# Adapted from pytorch torchnet
# https://github.com/pytorch/tnt/blob/master/torchnet/transform.py
###############################################################################
# BSD 3-Clause License

# Copyright (c) 2017- Sergey Zagoruyko,
# Copyright (c) 2017- Sasank Chilamkurthy,
# All rights reserved.

# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:

# * Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer.

# * Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.

# * Neither the name of the copyright holder nor the names of its
#   contributors may be used to endorse or promote products derived from
#   this software without specific prior written permission.

# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
###############################################################################

import boardom as bd


def compose(*transforms):
    """Composes list of transforms (each accept and return one item).

    (From PyTorchNet_)

    .. _PyTorchNet: https://github.com/pytorch/tnt

    Args:
         transforms: Callables, each accepting
             and returning one item.

    Returns:
         callable: The composed transforms.
    Note:
         If no arguments are given, identity is returned

    """
    if transforms:
        for transform in transforms:
            assert callable(transform), "list of functions expected"

        def composition(obj):
            "Composite function"
            for transform in transforms:
                obj = transform(obj)
            return obj

        return composition
    else:
        return bd.identity
