#   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 unittest

import numpy as np
from op_test import OpTest, convert_float_to_uint16
from test_fusion_gru_op import fusion_gru
from test_fusion_lstm_op import ACTIVATION

from paddle.base import core


@unittest.skipIf(
    not core.supports_bfloat16(), "place does not support BF16 evaluation"
)
class TestFusionGRUBF16MKLDNNOp(OpTest):
    def set_confs(self):
        pass

    def test_check_output(self):
        for use_seq in {True, False}:
            self.attrs['use_seq'] = use_seq
            self.check_output(check_dygraph=False)

    def setUp(self):
        self.op_type = "fusion_gru"
        self.lod = [[2, 4, 3]]
        self.M = 3
        self.D = 5
        self.is_reverse = False
        self.with_h0 = False
        self.use_mkldnn = True
        self._cpu_only = True
        self.with_bias = True
        self.act_state = 'tanh'
        self.act_gate = 'sigmoid'
        self.origin_mode = False
        self.use_mkldnn = True
        self.mkldnn_data_type = "bfloat16"
        self.force_fp32_output = False
        self.weights_dtype = 'fp32'
        self.set_confs()

        T = sum(self.lod[0])
        N = len(self.lod[0])

        # fp32 X input for reference implementation and
        # corressponding bf16 data as input to GRU oneDNN bf16 kernel
        x_fp32 = np.random.rand(T, self.M).astype('float32')
        x_bf16 = convert_float_to_uint16(x_fp32)

        wx_fp32 = np.random.rand(self.M, 3 * self.D).astype('float32')
        wh_fp32 = np.random.rand(self.D, 3 * self.D).astype('float32')

        wx_bf16 = convert_float_to_uint16(wx_fp32)
        wh_bf16 = convert_float_to_uint16(wh_fp32)

        # bias is fp32 despite other inputs being in bf16
        bias = (
            np.random.rand(1, 3 * self.D).astype('float32')
            if self.with_bias
            else np.zeros((1, 3 * self.D), dtype='float32')
        )

        h0_fp32 = (
            np.random.rand(N, self.D).astype('float32')
            if self.with_h0
            else np.zeros((N, self.D), dtype='float32')
        )

        _, _, _, hidden = fusion_gru(
            x_fp32,
            self.lod,
            h0_fp32,
            wx_fp32,
            wh_fp32,
            bias,
            self.is_reverse,
            self.origin_mode,
            ACTIVATION[self.act_state],
            ACTIVATION[self.act_gate],
        )

        hidden_bf16 = convert_float_to_uint16(hidden)

        if self.weights_dtype == 'bf16':
            self.inputs = {
                'X': (x_bf16, self.lod),
                'WeightX': wx_bf16,
                'WeightH': wh_bf16,
            }
        elif self.weights_dtype == 'fp32':
            self.inputs = {
                'X': (x_bf16, self.lod),
                'WeightX': wx_fp32,
                'WeightH': wh_fp32,
            }

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

        h0_bf16 = convert_float_to_uint16(h0_fp32)

        if self.with_h0:
            if self.weights_dtype == 'bf16':
                self.inputs['H0'] = h0_bf16
            elif self.weights_dtype == 'fp32':
                self.inputs['H0'] = h0_fp32

        self.outputs = {'Hidden': (hidden, self.lod)}

        self.attrs = {
            'activation': self.act_state,
            'gate_activation': self.act_gate,
            'is_reverse': self.is_reverse,
            'origin_mode': self.origin_mode,
            'force_fp32_output': self.force_fp32_output,
            'use_mkldnn': self.use_mkldnn,
            'mkldnn_data_type': self.mkldnn_data_type,
        }


class TestFusionGRUINT8MKLDNNOp2(TestFusionGRUBF16MKLDNNOp):
    def set_confs(self):
        self.origin_mode = False


class TestFusionGRUINT8MKLDNNOp3(TestFusionGRUBF16MKLDNNOp):
    def set_confs(self):
        self.with_bias = False


class TestFusionGRUINT8MKLDNNBF16WeightsOp(TestFusionGRUBF16MKLDNNOp):
    def set_confs(self):
        self.weights_dtype = 'bf16'


if __name__ == "__main__":
    from paddle import enable_static

    enable_static()
    unittest.main()
