#   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.

import functools
import unittest

import numpy as np
from eager_op_test import OpTest
from test_lstm_op import ACTIVATION


def gru(
    input,  # T x 3D
    lod,  # 1 x N
    h0,  # N x D
    weight,  # D x 3D
    bias,  # 1 x 3D
    is_reverse,
    act_state,
    act_gate,
    dtype='float32',
    origin_mode=False,
):
    def _seq_to_batch(lod, is_reverse):
        idx_in_seq_list = []
        seq_lens = lod[0]
        seq_starts = [0]
        for i in range(len(seq_lens)):
            seq_starts.append(seq_starts[-1] + seq_lens[i])
        sorted_seqs = sorted(
            range(len(seq_lens)),
            key=functools.cmp_to_key(lambda x, y: seq_lens[y] - seq_lens[x]),
        )
        num_batch = seq_lens[sorted_seqs[0]]
        for batch_idx in range(num_batch):
            idx_in_seq = []
            for i in range(len(seq_lens)):
                if seq_lens[sorted_seqs[i]] <= batch_idx:
                    break
                idx = (
                    (seq_starts[sorted_seqs[i] + 1] - 1 - batch_idx)
                    if is_reverse
                    else (seq_starts[sorted_seqs[i]] + batch_idx)
                )
                idx_in_seq.append(idx)
            idx_in_seq_list.append(idx_in_seq)
        return idx_in_seq_list, sorted_seqs

    def _step(x, h_p, w, b, act_state, act_gate):
        T = x.shape[0]
        D = w.shape[0]
        g = x + np.tile(b, (T, 1))
        w_u_r = w.flatten()[: D * D * 2].reshape((D, D * 2))
        u_r = act_gate(np.dot(h_p, w_u_r) + g[:, : D * 2])
        u = u_r[:, :D]
        r = u_r[:, D : D * 2]
        r_h_p = r * h_p
        w_c = w.flatten()[D * D * 2 :].reshape((D, D))
        c = act_state(np.dot(r_h_p, w_c) + g[:, D * 2 :])
        g = np.hstack((u_r, c))
        if origin_mode:
            h = (1 - u) * c + u * h_p
        else:
            h = u * c + (1 - u) * h_p
        return g, r_h_p, h

    T = sum(lod[0])
    N = len(lod[0])
    D = weight.shape[0]
    batch_gate = np.zeros((T, 3 * D), dtype=dtype)
    batch_reset_hidden_prev = np.zeros((T, D), dtype=dtype)
    batch_hidden = np.zeros((T, D), dtype=dtype)
    hidden = np.zeros((T, D), dtype=dtype)

    idx_in_seq_list, sorted_seqs = _seq_to_batch(lod, is_reverse)
    h_p = h0[[seq for seq in sorted_seqs if lod[0][seq] > 0]]

    max_seq_len = len(idx_in_seq_list)
    end_idx = 0
    for batch_idx in range(max_seq_len):
        x = input[idx_in_seq_list[batch_idx]]
        g, r_h_p, h = _step(x, h_p, weight, bias, act_state, act_gate)
        if batch_idx < (max_seq_len - 1):
            h_p = h[: len(idx_in_seq_list[batch_idx + 1])]
        start_idx = end_idx
        end_idx = start_idx + len(idx_in_seq_list[batch_idx])
        batch_gate[start_idx:end_idx] = g
        batch_reset_hidden_prev[start_idx:end_idx] = r_h_p
        batch_hidden[start_idx:end_idx] = h
        hidden[idx_in_seq_list[batch_idx]] = h
    return batch_gate, batch_reset_hidden_prev, batch_hidden, hidden


class TestGRUOp(OpTest):
    def set_confs(self):
        pass

    def set_is_test(self):
        self.is_test = False

    def setUp(self):
        self.op_type = "gru"
        self.lod = [[2, 4, 3]]
        self.D = 40
        self.is_reverse = False
        self.with_h0 = True
        self.with_bias = True
        self.act_state = 'tanh'
        self.act_gate = 'sigmoid'
        self.dtype = 'float64'
        self.origin_mode = False
        self.set_confs()
        self.set_is_test()

        T = sum(self.lod[0])
        N = len(self.lod[0])
        input = np.random.rand(T, 3 * self.D).astype(self.dtype)
        weight = np.random.rand(self.D, 3 * self.D).astype(self.dtype)
        bias = (
            np.random.rand(1, 3 * self.D).astype(self.dtype)
            if self.with_bias
            else np.zeros((1, 3 * self.D), dtype=self.dtype)
        )
        h0 = (
            np.random.rand(N, self.D).astype(self.dtype)
            if self.with_h0
            else np.zeros((N, self.D), dtype=self.dtype)
        )

        batch_gate, batch_reset_hidden_prev, batch_hidden, hidden = gru(
            input,
            self.lod,
            h0,
            weight,
            bias,
            self.is_reverse,
            ACTIVATION[self.act_state],
            ACTIVATION[self.act_gate],
            self.dtype,
            self.origin_mode,
        )
        self.inputs = {'Input': (input, self.lod), 'Weight': weight}

        if self.with_bias:
            self.inputs['Bias'] = bias

        if self.with_h0:
            self.inputs['H0'] = h0

        self.outputs = {
            'Hidden': (hidden, self.lod),
            'BatchGate': batch_gate,
            'BatchResetHiddenPrev': batch_reset_hidden_prev,
            'BatchHidden': batch_hidden,
        }

        self.attrs = {
            'activation': self.act_state,
            'gate_activation': self.act_gate,
            'is_reverse': self.is_reverse,
            'origin_mode': self.origin_mode,
            'is_test': self.is_test,
        }

    def test_check_output(self):
        self.check_output(atol=1e-8, check_dygraph=False)

    def test_check_grad(self):
        self.check_grad(
            ['Input', 'H0', 'Weight', 'Bias'], ['Hidden'], check_dygraph=False
        )


class TestGRUOriginMode(TestGRUOp):
    def set_confs(self):
        self.origin_mode = True


class TestGRUOp2(TestGRUOp):
    def set_confs(self):
        self.dtype = 'float64'


class TestGRUOp2Len0(TestGRUOp):
    def set_confs(self):
        self.lod = [[2, 0, 4]]
        self.dtype = 'float64'


class TestGRUOp2OriginMode(TestGRUOp):
    def set_confs(self):
        self.dtype = 'float64'
        self.origin_mode = True


class TestGRUOp2OriginModeLen0(TestGRUOp):
    def set_confs(self):
        self.lod = [[0, 3, 4]]
        self.dtype = 'float64'
        self.origin_mode = True


class TestGRUOp2OriginModeLastLen0(TestGRUOp):
    def set_confs(self):
        self.lod = [[0, 3, 0]]
        self.dtype = 'float64'
        self.origin_mode = True


class TestGRUOpNoInitial(TestGRUOp):
    def set_confs(self):
        self.with_h0 = False

    def test_check_grad(self):
        self.check_grad(
            ['Input', 'Weight', 'Bias'], ['Hidden'], check_dygraph=False
        )


class TestGRUOpNoBias(TestGRUOp):
    def set_confs(self):
        self.with_bias = False

    def test_check_grad(self):
        self.check_grad(
            ['Input', 'H0', 'Weight'], ['Hidden'], check_dygraph=False
        )


class TestGRUOpReverse(TestGRUOp):
    def set_confs(self):
        self.is_reverse = True


class TestGRUOpReverseOriginMode(TestGRUOp):
    def set_confs(self):
        self.is_reverse = True
        self.origin_mode = True


class TestGRUOpInference(TestGRUOp):
    def set_is_test(self):
        self.is_test = True

    def test_check_output(self):
        new_outputs = {}
        new_outputs['Hidden'] = self.outputs['Hidden']
        self.outputs = new_outputs
        super().test_check_output()

    # avoid checking gradient
    def test_check_grad(self):
        pass


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