"""Script for the model plants reference genomes dataset"""

from typing import List
import datasets
from Bio import SeqIO
import os

_CITATION = ""

_DESCRIPTION = """\
Dataset made of model plants genomes available on NCBI.
Default configuration "6kbp" yields chunks of 6.2kbp (100bp overlap on each side). The chunks of DNA are cleaned and processed so that
they can only contain the letters A, T, C, G and N.
"""
_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/"

_LICENSE = "https://www.ncbi.nlm.nih.gov/home/about/policies/"

_CHUNK_LENGTHS = [6000,]


def filter_fn(char: str) -> str:
    """
    Transforms any letter different from a base nucleotide into an 'N'.
    """
    if char in {'A', 'T', 'C', 'G'}:
        return char
    else:
        return 'N'


def clean_sequence(seq: str) -> str:
    """
    Process a chunk of DNA to have all letters in upper and restricted to
    A, T, C, G and N.
    """
    seq = seq.upper()
    seq = map(filter_fn, seq)
    seq = ''.join(list(seq))
    return seq


class PlantSingleSpeciesGenomesConfig(datasets.BuilderConfig):
    """BuilderConfig for the Plant Single Species Pre-training Dataset."""

    def __init__(self, *args, chunk_length: int, overlap: int = 100, **kwargs):
        """BuilderConfig for the single species genome.
        Args:
            chunk_length (:obj:`int`): Chunk length.
            overlap: (:obj:`int`): Overlap in base pairs for two consecutive chunks (defaults to 100).
            **kwargs: keyword arguments forwarded to super.
        """
        num_kbp = int(chunk_length/1000)
        super().__init__(
            *args,
            name=f'{num_kbp}kbp',
            **kwargs,
        )
        self.chunk_length = chunk_length
        self.overlap = overlap


class PlantSingleSpeciesGenomes(datasets.GeneratorBasedBuilder):
    """Genome from a single species, filtered and split into chunks of consecutive
    nucleotides."""

    VERSION = datasets.Version("1.1.0")
    BUILDER_CONFIG_CLASS = PlantSingleSpeciesGenomesConfig
    BUILDER_CONFIGS = [PlantSingleSpeciesGenomesConfig(chunk_length=chunk_length) for chunk_length in _CHUNK_LENGTHS]
    DEFAULT_CONFIG_NAME = "6kbp"

    def _info(self):

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

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        # A.thaliana reference genome
        filepath = "GCF_000001735.4_TAIR10.1_genomic.fna.gz"
        downloaded_file = dl_manager.download_and_extract(filepath)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"file": downloaded_file, "chunk_length": self.config.chunk_length})
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, file, chunk_length):
        key = 0
        with open(file, 'rt') as f:
            fasta_sequences = SeqIO.parse(f, 'fasta')

            for record in fasta_sequences:
                sequence, description = str(record.seq), record.description

                # clean chromosome sequence
                sequence = clean_sequence(sequence)
                seq_length = len(sequence)

                # split into chunks
                num_chunks = (seq_length - 2 * self.config.overlap) // chunk_length

                if num_chunks < 1:
                    continue

                sequence = sequence[:(chunk_length * num_chunks + 2 * self.config.overlap)]
                seq_length = len(sequence)

                for i in range(num_chunks):
                    # get chunk
                    start_pos = i * chunk_length
                    end_pos = min(seq_length, (i+1) * chunk_length + 2 * self.config.overlap)
                    chunk_sequence = sequence[start_pos:end_pos]

                    # yield chunk
                    yield key, {
                        'sequence': chunk_sequence,
                        'description': description,
                        'start_pos': start_pos,
                        'end_pos': end_pos,
                    }
                    key += 1