# Copyright 2014-2017 Intel Corporation
#
# 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 sys import version_info
if version_info >= (2, 6, 0):
    def swig_import_helper():
        from os.path import dirname
        import imp
        fp = None
        try:
            fp, pathname, description = imp.find_module('_backward20', [dirname(__file__)])
        except ImportError:
            import _backward20
            return _backward20
        if fp is not None:
            try:
                _mod = imp.load_module('_backward20', fp, pathname, description)
            finally:
                fp.close()
            return _mod
    _backward20 = swig_import_helper()
    del swig_import_helper
else:
    import _backward20
del version_info
try:
    _swig_property = property
except NameError:
    pass  # Python < 2.2 doesn't have 'property'.


def _swig_setattr_nondynamic(self, class_type, name, value, static=1):
    if (name == "thisown"):
        return self.this.own(value)
    if (name == "this"):
        if type(value).__name__ == 'SwigPyObject':
            self.__dict__[name] = value
            return
    method = class_type.__swig_setmethods__.get(name, None)
    if method:
        return method(self, value)
    if (not static):
        if _newclass:
            object.__setattr__(self, name, value)
        else:
            self.__dict__[name] = value
    else:
        raise AttributeError("You cannot add attributes to %s" % self)


def _swig_setattr(self, class_type, name, value):
    return _swig_setattr_nondynamic(self, class_type, name, value, 0)


def _swig_getattr_nondynamic(self, class_type, name, static=1):
    if (name == "thisown"):
        return self.this.own()
    method = class_type.__swig_getmethods__.get(name, None)
    if method:
        return method(self)
    if (not static):
        return object.__getattr__(self, name)
    else:
        raise AttributeError(name)

def _swig_getattr(self, class_type, name):
    return _swig_getattr_nondynamic(self, class_type, name, 0)


def _swig_repr(self):
    try:
        strthis = "proxy of " + self.this.__repr__()
    except:
        strthis = ""
    return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)

try:
    _object = object
    _newclass = 1
except AttributeError:
    class _object:
        pass
    _newclass = 0


import daal.algorithms.neural_networks.initializers
import daal.algorithms.neural_networks
import daal.algorithms
import daal.data_management
import daal
import daal.services
import daal.data_management.data_feature_utils
import daal.algorithms.neural_networks.layers.backward
import daal.algorithms.neural_networks.layers
import daal.algorithms.neural_networks.layers.forward
import daal.algorithms.neural_networks.layers.pooling1d
class Input(daal.algorithms.neural_networks.layers.backward.Input):
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.neural_networks.layers.backward.Input]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Input, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.neural_networks.layers.backward.Input]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Input, name)

    def __init__(self, *args, **kwargs):
        raise AttributeError("No constructor defined - class is abstract")
    __repr__ = _swig_repr
    __swig_destroy__ = _backward20.delete_Input
    __del__ = lambda self: None

    def getGradientSize(self):
        return _backward20.Input_getGradientSize(self)
Input_swigregister = _backward20.Input_swigregister
Input_swigregister(Input)

class Result(daal.algorithms.neural_networks.layers.backward.Result):
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.neural_networks.layers.backward.Result]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Result, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.neural_networks.layers.backward.Result]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Result, name)
    __repr__ = _swig_repr

    def __init__(self):
        this = _backward20.new_Result()
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _backward20.delete_Result
    __del__ = lambda self: None

    def check(self, input, parameter, method):
        return _backward20.Result_check(self, input, parameter, method)

    def allocate_Float64(self, input, parameter, method):
        r"""
This function is specialized for float64"""
        return _backward20.Result_allocate_Float64(self, input, parameter, method)


    def allocate_Float32(self, input, parameter, method):
        r"""
This function is specialized for float32"""
        return _backward20.Result_allocate_Float32(self, input, parameter, method)

Result_swigregister = _backward20.Result_swigregister
Result_swigregister(Result)

from numpy import float64, float32, intc


# This file is compatible with both classic and new-style classes.


