# -*- coding: utf-8 -*-
# File: varreplace.py
# Credit: Qinyao He

from contextlib import contextmanager

from ..compat import tfv1 as tf
from .common import get_tf_version_tuple

__all__ = ['custom_getter_scope', 'freeze_variables', 'remap_variables']


@contextmanager
def custom_getter_scope(custom_getter):
    """
    Args:
        custom_getter: the same as in :func:`tf.get_variable`

    Returns:
        The current variable scope with a custom_getter.
    """
    scope = tf.get_variable_scope()
    if get_tf_version_tuple() >= (1, 5):
        with tf.variable_scope(
                scope, custom_getter=custom_getter,
                auxiliary_name_scope=False):
            yield
    else:
        ns = tf.get_default_graph().get_name_scope()
        with tf.variable_scope(
                scope, custom_getter=custom_getter):
            with tf.name_scope(ns + '/' if ns else ''):
                yield


def remap_variables(fn):
    """
    Use fn to map the output of any variable getter.

    Args:
        fn (tf.Variable -> tf.Tensor)

    Returns:
        The current variable scope with a custom_getter that maps
        all the variables by fn.

    Example:
        .. code-block:: python

            with varreplace.remap_variables(lambda var: quantize(var)):
                x = FullyConnected('fc', x, 1000)   # fc/{W,b} will be quantized
    """
    def custom_getter(getter, *args, **kwargs):
        v = getter(*args, **kwargs)
        return fn(v)
    return custom_getter_scope(custom_getter)


def freeze_variables(stop_gradient=True, skip_collection=False):
    """
    Return a context to freeze variables,
    by wrapping ``tf.get_variable`` with a custom getter.
    It works by either applying ``tf.stop_gradient`` on the variables,
    or by keeping them out of the ``TRAINABLE_VARIABLES`` collection, or
    both.

    Example:
        .. code-block:: python

            with varreplace.freeze_variable(stop_gradient=False, skip_collection=True):
                x = FullyConnected('fc', x, 1000)   # fc/* will not be trained

    Args:
        stop_gradient (bool): if True, variables returned from `get_variable`
            will be wrapped with `tf.stop_gradient` and therefore has no
            gradient when used later.
            Note that the created variables may still have gradient when accessed
            by other approaches (e.g. by name, or by collection).
            Also note that this makes `tf.get_variable` returns a Tensor instead of a Variable,
            which may break existing code.
            Therefore, it's recommended to use the `skip_collection` option instead.
        skip_collection (bool): if True, do not add the variable to
            ``TRAINABLE_VARIABLES`` collection, but to ``MODEL_VARIABLES``
            collection. As a result they will not be trained by default.
    """
    def custom_getter(getter, *args, **kwargs):
        trainable = kwargs.get('trainable', True)
        name = args[0] if len(args) else kwargs.get('name')
        if skip_collection:
            kwargs['trainable'] = False
        v = getter(*args, **kwargs)
        if skip_collection:
            tf.add_to_collection(tf.GraphKeys.MODEL_VARIABLES, v)
        if trainable and stop_gradient:
            v = tf.stop_gradient(v, name='freezed_' + name)
        return v
    return custom_getter_scope(custom_getter)
