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# Copyright (C) 2021-2024, Mindee.
# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
# Adapted from https://github.com/huggingface/transformers/blob/master/src/transformers/file_utils.py
import importlib.metadata
import importlib.util
import logging
import os
from typing import Optional
CLASS_NAME: str = "words"
__all__ = ["is_tf_available", "is_torch_available", "requires_package", "CLASS_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()
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")
logging.info(f"PyTorch version {_torch_version} available.")
except importlib.metadata.PackageNotFoundError: # pragma: no cover
_torch_available = False
else: # pragma: no cover
logging.info("Disabling PyTorch because USE_TF is set")
_torch_available = False
if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
_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",
"tensorflow-rocm",
"tensorflow-macos",
)
_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 int(_tf_version.split(".")[0]) < 2: # type: ignore[union-attr] # pragma: no cover
logging.info(f"TensorFlow found but with version {_tf_version}. DocTR requires version 2 minimum.")
_tf_available = False
else:
logging.info(f"TensorFlow version {_tf_version} available.")
else: # pragma: no cover
logging.info("Disabling Tensorflow because USE_TORCH is set")
_tf_available = False
if not _torch_available and not _tf_available: # pragma: no cover
raise ModuleNotFoundError(
"DocTR requires either TensorFlow or PyTorch to be installed. Please ensure one of them"
" is installed and that either USE_TF or USE_TORCH is enabled."
)
def requires_package(name: str, extra_message: Optional[str] = None) -> None: # pragma: no cover
"""
package requirement helper
Args:
----
name: name of the package
extra_message: additional message to display if the package is not found
"""
try:
_pkg_version = importlib.metadata.version(name)
logging.info(f"{name} version {_pkg_version} available.")
except importlib.metadata.PackageNotFoundError:
raise ImportError(
f"\n\n{extra_message if extra_message is not None else ''} "
f"\nPlease install it with the following command: pip install {name}\n"
)
def is_torch_available():
"""Whether PyTorch is installed."""
return _torch_available
def is_tf_available():
"""Whether TensorFlow is installed."""
return _tf_available
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