Source code for torchtext.utils

import gzip
import hashlib
import logging
import os
import tarfile
import zipfile

import torch
from torchtext import _CACHE_DIR

from ._download_hooks import _DATASET_DOWNLOAD_MANAGER

logger = logging.getLogger(__name__)

[docs]def reporthook(t): """ """ last_b = [0] def inner(b=1, bsize=1, tsize=None): """ b: int, optional Number of blocks just transferred [default: 1]. bsize: int, optional Size of each block (in tqdm units) [default: 1]. tsize: int, optional Total size (in tqdm units). If [default: None] remains unchanged. """ if tsize is not None: = tsize t.update((b - last_b[0]) * bsize) last_b[0] = b return inner
def validate_file(file_obj, hash_value, hash_type="sha256"): """Validate a given file object with its hash. Args: file_obj: File object to read from. hash_value (str): Hash for url. hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``). Returns: bool: return True if its a valid file, else False. """ if hash_type == "sha256": hash_func = hashlib.sha256() elif hash_type == "md5": hash_func = hashlib.md5() else: raise ValueError while True: # Read by chunk to avoid filling memory chunk = ** 2) if not chunk: break hash_func.update(chunk) return hash_func.hexdigest() == hash_value def _check_hash(path, hash_value, hash_type):"Validating hash {} matches hash of {}".format(hash_value, path)) with open(path, "rb") as file_obj: if not validate_file(file_obj, hash_value, hash_type): raise RuntimeError( "The hash of {} does not match. Delete the file manually and retry.".format(os.path.abspath(path)) )
[docs]def download_from_url(url, path=None, root=".data", overwrite=False, hash_value=None, hash_type="sha256"): """Download file, with logic (from tensor2tensor) for Google Drive. Returns the path to the downloaded file. Args: url: the url of the file from URL header. (None) path: path where file will be saved root: download folder used to store the file in (.data) overwrite: overwrite existing files (False) hash_value (str, optional): hash for url (Default: ``None``). hash_type (str, optional): hash type, among "sha256" and "md5" (Default: ``"sha256"``). Examples: >>> url = '' >>> torchtext.utils.download_from_url(url) >>> url = '' >>> torchtext.utils.download_from_url(url) >>> '.data/validation.tar.gz' """ # figure out filename and root if path is None: _, filename = os.path.split(url) root = os.path.abspath(root) path = os.path.join(root, filename) else: path = os.path.abspath(path) root, filename = os.path.split(os.path.abspath(path)) # skip download if path exists and overwrite is not True if os.path.exists(path):"File %s already exists." % path) if not overwrite: if hash_value: _check_hash(path, hash_value, hash_type) return path # make root dir if does not exist if not os.path.exists(root): try: os.makedirs(root) except OSError: raise OSError("Can't create the download directory {}.".format(root)) # download data and move to path _DATASET_DOWNLOAD_MANAGER.get_local_path(url, destination=path)"File {} downloaded.".format(path)) # validate if hash_value: _check_hash(path, hash_value, hash_type) # all good return path
[docs]def extract_archive(from_path, to_path=None, overwrite=False): """Extract archive. Args: from_path: the path of the archive. to_path: the root path of the extracted files (directory of from_path) overwrite: overwrite existing files (False) Returns: List of paths to extracted files even if not overwritten. Examples: >>> url = '' >>> from_path = './validation.tar.gz' >>> to_path = './' >>> torchtext.utils.download_from_url(url, from_path) >>> torchtext.utils.extract_archive(from_path, to_path) >>> ['.data/', '.data/val.en'] >>> torchtext.utils.download_from_url(url, from_path) >>> torchtext.utils.extract_archive(from_path, to_path) >>> ['.data/', '.data/val.en'] """ if to_path is None: to_path = os.path.dirname(from_path) if from_path.endswith((".tar.gz", ".tgz")):"Opening tar file {}.".format(from_path)) with, "r") as tar: files = [] for file_ in tar: file_path = os.path.join(to_path, if file_.isfile(): files.append(file_path) if os.path.exists(file_path):"{} already extracted.".format(file_path)) if not overwrite: continue tar.extract(file_, to_path)"Finished extracting tar file {}.".format(from_path)) return files elif from_path.endswith(".zip"): assert zipfile.is_zipfile(from_path), from_path"Opening zip file {}.".format(from_path)) with zipfile.ZipFile(from_path, "r") as zfile: files = [] for file_ in zfile.namelist(): file_path = os.path.join(to_path, file_) files.append(file_path) if os.path.exists(file_path):"{} already extracted.".format(file_path)) if not overwrite: continue zfile.extract(file_, to_path) files = [f for f in files if os.path.isfile(f)]"Finished extracting zip file {}.".format(from_path)) return files elif from_path.endswith(".gz"):"Opening gz file {}.".format(from_path)) default_block_size = 65536 filename = from_path[:-3] files = [filename] with, "rb") as gzfile, open(filename, "wb") as d_file: while True: block = if not block: break else: d_file.write(block) d_file.write(block)"Finished extracting gz file {}.".format(from_path)) return files else: raise NotImplementedError("We currently only support tar.gz, .tgz, .gz and zip achives.")
def _log_class_usage(klass): identifier = "torchtext" if klass and hasattr(klass, "__name__"): identifier += f".{klass.__name__}" torch._C._log_api_usage_once(identifier) def get_asset_local_path(asset_path: str) -> str: """Get local path for assets. Download if path does not exost locally Args: asset_path: Local path to asset or remote URL Returns: bool: local path of the asset after downloading or reading from cache Examples: >>> url = 'http://<HOST>/file.txt' >>> torchtext.utils.get_asset_local_path(url) >>> '.data/file.txt' >>> torchtext.utils.get_asset_local_path('/home/user/file.txt') >>> '/home/user/file.txt' """ if os.path.exists(asset_path): local_path = asset_path else: local_path = download_from_url(url=asset_path, root=_CACHE_DIR) return local_path


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