# Copyright (c) 2017 Sony 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 nnabla.functions as F
from .utils import no_grad, positive_axis


def log_softmax_backward(inputs, axis=None):
    """
    Args:
      inputs (list of nn.Variable): Incomming grads/inputs to/of the forward function.
      kwargs (dict of arguments): Dictionary of the corresponding function arguments.

    Return:
      list of Variable: Return the gradients wrt inputs of the corresponding function.
    """
    dy = inputs[0]
    x0 = inputs[1]
    y0 = F.softmax(x0, axis=axis)
    D = len(x0.shape)
    axis = positive_axis(axis, D)
    dx0 = dy - y0 * F.sum(dy, axis=axis, keepdims=True)
    return dx0

# Copyright (c) 2017 Sony 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 nnabla.functions as F
from .utils import no_grad, positive_axis, get_output


def log_softmax_backward(inputs, axis=None):
    """
    Args:
      inputs (list of nn.Variable): Incomming grads/inputs to/of the forward function.
      kwargs (dict of arguments): Dictionary of the corresponding function arguments.

    Return:
      list of Variable: Return the gradients wrt inputs of the corresponding function.
    """
    dy = inputs[0]
    x0 = inputs[1]
    y0 = get_output(x0, "LogSoftmax")
    D = len(x0.shape)
    axis = positive_axis(axis, D)
    dx0 = dy - F.exp(y0) * F.sum(dy, axis=axis, keepdims=True)
    return dx0

modify to use get_output in log_softmax_backward