file_utils.py

"""
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""


import fnmatch
import json
import logging
import os
import sys
import tempfile
from contextlib import contextmanager
from functools import partial, wraps
from hashlib import sha256
from urllib.parse import urlparse

import boto3
import requests
from botocore.config import Config
from botocore.exceptions import ClientError
from filelock import FileLock
from tqdm.auto import tqdm

from . import __version__


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

try:
    os.environ.setdefault("USE_TORCH", "YES")
    if os.environ["USE_TORCH"].upper() in ("1", "ON", "YES"):
        import torch

        _torch_available = True  # pylint: disable=invalid-name
        logger.info("PyTorch version {} available.".format(torch.__version__))
    else:
        logger.info("USE_TORCH override through env variable, disabling PyTorch")
        _torch_available = False
except ImportError:
    _torch_available = False  # pylint: disable=invalid-name

try:
    os.environ.setdefault("USE_TF", "YES")
    if os.environ["USE_TF"].upper() in ("1", "ON", "YES"):
        import tensorflow as tf

        assert hasattr(tf, "__version__") and int(tf.__version__[0]) >= 2
        _tf_available = True  # pylint: disable=invalid-name
        logger.info("TensorFlow version {} available.".format(tf.__version__))
    else:
        logger.info("USE_TF override through env variable, disabling Tensorflow")
        _tf_available = False
except (ImportError, AssertionError):
    _tf_available = False  # pylint: disable=invalid-name

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, "transformers")


try:
    from pathlib import Path

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

PYTORCH_TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE  # Kept for backward compatibility
TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE  # Kept for backward compatibility

WEIGHTS_NAME = "pytorch_model.bin"
TF2_WEIGHTS_NAME = "tf_model.h5"
TF_WEIGHTS_NAME = "model.ckpt"
CONFIG_NAME = "config.json"
MODEL_CARD_NAME = "modelcard.json"

DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]

S3_BUCKET_PREFIX = "https://s3.amazonaws.com/models.huggingface.co/bert"
CLOUDFRONT_DISTRIB_PREFIX = "https://d2ws9o8vfrpkyk.cloudfront.net"


def is_torch_available():
    return _torch_available


def is_tf_available():
    return _tf_available


def add_start_docstrings(*docstr):
    def docstring_decorator(fn):
        fn.__doc__ = "".join(docstr) + fn.__doc__
        return fn

    return docstring_decorator


def add_end_docstrings(*docstr):
    def docstring_decorator(fn):
        fn.__doc__ = fn.__doc__ + "".join(docstr)
        return fn

    return docstring_decorator


def is_remote_url(url_or_filename):
    parsed = urlparse(url_or_filename)
    return parsed.scheme in ("http", "https", "s3")


def hf_bucket_url(identifier, postfix=None, cdn=False):
    endpoint = CLOUDFRONT_DISTRIB_PREFIX if cdn else S3_BUCKET_PREFIX
    if postfix is None:
        return "/".join((endpoint, identifier))
    else:
        return "/".join((endpoint, identifier, postfix))


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.
    If the url ends with .h5 (Keras HDF5 weights) adds '.h5' to the name
    so that TF 2.0 can identify it as a HDF5 file
    (see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380)
    """
    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()

    if url.endswith(".h5"):
        filename += ".h5"

    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 = TRANSFORMERS_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(
    url_or_filename, cache_dir=None, force_download=False, proxies=None, resume_download=False, user_agent=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.
    Args:
        cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
        force_download: if True, re-dowload the file even if it's already cached in the cache dir.
        resume_download: if True, resume the download if incompletly recieved file is found.
        user_agent: Optional string or dict that will be appended to the user-agent on remote requests.
    """
    if cache_dir is None:
        cache_dir = TRANSFORMERS_CACHE
    if isinstance(url_or_filename, Path):
        url_or_filename = str(url_or_filename)
    if isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    if is_remote_url(url_or_filename):
        # URL, so get it from the cache (downloading if necessary)
        return get_from_cache(
            url_or_filename,
            cache_dir=cache_dir,
            force_download=force_download,
            proxies=proxies,
            resume_download=resume_download,
            user_agent=user_agent,
        )
    elif os.path.exists(url_or_filename):
        # File, and it exists.
        return url_or_filename
    elif urlparse(url_or_filename).scheme == "":
        # File, but it doesn't exist.
        raise EnvironmentError("file {} not found".format(url_or_filename))
    else:
        # 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):
        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, proxies=None):
    """Check ETag on S3 object."""
    s3_resource = boto3.resource("s3", config=Config(proxies=proxies))
    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, proxies=None):
    """Pull a file directly from S3."""
    s3_resource = boto3.resource("s3", config=Config(proxies=proxies))
    bucket_name, s3_path = split_s3_path(url)
    s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)


def http_get(url, temp_file, proxies=None, resume_size=0, user_agent=None):
    ua = "transformers/{}; python/{}".format(__version__, sys.version.split()[0])
    if is_torch_available():
        ua += "; torch/{}".format(torch.__version__)
    if is_tf_available():
        ua += "; tensorflow/{}".format(tf.__version__)
    if isinstance(user_agent, dict):
        ua += "; " + "; ".join("{}/{}".format(k, v) for k, v in user_agent.items())
    elif isinstance(user_agent, str):
        ua += "; " + user_agent
    headers = {"user-agent": ua}
    if resume_size > 0:
        headers["Range"] = "bytes=%d-" % (resume_size,)
    response = requests.get(url, stream=True, proxies=proxies, headers=headers)
    if response.status_code == 416:  # Range not satisfiable
        return
    content_length = response.headers.get("Content-Length")
    total = resume_size + int(content_length) if content_length is not None else None
    progress = tqdm(
        unit="B",
        unit_scale=True,
        total=total,
        initial=resume_size,
        desc="Downloading",
        disable=bool(logger.getEffectiveLevel() == logging.NOTSET),
    )
    for chunk in response.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, force_download=False, proxies=None, etag_timeout=10, resume_download=False, user_agent=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 = TRANSFORMERS_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, proxies=proxies)
    else:
        try:
            response = requests.head(url, allow_redirects=True, proxies=proxies, timeout=etag_timeout)
            if response.status_code != 200:
                etag = None
            else:
                etag = response.headers.get("ETag")
        except (EnvironmentError, requests.exceptions.Timeout):
            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 = [
            file
            for file in fnmatch.filter(os.listdir(cache_dir), filename + ".*")
            if not file.endswith(".json") and not file.endswith(".lock")
        ]
        if matching_files:
            cache_path = os.path.join(cache_dir, matching_files[-1])

    # Prevent parallel downloads of the same file with a lock.
    lock_path = cache_path + ".lock"
    with FileLock(lock_path):

        if resume_download:
            incomplete_path = cache_path + ".incomplete"

            @contextmanager
            def _resumable_file_manager():
                with open(incomplete_path, "a+b") as f:
                    yield f

            temp_file_manager = _resumable_file_manager
            if os.path.exists(incomplete_path):
                resume_size = os.stat(incomplete_path).st_size
            else:
                resume_size = 0
        else:
            temp_file_manager = partial(tempfile.NamedTemporaryFile, dir=cache_dir, delete=False)
            resume_size = 0

        if etag is not None and (not os.path.exists(cache_path) or force_download):
            # Download to temporary file, then copy to cache dir once finished.
            # Otherwise you get corrupt cache entries if the download gets interrupted.
            with temp_file_manager() as temp_file:
                logger.info(
                    "%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name
                )

                # GET file object
                if url.startswith("s3://"):
                    if resume_download:
                        logger.warn('Warning: resumable downloads are not implemented for "s3://" urls')
                    s3_get(url, temp_file, proxies=proxies)
                else:
                    http_get(url, temp_file, proxies=proxies, resume_size=resume_size, user_agent=user_agent)

                # we are copying the file before closing it, so flush to avoid truncation
                temp_file.flush()

                logger.info("storing %s in cache at %s", url, cache_path)
                os.rename(temp_file.name, cache_path)

                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:
                    json.dump(meta, meta_file)

    return cache_path