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# Copyright 2022 Cristóbal Alcázar
#
# 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.
"""Rock Glacier dataset with images of the chilean andes."""

import os

import datasets
from datasets.tasks import ImageClassification

_HOMEPAGE = "https://github.com/alcazar90/rock-glacier-detection"


_CITATION = """\
@ONLINE {rock-glacier-dataset,
    author="CMM-Glaciares",
    title="Rock Glacier Dataset",
    month="October",
    year="2022",
    url="https://github.com/alcazar90/rock-glacier-detection"
}
"""

_DESCRIPTION = """\
TODO: Add a description...
"""

_MASKS_URLS = ["https://huggingface.co/datasets/alkzar90/rock-glacier-dataset/resolve/main/data/glaciar_masks_trainset.zip"]

_URLS = {
	"train": "https://huggingface.co/datasets/alkzar90/rock-glacier-dataset/resolve/main/data/train.zip",
	"validation": "https://huggingface.co/datasets/alkzar90/rock-glacier-dataset/resolve/main/data/validation.zip"
}

_NAMES = ["glaciar", "cordillera"]


class RockGlacierConfig(datasets.BuilderConfig):
   def __init__(self, name, **kwargs):
      super(RockGlacierConfig, self).__init__(
         version=datasets.Version("1.0.0"),
         name=name,
         description="Rock Glacier Dataset",
         **kwargs,
         )
         
         
class RockGlacierDataset(datasets.GeneratorBasedBuilder):
   """Rock Glacier images dataset."""
   
   BUILDER_CONFIGS = [
   		RockGlacierConfig("image-classification"),
   		RockGlacierConfig("image-segmentation"),
   ]

   def _info(self):
      if self.config.name == "image-classification":
         features = datasets.Features({
            "image": datasets.Image(),
            "labels": datasets.features.ClassLabel(names=_NAMES),
            })
         keys = ("image", "labels")
         
      if self.config.name == "image-segmentation":
         features = datasets.Features({
            "image": datasets.Image(),
            "labels": datasets.Image(),
            })
         keys = ("image", "labels")
         
         
      return datasets.DatasetInfo(
               description=_DESCRIPTION,
               features=features,
               supervised_keys=keys,
               homepage=_HOMEPAGE,
               citation=_CITATION,
               )
	

   def _split_generators(self, dl_manager):
       data_files = dl_manager.download_and_extract(_URLS)
       return [
	   datasets.SplitGenerator(
	       name=datasets.Split.TRAIN,
	       gen_kwargs={
		   "files": dl_manager.iter_files([data_files["train"]]),
	       },
	   ),
	   datasets.SplitGenerator(
	       name=datasets.Split.VALIDATION,
	       gen_kwargs={
		   "files": dl_manager.iter_files([data_files["validation"]]),
	       },
 	   ),
       ]

   def _generate_examples(self, files):
   
      if selg.config.name == "image-classification":
         for i, path in enumerate(files):
            file_name = os.path.basename(path)
            if file_name.endswith(".png"):
               yield i, {
                  "image": path,
                  "labels": os.path.basename(os.path.dirname(path)).lower(),
                  }