# 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.

"""CelebA dataset.

Large-scale CelebFaces Attributes (CelebA) Dataset

Deep Learning Face Attributes in the Wild
Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang
"""

from __future__ import annotations

import os

from etils import epath
import numpy as np
import tensorflow_datasets.public_api as tfds

IMG_ALIGNED_DATA = (
    "https://drive.google.com/uc?export=download&"
    "id=0B7EVK8r0v71pZjFTYXZWM3FlRnM"
)
EVAL_LIST = (
    "https://drive.google.com/uc?export=download&"
    "id=0B7EVK8r0v71pY0NSMzRuSXJEVkk"
)
# Landmark coordinates: left_eye, right_eye etc.
LANDMARKS_DATA = (
    "https://drive.google.com/uc?export=download&"
    "id=0B7EVK8r0v71pd0FJY3Blby1HUTQ"
)

# Attributes in the image (Eyeglasses, Mustache etc).
ATTR_DATA = (
    "https://drive.google.com/uc?export=download&"
    "id=0B7EVK8r0v71pblRyaVFSWGxPY0U"
)

LANDMARK_HEADINGS = (
    "lefteye_x lefteye_y righteye_x righteye_y "
    "nose_x nose_y leftmouth_x leftmouth_y rightmouth_x "
    "rightmouth_y"
).split()
ATTR_HEADINGS = (
    "5_o_Clock_Shadow Arched_Eyebrows Attractive Bags_Under_Eyes Bald Bangs "
    "Big_Lips Big_Nose Black_Hair Blond_Hair Blurry Brown_Hair "
    "Bushy_Eyebrows Chubby Double_Chin Eyeglasses Goatee Gray_Hair "
    "Heavy_Makeup High_Cheekbones Male Mouth_Slightly_Open Mustache "
    "Narrow_Eyes No_Beard Oval_Face Pale_Skin Pointy_Nose Receding_Hairline "
    "Rosy_Cheeks Sideburns Smiling Straight_Hair Wavy_Hair Wearing_Earrings "
    "Wearing_Hat Wearing_Lipstick Wearing_Necklace Wearing_Necktie Young"
).split()


class Builder(tfds.core.GeneratorBasedBuilder):
  """CelebA dataset. Aligned and cropped. With metadata."""

  VERSION = tfds.core.Version("2.0.1")
  SUPPORTED_VERSIONS = [
      tfds.core.Version("2.0.0"),
  ]
  RELEASE_NOTES = {
      "2.0.1": "New split API (https://tensorflow.org/datasets/splits)",
  }

  def _info(self):
    return self.dataset_info_from_configs(
        features=tfds.features.FeaturesDict({
            "image": tfds.features.Image(
                shape=(218, 178, 3), encoding_format="jpeg"
            ),
            "landmarks": {name: np.int64 for name in LANDMARK_HEADINGS},
            # Attributes could be some special MultiLabel FeatureConnector
            "attributes": {name: np.bool_ for name in ATTR_HEADINGS},
        }),
        homepage="http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html",
    )

  def _split_generators(self, dl_manager):
    downloaded_dirs = dl_manager.download({
        "img_align_celeba": IMG_ALIGNED_DATA,
        "list_eval_partition": EVAL_LIST,
        "list_attr_celeba": ATTR_DATA,
        "landmarks_celeba": LANDMARKS_DATA,
    })

    # Load all images in memory (~1 GiB)
    # Use split to convert: `img_align_celeba/000005.jpg` -> `000005.jpg`
    all_images = {
        os.path.split(k)[-1]: img
        for k, img in dl_manager.iter_archive(
            downloaded_dirs["img_align_celeba"]
        )
    }

    return [
        tfds.core.SplitGenerator(
            name=tfds.Split.TRAIN,
            gen_kwargs={
                "file_id": 0,
                "downloaded_dirs": downloaded_dirs,
                "downloaded_images": all_images,
            },
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.VALIDATION,
            gen_kwargs={
                "file_id": 1,
                "downloaded_dirs": downloaded_dirs,
                "downloaded_images": all_images,
            },
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.TEST,
            gen_kwargs={
                "file_id": 2,
                "downloaded_dirs": downloaded_dirs,
                "downloaded_images": all_images,
            },
        ),
    ]

  def _process_celeba_config_file(self, file_path):
    """Unpack the celeba config file.

    The file starts with the number of lines, and a header.
    Afterwards, there is a configuration for each file: one per line.

    Args:
      file_path: Path to the file with the configuration.

    Returns:
      keys: names of the attributes
      values: map from the file name to the list of attribute values for
              this file.
    """
    with epath.Path(file_path).open() as f:
      data_raw = f.read()
    lines = data_raw.split("\n")

    keys = lines[1].strip().split()
    values = {}
    # Go over each line (skip the last one, as it is empty).
    for line in lines[2:-1]:
      row_values = line.strip().split()
      # Each row start with the 'file_name' and then space-separated values.
      values[row_values[0]] = [int(v) for v in row_values[1:]]
    return keys, values

  def _generate_examples(self, file_id, downloaded_dirs, downloaded_images):
    """Yields examples."""

    img_list_path = downloaded_dirs["list_eval_partition"]
    landmarks_path = downloaded_dirs["landmarks_celeba"]
    attr_path = downloaded_dirs["list_attr_celeba"]

    with epath.Path(img_list_path).open() as f:
      files = [
          line.split()[0]
          for line in f.readlines()
          if int(line.split()[1]) == file_id
      ]

    attributes = self._process_celeba_config_file(attr_path)
    landmarks = self._process_celeba_config_file(landmarks_path)

    for file_name in sorted(files):
      record = {
          "image": downloaded_images[file_name],
          "landmarks": {
              k: v for k, v in zip(landmarks[0], landmarks[1][file_name])
          },
          "attributes": {
              # atributes value are either 1 or -1, so convert to bool
              k: v > 0
              for k, v in zip(attributes[0], attributes[1][file_name])
          },
      }
      yield file_name, record
