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def recursive_setattr(obj, name, value):
    if '.' in name:
        parent, child = name.split('.')[0], '.'.join(name.split('.')[1:])
        recursive_setattr(getattr(obj, parent), child, value)
    else:
        setattr(obj, name, value)

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""" Argparser to wandb query. 

References
----------
Short tutorial: https://docs.wandb.ai/ref/export-api
Wandb API: https://docs.wandb.ai/ref/export-api/api
  - Api.runs
  - RUN class
Mongodb query: https://docs.mongodb.com/manual/reference/operator/query/
"""

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class MLPEncoder(nn.Module):
    def __init__(self):
        super(MLPEncoder, self).__init__()

        # TODO: Fix hard coding
        self.model = nn.Sequential(OrderedDict([
          ('layer1', nn.Linear(784, 400)),
          ('relu1', nn.ReLU()),
          ('layer2', nn.Linear(400, 400)),
          ('relu2', nn.ReLU()),

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# # -*- coding: utf-8 -*-
import itertools
from torch.utils import data


def l2diff(x1, x2):
    """
    standard euclidean norm
    """
    return ((x1-x2)**2).sum().sqrt()

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# # -*- coding: utf-8 -*-
"""Utils to handle saved information in mongodb."""

from future.utils import iteritems

import pandas as pd
from pymongo import MongoClient


def expand_config(config):

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import keras.backend as K
import numpy as np
from keras.layers import Input, Dense, Lambda, Activation
from keras.models import Model


def temped_softmax(x, T=1):
    """Compute softmax values for each sets of scores in x."""
    return np.exp(x/T) / np.sum(np.exp(x/T), axis=0)

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