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import_utils.py
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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