"""
 Copyright (C) 2018-2020 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.caffe.extractors.utils import embed_input
from mo.front.common.partial_infer.caffe_fallback import caffe_native_node_infer


def blob_name(i):
    """
    Implements legacy schema for blobs naming:
        0-th blob is called 'weights'
        1-th blob is called 'biases'
    then, next blobs are called according to the new default schema
    with 'custom_' prefix: custom_2, custom_3 and so on.
    """
    predefined_names = ['weights', 'biases']
    if i < len(predefined_names):
        return predefined_names[i]
    else:
        return 'custom_{}'.format(i)


def extract_custom_blobs(node):
    """
    Enumerate all blobs in node.model_pb, for each blob
    creates a new embedded input of name 'custom_X', where X is an index >= 0 according
    to the order blobs appear in node.model_pb. The order is also enforced by input port index.
    So the order of blobs is preserved in the final IR generation.
    Order is important because they can be accessed by indices (in addition to names).
    Update node attributes in-place.
    """
    base_port = len(node.in_nodes())
    if not hasattr(node.model_pb, 'blobs'):
        return
    for i, blob in enumerate(node.model_pb.blobs):
        port = base_port + i
        internal_name = '_custom_blob_' + str(i)
        log.debug("Found new custom blob of length {} for node {}. ".format(
            len(blob.data),
            node.name if node.has_valid('name') else '<UNKNOWN>'
        ) +
                  "It will appear as input {} and internal attribute {}.".format(
                      port,
                      internal_name))
        embed_input(node.graph.node[node.id], port, internal_name, blob.data, blob_name(i))


def native_caffe_node_extractor(node):
    extract_custom_blobs(node)
    return dict(infer=caffe_native_node_infer, top=list(node.pb.top)[0])
