# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""test_image_summary"""
import logging
import os
import numpy as np

import mindspore.nn as nn
from mindspore import context
from mindspore.train import Model, Callback
from mindspore import Tensor
from mindspore.nn.optim import Momentum
from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
from tests.security_utils import security_off_wrap
from .....dataset_mock import MindData

CUR_DIR = os.getcwd()
SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"

log = logging.getLogger("test")
log.setLevel(level=logging.ERROR)


def make_image_tensor(shape, dtype=float):
    """ make_image_tensor """
    number = np.prod(shape)
    x = (np.arange(number, dtype=dtype)).reshape(shape)
    return x


def METHOD_NAME(step):
    """ get_test_data """
    test_data_list = []
    tag1 = "x1[:Image]"
    tag2 = "x2[:Image]"
    np1 = make_image_tensor([2, 3, 8, 8])
    np2 = make_image_tensor([step, 3, 8, 8])

    dict1 = {}
    dict1["name"] = tag1
    dict1["data"] = Tensor(np1)

    dict2 = {}
    dict2["name"] = tag2
    dict2["data"] = Tensor(np2)

    test_data_list.append(dict1)
    test_data_list.append(dict2)

    return test_data_list


# Test: call method on parse graph code
@security_off_wrap
def test_image_summary_sample():
    """ test_image_summary_sample """
    with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:

        for i in range(1, 5):
            test_data = METHOD_NAME(i)
            _cache_summary_tensor_data(test_data)
            test_writer.record(i)
            test_writer.flush()


class Net(nn.Cell):
    """ Net definition """

    def __init__(self):
        super(Net, self).__init__()
        self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal',
                              pad_mode='valid')
        self.bn = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
        self.flatten = nn.Flatten()
        self.fc = nn.Dense(64 * 222 * 222, 3)  # padding=0

    def construct(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        x = self.flatten(x)
        out = self.fc(x)
        return out


class LossNet(nn.Cell):
    """ LossNet definition """

    def __init__(self):
        super(LossNet, self).__init__()
        self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal',
                              pad_mode='valid')
        self.bn = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
        self.flatten = nn.Flatten()
        self.fc = nn.Dense(64 * 222 * 222, 3)  # padding=0
        self.loss = nn.SoftmaxCrossEntropyWithLogits()

    def construct(self, x, y):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        x = self.flatten(x)
        x = self.fc(x)
        out = self.loss(x, y)
        return out


def get_model():
    """ get_model """
    net = Net()
    loss = nn.SoftmaxCrossEntropyWithLogits()
    optim = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
    context.set_context(mode=context.GRAPH_MODE)
    model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
    return model


def get_dataset():
    """ get_dataset """
    dataset_types = (np.float32, np.float32)
    dataset_shapes = ((2, 3, 224, 224), (2, 3))

    dataset = MindData(size=2, batch_size=2,
                       np_types=dataset_types,
                       output_shapes=dataset_shapes,
                       input_indexs=(0, 1))
    return dataset


class ImageSummaryCallback(Callback):
    """Image summary callback."""

    def __init__(self, summary_record):
        self._summary_record = summary_record

    def __enter__(self):
        return self

    def __exit__(self, *err):
        self._summary_record.close()

    def record(self, step, train_network=None):
        """record data."""
        self._summary_record.record(step, train_network)
        self._summary_record.flush()


@security_off_wrap
def test_image_summary_train():
    """ test_image_summary_train """
    dataset = get_dataset()
    with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:
        model = get_model()
        callback = ImageSummaryCallback(test_writer)
        model.train(2, dataset, callbacks=[callback])


@security_off_wrap
def test_image_summary_data():
    """ test_image_summary_data """
    dataset = get_dataset()

    test_data_list = []
    i = 1
    for next_element in dataset:
        tag = "image_" + str(i) + "[:Image]"
        dct = {}
        dct["name"] = tag
        dct["data"] = Tensor(next_element[0])
        test_data_list.append(dct)
        i += 1

    with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:
        _cache_summary_tensor_data(test_data_list)
        test_writer.record(1)