# Copyright (c) Megvii Inc. All rights reserved.
# Copyright © Alibaba, Inc. and its affiliates.

import functools
import os
from collections import defaultdict, deque

import numpy as np
import torch

__all__ = [
    'AverageMeter',
    'MeterBuffer',
    'gpu_mem_usage',
]


def gpu_mem_usage():
    """
    Compute the GPU memory usage for the current device (MB).
    """
    mem_usage_bytes = torch.cuda.max_memory_allocated()
    return mem_usage_bytes / (1024 * 1024)


class AverageMeter:
    """Track a series of values and provide access to smoothed values over a
    window or the global series average.
    """
    def __init__(self, window_size=50):
        self._deque = deque(maxlen=window_size)
        self._total = 0.0
        self._count = 0

    def update(self, value):
        self._deque.append(value)
        self._count += 1
        self._total += value

    @property
    def median(self):
        d = np.array(list(self._deque))
        return np.median(d)

    @property
    def avg(self):
        # if deque is empty, nan will be returned.
        d = np.array(list(self._deque))
        return d.mean()

    @property
    def global_avg(self):
        return self._total / max(self._count, 1e-5)

    @property
    def latest(self):
        return self._deque[-1] if len(self._deque) > 0 else None

    @property
    def total(self):
        return self._total

    def reset(self):
        self._deque.clear()
        self._total = 0.0
        self._count = 0

    def clear(self):
        self._deque.clear()


class MeterBuffer(defaultdict):
    """Computes and stores the average and current value"""
    def __init__(self, window_size=20):
        factory = functools.partial(AverageMeter, window_size=window_size)
        super().__init__(factory)

    def reset(self):
        for v in self.values():
            v.reset()

    def get_filtered_meter(self, filter_key='time'):
        return {k: v for k, v in self.items() if filter_key in k}

    def update(self, values=None, **kwargs):
        if values is None:
            values = {}
        values.update(kwargs)
        for k, v in values.items():
            if isinstance(v, torch.Tensor):
                v = v.detach()
            self[k].update(v)

    def clear_meters(self):
        for v in self.values():
            v.clear()