# 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 multi-species genomes dataset. This dataset contains the genomes
from 850 different species."""

from typing import List
import datasets
import pandas as pd
from Bio import SeqIO


# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{o2016reference,
  title={Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation},
  author={O'Leary, Nuala A and Wright, Mathew W and Brister, J Rodney and Ciufo, Stacy and Haddad, Diana and McVeigh, Rich and Rajput, Bhanu and Robbertse, Barbara and Smith-White, Brian and Ako-Adjei, Danso and others},
  journal={Nucleic acids research},
  volume={44},
  number={D1},
  pages={D733--D745},
  year={2016},
  publisher={Oxford University Press}
}
"""

# You can copy an official description
_DESCRIPTION = """\
Genomes from 850 different species.
"""

_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/"

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

url_df = pd.read_csv('urls.csv')
urls = list(url_df['URL'])
_TEST_URLS = urls[-50:]   # 50 genomes for test set
_VALIDATION_URLS = urls[-100:-50]  # 50 genomes for validation set
_TRAIN_URLS = urls[:-100]  # 800 genomes for training

_CHUNK_LENGTHS = [6000, 12000]
_OVERLAP = 100


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 MultiSpeciesGenomesConfig(datasets.BuilderConfig):
    """BuilderConfig for The Human Reference Genome."""

    def __init__(self, *args, chunk_length: int, **kwargs):
        """BuilderConfig for the multi species genomes.
        Args:
            chunk_length (:obj:`int`): Chunk length.
            **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


class MultiSpeciesGenomes(datasets.GeneratorBasedBuilder):
    """Genomes from 850 species, filtered and split into chunks of consecutive
    nucleotides. 50 genomes are taken for test, 50 for validation and 800
    for training."""

    VERSION = datasets.Version("1.1.0")
    BUILDER_CONFIG_CLASS = MultiSpeciesGenomesConfig
    BUILDER_CONFIGS = [MultiSpeciesGenomesConfig(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("int"),
                "end_pos": datasets.Value("int"),
            }
        )
        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]:

        train_downloaded_files = dl_manager.download_and_extract(_TRAIN_URLS)
        test_downloaded_files = dl_manager.download_and_extract(_TEST_URLS)
        validation_downloaded_files = dl_manager.download_and_extract(_VALIDATION_URLS)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": train_downloaded_files, "chunk_length": self.config.chunk_length}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"files": validation_downloaded_files, "chunk_length": self.config.chunk_length}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"files": test_downloaded_files, "chunk_length": self.config.chunk_length}),
        ]

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

                for record in fasta_sequences:

                    # parse descriptions in the fasta file
                    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 * _OVERLAP) // chunk_length

                    if num_chunks < 1:
                        continue

                    sequence = sequence[:(chunk_length * num_chunks + 2 * _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 * _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