# 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 print_function

import unittest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.dygraph import GRUCell

import numpy as np

np.random.seed = 123


def sigmoid(x):
    return 1. / (1. + np.exp(-x))


def tanh(x):
    return 2. * sigmoid(2. * x) - 1.


def cudnn_step(step_input_np, pre_hidden_np, weight_ih, bias_ih, weight_hh,
               bias_hh):
    igates = np.matmul(step_input_np, weight_ih.transpose(1, 0))
    igates += bias_ih
    hgates = np.matmul(pre_hidden_np, weight_hh.transpose(1, 0))
    hgates += bias_hh

    chunked_igates = np.split(igates, indices_or_sections=3, axis=1)
    chunked_hgates = np.split(hgates, indices_or_sections=3, axis=1)

    reset_gate = chunked_igates[0] + chunked_hgates[0]
    reset_gate = sigmoid(reset_gate)

    input_gate = chunked_igates[1] + chunked_hgates[1]
    input_gate = sigmoid(input_gate)

    _temp = reset_gate * chunked_hgates[2]
    new_gate = chunked_igates[2] + _temp
    new_gate = tanh(new_gate)

    new_hidden = (pre_hidden_np - new_gate) * input_gate + new_gate

    return new_hidden


def non_cudnn_step(step_in, pre_hidden, gate_w, gate_b, candidate_w,
                   candidate_b):
    concat_1 = np.concatenate([step_in, pre_hidden], 1)

    gate_input = np.matmul(concat_1, gate_w)
    gate_input += gate_b
    gate_input = sigmoid(gate_input)
    r, u = np.split(gate_input, indices_or_sections=2, axis=1)

    r_hidden = r * pre_hidden

    candidate = np.matmul(np.concatenate([step_in, r_hidden], 1), candidate_w)

    candidate += candidate_b
    c = tanh(candidate)

    new_hidden = u * pre_hidden + (1 - u) * c

    return new_hidden


class TestCudnnGRU(unittest.TestCase):
    def setUp(self):
        self.input_size = 100
        self.hidden_size = 200
        self.batch_size = 64

    def test_run(self):

        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()

        with fluid.dygraph.guard(place):
            param_attr = fluid.ParamAttr(name="param_attr")
            bias_attr = fluid.ParamAttr(name="bias_attr")
            named_cudnn_gru = GRUCell(self.hidden_size, self.input_size,
                                      param_attr, bias_attr)
            cudnn_gru = GRUCell(self.hidden_size, self.input_size)

            param_list = cudnn_gru.state_dict()
            named_param_list = named_cudnn_gru.state_dict()

            # process weight and bias

            weight_ih_name = "_weight_ih"
            bias_ih_name = "_bias_ih"
            weight_hh_name = "_weight_hh"
            bias_hh_name = "_bias_hh"

            weight_ih = param_list[weight_ih_name].numpy()
            weight_ih = np.random.uniform(
                -0.1, 0.1, size=weight_ih.shape).astype('float64')
            param_list[weight_ih_name].set_value(weight_ih)
            named_param_list[weight_ih_name].set_value(weight_ih)

            bias_ih = param_list[bias_ih_name].numpy()
            bias_ih = np.random.uniform(
                -0.1, 0.1, size=bias_ih.shape).astype('float64')
            param_list[bias_ih_name].set_value(bias_ih)
            named_param_list[bias_ih_name].set_value(bias_ih)

            weight_hh = param_list[weight_hh_name].numpy()
            weight_hh = np.random.uniform(
                -0.1, 0.1, size=weight_hh.shape).astype('float64')
            param_list[weight_hh_name].set_value(weight_hh)
            named_param_list[weight_hh_name].set_value(weight_hh)

            bias_hh = param_list[bias_hh_name].numpy()
            bias_hh = np.random.uniform(
                -0.1, 0.1, size=bias_hh.shape).astype('float64')
            param_list[bias_hh_name].set_value(bias_hh)
            named_param_list[bias_hh_name].set_value(bias_hh)

            step_input_np = np.random.uniform(-0.1, 0.1, (
                self.batch_size, self.input_size)).astype('float64')
            pre_hidden_np = np.random.uniform(-0.1, 0.1, (
                self.batch_size, self.hidden_size)).astype('float64')

            step_input_var = fluid.dygraph.to_variable(step_input_np)
            pre_hidden_var = fluid.dygraph.to_variable(pre_hidden_np)
            api_out = cudnn_gru(step_input_var, pre_hidden_var)
            named_api_out = named_cudnn_gru(step_input_var, pre_hidden_var)

        np_out = cudnn_step(step_input_np, pre_hidden_np, weight_ih, bias_ih,
                            weight_hh, bias_hh)

        self.assertTrue(np.allclose(api_out.numpy(), np_out, rtol=1e-5, atol=0))
        self.assertTrue(
            np.allclose(
                named_api_out.numpy(), np_out, rtol=1e-5, atol=0))


class TestNonCudnnGRU(unittest.TestCase):
    def setUp(self):
        self.input_size = 100
        self.hidden_size = 200
        self.batch_size = 64

    def test_run(self):

        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()

        with fluid.dygraph.guard(place):
            param_attr = fluid.ParamAttr(name="param_attr")
            bias_attr = fluid.ParamAttr(name="bias_attr")
            named_non_cudnn_gru = GRUCell(
                self.hidden_size,
                self.input_size,
                param_attr,
                bias_attr,
                use_cudnn_impl=False)
            non_cudnn_gru = GRUCell(
                self.hidden_size, self.input_size, use_cudnn_impl=False)

            param_list = non_cudnn_gru.state_dict()
            named_param_list = named_non_cudnn_gru.state_dict()

            # process weight and bias

            gate_w_name = "_gate_weight"
            gate_b_name = "_gate_bias"
            candidate_w_name = "_candidate_weight"
            candidate_b_name = "_candidate_bias"

            gate_w = param_list[gate_w_name].numpy()
            gate_w = np.random.uniform(
                -0.1, 0.1, size=gate_w.shape).astype('float64')
            param_list[gate_w_name].set_value(gate_w)
            named_param_list[gate_w_name].set_value(gate_w)

            gate_b = param_list[gate_b_name].numpy()
            gate_b = np.random.uniform(
                -0.1, 0.1, size=gate_b.shape).astype('float64')
            param_list[gate_b_name].set_value(gate_b)
            named_param_list[gate_b_name].set_value(gate_b)

            candidate_w = param_list[candidate_w_name].numpy()
            candidate_w = np.random.uniform(
                -0.1, 0.1, size=candidate_w.shape).astype('float64')
            param_list[candidate_w_name].set_value(candidate_w)
            named_param_list[candidate_w_name].set_value(candidate_w)

            candidate_b = param_list[candidate_b_name].numpy()
            candidate_b = np.random.uniform(
                -0.1, 0.1, size=candidate_b.shape).astype('float64')
            param_list[candidate_b_name].set_value(candidate_b)
            named_param_list[candidate_b_name].set_value(candidate_b)

            step_input_np = np.random.uniform(-0.1, 0.1, (
                self.batch_size, self.input_size)).astype('float64')
            pre_hidden_np = np.random.uniform(-0.1, 0.1, (
                self.batch_size, self.hidden_size)).astype('float64')

            step_input_var = fluid.dygraph.to_variable(step_input_np)
            pre_hidden_var = fluid.dygraph.to_variable(pre_hidden_np)
            api_out = non_cudnn_gru(step_input_var, pre_hidden_var)
            named_api_out = named_non_cudnn_gru(step_input_var, pre_hidden_var)

        np_out = non_cudnn_step(step_input_np, pre_hidden_np, gate_w, gate_b,
                                candidate_w, candidate_b)

        self.assertTrue(np.allclose(api_out.numpy(), np_out, rtol=1e-5, atol=0))
        self.assertTrue(
            np.allclose(
                named_api_out.numpy(), np_out, rtol=1e-5, atol=0))


if __name__ == '__main__':
    unittest.main()
