# 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 itertools import chain

from types import ModuleType

from typing import Any, Union

from huggingface_hub.utils import is_jinja_available # noqa: F401

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()

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}

_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

_torch_xla_available = importlib.util.find_spec("torch_xla") is not None

if _torch_xla_available:

try:

_torch_xla_version = importlib_metadata.version("torch_xla")

logger.info(f"PyTorch XLA version {_torch_xla_version} available.")

except ImportError:

_torch_xla_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:

_torch_version = importlib_metadata.version("torch")

if version.Version(_torch_version) < version.Version("1.12"):

raise ValueError("xformers is installed in your environment and requires PyTorch >= 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

_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

_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

_peft_available = importlib.util.find_spec("peft") is not None

try:

_peft_version = importlib_metadata.version("peft")

logger.debug(f"Successfully imported peft version {_peft_version}")

except importlib_metadata.PackageNotFoundError:

_peft_available = False

def is_torch_available():

return _torch_available

def is_torch_xla_available():

return _torch_xla_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

# 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`

"""

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)),

]

)

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):

"""

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)

def get_objects_from_module(module):

"""

Args:

Returns a dict of object names and values in a module, while skipping private/internal objects

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))