# 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.

"""
This dataset contains example data for running through the multiplexed imaging data pipeline in
Ark Analysis: https://github.com/angelolab/ark-analysis
"""

import json
import os

import datasets
import pathlib
import glob
import tifffile

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Ark Analysis Example Dataset},
author={Angelo Lab},
year={2022}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This dataset contains 11 Field of Views (FOVs), each with 22 channels. 
"""

_HOMEPAGE = "https://github.com/angelolab/ark-analysis"

_LICENSE = "https://github.com/angelolab/ark-analysis/blob/main/LICENSE"

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL_REPO = "https://huggingface.co/datasets/angelolab/ark_example/resolve/main/"


_URLS = {"base_dataset": f"{_URL_REPO}/data/fovs.zip"}

"""
https://huggingface.co/docs/datasets/dataset_script
https://huggingface.co/docs/datasets/share
https://huggingface.co/datasets/allenai/wmt22_african/blob/main/wmt22_african.py
https://huggingface.co/docs/datasets/repository_structure
"""

# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ArkExample(datasets.GeneratorBasedBuilder):
    """The Dataset consists of 12 FOVs"""

    VERSION = datasets.Version("0.0.1")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'base_dataset')
    # data = datasets.load_dataset('my_dataset', 'dev_dataset')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="base_dataset",
            version=VERSION,
            description="This dataset contains only the 12 FOVs.",
        ),
        datasets.BuilderConfig(
            name="dev_dataset",
            version=VERSION,
            description="This dataset is a superset of the base_dataset, and contains intermediate data for all notebooks. \
                Therefore you can start at any notebook with this dataset.",
        ),
    ]

    DEFAULT_CONFIG_NAME = "base_dataset"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains information and typings for the dataset
        if (
            self.config.name == "base_dataset"
        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "fov": datasets.Sequence({"channel": datasets.Image()}),
                }
            )
        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
            features = datasets.Features(
                {
                    "sentence": datasets.Value("string"),
                    "option2": datasets.Value("string"),
                    "second_domain_answer": datasets.Value("string")
                    # These are the features of your dataset like images, labels ...
                }
            )
        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
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    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
        urls = _URLS[self.config.name]
        data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name="BASE_DATASET",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": pathlib.Path(data_dir) / "fovs"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath: pathlib.Path):
        # 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.

        file_paths = list(filepath.rglob("*"))
        
        for fp in file_paths:
            if fp.suffix in [".tiff", ".tif"]:
                
                image_data = tifffile.imread(fp, key=0)
                if self.config.name == "base_dataset":
                    yield fp.parent.stem, {
                        "chan": image_data,
                        "path": fp
                    }
        
        # with open(filepath, encoding="utf-8") as f:
        #     for key, row in enumerate(f):
        #         data = json.loads(row)
        #         if self.config.name == "first_domain":
        #             # Yields examples as (key, example) tuples
        #             yield key, {
        #                 "sentence": data["sentence"],
        #                 "option1": data["option1"],
        #                 "answer": "" if split == "test" else data["answer"],
        #             }
        #         else:
        #             yield key, {
        #                 "sentence": data["sentence"],
        #                 "option2": data["option2"],
        #                 "second_domain_answer": ""
        #                 if split == "test"
        #                 else data["second_domain_answer"],
        #             }