import os
import datasets

#datasets.logging.set_verbosity_debug()
#datasets.logging.set_verbosity_info()
#logger = datasets.logging.get_logger(__name__)


_DESCRIPTION = """\
A segmentation dataset for [TODO: complete...]
"""


_HOMEPAGE = "https://huggingface.co/datasets/alkzar90/cell_benchmark"
_EXTENSION = [".jpg", ".png"]
_URL_BASE = "https://huggingface.co/datasets/alkzar90/cell_benchmark/resolve/main/data/"
_SPLIT_URLS = {
  "train":  _URL_BASE + "train.zip",
  "val":    _URL_BASE + "val.zip",
  "test":   _URL_BASE + "test.zip",
  "masks_train":  _URL_BASE + "masks/train.zip",
  "masks_val":  _URL_BASE + "masks/val.zip",
  "masks_test":  _URL_BASE + "masks/test.zip",
}



class Cellsegmentation(datasets.GeneratorBasedBuilder):

  def _info(self):
    features = datasets.Features({
         "image": datasets.Image(),
         "masks": datasets.Image(),
         #"path" : datasets.Value("string"),
      })
    return datasets.DatasetInfo(
        description=_DESCRIPTION,
        features=datasets.Features(features),
        supervised_keys=("image", "masks"),
        homepage=_HOMEPAGE,
        citation="",
    )


  def _split_generators(self, dl_manager):
    data_files = dl_manager.download_and_extract(_SPLIT_URLS)      
    splits = [
           datasets.SplitGenerator(
              name=datasets.Split.TRAIN,
              gen_kwargs={
                  "files" : dl_manager.iter_files([data_files["train"]]),
                  "masks": dl_manager.iter_files([data_files["masks_train"]]),
                  "split":  "training",
              },
           ),
           datasets.SplitGenerator(
              name=datasets.Split.VALIDATION,
              gen_kwargs={
                  "files" : dl_manager.iter_files([data_files["val"]]),
                  "masks": dl_manager.iter_files([data_files["masks_val"]]),
                  "split": "validation",
              },
           ),
           datasets.SplitGenerator(
              name=datasets.Split.TEST,
              gen_kwargs={
                  "files" : dl_manager.iter_files([data_files["test"]]),
                  "masks": dl_manager.iter_files([data_files["masks_test"]]),
                  "split": "test",
              }
           )
    ]
    return splits


  def _generate_examples(self, files, masks, split):
    for i, path in enumerate(zip(files, masks)):
      yield i, {
           "image": path[0],
           "masks": path[1],
      }