ppdiffusers/utils/import_utils.py

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 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.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_PADDLE = os.environ.get("USE_PADDLE", "AUTO").upper()

STR_OPERATION_TO_FUNC = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}

_paddle_version = "N/A"
if USE_PADDLE in ENV_VARS_TRUE_AND_AUTO_VALUES:
    _paddle_available = importlib.util.find_spec("paddle") is not None
    if _paddle_available:
        try:
            import paddle

            _paddle_version = paddle.__version__
            logger.info(f"Paddle version {_paddle_version} available.")
        except importlib_metadata.PackageNotFoundError:
            _paddle_available = False
else:
    logger.info("Disabling Paddle because USE_PADDLE is not set.")
    _paddle_available = False

_paddlenlp_available = importlib.util.find_spec("paddlenlp") is not None
try:
    _paddlenlp_version = importlib_metadata.version("paddlenlp")
    logger.debug(f"Successfully imported paddlenlp version {_paddlenlp_version}")
except importlib_metadata.PackageNotFoundError:
    _paddlenlp_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

_modelcards_available = importlib.util.find_spec("modelcards") is not None
try:
    _modelcards_version = importlib_metadata.version("modelcards")
    logger.debug(f"Successfully imported modelcards version {_modelcards_version}")
except importlib_metadata.PackageNotFoundError:
    _modelcards_available = False

_onnxruntime_version = "N/A"
_onnx_available = importlib.util.find_spec("onnxruntime") is not None
if _onnx_available:
    candidates = (
        "onnxruntime",
        "onnxruntime-gpu",
        "onnxruntime-directml",
        "onnxruntime-openvino",
        "ort_nightly_directml",
    )
    _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}")

_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

_fastdeploy_available = importlib.util.find_spec("fastdeploy") is not None
if _fastdeploy_available:
    candidates = ("fastdeploy_gpu_python", "fastdeploy_python")
    _fastdeploy_version = None
    # For the metadata, we have to look for both fastdeploy_python and fastdeploy_gpu_python
    for pkg in candidates:
        try:
            _fastdeploy_version = importlib_metadata.version(pkg)
            break
        except importlib_metadata.PackageNotFoundError:
            pass
    _fastdeploy_available = _fastdeploy_version is not None
    if _fastdeploy_available:
        logger.debug(f"Successfully imported fastdeploy version {_fastdeploy_version}")


_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 = True

_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


def is_paddle_available():
    return _paddle_available


def is_paddlenlp_available():
    return _paddlenlp_available


def is_inflect_available():
    return _inflect_available


def is_unidecode_available():
    return _unidecode_available


def is_modelcards_available():
    return _modelcards_available


def is_onnx_available():
    return _onnx_available


def is_scipy_available():
    return _scipy_available


def is_librosa_available():
    return _librosa_available


def is_fastdeploy_available():
    return _fastdeploy_available


def is_k_diffusion_available():
    return _k_diffusion_available


def is_wandb_available():
    return _wandb_available


# docstyle-ignore
FASTDEPLOY_IMPORT_ERROR = """
{0} requires the fastdeploy library but it was not found in your environment. You can install it with pip: `pip install
fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html`
"""

# 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
PADDLE_IMPORT_ERROR = """
{0} requires the Paddle library but it was not found in your environment. Checkout the instructions on the
installation page: https://www.paddlepaddle.org.cn/install/quick and follow the ones that match your environment.
"""

# 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
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
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
PADDLENLP_IMPORT_ERROR = """
{0} requires the paddlenlp library but it was not found in your environment. You can install it with pip: `pip
install paddlenlp`
"""

# 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
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`
"""

BACKENDS_MAPPING = OrderedDict(
    [
        ("fastdeploy", (is_fastdeploy_available, FASTDEPLOY_IMPORT_ERROR)),
        ("inflect", (is_inflect_available, INFLECT_IMPORT_ERROR)),
        ("onnx", (is_onnx_available, ONNX_IMPORT_ERROR)),
        ("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)),
        ("paddle", (is_paddle_available, PADDLE_IMPORT_ERROR)),
        ("paddlenlp", (is_paddlenlp_available, PADDLENLP_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)),
        ("wandb", (is_wandb_available, WANDB_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))


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_paddle_version(operation: str, version: str):
    """
    Args:
    Compares the current Paddle 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 Paddle
    """
    return compare_versions(parse(_paddle_version), operation, version)


class OptionalDependencyNotAvailable(BaseException):
    """An error indicating that an optional dependency of Diffusers was not found in the environment."""