# Copyright 2025 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()
                    # Infer top-level package names from file structure
                    _inferred_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, _inferred_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
    _torch_version = "N/A"

_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_SAFETENSORS 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")
_kernels_available, _kernels_version = _is_package_available("kernels")
_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")
_sageattention_available, _sageattention_version = _is_package_available("sageattention")
_flash_attn_available, _flash_attn_version = _is_package_available("flash_attn")
_flash_attn_3_available, _flash_attn_3_version = _is_package_available("flash_attn_3")
_kornia_available, _kornia_version = _is_package_available("kornia")
_nvidia_modelopt_available, _nvidia_modelopt_version = _is_package_available("modelopt", get_dist_name=True)


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_kernels_available():
    return _kernels_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_nvidia_modelopt_available():
    return _nvidia_modelopt_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"))


def is_sageattention_available():
    return _sageattention_available


def is_flash_attn_available():
    return _flash_attn_available


def is_flash_attn_3_available():
    return _flash_attn_3_available


def is_kornia_available():
    return _kornia_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`
"""

# 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 is_nvidia_modelopt_version(operation: str, version: str):
    """
    Compares the current Nvidia ModelOpt 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 _nvidia_modelopt_available:
        return False
    return compare_versions(parse(_nvidia_modelopt_version), operation, version)


def is_xformers_version(operation: str, version: str):
    """
    Compares the current xformers 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 _xformers_available:
        return False
    return compare_versions(parse(_xformers_version), operation, version)


def is_sageattention_version(operation: str, version: str):
    """
    Compares the current sageattention 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 _sageattention_available:
        return False
    return compare_versions(parse(_sageattention_version), operation, version)


def is_flash_attn_version(operation: str, version: str):
    """
    Compares the current flash-attention 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 _flash_attn_available:
        return False
    return compare_versions(parse(_flash_attn_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))
