# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import numpy as np

import torch
import torch.nn as nn


class ValueNorm(nn.Module):
    """ Normalize a vector of observations - across the first norm_axes dimensions"""

    def __init__(self,
                 input_shape,
                 norm_axes=1,
                 beta=0.99999,
                 per_element_update=False,
                 epsilon=1e-5,
                 device=torch.device("cpu")):
        super(ValueNorm, self).__init__()

        self.input_shape = input_shape
        self.norm_axes = norm_axes
        self.epsilon = epsilon
        self.beta = beta
        self.per_element_update = per_element_update
        self.tpdv = dict(dtype=torch.float32, device=device)

        self.running_mean = nn.Parameter(torch.zeros(input_shape),
                                         requires_grad=False).to(**self.tpdv)
        self.running_mean_sq = nn.Parameter(
            torch.zeros(input_shape), requires_grad=False).to(**self.tpdv)
        self.debiasing_term = nn.Parameter(torch.tensor(0.0),
                                           requires_grad=False).to(**self.tpdv)

        self.reset_parameters()

    def reset_parameters(self):
        self.running_mean.zero_()
        self.running_mean_sq.zero_()
        self.debiasing_term.zero_()

    def running_mean_var(self):
        debiased_mean = self.running_mean / self.debiasing_term.clamp(
            min=self.epsilon)
        debiased_mean_sq = self.running_mean_sq / self.debiasing_term.clamp(
            min=self.epsilon)
        debiased_var = (debiased_mean_sq - debiased_mean**2).clamp(min=1e-2)
        return debiased_mean, debiased_var

    @torch.no_grad()
    def update(self, input_vector):
        if type(input_vector) == np.ndarray:
            input_vector = torch.from_numpy(input_vector)
        input_vector = input_vector.to(**self.tpdv)

        batch_mean = input_vector.mean(dim=tuple(range(self.norm_axes)))
        batch_sq_mean = (input_vector**2).mean(
            dim=tuple(range(self.norm_axes)))

        if self.per_element_update:
            batch_size = np.prod(input_vector.size()[:self.norm_axes])
            weight = self.beta**batch_size
        else:
            weight = self.beta

        self.running_mean.mul_(weight).add_(batch_mean * (1.0 - weight))
        self.running_mean_sq.mul_(weight).add_(batch_sq_mean * (1.0 - weight))
        self.debiasing_term.mul_(weight).add_(1.0 * (1.0 - weight))

    def normalize(self, input_vector):
        # Make sure input is float32
        if type(input_vector) == np.ndarray:
            input_vector = torch.from_numpy(input_vector)
        input_vector = input_vector.to(**self.tpdv)

        mean, var = self.running_mean_var()
        out = (input_vector - mean[(None, ) * self.norm_axes]
               ) / torch.sqrt(var)[(None, ) * self.norm_axes]

        return out

    def denormalize(self, input_vector):
        """ Transform normalized data back into original distribution """
        if type(input_vector) == np.ndarray:
            input_vector = torch.from_numpy(input_vector)
        input_vector = input_vector.to(**self.tpdv)

        mean, var = self.running_mean_var()
        out = input_vector * torch.sqrt(var)[(None, ) * self.norm_axes] + mean[
            (None, ) * self.norm_axes]

        out = out.cpu().numpy()

        return out
