#   Copyright (c) 2018 PaddlePaddle Authors. 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.

from __future__ import print_function
import os
from .layer_function_generator import generate_layer_fn, generate_activation_fn
from .. import core
from ..framework import convert_np_dtype_to_dtype_

__activations_noattr__ = [
    'sigmoid',
    'logsigmoid',
    'exp',
    'tanh',
    'atan',
    'tanh_shrink',
    'sqrt',
    'rsqrt',
    'abs',
    'ceil',
    'floor',
    'cos',
    'acos',
    'asin',
    'sin',
    'round',
    'reciprocal',
    'square',
    'softplus',
    'softsign',
]

__all__ = []

for _OP in set(__all__):
    globals()[_OP] = generate_layer_fn(_OP)

# It is a hot fix in some unittest using:
#   fluid.layers.scale(x=x, scale=10.0, out=out_var)
# e.g.: test_program_code.py, test_dist_train.py
globals()['_scale'] = generate_layer_fn('scale')

globals()['_elementwise_div'] = generate_layer_fn('elementwise_div')

__all__ += __activations_noattr__

for _OP in set(__activations_noattr__):
    globals()[_OP] = generate_activation_fn(_OP)

__all__ += ['softshrink']

_softshrink_ = generate_layer_fn('softshrink')


def softshrink(x, alpha=None):
    locals_var = locals().copy()
    kwargs = dict()
    for name, val in locals_var.items():
        if val is not None:
            if name == 'alpha':
                kwargs['lambda'] = val
            else:
                kwargs[name] = val
    return _softshrink_(**kwargs)


softshrink.__doc__ = """
:strong:`Softshrink Activation Operator`

..  math::
    out = \begin{cases}
            x - \alpha, \text{if } x > \alpha \\
            x + \alpha, \text{if } x < -\alpha \\
            0,  \text{otherwise}
            \end{cases}


Args:
    x: Input of Softshrink operator
    alpha (FLOAT): non-negative offset
    
Returns:
    Output of Softshrink operator

Examples:
    .. code-block:: python
    
        import paddle.fluid as fluid
        data = fluid.layers.data(name="input", shape=[784])
        result = fluid.layers.softshrink(x=data, alpha=0.3)
"""

__all__ += ['hard_shrink']

_hard_shrink_ = generate_layer_fn('hard_shrink')


def hard_shrink(x, threshold=None):
    locals_var = locals().copy()
    kwargs = dict()
    for name, val in locals_var.items():
        if val is not None:
            kwargs[name] = val
    return _hard_shrink_(**kwargs)


hard_shrink.__doc__ = _hard_shrink_.__doc__ + """
Examples:

    >>> import paddle.fluid as fluid
    >>> data = fluid.layers.data(name="input", shape=[784])
    >>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
"""

__all__ += ['cumsum']

_cum_sum_ = generate_layer_fn('cumsum')


def cumsum(x, axis=None, exclusive=None, reverse=None):
    locals_var = locals().copy()
    kwargs = dict()
    for name, val in locals_var.items():
        if val is not None:
            kwargs[name] = val
    return _cum_sum_(**kwargs)


cumsum.__doc__ = """
The cumulative sum of the elements along a given axis. By default, the first element of the result is the same of the first element of the input. If exlusive is true, the first element of the result is 0.

Args:
    x (Variable): Input of cumsum operator, the Tensor/LoDTensor needed to be cumsumed. 
    axis (int, optional): The dimenstion to accumulate along. -1 means the last dimenstion. Default is -1.
    exclusive (bool, optional): Whether to perform exclusive cumsum. Default is False.
    reverse (bool, optional): If true, the cumsum is performed in the reversed direction. Default is False.

Returns:
    Variable(Tensor/LoDTensor): The result of cumsum operator, output of cumsum operator. 

Examples:
    .. code-block:: python
        
        import paddle.fluid as fluid
        data = fluid.layers.data(name="input", shape=[32, 784])
        result = fluid.layers.cumsum(data, axis=0)
"""

__all__ += ['thresholded_relu']

_thresholded_relu_ = generate_layer_fn('thresholded_relu')


def thresholded_relu(x, threshold=None):
    locals_var = locals().copy()
    kwargs = dict()
    for name, val in locals_var.items():
        if val is not None:
            kwargs[name] = val

    return _thresholded_relu_(**kwargs)


thresholded_relu.__doc__ = _thresholded_relu_.__doc__ + """
Examples:

    >>> import paddle.fluid as fluid
    >>> data = fluid.layers.data(name="input", shape=[1])
    >>> result = fluid.layers.thresholded_relu(data, threshold=0.4)
"""