# 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 re

import datasets
from datasets.tasks import ImageClassification

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


_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...
"""


_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",
	"train_mask": "https://huggingface.co/datasets/alkzar90/rock-glacier-dataset/resolve/main/data/glaciar_masks_trainset.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)
       
       if self.config.name == "image-classification":
          return [
	          datasets.SplitGenerator(
	          name=datasets.Split.TRAIN,
	          gen_kwargs={
		          "files": dl_manager.iter_files([data_files["train"]]),
		          "split": "training",
	          },
	          ),
	          datasets.SplitGenerator(
	          name=datasets.Split.VALIDATION,
	          gen_kwargs={
		          "files": dl_manager.iter_files([data_files["validation"]]),
		          "split": "validation",
	          },
 	         ),
           ]
           
       if self.config.name == "image-segmentation":
          train_data = dl_manager.iter_files([data_files["train"]]), dl_manager.iter_files([data_files["train_mask"]])
          return [
             datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                   "files": train_data,
                   "split": "training",
                },
             )]
                

   def _generate_examples(self, files, split):
   
      if self.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(),
                  }
      
      if self.config.name == "image-segmentation":
         if split == "training":
            images, masks = files
            imageId2mask = {}
            # iterate trought masks
            for mask_path in masks:
               mask_id = re.search('\d+', mask_path).group(0)
               imageId2mask[mask_id] = mask_path
            logger.info(f"imageId2mask check paths: {imageId2mask}")     
            for i, path in enumerate(files):
               file_name = os.path.basename(path)
               if file_name.endswith(".png"):
                  yield i, {
                     "image": path,
                     "labels": imageId2mask[re.search('\d+', file_name).group(0)]
                  }