fairseq/fairseq/file_utils.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

"""
Utilities for working with the local dataset cache.
This file is adapted from `AllenNLP <https://github.com/allenai/allennlp>`_.
and `huggingface <https://github.com/huggingface>`_.
"""

import fnmatch
import json
import logging
import os
import shutil
import tarfile
import tempfile
from functools import partial, wraps
from hashlib import sha256
from io import open


try:
    from torch.hub import _get_torch_home

    torch_cache_home = _get_torch_home()
except ImportError:
    torch_cache_home = os.path.expanduser(
        os.getenv(
            "TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch")
        )
    )
default_cache_path = os.path.join(torch_cache_home, "pytorch_fairseq")

try:
    from urllib.parse import urlparse
except ImportError:
    from urlparse import urlparse

try:
    from pathlib import Path

    PYTORCH_FAIRSEQ_CACHE = Path(os.getenv("PYTORCH_FAIRSEQ_CACHE", default_cache_path))
except (AttributeError, ImportError):
    PYTORCH_FAIRSEQ_CACHE = os.getenv("PYTORCH_FAIRSEQ_CACHE", default_cache_path)

CONFIG_NAME = "config.json"
WEIGHTS_NAME = "pytorch_model.bin"

logger = logging.getLogger(__name__)  # pylint: disable=invalid-name


def load_archive_file(archive_file):
    # redirect to the cache, if necessary
    try:
        resolved_archive_file = cached_path(archive_file, cache_dir=None)
    except EnvironmentError:
        logger.info(
            "Archive name '{}' was not found in archive name list. "
            "We assumed '{}' was a path or URL but couldn't find any file "
            "associated to this path or URL.".format(
                archive_file,
                archive_file,
            )
        )
        return None

    if resolved_archive_file == archive_file:
        logger.info("loading archive file {}".format(archive_file))
    else:
        logger.info(
            "loading archive file {} from cache at {}".format(
                archive_file, resolved_archive_file
            )
        )

    # Extract archive to temp dir and replace .tar.bz2 if necessary
    tempdir = None
    if not os.path.isdir(resolved_archive_file):
        tempdir = tempfile.mkdtemp()
        logger.info(
            "extracting archive file {} to temp dir {}".format(
                resolved_archive_file, tempdir
            )
        )
        ext = os.path.splitext(archive_file)[1][1:]
        with tarfile.open(resolved_archive_file, "r:" + ext) as archive:
            top_dir = os.path.commonprefix(archive.getnames())
            archive.extractall(tempdir)
        os.remove(resolved_archive_file)
        shutil.move(os.path.join(tempdir, top_dir), resolved_archive_file)
        shutil.rmtree(tempdir)

    return resolved_archive_file


def url_to_filename(url, etag=None):
    """
    Convert `url` into a hashed filename in a repeatable way.
    If `etag` is specified, append its hash to the URL's, delimited
    by a period.
    """
    url_bytes = url.encode("utf-8")
    url_hash = sha256(url_bytes)
    filename = url_hash.hexdigest()

    if etag:
        etag_bytes = etag.encode("utf-8")
        etag_hash = sha256(etag_bytes)
        filename += "." + etag_hash.hexdigest()

    return filename


def filename_to_url(filename, cache_dir=None):
    """
    Return the url and etag (which may be ``None``) stored for `filename`.
    Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
    """
    if cache_dir is None:
        cache_dir = PYTORCH_FAIRSEQ_CACHE
    if isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    cache_path = os.path.join(cache_dir, filename)
    if not os.path.exists(cache_path):
        raise EnvironmentError("file {} not found".format(cache_path))

    meta_path = cache_path + ".json"
    if not os.path.exists(meta_path):
        raise EnvironmentError("file {} not found".format(meta_path))

    with open(meta_path, encoding="utf-8") as meta_file:
        metadata = json.load(meta_file)
    url = metadata["url"]
    etag = metadata["etag"]

    return url, etag


def cached_path_from_pm(url_or_filename):
    """
    Tries to cache the specified URL using PathManager class.
    Returns the cached path if success otherwise failure.
    """
    try:
        from fairseq.file_io import PathManager
        local_path = PathManager.get_local_path(url_or_filename)
        return local_path
    except Exception:
        return None


def cached_path(url_or_filename, cache_dir=None):
    """
    Given something that might be a URL (or might be a local path),
    determine which. If it's a URL, download the file and cache it, and
    return the path to the cached file. If it's already a local path,
    make sure the file exists and then return the path.
    """
    if cache_dir is None:
        cache_dir = PYTORCH_FAIRSEQ_CACHE
    if isinstance(url_or_filename, Path):
        url_or_filename = str(url_or_filename)
    if isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    parsed = urlparse(url_or_filename)

    if parsed.scheme in ("http", "https", "s3"):
        # URL, so get it from the cache (downloading if necessary)
        return get_from_cache(url_or_filename, cache_dir)
    elif os.path.exists(url_or_filename):
        # File, and it exists.
        return url_or_filename
    elif parsed.scheme == "":
        # File, but it doesn't exist.
        raise EnvironmentError("file {} not found".format(url_or_filename))
    else:
        cached_path = cached_path_from_pm(url_or_filename)
        if cached_path:
            return cached_path
        # Something unknown
        raise ValueError(
            "unable to parse {} as a URL or as a local path".format(url_or_filename)
        )


def split_s3_path(url):
    """Split a full s3 path into the bucket name and path."""
    parsed = urlparse(url)
    if not parsed.netloc or not parsed.path:
        raise ValueError("bad s3 path {}".format(url))
    bucket_name = parsed.netloc
    s3_path = parsed.path
    # Remove '/' at beginning of path.
    if s3_path.startswith("/"):
        s3_path = s3_path[1:]
    return bucket_name, s3_path


def s3_request(func):
    """
    Wrapper function for s3 requests in order to create more helpful error
    messages.
    """

    @wraps(func)
    def wrapper(url, *args, **kwargs):
        from botocore.exceptions import ClientError

        try:
            return func(url, *args, **kwargs)
        except ClientError as exc:
            if int(exc.response["Error"]["Code"]) == 404:
                raise EnvironmentError("file {} not found".format(url))
            else:
                raise

    return wrapper


@s3_request
def s3_etag(url):
    """Check ETag on S3 object."""
    import boto3

    s3_resource = boto3.resource("s3")
    bucket_name, s3_path = split_s3_path(url)
    s3_object = s3_resource.Object(bucket_name, s3_path)
    return s3_object.e_tag


@s3_request
def s3_get(url, temp_file):
    """Pull a file directly from S3."""
    import boto3

    s3_resource = boto3.resource("s3")
    bucket_name, s3_path = split_s3_path(url)
    s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)


def request_wrap_timeout(func, url):
    import requests

    for attempt, timeout in enumerate([10, 20, 40, 60, 60]):
        try:
            return func(timeout=timeout)
        except requests.exceptions.Timeout as e:
            logger.warning(
                "Request for %s timed-out (attempt %d). Retrying with a timeout of %d secs",
                url,
                attempt,
                timeout,
                exc_info=e,
            )
            continue
    raise RuntimeError(f"Unable to fetch file {url}")


def http_get(url, temp_file):
    import requests
    from tqdm import tqdm

    req = request_wrap_timeout(partial(requests.get, url, stream=True), url)
    content_length = req.headers.get("Content-Length")
    total = int(content_length) if content_length is not None else None
    progress = tqdm(unit="B", total=total)
    for chunk in req.iter_content(chunk_size=1024):
        if chunk:  # filter out keep-alive new chunks
            progress.update(len(chunk))
            temp_file.write(chunk)
    progress.close()


def get_from_cache(url, cache_dir=None):
    """
    Given a URL, look for the corresponding dataset in the local cache.
    If it's not there, download it. Then return the path to the cached file.
    """
    if cache_dir is None:
        cache_dir = PYTORCH_FAIRSEQ_CACHE
    if isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    if not os.path.exists(cache_dir):
        os.makedirs(cache_dir)

    # Get eTag to add to filename, if it exists.
    if url.startswith("s3://"):
        etag = s3_etag(url)
    else:
        try:
            import requests

            response = request_wrap_timeout(
                partial(requests.head, url, allow_redirects=True), url
            )
            if response.status_code != 200:
                etag = None
            else:
                etag = response.headers.get("ETag")
        except RuntimeError:
            etag = None

    filename = url_to_filename(url, etag)

    # get cache path to put the file
    cache_path = os.path.join(cache_dir, filename)

    # If we don't have a connection (etag is None) and can't identify the file
    # try to get the last downloaded one
    if not os.path.exists(cache_path) and etag is None:
        matching_files = fnmatch.filter(os.listdir(cache_dir), filename + ".*")
        matching_files = list(filter(lambda s: not s.endswith(".json"), matching_files))
        if matching_files:
            cache_path = os.path.join(cache_dir, matching_files[-1])

    if not os.path.exists(cache_path):
        # Download to temporary file, then copy to cache dir once finished.
        # Otherwise you get corrupt cache entries if the download gets interrupted.
        with tempfile.NamedTemporaryFile() as temp_file:
            logger.info("%s not found in cache, downloading to %s", url, temp_file.name)

            # GET file object
            if url.startswith("s3://"):
                s3_get(url, temp_file)
            else:
                http_get(url, temp_file)

            # we are copying the file before closing it, so flush to avoid truncation
            temp_file.flush()
            # shutil.copyfileobj() starts at the current position, so go to the start
            temp_file.seek(0)

            logger.info("copying %s to cache at %s", temp_file.name, cache_path)
            with open(cache_path, "wb") as cache_file:
                shutil.copyfileobj(temp_file, cache_file)

            logger.info("creating metadata file for %s", cache_path)
            meta = {"url": url, "etag": etag}
            meta_path = cache_path + ".json"
            with open(meta_path, "w") as meta_file:
                output_string = json.dumps(meta)
                meta_file.write(output_string)

            logger.info("removing temp file %s", temp_file.name)

    return cache_path


def read_set_from_file(filename):
    """
    Extract a de-duped collection (set) of text from a file.
    Expected file format is one item per line.
    """
    collection = set()
    with open(filename, "r", encoding="utf-8") as file_:
        for line in file_:
            collection.add(line.rstrip())
    return collection


def get_file_extension(path, dot=True, lower=True):
    ext = os.path.splitext(path)[1]
    ext = ext if dot else ext[1:]
    return ext.lower() if lower else ext