# coding=utf-8
# Copyright 2019 The TensorFlow Datasets Authors.
#
# 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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""SCV dataset from http://arxiv.org/abs/1812.01717 ."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from absl import logging
import tensorflow as tf

import tensorflow_datasets.public_api as tfds

DATA_URL_DIR = "https://storage.googleapis.com/scv_dataset/data/"
_CITATION = """\
@article{DBLP:journals/corr/abs-1812-01717,
  author    = {Thomas Unterthiner and
               Sjoerd van Steenkiste and
               Karol Kurach and
               Rapha{\"{e}}l Marinier and
               Marcin Michalski and
               Sylvain Gelly},
  title     = {Towards Accurate Generative Models of Video: {A} New Metric and
               Challenges},
  journal   = {CoRR},
  volume    = {abs/1812.01717},
  year      = {2018},
  url       = {http://arxiv.org/abs/1812.01717},
  archivePrefix = {arXiv},
  eprint    = {1812.01717},
  timestamp = {Tue, 01 Jan 2019 15:01:25 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1812-01717},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""


class StarcraftVideoConfig(tfds.core.BuilderConfig):
    """Config for StarcraftVideo dataset."""

    @tfds.core.disallow_positional_args
    def __init__(self, map_name, resolution, size_in_gb, **kwargs):
        super(StarcraftVideoConfig, self).__init__(
            version=tfds.core.Version(
                "0.1.2", experiments={tfds.core.Experiment.S3: False}
            ),
            supported_versions=[
                tfds.core.Version(
                    "1.0.0", "New split API (https://tensorflow.org/datasets/splits)"
                )
            ],
            **kwargs
        )
        self.map_name = map_name
        self.resolution = resolution
        self.size_in_gb = size_in_gb


class StarcraftVideo(tfds.core.GeneratorBasedBuilder):
    """Starcraft video datasets."""

    BUILDER_CONFIGS = [
        StarcraftVideoConfig(
            name="brawl_64",
            description="Brawl map with 64x64 resolution.",
            map_name="Brawl",
            resolution=64,
            size_in_gb=6.3,
        ),
        StarcraftVideoConfig(
            name="brawl_128",
            description="Brawl map with 128x128 resolution.",
            map_name="Brawl",
            resolution=128,
            size_in_gb=20.7,
        ),
        StarcraftVideoConfig(
            name="collect_mineral_shards_64",
            description="CollectMineralShards map with 64x64 resolution.",
            map_name="CollectMineralShards",
            resolution=64,
            size_in_gb=6.3,
        ),
        StarcraftVideoConfig(
            name="collect_mineral_shards_128",
            description="CollectMineralShards map with 128x128 resolution.",
            map_name="CollectMineralShards",
            resolution=128,
            size_in_gb=20.7,
        ),
        StarcraftVideoConfig(
            name="move_unit_to_border_64",
            description="MoveUnitToBorder map with 64x64 resolution.",
            map_name="MoveUnitToBorder",
            resolution=64,
            size_in_gb=5.8,
        ),
        StarcraftVideoConfig(
            name="move_unit_to_border_128",
            description="MoveUnitToBorder map with 128x128 resolution.",
            map_name="MoveUnitToBorder",
            resolution=128,
            size_in_gb=20.7,
        ),
        StarcraftVideoConfig(
            name="road_trip_with_medivac_64",
            description="RoadTripWithMedivac map with 64x64 resolution.",
            map_name="RoadTripWithMedivac",
            resolution=64,
            size_in_gb=2.4,
        ),
        StarcraftVideoConfig(
            name="road_trip_with_medivac_128",
            description="RoadTripWithMedivac map with 128x128 resolution.",
            map_name="RoadTripWithMedivac",
            resolution=128,
            size_in_gb=7.9,
        ),
    ]

    def _info(self):
        features = tfds.features.FeaturesDict(
            {
                "rgb_screen": tfds.features.Video(
                    shape=(
                        None,
                        self.builder_config.resolution,
                        self.builder_config.resolution,
                        3,
                    )
                ),
            }
        )
        return tfds.core.DatasetInfo(
            builder=self,
            description="This data set contains videos generated from Starcraft.",
            features=features,
            homepage="https://storage.googleapis.com/scv_dataset/README.html",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        url = DATA_URL_DIR + "%s_%dx%d_png/" % (
            self.builder_config.map_name,
            self.builder_config.resolution,
            self.builder_config.resolution,
        )

        urls_to_download = {
            "train_%d" % i: url + "train-0000%d-of-00010.tfrecords" % i
            for i in range(10)
        }
        urls_to_download["valid"] = url + "valid-00000-of-00001.tfrecords"
        urls_to_download["test"] = url + "test-00000-of-00001.tfrecords"

        downloaded_urls = dl_manager.download_and_extract(urls_to_download)

        return [
            tfds.core.SplitGenerator(
                name=tfds.Split.TRAIN,
                num_shards=10,
                gen_kwargs={
                    "files": [
                        download
                        for name, download in downloaded_urls.items()
                        if "train" in name
                    ]
                },
            ),
            tfds.core.SplitGenerator(
                name=tfds.Split.TEST,
                num_shards=1,
                gen_kwargs={"files": [downloaded_urls["test"]]},
            ),
            tfds.core.SplitGenerator(
                name=tfds.Split.VALIDATION,
                num_shards=1,
                gen_kwargs={"files": [downloaded_urls["valid"]]},
            ),
        ]

    def _parse_single_video(self, example_proto):
        """Parses single video from the input tfrecords.

    Args:
      example_proto: tfExample proto with a single video.

    Returns:
      dict with all frames, positions and actions.
    """
        context_features = {
            "game_duration_loops": tf.io.FixedLenFeature([1], tf.int64),
            "game_duration_seconds": tf.io.FixedLenFeature([1], tf.float32),
            "n_steps": tf.io.FixedLenFeature([1], tf.int64),
            "screen_size": tf.io.FixedLenFeature([2], tf.int64),
        }

        sequence_features = {
            "rgb_screen": tf.io.FixedLenSequenceFeature([], tf.string),
        }

        _, seq_feat = tf.io.parse_single_sequence_example(
            example_proto,
            context_features=context_features,
            sequence_features=sequence_features,
        )

        video_frames = tf.map_fn(
            tf.image.decode_png, seq_feat["rgb_screen"], dtype=tf.uint8
        )
        return video_frames

    def _generate_examples(self, files):
        logging.info("Reading data from %s.", ",".join(files))
        with tf.Graph().as_default():
            ds = tf.data.TFRecordDataset(sorted(files))
            ds = ds.map(
                self._parse_single_video,
                num_parallel_calls=tf.data.experimental.AUTOTUNE,
            )
            iterator = tf.compat.v1.data.make_one_shot_iterator(ds).get_next()
            with tf.compat.v1.Session() as sess:
                sess.run(tf.compat.v1.global_variables_initializer())
                try:
                    i = 0
                    while True:
                        video = sess.run(iterator)
                        yield i, {"rgb_screen": video}
                        i += 1

                except tf.errors.OutOfRangeError:
                    # End of file.
                    return
