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# Copyright (c) 2021 ETH Zurich, Nikita Rudin

import torch
import numpy as np
from typing import Tuple


def split_and_pad_trajectories(tensor, dones):
    """ Splits trajectories at done indices. Then concatenates them and padds with zeros up to the length og the longest trajectory.
    Returns masks corresponding to valid parts of the trajectories
    Example: 
        Input: [ [a1, a2, a3, a4 | a5, a6],
                 [b1, b2 | b3, b4, b5 | b6]
                ]

        Output:[ [a1, a2, a3, a4], | [  [True, True, True, True],
                 [a5, a6, 0, 0],   |    [True, True, False, False],
                 [b1, b2, 0, 0],   |    [True, True, False, False],
                 [b3, b4, b5, 0],  |    [True, True, True, False],
                 [b6, 0, 0, 0]     |    [True, False, False, False],
                ]                  | ]    
            
    Assumes that the inputy has the following dimension order: [time, number of envs, aditional dimensions]
    """
    dones = dones.clone()
    dones[-1] = 1
    # Permute the buffers to have order (num_envs, num_transitions_per_env, ...), for correct reshaping
    flat_dones = dones.transpose(1, 0).reshape(-1, 1)

    # Get length of trajectory by counting the number of successive not done elements
    done_indices = torch.cat((flat_dones.new_tensor([-1], dtype=torch.int64), flat_dones.nonzero()[:, 0]))
    trajectory_lengths = done_indices[1:] - done_indices[:-1]
    trajectory_lengths_list = trajectory_lengths.tolist()
    # Extract the individual trajectories
    trajectories = torch.split(tensor.transpose(1, 0).flatten(0, 1),trajectory_lengths_list)
    padded_trajectories = torch.nn.utils.rnn.pad_sequence(trajectories)


    trajectory_masks = trajectory_lengths > torch.arange(0, tensor.shape[0], device=tensor.device).unsqueeze(1)
    return padded_trajectories, trajectory_masks

def unpad_trajectories(trajectories, masks):
    """ Does the inverse operation of  split_and_pad_trajectories()
    """
    # Need to transpose before and after the masking to have proper reshaping
    return trajectories.transpose(1, 0)[masks.transpose(1, 0)].view(-1, trajectories.shape[0], trajectories.shape[-1]).transpose(1, 0)


class RunningMeanStd(object):
    def __init__(self, epsilon: float = 1e-4, shape: Tuple[int, ...] = ()):
        """
        Calulates the running mean and std of a data stream
        https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
        :param epsilon: helps with arithmetic issues
        :param shape: the shape of the data stream's output
        """
        self.mean = np.zeros(shape, np.float64)
        self.var = np.ones(shape, np.float64)
        self.count = epsilon

    def update(self, arr: np.ndarray) -> None:
        batch_mean = np.mean(arr, axis=0)
        batch_var = np.var(arr, axis=0)
        batch_count = arr.shape[0]
        self.update_from_moments(batch_mean, batch_var, batch_count)

    def update_from_moments(self, batch_mean: np.ndarray, batch_var: np.ndarray, batch_count: int) -> None:
        delta = batch_mean - self.mean
        tot_count = self.count + batch_count

        new_mean = self.mean + delta * batch_count / tot_count
        m_a = self.var * self.count
        m_b = batch_var * batch_count
        m_2 = m_a + m_b + np.square(delta) * self.count * batch_count / (self.count + batch_count)
        new_var = m_2 / (self.count + batch_count)

        new_count = batch_count + self.count

        self.mean = new_mean
        self.var = new_var
        self.count = new_count


class Normalizer(RunningMeanStd):
    def __init__(self, input_dim, epsilon=1e-4, clip_obs=10.0):
        super().__init__(shape=input_dim)
        self.epsilon = epsilon
        self.clip_obs = clip_obs

    def normalize(self, input):
        return np.clip(
            (input - self.mean) / np.sqrt(self.var + self.epsilon),
            -self.clip_obs, self.clip_obs)

    def normalize_torch(self, input, device):
        mean_torch = torch.tensor(
            self.mean, device=device, dtype=torch.float32)
        std_torch = torch.sqrt(torch.tensor(
            self.var + self.epsilon, device=device, dtype=torch.float32))
        return torch.clamp(
            (input - mean_torch) / std_torch, -self.clip_obs, self.clip_obs)

    def update_normalizer(self, rollouts, expert_loader):
        policy_data_generator = rollouts.feed_forward_generator_amp(
            None, mini_batch_size=expert_loader.batch_size)
        expert_data_generator = expert_loader.dataset.feed_forward_generator_amp(
                expert_loader.batch_size)

        for expert_batch, policy_batch in zip(expert_data_generator, policy_data_generator):
            self.update(
                torch.vstack(tuple(policy_batch) + tuple(expert_batch)).cpu().numpy())