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import tensorflow as tf
import torch

from torchmetrics import Metric


def tf2pt(x_tf=None):
    if x_tf is None:
        return None

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from sklearn.datasets import load_boston
from sklearn.linear_model import (LinearRegression, Ridge, SGDRegressor,
                                  Lasso, ElasticNetCV)

from sklearn.preprocessing import MinMaxScaler
import numpy as np
#from minepy import MINE

from sklearn.metrics import mean_squared_error
 

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from sklearn.datasets import load_boston
from sklearn.linear_model import (LinearRegression, Ridge, LassoCV, ElasticNetCV,
                                  ElasticNet, Lasso, RandomizedLasso)
from sklearn.feature_selection import RFE, f_regression
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
import numpy as np
import pdb
#from minepy import MINE

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from sklearn.datasets import load_boston
from sklearn.linear_model import (LinearRegression, Ridge, 
                                  Lasso, RandomizedLasso)
from sklearn.feature_selection import RFE, f_regression
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
import numpy as np
#from minepy import MINE

from sklearn.metrics import mean_squared_error

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import torch
import torch.nn as nn


class EvoNorm2d(nn.Module):
    __constants__ = ['num_features', 'eps', 'nonlinearity']
    
    def __init__(self, num_features, eps=1e-5, nonlinearity=True):
        super(EvoNorm2d, self).__init__()
        

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def input_fn(file_pattern, labels,
             image_size=(224,224),
             shuffle=False,
             batch_size=64, 
             num_epochs=None, 
             buffer_size=4096,
             prefetch_buffer_size=None):

    table = tf.contrib.lookup.index_table_from_tensor(mapping=tf.constant(labels))
    num_classes = len(labels)

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class AccSGD(Optimizer):
    """AccSGD optimizer.

    Arguments:
        lr (float): learning rate
        kappa (float, optional): ratio of long to short step (default: 1000)
        xi (float, optional): statistical advantage parameter (default: 10)
        smallConst (float, optional): any value <=1 (default: 0.7)

    # References

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class AMSgrad(Optimizer):
    """AMSGrad optimizer.

    Default parameters follow those provided in the Adam paper.

    # Arguments
        lr: float >= 0. Learning rate.
        beta_1: float, 0 < beta < 1. Generally close to 1.
        beta_2: float, 0 < beta < 1. Generally close to 1.
        epsilon: float >= 0. Fuzz factor.

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'''Trains a simple convnet on the Zalando MNIST dataset.

Gets to 81.03% test accuracy after 30 epochs
(there is still a lot of margin for parameter tuning).
3 seconds per epoch on a GeForce GTX 980 GPU with CuDNN 5.
'''

from __future__ import print_function
import numpy as np
from mnist import MNIST

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import numpy as np
import gym
from gym.spaces import Discrete, Box
from gym.wrappers import Monitor
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten


# ================================================================
# Policies