"""
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 typing import Optional
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:
USE_TF = os.environ.get("USE_TF", "AUTO").upper()
USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
if USE_TORCH in ("1", "ON", "YES", "AUTO") and USE_TF not in ("1", "ON", "YES"):
import torch
_torch_available = True # pylint: disable=invalid-name
logger.info("PyTorch version {} available.".format(torch.__version__))
else:
logger.info("Disabling PyTorch because USE_TF is set")
_torch_available = False
except ImportError:
_torch_available = False # pylint: disable=invalid-name
try:
USE_TF = os.environ.get("USE_TF", "AUTO").upper()
USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
if USE_TF in ("1", "ON", "YES", "AUTO") and USE_TORCH not 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("Disabling Tensorflow because USE_TORCH is set")
_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"
MULTIPLE_CHOICE_DUMMY_INPUTS = [[[0], [1]], [[0], [1]]]
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__ if fn.__doc__ is not None else "")
return fn
return docstring_decorator
def add_start_docstrings_to_callable(*docstr):
def docstring_decorator(fn):
class_name = ":class:`~transformers.{}`".format(fn.__qualname__.split(".")[0])
intro = " The {} forward method, overrides the :func:`__call__` special method.".format(class_name)
note = r"""
.. note::
Although the recipe for forward pass needs to be defined within
this function, one should call the :class:`Module` instance afterwards
instead of this since the former takes care of running the
pre and post processing steps while the latter silently ignores them.
"""
fn.__doc__ = intro + note + "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
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) -> str:
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
) -> Optional[str]:
"""
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.
Return:
None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
Local path (string) otherwise
"""
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
) -> Optional[str]:
"""
Given a URL, look for the corresponding file in the local cache.
If it's not there, download it. Then return the path to the cached file.
Return:
None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
Local path (string) otherwise
"""
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
os.makedirs(cache_dir, exist_ok=True)
# 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)
# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
# try to get the last downloaded one
if etag is None:
if os.path.exists(cache_path):
return cache_path
else:
matching_files = [
file
for file in fnmatch.filter(os.listdir(cache_dir), filename + ".*")
if not file.endswith(".json") and not file.endswith(".lock")
]
if len(matching_files) > 0:
return os.path.join(cache_dir, matching_files[-1])
else:
return None
# From now on, etag is not None.
if os.path.exists(cache_path) and not force_download:
return cache_path
# 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
# 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)
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
