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Grounded-Segment-Anything / transformers_4_35_0 /utils /import_utils.py

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	# 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.metadata
	import importlib.util
	import json
	import os
	import shutil
	import subprocess
	import sys
	import warnings
	from collections import OrderedDict
	from functools import lru_cache
	from itertools import chain
	from types import ModuleType
	from typing import Any, Tuple, Union

	from packaging import version

	from . import logging


	logger = logging.get_logger(__name__) # pylint: disable=invalid-name


	# TODO: This doesn't work for all packages (`bs4`, `faiss`, etc.) Talk to Sylvain to see how to do with it better.
	def _is_package_available(pkg_name: str, return_version: bool = False) -> Union[Tuple[bool, str], bool]:
	# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
	package_exists = importlib.util.find_spec(pkg_name) is not None
	package_version = "N/A"
	if package_exists:
	try:
	package_version = importlib.metadata.version(pkg_name)
	package_exists = True
	except importlib.metadata.PackageNotFoundError:
	package_exists = False
	logger.debug(f"Detected {pkg_name} version {package_version}")
	if return_version:
	return package_exists, package_version
	else:
	return package_exists


	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()

	FORCE_TF_AVAILABLE = os.environ.get("FORCE_TF_AVAILABLE", "AUTO").upper()

	# This is the version of torch required to run torch.fx features and torch.onnx with dictionary inputs.
	TORCH_FX_REQUIRED_VERSION = version.parse("1.10")


	_accelerate_available, _accelerate_version = _is_package_available("accelerate", return_version=True)
	_apex_available = _is_package_available("apex")
	_bitsandbytes_available = _is_package_available("bitsandbytes")
	_flash_attn_available = _is_package_available("flash_attn")
	# `importlib.metadata.version` doesn't work with `bs4` but `beautifulsoup4`. For `importlib.util.find_spec`, reversed.
	_bs4_available = importlib.util.find_spec("bs4") is not None
	_coloredlogs_available = _is_package_available("coloredlogs")
	# `importlib.metadata.util` doesn't work with `opencv-python-headless`.
	_cv2_available = importlib.util.find_spec("cv2") is not None
	_datasets_available = _is_package_available("datasets")
	_decord_available = importlib.util.find_spec("decord") is not None
	_detectron2_available = _is_package_available("detectron2")
	# We need to check both `faiss` and `faiss-cpu`.
	_faiss_available = importlib.util.find_spec("faiss") is not None
	try:
	_faiss_version = importlib.metadata.version("faiss")
	logger.debug(f"Successfully imported faiss version {_faiss_version}")
	except importlib.metadata.PackageNotFoundError:
	try:
	_faiss_version = importlib.metadata.version("faiss-cpu")
	logger.debug(f"Successfully imported faiss version {_faiss_version}")
	except importlib.metadata.PackageNotFoundError:
	_faiss_available = False
	_ftfy_available = _is_package_available("ftfy")
	_ipex_available, _ipex_version = _is_package_available("intel_extension_for_pytorch", return_version=True)
	_jieba_available = _is_package_available("jieba")
	_jinja_available = _is_package_available("jinja2")
	_kenlm_available = _is_package_available("kenlm")
	_keras_nlp_available = _is_package_available("keras_nlp")
	_levenshtein_available = _is_package_available("Levenshtein")
	_librosa_available = _is_package_available("librosa")
	_natten_available = _is_package_available("natten")
	_nltk_available = _is_package_available("nltk")
	_onnx_available = _is_package_available("onnx")
	_openai_available = _is_package_available("openai")
	_optimum_available = _is_package_available("optimum")
	_auto_gptq_available = _is_package_available("auto_gptq")
	_pandas_available = _is_package_available("pandas")
	_peft_available = _is_package_available("peft")
	_phonemizer_available = _is_package_available("phonemizer")
	_psutil_available = _is_package_available("psutil")
	_py3nvml_available = _is_package_available("py3nvml")
	_pyctcdecode_available = _is_package_available("pyctcdecode")
	_pytesseract_available = _is_package_available("pytesseract")
	_pytest_available = _is_package_available("pytest")
	_pytorch_quantization_available = _is_package_available("pytorch_quantization")
	_rjieba_available = _is_package_available("rjieba")
	_sacremoses_available = _is_package_available("sacremoses")
	_safetensors_available = _is_package_available("safetensors")
	_scipy_available = _is_package_available("scipy")
	_sentencepiece_available = _is_package_available("sentencepiece")
	_is_seqio_available = _is_package_available("seqio")
	_sklearn_available = importlib.util.find_spec("sklearn") is not None
	if _sklearn_available:
	try:
	importlib.metadata.version("scikit-learn")
	except importlib.metadata.PackageNotFoundError:
	_sklearn_available = False
	_smdistributed_available = importlib.util.find_spec("smdistributed") is not None
	_soundfile_available = _is_package_available("soundfile")
	_spacy_available = _is_package_available("spacy")
	_sudachipy_available = _is_package_available("sudachipy")
	_tensorflow_probability_available = _is_package_available("tensorflow_probability")
	_tensorflow_text_available = _is_package_available("tensorflow_text")
	_tf2onnx_available = _is_package_available("tf2onnx")
	_timm_available = _is_package_available("timm")
	_tokenizers_available = _is_package_available("tokenizers")
	_torchaudio_available = _is_package_available("torchaudio")
	_torchdistx_available = _is_package_available("torchdistx")
	_torchvision_available = _is_package_available("torchvision")


	_torch_version = "N/A"
	_torch_available = False
	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", return_version=True)
	else:
	logger.info("Disabling PyTorch because USE_TF is set")
	_torch_available = False


	_tf_version = "N/A"
	_tf_available = False
	if FORCE_TF_AVAILABLE in ENV_VARS_TRUE_VALUES:
	_tf_available = True
	else:
	if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
	# Note: _is_package_available("tensorflow") fails for tensorflow-cpu. Please test any changes to the line below
	# with tensorflow-cpu to make sure it still works!
	_tf_available = importlib.util.find_spec("tensorflow") is not None
	if _tf_available:
	candidates = (
	"tensorflow",
	"tensorflow-cpu",
	"tensorflow-gpu",
	"tf-nightly",
	"tf-nightly-cpu",
	"tf-nightly-gpu",
	"intel-tensorflow",
	"intel-tensorflow-avx512",
	"tensorflow-rocm",
	"tensorflow-macos",
	"tensorflow-aarch64",
	)
	_tf_version = None
	# For the metadata, we have to look for both tensorflow and tensorflow-cpu
	for pkg in candidates:
	try:
	_tf_version = importlib.metadata.version(pkg)
	break
	except importlib.metadata.PackageNotFoundError:
	pass
	_tf_available = _tf_version is not None
	if _tf_available:
	if version.parse(_tf_version) < version.parse("2"):
	logger.info(
	f"TensorFlow found but with version {_tf_version}. Transformers requires version 2 minimum."
	)
	_tf_available = False
	else:
	logger.info("Disabling Tensorflow because USE_TORCH is set")


	_essentia_available = importlib.util.find_spec("essentia") is not None
	try:
	_essentia_version = importlib.metadata.version("essentia")
	logger.debug(f"Successfully imported essentia version {_essentia_version}")
	except importlib.metadata.PackageNotFoundError:
	_essentia_version = False


	_pretty_midi_available = importlib.util.find_spec("pretty_midi") is not None
	try:
	_pretty_midi_version = importlib.metadata.version("pretty_midi")
	logger.debug(f"Successfully imported pretty_midi version {_pretty_midi_version}")
	except importlib.metadata.PackageNotFoundError:
	_pretty_midi_available = False


	ccl_version = "N/A"
	_is_ccl_available = (
	importlib.util.find_spec("torch_ccl") is not None
	or importlib.util.find_spec("oneccl_bindings_for_pytorch") is not None
	)
	try:
	ccl_version = importlib.metadata.version("oneccl_bind_pt")
	logger.debug(f"Detected oneccl_bind_pt version {ccl_version}")
	except importlib.metadata.PackageNotFoundError:
	_is_ccl_available = False


	_flax_available = False
	if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
	_flax_available, _flax_version = _is_package_available("flax", return_version=True)
	if _flax_available:
	_jax_available, _jax_version = _is_package_available("jax", return_version=True)
	if _jax_available:
	logger.info(f"JAX version {_jax_version}, Flax version {_flax_version} available.")
	else:
	_flax_available = _jax_available = False
	_jax_version = _flax_version = "N/A"


	_torch_fx_available = False
	if _torch_available:
	torch_version = version.parse(_torch_version)
	_torch_fx_available = (torch_version.major, torch_version.minor) >= (
	TORCH_FX_REQUIRED_VERSION.major,
	TORCH_FX_REQUIRED_VERSION.minor,
	)


	def is_kenlm_available():
	return _kenlm_available


	def is_cv2_available():
	return _cv2_available


	def is_torch_available():
	return _torch_available


	def get_torch_version():
	return _torch_version


	def is_torchvision_available():
	return _torchvision_available


	def is_pyctcdecode_available():
	return _pyctcdecode_available


	def is_librosa_available():
	return _librosa_available


	def is_essentia_available():
	return _essentia_available


	def is_pretty_midi_available():
	return _pretty_midi_available


	def is_torch_cuda_available():
	if is_torch_available():
	import torch

	return torch.cuda.is_available()
	else:
	return False


	def is_torch_mps_available():
	if is_torch_available():
	import torch

	if hasattr(torch.backends, "mps"):
	return torch.backends.mps.is_available()
	return False


	def is_torch_bf16_gpu_available():
	if not is_torch_available():
	return False

	import torch

	# since currently no utility function is available we build our own.
	# some bits come from https://github.com/pytorch/pytorch/blob/2289a12f21c54da93bf5d696e3f9aea83dd9c10d/torch/testing/_internal/common_cuda.py#L51
	# with additional check for torch version
	# to succeed: (torch is required to be >= 1.10 anyway)
	# 1. the hardware needs to support bf16 (GPU arch >= Ampere, or CPU)
	# 2. if using gpu, CUDA >= 11
	# 3. torch.autocast exists
	# XXX: one problem here is that it may give invalid results on mixed gpus setup, so it's
	# really only correct for the 0th gpu (or currently set default device if different from 0)
	if torch.cuda.is_available() and torch.version.cuda is not None:
	if torch.cuda.get_device_properties(torch.cuda.current_device()).major < 8:
	return False
	if int(torch.version.cuda.split(".")[0]) < 11:
	return False
	if not hasattr(torch.cuda.amp, "autocast"):
	return False
	else:
	return False

	return True


	def is_torch_bf16_cpu_available():
	if not is_torch_available():
	return False

	import torch

	try:
	# multiple levels of AttributeError depending on the pytorch version so do them all in one check
	_ = torch.cpu.amp.autocast
	except AttributeError:
	return False

	return True


	def is_torch_bf16_available():
	# the original bf16 check was for gpu only, but later a cpu/bf16 combo has emerged so this util
	# has become ambiguous and therefore deprecated
	warnings.warn(
	"The util is_torch_bf16_available is deprecated, please use is_torch_bf16_gpu_available "
	"or is_torch_bf16_cpu_available instead according to whether it's used with cpu or gpu",
	FutureWarning,
	)
	return is_torch_bf16_gpu_available()


	def is_torch_tf32_available():
	if not is_torch_available():
	return False

	import torch

	if not torch.cuda.is_available() or torch.version.cuda is None:
	return False
	if torch.cuda.get_device_properties(torch.cuda.current_device()).major < 8:
	return False
	if int(torch.version.cuda.split(".")[0]) < 11:
	return False
	if version.parse(version.parse(torch.__version__).base_version) < version.parse("1.7"):
	return False

	return True


	def is_torch_fx_available():
	return _torch_fx_available


	def is_peft_available():
	return _peft_available


	def is_bs4_available():
	return _bs4_available


	def is_tf_available():
	return _tf_available


	def is_coloredlogs_available():
	return _coloredlogs_available


	def is_tf2onnx_available():
	return _tf2onnx_available


	def is_onnx_available():
	return _onnx_available


	def is_openai_available():
	return _openai_available


	def is_flax_available():
	return _flax_available


	def is_ftfy_available():
	return _ftfy_available


	@lru_cache()
	def is_torch_tpu_available(check_device=True):
	"Checks if `torch_xla` is installed and potentially if a TPU is in the environment"
	if not _torch_available:
	return False
	if importlib.util.find_spec("torch_xla") is not None:
	if check_device:
	# We need to check if `xla_device` can be found, will raise a RuntimeError if not
	try:
	import torch_xla.core.xla_model as xm

	_ = xm.xla_device()
	return True
	except RuntimeError:
	return False
	return True
	return False


	@lru_cache()
	def is_torch_neuroncore_available(check_device=True):
	if importlib.util.find_spec("torch_neuronx") is not None:
	return is_torch_tpu_available(check_device)
	return False


	@lru_cache()
	def is_torch_npu_available(check_device=False):
	"Checks if `torch_npu` is installed and potentially if a NPU is in the environment"
	if not _torch_available or importlib.util.find_spec("torch_npu") is None:
	return False

	import torch
	import torch_npu # noqa: F401

	if check_device:
	try:
	# Will raise a RuntimeError if no NPU is found
	_ = torch.npu.device_count()
	return torch.npu.is_available()
	except RuntimeError:
	return False
	return hasattr(torch, "npu") and torch.npu.is_available()


	def is_torchdynamo_available():
	if not is_torch_available():
	return False
	try:
	import torch._dynamo as dynamo # noqa: F401

	return True
	except Exception:
	return False


	def is_torch_compile_available():
	if not is_torch_available():
	return False

	import torch

	# We don't do any version check here to support nighlies marked as 1.14. Ultimately needs to check version against
	# 2.0 but let's do it later.
	return hasattr(torch, "compile")


	def is_torchdynamo_compiling():
	if not is_torch_available():
	return False
	try:
	import torch._dynamo as dynamo # noqa: F401

	return dynamo.is_compiling()
	except Exception:
	return False


	def is_torch_tensorrt_fx_available():
	if importlib.util.find_spec("torch_tensorrt") is None:
	return False
	return importlib.util.find_spec("torch_tensorrt.fx") is not None


	def is_datasets_available():
	return _datasets_available


	def is_detectron2_available():
	return _detectron2_available


	def is_rjieba_available():
	return _rjieba_available


	def is_psutil_available():
	return _psutil_available


	def is_py3nvml_available():
	return _py3nvml_available


	def is_sacremoses_available():
	return _sacremoses_available


	def is_apex_available():
	return _apex_available


	def is_ninja_available():
	r"""
	Code comes from torch.utils.cpp_extension.is_ninja_available(). Returns `True` if the
	[ninja](https://ninja-build.org/) build system is available on the system, `False` otherwise.
	"""
	try:
	subprocess.check_output("ninja --version".split())
	except Exception:
	return False
	else:
	return True


	def is_ipex_available():
	def get_major_and_minor_from_version(full_version):
	return str(version.parse(full_version).major) + "." + str(version.parse(full_version).minor)

	if not is_torch_available() or not _ipex_available:
	return False

	torch_major_and_minor = get_major_and_minor_from_version(_torch_version)
	ipex_major_and_minor = get_major_and_minor_from_version(_ipex_version)
	if torch_major_and_minor != ipex_major_and_minor:
	logger.warning(
	f"Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"
	f" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."
	)
	return False
	return True


	@lru_cache
	def is_torch_xpu_available(check_device=False):
	"Checks if `intel_extension_for_pytorch` is installed and potentially if a XPU is in the environment"
	if not is_ipex_available():
	return False

	import intel_extension_for_pytorch # noqa: F401
	import torch

	if check_device:
	try:
	# Will raise a RuntimeError if no XPU is found
	_ = torch.xpu.device_count()
	return torch.xpu.is_available()
	except RuntimeError:
	return False
	return hasattr(torch, "xpu") and torch.xpu.is_available()


	def is_bitsandbytes_available():
	if not is_torch_available():
	return False

	# bitsandbytes throws an error if cuda is not available
	# let's avoid that by adding a simple check
	import torch

	return _bitsandbytes_available and torch.cuda.is_available()


	def is_flash_attn_available():
	if not is_torch_available():
	return False

	# Let's add an extra check to see if cuda is available
	import torch

	return _flash_attn_available and torch.cuda.is_available()


	def is_torchdistx_available():
	return _torchdistx_available


	def is_faiss_available():
	return _faiss_available


	def is_scipy_available():
	return _scipy_available


	def is_sklearn_available():
	return _sklearn_available


	def is_sentencepiece_available():
	return _sentencepiece_available


	def is_seqio_available():
	return _is_seqio_available


	def is_protobuf_available():
	if importlib.util.find_spec("google") is None:
	return False
	return importlib.util.find_spec("google.protobuf") is not None


	def is_accelerate_available(min_version: str = None):
	if min_version is not None:
	return _accelerate_available and version.parse(_accelerate_version) >= version.parse(min_version)
	return _accelerate_available


	def is_fsdp_available(min_version: str = "1.12.0"):
	return is_torch_available() and version.parse(_torch_version) >= version.parse(min_version)


	def is_optimum_available():
	return _optimum_available


	def is_auto_gptq_available():
	return _auto_gptq_available


	def is_levenshtein_available():
	return _levenshtein_available


	def is_optimum_neuron_available():
	return _optimum_available and _is_package_available("optimum.neuron")


	def is_safetensors_available():
	return _safetensors_available


	def is_tokenizers_available():
	return _tokenizers_available


	def is_vision_available():
	_pil_available = importlib.util.find_spec("PIL") is not None
	if _pil_available:
	try:
	package_version = importlib.metadata.version("Pillow")
	except importlib.metadata.PackageNotFoundError:
	try:
	package_version = importlib.metadata.version("Pillow-SIMD")
	except importlib.metadata.PackageNotFoundError:
	return False
	logger.debug(f"Detected PIL version {package_version}")
	return _pil_available


	def is_pytesseract_available():
	return _pytesseract_available


	def is_pytest_available():
	return _pytest_available


	def is_spacy_available():
	return _spacy_available


	def is_tensorflow_text_available():
	return is_tf_available() and _tensorflow_text_available


	def is_keras_nlp_available():
	return is_tensorflow_text_available() and _keras_nlp_available


	def is_in_notebook():
	try:
	# Test adapted from tqdm.autonotebook: https://github.com/tqdm/tqdm/blob/master/tqdm/autonotebook.py
	get_ipython = sys.modules["IPython"].get_ipython
	if "IPKernelApp" not in get_ipython().config:
	raise ImportError("console")
	if "VSCODE_PID" in os.environ:
	raise ImportError("vscode")
	if "DATABRICKS_RUNTIME_VERSION" in os.environ and os.environ["DATABRICKS_RUNTIME_VERSION"] < "11.0":
	# Databricks Runtime 11.0 and above uses IPython kernel by default so it should be compatible with Jupyter notebook
	# https://docs.microsoft.com/en-us/azure/databricks/notebooks/ipython-kernel
	raise ImportError("databricks")

	return importlib.util.find_spec("IPython") is not None
	except (AttributeError, ImportError, KeyError):
	return False


	def is_pytorch_quantization_available():
	return _pytorch_quantization_available


	def is_tensorflow_probability_available():
	return _tensorflow_probability_available


	def is_pandas_available():
	return _pandas_available


	def is_sagemaker_dp_enabled():
	# Get the sagemaker specific env variable.
	sagemaker_params = os.getenv("SM_FRAMEWORK_PARAMS", "{}")
	try:
	# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
	sagemaker_params = json.loads(sagemaker_params)
	if not sagemaker_params.get("sagemaker_distributed_dataparallel_enabled", False):
	return False
	except json.JSONDecodeError:
	return False
	# Lastly, check if the `smdistributed` module is present.
	return _smdistributed_available


	def is_sagemaker_mp_enabled():
	# Get the sagemaker specific mp parameters from smp_options variable.
	smp_options = os.getenv("SM_HP_MP_PARAMETERS", "{}")
	try:
	# Parse it and check the field "partitions" is included, it is required for model parallel.
	smp_options = json.loads(smp_options)
	if "partitions" not in smp_options:
	return False
	except json.JSONDecodeError:
	return False

	# Get the sagemaker specific framework parameters from mpi_options variable.
	mpi_options = os.getenv("SM_FRAMEWORK_PARAMS", "{}")
	try:
	# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
	mpi_options = json.loads(mpi_options)
	if not mpi_options.get("sagemaker_mpi_enabled", False):
	return False
	except json.JSONDecodeError:
	return False
	# Lastly, check if the `smdistributed` module is present.
	return _smdistributed_available


	def is_training_run_on_sagemaker():
	return "SAGEMAKER_JOB_NAME" in os.environ


	def is_soundfile_availble():
	return _soundfile_available


	def is_timm_available():
	return _timm_available


	def is_natten_available():
	return _natten_available


	def is_nltk_available():
	return _nltk_available


	def is_torchaudio_available():
	return _torchaudio_available


	def is_speech_available():
	# For now this depends on torchaudio but the exact dependency might evolve in the future.
	return _torchaudio_available


	def is_phonemizer_available():
	return _phonemizer_available


	def torch_only_method(fn):
	def wrapper(args, *kwargs):
	if not _torch_available:
	raise ImportError(
	"You need to install pytorch to use this method or class, "
	"or activate it with environment variables USE_TORCH=1 and USE_TF=0."
	)
	else:
	return fn(args, *kwargs)

	return wrapper


	def is_ccl_available():
	return _is_ccl_available


	def is_decord_available():
	return _decord_available


	def is_sudachi_available():
	return _sudachipy_available


	def is_jumanpp_available():
	return (importlib.util.find_spec("rhoknp") is not None) and (shutil.which("jumanpp") is not None)


	def is_cython_available():
	return importlib.util.find_spec("pyximport") is not None


	def is_jieba_available():
	return _jieba_available


	def is_jinja_available():
	return _jinja_available


	# docstyle-ignore
	CV2_IMPORT_ERROR = """
	{0} requires the OpenCV library but it was not found in your environment. You can install it with:
	```
	pip install opencv-python
	```
	Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	DATASETS_IMPORT_ERROR = """
	{0} requires the 🤗 Datasets library but it was not found in your environment. You can install it with:
	```
	pip install datasets
	```
	In a notebook or a colab, you can install it by executing a cell with
	```
	!pip install datasets
	```
	then restarting your kernel.

	Note that if you have a local folder named `datasets` or a local python file named `datasets.py` in your current
	working directory, python may try to import this instead of the 🤗 Datasets library. You should rename this folder or
	that python file if that's the case. Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	TOKENIZERS_IMPORT_ERROR = """
	{0} requires the 🤗 Tokenizers library but it was not found in your environment. You can install it with:
	```
	pip install tokenizers
	```
	In a notebook or a colab, you can install it by executing a cell with
	```
	!pip install tokenizers
	```
	Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	SENTENCEPIECE_IMPORT_ERROR = """
	{0} requires the SentencePiece library but it was not found in your environment. Checkout the instructions on the
	installation page of its repo: https://github.com/google/sentencepiece#installation and follow the ones
	that match your environment. Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	PROTOBUF_IMPORT_ERROR = """
	{0} requires the protobuf library but it was not found in your environment. Checkout the instructions on the
	installation page of its repo: https://github.com/protocolbuffers/protobuf/tree/master/python#installation and follow the ones
	that match your environment. Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	FAISS_IMPORT_ERROR = """
	{0} requires the faiss library but it was not found in your environment. Checkout the instructions on the
	installation page of its repo: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md and follow the ones
	that match your environment. Please note that you may need to restart your runtime after installation.
	"""


	# 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.
	Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	TORCHVISION_IMPORT_ERROR = """
	{0} requires the Torchvision 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.
	Please note that you may need to restart your runtime after installation.
	"""

	# docstyle-ignore
	PYTORCH_IMPORT_ERROR_WITH_TF = """
	{0} requires the PyTorch library but it was not found in your environment.
	However, we were able to find a TensorFlow installation. TensorFlow classes begin
	with "TF", but are otherwise identically named to our PyTorch classes. This
	means that the TF equivalent of the class you tried to import would be "TF{0}".
	If you want to use TensorFlow, please use TF classes instead!

	If you really do want to use PyTorch please go to
	https://pytorch.org/get-started/locally/ and follow the instructions that
	match your environment.
	"""

	# docstyle-ignore
	TF_IMPORT_ERROR_WITH_PYTORCH = """
	{0} requires the TensorFlow library but it was not found in your environment.
	However, we were able to find a PyTorch installation. PyTorch classes do not begin
	with "TF", but are otherwise identically named to our TF classes.
	If you want to use PyTorch, please use those classes instead!

	If you really do want to use TensorFlow, please follow the instructions on the
	installation page https://www.tensorflow.org/install that match your environment.
	"""

	# 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
	SKLEARN_IMPORT_ERROR = """
	{0} requires the scikit-learn library but it was not found in your environment. You can install it with:
	```
	pip install -U scikit-learn
	```
	In a notebook or a colab, you can install it by executing a cell with
	```
	!pip install -U scikit-learn
	```
	Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	TENSORFLOW_IMPORT_ERROR = """
	{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the
	installation page: https://www.tensorflow.org/install and follow the ones that match your environment.
	Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	DETECTRON2_IMPORT_ERROR = """
	{0} requires the detectron2 library but it was not found in your environment. Checkout the instructions on the
	installation page: https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md and follow the ones
	that match your environment. Please note that you may need to restart your runtime after installation.
	"""


	# 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.
	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.
	"""

	LEVENSHTEIN_IMPORT_ERROR = """
	{0} requires the python-Levenshtein library but it was not found in your environment. You can install it with pip: `pip
	install python-Levenshtein`. Please note that you may need to restart your runtime after installation.
	"""

	# docstyle-ignore
	PYTORCH_QUANTIZATION_IMPORT_ERROR = """
	{0} requires the pytorch-quantization library but it was not found in your environment. You can install it with pip:
	`pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com`
	Please note that you may need to restart your runtime after installation.
	"""

	# docstyle-ignore
	TENSORFLOW_PROBABILITY_IMPORT_ERROR = """
	{0} requires the tensorflow_probability library but it was not found in your environment. You can install it with pip as
	explained here: https://github.com/tensorflow/probability. Please note that you may need to restart your runtime after installation.
	"""

	# docstyle-ignore
	TENSORFLOW_TEXT_IMPORT_ERROR = """
	{0} requires the tensorflow_text library but it was not found in your environment. You can install it with pip as
	explained here: https://www.tensorflow.org/text/guide/tf_text_intro.
	Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	PANDAS_IMPORT_ERROR = """
	{0} requires the pandas library but it was not found in your environment. You can install it with pip as
	explained here: https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html.
	Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	PHONEMIZER_IMPORT_ERROR = """
	{0} requires the phonemizer library but it was not found in your environment. You can install it with pip:
	`pip install phonemizer`. Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	SACREMOSES_IMPORT_ERROR = """
	{0} requires the sacremoses library but it was not found in your environment. You can install it with pip:
	`pip install sacremoses`. Please note that you may need to restart your runtime after installation.
	"""


	# 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`. Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	SPEECH_IMPORT_ERROR = """
	{0} requires the torchaudio library but it was not found in your environment. You can install it with pip:
	`pip install torchaudio`. Please note that you may need to restart your runtime after installation.
	"""

	# docstyle-ignore
	TIMM_IMPORT_ERROR = """
	{0} requires the timm library but it was not found in your environment. You can install it with pip:
	`pip install timm`. Please note that you may need to restart your runtime after installation.
	"""

	# docstyle-ignore
	NATTEN_IMPORT_ERROR = """
	{0} requires the natten library but it was not found in your environment. You can install it by referring to:
	shi-labs.com/natten . You can also install it with pip (may take longer to build):
	`pip install natten`. Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	NLTK_IMPORT_ERROR = """
	{0} requires the NLTK library but it was not found in your environment. You can install it by referring to:
	https://www.nltk.org/install.html. Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	VISION_IMPORT_ERROR = """
	{0} requires the PIL library but it was not found in your environment. You can install it with pip:
	`pip install pillow`. Please note that you may need to restart your runtime after installation.
	"""


	# docstyle-ignore
	PYTESSERACT_IMPORT_ERROR = """
	{0} requires the PyTesseract library but it was not found in your environment. You can install it with pip:
	`pip install pytesseract`. Please note that you may need to restart your runtime after installation.
	"""

	# docstyle-ignore
	PYCTCDECODE_IMPORT_ERROR = """
	{0} requires the pyctcdecode library but it was not found in your environment. You can install it with pip:
	`pip install pyctcdecode`. Please note that you may need to restart your runtime after installation.
	"""

	# docstyle-ignore
	ACCELERATE_IMPORT_ERROR = """
	{0} requires the accelerate library but it was not found in your environment. You can install it with pip:
	`pip install accelerate`. Please note that you may need to restart your runtime after installation.
	"""

	# docstyle-ignore
	CCL_IMPORT_ERROR = """
	{0} requires the torch ccl library but it was not found in your environment. You can install it with pip:
	`pip install oneccl_bind_pt -f https://developer.intel.com/ipex-whl-stable`
	Please note that you may need to restart your runtime after installation.
	"""

	# docstyle-ignore
	ESSENTIA_IMPORT_ERROR = """
	{0} requires essentia library. But that was not found in your environment. You can install them with pip:
	`pip install essentia==2.1b6.dev1034`
	Please note that you may need to restart your runtime after installation.
	"""

	# docstyle-ignore
	LIBROSA_IMPORT_ERROR = """
	{0} requires thes librosa library. But that was not found in your environment. You can install them with pip:
	`pip install librosa`
	Please note that you may need to restart your runtime after installation.
	"""

	# docstyle-ignore
	PRETTY_MIDI_IMPORT_ERROR = """
	{0} requires thes pretty_midi library. But that was not found in your environment. You can install them with pip:
	`pip install pretty_midi`
	Please note that you may need to restart your runtime after installation.
	"""

	DECORD_IMPORT_ERROR = """
	{0} requires the decord library but it was not found in your environment. You can install it with pip: `pip install
	decord`. Please note that you may need to restart your runtime after installation.
	"""

	CYTHON_IMPORT_ERROR = """
	{0} requires the Cython library but it was not found in your environment. You can install it with pip: `pip install
	Cython`. Please note that you may need to restart your runtime after installation.
	"""

	JIEBA_IMPORT_ERROR = """
	{0} requires the jieba library but it was not found in your environment. You can install it with pip: `pip install
	jieba`. Please note that you may need to restart your runtime after installation.
	"""

	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`. Please note that you may need to restart your runtime after installation.
	"""

	JINJA_IMPORT_ERROR = """
	{0} requires the jinja library but it was not found in your environment. You can install it with pip: `pip install
	jinja2`. Please note that you may need to restart your runtime after installation.
	"""

	BACKENDS_MAPPING = OrderedDict(
	[
	("bs4", (is_bs4_available, BS4_IMPORT_ERROR)),
	("cv2", (is_cv2_available, CV2_IMPORT_ERROR)),
	("datasets", (is_datasets_available, DATASETS_IMPORT_ERROR)),
	("detectron2", (is_detectron2_available, DETECTRON2_IMPORT_ERROR)),
	("essentia", (is_essentia_available, ESSENTIA_IMPORT_ERROR)),
	("faiss", (is_faiss_available, FAISS_IMPORT_ERROR)),
	("flax", (is_flax_available, FLAX_IMPORT_ERROR)),
	("ftfy", (is_ftfy_available, FTFY_IMPORT_ERROR)),
	("pandas", (is_pandas_available, PANDAS_IMPORT_ERROR)),
	("phonemizer", (is_phonemizer_available, PHONEMIZER_IMPORT_ERROR)),
	("pretty_midi", (is_pretty_midi_available, PRETTY_MIDI_IMPORT_ERROR)),
	("levenshtein", (is_levenshtein_available, LEVENSHTEIN_IMPORT_ERROR)),
	("librosa", (is_librosa_available, LIBROSA_IMPORT_ERROR)),
	("protobuf", (is_protobuf_available, PROTOBUF_IMPORT_ERROR)),
	("pyctcdecode", (is_pyctcdecode_available, PYCTCDECODE_IMPORT_ERROR)),
	("pytesseract", (is_pytesseract_available, PYTESSERACT_IMPORT_ERROR)),
	("sacremoses", (is_sacremoses_available, SACREMOSES_IMPORT_ERROR)),
	("pytorch_quantization", (is_pytorch_quantization_available, PYTORCH_QUANTIZATION_IMPORT_ERROR)),
	("sentencepiece", (is_sentencepiece_available, SENTENCEPIECE_IMPORT_ERROR)),
	("sklearn", (is_sklearn_available, SKLEARN_IMPORT_ERROR)),
	("speech", (is_speech_available, SPEECH_IMPORT_ERROR)),
	("tensorflow_probability", (is_tensorflow_probability_available, TENSORFLOW_PROBABILITY_IMPORT_ERROR)),
	("tf", (is_tf_available, TENSORFLOW_IMPORT_ERROR)),
	("tensorflow_text", (is_tensorflow_text_available, TENSORFLOW_TEXT_IMPORT_ERROR)),
	("timm", (is_timm_available, TIMM_IMPORT_ERROR)),
	("natten", (is_natten_available, NATTEN_IMPORT_ERROR)),
	("nltk", (is_nltk_available, NLTK_IMPORT_ERROR)),
	("tokenizers", (is_tokenizers_available, TOKENIZERS_IMPORT_ERROR)),
	("torch", (is_torch_available, PYTORCH_IMPORT_ERROR)),
	("torchvision", (is_torchvision_available, TORCHVISION_IMPORT_ERROR)),
	("vision", (is_vision_available, VISION_IMPORT_ERROR)),
	("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)),
	("accelerate", (is_accelerate_available, ACCELERATE_IMPORT_ERROR)),
	("oneccl_bind_pt", (is_ccl_available, CCL_IMPORT_ERROR)),
	("decord", (is_decord_available, DECORD_IMPORT_ERROR)),
	("cython", (is_cython_available, CYTHON_IMPORT_ERROR)),
	("jieba", (is_jieba_available, JIEBA_IMPORT_ERROR)),
	("peft", (is_peft_available, PEFT_IMPORT_ERROR)),
	("jinja", (is_jinja_available, JINJA_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__

	# Raise an error for users who might not realize that classes without "TF" are torch-only
	if "torch" in backends and "tf" not in backends and not is_torch_available() and is_tf_available():
	raise ImportError(PYTORCH_IMPORT_ERROR_WITH_TF.format(name))

	# Raise the inverse error for PyTorch users trying to load TF classes
	if "tf" in backends and "torch" not in backends and is_torch_available() and not is_tf_available():
	raise ImportError(TF_IMPORT_ERROR_WITH_PYTORCH.format(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 __getattribute__(cls, key):
	if key.startswith("_") and key != "_from_config":
	return super().__getattribute__(key)
	requires_backends(cls, cls._backends)


	def is_torch_fx_proxy(x):
	if is_torch_fx_available():
	import torch.fx

	return isinstance(x, torch.fx.Proxy)
	return False


	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))


	class OptionalDependencyNotAvailable(BaseException):
	"""Internally used error class for signalling an optional dependency was not found."""


	def direct_transformers_import(path: str, file="__init__.py") -> ModuleType:
	"""Imports transformers directly

	Args:
	path (`str`): The path to the source file
	file (`str`, optional): The file to join with the path. Defaults to "__init__.py".

	Returns:
	`ModuleType`: The resulting imported module
	"""
	name = "transformers"
	location = os.path.join(path, file)
	spec = importlib.util.spec_from_file_location(name, location, submodule_search_locations=[path])
	module = importlib.util.module_from_spec(spec)
	spec.loader.exec_module(module)
	module = sys.modules[name]
	return module