#!/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.
"""Inject dummy BiasAdd Graph Rewriter."""

import tensorflow as tf
from tensorflow.python.framework import dtypes
from neural_compressor.utils.utility import dump_elapsed_time

from ..graph_base import GraphRewriterBase
from neural_compressor.adaptor.tf_utils.graph_util import GraphAnalyzer
from neural_compressor.adaptor.tf_utils.graph_util import GraphRewriterHelper as Helper
from neural_compressor.adaptor.tf_utils.util import version1_gt_version2


class InjectDummyBiasAddOptimizer(GraphRewriterBase):
    """Inject dummy BiasAdd for MatMul, Conv2D for pattern fusion."""
    def __init__(self, model, outputs):
        """Initilization."""
        super().__init__(model)
        self.outputs = outputs

    @dump_elapsed_time("Pass InjectDummyBiasAddOptimizer")
    def do_transformation(self):
        """Inject dummy BiasAdd if MatMul, Conv2D missing the valid add ops behind them."""
        g = GraphAnalyzer()
        g.graph = self.model
        graph_info = g.parse_graph()
        g.get_frame_info()
        valid_ops = ('BiasAdd', 'Add', 'AddV2', 'AddN')
        target_nodes = g.query_fusion_pattern_nodes([['MatMul', 'Conv2D'],])
        for i in target_nodes:
            # only apply this pass for tensorflow old quantization API, pre_optimize does this check
            # use conv+dummy_biasadd+relu because TF do not support conv+relu now. 
            if i[0] in self.outputs:
                continue
            next_node_names = graph_info[i[0]].outputs
            if next_node_names and len(next_node_names) == 1 and \
                graph_info[Helper.node_name_from_input(next_node_names[0])].node.op in valid_ops:
                continue
            bias_node_name = i[0] + '_dummy_biasadd'
            bias_const_node_name = i[0] + '_dummy_biasadd_const'
            matmul_a_node_name = Helper.node_name_from_input(graph_info[i[0]].node.input[0])
            matmul_a_node = graph_info[matmul_a_node_name].node
            matmul_b_node_name = Helper.node_name_from_input(graph_info[i[0]].node.input[1])
            matmul_b_node = graph_info[matmul_b_node_name].node

            if matmul_a_node.op == 'Const' or matmul_b_node.op not in ['Const', 'Enter']:
                continue
            if matmul_b_node.op == 'Enter': # pragma: no cover
                parent_node = graph_info[Helper.node_name_from_input(matmul_b_node.input[0])].node
                if parent_node.op != 'Const':
                    continue
                else:
                    matmul_b_node = parent_node
                    matmul_b_node_name = matmul_b_node.name

            if graph_info[i[0]].node.op == 'MatMul':
                t_b_index = 0 if graph_info[i[0]].node.attr['transpose_b'].b else 1
            elif graph_info[i[0]].node.op == 'Conv2D' and graph_info[i[0]].node.attr['data_format'].s == b'NHWC':
                t_b_index = 3
            elif graph_info[i[0]].node.op == 'Conv2D' and graph_info[i[0]].node.attr['data_format'].s == b'NCHW':
                t_b_index = 1
            else:
                continue

            bias_add_length = matmul_b_node.attr['value'].tensor.tensor_shape.dim[t_b_index].size

            bias_add_content = [0.] * bias_add_length

            bias_const_node = Helper.create_constant_node(
                bias_const_node_name, bias_add_content, dtypes.float32, shape=[bias_add_length])

            if i[0] in g.parent_frame_details and g.parent_frame_details[i[0]]:         # pragma: no cover
                bias_const_enter_node = Helper.create_node(
                    'Enter', bias_const_node_name+'_enter', [bias_const_node_name])
                Helper.set_attr_string(bias_const_enter_node,
                                       'frame_name', g.parent_frame_details[i[0]].attr['frame_name'].s)
                Helper.set_attr_dtype(bias_const_enter_node, 'T', dtypes.float32)
                Helper.set_attr_bool(bias_const_enter_node, 'is_constant', True)
                Helper.set_attr_int(bias_const_enter_node, 'parallel_iterations', \
                                    g.parent_frame_details[i[0]].attr['parallel_iterations'].i)

            bias_node = Helper.create_node('BiasAdd', bias_node_name, \
                        [i[0], bias_const_enter_node.name if i[0] in g.parent_frame_details \
                        and g.parent_frame_details[i[0]] else bias_const_node_name])
            Helper.set_attr_dtype(bias_node, "T", dtypes.float32)
            g.add_node(bias_node, i[0], next_node_names)
            if i[0] in g.parent_frame_details and g.parent_frame_details[i[0]]: # pragma: no cover
                g.add_node(bias_const_node, None, [bias_const_enter_node.name])
                g.add_node(bias_const_enter_node, bias_const_node_name, [bias_node_name])
            else:
                g.add_node(bias_const_node, None, [bias_node_name])

        return g.dump_graph()
