"""
General utils

Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
Please cite our work if the code is helpful to you.
"""

# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from datetime import datetime


@torch.no_grad()
def offset2bincount(offset):
    return torch.diff(
        offset, prepend=torch.tensor([0], device=offset.device, dtype=torch.long)
    )


@torch.no_grad()
def bincount2offset(bincount):
    return torch.cumsum(bincount, dim=0)


@torch.no_grad()
def offset2batch(offset):
    bincount = offset2bincount(offset)
    return torch.arange(
        len(bincount), device=offset.device, dtype=torch.long
    ).repeat_interleave(bincount)


@torch.no_grad()
def batch2offset(batch):
    return torch.cumsum(batch.bincount(), dim=0).long()


def get_random_seed():
    seed = (
        os.getpid()
        + int(datetime.now().strftime("%S%f"))
        + int.from_bytes(os.urandom(2), "big")
    )
    return seed


def set_seed(seed=None):
    if seed is None:
        seed = get_random_seed()
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    cudnn.benchmark = False
    cudnn.deterministic = True
    os.environ["PYTHONHASHSEED"] = str(seed)