# 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):
	"""Rock Glacier dataset configuration"""
	def __init__(self, name, **kwargs):
		super(RockGlacierConfig).__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 = dataset.Features({
   	   			"image": datasets.Image(),
   	   			"labels": datasets.features.ClassLabel(names=_NAMES),
   	   		})
   	   		keys = ("image", "labels")
   	   		task = [ImageClassification(image_column="image", label_column="labels")]
   	   		
   	   if self.config.name == "image-segmentation":
   	   		pass
   	   		
       return datasets.DatasetInfo(
       	   		description=_DESCRIPTION,
       	  		features=features,
  				supervised_keys=("image", "labels"),
	   			homepage=_HOMEPAGE,
	   			citation=_CITATION,
	   			task_templates=task,
				)
	

   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(),
	                }