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

from __future__ import annotations

import random
from typing import Any

from ultralytics.utils import LOGGER


class GPUInfo:
    """Manages NVIDIA GPU information via pynvml with robust error handling.

    Provides methods to query detailed GPU statistics (utilization, memory, temp, power) and select the most idle GPUs
    based on configurable criteria. It safely handles the absence or initialization failure of the pynvml library by
    logging warnings and disabling related features, preventing application crashes.

    Includes fallback logic using `torch.cuda` for basic device counting if NVML is unavailable during GPU
    selection. Manages NVML initialization and shutdown internally.

    Attributes:
        pynvml (module | None): The `pynvml` module if successfully imported and initialized, otherwise `None`.
        nvml_available (bool): Indicates if `pynvml` is ready for use. True if `nvmlInit()` succeeded, False otherwise.
        gpu_stats (list[dict[str, Any]]): A list of dictionaries, each holding stats for one GPU, populated on
        initialization and by `refresh_stats()`. Keys include: 'index', 'name', 'utilization' (%), 'memory_used' (MiB),
            'memory_total' (MiB), 'memory_free' (MiB), 'temperature' (C), 'power_draw' (W), 'power_limit' (W or 'N/A').
            Empty if NVML is unavailable or queries fail.

    Methods:
        refresh_stats: Refresh the internal gpu_stats list by querying NVML.
        print_status: Print GPU status in a compact table format using current stats.
        select_idle_gpu: Select the most idle GPUs based on utilization and free memory.
        shutdown: Shut down NVML if it was initialized.

    Examples:
        Initialize GPUInfo and print status
        >>> gpu_info = GPUInfo()
        >>> gpu_info.print_status()

        Select idle GPUs with minimum memory requirements
        >>> selected = gpu_info.select_idle_gpu(count=2, min_memory_fraction=0.2)
        >>> print(f"Selected GPU indices: {selected}")
    """

    def __init__(self):
        """Initialize GPUInfo, attempting to import and initialize pynvml."""
        self.pynvml: Any | None = None
        self.nvml_available: bool = False
        self.gpu_stats: list[dict[str, Any]] = []

        try:
            import pynvml  # scoped as slow import

            self.pynvml = pynvml
            pynvml.nvmlInit()
            self.nvml_available = True
            self.refresh_stats()
        except Exception as e:
            LOGGER.warning(f"Failed to initialize pynvml, GPU stats disabled: {e}")

    def __del__(self):
        """Ensure NVML is shut down when the object is garbage collected."""
        self.shutdown()

    def shutdown(self):
        """Shut down NVML if it was initialized."""
        if self.nvml_available and self.pynvml:
            try:
                self.pynvml.nvmlShutdown()
            except Exception:
                pass
            self.nvml_available = False

    def refresh_stats(self):
        """Refresh the internal gpu_stats list by querying NVML."""
        self.gpu_stats = []
        if not self.nvml_available or not self.pynvml:
            return

        try:
            device_count = self.pynvml.nvmlDeviceGetCount()
            self.gpu_stats.extend(self._get_device_stats(i) for i in range(device_count))
        except Exception as e:
            LOGGER.warning(f"Error during device query: {e}")
            self.gpu_stats = []

    def _get_device_stats(self, index: int) -> dict[str, Any]:
        """Get stats for a single GPU device."""
        handle = self.pynvml.nvmlDeviceGetHandleByIndex(index)
        memory = self.pynvml.nvmlDeviceGetMemoryInfo(handle)
        util = self.pynvml.nvmlDeviceGetUtilizationRates(handle)

        def safe_get(func, *args, default=-1, divisor=1):
            try:
                val = func(*args)
                return val // divisor if divisor != 1 and isinstance(val, (int, float)) else val
            except Exception:
                return default

        temp_type = getattr(self.pynvml, "NVML_TEMPERATURE_GPU", -1)

        return {
            "index": index,
            "name": self.pynvml.nvmlDeviceGetName(handle),
            "utilization": util.gpu if util else -1,
            "memory_used": memory.used >> 20 if memory else -1,  # Convert bytes to MiB
            "memory_total": memory.total >> 20 if memory else -1,
            "memory_free": memory.free >> 20 if memory else -1,
            "temperature": safe_get(self.pynvml.nvmlDeviceGetTemperature, handle, temp_type),
            "power_draw": safe_get(self.pynvml.nvmlDeviceGetPowerUsage, handle, divisor=1000),  # Convert mW to W
            "power_limit": safe_get(self.pynvml.nvmlDeviceGetEnforcedPowerLimit, handle, divisor=1000),
        }

    def print_status(self):
        """Print GPU status in a compact table format using current stats."""
        self.refresh_stats()
        if not self.gpu_stats:
            LOGGER.warning("No GPU stats available.")
            return

        stats = self.gpu_stats
        name_len = max(len(gpu.get("name", "N/A")) for gpu in stats)
        hdr = f"{'Idx':<3} {'Name':<{name_len}} {'Util':>6} {'Mem (MiB)':>15} {'Temp':>5} {'Pwr (W)':>10}"
        LOGGER.info(f"\n--- GPU Status ---\n{hdr}\n{'-' * len(hdr)}")

        for gpu in stats:
            u = f"{gpu['utilization']:>5}%" if gpu["utilization"] >= 0 else " N/A "
            m = f"{gpu['memory_used']:>6}/{gpu['memory_total']:<6}" if gpu["memory_used"] >= 0 else " N/A / N/A "
            t = f"{gpu['temperature']}C" if gpu["temperature"] >= 0 else " N/A "
            p = f"{gpu['power_draw']:>3}/{gpu['power_limit']:<3}" if gpu["power_draw"] >= 0 else " N/A "

            LOGGER.info(f"{gpu.get('index'):<3d} {gpu.get('name', 'N/A'):<{name_len}} {u:>6} {m:>15} {t:>5} {p:>10}")

        LOGGER.info(f"{'-' * len(hdr)}\n")

    def select_idle_gpu(
        self,
        count: int = 1,
        min_memory_fraction: float = 0,
        min_util_fraction: float = 0,
        indices: list[int] | None = None,
    ) -> list[int]:
        """Select the most idle GPUs based on utilization and free memory.

        Args:
            count (int): The number of idle GPUs to select.
            min_memory_fraction (float): Minimum free memory required as a fraction of total memory.
            min_util_fraction (float): Minimum free utilization rate required from 0.0 - 1.0.
            indices (list[int] | None): Restrict selection to these GPU indices, e.g. the GPUs visible under an external
                CUDA_VISIBLE_DEVICES restriction. None searches all GPUs.

        Returns:
            (list[int]): Indices of the selected GPUs, sorted by idleness (lowest utilization first).

        Notes:
             Returns fewer than 'count' if not enough qualify or exist.
             Returns empty list if NVML stats are unavailable or no GPUs meet the criteria.
        """
        assert min_memory_fraction <= 1.0, f"min_memory_fraction must be <= 1.0, got {min_memory_fraction}"
        assert min_util_fraction <= 1.0, f"min_util_fraction must be <= 1.0, got {min_util_fraction}"
        criteria = (
            f"free memory >= {min_memory_fraction * 100:.1f}% and free utilization >= {min_util_fraction * 100:.1f}%"
        )
        LOGGER.info(f"Searching for {count} idle GPUs with {criteria}...")

        if count <= 0:
            return []

        self.refresh_stats()
        if not self.gpu_stats:
            LOGGER.warning("NVML stats unavailable.")
            return []

        # Filter and sort eligible GPUs
        eligible_gpus = [
            gpu
            for gpu in self.gpu_stats
            if (indices is None or gpu["index"] in indices)
            and gpu.get("memory_free", 0) / gpu.get("memory_total", 1) >= min_memory_fraction
            and (100 - gpu.get("utilization", 100)) >= min_util_fraction * 100
        ]
        # Random tiebreaker prevents race conditions when multiple processes start simultaneously
        # and all GPUs appear equally idle (same utilization and free memory)
        eligible_gpus.sort(key=lambda x: (x.get("utilization", 101), -x.get("memory_free", 0), random.random()))

        # Select top 'count' indices
        selected = [gpu["index"] for gpu in eligible_gpus[:count]]

        if selected:
            if len(selected) < count:
                LOGGER.warning(f"Requested {count} GPUs but only {len(selected)} met the idle criteria.")
            LOGGER.info(f"Selected idle CUDA devices {selected}")
        else:
            LOGGER.warning(f"No GPUs met criteria ({criteria}).")

        return selected


if __name__ == "__main__":
    required_free_mem_fraction = 0.2  # Require 20% free VRAM
    required_free_util_fraction = 0.2  # Require 20% free utilization
    num_gpus_to_select = 1

    gpu_info = GPUInfo()
    gpu_info.print_status()

    if selected := gpu_info.select_idle_gpu(
        count=num_gpus_to_select,
        min_memory_fraction=required_free_mem_fraction,
        min_util_fraction=required_free_util_fraction,
    ):
        print(f"\n==> Using selected GPU indices: {selected}")
        devices = [f"cuda:{idx}" for idx in selected]
        print(f"    Target devices: {devices}")
