# 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.
import random
import re
import time

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from omegaconf import OmegaConf
from torch import distributions as pyd
from torch.distributions.utils import _standard_normal


class eval_mode:
    def __init__(self, *models):
        self.models = models

    def __enter__(self):
        self.prev_states = []
        for model in self.models:
            self.prev_states.append(model.training)
            model.train(False)

    def __exit__(self, *args):
        for model, state in zip(self.models, self.prev_states):
            model.train(state)
        return False


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


def soft_update_params(net, target_net, tau):
    for param, target_param in zip(net.parameters(), target_net.parameters()):
        target_param.data.copy_(tau * param.data +
                                (1 - tau) * target_param.data)


def to_torch(xs, device):
    return tuple(torch.as_tensor(x, device=device) for x in xs)


def weight_init(m):
    if isinstance(m, nn.Linear):
        nn.init.orthogonal_(m.weight.data)
        if hasattr(m.bias, 'data'):
            m.bias.data.fill_(0.0)
    elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
        gain = nn.init.calculate_gain('relu')
        nn.init.orthogonal_(m.weight.data, gain)
        if hasattr(m.bias, 'data'):
            m.bias.data.fill_(0.0)


def update_mean_var_count_from_moments(
    mean, var, count, batch_mean, batch_var, batch_count
):
    delta = batch_mean - mean
    tot_count = count + batch_count

    new_mean = mean + delta + batch_count / tot_count
    m_a = var * count
    m_b = batch_var * batch_count
    M2 = m_a + m_b + torch.pow(delta, 2) * count * batch_count / tot_count
    new_var = M2 / tot_count
    new_count = tot_count

    return new_mean, new_var, new_count

class TorchRunningMeanStd:
    def __init__(self, epsilon=1e-4, shape=(), device=None):
        self.mean = torch.zeros(shape, device=device)
        self.var = torch.ones(shape, device=device)
        self.count = epsilon

    def update(self, x):
        with torch.no_grad():
            batch_mean = torch.mean(x, axis=0)
            batch_var = torch.var(x, axis=0)
            batch_count = x.shape[0]
            self.update_from_moments(batch_mean, batch_var, batch_count)

    def update_from_moments(self, batch_mean, batch_var, batch_count):
        self.mean, self.var, self.count = update_mean_var_count_from_moments(
            self.mean, self.var, self.count, batch_mean, batch_var, batch_count
        )

    @property
    def std(self):
        return torch.sqrt(self.var)


class Until:
    def __init__(self, until, action_repeat=1):
        self._until = until
        self._action_repeat = action_repeat

    def __call__(self, step):
        if self._until is None:
            return True
        until = self._until // self._action_repeat
        return step < until


class Every:
    def __init__(self, every, action_repeat=1):
        self._every = every
        self._action_repeat = action_repeat

    def __call__(self, step):
        if self._every is None:
            return False
        every = self._every // self._action_repeat
        if step % every == 0:
            return True
        return False


class Timer:
    def __init__(self):
        self._start_time = time.time()
        self._last_time = time.time()

    def reset(self):
        elapsed_time = time.time() - self._last_time
        self._last_time = time.time()
        total_time = time.time() - self._start_time
        return elapsed_time, total_time

    def total_time(self):
        return time.time() - self._start_time


class TruncatedNormal(pyd.Normal):
    def __init__(self, loc, scale, low=-1.0, high=1.0, eps=1e-6):
        super().__init__(loc, scale, validate_args=False)
        self.low = low
        self.high = high
        self.eps = eps

    def _clamp(self, x):
        clamped_x = torch.clamp(x, self.low + self.eps, self.high - self.eps)
        x = x - x.detach() + clamped_x.detach()
        return x

    def sample(self, clip=None, sample_shape=torch.Size()):
        shape = self._extended_shape(sample_shape)
        eps = _standard_normal(shape,
                               dtype=self.loc.dtype,
                               device=self.loc.device)
        eps *= self.scale
        if clip is not None:
            eps = torch.clamp(eps, -clip, clip)
        x = self.loc + eps
        return self._clamp(x)


def schedule(schdl, step):
    try:
        return float(schdl)
    except ValueError:
        match = re.match(r'linear\((.+),(.+),(.+)\)', schdl)
        if match:
            init, final, duration = [float(g) for g in match.groups()]
            mix = np.clip(step / duration, 0.0, 1.0)
            return (1.0 - mix) * init + mix * final
        match = re.match(r'step_linear\((.+),(.+),(.+),(.+),(.+)\)', schdl)
        if match:
            init, final1, duration1, final2, duration2 = [
                float(g) for g in match.groups()
            ]
            if step <= duration1:
                mix = np.clip(step / duration1, 0.0, 1.0)
                return (1.0 - mix) * init + mix * final1
            else:
                mix = np.clip((step - duration1) / duration2, 0.0, 1.0)
                return (1.0 - mix) * final1 + mix * final2
    raise NotImplementedError(schdl)

class PNorm(nn.Module):
    def __init__(self, eps=1e-10):
        super().__init__()
        self.eps = eps
    def forward(self, x):
        assert len(x.shape) == 2
        return F.normalize(x, dim=1, eps=self.eps)
    
def avg_q(q1, q2):
    return (q1+q2)/2.0