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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 | |