#   Copyright (c) 2018 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.layers as layers
import numpy as np
import paddle.fluid.core as core

from paddle.fluid import ParamAttr
from paddle.fluid.framework import Program, grad_var_name
from paddle.fluid.executor import Executor
from paddle.fluid.backward import append_backward

np.random.seed(123)


class PyRNNBase(object):
    def __init__(self, input_shape, output_shape):
        self.x = np.ones(shape=input_shape).astype("float32")
        self.y = np.zeros(shape=output_shape).astype("float32")

    def step(self, step_id, x):
        raise NotImplementedError

    def forward(self):
        for step_id in range(self.x.shape[0]):
            self.step(step_id, self.x[step_id])
        return np.array([np.mean(self.y)])

    def segment_inputs(self):
        return [self.x[i] for i in range(self.x.shape[0])]


class PySimpleRNN1(PyRNNBase):
    def __init__(self, input_shape, output_shape):
        super(PySimpleRNN1, self).__init__(input_shape, output_shape)

        seq_len, batch_size, input_dim = input_shape
        self.h_boot = np.random.normal(size=(batch_size,
                                             input_dim)).astype("float32")

        self.scale = 1.0 / 2.0
        men_dim = (seq_len, batch_size, input_dim)
        self.mems = np.zeros(shape=men_dim).astype("float32")

    def step(self, step_id, x):
        if step_id == 0:
            pre_mem = self.h_boot
        else:
            pre_mem = self.mems[step_id - 1]
        self.mems[step_id] = (pre_mem + x) * self.scale
        self.y[step_id] = self.mems[step_id]


class PySimpleRNN2(PyRNNBase):
    def __init__(self, input_shape, output_shape):
        super(PySimpleRNN2, self).__init__(input_shape, output_shape)

        seq_len, batch_size, input_dim = input_shape
        self.W = np.ones(shape=(input_dim, input_dim)).astype("float32")
        self.U = np.zeros(shape=(input_dim, input_dim)).astype("float32")
        self.h_boot = np.ones(shape=(batch_size, input_dim)).astype("float32")

        men_dim = (seq_len, batch_size, input_dim)
        self.mems = np.zeros(shape=men_dim).astype("float32")

    def step(self, step_id, x):
        if step_id > 0:
            pre_mem = self.mems[step_id - 1]
        else:
            pre_mem = self.h_boot
        xW = np.matmul(x, self.W).astype("float32")
        hU = np.matmul(pre_mem, self.U).astype("float32")

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

        self.mems[step_id] = py_sigmoid(xW + hU)
        self.y[step_id] = self.mems[step_id]


def create_tensor(np_data, place):
    tensor = core.LoDTensor()
    tensor.set(np_data, place)
    return tensor


class RecurrentOpTest1(unittest.TestCase):
    '''
    Test RNNOp
    equation:
        h_t = ( x_t + h_{t-1} ) / scale
    vars:
        - x
    memories:
        - h
    outputs:
        - h
    '''

    input_dim = 2
    batch_size = 1
    sent_len = 1

    def setup_program(self):
        self.main_program = Program()
        self.startup_program = Program()
        self.place = core.CPUPlace()

    def setUp(self):
        self.setup_program()
        self.feed_data_field = {"x", "h_boot"}
        self.grad_data_field = self.feed_data_field

        self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
        self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
        self.py_rnn = PySimpleRNN1(self.input_shape, self.output_shape)

        with fluid.program_guard(self.main_program, self.startup_program):
            self.output = layers.mean(self.create_rnn_op())

    def create_rnn_op(self):
        x = layers.data(
            shape=[self.sent_len, self.batch_size, self.input_dim],
            dtype='float32',
            name='x',
            append_batch_size=False)
        x.stop_gradient = False
        h_boot = layers.data(
            shape=[self.input_dim], dtype='float32', name='h_boot')
        h_boot.stop_gradient = False

        rnn = layers.StaticRNN()
        with rnn.step():
            h_pre = rnn.memory(init=h_boot)
            x_t = rnn.step_input(x)

            h = layers.scale(
                x=layers.elementwise_add(
                    x=h_pre, y=x_t),
                scale=self.py_rnn.scale)

            rnn.update_memory(h_pre, h)
            rnn.output(h)

        return rnn()

    def forward(self):
        self.feed_map = {
            x: create_tensor(getattr(self.py_rnn, x), self.place)
            for x in self.feed_data_field
        }
        exe = Executor(self.place)
        out = exe.run(self.main_program,
                      feed=self.feed_map,
                      fetch_list=[self.output])

        return out[0]

    def backward(self):
        self.feed_map = {
            x: create_tensor(getattr(self.py_rnn, x), self.place)
            for x in self.feed_data_field
        }
        fetch_list = [
            self.main_program.global_block().var(grad_var_name(x))
            for x in self.grad_data_field
        ]

        exe = Executor(self.place)
        return exe.run(self.main_program,
                       feed=self.feed_map,
                       fetch_list=fetch_list,
                       return_numpy=False)

    def test_backward(self, rtol=0.01):
        self.check_forward()

        with fluid.program_guard(self.main_program, self.startup_program):
            append_backward(self.output)

        ana_grad = [np.array(x) for x in self.backward()]

        num_grad = self.get_numerical_gradient()
        for idx, name in enumerate(self.grad_data_field):
            self.assertEqual(num_grad[idx].shape, ana_grad[idx].shape)
            self.assertTrue(
                np.isclose(
                    num_grad[idx], ana_grad[idx], rtol=rtol).all(),
                "num_grad (" + name + ") has diff at " + str(self.place) +
                "\nExpect " + str(num_grad[idx]) + "\n" + "But Got" +
                str(ana_grad[idx]) + " in class " + self.__class__.__name__)

    def check_forward(self):
        pd_output = self.forward()
        py_output = self.py_rnn.forward()
        self.assertEqual(pd_output.shape, py_output.shape)
        self.assertTrue(np.isclose(pd_output, py_output, rtol=0.01).all())

    def get_numerical_gradient(self, delta=0.005):
        dloss_dout = 1.0
        feed_list = [getattr(self.py_rnn, x) for x in self.grad_data_field]
        grad_list = [np.zeros_like(x) for x in feed_list]
        for feed, grad in zip(feed_list, grad_list):
            for f, g in np.nditer([feed, grad], op_flags=['readwrite']):
                o = float(f)
                f[...] = o + delta
                y_pos = self.forward()

                f[...] = o - delta
                y_neg = self.forward()

                f[...] = o
                dout_dfeed = (y_pos - y_neg) / (delta * 2)
                g[...] = dout_dfeed[0]

        return grad_list


class RecurrentOpTest2(RecurrentOpTest1):
    r'''
    Test RNNOp
    equation:
        h_t = \sigma (W x_t + U h_{t-1})
    weights:
        - W
        - U
    vars:
        - x
    memories:
        - h
    outputs:
       - h
    '''

    input_dim = 2
    batch_size = 10
    sent_len = 2

    def setUp(self):
        self.setup_program()

        self.feed_data_field = {"x", "h_boot", "W", "U"}
        self.grad_data_field = self.feed_data_field

        self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
        self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
        self.py_rnn = PySimpleRNN2(self.input_shape, self.output_shape)

        with fluid.program_guard(self.main_program, self.startup_program):
            self.output = layers.mean(self.create_rnn_op())

    def create_rnn_op(self):
        x = layers.data(
            shape=[self.sent_len, self.batch_size, self.input_dim],
            dtype='float32',
            name='x',
            append_batch_size=False)
        x.stop_gradient = False
        h_boot = layers.data(
            shape=[self.input_dim], dtype='float32', name='h_boot')
        h_boot.stop_gradient = False

        rnn = layers.StaticRNN()
        with rnn.step():
            h_pre = rnn.memory(init=h_boot)
            x_t = rnn.step_input(x)

            temp_l = layers.fc(
                input=x_t,
                size=self.input_dim,
                param_attr=ParamAttr(
                    name='W',
                    initializer=fluid.initializer.ConstantInitializer(1.0)),
                bias_attr=False)
            temp_r = layers.fc(
                input=h_pre,
                size=self.input_dim,
                param_attr=ParamAttr(
                    name='U',
                    initializer=fluid.initializer.ConstantInitializer(0.0)),
                bias_attr=False)

            h = layers.sigmoid(x=layers.elementwise_add(x=temp_l, y=temp_r))

            rnn.update_memory(h_pre, h)
            rnn.output(h)

        return rnn()

    def test_backward(self):
        super(RecurrentOpTest2, self).test_backward(rtol=0.01)


class RecurrentOpMultipleMemoryTest(RecurrentOpTest1):
    '''
    Test RNNOp with two memories
    equation:
        h_1 = h_pre_1
        h_2 = h_pre_2
        y = h_1 + h_2
    vars:
        - x
    memories:
        - h_1, h_2
    outputs:
       - y
    '''

    class PySimpleRNN3(PyRNNBase):
        def __init__(self, input_shape, output_shape):
            super(RecurrentOpMultipleMemoryTest.PySimpleRNN3, self).__init__(
                input_shape, output_shape)

            seq_len, batch_size, input_dim = input_shape
            self.h_boot1 = np.random.normal(size=(batch_size,
                                                  input_dim)).astype("float32")
            self.h_boot2 = np.random.normal(size=(batch_size,
                                                  input_dim)).astype("float32")

            men_dim = (seq_len, batch_size, input_dim)
            self.mems1 = np.zeros(shape=men_dim).astype("float32")
            self.mems2 = np.zeros(shape=men_dim).astype("float32")

        def step(self, step_id, x):
            if step_id == 0:
                pre_mem1 = self.h_boot1
                pre_mem2 = self.h_boot2
            else:
                pre_mem1 = self.mems1[step_id - 1]
                pre_mem2 = self.mems2[step_id - 1]
            self.mems1[step_id] = pre_mem1
            self.mems2[step_id] = pre_mem2
            self.y[step_id] = self.mems1[step_id] + self.mems2[step_id] + x

    input_dim = 1
    batch_size = 1
    sent_len = 2

    def setUp(self):
        self.setup_program()

        self.feed_data_field = {"x", "h_boot1", "h_boot2"}
        self.grad_data_field = self.feed_data_field

        self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
        self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
        self.py_rnn = RecurrentOpMultipleMemoryTest.PySimpleRNN3(
            self.input_shape, self.output_shape)

        with fluid.program_guard(self.main_program, self.startup_program):
            self.output = layers.mean(self.create_rnn_op())

    def create_rnn_op(self):
        x = layers.data(
            shape=[self.sent_len, self.batch_size, self.input_dim],
            dtype='float32',
            name='x',
            append_batch_size=False)
        x.stop_gradient = False
        h_boot1 = layers.data(
            shape=[self.batch_size, self.input_dim],
            dtype='float32',
            name='h_boot1',
            append_batch_size=False)
        h_boot1.stop_gradient = False
        h_boot2 = layers.data(
            shape=[self.batch_size, self.input_dim],
            dtype='float32',
            name='h_boot2',
            append_batch_size=False)
        h_boot2.stop_gradient = False

        rnn = layers.StaticRNN()
        with rnn.step():
            h_pre1 = rnn.memory(init=h_boot1)
            h_pre2 = rnn.memory(init=h_boot2)
            x_t = rnn.step_input(x)

            mem1 = layers.scale(x=h_pre1, scale=1.0)
            mem2 = layers.scale(x=h_pre2, scale=1.0)
            out = layers.sums(input=[mem1, x_t, mem2])

            rnn.update_memory(h_pre1, mem1)
            rnn.update_memory(h_pre2, mem2)
            rnn.output(out)

        return rnn()


class RecurrentOpNoMemBootTest(RecurrentOpTest1):
    '''
    Test RNNOp with two memories
    equation:
        mem = x + mem_pre
        y = mem
    vars:
        - x
    memories:
        - mem
    outputs:
       - y
    '''

    class PySimpleRNN4(PyRNNBase):
        def __init__(self, input_shape, output_shape):
            super(RecurrentOpNoMemBootTest.PySimpleRNN4, self).__init__(
                input_shape, output_shape)
            men_dim = input_shape
            self.mems = np.zeros(shape=men_dim).astype("float32")

        def step(self, step_id, x):
            if step_id == 0:
                pre_mem = np.zeros_like(x)
            else:
                pre_mem = self.mems[step_id - 1]
            self.mems[step_id] = pre_mem + x
            self.y[step_id] = self.mems[step_id]

    input_dim = 1
    batch_size = 1
    sent_len = 2

    def setUp(self):
        self.setup_program()

        self.feed_data_field = {"x"}
        self.grad_data_field = self.feed_data_field

        self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
        self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
        self.py_rnn = RecurrentOpNoMemBootTest.PySimpleRNN4(self.input_shape,
                                                            self.output_shape)

        with fluid.program_guard(self.main_program, self.startup_program):
            self.output = layers.mean(self.create_rnn_op())

    def create_rnn_op(self):
        x = layers.data(
            shape=[self.sent_len, self.batch_size, self.input_dim],
            dtype='float32',
            name='x',
            append_batch_size=False)
        x.stop_gradient = False

        rnn = layers.StaticRNN()
        with rnn.step():
            mem_pre = rnn.memory(shape=[-1, self.input_dim], batch_ref=x)
            x_t = rnn.step_input(x)
            mem = layers.elementwise_add(x=mem_pre, y=x_t)
            rnn.update_memory(mem_pre, mem)
            rnn.output(mem)

        return rnn()


class RecurrentOpSubBlockTest(RecurrentOpTest1):
    r'''
    Test RNNOp with subblock variable
    equation:
        y_ = emb * w1
        h_t = \concat([x, h_{t-1}])
        h_t = h_t * w2
        h_t = \\unsqueeze(h_t, 1)
        h_t = \dot_attention(h_t, y_)
        h_t = \squeeze(h_t, 1)
        y = h_t
    vars:
        - x
        - w1
        - w2
    memories:
        - h
    outputs:
       - y
    '''

    class PySimpleRNN5(PyRNNBase):
        def __init__(self, input_shape, output_shape):
            super(RecurrentOpSubBlockTest.PySimpleRNN5, self).__init__(
                input_shape, output_shape)

            seq_len, batch_size, input_dim = input_shape
            self.w1 = np.random.uniform(
                -0.1, 0.1, size=(input_dim, input_dim)).astype("float32")
            self.w2 = np.random.uniform(
                -0.1, 0.1, size=(input_dim * 2, input_dim)).astype("float32")

            self.emb = np.random.uniform(
                -0.1, 0.1, size=(seq_len, batch_size,
                                 input_dim)).astype("float32")

            men_dim = (seq_len, batch_size, input_dim)
            self.mems = np.zeros(shape=men_dim).astype("float32")
            self.oy = np.matmul(self.emb, self.w1)

        def step(self, step_id, x):
            def dot_attention(query, memory):
                attn = np.matmul(query, memory.transpose((0, 2, 1)))
                weight = softmax(attn)
                weight_memory = np.matmul(weight, memory)
                return weight_memory, weight

            def softmax(x):
                return np.exp(x) / sum(np.exp(x))

            if step_id == 0:
                pre_mem = np.zeros_like(x)
            else:
                pre_mem = self.mems[step_id - 1]
            concat_in = np.concatenate([x, pre_mem], 1)
            new_mem = np.matmul(concat_in, self.w2)

            new_mem = np.expand_dims(new_mem, 1)
            new_mem, _ = dot_attention(new_mem, self.oy)
            new_mem = np.squeeze(new_mem, 1)

            self.mems[step_id] = new_mem
            self.y[step_id] = self.mems[step_id]

    input_dim = 2
    batch_size = 3
    sent_len = 3

    def setUp(self):
        self.setup_program()

        self.feed_data_field = {"x", "emb", "w1", "w2"}
        self.grad_data_field = self.feed_data_field

        self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
        self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
        self.py_rnn = RecurrentOpSubBlockTest.PySimpleRNN5(self.input_shape,
                                                           self.output_shape)

        with fluid.program_guard(self.main_program, self.startup_program):
            rnn_out = self.create_rnn_op()
            self.output = layers.mean(rnn_out)

    def create_rnn_op(self):
        x = layers.data(
            shape=[self.sent_len, self.batch_size, self.input_dim],
            dtype='float32',
            name='x',
            append_batch_size=False)
        x.stop_gradient = False

        emb = layers.data(
            name='emb',
            shape=[self.sent_len, self.batch_size, self.input_dim],
            dtype='float32',
            append_batch_size=False)
        emb.stop_gradient = False

        w1 = layers.data(
            shape=[self.input_dim, self.input_dim],
            dtype='float32',
            name='w1',
            append_batch_size=False)
        w1.stop_gradient = False
        w2 = layers.data(
            shape=[self.input_dim * 2, self.input_dim],
            dtype='float32',
            name='w2',
            append_batch_size=False)
        w2.stop_gradient = False

        rnn = layers.StaticRNN()

        def dot_attention(query, memory):
            attn = layers.matmul(query, memory, transpose_y=True)
            weight = layers.softmax(attn)
            weight_memory = layers.matmul(weight, memory)

            return weight_memory, weight

        y = layers.matmul(emb, w1)
        with rnn.step():
            pre_h = rnn.memory(
                shape=(self.sent_len, self.input_dim),
                batch_ref=x,
                init_value=0.0)
            step_in = rnn.step_input(x)
            concat_in = layers.concat([step_in, pre_h], 1)
            new_h = layers.matmul(concat_in, w2)
            new_h = layers.unsqueeze(new_h, [1])
            new_h, _ = dot_attention(new_h, y)
            new_h = layers.squeeze(new_h, [1])

            rnn.update_memory(pre_h, new_h)
            rnn.step_output(new_h)

        return rnn()


class RecurrentOpStopGradientTest(RecurrentOpTest1):
    r"""
    Test RNNOp with stop_gradient = True
    equation:
        h_t = \sigma (W x_t + U h_{t-1})
    weights:
        - W
	- U
    vars:
        - x
    memories:
        - h
    output:
        - h
    """

    input_dim = 2
    batch_size = 10
    sent_len = 2

    def setUp(self):
        self.setup_program()
        self.feed_data_field = {"x", "h_boot", "W", "U"}
        self.grad_data_field = {"x", "W", "U"}

        self.input_shape = (self.sent_len, self.batch_size, self.input_dim)
        self.output_shape = (self.sent_len, self.batch_size, self.input_dim)
        self.py_rnn = PySimpleRNN2(self.input_shape, self.output_shape)

        with fluid.program_guard(self.main_program, self.startup_program):
            self.output = layers.mean(self.create_rnn_op())

    def create_rnn_op(self):
        x = layers.data(
            shape=[self.sent_len, self.batch_size, self.input_dim],
            dtype="float32",
            name="x",
            append_batch_size=False)
        x.stop_gradient = False
        h_boot = layers.data(
            shape=[self.input_dim], dtype="float32", name="h_boot")
        h_boot.stop_gradient = True

        rnn = layers.StaticRNN()
        with rnn.step():
            h_pre = rnn.memory(init=h_boot)  # init doesn't have gradient
            x_t = rnn.step_input(x)

            temp_l = layers.fc(
                input=x_t,
                size=self.input_dim,
                param_attr=ParamAttr(
                    name="W",
                    initializer=fluid.initializer.ConstantInitializer(1.0)),
                bias_attr=False)
            temp_r = layers.fc(
                input=h_pre,
                size=self.input_dim,
                param_attr=ParamAttr(
                    name="U",
                    initializer=fluid.initializer.ConstantInitializer(0.0)),
                bias_attr=False)

            h = layers.sigmoid(x=layers.elementwise_add(temp_l, temp_r))

            rnn.update_memory(h_pre, h)
            rnn.output(h)

        return rnn()


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