"""LyNoS: Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding."""


import os
import csv
import json

import datasets


_DESCRIPTION = """\
LyNoS: Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding.
"""

_HOMEPAGE = "https://github.com/raidionics/LyNoS"

_LICENSE = "MIT"

_CITATION = """\
@article{bouget2023mediastinal,
  title={Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding},
  author={Bouget, David and Pedersen, Andr{\'e} and Vanel, Johanna and Leira, Haakon O and Lang{\o}, Thomas},
  journal={Computer Methods in Biomechanics and Biomedical Engineering: Imaging \& Visualization},
  volume={11},
  number={1},
  pages={44--58},
  year={2023},
  publisher={Taylor \& Francis}
}

"""

_URLS = {"zenodo": "https://zenodo.org/records/10102261/files/LyNoS.zip?download=1"}


class LyNoS(datasets.GeneratorBasedBuilder):
    """A segmentation benchmark dataset for enlarged lymph nodes in patients with primary lung cancer."""

    VERSION = datasets.Version("1.0.0")
    DEFAULT_CONFIG_NAME = "zenodo"
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="zenodo", version=VERSION, description="This includes all 15 CTs stored as a single zip on Zenodo"),
    ]

    DEFAULT_CONFIG_NAME = "zenodo"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.DATA_DIR = None

    def get_patient(self, patient_id):
        if (patient_id < 1) or (patiend_id > 15):
            raise ValueError("patient_id should be an integer in range [1, 15].")

    def _info(self):
       # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
       if self.config.name == "zenodo":  # This is the name of the configuration selected in BUILDER_CONFIGS above
           features = datasets.Features(
               {
                   "ct": datasets.Value("string"),
                   "lymphnodes": datasets.Value("string"),
                   "azygos": datasets.Value("string"),
                   "brachiocephalicveins": datasets.Value("string"),
                   "esophagus": datasets.Value("string"),
                   "subclaviancarotidarteries": datasets.Value("string")
               }
           )
       else:  
           raise ValueError("Only 'zenodo' is supported.")# This is an example to show how to have different features for "first_domain" and "second_domain"

       return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def get_data_dir(self):
        return self.DATA_DIR
    
    def _split_generators(self, dl_manager):
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        urls = _URLS[self.config.name]
        self.DATA_DIR = dl_manager.download_and_extract(urls)

        # append AeroPath
        self.DATA_DIR = os.path.join(self.DATA_DIR, "Benchmark")

        print("data is downloaded to:", self.DATA_DIR)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "split": "test",
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, split):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        for patient_id in os.listdir(self.DATA_DIR):
            curr_path = os.path.join(self.DATA_DIR, patient_id)
            if patient_id in ["README.md", "license.md", "stations_sto.csv"]:
                continue
            yield patient_id, {
                "ct": os.path.join(curr_path, patient_id.lower() + "_data.nii.gz"),
                "lymphnodes": os.path.join(curr_path, patient_id.lower() + "_labels_LymphNodes.nii.gz"),
                "azygos": os.path.join(curr_path, patient_id.lower() + "_labels_Azygos.nii.gz"),
                "brachiocephalicveins": os.path.join(curr_path, patient_id.lower() + "_labels_BrachiocephalicVeins.nii.gz"),
                "esophagus": os.path.join(curr_path, patient_id.lower() + "_labels_Esophagus.nii.gz"),
                "subclaviancarotidarteries": os.path.join(curr_path, patient_id.lower() + "_labels_SubCarArt.nii.gz")
            }