# 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 inspect import operator as op import os import sys from collections import OrderedDict, defaultdict from itertools import chain from types import ModuleType from typing import Any, Tuple, 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 try: _package_map = importlib_metadata.packages_distributions() # load-once to avoid expensive calls except Exception: _package_map = None 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, get_dist_name: bool = False) -> Tuple[bool, str]: global _package_map pkg_exists = importlib.util.find_spec(pkg_name) is not None pkg_version = "N/A" if pkg_exists: if _package_map is None: _package_map = defaultdict(list) try: # Fallback for Python < 3.10 for dist in importlib_metadata.distributions(): _top_level_declared = (dist.read_text("top_level.txt") or "").split() _infered_opt_names = { f.parts[0] if len(f.parts) > 1 else inspect.getmodulename(f) for f in (dist.files or []) } - {None} _top_level_inferred = filter(lambda name: "." not in name, _infered_opt_names) for pkg in _top_level_declared or _top_level_inferred: _package_map[pkg].append(dist.metadata["Name"]) except Exception as _: pass try: if get_dist_name and pkg_name in _package_map and _package_map[pkg_name]: if len(_package_map[pkg_name]) > 1: logger.warning( f"Multiple distributions found for package {pkg_name}. Picked distribution: {_package_map[pkg_name][0]}" ) pkg_name = _package_map[pkg_name][0] 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-cann", "onnxruntime-directml", "ort_nightly_directml", "onnxruntime-gpu", "ort_nightly_gpu", "onnxruntime-migraphx", "onnxruntime-openvino", "onnxruntime-qnn", "onnxruntime-rocm", "onnxruntime-training", "onnxruntime-vitisai", ) _onnxruntime_version = None # For the metadata, we have to look for both onnxruntime and onnxruntime-x 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") _optimum_quanto_available, _optimum_quanto_version = _is_package_available("optimum", get_dist_name=True) _pytorch_retinaface_available, _pytorch_retinaface_version = _is_package_available("pytorch_retinaface") _better_profanity_available, _better_profanity_version = _is_package_available("better_profanity") _nltk_available, _nltk_version = _is_package_available("nltk") _cosmos_guardrail_available, _cosmos_guardrail_version = _is_package_available("cosmos_guardrail") 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 def is_pytorch_retinaface_available(): return _pytorch_retinaface_available def is_better_profanity_available(): return _better_profanity_available def is_nltk_available(): return _nltk_available def is_cosmos_guardrail_available(): return _cosmos_guardrail_available def is_hpu_available(): return all(importlib.util.find_spec(lib) for lib in ("habana_frameworks", "habana_frameworks.torch")) # 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` """ # docstyle-ignore PYTORCH_RETINAFACE_IMPORT_ERROR = """ {0} requires the pytorch_retinaface library but it was not found in your environment. You can install it with pip: `pip install pytorch_retinaface` """ # docstyle-ignore BETTER_PROFANITY_IMPORT_ERROR = """ {0} requires the better_profanity library but it was not found in your environment. You can install it with pip: `pip install better_profanity` """ # docstyle-ignore NLTK_IMPORT_ERROR = """ {0} requires the nltk library but it was not found in your environment. You can install it with pip: `pip install nltk` """ 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)), ("pytorch_retinaface", (is_pytorch_retinaface_available, PYTORCH_RETINAFACE_IMPORT_ERROR)), ("better_profanity", (is_better_profanity_available, BETTER_PROFANITY_IMPORT_ERROR)), ("nltk", (is_nltk_available, NLTK_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))