# -*- coding: UTF-8 -*-
# File: utils.py


import os
import sys
from contextlib import contextmanager
import inspect
from datetime import datetime
from tqdm import tqdm
import numpy as np


__all__ = ['change_env',
           'get_rng',
           'fix_rng_seed',
           'get_tqdm',
           'execute_only_once',
           ]


@contextmanager
def change_env(name, val):
    """
    Args:
        name(str), val(str):

    Returns:
        a context where the environment variable ``name`` being set to
        ``val``. It will be set back after the context exits.
    """
    oldval = os.environ.get(name, None)
    os.environ[name] = val
    yield
    if oldval is None:
        del os.environ[name]
    else:
        os.environ[name] = oldval


_RNG_SEED = None


def fix_rng_seed(seed):
    """
    Call this function at the beginning of program to fix rng seed within tensorpack.

    Args:
        seed (int):

    Note:
        See https://github.com/ppwwyyxx/tensorpack/issues/196.
    """
    global _RNG_SEED
    _RNG_SEED = int(seed)


def get_rng(obj=None):
    """
    Get a good RNG seeded with time, pid and the object.

    Args:
        obj: some object to use to generate random seed.
    Returns:
        np.random.RandomState: the RNG.
    """
    seed = (id(obj) + os.getpid() +
            int(datetime.now().strftime("%Y%m%d%H%M%S%f"))) % 4294967295
    if _RNG_SEED is not None:
        seed = _RNG_SEED
    return np.random.RandomState(seed)


_EXECUTE_HISTORY = set()


def execute_only_once():
    """
    Each called in the code to this function is guranteed to return True the
    first time and False afterwards.

    Returns:
        bool: whether this is the first time this function gets called from this line of code.

    Example:
        .. code-block:: python

            if execute_only_once():
                # do something only once
    """
    f = inspect.currentframe().f_back
    ident = (f.f_code.co_filename, f.f_lineno)
    if ident in _EXECUTE_HISTORY:
        return False
    _EXECUTE_HISTORY.add(ident)
    return True


def get_tqdm_kwargs(**kwargs):
    """
    Return default arguments to be used with tqdm.

    Args:
        kwargs: extra arguments to be used.
    Returns:
        dict:
    """
    default = dict(
        smoothing=0.5,
        dynamic_ncols=True,
        ascii=True,
        bar_format='{l_bar}{bar}|{n_fmt}/{total_fmt}[{elapsed}<{remaining},{rate_noinv_fmt}]'
    )

    f = kwargs.get('file', sys.stderr)
    isatty = f.isatty()
    # TODO when run under mpirun, isatty is always False
    # Jupyter notebook should be recognized as tty.
    # Wait for https://github.com/ipython/ipykernel/issues/268
    try:
        from ipykernel import iostream
        if isinstance(f, iostream.OutStream):
            isatty = True
    except ImportError:
        pass

    if isatty:
        default['mininterval'] = 0.5
    else:
        # If not a tty, don't refresh progress bar that often
        default['mininterval'] = 300
    default.update(kwargs)
    return default


def get_tqdm(**kwargs):
    """ Similar to :func:`get_tqdm_kwargs`,
    but returns the tqdm object directly. """
    return tqdm(**get_tqdm_kwargs(**kwargs))

@@ -988,16 +988,302 @@
 ues/196.
+%0A%0A    Examples:%0A%0A        Fix random seed in both tensorpack and tensorflow.%0A%0A    .. code-block:: python%0A%0A            import tensorpack.utils.utils as utils%0A%0A            seed = 42%0A            utils.fix_rng_seed(seed)%0A            tesnorflow.set_random_seed(seed)%0A            # run trainer
 %0A    %22%22%22

Update utils.py (#616)