pipeline._options.get_all_options().get("temp_location")
if remote_dir is None:
raise ValueError("You need to specify 'temp_location' in PipelineOptions to upload files")
def upload(local_file_path):
remote_file_path = posixpath.join(
remote_dir, config.DOWNLOADED_DATASETS_DIR, os.path.basename(local_file_path)
)
logger.info(
f"Uploading {local_file_path} ({size_str(os.path.getsize(local_file_path))}) to {remote_file_path}."
)
upload_local_to_remote(local_file_path, remote_file_path)
return remote_file_path
uploaded_path_or_paths = map_nested(
lambda local_file_path: upload(local_file_path),
downloaded_path_or_paths,
)
return uploaded_path_or_paths
def _record_sizes_checksums(self, url_or_urls: NestedDataStructure, downloaded_path_or_paths: NestedDataStructure):
"""Record size/checksum of downloaded files."""
delay = 5
for url, path in hf_tqdm(
list(zip(url_or_urls.flatten(), downloaded_path_or_paths.flatten())),
delay=delay,
desc="Computing checksums",
):
# call str to support PathLike objects
self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(
path, record_checksum=self.record_checksums
)
@deprecated("Use `.download`/`.download_and_extract` with `fsspec` URLs instead.")
def download_custom(self, url_or_urls, custom_download):
"""
Download given urls(s) by calling `custom_download`.
Args:
url_or_urls (`str` or `list` or `dict`):
URL or `list` or `dict` of URLs to download and extract. Each URL is a `str`.
custom_download (`Callable[src_url, dst_path]`):
The source URL and destination path. For example
`tf.io.gfile.copy`, that lets you download from Google storage.
Returns:
downloaded_path(s): `str`, The downloaded paths matching the given input
`url_or_urls`.
Example:
```py
>>> downloaded_files = dl_manager.download_custom('s3://my-bucket/data.zip', custom_download_for_my_private_bucket)
```
"""
cache_dir = self.download_config.cache_dir or config.DOWNLOADED_DATASETS_PATH
max_retries = self.download_config.max_retries
def url_to_downloaded_path(url):
return os.path.join(cache_dir, hash_url_to_filename(url))
downloaded_path_or_paths = map_nested(url_to_downloaded_path, url_or_urls)
url_or_urls = NestedDataStructure(url_or_urls)
downloaded_path_or_paths = NestedDataStructure(downloaded_path_or_paths)
for url, path in zip(url_or_urls.flatten(), downloaded_path_or_paths.flatten()):
try:
get_from_cache(
url, cache_dir=cache_dir, local_files_only=True, use_etag=False, max_retries=max_retries
)
cached = True
except FileNotFoundError:
cached = False
if not cached or self.download_config.force_download:
custom_download(url, path)
get_from_cache(
url, cache_dir=cache_dir, local_files_only=True, use_etag=False, max_retries=max_retries
)
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
return downloaded_path_or_paths.data
def download(self, url_or_urls):
"""Download given URL(s).
By default, only one process is used for download. Pass customized `download_config.num_proc` to change this behavior.
Args:
url_or_urls (`str` or `list` or `dict`):
URL or `list` or `dict` of URLs to download. Each URL is a `str`.
Returns:
`str` or `list` or `dict`:
The downloaded paths matching the given input `url_or_urls`.
Example:
```py
>>> downloaded_files = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')
```
"""
download_config = self.download_config.copy()
download_config.extract_compressed_file = False
if download_config.download_desc is None:
download_config.download_desc = "Downloading data"
download_func = partial(self._download_batched, download_config=download_config)
start_time = datetime.now()
with stack_multiprocessing_download_progress_bars():
downloaded_path_or_paths = map_nested(
download_func,
url_or_urls,
map_tuple=True,
num_proc=download_config.num_proc,
desc="Downloading data files",
batched=True,
batch_size=-1,
)
duration = datetime.now() - start_time
logger.info(f"Downloading took {duration.total_seconds() // 60} min")
url_or_urls = NestedDataStructure(url_or_urls)
downloaded_path_or_paths = NestedDataStructure(downloaded_path_or_paths)
self.downloaded_paths.update(dict(zip(url_or_urls.flatten(), downloaded_path_or_paths.flatten())))
start_time = datetime.now()
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
duration = datetime.now() - start_time
logger.info(f"Checksum Computation took {duration.total_seconds() // 60} min")
return downloaded_path_or_paths.data
def _download_batched(
self,
url_or_filenames: List[str],
download_config: DownloadConfig,
) -> List[str]:
if len(url_or_filenames) >= 16:
download_config = download_config.copy()
download_config.disable_tqdm = True
download_func = partial(self._download_single, download_config=download_config)
fs: fsspec.AbstractFileSystem
fs, path = url_to_fs(url_or_filenames[0], **download_config.storage_options)
size = 0
try:
size = fs.info(path).get("size", 0)
except Exception:
pass
max_workers = (
config.HF_DATASETS_MULTITHREADING_MAX_WORKERS if size < (20 << 20) else 1
) # enable multithreading if files are small
return thread_map(
download_func,
url_or_filenames,
desc=download_config.download_desc or "Downloading",
unit="files",
position=multiprocessing.current_process()._identity[-1] # contains the ranks of subprocesses
if os.environ.get("HF_DATASETS_STACK_MULTIPROCESSING_DOWNLOAD_PROGRESS_BARS") == "1"
and multiprocessing.current_process()._identity
else None,
max_workers=max_workers,
tqdm_class=tqdm,
)
else:
return [
self._download_single(url_or_filename, download_config=download_config)
for url_or_filename in url_or_filenames
]
def _download_single(self, url_or_filename: str, download_config: DownloadConfig) -> str:
url_or_filename = str(url_or_filename)
if is_relative_path(url_or_filename):
# append the relative path to the base_path
url_or_filename = url_or_path_join(self._base_path, url_or_filename)
out = cached_path(url_or_filename, download_config=download_config)
out = tracked_str(out)
out.set_origin(url_or_filename)
return out
def iter_archive(self, path_or_buf: Union[str, io.BufferedReader]):
"""Iterate over files within an archive.
Args:
path_or_buf (`str` or `io.BufferedReader`):
Archive path or archive binary file object.
Yields:
`tuple[str, io.BufferedReader]`:
2-tuple (path_within_archive, file_object).
File object is opened in binary mode.
Example:
```py
>>> archive = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')
>>> files = dl_manager.iter_archive(archive)
```
"""
if hasattr(path_or_buf, "read"):
return ArchiveIterable.from_buf(path_or_buf)
else:
return ArchiveIterable.from_urlpath(path_or_buf)
def iter_files(self, paths: Union[str, List[str]]):
"""Iterate over file paths.
Args:
paths (`str` or `list` of `str`):
Root paths.
Yields:
`str`: File path.
Example:
```py
>>> files = dl_manager.download_and_extract('https://huggingface.co/datasets/beans/resolve/main/data/train.zip')
>>> files = dl_manager.iter_files(files)
```
"""
return FilesIterable.from_urlpaths(paths)
def extract(self, path_or_paths, num_proc="deprecated"):
"""Extract given path(s).
Args:
path_or_paths (path or `list` or `dict`):
Path of file to extract. Each path is a `str`.
num_proc (`int`):
Use multi-processing if `num_proc` > 1 and the length of
`path_or_paths` is larger than `num_proc`.
Pass `DownloadConfig(num_proc=)` to the initializer instead.
Returns:
extracted_path(s): `str`, The extracted paths matching the given input
path_or_paths.
Example:
```py
>>> downloaded_files = dl_manager.download('https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz')
>>> extracted_files = dl_manager.extract(downloaded_files)
```
"""
if num_proc != "deprecated":
warnings.warn(
"'num_proc' was deprecated in version 2.6.2 and will be removed in 3.0.0. Pass `DownloadConfig(num_proc=)` to the initializer instead.",
FutureWarning,
)
download_config = self.download_config.copy()
download_config.extract_compressed_file = True
extract_func = partial(self._download_single, download_config=download_config)
extracted_paths = map_nested(
extract_func,
path_or_paths,
num_proc=download_config.num_proc,
desc="Extracting data files",
)
path_or_paths = NestedDataStructure(path_or_paths)
extracted_paths = NestedDataStructure(extracted_paths)
self.extracted_paths.update(dict(zip(path_or_paths.flatten(), extracted_paths.flatten())))
return extracted_paths.data
def download_and_extract(self, url_or_urls):
"""Download and extract given `url_or_urls`.
Is roughly equivalent to:
```
extracted_paths = dl_manager.extract(dl_manager.download(url_or_urls))
```
Args:
url_or_urls (`str` or `list` or `dict`):
URL or `list` or `dict` of URLs to download and extract. Each URL is a `str`.
Returns:
extracted_path(s): `str`, extracted paths of given URL(s).
"""
return self.extract(self.download(url_or_urls))
def get_recorded_sizes_checksums(self):
return self._recorded_sizes_checksums.copy()
def delete_extracted_files(self):
paths_to_delete = set(self.extracted_paths.values()) - set(self.downloaded_paths.values())
for key, path in list(self.extracted_paths.items()):
if path in paths_to_delete and os.path.isfile(path):
os.remove(path)
del self.extracted_paths[key]
def manage_extracted_files(self):
if self.download_config.delete_extracted:
self.delete_extracted_files()