# Copyright 2020 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.
# ============================================================================
"""
@File  : test_image_summary.py
@Author:
@Date  : 2019-07-4
@Desc  : test summary function
"""
import logging
import os
import numpy as np

import mindspore.nn as nn
from mindspore import Model, context
from mindspore import Tensor
from mindspore.nn.optim import Momentum
from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
from mindspore.train.callback import Callback
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 """
    # pylint: disable=unused-argument
    numel = np.prod(shape)
    x = (np.arange(numel, dtype=float)).reshape(shape)
    return x


def get_test_data(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
def test_image_summary_sample():
    """ test_image_summary_sample """
    log.debug("begin test_image_summary_sample")
    # step 0: create the thread
    with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:
        # step 1: create the test data for summary

        # step 2: create the Event
        for i in range(1, 5):
            test_data = get_test_data(i)
            _cache_summary_tensor_data(test_data)
            test_writer.record(i)
            test_writer.flush()

        # step 3: send the event to mq

        # step 4: accept the event and write the file
        log.debug("finished test_image_summary_sample")


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()


def test_image_summary_train():
    """ test_image_summary_train """
    dataset = get_dataset()

    log.debug("begin test_image_summary_sample")
    # step 0: create the thread
    with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:
        # step 1: create the test data for summary

        # step 2: create the Event

        model = get_model()
        callback = ImageSummaryCallback(test_writer)
        model.train(2, dataset, callbacks=[callback])

        # step 3: send the event to mq

        # step 4: accept the event and write the file
        log.debug("finished test_image_summary_sample")


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

    log.debug("begin test_image_summary_sample")
    # step 0: create the thread
    with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:
        # step 1: create the test data for summary

        # step 2: create the Event
        _cache_summary_tensor_data(test_data_list)
        test_writer.record(1)

        log.debug("finished test_image_summary_sample")