# 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.
"""Script for the dataset containing tasks for genome origin detection."""

from typing import List

import datasets


# Custom function to parse lines in the dataset
def parse_text(fp):
    for line in fp:
        line = line.strip()
        if not line:  # Skip empty lines
            continue
        seq, label = line.split(",")
        yield seq, label


# Dataset metadata
_CITATION = """TBD"""
_DESCRIPTION = """TBD"""
_HOMEPAGE = "TBD"
_LICENSE = "TBD"

# Tasks available in the dataset
_TASKS = [
    "four_kingdoms",
    "plasmid_detection",
]


class GenomeOriginTasksConfig(datasets.BuilderConfig):
    """BuilderConfig for Genome Origin tasks dataset."""

    def __init__(self, *args, task: str, **kwargs):
        """
        BuilderConfig for Genome Origin tasks dataset.

        Args:
            task (str): Task name.
            **kwargs: Additional keyword arguments forwarded to super.
        """
        super().__init__(
            *args,
            name=f"{task}",
            **kwargs,
        )
        self.task = task


class GenomeOriginTasks(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.1.0")
    BUILDER_CONFIG_CLASS = GenomeOriginTasksConfig
    BUILDER_CONFIGS = [GenomeOriginTasksConfig(task=task) for task in _TASKS]
    DEFAULT_CONFIG_NAME = "four_kingdoms"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "sequence": datasets.Value("string"),
                    "label": datasets.Value("int32"),
                }
            ),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        """Returns SplitGenerators."""
        
        train_file = dl_manager.download_and_extract(self.config.task + "/train.txt")
        test_file = dl_manager.download_and_extract(self.config.task + "/test.txt")

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"file_path": train_file}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"file_path": test_file}
            ),
        ]

    def _generate_examples(self, file_path):
        """Generates examples from a given file."""
        key = 0
        with open(file_path, "r") as f:
            for seq, label in parse_text(f):
                yield key, {
                    "sequence": seq.upper(),  # Ensure sequences are uppercase
                    "label": int(label),  # Convert label to integer
                }
                key += 1