import time
import torch
from .WandbLog import TensorboardLog

_GLOBAL_TIMERS = None
_GLOBAL_TENSORBOARD_WRITER = None


def set_global_variables(launch_time, tensorboard_path):
    _set_timers()
    _set_tensorboard_writer(launch_time, tensorboard_path)

def _set_timers():
    """Initialize timers."""
    global _GLOBAL_TIMERS
    _ensure_var_is_not_initialized(_GLOBAL_TIMERS, 'timers')
    _GLOBAL_TIMERS = Timers()

def _set_tensorboard_writer(launch_time, tensorboard_path):
    """Set tensorboard writer."""
    global _GLOBAL_TENSORBOARD_WRITER
    _ensure_var_is_not_initialized(_GLOBAL_TENSORBOARD_WRITER,
                                   'tensorboard writer')
    if torch.distributed.get_rank() == 0:
        _GLOBAL_TENSORBOARD_WRITER = TensorboardLog(tensorboard_path + f'/{launch_time}', launch_time)
    
def get_timers():
    """Return timers."""
    _ensure_var_is_initialized(_GLOBAL_TIMERS, 'timers')
    return _GLOBAL_TIMERS

def get_tensorboard_writer():
    """Return tensorboard writer. It can be None so no need
    to check if it is initialized."""
    return _GLOBAL_TENSORBOARD_WRITER

def _ensure_var_is_initialized(var, name):
    """Make sure the input variable is not None."""
    assert var is not None, '{} is not initialized.'.format(name)


def _ensure_var_is_not_initialized(var, name):
    """Make sure the input variable is not None."""
    assert var is None, '{} is already initialized.'.format(name)


class _Timer:
    """Timer."""

    def __init__(self, name):
        self.name_ = name
        self.elapsed_ = 0.0
        self.started_ = False
        self.start_time = time.time()

    def start(self):
        """Start the timer."""
        # assert not self.started_, 'timer has already been started'
        torch.cuda.synchronize()
        self.start_time = time.time()
        self.started_ = True

    def stop(self):
        """Stop the timer."""
        assert self.started_, 'timer is not started'
        torch.cuda.synchronize()
        self.elapsed_ += (time.time() - self.start_time)
        self.started_ = False

    def reset(self):
        """Reset timer."""
        self.elapsed_ = 0.0
        self.started_ = False

    def elapsed(self, reset=True):
        """Calculate the elapsed time."""
        started_ = self.started_
        # If the timing in progress, end it first.
        if self.started_:
            self.stop()
        # Get the elapsed time.
        elapsed_ = self.elapsed_
        # Reset the elapsed time
        if reset:
            self.reset()
        # If timing was in progress, set it back.
        if started_:
            self.start()
        return elapsed_


class Timers:
    """Group of timers."""

    def __init__(self):
        self.timers = {}

    def __call__(self, name):
        if name not in self.timers:
            self.timers[name] = _Timer(name)
        return self.timers[name]

    def write(self, names, writer, iteration, normalizer=1.0, reset=False):
        """Write timers to a tensorboard writer"""
        # currently when using add_scalars,
        # torch.utils.add_scalars makes each timer its own run, which
        # polutes the runs list, so we just add each as a scalar
        assert normalizer > 0.0
        for name in names:
            value = self.timers[name].elapsed(reset=reset) / normalizer
            writer.add_scalar(name + '-time', value, iteration)

    def log(self, names, normalizer=1.0, reset=True):
        """Log a group of timers."""
        assert normalizer > 0.0
        string = 'time (ms)'
        for name in names:
            elapsed_time = self.timers[name].elapsed(
                reset=reset) * 1000.0 / normalizer
            string += ' | {}: {:.2f}'.format(name, elapsed_time)
        if torch.distributed.is_initialized():
            if torch.distributed.get_rank() == (
                    torch.distributed.get_world_size() - 1):
                print(string, flush=True)
        else:
            print(string, flush=True)
