"""
 Copyright (c) 2018-2019 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.
"""

import logging as log

from mo.front.common.partial_infer.elemental import copy_shape_infer


def tf_lrn_ext(pb):
    """
    TF and IE(CAFFE) parameters in LRN differs in several places :
        region (IE) : in TF there is no such parameter, they just use last dimension (feature dimension in case of NHWC)
        local-size (IE) : it's the size of 1D vector in Caffe. In TF they have 'depth_radius' that eq
        '(local-size * 2) + 1'
        alpha (IE) : in Caffe 'alpha' divides on local-size, so we should multiply alpha on local-size

    Caffe ref : http://caffe.berkeleyvision.org/tutorial/layers/lrn.html
    TF ref : https://www.tensorflow.org/api_docs/python/tf/nn/local_response_normalization
    """

    return {
        'type': 'Norm',
        'alpha': pb.attr['alpha'].f * (2. * pb.attr['depth_radius'].i + 1.),
        'beta': pb.attr['beta'].f,
        'bias': pb.attr['bias'].f,
        'local_size': (2 * pb.attr['depth_radius'].i + 1),
        'region': 'across',
        'infer': copy_shape_infer
    }
