import_utils.py 30.8 KB
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# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
# 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.
"""
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import importlib.util
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import operator as op
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import os
import sys
from collections import OrderedDict
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from itertools import chain
from types import ModuleType
from typing import Any, Union
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from huggingface_hub.utils import is_jinja_available  # noqa: F401
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from packaging import version
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from packaging.version import Version, parse
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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()
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USE_SAFETENSORS = os.environ.get("USE_SAFETENSORS", "AUTO").upper()
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DIFFUSERS_SLOW_IMPORT = os.environ.get("DIFFUSERS_SLOW_IMPORT", "FALSE").upper()
DIFFUSERS_SLOW_IMPORT = DIFFUSERS_SLOW_IMPORT in ENV_VARS_TRUE_VALUES
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STR_OPERATION_TO_FUNC = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}

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_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:
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    logger.info("Disabling PyTorch because USE_TORCH is set")
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    _torch_available = False

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_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

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# check whether torch_npu is available
_torch_npu_available = importlib.util.find_spec("torch_npu") is not None
if _torch_npu_available:
    try:
        _torch_npu_version = importlib_metadata.version("torch_npu")
        logger.info(f"torch_npu version {_torch_npu_version} available.")
    except ImportError:
        _torch_npu_available = False

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_jax_version = "N/A"
_flax_version = "N/A"
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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

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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
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_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

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_onnxruntime_version = "N/A"
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_onnx_available = importlib.util.find_spec("onnxruntime") is not None
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if _onnx_available:
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    candidates = (
        "onnxruntime",
        "onnxruntime-gpu",
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        "ort_nightly_gpu",
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        "onnxruntime-directml",
        "onnxruntime-openvino",
        "ort_nightly_directml",
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        "onnxruntime-rocm",
        "onnxruntime-training",
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    )
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    _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}")
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# (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:
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    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}")
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except importlib_metadata.PackageNotFoundError:
    _opencv_available = False
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_scipy_available = importlib.util.find_spec("scipy") is not None
try:
    _scipy_version = importlib_metadata.version("scipy")
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    logger.debug(f"Successfully imported scipy version {_scipy_version}")
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except importlib_metadata.PackageNotFoundError:
    _scipy_available = False

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_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

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_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

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_xformers_available = importlib.util.find_spec("xformers") is not None
try:
    _xformers_version = importlib_metadata.version("xformers")
    if _torch_available:
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        _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")
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    logger.debug(f"Successfully imported xformers version {_xformers_version}")
except importlib_metadata.PackageNotFoundError:
    _xformers_available = False

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_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

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_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

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

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_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

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_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


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_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

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_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

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_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

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_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

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

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_sentencepiece_available = importlib.util.find_spec("sentencepiece") is not None
try:
    _sentencepiece_version = importlib_metadata.version("sentencepiece")
    logger.info(f"Successfully imported sentencepiece version {_sentencepiece_version}")
except importlib_metadata.PackageNotFoundError:
    _sentencepiece_available = False

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

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_timm_available = importlib.util.find_spec("timm") is not None
if _timm_available:
    try:
        _timm_version = importlib_metadata.version("timm")
        logger.info(f"Timm version {_timm_version} available.")
    except importlib_metadata.PackageNotFoundError:
        _timm_available = False


def is_timm_available():
    return _timm_available


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

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_is_google_colab = "google.colab" in sys.modules or any(k.startswith("COLAB_") for k in os.environ)
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_imageio_available = importlib.util.find_spec("imageio") is not None
if _imageio_available:
    try:
        _imageio_version = importlib_metadata.version("imageio")
        logger.debug(f"Successfully imported imageio version {_imageio_version}")

    except importlib_metadata.PackageNotFoundError:
        _imageio_available = False

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_is_torchao_available = importlib.util.find_spec("torchao") is not None
if _is_torchao_available:
    try:
        _torchao_version = importlib_metadata.version("torchao")
        logger.debug(f"Successfully import torchao version {_torchao_version}")
    except importlib_metadata.PackageNotFoundError:
        _is_torchao_available = False


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def is_torch_available():
    return _torch_available


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def is_torch_xla_available():
    return _torch_xla_available


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def is_torch_npu_available():
    return _torch_npu_available


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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


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def is_onnx_available():
    return _onnx_available


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def is_opencv_available():
    return _opencv_available


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def is_scipy_available():
    return _scipy_available


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def is_librosa_available():
    return _librosa_available


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def is_xformers_available():
    return _xformers_available


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def is_accelerate_available():
    return _accelerate_available


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def is_k_diffusion_available():
    return _k_diffusion_available


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def is_note_seq_available():
    return _note_seq_available


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def is_wandb_available():
    return _wandb_available


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def is_tensorboard_available():
    return _tensorboard_available


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def is_compel_available():
    return _compel_available


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def is_ftfy_available():
    return _ftfy_available


def is_bs4_available():
    return _bs4_available


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def is_torchsde_available():
    return _torchsde_available


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def is_invisible_watermark_available():
    return _invisible_watermark_available


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def is_peft_available():
    return _peft_available


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def is_torchvision_available():
    return _torchvision_available


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def is_matplotlib_available():
    return _matplotlib_available


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def is_safetensors_available():
    return _safetensors_available


def is_bitsandbytes_available():
    return _bitsandbytes_available


def is_google_colab():
    return _is_google_colab


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def is_sentencepiece_available():
    return _sentencepiece_available


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def is_imageio_available():
    return _imageio_available


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def is_torchao_available():
    return _is_torchao_available


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# 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.
"""

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# 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`
"""

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# 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`
"""

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# 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`
"""

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# 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.
"""

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# 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`
"""

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# 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`
"""

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# 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`
"""

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# 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`
"""

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# 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`
"""

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# 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`
"""

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# 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.
"""

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# 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`
"""

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# docstyle-ignore
INVISIBLE_WATERMARK_IMPORT_ERROR = """
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{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`
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"""

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# 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`
"""

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# 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`
"""


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# 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`
"""
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# 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`
"""

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# docstyle-ignore
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`
"""

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BACKENDS_MAPPING = OrderedDict(
    [
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        ("bs4", (is_bs4_available, BS4_IMPORT_ERROR)),
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        ("flax", (is_flax_available, FLAX_IMPORT_ERROR)),
        ("inflect", (is_inflect_available, INFLECT_IMPORT_ERROR)),
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        ("onnx", (is_onnx_available, ONNX_IMPORT_ERROR)),
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        ("opencv", (is_opencv_available, OPENCV_IMPORT_ERROR)),
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        ("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)),
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        ("librosa", (is_librosa_available, LIBROSA_IMPORT_ERROR)),
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        ("k_diffusion", (is_k_diffusion_available, K_DIFFUSION_IMPORT_ERROR)),
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        ("note_seq", (is_note_seq_available, NOTE_SEQ_IMPORT_ERROR)),
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        ("wandb", (is_wandb_available, WANDB_IMPORT_ERROR)),
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        ("tensorboard", (is_tensorboard_available, TENSORBOARD_IMPORT_ERROR)),
        ("compel", (is_compel_available, COMPEL_IMPORT_ERROR)),
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        ("ftfy", (is_ftfy_available, FTFY_IMPORT_ERROR)),
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        ("torchsde", (is_torchsde_available, TORCHSDE_IMPORT_ERROR)),
        ("invisible_watermark", (is_invisible_watermark_available, INVISIBLE_WATERMARK_IMPORT_ERROR)),
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        ("peft", (is_peft_available, PEFT_IMPORT_ERROR)),
        ("safetensors", (is_safetensors_available, SAFETENSORS_IMPORT_ERROR)),
        ("bitsandbytes", (is_bitsandbytes_available, BITSANDBYTES_IMPORT_ERROR)),
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        ("sentencepiece", (is_sentencepiece_available, SENTENCEPIECE_IMPORT_ERROR)),
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        ("imageio", (is_imageio_available, IMAGEIO_IMPORT_ERROR)),
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        ("torchao", (is_torchao_available, TORCHAO_IMPORT_ERROR)),
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    ]
)


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))

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    if name in [
        "VersatileDiffusionTextToImagePipeline",
        "VersatileDiffusionPipeline",
        "VersatileDiffusionDualGuidedPipeline",
        "StableDiffusionImageVariationPipeline",
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        "UnCLIPPipeline",
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    ] 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```"
        )

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    if name in ["StableDiffusionDepth2ImgPipeline", "StableDiffusionPix2PixZeroPipeline"] and is_transformers_version(
        "<", "4.26.0"
    ):
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        raise ImportError(
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            f"You need to install `transformers>=4.26` in order to use {name}: \n```\n pip install"
            " --upgrade transformers \n```"
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        )

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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):
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        if key.startswith("_") and key not in ["_load_connected_pipes", "_is_onnx"]:
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            return super().__getattr__(cls, key)
        requires_backends(cls, cls._backends)
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# 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.
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    Args:
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        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.
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    Args:
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        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)
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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)


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def is_transformers_version(operation: str, version: str):
    """
    Compares the current Transformers version to a given reference with an operation.
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    Args:
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        operation (`str`):
            A string representation of an operator, such as `">"` or `"<="`
        version (`str`):
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            A version string
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    """
    if not _transformers_available:
        return False
    return compare_versions(parse(_transformers_version), operation, version)
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def is_accelerate_version(operation: str, version: str):
    """
    Compares the current Accelerate version to a given reference with an operation.
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    Args:
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        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)


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def is_peft_version(operation: str, version: str):
    """
    Compares the current PEFT version to a given reference with an operation.
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    Args:
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        operation (`str`):
            A string representation of an operator, such as `">"` or `"<="`
        version (`str`):
            A version string
    """
    if not _peft_version:
        return False
    return compare_versions(parse(_peft_version), operation, version)


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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_version:
        return False
    return compare_versions(parse(_bitsandbytes_version), operation, version)


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def is_k_diffusion_version(operation: str, version: str):
    """
    Compares the current k-diffusion version to a given reference with an operation.
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    Args:
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        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)


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def get_objects_from_module(module):
    """
    Returns a dict of object names and values in a module, while skipping private/internal objects
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    Args:
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        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


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