import random
import numpy
import torch
import collections


def seed(seed):
    random.seed(seed)
    numpy.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)


def synthesize(array):
    d = collections.OrderedDict()
    d["mean"] = numpy.mean(array)
    d["std"] = numpy.std(array)
    d["min"] = numpy.amin(array)
    d["max"] = numpy.amax(array)
    return d

# Function from https://github.com/ikostrikov/pytorch-a2c-ppo-acktr/blob/master/model.py
def init_params(m):
    classname = m.__class__.__name__
    if classname.find("Linear") != -1:
        m.weight.data.normal_(0, 1)
        m.weight.data *= 1 / torch.sqrt(m.weight.data.pow(2).sum(1, keepdim=True))
        if m.bias is not None:
            m.bias.data.fill_(0)