# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""

import pandas as pd
import numpy as np
from pathlib import Path
import jsonlines
from connect_later.split_dataset_into_files import split_dataset_into_files
import torch
import pdb

import datasets

DATASET_PATH = "/pscratch/sd/h/helenqu/plasticc/raw/plasticc_raw_examples"

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class NewDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        # if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
        features = datasets.Features(
            {
                "objid": datasets.Value("int32"),
                "times_wv": datasets.Array2D(shape=(300, 2), dtype='float64'), # ith row is [time, central wv of band]
                "target": datasets.Array2D(shape=(300, 2), dtype='float64'), # the time series data, ith row is [flux, flux_err]
            }
        )
        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
        )

    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
        dataset_path = Path(DATASET_PATH)
        # if not (dataset_path / 'train.csv').exists():
        #     print('Splitting dataset into files...')
        #     split_dataset_into_files(dataset_path, "prepr*csv", 0.8, fraction=0.15, required_paths=[dataset_path / "orig_train_set.csv"]) # full dataset size is 256G, trying to keep it under 40G for now since that's the size of the GPU mem

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": self.config.data_files['train'] if self.config.data_files is not None else dataset_path.glob('*.jsonl'),
                    "split": "train",
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, 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 path in filepath:
            with jsonlines.open(path) as reader:
                for obj in reader:
                    yield int(obj['object_id']), {
                        "objid": int(obj['object_id']),
                        "times_wv": obj['times_wv'],
                        # "target": np.transpose(np.array(obj['lightcurve'], dtype='float64')),
                        "target": obj['lightcurve'],
                    }