# coding=utf-8
# Copyright 2023 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
#
# 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.

"""PatchCamelyon images dataset."""

import tensorflow_datasets.public_api as tfds

_URL = 'https://patchcamelyon.grand-challenge.org/'


class Builder(tfds.core.GeneratorBasedBuilder):
  """PatchCamelyon."""

  VERSION = tfds.core.Version('2.0.0')
  RELEASE_NOTES = {
      '2.0.0': 'New split API (https://tensorflow.org/datasets/splits)',
  }

  def _info(self):
    return self.dataset_info_from_configs(
        features=tfds.features.FeaturesDict({
            'id': tfds.features.Text(),
            'image': tfds.features.Image(
                shape=(96, 96, 3), encoding_format='png'
            ),
            'label': tfds.features.ClassLabel(num_classes=2),
        }),
        supervised_keys=('image', 'label'),
        homepage=_URL,
    )

  def _split_generators(self, dl_manager):
    base_url = 'https://zenodo.org/record/2546921/files/'
    resources = {
        'test_x': base_url + 'camelyonpatch_level_2_split_test_x.h5.gz',
        'test_y': base_url + 'camelyonpatch_level_2_split_test_y.h5.gz',
        'train_x': base_url + 'camelyonpatch_level_2_split_train_x.h5.gz',
        'train_y': base_url + 'camelyonpatch_level_2_split_train_y.h5.gz',
        'valid_x': base_url + 'camelyonpatch_level_2_split_valid_x.h5.gz',
        'valid_y': base_url + 'camelyonpatch_level_2_split_valid_y.h5.gz',
    }
    paths = dl_manager.download_and_extract(resources)
    return [
        tfds.core.SplitGenerator(
            name=tfds.Split.TEST, gen_kwargs=dict(split='test', paths=paths)
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.TRAIN, gen_kwargs=dict(split='train', paths=paths)
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.VALIDATION,
            gen_kwargs=dict(split='valid', paths=paths),
        ),
    ]

  def _generate_examples(self, split, paths):
    """Generates images and labels given the image directory path.

    Args:
      split: name of the split to generate examples for (test, train, valid).
      paths: dictionary with the paths to the h5 files for each split.

    Yields:
      A dictionary with the image and the corresponding label.
    """
    h5py = tfds.core.lazy_imports.h5py

    filepath_x = paths[split + '_x']
    filepath_y = paths[split + '_y']
    with h5py.File(filepath_x, 'r') as f_x, h5py.File(filepath_y, 'r') as f_y:
      images = f_x['x']
      labels = f_y['y']  # Note: Labels are in a N x 1 x 1 x 1 tensor.

      for i, (image, label) in enumerate(zip(images, labels)):
        label = label.flatten()[0]
        id_ = '%s_%d' % (split, i)
        record = {'id': id_, 'image': image, 'label': label}
        yield id_, record
