# Copyright 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 os
import time

import pytest

from sagemaker.inputs import FileSystemInput
from sagemaker.parameter import IntegerParameter
from sagemaker.tensorflow import TensorFlow
from sagemaker.tuner import HyperparameterTuner
from sagemaker.utils import unique_name_from_base
import tests
from tests.integ import TRAINING_DEFAULT_TIMEOUT_MINUTES, TUNING_DEFAULT_TIMEOUT_MINUTES
from tests.integ.file_system_input_utils import tear_down, set_up_efs_fsx
from tests.integ.s3_utils import assert_s3_file_patterns_exist
from tests.integ.timeout import timeout

RESOURCE_PATH = os.path.join(os.path.dirname(__file__), "..", "data")
MNIST_RESOURCE_PATH = os.path.join(RESOURCE_PATH, "tensorflow_mnist")
SCRIPT = os.path.join(MNIST_RESOURCE_PATH, "mnist.py")
TFS_RESOURCE_PATH = os.path.join(RESOURCE_PATH, "tfs", "tfs-test-entrypoint-with-handler")
EFS_DIR_PATH = "/tensorflow"
FSX_DIR_PATH = "/fsx/tensorflow"
MAX_JOBS = 2
MAX_PARALLEL_JOBS = 2
PY_VERSION = "py37"


@pytest.fixture(scope="module")
def efs_fsx_setup(sagemaker_session, ec2_instance_type):
    fs_resources = None
    try:
        fs_resources = set_up_efs_fsx(sagemaker_session, ec2_instance_type)
        yield fs_resources
    finally:
        if fs_resources:
            tear_down(sagemaker_session, fs_resources)


@pytest.mark.skipif(
    tests.integ.test_region() not in tests.integ.EFS_TEST_ENABLED_REGION,
    reason="EFS integration tests need to be fixed before running in all regions.",
)
def test_mnist_efs(
    efs_fsx_setup,
    sagemaker_session,
    cpu_instance_type,
    tensorflow_training_latest_version,
    tensorflow_training_latest_py_version,
):
    role = efs_fsx_setup["role_name"]
    subnets = [efs_fsx_setup["subnet_id"]]
    security_group_ids = efs_fsx_setup["security_group_ids"]

    estimator = TensorFlow(
        entry_point=SCRIPT,
        role=role,
        instance_count=1,
        instance_type=cpu_instance_type,
        sagemaker_session=sagemaker_session,
        framework_version=tensorflow_training_latest_version,
        py_version=tensorflow_training_latest_py_version,
        subnets=subnets,
        security_group_ids=security_group_ids,
    )

    file_system_efs_id = efs_fsx_setup["file_system_efs_id"]
    content_type = "application/json"
    file_system_input = FileSystemInput(
        file_system_id=file_system_efs_id,
        file_system_type="EFS",
        directory_path=EFS_DIR_PATH,
        content_type=content_type,
    )
    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
        estimator.fit(inputs=file_system_input, job_name=unique_name_from_base("test-mnist-efs"))

    assert_s3_file_patterns_exist(
        sagemaker_session,
        estimator.model_dir,
        [r"model\.ckpt-\d+\.index", r"checkpoint"],
    )


@pytest.mark.skipif(
    tests.integ.test_region() not in tests.integ.EFS_TEST_ENABLED_REGION,
    reason="EFS integration tests need to be fixed before running in all regions.",
)
def test_mnist_lustre(
    efs_fsx_setup,
    sagemaker_session,
    cpu_instance_type,
    tensorflow_training_latest_version,
    tensorflow_training_latest_py_version,
):
    role = efs_fsx_setup["role_name"]
    subnets = [efs_fsx_setup["subnet_id"]]
    security_group_ids = efs_fsx_setup["security_group_ids"]

    estimator = TensorFlow(
        entry_point=SCRIPT,
        role=role,
        instance_count=1,
        instance_type=cpu_instance_type,
        sagemaker_session=sagemaker_session,
        framework_version=tensorflow_training_latest_version,
        py_version=tensorflow_training_latest_py_version,
        subnets=subnets,
        security_group_ids=security_group_ids,
    )

    file_system_fsx_id = efs_fsx_setup["file_system_fsx_id"]
    file_system_input = FileSystemInput(
        file_system_id=file_system_fsx_id, file_system_type="FSxLustre", directory_path=FSX_DIR_PATH
    )

    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
        estimator.fit(inputs=file_system_input, job_name=unique_name_from_base("test-mnist-lustre"))
    assert_s3_file_patterns_exist(
        sagemaker_session,
        estimator.model_dir,
        [r"model\.ckpt-\d+\.index", r"checkpoint"],
    )


@pytest.mark.skipif(
    tests.integ.test_region() not in tests.integ.EFS_TEST_ENABLED_REGION,
    reason="EFS integration tests need to be fixed before running in all regions.",
)
def test_tuning_tf_efs(
    efs_fsx_setup,
    sagemaker_session,
    cpu_instance_type,
    tensorflow_training_latest_version,
    tensorflow_training_latest_py_version,
):
    role = efs_fsx_setup["role_name"]
    subnets = [efs_fsx_setup["subnet_id"]]
    security_group_ids = efs_fsx_setup["security_group_ids"]

    estimator = TensorFlow(
        entry_point=SCRIPT,
        role=role,
        instance_count=1,
        instance_type=cpu_instance_type,
        sagemaker_session=sagemaker_session,
        framework_version=tensorflow_training_latest_version,
        py_version=tensorflow_training_latest_py_version,
        subnets=subnets,
        security_group_ids=security_group_ids,
    )

    hyperparameter_ranges = {"epochs": IntegerParameter(1, 2)}
    objective_metric_name = "accuracy"
    metric_definitions = [{"Name": objective_metric_name, "Regex": "accuracy = ([0-9\\.]+)"}]
    tuner = HyperparameterTuner(
        estimator,
        objective_metric_name,
        hyperparameter_ranges,
        metric_definitions,
        max_jobs=MAX_JOBS,
        max_parallel_jobs=MAX_PARALLEL_JOBS,
    )

    file_system_efs_id = efs_fsx_setup["file_system_efs_id"]
    file_system_input = FileSystemInput(
        file_system_id=file_system_efs_id, file_system_type="EFS", directory_path=EFS_DIR_PATH
    )

    with timeout(minutes=TUNING_DEFAULT_TIMEOUT_MINUTES):
        tuning_job_name = unique_name_from_base("test-tuning-tf-script-mode-efs", max_length=32)
        tuner.fit(file_system_input, job_name=tuning_job_name)
        time.sleep(15)
        tuner.wait()
    best_training_job = tuner.best_training_job()
    assert best_training_job


@pytest.mark.skipif(
    tests.integ.test_region() not in tests.integ.EFS_TEST_ENABLED_REGION,
    reason="EFS integration tests need to be fixed before running in all regions.",
)
def test_tuning_tf_lustre(
    efs_fsx_setup,
    sagemaker_session,
    cpu_instance_type,
    tensorflow_training_latest_version,
    tensorflow_training_latest_py_version,
):
    role = efs_fsx_setup["role_name"]
    subnets = [efs_fsx_setup["subnet_id"]]
    security_group_ids = efs_fsx_setup["security_group_ids"]

    estimator = TensorFlow(
        entry_point=SCRIPT,
        role=role,
        instance_count=1,
        instance_type=cpu_instance_type,
        sagemaker_session=sagemaker_session,
        framework_version=tensorflow_training_latest_version,
        py_version=tensorflow_training_latest_py_version,
        subnets=subnets,
        security_group_ids=security_group_ids,
    )

    hyperparameter_ranges = {"epochs": IntegerParameter(1, 2)}
    objective_metric_name = "accuracy"
    metric_definitions = [{"Name": objective_metric_name, "Regex": "accuracy = ([0-9\\.]+)"}]
    tuner = HyperparameterTuner(
        estimator,
        objective_metric_name,
        hyperparameter_ranges,
        metric_definitions,
        max_jobs=MAX_JOBS,
        max_parallel_jobs=MAX_PARALLEL_JOBS,
    )

    file_system_fsx_id = efs_fsx_setup["file_system_fsx_id"]
    file_system_input = FileSystemInput(
        file_system_id=file_system_fsx_id, file_system_type="FSxLustre", directory_path=FSX_DIR_PATH
    )

    with timeout(minutes=TUNING_DEFAULT_TIMEOUT_MINUTES):
        tuning_job_name = unique_name_from_base("test-tuning-tf-script-mode-lustre", max_length=32)
        tuner.fit(file_system_input, job_name=tuning_job_name)
        time.sleep(15)
        tuner.wait()
    best_training_job = tuner.best_training_job()
    assert best_training_job