#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2021 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 tensorflow.core.framework import attr_value_pb2
from tensorflow.python.framework import tensor_util
from tensorflow.python.framework import dtypes
from lpot.utils.utility import dump_elapsed_time

from ..graph_base import GraphRewriterBase
from ..graph_util import GraphAnalyzer
from ..graph_util import GraphRewriterHelper as Helper


class FuseColumnWiseMulOptimizer(GraphRewriterBase):
    """Fuse Mul op into Conv2D/DepthwiseConv2dNative/MatMul
    Mul + Conv2D/DepthwiseConv2dNative/MatMul --> Conv2D/DepthwiseConv2dNative/MatMul
    """
    @dump_elapsed_time("Pass FuseColumnWiseMulOptimizer")
    def do_transformation(self):
        cur_graph = GraphAnalyzer()
        cur_graph.graph = self.model

        graph_info = cur_graph.parse_graph()
        target_nodes = cur_graph.query_fusion_pattern_nodes(
            [["Conv2D", "DepthwiseConv2dNative", "MatMul"], "Mul"])

        for node_combination in target_nodes:
            upper_node = graph_info[node_combination[0]].node
            mul_node = graph_info[node_combination[1]].node
            if graph_info[Helper.node_name_from_input(mul_node.input[1])].node.op != "Const":
                continue
            weights_node = graph_info[graph_info[node_combination[0]].node.input[1]].node
            mul_value_node = graph_info[graph_info[node_combination[1]].node.input[1]].node
            upper_node_type = upper_node.op

            if upper_node_type == 'Conv2D':
                weights_col = weights_node.attr['value'].tensor.tensor_shape.dim[3].size
            elif upper_node_type == 'DepthwiseConv2dNative':
                weights_col = weights_node.attr['value'].tensor.tensor_shape.dim[2].size * \
                    weights_node.attr['value'].tensor.tensor_shape.dim[3].size
            else:
                weights_col = weights_node.attr['value'].tensor.tensor_shape.dim[1].size

            mul_value_node_tensor = mul_value_node.attr['value'].tensor
            weights_node_tensor = weights_node.attr['value'].tensor
            if len(mul_value_node_tensor.tensor_shape.dim
                   ) != 1 or mul_value_node_tensor.tensor_shape.dim[0].size != weights_col:
                self.logger.debug("Invalid Mul OP fusion.")
                return self.model

            mul_value_node_list = [i for i in tensor_util.MakeNdarray(mul_value_node_tensor).flat]
            new_weights = []
            for index, i in enumerate(tensor_util.MakeNdarray(weights_node_tensor).flat):
                new_weights_value = i * mul_value_node_list[index % len(mul_value_node_list)]
                new_weights.append(new_weights_value)

            weights_node.attr['value'].CopyFrom(
                attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
                    new_weights, dtypes.float32,
                    tensor_util.MakeNdarray(weights_node_tensor).shape)))

            cur_graph.remove_node_with_single_input_output(mul_node.name)
            cur_graph.remove_node(mul_node.input[1])

        return cur_graph.dump_graph()
