#   Copyright (c) 2022 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 random
import sys
import unittest

import numpy as np

sys.path.append('../rnn')

from convert import get_params_for_net
from get_test_cover_info import (
    XPUOpTestWrapper,
    create_test_class,
    get_xpu_op_support_types,
)
from op_test_xpu import XPUOpTest
from rnn_numpy import LSTM

import paddle
from paddle.fluid import core

random.seed(2)
np.set_printoptions(threshold=np.inf)
paddle.enable_static()


class XPUTestRNNOp(XPUOpTestWrapper):
    def __init__(self):
        self.op_name = 'rnn'
        self.use_dynamic_create_class = False

    class TestRNNOp(XPUOpTest):
        def setUp(self):
            self.init_size()
            self.init_dtype()
            self.op_type = "rnn"
            self.place = paddle.XPUPlace(0)
            self.sequence_length = np.array([12, 11, 10, 9, 8], dtype=np.int32)
            self.num_layers = 1
            self.is_bidirec = False
            self.set_attrs()
            self.mode = "LSTM"
            self.is_test = False
            self.dropout = 0.0

            self.direction_num = 2 if self.is_bidirec else 1
            direction = "bidirectional" if self.is_bidirec else "forward"

            input = np.random.uniform(
                low=-0.1,
                high=0.1,
                size=(self.seq_length, self.batch_size, self.input_size),
            ).astype(self.dtype)
            input[11][1:][:] = 0
            input[10][2:][:] = 0
            input[9][3:][:] = 0
            input[8][4:][:] = 0

            rnn1 = LSTM(
                self.input_size,
                self.hidden_size,
                num_layers=self.num_layers,
                time_major=True,
                direction=direction,
                dropout=self.dropout,
                dtype=self.dtype,
            )

            flat_w = get_params_for_net(rnn1)
            output, (last_hidden, last_cell) = rnn1(
                input, sequence_length=self.sequence_length
            )

            init_h = np.zeros(
                (
                    self.num_layers * self.direction_num,
                    self.batch_size,
                    self.hidden_size,
                )
            ).astype(self.dtype)
            init_c = np.zeros(
                (
                    self.num_layers * self.direction_num,
                    self.batch_size,
                    self.hidden_size,
                )
            ).astype(self.dtype)
            state_out = np.ndarray(300).astype("uint8")

            self.inputs = {
                'Input': input,
                'WeightList': flat_w,
                'PreState': [('init_h', init_h), ('init_c', init_c)],
                'SequenceLength': self.sequence_length,
            }
            if self.sequence_length is None:
                self.inputs = {
                    'Input': input,
                    'WeightList': flat_w,
                    'PreState': [('init_h', init_h), ('init_c', init_c)],
                }
            self.attrs = {
                'dropout_prob': self.dropout,
                'is_bidirec': self.is_bidirec,
                'input_size': self.input_size,
                'hidden_size': self.hidden_size,
                'num_layers': self.num_layers,
                'mode': self.mode,
                'is_test': self.is_test,
            }
            self.outputs = {
                'Out': output,
                "State": [
                    ('last_hidden', last_hidden),
                    ('last_cell', last_cell),
                ],
                'Reserve': np.ndarray(400).astype("uint8"),
                'DropoutState': state_out,
            }

        def init_dtype(self):
            self.dtype = self.in_type

        def set_xpu(self):
            self.__class__.use_xpu = True
            self.__class__.no_need_check_grad = False
            self.__class__.op_type = self.in_type

        def test_check_output(self):
            self.check_output_with_place(
                self.place, atol=0.01, no_check_set=['Reserve', 'DropoutState']
            )

        def test_grad(self):
            if not self.is_test:
                var_name_list = self.get_weight_names()
                grad_check_list = ['Input', 'init_h', 'init_c']
                grad_check_list.extend(var_name_list)
                self.check_grad_with_place(
                    self.place,
                    set(grad_check_list),
                    ['Out', 'last_hidden', 'last_cell'],
                )

        def init_size(self):
            self.seq_length = 12
            self.batch_size = 5
            self.input_size = 3
            self.hidden_size = 2

        def get_weight_names(self):
            weight_names = []
            for i in range(self.num_layers):
                for j in range(0, 2 * self.direction_num):
                    weight_names.append(f"{i}.weight_{j}")
            for i in range(self.num_layers):
                for j in range(0, 2 * self.direction_num):
                    weight_names.append(f"{i}.bias_{j}")
            return weight_names

        def set_attrs(self):
            pass

    class TestRNNOp1(TestRNNOp):
        def set_attrs(self):
            self.sequence_length = None

    class TestRNNOp2(TestRNNOp):
        def set_attrs(self):
            self.num_layers = 1
            self.is_bidirec = True

    class TestRNNOp3(TestRNNOp):
        def set_attrs(self):
            self.num_layers = 2
            self.is_bidirec = False

    class TestRNNOp4(TestRNNOp):
        def set_attrs(self):
            self.num_layers = 3
            self.is_bidirec = False

    class TestRNNOp5(TestRNNOp):
        def set_attrs(self):
            self.num_layers = 2
            self.is_bidirec = True

    class TestRNNOp6(TestRNNOp):
        def set_attrs(self):
            self.num_layers = 2
            self.is_bidirec = True
            self.sequence_length = None

    class TestRNNOp7(TestRNNOp):
        def set_attrs(self):
            self.num_layers = 3
            self.is_bidirec = True


support_types = get_xpu_op_support_types('rnn')
for stype in support_types:
    create_test_class(
        globals(),
        XPUTestRNNOp,
        stype,
        ignore_device_version=[core.XPUVersion.XPU1],
    )

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