import contextlib
import inspect
import logging.config
import os
import platform
import sys
import tempfile
import threading
import uuid
from pathlib import Path

import cv2
import pandas as pd
import yaml

# Constants
FILE = Path(__file__).resolve()
ROOT = FILE.parents[2]  # YOLO
DEFAULT_CONFIG = ROOT / "yolo/configs/default.yaml"
RANK = int(os.getenv('RANK', -1))
DATASETS_DIR = Path(os.getenv('YOLOv5_DATASETS_DIR', ROOT.parent / 'datasets'))  # global datasets directory
NUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number of YOLOv5 multiprocessing threads
AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true'  # global auto-install mode
FONT = 'Arial.ttf'  # https://ultralytics.com/assets/Arial.ttf
VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true'  # global verbose mode
TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}'  # tqdm bar format
LOGGING_NAME = 'yolov5'
HELP_MSG = \
    """
    Usage examples for running YOLOv8:

    1. Install the ultralytics package:

        pip install ultralytics

    2. Use the Python SDK:

        from ultralytics import YOLO

        model = YOLO('yolov8n.yaml')                # build a new model from scratch
        model = YOLO('yolov8n.pt')                  # load a pretrained model (recommended for best training results)
        results = model.train(data='coco128.yaml')  # train the model
        results = model.val()                       # evaluate model performance on the validation set
        results = model.predict(source='bus.jpg')   # predict on an image
        success = model.export(format='onnx')       # export the model to ONNX format

    3. Use the command line interface (CLI):

        yolo task=detect    mode=train    model=yolov8n.yaml      args...
                  classify       predict        yolov8n-cls.yaml  args...
                  segment        val            yolov8n-seg.yaml  args...
                                 export         yolov8n.pt        format=onnx  args...

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

# Settings
# torch.set_printoptions(linewidth=320, precision=5, profile='long')
# np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
pd.options.display.max_columns = 10
cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS)  # NumExpr max threads
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'  # for deterministic training

# Default config dictionary
with open(DEFAULT_CONFIG, errors='ignore') as f:
    DEFAULT_CONFIG_DICT = yaml.safe_load(f)
DEFAULT_CONFIG_KEYS = DEFAULT_CONFIG_DICT.keys()


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.
    """
    # Check if the google.colab module is present in sys.modules
    return 'google.colab' in sys.modules


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_notebook():
    """
    Check if the current script is running inside a Jupyter Notebook.
    Verified on Colab, Jupyterlab, Kaggle, Paperspace.

    Returns:
        bool: True if running inside a Jupyter Notebook, False otherwise.
    """
    # Check if the get_ipython function exists
    # (it does not exist when running as a standalone script)
    try:
        from IPython import get_ipython
        return get_ipython() is not None
    except ImportError:
        return False


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.
    """
    with open('/proc/self/cgroup') as f:
        return 'docker' in f.read()


def is_dir_writeable(dir_path: str) -> bool:
    """
    Check if a directory is writeable.

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

    Returns:
        bool: True if the directory is writeable, False otherwise.
    """
    try:
        with tempfile.TemporaryFile(dir=dir_path):
            pass
        return True
    except OSError:
        return False


def get_default_args(func):
    # Get func() default arguments
    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_user_config_dir(sub_dir='Ultralytics'):
    """
    Get the user config directory.

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

    Returns:
        Path: The path to the user config directory.
    """
    # Get the operating system name
    os_name = platform.system()

    # Return the appropriate config directory for each operating system
    if os_name == 'Windows':
        path = Path.home() / 'AppData' / 'Roaming' / sub_dir
    elif os_name == 'Darwin':  # macOS
        path = Path.home() / 'Library' / 'Application Support' / sub_dir
    elif os_name == 'Linux':
        path = Path.home() / '.config' / sub_dir
    else:
        raise ValueError(f'Unsupported operating system: {os_name}')

    # GCP and AWS lambda fix, only /tmp is writeable
    if not is_dir_writeable(str(path.parent)):
        path = Path('/tmp') / sub_dir

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

    return path


USER_CONFIG_DIR = get_user_config_dir()  # Ultralytics settings dir


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


def colorstr(*input):
    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
    *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 set_logging(name=LOGGING_NAME, verbose=True):
    # sets up logging for the given name
    rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings
    level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
    logging.config.dictConfig({
        "version": 1,
        "disable_existing_loggers": False,
        "formatters": {
            name: {
                "format": "%(message)s"}},
        "handlers": {
            name: {
                "class": "logging.StreamHandler",
                "formatter": name,
                "level": level,}},
        "loggers": {
            name: {
                "level": level,
                "handlers": [name],
                "propagate": False,}}})


class TryExcept(contextlib.ContextDecorator):
    # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
    def __init__(self, msg=''):
        self.msg = msg

    def __enter__(self):
        pass

    def __exit__(self, exc_type, value, traceback):
        if value:
            print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
        return True


def threaded(func):
    # Multi-threads a target function and returns thread. Usage: @threaded decorator
    def wrapper(*args, **kwargs):
        thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
        thread.start()
        return thread

    return wrapper


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

    Args:
        file (str, optional): File name. Default is 'data.yaml'.
        data (dict, optional): Data to save in YAML format. Default is None.

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

    with open(file, 'w') as f:
        # Dump data to file in YAML format, converting Path objects to strings
        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)


def yaml_load(file='data.yaml', append_filename=True):
    """
    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 True.

    Returns:
        dict: YAML data and file name.
    """
    with open(file, errors='ignore') as f:
        # Add YAML filename to dict and return
        return {**yaml.safe_load(f), 'yaml_file': str(file)} if append_filename else yaml.safe_load(f)


def get_settings(file=USER_CONFIG_DIR / 'settings.yaml'):
    """
    Loads a global settings YAML file or creates one with default values if it does not exist.

    Args:
        file (Path): Path to the settings YAML file. Defaults to 'settings.yaml' in the USER_CONFIG_DIR.

    Returns:
        dict: Dictionary of settings key-value pairs.
    """
    from ultralytics.yolo.utils.torch_utils import torch_distributed_zero_first

    defaults = {
        'datasets_dir': None,  # default datasets directory. If None, current working directory is used.
        'weights_dir': None,  # default weights directory. If None, current working directory is used.
        'runs_dir': None,  # default runs directory. If None, current working directory is used.
        'sync': True,  # sync analytics to help with YOLO development
        'uuid': uuid.getnode(),  # device UUID to align analytics
        'yaml_file': str(file)}  # setting YAML file path

    with torch_distributed_zero_first(RANK):
        if not file.exists():
            yaml_save(file, defaults)

        settings = yaml_load(file)
        if settings.keys() != defaults.keys():
            settings = {**defaults, **settings}  # merge **defaults with **settings (prefer **settings)
            yaml_save(file, settings)  # save updated defaults

        return settings


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

# Set logger
set_logging(LOGGING_NAME)  # run before defining LOGGER
LOGGER = logging.getLogger(LOGGING_NAME)  # define globally (used in train.py, val.py, detect.py, etc.)
if platform.system() == 'Windows':
    for fn in LOGGER.info, LOGGER.warning:
        setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x)))  # emoji safe logging

# Check first-install steps
SETTINGS = get_settings()


def set_settings(kwargs, file=USER_CONFIG_DIR / 'settings.yaml'):
    """
    Function that runs on a first-time ultralytics package installation to set up global settings and create necessary
    directories.
    """
    SETTINGS.update(kwargs)

    yaml_save(file, SETTINGS)