third_party/yoloworld/utils/__init__.py

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

import contextlib
import importlib.metadata
import inspect
import json
import logging.config
import os
import platform
import re
import subprocess
import sys
import threading
import time
import uuid
import warnings
from pathlib import Path
from threading import Lock
from types import SimpleNamespace
from typing import Union
from urllib.parse import unquote

import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
import tqdm
import yaml

from ultralytics import __version__
from ultralytics.utils.patches import imread, imshow, imwrite, torch_load, torch_save  # for patches

# PyTorch Multi-GPU DDP Constants
RANK = int(os.getenv("RANK", -1))
LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1))  # https://pytorch.org/docs/stable/elastic/run.html

# Other Constants
ARGV = sys.argv or ["", ""]  # sometimes sys.argv = []
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]  # YOLO
ASSETS = ROOT / "assets"  # default images
ASSETS_URL = "https://github.com/ultralytics/assets/releases/download/v0.0.0"  # assets GitHub URL
DEFAULT_CFG_PATH = ROOT / "cfg/default.yaml"
DEFAULT_SOL_CFG_PATH = ROOT / "cfg/solutions/default.yaml"  # Ultralytics solutions yaml path
NUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number of YOLO multiprocessing threads
AUTOINSTALL = str(os.getenv("YOLO_AUTOINSTALL", True)).lower() == "true"  # global auto-install mode
VERBOSE = str(os.getenv("YOLO_VERBOSE", True)).lower() == "true"  # global verbose mode
TQDM_BAR_FORMAT = "{l_bar}{bar:10}{r_bar}" if VERBOSE else None  # tqdm bar format
LOGGING_NAME = "ultralytics"
MACOS, LINUX, WINDOWS = (platform.system() == x for x in ["Darwin", "Linux", "Windows"])  # environment booleans
MACOS_VERSION = platform.mac_ver()[0] if MACOS else None
ARM64 = platform.machine() in {"arm64", "aarch64"}  # ARM64 booleans
PYTHON_VERSION = platform.python_version()
TORCH_VERSION = torch.__version__
TORCHVISION_VERSION = importlib.metadata.version("torchvision")  # faster than importing torchvision
IS_VSCODE = os.environ.get("TERM_PROGRAM", False) == "vscode"
RKNN_CHIPS = frozenset(
    {
        "rk3588",
        "rk3576",
        "rk3566",
        "rk3568",
        "rk3562",
        "rv1103",
        "rv1106",
        "rv1103b",
        "rv1106b",
        "rk2118",
    }
)  # Rockchip processors available for export
HELP_MSG = """
    Examples for running Ultralytics:

    1. Install the ultralytics package:

        pip install ultralytics

    2. Use the Python SDK:

        from ultralytics import YOLO

        # Load a model
        model = YOLO("yolo11n.yaml")  # build a new model from scratch
        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)

        # Use the model
        results = model.train(data="coco8.yaml", epochs=3)  # train the model
        results = model.val()  # evaluate model performance on the validation set
        results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
        success = model.export(format="onnx")  # export the model to ONNX format

    3. Use the command line interface (CLI):

        Ultralytics 'yolo' CLI commands use the following syntax:

            yolo TASK MODE ARGS

            Where   TASK (optional) is one of [detect, segment, classify, pose, obb]
                    MODE (required) is one of [train, val, predict, export, track, benchmark]
                    ARGS (optional) are any number of custom "arg=value" pairs like "imgsz=320" that override defaults.
                        See all ARGS at https://docs.ultralytics.com/usage/cfg or with "yolo cfg"

        - Train a detection model for 10 epochs with an initial learning_rate of 0.01
            yolo detect train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01

        - Predict a YouTube video using a pretrained segmentation model at image size 320:
            yolo segment predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320

        - Val a pretrained detection model at batch-size 1 and image size 640:
            yolo detect val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640

        - Export a YOLO11n classification model to ONNX format at image size 224 by 128 (no TASK required)
            yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128

        - Run special commands:
            yolo help
            yolo checks
            yolo version
            yolo settings
            yolo copy-cfg
            yolo cfg

    Docs: https://docs.ultralytics.com
    Community: https://community.ultralytics.com
    GitHub: https://github.com/ultralytics/ultralytics
    """

# Settings and Environment Variables
torch.set_printoptions(linewidth=320, precision=4, profile="default")
np.set_printoptions(linewidth=320, formatter=dict(float_kind="{:11.5g}".format))  # format short g, %precision=5
cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
os.environ["NUMEXPR_MAX_THREADS"] = str(NUM_THREADS)  # NumExpr max threads
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"  # suppress verbose TF compiler warnings in Colab
os.environ["TORCH_CPP_LOG_LEVEL"] = "ERROR"  # suppress "NNPACK.cpp could not initialize NNPACK" warnings
os.environ["KINETO_LOG_LEVEL"] = "5"  # suppress verbose PyTorch profiler output when computing FLOPs

if TQDM_RICH := str(os.getenv("YOLO_TQDM_RICH", False)).lower() == "true":
    from tqdm import rich


class TQDM(rich.tqdm if TQDM_RICH else tqdm.tqdm):
    """
    A custom TQDM progress bar class that extends the original tqdm functionality.

    This class modifies the behavior of the original tqdm progress bar based on global settings and provides
    additional customization options.

    Attributes:
        disable (bool): Whether to disable the progress bar. Determined by the global VERBOSE setting and
            any passed 'disable' argument.
        bar_format (str): The format string for the progress bar. Uses the global TQDM_BAR_FORMAT if not
            explicitly set.

    Methods:
        __init__: Initializes the TQDM object with custom settings.

    Examples:
        >>> from ultralytics.utils import TQDM
        >>> for i in TQDM(range(100)):
        ...     # Your processing code here
        ...     pass
    """

    def __init__(self, *args, **kwargs):
        """
        Initializes a custom TQDM progress bar.

        This class extends the original tqdm class to provide customized behavior for Ultralytics projects.

        Args:
            *args (Any): Variable length argument list to be passed to the original tqdm constructor.
            **kwargs (Any): Arbitrary keyword arguments to be passed to the original tqdm constructor.

        Notes:
            - The progress bar is disabled if VERBOSE is False or if 'disable' is explicitly set to True in kwargs.
            - The default bar format is set to TQDM_BAR_FORMAT unless overridden in kwargs.

        Examples:
            >>> from ultralytics.utils import TQDM
            >>> for i in TQDM(range(100)):
            ...     # Your code here
            ...     pass
        """
        warnings.filterwarnings("ignore", category=tqdm.TqdmExperimentalWarning)  # suppress tqdm.rich warning
        kwargs["disable"] = not VERBOSE or kwargs.get("disable", False)
        kwargs.setdefault("bar_format", TQDM_BAR_FORMAT)  # override default value if passed
        super().__init__(*args, **kwargs)


class SimpleClass:
    """
    A simple base class for creating objects with string representations of their attributes.

    This class provides a foundation for creating objects that can be easily printed or represented as strings,
    showing all their non-callable attributes. It's useful for debugging and introspection of object states.

    Methods:
        __str__: Returns a human-readable string representation of the object.
        __repr__: Returns a machine-readable string representation of the object.
        __getattr__: Provides a custom attribute access error message with helpful information.

    Examples:
        >>> class MyClass(SimpleClass):
        ...     def __init__(self):
        ...         self.x = 10
        ...         self.y = "hello"
        >>> obj = MyClass()
        >>> print(obj)
        __main__.MyClass object with attributes:

        x: 10
        y: 'hello'

    Notes:
        - This class is designed to be subclassed. It provides a convenient way to inspect object attributes.
        - The string representation includes the module and class name of the object.
        - Callable attributes and attributes starting with an underscore are excluded from the string representation.
    """

    def __str__(self):
        """Return a human-readable string representation of the object."""
        attr = []
        for a in dir(self):
            v = getattr(self, a)
            if not callable(v) and not a.startswith("_"):
                if isinstance(v, SimpleClass):
                    # Display only the module and class name for subclasses
                    s = f"{a}: {v.__module__}.{v.__class__.__name__} object"
                else:
                    s = f"{a}: {repr(v)}"
                attr.append(s)
        return f"{self.__module__}.{self.__class__.__name__} object with attributes:\n\n" + "\n".join(attr)

    def __repr__(self):
        """Return a machine-readable string representation of the object."""
        return self.__str__()

    def __getattr__(self, attr):
        """Custom attribute access error message with helpful information."""
        name = self.__class__.__name__
        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")


class IterableSimpleNamespace(SimpleNamespace):
    """
    An iterable SimpleNamespace class that provides enhanced functionality for attribute access and iteration.

    This class extends the SimpleNamespace class with additional methods for iteration, string representation,
    and attribute access. It is designed to be used as a convenient container for storing and accessing
    configuration parameters.

    Methods:
        __iter__: Returns an iterator of key-value pairs from the namespace's attributes.
        __str__: Returns a human-readable string representation of the object.
        __getattr__: Provides a custom attribute access error message with helpful information.
        get: Retrieves the value of a specified key, or a default value if the key doesn't exist.

    Examples:
        >>> cfg = IterableSimpleNamespace(a=1, b=2, c=3)
        >>> for k, v in cfg:
        ...     print(f"{k}: {v}")
        a: 1
        b: 2
        c: 3
        >>> print(cfg)
        a=1
        b=2
        c=3
        >>> cfg.get("b")
        2
        >>> cfg.get("d", "default")
        'default'

    Notes:
        This class is particularly useful for storing configuration parameters in a more accessible
        and iterable format compared to a standard dictionary.
    """

    def __iter__(self):
        """Return an iterator of key-value pairs from the namespace's attributes."""
        return iter(vars(self).items())

    def __str__(self):
        """Return a human-readable string representation of the object."""
        return "\n".join(f"{k}={v}" for k, v in vars(self).items())

    def __getattr__(self, attr):
        """Custom attribute access error message with helpful information."""
        name = self.__class__.__name__
        raise AttributeError(
            f"""
            '{name}' object has no attribute '{attr}'. This may be caused by a modified or out of date ultralytics
            'default.yaml' file.\nPlease update your code with 'pip install -U ultralytics' and if necessary replace
            {DEFAULT_CFG_PATH} with the latest version from
            https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/default.yaml
            """
        )

    def get(self, key, default=None):
        """Return the value of the specified key if it exists; otherwise, return the default value."""
        return getattr(self, key, default)


def plt_settings(rcparams=None, backend="Agg"):
    """
    Decorator to temporarily set rc parameters and the backend for a plotting function.

    Args:
        rcparams (dict, optional): Dictionary of rc parameters to set.
        backend (str, optional): Name of the backend to use. Defaults to 'Agg'.

    Returns:
        (Callable): Decorated function with temporarily set rc parameters and backend.

    Examples:
        >>> @plt_settings({"font.size": 12})
        >>> def plot_function():
        ...     plt.figure()
        ...     plt.plot([1, 2, 3])
        ...     plt.show()

        >>> with plt_settings({"font.size": 12}):
        ...     plt.figure()
        ...     plt.plot([1, 2, 3])
        ...     plt.show()
    """
    if rcparams is None:
        rcparams = {"font.size": 11}

    def decorator(func):
        """Decorator to apply temporary rc parameters and backend to a function."""

        def wrapper(*args, **kwargs):
            """Sets rc parameters and backend, calls the original function, and restores the settings."""
            original_backend = plt.get_backend()
            switch = backend.lower() != original_backend.lower()
            if switch:
                plt.close("all")  # auto-close()ing of figures upon backend switching is deprecated since 3.8
                plt.switch_backend(backend)

            # Plot with backend and always revert to original backend
            try:
                with plt.rc_context(rcparams):
                    result = func(*args, **kwargs)
            finally:
                if switch:
                    plt.close("all")
                    plt.switch_backend(original_backend)
            return result

        return wrapper

    return decorator


def set_logging(name="LOGGING_NAME", verbose=True):
    """
    Sets up logging with UTF-8 encoding and configurable verbosity.

    This function configures logging for the Ultralytics library, setting the appropriate logging level and
    formatter based on the verbosity flag and the current process rank. It handles special cases for Windows
    environments where UTF-8 encoding might not be the default.

    Args:
        name (str): Name of the logger. Defaults to "LOGGING_NAME".
        verbose (bool): Flag to set logging level to INFO if True, ERROR otherwise. Defaults to True.

    Returns:
        (logging.Logger): Configured logger object.

    Examples:
        >>> set_logging(name="ultralytics", verbose=True)
        >>> logger = logging.getLogger("ultralytics")
        >>> logger.info("This is an info message")

    Notes:
        - On Windows, this function attempts to reconfigure stdout to use UTF-8 encoding if possible.
        - If reconfiguration is not possible, it falls back to a custom formatter that handles non-UTF-8 environments.
        - The function sets up a StreamHandler with the appropriate formatter and level.
        - The logger's propagate flag is set to False to prevent duplicate logging in parent loggers.
    """
    level = logging.INFO if verbose and RANK in {-1, 0} else logging.ERROR  # rank in world for Multi-GPU trainings

    # Configure the console (stdout) encoding to UTF-8, with checks for compatibility
    formatter = logging.Formatter("%(message)s")  # Default formatter
    if WINDOWS and hasattr(sys.stdout, "encoding") and sys.stdout.encoding != "utf-8":

        class CustomFormatter(logging.Formatter):
            def format(self, record):
                """Format log records with UTF-8 encoding for Windows compatibility."""
                return emojis(super().format(record))

        try:
            # Attempt to reconfigure stdout to use UTF-8 encoding if possible
            if hasattr(sys.stdout, "reconfigure"):
                sys.stdout.reconfigure(encoding="utf-8")
            # For environments where reconfigure is not available, wrap stdout in a TextIOWrapper
            elif hasattr(sys.stdout, "buffer"):
                import io

                sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8")
            else:
                formatter = CustomFormatter("%(message)s")
        except Exception as e:
            print(f"Creating custom formatter for non UTF-8 environments due to {e}")
            formatter = CustomFormatter("%(message)s")

    # Create and configure the StreamHandler with the appropriate formatter and level
    stream_handler = logging.StreamHandler(sys.stdout)
    stream_handler.setFormatter(formatter)
    stream_handler.setLevel(level)

    # Set up the logger
    logger = logging.getLogger(name)
    logger.setLevel(level)
    logger.addHandler(stream_handler)
    logger.propagate = False
    return logger


# Set logger
LOGGER = set_logging(LOGGING_NAME, verbose=VERBOSE)  # define globally (used in train.py, val.py, predict.py, etc.)
for logger in "sentry_sdk", "urllib3.connectionpool":
    logging.getLogger(logger).setLevel(logging.CRITICAL + 1)


def emojis(string=""):
    """Return platform-dependent emoji-safe version of string."""
    return string.encode().decode("ascii", "ignore") if WINDOWS else string


class ThreadingLocked:
    """
    A decorator class for ensuring thread-safe execution of a function or method.

    This class can be used as a decorator to make sure that if the decorated function is called from multiple threads,
    only one thread at a time will be able to execute the function.

    Attributes:
        lock (threading.Lock): A lock object used to manage access to the decorated function.

    Examples:
        >>> from ultralytics.utils import ThreadingLocked
        >>> @ThreadingLocked()
        >>> def my_function():
        ...    # Your code here
    """

    def __init__(self):
        """Initialize the decorator class with a threading lock."""
        self.lock = threading.Lock()

    def __call__(self, f):
        """Run thread-safe execution of function or method."""
        from functools import wraps

        @wraps(f)
        def decorated(*args, **kwargs):
            """Applies thread-safety to the decorated function or method."""
            with self.lock:
                return f(*args, **kwargs)

        return decorated


def yaml_save(file="data.yaml", data=None, header=""):
    """
    Save YAML data to a file.

    Args:
        file (str, optional): File name. Default is 'data.yaml'.
        data (dict): Data to save in YAML format.
        header (str, optional): YAML header to add.

    Returns:
        (None): Data is saved to the specified file.
    """
    if data is None:
        data = {}
    file = Path(file)
    if not file.parent.exists():
        # Create parent directories if they don't exist
        file.parent.mkdir(parents=True, exist_ok=True)

    # Convert Path objects to strings
    valid_types = int, float, str, bool, list, tuple, dict, type(None)
    for k, v in data.items():
        if not isinstance(v, valid_types):
            data[k] = str(v)

    # Dump data to file in YAML format
    with open(file, "w", errors="ignore", encoding="utf-8") as f:
        if header:
            f.write(header)
        yaml.safe_dump(data, f, sort_keys=False, allow_unicode=True)


def yaml_load(file="data.yaml", append_filename=False):
    """
    Load YAML data from a file.

    Args:
        file (str, optional): File name. Default is 'data.yaml'.
        append_filename (bool): Add the YAML filename to the YAML dictionary. Default is False.

    Returns:
        (dict): YAML data and file name.
    """
    assert Path(file).suffix in {".yaml", ".yml"}, f"Attempting to load non-YAML file {file} with yaml_load()"
    with open(file, errors="ignore", encoding="utf-8") as f:
        s = f.read()  # string

        # Remove special characters
        if not s.isprintable():
            s = re.sub(r"[^\x09\x0A\x0D\x20-\x7E\x85\xA0-\uD7FF\uE000-\uFFFD\U00010000-\U0010ffff]+", "", s)

        # Add YAML filename to dict and return
        data = yaml.safe_load(s) or {}  # always return a dict (yaml.safe_load() may return None for empty files)
        if append_filename:
            data["yaml_file"] = str(file)
        return data


def yaml_print(yaml_file: Union[str, Path, dict]) -> None:
    """
    Pretty prints a YAML file or a YAML-formatted dictionary.

    Args:
        yaml_file: The file path of the YAML file or a YAML-formatted dictionary.

    Returns:
        (None)
    """
    yaml_dict = yaml_load(yaml_file) if isinstance(yaml_file, (str, Path)) else yaml_file
    dump = yaml.dump(yaml_dict, sort_keys=False, allow_unicode=True, width=float("inf"))
    LOGGER.info(f"Printing '{colorstr('bold', 'black', yaml_file)}'\n\n{dump}")


# Default configuration
DEFAULT_CFG_DICT = yaml_load(DEFAULT_CFG_PATH)
DEFAULT_SOL_DICT = yaml_load(DEFAULT_SOL_CFG_PATH)  # Ultralytics solutions configuration
for k, v in DEFAULT_CFG_DICT.items():
    if isinstance(v, str) and v.lower() == "none":
        DEFAULT_CFG_DICT[k] = None
DEFAULT_CFG_KEYS = DEFAULT_CFG_DICT.keys()
DEFAULT_CFG = IterableSimpleNamespace(**DEFAULT_CFG_DICT)


def read_device_model() -> str:
    """
    Reads the device model information from the system and caches it for quick access.

    Returns:
        (str): Kernel release information.
    """
    return platform.release().lower()


def is_ubuntu() -> bool:
    """
    Check if the OS is Ubuntu.

    Returns:
        (bool): True if OS is Ubuntu, False otherwise.
    """
    try:
        with open("/etc/os-release") as f:
            return "ID=ubuntu" in f.read()
    except FileNotFoundError:
        return False


def is_colab():
    """
    Check if the current script is running inside a Google Colab notebook.

    Returns:
        (bool): True if running inside a Colab notebook, False otherwise.
    """
    return "COLAB_RELEASE_TAG" in os.environ or "COLAB_BACKEND_VERSION" in os.environ


def is_kaggle():
    """
    Check if the current script is running inside a Kaggle kernel.

    Returns:
        (bool): True if running inside a Kaggle kernel, False otherwise.
    """
    return os.environ.get("PWD") == "/kaggle/working" and os.environ.get("KAGGLE_URL_BASE") == "https://www.kaggle.com"


def is_jupyter():
    """
    Check if the current script is running inside a Jupyter Notebook.

    Returns:
        (bool): True if running inside a Jupyter Notebook, False otherwise.

    Note:
        - Only works on Colab and Kaggle, other environments like Jupyterlab and Paperspace are not reliably detectable.
        - "get_ipython" in globals() method suffers false positives when IPython package installed manually.
    """
    return IS_COLAB or IS_KAGGLE


def is_runpod():
    """
    Check if the current script is running inside a RunPod container.

    Returns:
        (bool): True if running in RunPod, False otherwise.
    """
    return "RUNPOD_POD_ID" in os.environ


def is_docker() -> bool:
    """
    Determine if the script is running inside a Docker container.

    Returns:
        (bool): True if the script is running inside a Docker container, False otherwise.
    """
    try:
        with open("/proc/self/cgroup") as f:
            return "docker" in f.read()
    except Exception:
        return False


def is_raspberrypi() -> bool:
    """
    Determines if the Python environment is running on a Raspberry Pi.

    Returns:
        (bool): True if running on a Raspberry Pi, False otherwise.
    """
    return "rpi" in DEVICE_MODEL


def is_jetson() -> bool:
    """
    Determines if the Python environment is running on an NVIDIA Jetson device.

    Returns:
        (bool): True if running on an NVIDIA Jetson device, False otherwise.
    """
    return "tegra" in DEVICE_MODEL


def is_online() -> bool:
    """
    Check internet connectivity by attempting to connect to a known online host.

    Returns:
        (bool): True if connection is successful, False otherwise.
    """
    try:
        assert str(os.getenv("YOLO_OFFLINE", "")).lower() != "true"  # check if ENV var YOLO_OFFLINE="True"
        import socket

        for dns in ("1.1.1.1", "8.8.8.8"):  # check Cloudflare and Google DNS
            socket.create_connection(address=(dns, 80), timeout=2.0).close()
            return True
    except Exception:
        return False


def is_pip_package(filepath: str = __name__) -> bool:
    """
    Determines if the file at the given filepath is part of a pip package.

    Args:
        filepath (str): The filepath to check.

    Returns:
        (bool): True if the file is part of a pip package, False otherwise.
    """
    import importlib.util

    # Get the spec for the module
    spec = importlib.util.find_spec(filepath)

    # Return whether the spec is not None and the origin is not None (indicating it is a package)
    return spec is not None and spec.origin is not None


def is_dir_writeable(dir_path: Union[str, Path]) -> bool:
    """
    Check if a directory is writeable.

    Args:
        dir_path (str | Path): The path to the directory.

    Returns:
        (bool): True if the directory is writeable, False otherwise.
    """
    return os.access(str(dir_path), os.W_OK)


def is_pytest_running():
    """
    Determines whether pytest is currently running or not.

    Returns:
        (bool): True if pytest is running, False otherwise.
    """
    return ("PYTEST_CURRENT_TEST" in os.environ) or ("pytest" in sys.modules) or ("pytest" in Path(ARGV[0]).stem)


def is_github_action_running() -> bool:
    """
    Determine if the current environment is a GitHub Actions runner.

    Returns:
        (bool): True if the current environment is a GitHub Actions runner, False otherwise.
    """
    return "GITHUB_ACTIONS" in os.environ and "GITHUB_WORKFLOW" in os.environ and "RUNNER_OS" in os.environ


def get_git_dir():
    """
    Determines whether the current file is part of a git repository and if so, returns the repository root directory.

    Returns:
        (Path | None): Git root directory if found or None if not found.
    """
    for d in Path(__file__).parents:
        if (d / ".git").is_dir():
            return d


def is_git_dir():
    """
    Determines whether the current file is part of a git repository.

    Returns:
        (bool): True if current file is part of a git repository.
    """
    return GIT_DIR is not None


def get_git_origin_url():
    """
    Retrieves the origin URL of a git repository.

    Returns:
        (str | None): The origin URL of the git repository or None if not git directory.
    """
    if IS_GIT_DIR:
        try:
            origin = subprocess.check_output(["git", "config", "--get", "remote.origin.url"])
            return origin.decode().strip()
        except subprocess.CalledProcessError:
            return None


def get_git_branch():
    """
    Returns the current git branch name. If not in a git repository, returns None.

    Returns:
        (str | None): The current git branch name or None if not a git directory.
    """
    if IS_GIT_DIR:
        try:
            origin = subprocess.check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"])
            return origin.decode().strip()
        except subprocess.CalledProcessError:
            return None


def get_default_args(func):
    """
    Returns a dictionary of default arguments for a function.

    Args:
        func (callable): The function to inspect.

    Returns:
        (dict): A dictionary where each key is a parameter name, and each value is the default value of that parameter.
    """
    signature = inspect.signature(func)
    return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty}


def get_ubuntu_version():
    """
    Retrieve the Ubuntu version if the OS is Ubuntu.

    Returns:
        (str): Ubuntu version or None if not an Ubuntu OS.
    """
    if is_ubuntu():
        try:
            with open("/etc/os-release") as f:
                return re.search(r'VERSION_ID="(\d+\.\d+)"', f.read())[1]
        except (FileNotFoundError, AttributeError):
            return None


def get_user_config_dir(sub_dir="Ultralytics"):
    """
    Return the appropriate config directory based on the environment operating system.

    Args:
        sub_dir (str): The name of the subdirectory to create.

    Returns:
        (Path): The path to the user config directory.
    """
    if WINDOWS:
        path = Path.home() / "AppData" / "Roaming" / sub_dir
    elif MACOS:  # macOS
        path = Path.home() / "Library" / "Application Support" / sub_dir
    elif LINUX:
        path = Path.home() / ".config" / sub_dir
    else:
        raise ValueError(f"Unsupported operating system: {platform.system()}")

    # GCP and AWS lambda fix, only /tmp is writeable
    if not is_dir_writeable(path.parent):
        LOGGER.warning(
            f"WARNING ⚠️ user config directory '{path}' is not writeable, defaulting to '/tmp' or CWD."
            "Alternatively you can define a YOLO_CONFIG_DIR environment variable for this path."
        )
        path = Path("/tmp") / sub_dir if is_dir_writeable("/tmp") else Path().cwd() / sub_dir

    # Create the subdirectory if it does not exist
    path.mkdir(parents=True, exist_ok=True)

    return path


# Define constants (required below)
DEVICE_MODEL = read_device_model()  # is_jetson() and is_raspberrypi() depend on this constant
ONLINE = is_online()
IS_COLAB = is_colab()
IS_KAGGLE = is_kaggle()
IS_DOCKER = is_docker()
IS_JETSON = is_jetson()
IS_JUPYTER = is_jupyter()
IS_PIP_PACKAGE = is_pip_package()
IS_RASPBERRYPI = is_raspberrypi()
GIT_DIR = get_git_dir()
IS_GIT_DIR = is_git_dir()
USER_CONFIG_DIR = Path(os.getenv("YOLO_CONFIG_DIR") or get_user_config_dir())  # Ultralytics settings dir
SETTINGS_FILE = USER_CONFIG_DIR / "settings.json"


def colorstr(*input):
    r"""
    Colors a string based on the provided color and style arguments. Utilizes ANSI escape codes.
    See https://en.wikipedia.org/wiki/ANSI_escape_code for more details.

    This function can be called in two ways:
        - colorstr('color', 'style', 'your string')
        - colorstr('your string')

    In the second form, 'blue' and 'bold' will be applied by default.

    Args:
        *input (str | Path): A sequence of strings where the first n-1 strings are color and style arguments,
                      and the last string is the one to be colored.

    Supported Colors and Styles:
        Basic Colors: 'black', 'red', 'green', 'yellow', 'blue', 'magenta', 'cyan', 'white'
        Bright Colors: 'bright_black', 'bright_red', 'bright_green', 'bright_yellow',
                       'bright_blue', 'bright_magenta', 'bright_cyan', 'bright_white'
        Misc: 'end', 'bold', 'underline'

    Returns:
        (str): The input string wrapped with ANSI escape codes for the specified color and style.

    Examples:
        >>> colorstr("blue", "bold", "hello world")
        >>> "\033[34m\033[1mhello world\033[0m"
    """
    *args, string = input if len(input) > 1 else ("blue", "bold", input[0])  # color arguments, string
    colors = {
        "black": "\033[30m",  # basic colors
        "red": "\033[31m",
        "green": "\033[32m",
        "yellow": "\033[33m",
        "blue": "\033[34m",
        "magenta": "\033[35m",
        "cyan": "\033[36m",
        "white": "\033[37m",
        "bright_black": "\033[90m",  # bright colors
        "bright_red": "\033[91m",
        "bright_green": "\033[92m",
        "bright_yellow": "\033[93m",
        "bright_blue": "\033[94m",
        "bright_magenta": "\033[95m",
        "bright_cyan": "\033[96m",
        "bright_white": "\033[97m",
        "end": "\033[0m",  # misc
        "bold": "\033[1m",
        "underline": "\033[4m",
    }
    return "".join(colors[x] for x in args) + f"{string}" + colors["end"]


def remove_colorstr(input_string):
    """
    Removes ANSI escape codes from a string, effectively un-coloring it.

    Args:
        input_string (str): The string to remove color and style from.

    Returns:
        (str): A new string with all ANSI escape codes removed.

    Examples:
        >>> remove_colorstr(colorstr("blue", "bold", "hello world"))
        >>> "hello world"
    """
    ansi_escape = re.compile(r"\x1B\[[0-9;]*[A-Za-z]")
    return ansi_escape.sub("", input_string)


class TryExcept(contextlib.ContextDecorator):
    """
    Ultralytics TryExcept class. Use as @TryExcept() decorator or 'with TryExcept():' context manager.

    Examples:
        As a decorator:
        >>> @TryExcept(msg="Error occurred in func", verbose=True)
        >>> def func():
        >>> # Function logic here
        >>>     pass

        As a context manager:
        >>> with TryExcept(msg="Error occurred in block", verbose=True):
        >>> # Code block here
        >>>     pass
    """

    def __init__(self, msg="", verbose=True):
        """Initialize TryExcept class with optional message and verbosity settings."""
        self.msg = msg
        self.verbose = verbose

    def __enter__(self):
        """Executes when entering TryExcept context, initializes instance."""
        pass

    def __exit__(self, exc_type, value, traceback):
        """Defines behavior when exiting a 'with' block, prints error message if necessary."""
        if self.verbose and value:
            LOGGER.warning(f"{self.msg}{': ' if self.msg else ''}{value}")
        return True


class Retry(contextlib.ContextDecorator):
    """
    Retry class for function execution with exponential backoff.

    Can be used as a decorator to retry a function on exceptions, up to a specified number of times with an
    exponentially increasing delay between retries.

    Examples:
        Example usage as a decorator:
        >>> @Retry(times=3, delay=2)
        >>> def test_func():
        >>> # Replace with function logic that may raise exceptions
        >>>     return True
    """

    def __init__(self, times=3, delay=2):
        """Initialize Retry class with specified number of retries and delay."""
        self.times = times
        self.delay = delay
        self._attempts = 0

    def __call__(self, func):
        """Decorator implementation for Retry with exponential backoff."""

        def wrapped_func(*args, **kwargs):
            """Applies retries to the decorated function or method."""
            self._attempts = 0
            while self._attempts < self.times:
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    self._attempts += 1
                    print(f"Retry {self._attempts}/{self.times} failed: {e}")
                    if self._attempts >= self.times:
                        raise e
                    time.sleep(self.delay * (2**self._attempts))  # exponential backoff delay

        return wrapped_func


def threaded(func):
    """
    Multi-threads a target function by default and returns the thread or function result.

    This decorator provides flexible execution of the target function, either in a separate thread or synchronously.
    By default, the function runs in a thread, but this can be controlled via the 'threaded=False' keyword argument
    which is removed from kwargs before calling the function.

    Args:
        func (callable): The function to be potentially executed in a separate thread.

    Returns:
        (callable): A wrapper function that either returns a daemon thread or the direct function result.

    Examples:
        >>> @threaded
        ... def process_data(data):
        ...     return data
        >>>
        >>> thread = process_data(my_data)  # Runs in background thread
        >>> result = process_data(my_data, threaded=False)  # Runs synchronously, returns function result
    """

    def wrapper(*args, **kwargs):
        """Multi-threads a given function based on 'threaded' kwarg and returns the thread or function result."""
        if kwargs.pop("threaded", True):  # run in thread
            thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
            thread.start()
            return thread
        else:
            return func(*args, **kwargs)

    return wrapper


def set_sentry():
    """
    Initialize the Sentry SDK for error tracking and reporting.

    Only used if sentry_sdk package is installed and sync=True in settings. Run 'yolo settings' to see and update
    settings.

    Conditions required to send errors (ALL conditions must be met or no errors will be reported):
        - sentry_sdk package is installed
        - sync=True in YOLO settings
        - pytest is not running
        - running in a pip package installation
        - running in a non-git directory
        - running with rank -1 or 0
        - online environment
        - CLI used to run package (checked with 'yolo' as the name of the main CLI command)
    """
    if (
        not SETTINGS["sync"]
        or RANK not in {-1, 0}
        or Path(ARGV[0]).name != "yolo"
        or TESTS_RUNNING
        or not ONLINE
        or not IS_PIP_PACKAGE
        or IS_GIT_DIR
    ):
        return
    # If sentry_sdk package is not installed then return and do not use Sentry
    try:
        import sentry_sdk  # noqa
    except ImportError:
        return

    def before_send(event, hint):
        """
        Modify the event before sending it to Sentry based on specific exception types and messages.

        Args:
            event (dict): The event dictionary containing information about the error.
            hint (dict): A dictionary containing additional information about the error.

        Returns:
            dict: The modified event or None if the event should not be sent to Sentry.
        """
        if "exc_info" in hint:
            exc_type, exc_value, _ = hint["exc_info"]
            if exc_type in {KeyboardInterrupt, FileNotFoundError} or "out of memory" in str(exc_value):
                return None  # do not send event

        event["tags"] = {
            "sys_argv": ARGV[0],
            "sys_argv_name": Path(ARGV[0]).name,
            "install": "git" if IS_GIT_DIR else "pip" if IS_PIP_PACKAGE else "other",
            "os": ENVIRONMENT,
        }
        return event

    sentry_sdk.init(
        dsn="https://888e5a0778212e1d0314c37d4b9aae5d@o4504521589325824.ingest.us.sentry.io/4504521592406016",
        debug=False,
        auto_enabling_integrations=False,
        traces_sample_rate=1.0,
        release=__version__,
        environment="runpod" if is_runpod() else "production",
        before_send=before_send,
        ignore_errors=[KeyboardInterrupt, FileNotFoundError],
    )
    sentry_sdk.set_user({"id": SETTINGS["uuid"]})  # SHA-256 anonymized UUID hash


class JSONDict(dict):
    """
    A dictionary-like class that provides JSON persistence for its contents.

    This class extends the built-in dictionary to automatically save its contents to a JSON file whenever they are
    modified. It ensures thread-safe operations using a lock.

    Attributes:
        file_path (Path): The path to the JSON file used for persistence.
        lock (threading.Lock): A lock object to ensure thread-safe operations.

    Methods:
        _load: Loads the data from the JSON file into the dictionary.
        _save: Saves the current state of the dictionary to the JSON file.
        __setitem__: Stores a key-value pair and persists it to disk.
        __delitem__: Removes an item and updates the persistent storage.
        update: Updates the dictionary and persists changes.
        clear: Clears all entries and updates the persistent storage.

    Examples:
        >>> json_dict = JSONDict("data.json")
        >>> json_dict["key"] = "value"
        >>> print(json_dict["key"])
        value
        >>> del json_dict["key"]
        >>> json_dict.update({"new_key": "new_value"})
        >>> json_dict.clear()
    """

    def __init__(self, file_path: Union[str, Path] = "data.json"):
        """Initialize a JSONDict object with a specified file path for JSON persistence."""
        super().__init__()
        self.file_path = Path(file_path)
        self.lock = Lock()
        self._load()

    def _load(self):
        """Load the data from the JSON file into the dictionary."""
        try:
            if self.file_path.exists():
                with open(self.file_path) as f:
                    self.update(json.load(f))
        except json.JSONDecodeError:
            print(f"Error decoding JSON from {self.file_path}. Starting with an empty dictionary.")
        except Exception as e:
            print(f"Error reading from {self.file_path}: {e}")

    def _save(self):
        """Save the current state of the dictionary to the JSON file."""
        try:
            self.file_path.parent.mkdir(parents=True, exist_ok=True)
            with open(self.file_path, "w", encoding="utf-8") as f:
                json.dump(dict(self), f, indent=2, default=self._json_default)
        except Exception as e:
            print(f"Error writing to {self.file_path}: {e}")

    @staticmethod
    def _json_default(obj):
        """Handle JSON serialization of Path objects."""
        if isinstance(obj, Path):
            return str(obj)
        raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable")

    def __setitem__(self, key, value):
        """Store a key-value pair and persist to disk."""
        with self.lock:
            super().__setitem__(key, value)
            self._save()

    def __delitem__(self, key):
        """Remove an item and update the persistent storage."""
        with self.lock:
            super().__delitem__(key)
            self._save()

    def __str__(self):
        """Return a pretty-printed JSON string representation of the dictionary."""
        contents = json.dumps(dict(self), indent=2, ensure_ascii=False, default=self._json_default)
        return f'JSONDict("{self.file_path}"):\n{contents}'

    def update(self, *args, **kwargs):
        """Update the dictionary and persist changes."""
        with self.lock:
            super().update(*args, **kwargs)
            self._save()

    def clear(self):
        """Clear all entries and update the persistent storage."""
        with self.lock:
            super().clear()
            self._save()


class SettingsManager(JSONDict):
    """
    SettingsManager class for managing and persisting Ultralytics settings.

    This class extends JSONDict to provide JSON persistence for settings, ensuring thread-safe operations and default
    values. It validates settings on initialization and provides methods to update or reset settings.

    Attributes:
        file (Path): The path to the JSON file used for persistence.
        version (str): The version of the settings schema.
        defaults (dict): A dictionary containing default settings.
        help_msg (str): A help message for users on how to view and update settings.

    Methods:
        _validate_settings: Validates the current settings and resets if necessary.
        update: Updates settings, validating keys and types.
        reset: Resets the settings to default and saves them.

    Examples:
        Initialize and update settings:
        >>> settings = SettingsManager()
        >>> settings.update(runs_dir="/new/runs/dir")
        >>> print(settings["runs_dir"])
        /new/runs/dir
    """

    def __init__(self, file=SETTINGS_FILE, version="0.0.6"):
        """Initializes the SettingsManager with default settings and loads user settings."""
        import hashlib

        from ultralytics.utils.torch_utils import torch_distributed_zero_first

        root = GIT_DIR or Path()
        datasets_root = (root.parent if GIT_DIR and is_dir_writeable(root.parent) else root).resolve()

        self.file = Path(file)
        self.version = version
        self.defaults = {
            "settings_version": version,  # Settings schema version
            "datasets_dir": str(datasets_root / "datasets"),  # Datasets directory
            "weights_dir": str(root / "weights"),  # Model weights directory
            "runs_dir": str(root / "runs"),  # Experiment runs directory
            "uuid": hashlib.sha256(str(uuid.getnode()).encode()).hexdigest(),  # SHA-256 anonymized UUID hash
            "sync": True,  # Enable synchronization
            "api_key": "",  # Ultralytics API Key
            "openai_api_key": "",  # OpenAI API Key
            "clearml": True,  # ClearML integration
            "comet": True,  # Comet integration
            "dvc": True,  # DVC integration
            "hub": True,  # Ultralytics HUB integration
            "mlflow": True,  # MLflow integration
            "neptune": True,  # Neptune integration
            "raytune": True,  # Ray Tune integration
            "tensorboard": False,  # TensorBoard logging
            "wandb": False,  # Weights & Biases logging
            "vscode_msg": True,  # VSCode messaging
        }

        self.help_msg = (
            f"\nView Ultralytics Settings with 'yolo settings' or at '{self.file}'"
            "\nUpdate Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. "
            "For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings."
        )

        with torch_distributed_zero_first(RANK):
            super().__init__(self.file)

            if not self.file.exists() or not self:  # Check if file doesn't exist or is empty
                LOGGER.info(f"Creating new Ultralytics Settings v{version} file ✅ {self.help_msg}")
                self.reset()

            self._validate_settings()

    def _validate_settings(self):
        """Validate the current settings and reset if necessary."""
        correct_keys = frozenset(self.keys()) == frozenset(self.defaults.keys())
        correct_types = all(isinstance(self.get(k), type(v)) for k, v in self.defaults.items())
        correct_version = self.get("settings_version", "") == self.version

        if not (correct_keys and correct_types and correct_version):
            LOGGER.warning(
                "WARNING ⚠️ Ultralytics settings reset to default values. This may be due to a possible problem "
                f"with your settings or a recent ultralytics package update. {self.help_msg}"
            )
            self.reset()

        if self.get("datasets_dir") == self.get("runs_dir"):
            LOGGER.warning(
                f"WARNING ⚠️ Ultralytics setting 'datasets_dir: {self.get('datasets_dir')}' "
                f"must be different than 'runs_dir: {self.get('runs_dir')}'. "
                f"Please change one to avoid possible issues during training. {self.help_msg}"
            )

    def __setitem__(self, key, value):
        """Updates one key: value pair."""
        self.update({key: value})

    def update(self, *args, **kwargs):
        """Updates settings, validating keys and types."""
        for arg in args:
            if isinstance(arg, dict):
                kwargs.update(arg)
        for k, v in kwargs.items():
            if k not in self.defaults:
                raise KeyError(f"No Ultralytics setting '{k}'. {self.help_msg}")
            t = type(self.defaults[k])
            if not isinstance(v, t):
                raise TypeError(
                    f"Ultralytics setting '{k}' must be '{t.__name__}' type, not '{type(v).__name__}'. {self.help_msg}"
                )
        super().update(*args, **kwargs)

    def reset(self):
        """Resets the settings to default and saves them."""
        self.clear()
        self.update(self.defaults)


def deprecation_warn(arg, new_arg=None):
    """Issue a deprecation warning when a deprecated argument is used, suggesting an updated argument."""
    msg = f"WARNING ⚠️ '{arg}' is deprecated and will be removed in in the future."
    if new_arg is not None:
        msg += f" Use '{new_arg}' instead."
    LOGGER.warning(msg)


def clean_url(url):
    """Strip auth from URL, i.e. https://url.com/file.txt?auth -> https://url.com/file.txt."""
    url = Path(url).as_posix().replace(":/", "://")  # Pathlib turns :// -> :/, as_posix() for Windows
    return unquote(url).split("?")[0]  # '%2F' to '/', split https://url.com/file.txt?auth


def url2file(url):
    """Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt."""
    return Path(clean_url(url)).name


def vscode_msg(ext="ultralytics.ultralytics-snippets") -> str:
    """Display a message to install Ultralytics-Snippets for VS Code if not already installed."""
    path = (USER_CONFIG_DIR.parents[2] if WINDOWS else USER_CONFIG_DIR.parents[1]) / ".vscode/extensions"
    obs_file = path / ".obsolete"  # file tracks uninstalled extensions, while source directory remains
    installed = any(path.glob(f"{ext}*")) and ext not in (obs_file.read_text("utf-8") if obs_file.exists() else "")
    url = "https://docs.ultralytics.com/integrations/vscode"
    return "" if installed else f"{colorstr('VS Code:')} view Ultralytics VS Code Extension ⚡ at {url}"


# Run below code on utils init ------------------------------------------------------------------------------------

# Check first-install steps
PREFIX = colorstr("Ultralytics: ")
SETTINGS = SettingsManager()  # initialize settings
PERSISTENT_CACHE = JSONDict(USER_CONFIG_DIR / "persistent_cache.json")  # initialize persistent cache
DATASETS_DIR = Path(SETTINGS["datasets_dir"])  # global datasets directory
WEIGHTS_DIR = Path(SETTINGS["weights_dir"])  # global weights directory
RUNS_DIR = Path(SETTINGS["runs_dir"])  # global runs directory
ENVIRONMENT = (
    "Colab"
    if IS_COLAB
    else "Kaggle"
    if IS_KAGGLE
    else "Jupyter"
    if IS_JUPYTER
    else "Docker"
    if IS_DOCKER
    else platform.system()
)
TESTS_RUNNING = is_pytest_running() or is_github_action_running()
set_sentry()

# Apply monkey patches
torch.load = torch_load
torch.save = torch_save
if WINDOWS:
    # Apply cv2 patches for non-ASCII and non-UTF characters in image paths
    cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow