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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Import utilities: Utilities related to imports and our lazy inits.
"""
import importlib.util
import operator as op
import os
import sys
from collections import OrderedDict
from itertools import chain
from types import ModuleType
from typing import Any, Union
from huggingface_hub.utils import is_jinja_available # noqa: F401
from packaging.version import Version, parse
from . import logging
# The package importlib_metadata is in a different place, depending on the python version.
if sys.version_info < (3, 8):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})
USE_TF = os.environ.get("USE_TF", "AUTO").upper()
USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
USE_JAX = os.environ.get("USE_FLAX", "AUTO").upper()
USE_SAFETENSORS = os.environ.get("USE_SAFETENSORS", "AUTO").upper()
DIFFUSERS_SLOW_IMPORT = os.environ.get("DIFFUSERS_SLOW_IMPORT", "FALSE").upper()
DIFFUSERS_SLOW_IMPORT = DIFFUSERS_SLOW_IMPORT in ENV_VARS_TRUE_VALUES
STR_OPERATION_TO_FUNC = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}
_is_google_colab = "google.colab" in sys.modules or any(k.startswith("COLAB_") for k in os.environ)
def _is_package_available(pkg_name: str):
pkg_exists = importlib.util.find_spec(pkg_name) is not None
pkg_version = "N/A"
if pkg_exists:
try:
pkg_version = importlib_metadata.version(pkg_name)
logger.debug(f"Successfully imported {pkg_name} version {pkg_version}")
except (ImportError, importlib_metadata.PackageNotFoundError):
pkg_exists = False
return pkg_exists, pkg_version
if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
_torch_available, _torch_version = _is_package_available("torch")
else:
logger.info("Disabling PyTorch because USE_TORCH is set")
_torch_available = False
_jax_version = "N/A"
_flax_version = "N/A"
if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
_flax_available = importlib.util.find_spec("jax") is not None and importlib.util.find_spec("flax") is not None
if _flax_available:
try:
_jax_version = importlib_metadata.version("jax")
_flax_version = importlib_metadata.version("flax")
logger.info(f"JAX version {_jax_version}, Flax version {_flax_version} available.")
except importlib_metadata.PackageNotFoundError:
_flax_available = False
else:
_flax_available = False
if USE_SAFETENSORS in ENV_VARS_TRUE_AND_AUTO_VALUES:
_safetensors_available, _safetensors_version = _is_package_available("safetensors")
else:
logger.info("Disabling Safetensors because USE_TF is set")
_safetensors_available = False
_onnxruntime_version = "N/A"
_onnx_available = importlib.util.find_spec("onnxruntime") is not None
if _onnx_available:
candidates = (
"onnxruntime",
"onnxruntime-gpu",
"ort_nightly_gpu",
"onnxruntime-directml",
"onnxruntime-openvino",
"ort_nightly_directml",
"onnxruntime-rocm",
"onnxruntime-migraphx",
"onnxruntime-training",
)
_onnxruntime_version = None
# For the metadata, we have to look for both onnxruntime and onnxruntime-gpu
for pkg in candidates:
try:
_onnxruntime_version = importlib_metadata.version(pkg)
break
except importlib_metadata.PackageNotFoundError:
pass
_onnx_available = _onnxruntime_version is not None
if _onnx_available:
logger.debug(f"Successfully imported onnxruntime version {_onnxruntime_version}")
# (sayakpaul): importlib.util.find_spec("opencv-python") returns None even when it's installed.
# _opencv_available = importlib.util.find_spec("opencv-python") is not None
try:
candidates = (
"opencv-python",
"opencv-contrib-python",
"opencv-python-headless",
"opencv-contrib-python-headless",
)
_opencv_version = None
for pkg in candidates:
try:
_opencv_version = importlib_metadata.version(pkg)
break
except importlib_metadata.PackageNotFoundError:
pass
_opencv_available = _opencv_version is not None
if _opencv_available:
logger.debug(f"Successfully imported cv2 version {_opencv_version}")
except importlib_metadata.PackageNotFoundError:
_opencv_available = False
_bs4_available = importlib.util.find_spec("bs4") is not None
try:
# importlib metadata under different name
_bs4_version = importlib_metadata.version("beautifulsoup4")
logger.debug(f"Successfully imported ftfy version {_bs4_version}")
except importlib_metadata.PackageNotFoundError:
_bs4_available = False
_invisible_watermark_available = importlib.util.find_spec("imwatermark") is not None
try:
_invisible_watermark_version = importlib_metadata.version("invisible-watermark")
logger.debug(f"Successfully imported invisible-watermark version {_invisible_watermark_version}")
except importlib_metadata.PackageNotFoundError:
_invisible_watermark_available = False
_torch_xla_available, _torch_xla_version = _is_package_available("torch_xla")
_torch_npu_available, _torch_npu_version = _is_package_available("torch_npu")
_transformers_available, _transformers_version = _is_package_available("transformers")
_hf_hub_available, _hf_hub_version = _is_package_available("huggingface_hub")
_inflect_available, _inflect_version = _is_package_available("inflect")
_unidecode_available, _unidecode_version = _is_package_available("unidecode")
_k_diffusion_available, _k_diffusion_version = _is_package_available("k_diffusion")
_note_seq_available, _note_seq_version = _is_package_available("note_seq")
_wandb_available, _wandb_version = _is_package_available("wandb")
_tensorboard_available, _tensorboard_version = _is_package_available("tensorboard")
_compel_available, _compel_version = _is_package_available("compel")
_sentencepiece_available, _sentencepiece_version = _is_package_available("sentencepiece")
_torchsde_available, _torchsde_version = _is_package_available("torchsde")
_peft_available, _peft_version = _is_package_available("peft")
_torchvision_available, _torchvision_version = _is_package_available("torchvision")
_matplotlib_available, _matplotlib_version = _is_package_available("matplotlib")
_timm_available, _timm_version = _is_package_available("timm")
_bitsandbytes_available, _bitsandbytes_version = _is_package_available("bitsandbytes")
_imageio_available, _imageio_version = _is_package_available("imageio")
_ftfy_available, _ftfy_version = _is_package_available("ftfy")
_scipy_available, _scipy_version = _is_package_available("scipy")
_librosa_available, _librosa_version = _is_package_available("librosa")
_accelerate_available, _accelerate_version = _is_package_available("accelerate")
_xformers_available, _xformers_version = _is_package_available("xformers")
_gguf_available, _gguf_version = _is_package_available("gguf")
_torchao_available, _torchao_version = _is_package_available("torchao")
_bitsandbytes_available, _bitsandbytes_version = _is_package_available("bitsandbytes")
_torchao_available, _torchao_version = _is_package_available("torchao")
_optimum_quanto_available = importlib.util.find_spec("optimum") is not None
if _optimum_quanto_available:
try:
_optimum_quanto_version = importlib_metadata.version("optimum_quanto")
logger.debug(f"Successfully import optimum-quanto version {_optimum_quanto_version}")
except importlib_metadata.PackageNotFoundError:
_optimum_quanto_available = False
def is_torch_available():
return _torch_available
def is_torch_xla_available():
return _torch_xla_available
def is_torch_npu_available():
return _torch_npu_available
def is_flax_available():
return _flax_available
def is_transformers_available():
return _transformers_available
def is_inflect_available():
return _inflect_available
def is_unidecode_available():
return _unidecode_available
def is_onnx_available():
return _onnx_available
def is_opencv_available():
return _opencv_available
def is_scipy_available():
return _scipy_available
def is_librosa_available():
return _librosa_available
def is_xformers_available():
return _xformers_available
def is_accelerate_available():
return _accelerate_available
def is_k_diffusion_available():
return _k_diffusion_available
def is_note_seq_available():
return _note_seq_available
def is_wandb_available():
return _wandb_available
def is_tensorboard_available():
return _tensorboard_available
def is_compel_available():
return _compel_available
def is_ftfy_available():
return _ftfy_available
def is_bs4_available():
return _bs4_available
def is_torchsde_available():
return _torchsde_available
def is_invisible_watermark_available():
return _invisible_watermark_available
def is_peft_available():
return _peft_available
def is_torchvision_available():
return _torchvision_available
def is_matplotlib_available():
return _matplotlib_available
def is_safetensors_available():
return _safetensors_available
def is_bitsandbytes_available():
return _bitsandbytes_available
def is_google_colab():
return _is_google_colab
def is_sentencepiece_available():
return _sentencepiece_available
def is_imageio_available():
return _imageio_available
def is_gguf_available():
return _gguf_available
def is_torchao_available():
return _torchao_available
def is_optimum_quanto_available():
return _optimum_quanto_available
def is_timm_available():
return _timm_available
# docstyle-ignore
FLAX_IMPORT_ERROR = """
{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
installation page: https://github.com/google/flax and follow the ones that match your environment.
"""
# docstyle-ignore
INFLECT_IMPORT_ERROR = """
{0} requires the inflect library but it was not found in your environment. You can install it with pip: `pip install
inflect`
"""
# docstyle-ignore
PYTORCH_IMPORT_ERROR = """
{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
"""
# docstyle-ignore
ONNX_IMPORT_ERROR = """
{0} requires the onnxruntime library but it was not found in your environment. You can install it with pip: `pip
install onnxruntime`
"""
# docstyle-ignore
OPENCV_IMPORT_ERROR = """
{0} requires the OpenCV library but it was not found in your environment. You can install it with pip: `pip
install opencv-python`
"""
# docstyle-ignore
SCIPY_IMPORT_ERROR = """
{0} requires the scipy library but it was not found in your environment. You can install it with pip: `pip install
scipy`
"""
# docstyle-ignore
LIBROSA_IMPORT_ERROR = """
{0} requires the librosa library but it was not found in your environment. Checkout the instructions on the
installation page: https://librosa.org/doc/latest/install.html and follow the ones that match your environment.
"""
# docstyle-ignore
TRANSFORMERS_IMPORT_ERROR = """
{0} requires the transformers library but it was not found in your environment. You can install it with pip: `pip
install transformers`
"""
# docstyle-ignore
UNIDECODE_IMPORT_ERROR = """
{0} requires the unidecode library but it was not found in your environment. You can install it with pip: `pip install
Unidecode`
"""
# docstyle-ignore
K_DIFFUSION_IMPORT_ERROR = """
{0} requires the k-diffusion library but it was not found in your environment. You can install it with pip: `pip
install k-diffusion`
"""
# docstyle-ignore
NOTE_SEQ_IMPORT_ERROR = """
{0} requires the note-seq library but it was not found in your environment. You can install it with pip: `pip
install note-seq`
"""
# docstyle-ignore
WANDB_IMPORT_ERROR = """
{0} requires the wandb library but it was not found in your environment. You can install it with pip: `pip
install wandb`
"""
# docstyle-ignore
TENSORBOARD_IMPORT_ERROR = """
{0} requires the tensorboard library but it was not found in your environment. You can install it with pip: `pip
install tensorboard`
"""
# docstyle-ignore
COMPEL_IMPORT_ERROR = """
{0} requires the compel library but it was not found in your environment. You can install it with pip: `pip install compel`
"""
# docstyle-ignore
BS4_IMPORT_ERROR = """
{0} requires the Beautiful Soup library but it was not found in your environment. You can install it with pip:
`pip install beautifulsoup4`. Please note that you may need to restart your runtime after installation.
"""
# docstyle-ignore
FTFY_IMPORT_ERROR = """
{0} requires the ftfy library but it was not found in your environment. Checkout the instructions on the
installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones
that match your environment. Please note that you may need to restart your runtime after installation.
"""
# docstyle-ignore
TORCHSDE_IMPORT_ERROR = """
{0} requires the torchsde library but it was not found in your environment. You can install it with pip: `pip install torchsde`
"""
# docstyle-ignore
INVISIBLE_WATERMARK_IMPORT_ERROR = """
{0} requires the invisible-watermark library but it was not found in your environment. You can install it with pip: `pip install invisible-watermark>=0.2.0`
"""
# docstyle-ignore
PEFT_IMPORT_ERROR = """
{0} requires the peft library but it was not found in your environment. You can install it with pip: `pip install peft`
"""
# docstyle-ignore
SAFETENSORS_IMPORT_ERROR = """
{0} requires the safetensors library but it was not found in your environment. You can install it with pip: `pip install safetensors`
"""
# docstyle-ignore
SENTENCEPIECE_IMPORT_ERROR = """
{0} requires the sentencepiece library but it was not found in your environment. You can install it with pip: `pip install sentencepiece`
"""
# docstyle-ignore
BITSANDBYTES_IMPORT_ERROR = """
{0} requires the bitsandbytes library but it was not found in your environment. You can install it with pip: `pip install bitsandbytes`
"""
# docstyle-ignore
IMAGEIO_IMPORT_ERROR = """
{0} requires the imageio library and ffmpeg but it was not found in your environment. You can install it with pip: `pip install imageio imageio-ffmpeg`
"""
# docstyle-ignore
GGUF_IMPORT_ERROR = """
{0} requires the gguf library but it was not found in your environment. You can install it with pip: `pip install gguf`
"""
TORCHAO_IMPORT_ERROR = """
{0} requires the torchao library but it was not found in your environment. You can install it with pip: `pip install
torchao`
"""
QUANTO_IMPORT_ERROR = """
{0} requires the optimum-quanto library but it was not found in your environment. You can install it with pip: `pip
install optimum-quanto`
"""
BACKENDS_MAPPING = OrderedDict(
[
("bs4", (is_bs4_available, BS4_IMPORT_ERROR)),
("flax", (is_flax_available, FLAX_IMPORT_ERROR)),
("inflect", (is_inflect_available, INFLECT_IMPORT_ERROR)),
("onnx", (is_onnx_available, ONNX_IMPORT_ERROR)),
("opencv", (is_opencv_available, OPENCV_IMPORT_ERROR)),
("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)),
("torch", (is_torch_available, PYTORCH_IMPORT_ERROR)),
("transformers", (is_transformers_available, TRANSFORMERS_IMPORT_ERROR)),
("unidecode", (is_unidecode_available, UNIDECODE_IMPORT_ERROR)),
("librosa", (is_librosa_available, LIBROSA_IMPORT_ERROR)),
("k_diffusion", (is_k_diffusion_available, K_DIFFUSION_IMPORT_ERROR)),
("note_seq", (is_note_seq_available, NOTE_SEQ_IMPORT_ERROR)),
("wandb", (is_wandb_available, WANDB_IMPORT_ERROR)),
("tensorboard", (is_tensorboard_available, TENSORBOARD_IMPORT_ERROR)),
("compel", (is_compel_available, COMPEL_IMPORT_ERROR)),
("ftfy", (is_ftfy_available, FTFY_IMPORT_ERROR)),
("torchsde", (is_torchsde_available, TORCHSDE_IMPORT_ERROR)),
("invisible_watermark", (is_invisible_watermark_available, INVISIBLE_WATERMARK_IMPORT_ERROR)),
("peft", (is_peft_available, PEFT_IMPORT_ERROR)),
("safetensors", (is_safetensors_available, SAFETENSORS_IMPORT_ERROR)),
("bitsandbytes", (is_bitsandbytes_available, BITSANDBYTES_IMPORT_ERROR)),
("sentencepiece", (is_sentencepiece_available, SENTENCEPIECE_IMPORT_ERROR)),
("imageio", (is_imageio_available, IMAGEIO_IMPORT_ERROR)),
("gguf", (is_gguf_available, GGUF_IMPORT_ERROR)),
("torchao", (is_torchao_available, TORCHAO_IMPORT_ERROR)),
("quanto", (is_optimum_quanto_available, QUANTO_IMPORT_ERROR)),
]
)
def requires_backends(obj, backends):
if not isinstance(backends, (list, tuple)):
backends = [backends]
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
checks = (BACKENDS_MAPPING[backend] for backend in backends)
failed = [msg.format(name) for available, msg in checks if not available()]
if failed:
raise ImportError("".join(failed))
if name in [
"VersatileDiffusionTextToImagePipeline",
"VersatileDiffusionPipeline",
"VersatileDiffusionDualGuidedPipeline",
"StableDiffusionImageVariationPipeline",
"UnCLIPPipeline",
] and is_transformers_version("<", "4.25.0"):
raise ImportError(
f"You need to install `transformers>=4.25` in order to use {name}: \n```\n pip install"
" --upgrade transformers \n```"
)
if name in ["StableDiffusionDepth2ImgPipeline", "StableDiffusionPix2PixZeroPipeline"] and is_transformers_version(
"<", "4.26.0"
):
raise ImportError(
f"You need to install `transformers>=4.26` in order to use {name}: \n```\n pip install"
" --upgrade transformers \n```"
)
class DummyObject(type):
"""
Metaclass for the dummy objects. Any class inheriting from it will return the ImportError generated by
`requires_backend` each time a user tries to access any method of that class.
"""
def __getattr__(cls, key):
if key.startswith("_") and key not in ["_load_connected_pipes", "_is_onnx"]:
return super().__getattr__(cls, key)
requires_backends(cls, cls._backends)
# This function was copied from: https://github.com/huggingface/accelerate/blob/874c4967d94badd24f893064cc3bef45f57cadf7/src/accelerate/utils/versions.py#L319
def compare_versions(library_or_version: Union[str, Version], operation: str, requirement_version: str):
"""
Compares a library version to some requirement using a given operation.
Args:
library_or_version (`str` or `packaging.version.Version`):
A library name or a version to check.
operation (`str`):
A string representation of an operator, such as `">"` or `"<="`.
requirement_version (`str`):
The version to compare the library version against
"""
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(f"`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys())}, received {operation}")
operation = STR_OPERATION_TO_FUNC[operation]
if isinstance(library_or_version, str):
library_or_version = parse(importlib_metadata.version(library_or_version))
return operation(library_or_version, parse(requirement_version))
# This function was copied from: https://github.com/huggingface/accelerate/blob/874c4967d94badd24f893064cc3bef45f57cadf7/src/accelerate/utils/versions.py#L338
def is_torch_version(operation: str, version: str):
"""
Compares the current PyTorch version to a given reference with an operation.
Args:
operation (`str`):
A string representation of an operator, such as `">"` or `"<="`
version (`str`):
A string version of PyTorch
"""
return compare_versions(parse(_torch_version), operation, version)
def is_torch_xla_version(operation: str, version: str):
"""
Compares the current torch_xla version to a given reference with an operation.
Args:
operation (`str`):
A string representation of an operator, such as `">"` or `"<="`
version (`str`):
A string version of torch_xla
"""
if not is_torch_xla_available:
return False
return compare_versions(parse(_torch_xla_version), operation, version)
def is_transformers_version(operation: str, version: str):
"""
Compares the current Transformers version to a given reference with an operation.
Args:
operation (`str`):
A string representation of an operator, such as `">"` or `"<="`
version (`str`):
A version string
"""
if not _transformers_available:
return False
return compare_versions(parse(_transformers_version), operation, version)
def is_hf_hub_version(operation: str, version: str):
"""
Compares the current Hugging Face Hub version to a given reference with an operation.
Args:
operation (`str`):
A string representation of an operator, such as `">"` or `"<="`
version (`str`):
A version string
"""
if not _hf_hub_available:
return False
return compare_versions(parse(_hf_hub_version), operation, version)
def is_accelerate_version(operation: str, version: str):
"""
Compares the current Accelerate version to a given reference with an operation.
Args:
operation (`str`):
A string representation of an operator, such as `">"` or `"<="`
version (`str`):
A version string
"""
if not _accelerate_available:
return False
return compare_versions(parse(_accelerate_version), operation, version)
def is_peft_version(operation: str, version: str):
"""
Compares the current PEFT version to a given reference with an operation.
Args:
operation (`str`):
A string representation of an operator, such as `">"` or `"<="`
version (`str`):
A version string
"""
if not _peft_available:
return False
return compare_versions(parse(_peft_version), operation, version)
def is_bitsandbytes_version(operation: str, version: str):
"""
Args:
Compares the current bitsandbytes version to a given reference with an operation.
operation (`str`):
A string representation of an operator, such as `">"` or `"<="`
version (`str`):
A version string
"""
if not _bitsandbytes_available:
return False
return compare_versions(parse(_bitsandbytes_version), operation, version)
def is_gguf_version(operation: str, version: str):
"""
Compares the current Accelerate version to a given reference with an operation.
Args:
operation (`str`):
A string representation of an operator, such as `">"` or `"<="`
version (`str`):
A version string
"""
if not _gguf_available:
return False
return compare_versions(parse(_gguf_version), operation, version)
def is_torchao_version(operation: str, version: str):
"""
Compares the current torchao version to a given reference with an operation.
Args:
operation (`str`):
A string representation of an operator, such as `">"` or `"<="`
version (`str`):
A version string
"""
if not _torchao_available:
return False
return compare_versions(parse(_torchao_version), operation, version)
def is_k_diffusion_version(operation: str, version: str):
"""
Compares the current k-diffusion version to a given reference with an operation.
Args:
operation (`str`):
A string representation of an operator, such as `">"` or `"<="`
version (`str`):
A version string
"""
if not _k_diffusion_available:
return False
return compare_versions(parse(_k_diffusion_version), operation, version)
def is_optimum_quanto_version(operation: str, version: str):
"""
Compares the current Accelerate version to a given reference with an operation.
Args:
operation (`str`):
A string representation of an operator, such as `">"` or `"<="`
version (`str`):
A version string
"""
if not _optimum_quanto_available:
return False
return compare_versions(parse(_optimum_quanto_version), operation, version)
def get_objects_from_module(module):
"""
Returns a dict of object names and values in a module, while skipping private/internal objects
Args:
module (ModuleType):
Module to extract the objects from.
Returns:
dict: Dictionary of object names and corresponding values
"""
objects = {}
for name in dir(module):
if name.startswith("_"):
continue
objects[name] = getattr(module, name)
return objects
class OptionalDependencyNotAvailable(BaseException):
"""
An error indicating that an optional dependency of Diffusers was not found in the environment.
"""
class _LazyModule(ModuleType):
"""
Module class that surfaces all objects but only performs associated imports when the objects are requested.
"""
# Very heavily inspired by optuna.integration._IntegrationModule
# https://github.com/optuna/optuna/blob/master/optuna/integration/__init__.py
def __init__(self, name, module_file, import_structure, module_spec=None, extra_objects=None):
super().__init__(name)
self._modules = set(import_structure.keys())
self._class_to_module = {}
for key, values in import_structure.items():
for value in values:
self._class_to_module[value] = key
# Needed for autocompletion in an IDE
self.__all__ = list(import_structure.keys()) + list(chain(*import_structure.values()))
self.__file__ = module_file
self.__spec__ = module_spec
self.__path__ = [os.path.dirname(module_file)]
self._objects = {} if extra_objects is None else extra_objects
self._name = name
self._import_structure = import_structure
# Needed for autocompletion in an IDE
def __dir__(self):
result = super().__dir__()
# The elements of self.__all__ that are submodules may or may not be in the dir already, depending on whether
# they have been accessed or not. So we only add the elements of self.__all__ that are not already in the dir.
for attr in self.__all__:
if attr not in result:
result.append(attr)
return result
def __getattr__(self, name: str) -> Any:
if name in self._objects:
return self._objects[name]
if name in self._modules:
value = self._get_module(name)
elif name in self._class_to_module.keys():
module = self._get_module(self._class_to_module[name])
value = getattr(module, name)
else:
raise AttributeError(f"module {self.__name__} has no attribute {name}")
setattr(self, name, value)
return value
def _get_module(self, module_name: str):
try:
return importlib.import_module("." + module_name, self.__name__)
except Exception as e:
raise RuntimeError(
f"Failed to import {self.__name__}.{module_name} because of the following error (look up to see its"
f" traceback):\n{e}"
) from e
def __reduce__(self):
return (self.__class__, (self._name, self.__file__, self._import_structure))