# 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 numpy as np
import paddle

from paddle import Model, set_device
from paddle.static import InputSpec as Input
from paddle.metric import Accuracy
from paddle.vision.datasets import MNIST
from paddle.vision.models import LeNet
import paddle.static.amp as amp
import random
import argparse
import ast

SEED = 2
paddle.seed(SEED)
paddle.framework.random._manual_program_seed(SEED)
np.random.seed(SEED)
random.seed(SEED)

paddle.enable_static()
set_device('cpu')


def parse_args():
    parser = argparse.ArgumentParser("Lenet BF16 train static script")
    parser.add_argument(
        '-bf16',
        '--bf16',
        type=ast.literal_eval,
        default=False,
        help="whether use bf16",
    )
    args = parser.parse_args()
    return args


class MnistDataset(MNIST):
    def __init__(self, mode, return_label=True):
        super().__init__(mode=mode)
        self.return_label = return_label

    def __getitem__(self, idx):
        img = np.reshape(self.images[idx], [1, 28, 28])
        if self.return_label:
            return img, np.array(self.labels[idx]).astype('int64')
        return (img,)

    def __len__(self):
        return len(self.images)


def compute_accuracy(pred, gt):
    pred = np.argmax(pred, -1)
    gt = np.array(gt)

    correct = pred[:, np.newaxis] == gt

    return np.sum(correct) / correct.shape[0]


def main(args):
    print('download training data and load training data')
    train_dataset = MnistDataset(
        mode='train',
    )
    val_dataset = MnistDataset(
        mode='test',
    )
    test_dataset = MnistDataset(mode='test', return_label=False)

    im_shape = (-1, 1, 28, 28)
    batch_size = 64

    inputs = [Input(im_shape, 'float32', 'image')]
    labels = [Input([None, 1], 'int64', 'label')]

    model = Model(LeNet(), inputs, labels)
    optim = paddle.optimizer.SGD(learning_rate=0.001)
    if args.bf16:
        optim = amp.bf16.decorate_bf16(
            optim,
            amp_lists=amp.bf16.AutoMixedPrecisionListsBF16(
                custom_bf16_list={
                    'matmul_v2',
                    'pool2d',
                    'relu',
                    'scale',
                    'elementwise_add',
                    'reshape2',
                    'slice',
                    'reduce_mean',
                    'conv2d',
                },
            ),
        )

    # Configuration model
    model.prepare(optim, paddle.nn.CrossEntropyLoss(), Accuracy())
    # Training model #
    if args.bf16:
        print('Training BF16')
    else:
        print('Training FP32')
    model.fit(train_dataset, epochs=2, batch_size=batch_size, verbose=1)
    eval_result = model.evaluate(val_dataset, batch_size=batch_size, verbose=1)

    output = model.predict(
        test_dataset, batch_size=batch_size, stack_outputs=True
    )

    np.testing.assert_equal(output[0].shape[0], len(test_dataset))

    acc = compute_accuracy(output[0], val_dataset.labels)

    print("acc", acc)
    print("eval_result['acc']", eval_result['acc'])

    np.testing.assert_allclose(acc, eval_result['acc'])


if __name__ == "__main__":
    args = parse_args()
    main(args)
