"""
 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 numpy as np

from mo.front.caffe.extractors.utils import embed_input
from mo.front.common.replacement import FrontReplacementOp
from mo.graph.graph import Node, Graph
from mo.ops.activation import Activation
from mo.ops.clamp import Clamp
from mo.ops.eltwise import Eltwise
from mo.ops.inner_product import InnerProduct
from mo.ops.memory import Memory
from mo.ops.scale_shift import ScaleShiftOp
from mo.ops.split import Split


def unique_id(prefix: str = 'id') -> str:
    """
    Generates a unique id
    The optional string prefix can be specified.
    """
    index = len(unique_id.names)
    name = prefix
    while name in unique_id.names:
        name = '{}_{}'.format(prefix, index)
        index += 1
    unique_id.names.append(name)
    return name


unique_id.names = []


class ReplaceLSTMNodePattern(FrontReplacementOp):
    op = "LSTMCell"
    enabled = True

    # we need to rewrite this transform to fit unified pipeline (it should be a part of traditional FRONT phase)
    def run_before(self):
        from extensions.front.output_cut import OutputCut
        return [OutputCut]

    def run_after(self):
        return []

    def replace_op(self, graph: Graph, node: Node):
        input_node = node.in_node()

        memory_pair_input = unique_id('id')
        memory_pair_output = unique_id('id')

        # Input -> FullyConnected
        fc_layer_after_input_attrs = {'name': 'input_fullyconnected',
                                      'num_output': node.gifo_x_weights_shape[0],
                                      'bias_term': True
                                      }

        embed_input(fc_layer_after_input_attrs, 1, 'weights', node.gifo_x_weights)
        embed_input(fc_layer_after_input_attrs, 2, 'biases', node.gifo_biases)
        fc_layer_after_input = InnerProduct(graph, fc_layer_after_input_attrs).create_node([input_node])

        prev_lstm_output = Memory(graph, {'name': 'prev_memory_output',
                                          'id': memory_pair_input,
                                          'index': 1,
                                          'size': 2,
                                          'shape': np.array([node.gifo_r_weights_shape[1]], dtype=np.int64)
                                          }).create_node()

        # *Memory(output) -> FullyConnected
        fc_layer_from_prev_state_attrs = {'name': 'prev_memory_output_fullyconnected',
                                          'num_output': node.gifo_r_weights_shape[0],
                                          'bias_term': False
                                          }

        embed_input(fc_layer_from_prev_state_attrs, 1, 'weights', node.gifo_r_weights)
        fc_layer_from_prev_state = InnerProduct(graph, fc_layer_from_prev_state_attrs).create_node(
            [prev_lstm_output])

        # Memory -> FullyConnected  \
        #                           *Eltwise(sum)
        # Input -> FullyConnected   /
        join_input_prev_state_sum = Eltwise(graph, {'name': 'join_input_eltwise',
                                                    'operation': 'sum'
                                                    }).create_node([fc_layer_from_prev_state,
                                                                    fc_layer_after_input])

        # *Eltwise(sum) -> Split
        # it is split into 4 nodes: Act, Eltw*3
        # the following order is mandatory
        #       ___Tanh
        #      /
        # Split ---(2)Eltwise(sum)
        #     |\
        #     | \__(3)Eltwise(sum)
        #     |____(4)Eltwise(sum)
        split_joined_input = Split(graph, {'name': 'join_input_split',
                                           'axis': 1,
                                           'num_split': 4,
                                           'out_ports_count': 4,
                                           }).create_node([join_input_prev_state_sum])

        prev_lstm_state = Memory(graph, {'name': 'prev_memory_state',
                                         'id': memory_pair_output,
                                         'index': 1,
                                         'size': 2,
                                         'shape': np.array([node.input_gate_weights.shape[0]], dtype=np.int64)
                                         }).create_node()

        # *Memory(state) -> *ScaleShift(input)
        state_input_scaleshift_attrs = {'name': 'input_scaleshift',
                                        'bias_term': False
                                        }
        embed_input(state_input_scaleshift_attrs, 1, 'weights', node.input_gate_weights)
        state_input_scaleshift = ScaleShiftOp(graph, state_input_scaleshift_attrs).create_node([prev_lstm_state])

        # *Memory(state) -> *ScaleShift(forget)
        state_forget_scaleshift_attrs = {'name': 'forget_scaleshift',
                                         'bias_term': False
                                         }
        embed_input(state_forget_scaleshift_attrs, 1, 'weights', node.forget_gate_weights)
        state_forget_scaleshift = ScaleShiftOp(graph, state_forget_scaleshift_attrs).create_node([prev_lstm_state])

        # Split                                 \
        #                                       (2)Eltwise(sum)
        # Memory(state) -> *ScaleShift(input)  /
        join_prev_lstm_input_joined_input_sum = Eltwise(graph, {'name': 'join_prev_lstm_input_joined_input_eltwise',
                                                                'operation': 'sum'
                                                                }).create_node([(split_joined_input, 1),
                                                                                state_input_scaleshift
                                                                                ])
        # Split                                 \
        #                                       (3)Eltwise(sum)
        # Memory(state) -> *ScaleShift(forget)  /
        join_prev_lstm_input_joined_forget_sum = Eltwise(graph, {'name': 'join_prev_lstm_input_joined_forget_sum',
                                                                 'operation': 'sum'
                                                                 }).create_node([(split_joined_input, 2),
                                                                                 state_forget_scaleshift
                                                                                 ])

        # Split -> Tanh
        remember_tahn = Activation(graph, {'name': 'remember_tahnv',
                                           'operation': 'tanh'
                                           }).create_node([(split_joined_input, 0)])

        # Split -> (2)Eltwise(sum) -> *Sigmoid
        remember_sigmoid = Activation(graph, {'name': 'remember_sigmoid',
                                              'operation': 'sigmoid'
                                              }).create_node(
            [join_prev_lstm_input_joined_input_sum])

        # Split -> (3)Eltwise(sum) -> **Sigmoid
        forget_sigmoid = Activation(graph, {'name': 'forget_sigmoid',
                                            'operation': 'sigmoid'
                                            }).create_node(
            [join_prev_lstm_input_joined_forget_sum])

        # *Memory(state)                        \
        #                                       (6)Eltwise(mul)
        # Split -> (3)Eltwise(sum) -> **Sigmoid /
        join_forget_prev_state_mul = Eltwise(graph, {'name': 'join_forget_prev_state_mul',
                                                     'operation': 'mul'
                                                     }).create_node(
            [forget_sigmoid, prev_lstm_state])

        # Split -> Tahn                         \
        #                                       (5)Eltwise(mul)
        # Split -> (2)Eltwise(sum) -> *Sigmoid   /
        join_remember_candidates_mul = Eltwise(graph, {'name': 'join_remember_candidates_mul',
                                                       'operation': 'mul'
                                                       }).create_node(
            [remember_tahn, remember_sigmoid])

        # (5)Eltwise(mul)  \
        #               (7)Eltwise(sum)
        # (6)Eltwise(mul)   /
        join_forget_remember_sum = Eltwise(graph, {'name': 'join_forget_remember_sum',
                                                   'operation': 'sum'
                                                   }).create_node(
            [join_forget_prev_state_mul, join_remember_candidates_mul])

        # (7)Eltwise(sum) -> Clamp
        join_forget_clamp = Clamp(graph, {'name': 'join_forget_clamp',
                                          'max': node.clip_value,
                                          'min': -node.clip_value
                                          }).create_node(
            [join_forget_remember_sum])
        #
        # Clamp -> (2)Memory(state)
        Memory(graph, {'name': 'next_lstm_state',
                       'id': memory_pair_output,
                       'index': 0,
                       'size': 2,
                       'shape': np.array([node.input_gate_weights.shape[0]], dtype=np.int64)
                       }).create_node([join_forget_clamp])

        # Clamp -> (2)Tahn
        state_filtered_tahn = Activation(graph, {'name': 'state_filtered_tahn',
                                                 'operation': 'tanh'
                                                 }).create_node([join_forget_clamp])

        # Clamp -> (2)ScaleShift
        clamp_scaleshift_attrs = {'name': 'clamp_scaleshift',
                                  'bias_term': False}
        embed_input(clamp_scaleshift_attrs, 1, 'weights', node.output_gate_weights)
        clamp_scaleshift = ScaleShiftOp(graph, clamp_scaleshift_attrs).create_node([join_forget_clamp])

        # Split                 \
        #                       (4)Eltwise(sum)
        # Clamp -> (2)ScaleShift /
        join_next_lstm_input_joined_input_sum = Eltwise(graph, {'name': 'join_next_lstm_input_joined_input_sum',
                                                                'operation': 'sum'
                                                                }).create_node([(split_joined_input, 3), clamp_scaleshift])

        # (4)Eltwise(sum) -> (3)Sigmoid
        output_sigmoid = Activation(graph, {'name': 'output_sigmoid',
                                            'operation': 'sigmoid'
                                            }).create_node(
            [join_next_lstm_input_joined_input_sum])

        # (4)Eltwise(sum) -> (3)Sigmoid         \
        #                                       (5)Eltwise(mul)
        # Clamp -> (2)Tahn                      /
        joined_output_mul = Eltwise(graph, {'name': 'joined_output_mul',
                                            'operation': 'mul'
                                            }).create_node([state_filtered_tahn, output_sigmoid])

        # (5)Eltwise(mul) -> (3)FullyConnected
        fc_output_attrs = {'name': 'FullyConnected',
                           'num_output': node.projection_weights_shape[0],
                           'bias_term': False}
        embed_input(fc_output_attrs, 1, 'weights', node.projection_weights)
        fc_output = InnerProduct(graph, fc_output_attrs).create_node([joined_output_mul])

        #                   / (2)Memory(output)
        # (3)FullyConnected
        #                   \ Output (any next node) (edge created automatically after replacement)
        Memory(graph, {'name': 'next_lstm_output',
                       'id': memory_pair_input,
                       'index': 0,
                       'size': 2,
                       'shape': np.array([node.gifo_r_weights_shape[1]], dtype=np.int64)
                       }).create_node([fc_output])

        return [fc_output.id]
