#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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 __future__ import absolute_import

import numpy as np
from paddle2onnx.constant import dtypes
from paddle2onnx.op_mapper import OpMapper as op_mapper
from paddle2onnx.op_mapper import mapper_helper


@op_mapper(
    ['relu', 'tanh', 'log', 'sigmoid', 'sqrt'],
    mapper_dict={
        'relu': 'Relu',
        'tanh': 'Tanh',
        'log': 'Log',
        'sigmoid': 'Sigmoid',
        'sqrt': 'Sqrt',
    })
class ActivationOps():
    support_opset_verison_range = (1, 12)

    @classmethod
    def opset_1(cls, graph, node, **kw):
        onnx_type = kw['mapper_dict'][node.type]
        onnx_node = graph.make_node(
            onnx_type, inputs=node.input('X'), outputs=node.output('Out'))


@op_mapper('leaky_relu')
class LeakyRelu():
    support_opset_verison_range = (1, 12)

    @classmethod
    def opset_1(cls, graph, node, **kw):
        onnx_node = graph.make_node(
            'LeakyRelu',
            inputs=[node.input('X')[0]],
            outputs=node.output('Out'),
            alpha=node.attr('alpha'))


@op_mapper('prelu')
class PRelu():
    support_opset_verison_range = (1, 12)

    @classmethod
    def opset_1(cls, graph, node, **kw):
        onnx_node = graph.make_node(
            'PRelu',
            inputs=[node.input('X')[0], node.input('Alpha')[0]],
            outputs=node.output('Out'))


@op_mapper('relu6')
class Relu6():
    support_opset_verison_range = (1, 12)

    @classmethod
    def opset_1(cls, graph, node, **kw):
        mapper_helper.clip_helper(graph,
                                  node.input('X', 0),
                                  node.attr('threshold'), 0.0,
                                  node.output('Out', 0))


@op_mapper('gelu')
class Gelu():
    support_opset_verision_range = (7, 12)

    @classmethod
    def opset_7(cls, graph, node, **kw):
        if node.attr('approximate'):
            raise Exception("Not support approximate is True.")
        input = node.input('X', 0)
        sqrt2 = graph.make_node(
            'Constant', dtype=dtypes.ONNX.FLOAT, value=[1.4142135623730951])
        zero_point_five = graph.make_node(
            'Constant', dtype=dtypes.ONNX.FLOAT, value=[0.5])
        one = graph.make_node('Constant', dtype=dtypes.ONNX.FLOAT, value=[1])
        x = graph.make_node('Div', inputs=[input, sqrt2])
        x = graph.make_node('Erf', inputs=x)
        x = graph.make_node('Add', inputs=[x, one])
        x = graph.make_node('Mul', inputs=[input, x])
        graph.make_node(
            'Mul', inputs=[x, zero_point_five], outputs=node.output('Out'))


@op_mapper('hard_sigmoid')
class HardSigmoid():
    support_opset_verison_range = (1, 12)

    @classmethod
    def opset_1(cls, graph, node, **kw):
        slope = node.attr('slope')
        offset = node.attr('offset')
        graph.make_node(
            'HardSigmoid',
            inputs=node.input('X'),
            outputs=node.output('Out'),
            alpha=slope,
            beta=offset)


@op_mapper('swish')
class Swish():
    support_opset_verision_range = (7, 12)

    @classmethod
    def opset_7(cls, graph, node, **kw):
        beta_node = graph.make_node(
            'Constant',
            attrs={'dtype': dtypes.ONNX.FLOAT,
                   'value': [node.attr('beta')]})
        beta_x_node = graph.make_node(
            'Mul', inputs=[node.input('X')[0], beta_node])
        sigmoid_node = graph.make_node('Sigmoid', inputs=[beta_x_node])
        graph.make_node(
            'Mul',
            inputs=[node.input('X')[0], sigmoid_node],
            outputs=node.output('Out'))


@op_mapper('hard_swish')
class HardSwish():
    support_opset_verision_range = (7, 12)

    @classmethod
    def opset_7(cls, graph, node, **kw):
        scale_node = graph.make_node(
            'Constant',
            attrs={'dtype': dtypes.ONNX.FLOAT,
                   'value': node.attr('scale')})
        offset_node = graph.make_node(
            'Constant',
            attrs={'dtype': dtypes.ONNX.FLOAT,
                   'value': node.attr('offset')})

        node0 = graph.make_node('Add', inputs=[node.input('X')[0], offset_node])
        node1 = mapper_helper.clip_helper(graph, node0,
                                          node.attr('threshold'), 0.0)
        node2 = graph.make_node('Mul', inputs=[node.input('X')[0], node1])
        node3 = graph.make_node(
            'Div', inputs=[node2, scale_node], outputs=node.output('Out'))
