# Copyright 2023 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 typing import Union from packaging import version 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() STR_OPERATION_TO_FUNC = { ">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt } _torch_version = "N/A" if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES: _torch_available = importlib.util.find_spec("torch") is not None if _torch_available: try: _torch_version = importlib_metadata.version("torch") logger.info(f"PyTorch version {_torch_version} available.") except importlib_metadata.PackageNotFoundError: _torch_available = False else: logger.info("Disabling PyTorch because USE_TORCH is set") _torch_available = False _tf_version = "N/A" if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES: _tf_available = importlib.util.find_spec("tensorflow") is not None if _tf_available: candidates = ( "tensorflow", "tensorflow-cpu", "tensorflow-gpu", "tf-nightly", "tf-nightly-cpu", "tf-nightly-gpu", "intel-tensorflow", "intel-tensorflow-avx512", "tensorflow-rocm", "tensorflow-macos", "tensorflow-aarch64", ) _tf_version = None # For the metadata, we have to look for both tensorflow and tensorflow-cpu for pkg in candidates: try: _tf_version = importlib_metadata.version(pkg) break except importlib_metadata.PackageNotFoundError: pass _tf_available = _tf_version is not None if _tf_available: if version.parse(_tf_version) < version.parse("2"): logger.info(f"TensorFlow found but with version {_tf_version}. " "Diffusers requires version 2 minimum.") _tf_available = False else: logger.info(f"TensorFlow version {_tf_version} available.") else: logger.info("Disabling Tensorflow because USE_TORCH is set") _tf_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 = importlib.util.find_spec("safetensors") is not None if _safetensors_available: try: _safetensors_version = importlib_metadata.version("safetensors") logger.info(f"Safetensors version {_safetensors_version} available.") except importlib_metadata.PackageNotFoundError: _safetensors_available = False else: logger.info("Disabling Safetensors because USE_TF is set") _safetensors_available = False _transformers_available = importlib.util.find_spec("transformers") is not None try: _transformers_version = importlib_metadata.version("transformers") logger.debug(f"Successfully imported transformers version {_transformers_version}") except importlib_metadata.PackageNotFoundError: _transformers_available = False _inflect_available = importlib.util.find_spec("inflect") is not None try: _inflect_version = importlib_metadata.version("inflect") logger.debug(f"Successfully imported inflect version {_inflect_version}") except importlib_metadata.PackageNotFoundError: _inflect_available = False _unidecode_available = importlib.util.find_spec("unidecode") is not None try: _unidecode_version = importlib_metadata.version("unidecode") logger.debug(f"Successfully imported unidecode version {_unidecode_version}") except importlib_metadata.PackageNotFoundError: _unidecode_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-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 _scipy_available = importlib.util.find_spec("scipy") is not None try: _scipy_version = importlib_metadata.version("scipy") logger.debug(f"Successfully imported scipy version {_scipy_version}") except importlib_metadata.PackageNotFoundError: _scipy_available = False _librosa_available = importlib.util.find_spec("librosa") is not None try: _librosa_version = importlib_metadata.version("librosa") logger.debug(f"Successfully imported librosa version {_librosa_version}") except importlib_metadata.PackageNotFoundError: _librosa_available = False _accelerate_available = importlib.util.find_spec("accelerate") is not None try: _accelerate_version = importlib_metadata.version("accelerate") logger.debug(f"Successfully imported accelerate version {_accelerate_version}") except importlib_metadata.PackageNotFoundError: _accelerate_available = False _xformers_available = importlib.util.find_spec("xformers") is not None try: _xformers_version = importlib_metadata.version("xformers") if _torch_available: import torch if version.Version(torch.__version__) < version.Version("1.12"): raise ValueError("PyTorch should be >= 1.12") logger.debug(f"Successfully imported xformers version {_xformers_version}") except importlib_metadata.PackageNotFoundError: _xformers_available = False _k_diffusion_available = importlib.util.find_spec("k_diffusion") is not None try: _k_diffusion_version = importlib_metadata.version("k_diffusion") logger.debug(f"Successfully imported k-diffusion version {_k_diffusion_version}") except importlib_metadata.PackageNotFoundError: _k_diffusion_available = False _note_seq_available = importlib.util.find_spec("note_seq") is not None try: _note_seq_version = importlib_metadata.version("note_seq") logger.debug(f"Successfully imported note-seq version {_note_seq_version}") except importlib_metadata.PackageNotFoundError: _note_seq_available = False _wandb_available = importlib.util.find_spec("wandb") is not None try: _wandb_version = importlib_metadata.version("wandb") logger.debug(f"Successfully imported wandb version {_wandb_version }") except importlib_metadata.PackageNotFoundError: _wandb_available = False _omegaconf_available = importlib.util.find_spec("omegaconf") is not None try: _omegaconf_version = importlib_metadata.version("omegaconf") logger.debug(f"Successfully imported omegaconf version {_omegaconf_version}") except importlib_metadata.PackageNotFoundError: _omegaconf_available = False _tensorboard_available = importlib.util.find_spec("tensorboard") try: _tensorboard_version = importlib_metadata.version("tensorboard") logger.debug(f"Successfully imported tensorboard version {_tensorboard_version}") except importlib_metadata.PackageNotFoundError: _tensorboard_available = False _compel_available = importlib.util.find_spec("compel") try: _compel_version = importlib_metadata.version("compel") logger.debug(f"Successfully imported compel version {_compel_version}") except importlib_metadata.PackageNotFoundError: _compel_available = False _ftfy_available = importlib.util.find_spec("ftfy") is not None try: _ftfy_version = importlib_metadata.version("ftfy") logger.debug(f"Successfully imported ftfy version {_ftfy_version}") except importlib_metadata.PackageNotFoundError: _ftfy_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 _torchsde_available = importlib.util.find_spec("torchsde") is not None try: _torchsde_version = importlib_metadata.version("torchsde") logger.debug(f"Successfully imported torchsde version {_torchsde_version}") except importlib_metadata.PackageNotFoundError: _torchsde_available = False def is_torch_available(): return _torch_available def is_safetensors_available(): return _safetensors_available def is_tf_available(): return _tf_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_omegaconf_available(): return _omegaconf_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 # 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 OMEGACONF_IMPORT_ERROR = """ {0} requires the omegaconf library but it was not found in your environment. You can install it with pip: `pip install omegaconf` """ # 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` """ 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)), ("omegaconf", (is_omegaconf_available, OMEGACONF_IMPORT_ERROR)), ("tensorboard", (_tensorboard_available, TENSORBOARD_IMPORT_ERROR)), ("compel", (_compel_available, COMPEL_IMPORT_ERROR)), ("ftfy", (is_ftfy_available, FTFY_IMPORT_ERROR)), ("torchsde", (_torchsde_available, TORCHSDE_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("_"): 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): """ Args: Compares a library version to some requirement using a given operation. 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): """ Args: Compares the current PyTorch version to a given reference with an operation. 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_transformers_version(operation: str, version: str): """ Args: Compares the current Transformers 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 _transformers_available: return False return compare_versions(parse(_transformers_version), operation, version) def is_accelerate_version(operation: str, version: str): """ Args: Compares the current Accelerate 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 _accelerate_available: return False return compare_versions(parse(_accelerate_version), operation, version) def is_k_diffusion_version(operation: str, version: str): """ Args: Compares the current k-diffusion 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 _k_diffusion_available: return False return compare_versions(parse(_k_diffusion_version), operation, version) class OptionalDependencyNotAvailable(BaseException): """An error indicating that an optional dependency of Diffusers was not found in the environment."""