# Ultralytics YOLO 🚀, AGPL-3.0 license

import math
import os
import random
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
from typing import Union

import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torchvision

from ultralytics.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, __version__
from ultralytics.utils.checks import PYTHON_VERSION, check_version

try:
    import thop
except ImportError:
    thop = None

# Version checks (all default to version>=min_version)
TORCH_1_9 = check_version(torch.__version__, "1.9.0")
TORCH_1_13 = check_version(torch.__version__, "1.13.0")
TORCH_2_0 = check_version(torch.__version__, "2.0.0")
TORCHVISION_0_10 = check_version(torchvision.__version__, "0.10.0")
TORCHVISION_0_11 = check_version(torchvision.__version__, "0.11.0")
TORCHVISION_0_13 = check_version(torchvision.__version__, "0.13.0")


@contextmanager
def torch_distributed_zero_first(local_rank: int):
    """Decorator to make all processes in distributed training wait for each local_master to do something."""
    initialized = torch.distributed.is_available() and torch.distributed.is_initialized()
    if initialized and local_rank not in {-1, 0}:
        dist.barrier(device_ids=[local_rank])
    yield
    if initialized and local_rank == 0:
        dist.barrier(device_ids=[0])


def smart_inference_mode():
    """Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator."""

    def decorate(fn):
        """Applies appropriate torch decorator for inference mode based on torch version."""
        if TORCH_1_9 and torch.is_inference_mode_enabled():
            return fn  # already in inference_mode, act as a pass-through
        else:
            return (torch.inference_mode if TORCH_1_9 else torch.no_grad)()(fn)

    return decorate


def get_cpu_info():
    """Return a string with system CPU information, i.e. 'Apple M2'."""
    import cpuinfo  # pip install py-cpuinfo

    k = "brand_raw", "hardware_raw", "arch_string_raw"  # info keys sorted by preference (not all keys always available)
    info = cpuinfo.get_cpu_info()  # info dict
    string = info.get(k[0] if k[0] in info else k[1] if k[1] in info else k[2], "unknown")
    return string.replace("(R)", "").replace("CPU ", "").replace("@ ", "")


def select_device(device="", batch=0, newline=False, verbose=True):
    """
    Selects the appropriate PyTorch device based on the provided arguments.

    The function takes a string specifying the device or a torch.device object and returns a torch.device object
    representing the selected device. The function also validates the number of available devices and raises an
    exception if the requested device(s) are not available.

    Args:
        device (str | torch.device, optional): Device string or torch.device object.
            Options are 'None', 'cpu', or 'cuda', or '0' or '0,1,2,3'. Defaults to an empty string, which auto-selects
            the first available GPU, or CPU if no GPU is available.
        batch (int, optional): Batch size being used in your model. Defaults to 0.
        newline (bool, optional): If True, adds a newline at the end of the log string. Defaults to False.
        verbose (bool, optional): If True, logs the device information. Defaults to True.

    Returns:
        (torch.device): Selected device.

    Raises:
        ValueError: If the specified device is not available or if the batch size is not a multiple of the number of
            devices when using multiple GPUs.

    Examples:
        >>> select_device('cuda:0')
        device(type='cuda', index=0)

        >>> select_device('cpu')
        device(type='cpu')

    Note:
        Sets the 'CUDA_VISIBLE_DEVICES' environment variable for specifying which GPUs to use.
    """

    if isinstance(device, torch.device):
        return device

    s = f"Ultralytics YOLOv{__version__} 🚀 Python-{PYTHON_VERSION} torch-{torch.__version__} "
    device = str(device).lower()
    for remove in "cuda:", "none", "(", ")", "[", "]", "'", " ":
        device = device.replace(remove, "")  # to string, 'cuda:0' -> '0' and '(0, 1)' -> '0,1'
    cpu = device == "cpu"
    mps = device in {"mps", "mps:0"}  # Apple Metal Performance Shaders (MPS)
    if cpu or mps:
        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # force torch.cuda.is_available() = False
    elif device:  # non-cpu device requested
        if device == "cuda":
            device = "0"
        visible = os.environ.get("CUDA_VISIBLE_DEVICES", None)
        os.environ["CUDA_VISIBLE_DEVICES"] = device  # set environment variable - must be before assert is_available()
        if not (torch.cuda.is_available() and torch.cuda.device_count() >= len(device.split(","))):
            LOGGER.info(s)
            install = (
                "See https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no "
                "CUDA devices are seen by torch.\n"
                if torch.cuda.device_count() == 0
                else ""
            )
            raise ValueError(
                f"Invalid CUDA 'device={device}' requested."
                f" Use 'device=cpu' or pass valid CUDA device(s) if available,"
                f" i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n"
                f"\ntorch.cuda.is_available(): {torch.cuda.is_available()}"
                f"\ntorch.cuda.device_count(): {torch.cuda.device_count()}"
                f"\nos.environ['CUDA_VISIBLE_DEVICES']: {visible}\n"
                f"{install}"
            )

    if not cpu and not mps and torch.cuda.is_available():  # prefer GPU if available
        devices = device.split(",") if device else "0"  # range(torch.cuda.device_count())  # i.e. 0,1,6,7
        n = len(devices)  # device count
        if n > 1 and batch > 0 and batch % n != 0:  # check batch_size is divisible by device_count
            raise ValueError(
                f"'batch={batch}' must be a multiple of GPU count {n}. Try 'batch={batch // n * n}' or "
                f"'batch={batch // n * n + n}', the nearest batch sizes evenly divisible by {n}."
            )
        space = " " * (len(s) + 1)
        for i, d in enumerate(devices):
            p = torch.cuda.get_device_properties(i)
            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n"  # bytes to MB
        arg = "cuda:0"
    elif mps and TORCH_2_0 and torch.backends.mps.is_available():
        # Prefer MPS if available
        s += f"MPS ({get_cpu_info()})\n"
        arg = "mps"
    else:  # revert to CPU
        s += f"CPU ({get_cpu_info()})\n"
        arg = "cpu"

    if verbose:
        LOGGER.info(s if newline else s.rstrip())
    return torch.device(arg)


def time_sync():
    """PyTorch-accurate time."""
    if torch.cuda.is_available():
        torch.cuda.synchronize()
    return time.time()


def fuse_conv_and_bn(conv, bn):
    """Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/."""
    fusedconv = (
        nn.Conv2d(
            conv.in_channels,
            conv.out_channels,
            kernel_size=conv.kernel_size,
            stride=conv.stride,
            padding=conv.padding,
            dilation=conv.dilation,
            groups=conv.groups,
            bias=True,
        )
        .requires_grad_(False)
        .to(conv.weight.device)
    )

    # Prepare filters
    w_conv = conv.weight.clone().view(conv.out_channels, -1)
    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))

    # Prepare spatial bias
    b_conv = torch.zeros(conv.weight.shape[0], device=conv.weight.device) if conv.bias is None else conv.bias
    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)

    return fusedconv


def fuse_deconv_and_bn(deconv, bn):
    """Fuse ConvTranspose2d() and BatchNorm2d() layers."""
    fuseddconv = (
        nn.ConvTranspose2d(
            deconv.in_channels,
            deconv.out_channels,
            kernel_size=deconv.kernel_size,
            stride=deconv.stride,
            padding=deconv.padding,
            output_padding=deconv.output_padding,
            dilation=deconv.dilation,
            groups=deconv.groups,
            bias=True,
        )
        .requires_grad_(False)
        .to(deconv.weight.device)
    )

    # Prepare filters
    w_deconv = deconv.weight.clone().view(deconv.out_channels, -1)
    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
    fuseddconv.weight.copy_(torch.mm(w_bn, w_deconv).view(fuseddconv.weight.shape))

    # Prepare spatial bias
    b_conv = torch.zeros(deconv.weight.shape[1], device=deconv.weight.device) if deconv.bias is None else deconv.bias
    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
    fuseddconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)

    return fuseddconv


def model_info(model, detailed=False, verbose=True, imgsz=640):
    """
    Model information.

    imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320].
    """
    if not verbose:
        return
    n_p = get_num_params(model)  # number of parameters
    n_g = get_num_gradients(model)  # number of gradients
    n_l = len(list(model.modules()))  # number of layers
    if detailed:
        LOGGER.info(
            f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}"
        )
        for i, (name, p) in enumerate(model.named_parameters()):
            name = name.replace("module_list.", "")
            LOGGER.info(
                "%5g %40s %9s %12g %20s %10.3g %10.3g %10s"
                % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std(), p.dtype)
            )

    flops = get_flops(model, imgsz)
    fused = " (fused)" if getattr(model, "is_fused", lambda: False)() else ""
    fs = f", {flops:.1f} GFLOPs" if flops else ""
    yaml_file = getattr(model, "yaml_file", "") or getattr(model, "yaml", {}).get("yaml_file", "")
    model_name = Path(yaml_file).stem.replace("yolo", "YOLO") or "Model"
    LOGGER.info(f"{model_name} summary{fused}: {n_l} layers, {n_p} parameters, {n_g} gradients{fs}")
    return n_l, n_p, n_g, flops


def get_num_params(model):
    """Return the total number of parameters in a YOLO model."""
    return sum(x.numel() for x in model.parameters())


def get_num_gradients(model):
    """Return the total number of parameters with gradients in a YOLO model."""
    return sum(x.numel() for x in model.parameters() if x.requires_grad)


def model_info_for_loggers(trainer):
    """
    Return model info dict with useful model information.

    Example:
        YOLOv8n info for loggers
        ```python
        results = {'model/parameters': 3151904,
                   'model/GFLOPs': 8.746,
                   'model/speed_ONNX(ms)': 41.244,
                   'model/speed_TensorRT(ms)': 3.211,
                   'model/speed_PyTorch(ms)': 18.755}
        ```
    """
    if trainer.args.profile:  # profile ONNX and TensorRT times
        from ultralytics.utils.benchmarks import ProfileModels

        results = ProfileModels([trainer.last], device=trainer.device).profile()[0]
        results.pop("model/name")
    else:  # only return PyTorch times from most recent validation
        results = {
            "model/parameters": get_num_params(trainer.model),
            "model/GFLOPs": round(get_flops(trainer.model), 3),
        }
    results["model/speed_PyTorch(ms)"] = round(trainer.validator.speed["inference"], 3)
    return results


def get_flops(model, imgsz=640):
    """Return a YOLO model's FLOPs."""
    if not thop:
        return 0.0  # if not installed return 0.0 GFLOPs

    try:
        model = de_parallel(model)
        p = next(model.parameters())
        if not isinstance(imgsz, list):
            imgsz = [imgsz, imgsz]  # expand if int/float
        try:
            # Use stride size for input tensor
            stride = max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32  # max stride
            im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
            flops = thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1e9 * 2  # stride GFLOPs
            return flops * imgsz[0] / stride * imgsz[1] / stride  # imgsz GFLOPs
        except Exception:
            # Use actual image size for input tensor (i.e. required for RTDETR models)
            im = torch.empty((1, p.shape[1], *imgsz), device=p.device)  # input image in BCHW format
            return thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1e9 * 2  # imgsz GFLOPs
    except Exception:
        return 0.0


def get_flops_with_torch_profiler(model, imgsz=640):
    """Compute model FLOPs (thop alternative)."""
    if TORCH_2_0:
        model = de_parallel(model)
        p = next(model.parameters())
        stride = (max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32) * 2  # max stride
        im = torch.zeros((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
        with torch.profiler.profile(with_flops=True) as prof:
            model(im)
        flops = sum(x.flops for x in prof.key_averages()) / 1e9
        imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float
        flops = flops * imgsz[0] / stride * imgsz[1] / stride  # 640x640 GFLOPs
        return flops
    return 0


def initialize_weights(model):
    """Initialize model weights to random values."""
    for m in model.modules():
        t = type(m)
        if t is nn.Conv2d:
            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        elif t is nn.BatchNorm2d:
            m.eps = 1e-3
            m.momentum = 0.03
        elif t in {nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU}:
            m.inplace = True


def scale_img(img, ratio=1.0, same_shape=False, gs=32):
    """Scales and pads an image tensor of shape img(bs,3,y,x) based on given ratio and grid size gs, optionally
    retaining the original shape.
    """
    if ratio == 1.0:
        return img
    h, w = img.shape[2:]
    s = (int(h * ratio), int(w * ratio))  # new size
    img = F.interpolate(img, size=s, mode="bilinear", align_corners=False)  # resize
    if not same_shape:  # pad/crop img
        h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
    return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean


def make_divisible(x, divisor):
    """Returns nearest x divisible by divisor."""
    if isinstance(divisor, torch.Tensor):
        divisor = int(divisor.max())  # to int
    return math.ceil(x / divisor) * divisor


def copy_attr(a, b, include=(), exclude=()):
    """Copies attributes from object 'b' to object 'a', with options to include/exclude certain attributes."""
    for k, v in b.__dict__.items():
        if (len(include) and k not in include) or k.startswith("_") or k in exclude:
            continue
        else:
            setattr(a, k, v)


def get_latest_opset():
    """Return second-most (for maturity) recently supported ONNX opset by this version of torch."""
    return max(int(k[14:]) for k in vars(torch.onnx) if "symbolic_opset" in k) - 1  # opset


def intersect_dicts(da, db, exclude=()):
    """Returns a dictionary of intersecting keys with matching shapes, excluding 'exclude' keys, using da values."""
    return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape}


def is_parallel(model):
    """Returns True if model is of type DP or DDP."""
    return isinstance(model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel))


def de_parallel(model):
    """De-parallelize a model: returns single-GPU model if model is of type DP or DDP."""
    return model.module if is_parallel(model) else model


def one_cycle(y1=0.0, y2=1.0, steps=100):
    """Returns a lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf."""
    return lambda x: max((1 - math.cos(x * math.pi / steps)) / 2, 0) * (y2 - y1) + y1


def init_seeds(seed=0, deterministic=False):
    """Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html."""
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)  # for Multi-GPU, exception safe
    # torch.backends.cudnn.benchmark = True  # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287
    if deterministic:
        if TORCH_2_0:
            torch.use_deterministic_algorithms(True, warn_only=True)  # warn if deterministic is not possible
            torch.backends.cudnn.deterministic = True
            os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
            os.environ["PYTHONHASHSEED"] = str(seed)
        else:
            LOGGER.warning("WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.")
    else:
        torch.use_deterministic_algorithms(False)
        torch.backends.cudnn.deterministic = False


class ModelEMA:
    """Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
    Keeps a moving average of everything in the model state_dict (parameters and buffers)
    For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
    To disable EMA set the `enabled` attribute to `False`.
    """

    def __init__(self, model, decay=0.9999, tau=2000, updates=0):
        """Create EMA."""
        self.ema = deepcopy(de_parallel(model)).eval()  # FP32 EMA
        self.updates = updates  # number of EMA updates
        self.decay = lambda x: decay * (1 - math.exp(-x / tau))  # decay exponential ramp (to help early epochs)
        for p in self.ema.parameters():
            p.requires_grad_(False)
        self.enabled = True

    def update(self, model):
        """Update EMA parameters."""
        if self.enabled:
            self.updates += 1
            d = self.decay(self.updates)

            msd = de_parallel(model).state_dict()  # model state_dict
            for k, v in self.ema.state_dict().items():
                if v.dtype.is_floating_point:  # true for FP16 and FP32
                    v *= d
                    v += (1 - d) * msd[k].detach()
                    # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype},  model {msd[k].dtype}'

    def update_attr(self, model, include=(), exclude=("process_group", "reducer")):
        """Updates attributes and saves stripped model with optimizer removed."""
        if self.enabled:
            copy_attr(self.ema, model, include, exclude)


def strip_optimizer(f: Union[str, Path] = "best.pt", s: str = "") -> None:
    """
    Strip optimizer from 'f' to finalize training, optionally save as 's'.

    Args:
        f (str): file path to model to strip the optimizer from. Default is 'best.pt'.
        s (str): file path to save the model with stripped optimizer to. If not provided, 'f' will be overwritten.

    Returns:
        None

    Example:
        ```python
        from pathlib import Path
        from ultralytics.utils.torch_utils import strip_optimizer

        for f in Path('path/to/weights').rglob('*.pt'):
            strip_optimizer(f)
        ```
    """
    x = torch.load(f, map_location=torch.device("cpu"))
    if "model" not in x:
        LOGGER.info(f"Skipping {f}, not a valid Ultralytics model.")
        return

    if hasattr(x["model"], "args"):
        x["model"].args = dict(x["model"].args)  # convert from IterableSimpleNamespace to dict
    args = {**DEFAULT_CFG_DICT, **x["train_args"]} if "train_args" in x else None  # combine args
    if x.get("ema"):
        x["model"] = x["ema"]  # replace model with ema
    for k in "optimizer", "best_fitness", "ema", "updates":  # keys
        x[k] = None
    x["epoch"] = -1
    x["model"].half()  # to FP16
    for p in x["model"].parameters():
        p.requires_grad = False
    x["train_args"] = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS}  # strip non-default keys
    # x['model'].args = x['train_args']
    torch.save(x, s or f)
    mb = os.path.getsize(s or f) / 1e6  # file size
    LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")


def convert_optimizer_state_dict_to_fp16(state_dict):
    """
    Converts the state_dict of a given optimizer to FP16, focusing on the 'state' key for tensor conversions.

    This method aims to reduce storage size without altering 'param_groups' as they contain non-tensor data.
    """
    for state in state_dict["state"].values():
        for k, v in state.items():
            if k != "step" and isinstance(v, torch.Tensor) and v.dtype is torch.float32:
                state[k] = v.half()

    return state_dict


def profile(input, ops, n=10, device=None):
    """
    Ultralytics speed, memory and FLOPs profiler.

    Example:
        ```python
        from ultralytics.utils.torch_utils import profile

        input = torch.randn(16, 3, 640, 640)
        m1 = lambda x: x * torch.sigmoid(x)
        m2 = nn.SiLU()
        profile(input, [m1, m2], n=100)  # profile over 100 iterations
        ```
    """
    results = []
    if not isinstance(device, torch.device):
        device = select_device(device)
    LOGGER.info(
        f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
        f"{'input':>24s}{'output':>24s}"
    )

    for x in input if isinstance(input, list) else [input]:
        x = x.to(device)
        x.requires_grad = True
        for m in ops if isinstance(ops, list) else [ops]:
            m = m.to(device) if hasattr(m, "to") else m  # device
            m = m.half() if hasattr(m, "half") and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
            tf, tb, t = 0, 0, [0, 0, 0]  # dt forward, backward
            try:
                flops = thop.profile(m, inputs=[x], verbose=False)[0] / 1e9 * 2 if thop else 0  # GFLOPs
            except Exception:
                flops = 0

            try:
                for _ in range(n):
                    t[0] = time_sync()
                    y = m(x)
                    t[1] = time_sync()
                    try:
                        (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
                        t[2] = time_sync()
                    except Exception:  # no backward method
                        # print(e)  # for debug
                        t[2] = float("nan")
                    tf += (t[1] - t[0]) * 1000 / n  # ms per op forward
                    tb += (t[2] - t[1]) * 1000 / n  # ms per op backward
                mem = torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0  # (GB)
                s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else "list" for x in (x, y))  # shapes
                p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0  # parameters
                LOGGER.info(f"{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}")
                results.append([p, flops, mem, tf, tb, s_in, s_out])
            except Exception as e:
                LOGGER.info(e)
                results.append(None)
            torch.cuda.empty_cache()
    return results


class EarlyStopping:
    """Early stopping class that stops training when a specified number of epochs have passed without improvement."""

    def __init__(self, patience=50):
        """
        Initialize early stopping object.

        Args:
            patience (int, optional): Number of epochs to wait after fitness stops improving before stopping.
        """
        self.best_fitness = 0.0  # i.e. mAP
        self.best_epoch = 0
        self.patience = patience or float("inf")  # epochs to wait after fitness stops improving to stop
        self.possible_stop = False  # possible stop may occur next epoch

    def __call__(self, epoch, fitness):
        """
        Check whether to stop training.

        Args:
            epoch (int): Current epoch of training
            fitness (float): Fitness value of current epoch

        Returns:
            (bool): True if training should stop, False otherwise
        """
        if fitness is None:  # check if fitness=None (happens when val=False)
            return False

        if fitness >= self.best_fitness:  # >= 0 to allow for early zero-fitness stage of training
            self.best_epoch = epoch
            self.best_fitness = fitness
        delta = epoch - self.best_epoch  # epochs without improvement
        self.possible_stop = delta >= (self.patience - 1)  # possible stop may occur next epoch
        stop = delta >= self.patience  # stop training if patience exceeded
        if stop:
            prefix = colorstr("EarlyStopping: ")
            LOGGER.info(
                f"{prefix}Training stopped early as no improvement observed in last {self.patience} epochs. "
                f"Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n"
                f"To update EarlyStopping(patience={self.patience}) pass a new patience value, "
                f"i.e. `patience=300` or use `patience=0` to disable EarlyStopping."
            )
        return stop