# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. 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. A copy of
# the License is located at
#
#     http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file 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.
from __future__ import absolute_import

import gzip
import os
import pickle
import sys
import time

import pytest

from sagemaker import KMeans, KMeansModel
from sagemaker.utils import unique_name_from_base
from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES
from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name


@pytest.mark.continuous_testing
def test_kmeans(sagemaker_session):
    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
        data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
        pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}

        # Load the data into memory as numpy arrays
        with gzip.open(data_path, 'rb') as f:
            train_set, _, _ = pickle.load(f, **pickle_args)

        kmeans = KMeans(role='SageMakerRole', train_instance_count=1,
                        train_instance_type='ml.c4.xlarge',
                        k=10, sagemaker_session=sagemaker_session, base_job_name='test-kmeans')

        kmeans.init_method = 'random'
        kmeans.max_iterations = 1
        kmeans.tol = 1
        kmeans.num_trials = 1
        kmeans.local_init_method = 'kmeans++'
        kmeans.half_life_time_size = 1
        kmeans.epochs = 1
        kmeans.center_factor = 1

        assert kmeans.hyperparameters() == dict(
            init_method=kmeans.init_method,
            local_lloyd_max_iter=str(kmeans.max_iterations),
            local_lloyd_tol=str(kmeans.tol),
            local_lloyd_num_trials=str(kmeans.num_trials),
            local_lloyd_init_method=kmeans.local_init_method,
            half_life_time_size=str(kmeans.half_life_time_size),
            epochs=str(kmeans.epochs),
            extra_center_factor=str(kmeans.center_factor),
            k=str(kmeans.k),
            force_dense='True',
        )

        kmeans.fit(kmeans.record_set(train_set[0][:100]))

    endpoint_name = unique_name_from_base('kmeans')
    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
        model = KMeansModel(kmeans.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session)
        predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
        result = predictor.predict(train_set[0][:10])

        assert len(result) == 10
        for record in result:
            assert record.label["closest_cluster"] is not None
            assert record.label["distance_to_cluster"] is not None

    predictor.delete_model()
    with pytest.raises(Exception) as exception:
        sagemaker_session.sagemaker_client.describe_model(ModelName=model.name)
        assert 'Could not find model' in str(exception.value)


def test_async_kmeans(sagemaker_session):
    training_job_name = ""
    endpoint_name = unique_name_from_base('kmeans')

    with timeout(minutes=5):
        data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
        pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}

        # Load the data into memory as numpy arrays
        with gzip.open(data_path, 'rb') as f:
            train_set, _, _ = pickle.load(f, **pickle_args)

        kmeans = KMeans(role='SageMakerRole', train_instance_count=1,
                        train_instance_type='ml.c4.xlarge',
                        k=10, sagemaker_session=sagemaker_session, base_job_name='test-kmeans')

        kmeans.init_method = 'random'
        kmeans.max_iterations = 1
        kmeans.tol = 1
        kmeans.num_trials = 1
        kmeans.local_init_method = 'kmeans++'
        kmeans.half_life_time_size = 1
        kmeans.epochs = 1
        kmeans.center_factor = 1

        assert kmeans.hyperparameters() == dict(
            init_method=kmeans.init_method,
            local_lloyd_max_iter=str(kmeans.max_iterations),
            local_lloyd_tol=str(kmeans.tol),
            local_lloyd_num_trials=str(kmeans.num_trials),
            local_lloyd_init_method=kmeans.local_init_method,
            half_life_time_size=str(kmeans.half_life_time_size),
            epochs=str(kmeans.epochs),
            extra_center_factor=str(kmeans.center_factor),
            k=str(kmeans.k),
            force_dense='True',
        )

        kmeans.fit(kmeans.record_set(train_set[0][:100]), wait=False)
        training_job_name = kmeans.latest_training_job.name

        print("Detached from training job. Will re-attach in 20 seconds")
        time.sleep(20)
        print("attaching now...")

    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
        estimator = KMeans.attach(training_job_name=training_job_name, sagemaker_session=sagemaker_session)
        model = KMeansModel(estimator.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session)
        predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
        result = predictor.predict(train_set[0][:10])

        assert len(result) == 10
        for record in result:
            assert record.label["closest_cluster"] is not None
            assert record.label["distance_to_cluster"] is not None
