#!/usr/bin/env python
# Copyright 2019 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 argparse
import itertools
import os

from sagemaker import Session
from sagemaker.estimator import Framework
from sagemaker.tensorflow import TensorFlow

default_bucket = Session().default_bucket
dir_path = os.path.dirname(os.path.realpath(__file__))

_DEFAULT_HYPERPARAMETERS = {
    "batch_size": 32,
    "model": "resnet32",
    "num_epochs": 10,
    "data_format": "NHWC",
    "summary_verbosity": 1,
    "save_summaries_steps": 10,
    "data_name": "cifar10",
}


class ScriptModeTensorFlow(Framework):
    """This class is temporary until the final version of Script Mode is released.
    """

    __framework_name__ = "tensorflow-scriptmode-beta"

    create_model = TensorFlow.create_model

    def __init__(self, py_version="py3", **kwargs):
        super(ScriptModeTensorFlow, self).__init__(**kwargs)
        self.py_version = py_version
        self.image_name = None
        self.framework_version = "1.10.0"


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-t", "--instance-types", nargs="+", help="<Required> Set flag", required=True
    )
    parser.add_argument("-r", "--role", required=True)
    parser.add_argument("-w", "--wait", action="store_true")
    parser.add_argument("--region", default="us-west-2")
    parser.add_argument("--py-versions", nargs="+", help="<Required> Set flag", default=["py3"])
    parser.add_argument(
        "--checkpoint-path",
        default=os.path.join(default_bucket(), "benchmarks", "checkpoints"),
        help="The S3 location where the model checkpoints and tensorboard events are saved after training",
    )

    return parser.parse_known_args()


def main(args, script_args):
    for instance_type, py_version in itertools.product(args.instance_types, args.py_versions):
        base_name = "%s-%s-%s" % (py_version, instance_type[3:5], instance_type[6:])
        model_dir = os.path.join(args.checkpoint_path, base_name)

        job_hps = create_hyperparameters(model_dir, script_args)

        print("hyperparameters:")
        print(job_hps)

        estimator = ScriptModeTensorFlow(
            entry_point="tf_cnn_benchmarks.py",
            role="SageMakerRole",
            source_dir=os.path.join(dir_path, "tf_cnn_benchmarks"),
            base_job_name=base_name,
            train_instance_count=1,
            hyperparameters=job_hps,
            train_instance_type=instance_type,
        )

        input_dir = "s3://sagemaker-sample-data-%s/spark/mnist/train/" % args.region
        estimator.fit({"train": input_dir}, wait=args.wait)

    print("To use TensorBoard, execute the following command:")
    cmd = "S3_USE_HTTPS=0 S3_VERIFY_SSL=0  AWS_REGION=%s tensorboard --host localhost --port 6006 --logdir %s"
    print(cmd % (args.region, args.checkpoint_path))


def create_hyperparameters(model_dir, script_args):
    job_hps = _DEFAULT_HYPERPARAMETERS.copy()

    job_hps.update({"train_dir": model_dir, "eval_dir": model_dir})

    script_arg_keys_without_dashes = [
        key[2:] if key.startswith("--") else key[1:] for key in script_args[::2]
    ]
    script_arg_values = script_args[1::2]
    job_hps.update(dict(zip(script_arg_keys_without_dashes, script_arg_values)))

    return job_hps


if __name__ == "__main__":
    args, script_args = get_args()
    main(args, script_args)