# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
#
# 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 torch

from .global_vars import get_args
from .global_vars import get_current_global_batch_size
from .global_vars import get_num_microbatches
from .global_vars import get_signal_handler
from .global_vars import update_num_microbatches
from .global_vars import get_tokenizer
from .global_vars import get_tensorboard_writer
from .global_vars import get_adlr_autoresume
from .global_vars import get_timers
from .initialize  import initialize_megatron

import horovod.torch as hvd

def print_rank_0(message):
    """If distributed is initialized, print only on rank 0."""
    if torch.distributed.is_initialized():
        if torch.distributed.get_rank() == 0:
            print(message, flush=True)
    elif hvd.is_initialized():
        if hvd.rank() == 0:
            print(message, flush=True)
    else:
        print(message, flush=True)

def is_last_rank():
    return torch.distributed.get_rank() == (
        torch.distributed.get_world_size() - 1)

def print_rank_last(message):
    """If distributed is initialized, print only on last rank."""
    if torch.distributed.is_initialized():
        if is_last_rank():
            print(message, flush=True)
    else:
        print(message, flush=True)


def get_distributed_rank():
    if torch.distributed.is_initialized():
        return torch.distributed.get_rank()
    if hvd.is_initialized():
        return hvd.rank()
    return 0
