Spaces:
Sleeping
Sleeping
File size: 2,099 Bytes
8273cb9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
# 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.
"""isort:skip_file"""
import functools
import importlib
dependencies = [
"dataclasses",
"hydra",
"numpy",
"omegaconf",
"regex",
"requests",
"torch",
]
# Check for required dependencies and raise a RuntimeError if any are missing.
missing_deps = []
for dep in dependencies:
try:
importlib.import_module(dep)
except ImportError:
# Hack: the hydra package is provided under the "hydra-core" name in
# pypi. We don't want the user mistakenly calling `pip install hydra`
# since that will install an unrelated package.
if dep == "hydra":
dep = "hydra-core"
missing_deps.append(dep)
if len(missing_deps) > 0:
raise RuntimeError("Missing dependencies: {}".format(", ".join(missing_deps)))
# only do fairseq imports after checking for dependencies
from fairseq.hub_utils import ( # noqa; noqa
BPEHubInterface as bpe,
TokenizerHubInterface as tokenizer,
)
from fairseq.models import MODEL_REGISTRY # noqa
# torch.hub doesn't build Cython components, so if they are not found then try
# to build them here
try:
import fairseq.data.token_block_utils_fast # noqa
except ImportError:
try:
import cython # noqa
import os
from setuptools import sandbox
sandbox.run_setup(
os.path.join(os.path.dirname(__file__), "setup.py"),
["build_ext", "--inplace"],
)
except ImportError:
print(
"Unable to build Cython components. Please make sure Cython is "
"installed if the torch.hub model you are loading depends on it."
)
# automatically expose models defined in FairseqModel::hub_models
for _model_type, _cls in MODEL_REGISTRY.items():
for model_name in _cls.hub_models().keys():
globals()[model_name] = functools.partial(
_cls.from_pretrained,
model_name,
)
|