from collections import defaultdict, deque, OrderedDict
import copy
import datetime
import hashlib
import time
import paddle
import paddle.distributed as dist

import errno
import os


class SmoothedValue(object):
    """Track a series of values and provide access to smoothed values over a
    window or the global series average.
    """

    def __init__(self, window_size=20, fmt=None):
        if fmt is None:
            fmt = "{median:.4f} ({global_avg:.4f})"
        self.deque = deque(maxlen=window_size)
        self.total = 0.0
        self.count = 0
        self.fmt = fmt

    def update(self, value, n=1):
        self.deque.append(value)
        self.count += n
        self.total += value * n

    def synchronize_between_processes(self):
        """
        Warning: does not synchronize the deque!
        """
        t = paddle.to_tensor([self.count, self.total], dtype='float64')
        t = t.numpy().tolist()
        self.count = int(t[0])
        self.total = t[1]

    @property
    def median(self):
        d = paddle.to_tensor(list(self.deque))
        return d.median().numpy().item()

    @property
    def avg(self):
        d = paddle.to_tensor(list(self.deque), dtype='float32')
        return d.mean().numpy().item()

    @property
    def global_avg(self):
        return self.total / self.count

    @property
    def max(self):
        return max(self.deque)

    @property
    def value(self):
        return self.deque[-1]

    def __str__(self):
        return self.fmt.format(
            median=self.median,
            avg=self.avg,
            global_avg=self.global_avg,
            max=self.max,
            value=self.value)


class MetricLogger(object):
    def __init__(self, delimiter="\t"):
        self.meters = defaultdict(SmoothedValue)
        self.delimiter = delimiter

    def update(self, **kwargs):
        for k, v in kwargs.items():
            if isinstance(v, paddle.Tensor):
                v = v.item()
            assert isinstance(v, (float, int))
            self.meters[k].update(v)

    def __getattr__(self, attr):
        if attr in self.meters:
            return self.meters[attr]
        if attr in self.__dict__:
            return self.__dict__[attr]
        raise AttributeError("'{}' object has no attribute '{}'".format(
            type(self).__name__, attr))

    def __str__(self):
        loss_str = []
        for name, meter in self.meters.items():
            loss_str.append("{}: {}".format(name, str(meter)))
        return self.delimiter.join(loss_str)

    def synchronize_between_processes(self):
        for meter in self.meters.values():
            meter.synchronize_between_processes()

    def add_meter(self, name, meter):
        self.meters[name] = meter

    def log_every(self, iterable, print_freq, header=None):
        i = 0
        if not header:
            header = ''
        start_time = time.time()
        end = time.time()
        iter_time = SmoothedValue(fmt='{avg:.4f}')
        data_time = SmoothedValue(fmt='{avg:.4f}')
        space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
        log_msg = self.delimiter.join([
            header, '[{0' + space_fmt + '}/{1}]', 'eta: {eta}', '{meters}',
            'time: {time}', 'data: {data}'
        ])
        for obj in iterable:
            data_time.update(time.time() - end)
            yield obj
            iter_time.update(time.time() - end)
            if i % print_freq == 0:
                eta_seconds = iter_time.global_avg * (len(iterable) - i)
                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
                print(
                    log_msg.format(
                        i,
                        len(iterable),
                        eta=eta_string,
                        meters=str(self),
                        time=str(iter_time),
                        data=str(data_time)))
            i += 1
            end = time.time()
        total_time = time.time() - start_time
        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
        print('{} Total time: {}'.format(header, total_time_str))


def accuracy(output, target, topk=(1, )):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with paddle.no_grad():
        maxk = max(topk)
        batch_size = target.shape[0]

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.equal(target.astype("int64"))

        res = []
        for k in topk:
            correct_k = correct.astype(paddle.int32)[:k].flatten().sum(
                dtype='float32')
            res.append(correct_k / batch_size)
        return res


def get_world_size():
    return dist.get_world_size()


def mkdir(path):
    try:
        os.makedirs(path)
    except OSError as e:
        if e.errno != errno.EEXIST:
            raise
