# See the License for the specific language governing permissions and
# limitations under the License.

import os
from typing import List
import datasets

def parse_fasta(fp):
    name, seq = None, []
    for line in fp:
        line = line.rstrip()
        if line.startswith(">"):
            if name:
                # Slice to remove '>'
                yield (name[1:], "".join(seq))
            name, seq = line, []
        else:
            seq.append(line)
    if name:
        # Slice to remove '>'
        yield (name[1:], "".join(seq))


_CITATION = """\
@article{boshar2024gLMsForProteins,
  title={Are Genomic Language Models All You Need? Exploring Genomic Language Models on Protein Downstream Tasks},
  author={Sam Boshar, Evan Trop, Bernardo P. de Almeida, Lviua Copoiu, Thomas Pierrot},
  journal={bioRxiv},
  pages={},
  year={2024},
  publisher={}
}
'''
"""

# You can copy an official description
_DESCRIPTION = """\
This dataset comprises 5 downstream protein tasks with associated true CDS sequences considered in the paper. The tasks include five which are regression, and one which is multi-label classification. Each task corresponds to a dataset configuration.
"""

_HOMEPAGE = "https://github.com/instadeepai/gLMs-for-proteins"

_LICENSE = "https://github.com/instadeepai/nucleotide-transformer/LICENSE.md"

_TASKS =  ['beta_lactamase_complete',
            'beta_lactamase_unique',
            'ssp',
            'stability',
            'melting_point',
            'fluorescence'
            ]

class ProteinTrueCDSConfig(datasets.BuilderConfig):
    """BuilderConfig for protein True CDS tasks."""

    def __init__(self, *args, task: str, **kwargs):
        """BuilderConfig downstream tasks dataset.
        Args:
            task (:obj:`str`): Task name.
            **kwargs: keyword arguments forwarded to super.
        """
        super().__init__(
            *args,
            name=f"{task}",
            **kwargs,
        )
        self.task = task


class ProteinTrueCDSDownstreamTasks(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.1.0")
    BUILDER_CONFIG_CLASS = ProteinTrueCDSConfig
    BUILDER_CONFIGS = [
        ProteinTrueCDSConfig(task=task) for task in _TASKS
    ]

    # DEFAULT_CONFIG_NAME = "enhancers"

    def _info(self):
        if self.config.task == 'ssp':
            label_type = datasets.Sequence(datasets.Value("int32"))
        else:
            label_type = datasets.Value("float32")

        features = datasets.Features(
            {
                "sequence": datasets.Value("string"),
                "label": label_type,
            }
        )
        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,
            # 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: datasets.DownloadManager
    ) -> List[datasets.SplitGenerator]:

        if self.config.task == 'ssp':
            train_file = dl_manager.download_and_extract(self.config.task + "/train.fna")
            train_dataset = datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"file": train_file}
            )
            test_datasets = [
                datasets.SplitGenerator(
                    name=name, gen_kwargs={
                        "file": dl_manager.download_and_extract(self.config.task + f"/{name}.fna")
                    }
                ) for name in ['CASP12', 'CB513', 'TS115']]
            return [train_dataset] + test_datasets

        else:
            val_file = dl_manager.download_and_extract(self.config.task + "/val.fna")
            train_file = dl_manager.download_and_extract(self.config.task + "/train.fna")
            test_file = dl_manager.download_and_extract(self.config.task + "/test.fna")

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

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, file):
        key = 0
        with open(file, "rt") as f:
            fasta_sequences = parse_fasta(f)

            for name, seq in fasta_sequences:
                # parse descriptions in the fasta file
                sequence, name = str(seq), str(name)
                if self.config.task != 'ssp':
                    label = float(name.split("|")[-1])
                else:
                    label = [int(i) for i in name.split("|")[-1]]

                # yield example
                yield key, {
                    "sequence": sequence,
                    "label": label,
                }
                key += 1