"""
 Copyright (C) 2018-2021 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.
"""
import networkx as nx

from extensions.ops.gather import Gather
from extensions.ops.transpose import Transpose
from mo.front.common.partial_infer.utils import int64_array
from mo.graph.graph import Node
from mo.ops.const import Const


def get_node_with_permutation(node: Node, port_info: str):
    node_type, port = port_info.split(':')
    port = int(port)
    return node.in_node(port) if node_type == 'input' else node.out_node(port)


def axis(op_node: Node, port_info: str, input_port: int):
    """
    Performs layout change related transformation of the data on the in_port_idx port of op_node.
    Translates shape indexes from one layout to another according to inverse permutation

    Transformation inserts Gather operation with
        permutation as 0-port input data and
        actual data to translate as 1-port input indexes of Gather

    For example:
        NHWC Reduce operation has 0-port input with data of shape [1, 2, 3, 4] and
        1-port input with axis indices [0, 1].

        After translating such operation to NCHW layout:
            0-port input shape = [1, 4, 2, 3]
            1-port input axis indices = [0, 2]
    """
    graph = op_node.graph

    permutation_data_node = get_node_with_permutation(op_node, port_info)
    assert permutation_data_node.has_and_set('permutation'), 'Data node "{}" does not have permutation for node {}, ' \
                                                             'port_info "{}".'.format(permutation_data_node.id,
                                                                                      op_node.id, port_info)
    permutation = permutation_data_node.permutation
    if len(permutation.perm) == 0:
        return

    data_node = op_node.in_node(input_port)

    gather_name = op_node.soft_get('name', op_node.id) + '/AxisGather'
    const = Const(graph, {'value': permutation.inv, 'name': gather_name + '/const',
                          'need_shape_inference': True}).create_node_with_data()
    axis_const = Const(graph, {'value': int64_array(0), 'name': gather_name + '/axis'}).create_node_with_data()
    gather = Gather(graph, {'name': gather_name, 'need_shape_inference': True}).create_node_with_data(
        [const, data_node, axis_const])
    attrs = graph.get_edge_data(data_node.id, op_node.id, key=0).copy()
    graph.add_edge(gather.id, op_node.id, **attrs)
    graph.remove_edge(data_node.id, op_node.id)
    op_node['need_shape_inference'] = True


def order(op_node: Node, port_info: str, input_port: int):
    """
        Performs layout change related transformation of the data on the in_port_idx port of op_node.
        Translates ordered shape indexes from one layout to another according to permutation

        Transformation inserts two Gather operations

        1 Gather reorders data to new layout according to direct permutation:
            actual data to translate as 1-port input indexes of Gather and
            permutation as 0-port input data
        2 Gather translates shape indexes from one layout to another according to inverse permutation
            permutation as 0-port input data and
            actual data to translate as 1-port input indexes of Gather

    For example:
        NHWC Transpose operation has 0-port input with data of shape [1, 2, 3, 4] and
        1-port input with new order indices [0, 1, 3, 2].

        After translating such operation to NCHW layout:
            0-port input shape = [1, 4, 2, 3]

        1 phase (after first Gather insertion):
            1-port input order indices = [0, 2, 1, 3]
        2 phase (after second Gather insertion):
            1-port input order indices = [0, 3, 2, 1]
    """
    graph = op_node.graph
    permutation_data_node = get_node_with_permutation(op_node, port_info)
    assert permutation_data_node.has_and_set('permutation'), 'Data node "{}" does not have permutation for node {}, ' \
                                                             'port_info "{}".'.format(permutation_data_node.id,
                                                                                      op_node.id, port_info)
    permutation = permutation_data_node.permutation
    if len(permutation.perm) == 0:
        return

    data_node = op_node.in_node(input_port)

    gather_name = op_node.soft_get('name', op_node.id) + '/OrderGather_1'
    const = Const(graph, {'value': permutation.perm, 'name': gather_name + '/const',
                          'need_shape_inference': True}).create_node_with_data()
    axis_const = Const(graph, {'value': int64_array(0), 'name': gather_name + '/axis'}).create_node_with_data()
    gather = Gather(graph, {'name': gather_name,
                            'need_shape_inference': True}).create_node_with_data([data_node, const, axis_const])

    gather_1_name = op_node.soft_get('name', op_node.id) + '/OrderGather_2'
    const_1 = Const(graph, {'value': permutation.inv, 'name': gather_1_name + '/const',
                            'need_shape_inference': True}).create_node_with_data()
    axis_const_1 = Const(graph, {'value': int64_array(0), 'name': gather_1_name + '/axis'}).create_node_with_data()
    gather_1 = Gather(graph, {'name': gather_1_name,
                              'need_shape_inference': True}).create_node_with_data([const_1, gather, axis_const_1])

    attrs = graph.get_edge_data(data_node.id, op_node.id, key=0).copy()
    graph.add_edge(gather_1.id, op_node.id, **attrs)
    graph.remove_edge(data_node.id, op_node.id)
    op_node['need_shape_inference'] = True


def strided_slice(op_node: Node, port_info: str, input_port: int):
    """
    StridedSLice must be permuted even if input or output tensors have rank lesser than 4
    e.g. input_shape = (1, 10, 10), out = input[:, 0:10, :, new_axis], input_rank < 4
    input_shape = (1, 10, 10, 3), out = input[:, 0:5, 0:4, 0], output_rank < 4
    in both examples slice_rank is >= 4
    slice_rank is defined by length of begin, end, strides (they all are of the same length)
    """
    permutation_data_node = get_node_with_permutation(op_node, port_info)
    assert permutation_data_node.has_and_set('permutation'), 'Data node "{}" does not have permutation for node {}, ' \
                                                             'port_info "{}".'.format(permutation_data_node.id,
                                                                                      op_node.id, port_info)
    permute_indices_for_gather = permutation_data_node.permutation.perm
    if len(permute_indices_for_gather) == 0:
        return
    from mo.ops.op import PermuteAttrs

    slice_rank = op_node.in_port(input_port).data.get_shape()[0]  # length of begin, end or strides
    permute_indices_for_gather = PermuteAttrs.get_nhwc_to_nchw_permutation(slice_rank).perm
    reorder_inputs_for_shape_or_slice(op_node, input_port, permute_indices_for_gather)


def shape(op_node: Node, port_info: str, input_port: int):
    permutation_data_node = get_node_with_permutation(op_node, port_info)
    assert permutation_data_node.has_and_set('permutation'), 'Data node "{}" does not have permutation for node {}, ' \
                                                             'port_info "{}".'.format(permutation_data_node.id,
                                                                                      op_node.id, port_info)
    permute_indices_for_gather = permutation_data_node.permutation.perm
    if len(permute_indices_for_gather) == 0:
        return
    reorder_inputs_for_shape_or_slice(op_node, input_port, permute_indices_for_gather)


def reorder_inputs_for_shape_or_slice(op_node: Node, input_port: int, permute_indices_for_gather: list):
    """
    axis and slice permutations are almost the same the only difference is that for slice in general
    case permutation depends from slice_rank not from input_rank or output_rank
    """
    graph = op_node.graph
    data_node = op_node.in_node(input_port)

    gather_name = op_node.soft_get('name', op_node.id) + '/ShapeGather'
    const = Const(graph, {'value': permute_indices_for_gather, 'name': gather_name + '/const',
                          'need_shape_inference': True}).create_node_with_data()
    axis_const = Const(graph, {'value': int64_array(0), 'name': gather_name + '/axis'}).create_node_with_data()
    gather = Gather(graph, {'name': gather_name,
                            'need_shape_inference': True}).create_node_with_data([data_node, const, axis_const])
    attrs = graph.get_edge_data(data_node.id, op_node.id, key=0).copy()

    graph.add_edge(gather.id, op_node.id, **attrs)
    graph.remove_edge(data_node.id, op_node.id)

    # need to run manually to override output shape value to resolve shape collision for nodes with
    # 'correct_data_layout' output port attrs
    op_node['need_shape_inference'] = True


def transpose(op_node: Node, port_info: str, input_port: int):
    graph = op_node.graph
    permutation_data_node = get_node_with_permutation(op_node, port_info)
    assert permutation_data_node.has_and_set('permutation'), \
        'Data node "{}" does not have permutation for node {}, port_info "{}".'.format(
            permutation_data_node.id, op_node.id, port_info)
    permutation = permutation_data_node.permutation
    if len(permutation.perm) == 0:
        return

    transpose_name = op_node.soft_get('name', op_node.id) + '/Transpose'
    from mo.front.tf.graph_utils import create_op_with_const_inputs  # avoiding recursive imports
    transpose = create_op_with_const_inputs(
        graph, Transpose, {1: permutation.perm}, {'name': transpose_name, 'override_output_shape': True})
    op_node.in_port(input_port).get_connection().insert_node(transpose)
    transpose.infer(transpose)


def transpose_nchw_to_nhwc(op_node: Node, port_info: str, input_port: int):
    graph = op_node.graph
    permutation_data_node = get_node_with_permutation(op_node, port_info)
    rank = len(permutation_data_node.shape)
    assert rank >= 4, 'Rank must be 4D or higher for HCHW to HHWC permutation on node {}.'.format(op_node.id)

    perm = list(range(rank))
    perm.insert(1, perm.pop())
    perm = int64_array(perm)

    transpose_name = op_node.soft_get('name', op_node.id) + '/Transpose'
    from mo.front.tf.graph_utils import create_op_with_const_inputs  # avoiding recursive imports
    transpose = create_op_with_const_inputs(
        graph, Transpose, {1: perm}, {'name': transpose_name, 'override_output_shape': True})
    op_node.in_port(input_port).get_connection().insert_node(transpose)
    transpose.infer(transpose)


class PermuteInputs:
    input_permutes = {
        'axis': lambda node, port_info, input_port: axis(node, port_info, input_port),
        'slice': lambda node, port_info, input_port: strided_slice(node, port_info, input_port),
        'order': lambda node, port_info, input_port: order(node, port_info, input_port),
        'shape': lambda node, port_info, input_port: shape(node, port_info, input_port),
        'transpose': lambda node, port_info, input_port: transpose(node, port_info, input_port),
        'transpose_nchw_to_nhwc': lambda node, port_info, input_port: transpose_nchw_to_nhwc(node, port_info,
                                                                                             input_port),
    }

    shape_check_rules = {
        'rank': lambda port: bool(len(port.data.get_shape()) >= 4),
        'dim_size': lambda port: bool(port.data.get_shape()[0] >= 4),  # if input 'dim_size' >= 4 need to permute
    }

    def set_input_permutation(self, node1: Node, node2: Node, port_info: str, permutation_rule: str,
                              shape_check_rule: str = 'rank'):
        """
        Sets input permutation attribute on the edge between node1 and node2.
        Input permutation consists of function that perform input permutation and
        input port info 'input' or 'output' + <port_number> that points on the input with PermuteAttr.Permutation which
        current input depends on.

        shape_check_rule defines the check rule if the op node inputs need to be permuted.
        By default 'rank' rule is applied, 'dim_size' is used only for StridedSlice so far.
        """
        assert permutation_rule in self.input_permutes, 'No `{}` permutation rule in {}'.format(permutation_rule,
                                                                                                __class__.__name__)
        assert shape_check_rule in self.shape_check_rules, 'No `{}` permutation shape check rule ' \
                                                           'in {}'.format(shape_check_rule, __class__.__name__)
        nx.set_edge_attributes(G=node1.graph,
                               values={(node1.id, node2.id, 0): (self.input_permutes[permutation_rule], port_info,
                                                                 self.shape_check_rules[shape_check_rule])},
                               name='input_permutation')
