# coding=utf-8
# Copyright 2019 Google LLC.
#
# 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.

"""Implements oxford flowers 102 data class."""

from __future__ import absolute_import, division, print_function

import tensorflow_datasets as tfds

from . import base


class OxfordFlowers102Data(base.ImageTfdsData):
    """Provides Oxford 102 categories flowers dataset.

    See corresponding tfds dataset for details.

    URL: https://www.robots.ox.ac.uk/~vgg/data/flowers/102/
    """

    def __init__(self, data_dir=None, train_split_percent=None):
        dataset_builder = tfds.builder("oxford_flowers102:2.*.*", data_dir=data_dir)
        dataset_builder.download_and_prepare()

        # Example counts are retrieved from the tensorflow dataset info.
        train_count = dataset_builder.info.splits[tfds.Split.TRAIN].num_examples
        val_count = dataset_builder.info.splits[tfds.Split.VALIDATION].num_examples
        test_count = dataset_builder.info.splits[tfds.Split.TEST].num_examples

        if train_split_percent:
            tfds_splits = {
                "train": "train[:{s}%]+validation[:{s}%]".format(s=train_split_percent),
                "val": "train[-{s}%:]+validation[-{s}%:]".format(s=train_split_percent),
                "trainval": "train+validation",
                "test": "test",
                "train800": "train[:800]",
                "val200": "validation[:200]",
                "train800val200": "train[:800]+validation[:200]",
            }
            num_samples_splits = {
                "train": (((train_count + val_count) // 100) * train_split_percent),
                "val": (
                    ((train_count + val_count) // 100) * (100 - train_split_percent)
                ),
                "trainval": train_count + val_count,
                "test": test_count,
                "train800": 800,
                "val200": 200,
                "train800val200": 1000,
            }
        else:
            tfds_splits = {
                "train": "train",
                "val": "validation",
                "trainval": "train+validation",
                "test": "test",
                "train800": "train[:800]",
                "val200": "validation[:200]",
                "train800val200": "train[:800]+validation[:200]",
            }
            num_samples_splits = {
                "train": train_count,
                "val": val_count,
                "trainval": train_count + val_count,
                "test": test_count,
                "train800": 800,
                "val200": 200,
                "train800val200": 1000,
            }

        super(OxfordFlowers102Data, self).__init__(
            dataset_builder=dataset_builder,
            tfds_splits=tfds_splits,
            num_samples_splits=num_samples_splits,
            num_preprocessing_threads=400,
            shuffle_buffer_size=10000,
            # Note: Rename tensors but keep their original types.
            base_preprocess_fn=base.make_get_and_cast_tensors_fn(
                {
                    "image": ("image", None),
                    "label": ("label", None),
                }
            ),
            num_classes=dataset_builder.info.features["label"].num_classes,
        )
