# Copyright 2020-2023 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.
# ============================================================================
import os
import shutil

import sys
import csv

from tests.security_utils import security_off_wrap
import pytest

from mindspore import dataset as ds
from mindspore import nn, Tensor, context
from mindspore.nn.optim import Momentum
from mindspore.dataset.transforms import transforms as C
from mindspore.dataset.vision import transforms as CV
from mindspore.dataset.vision import Inter
from mindspore.common import dtype as mstype
from mindspore.common.initializer import TruncatedNormal
from mindspore.train import Model, Accuracy
from mindspore import Profiler


def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
    """weight initial for conv layer"""
    weight = weight_variable()
    return nn.Conv2d(in_channels, out_channels,
                     kernel_size=kernel_size, stride=stride, padding=padding,
                     weight_init=weight, has_bias=False, pad_mode="valid")


def fc_with_initialize(input_channels, out_channels):
    """weight initial for fc layer"""
    weight = weight_variable()
    bias = weight_variable()
    return nn.Dense(input_channels, out_channels, weight, bias)


def weight_variable():
    """weight initial"""
    return TruncatedNormal(0.02)


class LeNet5(nn.Cell):
    """Define LeNet5 network."""

    def __init__(self, num_class=10, channel=1):
        super(LeNet5, self).__init__()
        self.num_class = num_class
        self.conv1 = conv(channel, 6, 5)
        self.conv2 = conv(6, 16, 5)
        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
        self.fc2 = fc_with_initialize(120, 84)
        self.fc3 = fc_with_initialize(84, self.num_class)
        self.relu = nn.ReLU()
        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
        self.flatten = nn.Flatten()
        self.channel = Tensor(channel)

    def construct(self, data):
        """define construct."""
        output = self.conv1(data)
        output = self.relu(output)
        output = self.max_pool2d(output)
        output = self.conv2(output)
        output = self.relu(output)
        output = self.max_pool2d(output)
        output = self.flatten(output)
        output = self.fc1(output)
        output = self.relu(output)
        output = self.fc2(output)
        output = self.relu(output)
        output = self.fc3(output)
        return output


def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1):
    """create dataset for train"""
    # define dataset
    mnist_ds = ds.MnistDataset(data_path, num_samples=batch_size * 10)

    resize_height, resize_width = 32, 32
    rescale = 1.0 / 255.0
    rescale_nml = 1 / 0.3081
    shift_nml = -1 * 0.1307 / 0.3081

    # define map operations
    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Bilinear mode
    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
    rescale_op = CV.Rescale(rescale, shift=0.0)
    hwc2chw_op = CV.HWC2CHW()
    type_cast_op = C.TypeCast(mstype.int32)

    # apply map operations on images
    mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)

    # apply DatasetOps
    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
    mnist_ds = mnist_ds.repeat(repeat_size)

    return mnist_ds


def cleanup():
    data_path = os.path.join(os.getcwd(), "data")
    kernel_meta_path = os.path.join(os.getcwd(), "kernel_data")
    cache_path = os.path.join(os.getcwd(), "__pycache__")
    if os.path.exists(data_path):
        shutil.rmtree(data_path)
    if os.path.exists(kernel_meta_path):
        shutil.rmtree(kernel_meta_path)
    if os.path.exists(cache_path):
        shutil.rmtree(cache_path)


class TestProfiler:
    device_id = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
    rank_id = int(os.getenv('RANK_ID')) if os.getenv('RANK_ID') else 0
    mnist_path = '/home/workspace/mindspore_dataset/mnist'

    @classmethod
    def setup_class(cls):
        """Run begin all test case start."""
        cleanup()

    @staticmethod
    def teardown():
        """Run after each test case end."""
        cleanup()

    @pytest.mark.level2
    @pytest.mark.platform_x86_cpu
    @pytest.mark.env_onecard
    @security_off_wrap
    def test_cpu_profiler(self):
        if sys.platform != 'linux':
            return
        self._train_with_profiler(device_target="CPU", profile_memory=False)
        self._check_cpu_profiling_file()

    @pytest.mark.level1
    @pytest.mark.platform_x86_gpu_training
    @pytest.mark.env_onecard
    @security_off_wrap
    def test_gpu_profiler(self):
        self._train_with_profiler(device_target="GPU", profile_memory=False)
        self._check_gpu_profiling_file()
        self._check_host_profiling_file()

    @pytest.mark.level1
    @pytest.mark.platform_x86_gpu_training
    @pytest.mark.env_onecard
    @security_off_wrap
    def test_gpu_profiler_pynative(self):
        """
        Feature: profiler support GPU pynative mode.
        Description: profiling l2 GPU pynative mode data, analyze performance issues.
        Expectation: No exception.
        """
        self._train_with_profiler(device_target="GPU", profile_memory=False, context_mode=context.PYNATIVE_MODE)
        self._check_gpu_profiling_file()
        self._check_host_profiling_file()

    @pytest.mark.level1
    @pytest.mark.platform_arm_ascend_training
    @pytest.mark.platform_x86_ascend_training
    @pytest.mark.env_onecard
    @security_off_wrap
    def test_ascend_profiler(self):
        self._train_with_profiler(device_target="Ascend", profile_memory=True)
        self._check_d_profiling_file()
        self._check_host_profiling_file()

    @pytest.mark.level1
    @pytest.mark.platform_arm_ascend_training
    @pytest.mark.platform_x86_ascend_training
    @pytest.mark.env_onecard
    @security_off_wrap
    @pytest.mark.parametrize("profile_framework", ['all', 'time', 'memory', None])
    def test_host_profiler(self, profile_framework):
        self._train_with_profiler(device_target="Ascend", profile_memory=False, only_profile_host=True,
                                  profile_framework=profile_framework)
        self._check_host_profiling_file(profile_framework=profile_framework)

    def _train_with_profiler(self, device_target, profile_memory, context_mode=context.GRAPH_MODE,
                             only_profile_host=False, profile_framework='all'):
        context.set_context(mode=context_mode, device_target=device_target)
        ds_train = create_dataset(os.path.join(self.mnist_path, "train"))
        if ds_train.get_dataset_size() == 0:
            raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
        if only_profile_host:
            profiler = Profiler(output_path='data', op_time=False,
                                parallel_strategy=False, aicore_metrics=-1, data_process=False,
                                profile_framework=profile_framework)
        else:
            profiler = Profiler(profile_memory=profile_memory, output_path='data', profile_framework=profile_framework)
        profiler_name = os.listdir(os.path.join(os.getcwd(), 'data'))[0]
        self.profiler_path = os.path.join(os.getcwd(), f'data/{profiler_name}/')
        lenet = LeNet5()
        loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
        optim = Momentum(lenet.trainable_params(), learning_rate=0.1, momentum=0.9)
        model = Model(lenet, loss_fn=loss, optimizer=optim, metrics={'acc': Accuracy()})

        model.train(1, ds_train, dataset_sink_mode=True)
        profiler.analyse()
        if device_target != 'Ascend':
            profiler.op_analyse(op_name="Conv2D")

    def _check_gpu_profiling_file(self):
        op_detail_file = self.profiler_path + f'gpu_op_detail_info_{self.device_id}.csv'
        op_type_file = self.profiler_path + f'gpu_op_type_info_{self.device_id}.csv'
        activity_file = self.profiler_path + f'gpu_activity_data_{self.device_id}.csv'
        timeline_file = self.profiler_path + f'gpu_timeline_display_{self.device_id}.json'
        getnext_file = self.profiler_path + f'minddata_getnext_profiling_{self.device_id}.txt'
        pipeline_file = self.profiler_path + f'minddata_pipeline_raw_{self.device_id}.csv'
        framework_file = self.profiler_path + f'gpu_framework_{self.device_id}.txt'

        gpu_profiler_files = (op_detail_file, op_type_file, activity_file,
                              timeline_file, getnext_file, pipeline_file, framework_file)
        for file in gpu_profiler_files:
            assert os.path.isfile(file)

    def _check_d_profiling_file(self):
        aicore_file = self.profiler_path + f'aicore_intermediate_{self.rank_id}_detail.csv'
        step_trace_file = self.profiler_path + f'step_trace_raw_{self.rank_id}_detail_time.csv'
        timeline_file = self.profiler_path + f'ascend_timeline_display_{self.rank_id}.json'
        aicpu_file = self.profiler_path + f'aicpu_intermediate_{self.rank_id}.csv'
        minddata_pipeline_file = self.profiler_path + f'minddata_pipeline_raw_{self.rank_id}.csv'
        queue_profiling_file = self.profiler_path + f'device_queue_profiling_{self.rank_id}.txt'
        memory_file = self.profiler_path + f'memory_usage_{self.rank_id}.pb'

        d_profiler_files = (aicore_file, step_trace_file, timeline_file, aicpu_file,
                            minddata_pipeline_file, queue_profiling_file, memory_file)
        for file in d_profiler_files:
            assert os.path.isfile(file)

    def _check_cpu_profiling_file(self):
        op_detail_file = self.profiler_path + f'cpu_op_detail_info_{self.device_id}.csv'
        op_type_file = self.profiler_path + f'cpu_op_type_info_{self.device_id}.csv'
        timeline_file = self.profiler_path + f'cpu_op_execute_timestamp_{self.device_id}.txt'

        cpu_profiler_files = (op_detail_file, op_type_file, timeline_file)
        for file in cpu_profiler_files:
            assert os.path.isfile(file)

    def _check_host_profiling_file(self, profile_framework='all'):
        host_dir = os.path.join(self.profiler_path, 'host_info')
        if profile_framework is None:
            assert not os.path.exists(host_dir)
            return
        if profile_framework in ['all', 'time']:
            timeline_file = os.path.join(host_dir, f'timeline_{self.rank_id}.json')
            assert os.path.isfile(timeline_file)
        csv_file = os.path.join(host_dir, f'host_info_{self.rank_id}.csv')
        assert os.path.exists(csv_file)
        with open(csv_file, 'r') as f:
            f_reader = csv.reader(f)
            header = next(f_reader)
            assert header == ['tid', 'pid', 'parent_pid', 'module_name', 'event', 'stage', 'level', 'start_end',
                              'custom_info', 'memory_usage(kB)', 'time_stamp(us)']
            for row in f_reader:
                assert len(row) == 11
