Source code for datasets.utils.download_manager

# coding=utf-8
# 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
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# Unless required by applicable law or agreed to in writing, software
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# Lint as: python3
"""Download manager interface."""

import enum
import os
from datetime import datetime
from functools import partial
from typing import Dict, Union

from .file_utils import HF_DATASETS_CACHE, DownloadConfig, cached_path, get_from_cache, hash_url_to_filename
from .info_utils import get_size_checksum_dict
from .logging import get_logger
from .py_utils import flatten_nested, map_nested, size_str


logger = get_logger(__name__)


class GenerateMode(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"


[docs]class DownloadManager(object): def __init__( self, dataset_name=None, data_dir=None, download_config=None, ): """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 """ self._dataset_name = dataset_name self._data_dir = data_dir self._download_config = download_config or DownloadConfig() # To record what is being used: {url: {num_bytes: int, checksum: str}} self._recorded_sizes_checksums: Dict[str, Dict[str, Union[int, str]]] = {} @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()) def ship_files_with_pipeline(self, downloaded_path_or_paths, pipeline): """ Ship the files using Beam FileSystems to the pipeline temp dir. """ from datasets.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 = os.path.join(remote_dir, "downloads", os.path.basename(local_file_path)) logger.info( "Uploading {} ({}) to {}.".format( local_file_path, size_str(os.path.getsize(local_file_path)), 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, downloaded_path_or_paths): """Record size/checksum of downloaded files.""" flattened_urls_or_urls = flatten_nested(url_or_urls) flattened_downloaded_path_or_paths = flatten_nested(downloaded_path_or_paths) for url, path in zip(flattened_urls_or_urls, flattened_downloaded_path_or_paths): # call str to support PathLike objects self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path) def download_custom(self, url_or_urls, custom_download): """ Download given urls(s) by calling `custom_download`. Args: url_or_urls: url or `list`/`dict` of urls to download and extract. Each url is a `str`. custom_download: Callable with signature (src_url: str, dst_path: str) -> Any as 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. """ cache_dir = self._download_config.cache_dir or os.path.join(HF_DATASETS_CACHE, "downloads") 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) flattened_urls_or_urls = flatten_nested(url_or_urls) flattened_downloaded_path_or_paths = flatten_nested(downloaded_path_or_paths) for url, path in zip(flattened_urls_or_urls, flattened_downloaded_path_or_paths): 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 def download(self, url_or_urls): """Download given url(s). Args: url_or_urls: url or `list`/`dict` of urls to download and extract. Each url is a `str`. Returns: downloaded_path(s): `str`, The downloaded paths matching the given input url_or_urls. """ download_config = self._download_config.copy() download_config.extract_compressed_file = False # Default to using 16 parallel thread for downloading # Note that if we have less than 16 files, multi-processing is not activated if download_config.num_proc is None: download_config.num_proc = 16 download_func = partial(cached_path, download_config=download_config) start_time = datetime.now() downloaded_path_or_paths = map_nested( download_func, url_or_urls, map_tuple=True, num_proc=download_config.num_proc, ) duration = datetime.now() - start_time logger.info("Downloading took {} min".format(duration.total_seconds() // 60)) start_time = datetime.now() self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths) duration = datetime.now() - start_time logger.info("Checksum Computation took {} min".format(duration.total_seconds() // 60)) return downloaded_path_or_paths def iter_archive(self, path): """Returns iterator over files within archive. Args: path: path to archive. Returns: Generator yielding tuple (path_within_archive, file_obj). File-Obj are opened in byte mode (io.BufferedReader) """ logger.info("Extracting archive at %s", str(path)) extracted_path = self.extract(path) if os.path.isfile(extracted_path): with open(extracted_path, "rb") as file_obj: yield (extracted_path, file_obj) # We do this complex absolute/relative scheme to reproduce the API of iter_tar of tfds for root, dirs, files in os.walk(extracted_path, topdown=False): relative_dir_path = root.replace(os.path.abspath(extracted_path) + os.sep, "") for name in files: relative_file_path = os.path.join(relative_dir_path, name) absolute_file_path = os.path.join(root, name) with open(absolute_file_path, "rb") as file_obj: yield (relative_file_path, file_obj) def extract(self, path_or_paths, num_proc=None): """Extract given path(s). Args: path_or_paths: path or `list`/`dict` of path of file to extract. Each path is a `str`. num_proc: Use multi-processing if `num_proc` > 1 and the length of `path_or_paths` is larger than `num_proc` Returns: extracted_path(s): `str`, The extracted paths matching the given input path_or_paths. """ download_config = self._download_config.copy() download_config.extract_compressed_file = True download_config.force_extract = False return map_nested( partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, ) 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: url or `list`/`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()