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	# 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 human reference genome dataset.."""

	from typing import List
	import datasets
	from Bio import SeqIO
	import regex as re


	# 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 = """\
	Genome Reference Consortium Human Build 38 patch release 14 (GRCh38.p14)
	filtered and split into chunks.
	"""

	_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.40"

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

	_URLS = {
	f"fasta": "https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/GCF_000001405.40_GRCh38.p14/GCF_000001405.40_GRCh38.p14_genomic.fna.gz"
	}

	_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


	def continue_loop(split: str, chromosome: str) -> bool:
	"""
	Use to associate split and chromosome when looping over fasta file.
	"""
	validation_chromosome = '21'
	test_chromosome = '22'
	train_chromosomes = set(str(i) for i in range(1, 21))
	train_chromosomes.update({'X', 'Y'})
	if split == 'validation' and chromosome == validation_chromosome:
	return True
	elif split == 'test' and chromosome == test_chromosome:
	return True
	elif split == 'train' and chromosome in train_chromosomes:
	return True
	else:
	return False


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

	def __init__(self, args, chunk_length: int, *kwargs):
	"""BuilderConfig for The Pile.
	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 HumanReferenceGenome(datasets.GeneratorBasedBuilder):
	"""Human reference genome, filtered and split into chunks of consecutive
	nucleotides. The test set corresponds to chromosome 22, the validation set to
	chromosome 21 and all other chromosomes are used for training."""

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

	def _info(self):

	features = datasets.Features(
	{
	"sequence": datasets.Value("string"),
	"chromosome": datasets.Value("string"),
	"start_pos": datasets.Value("int32"),
	"end_pos": datasets.Value("int32"),
	}
	)
	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]:
	urls_to_download = _URLS
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files['fasta'], "split": "train", "chunk_length": self.config.chunk_length}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files['fasta'], "split": "validation", "chunk_length": self.config.chunk_length}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files['fasta'], "split": "test", "chunk_length": self.config.chunk_length}),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split, chunk_length):
	with open(filepath, 'rt') as f:
	fasta_sequences = SeqIO.parse(f, 'fasta')
	# regex to filter lines of interest in the FASTA
	prog = re.compile("NC_\d.\d Homo sapiens chromosome (\d*\|\w), GRCh38.p14 Primary Assembly")

	key = 0
	for record in fasta_sequences:

	# parse descriptions in the fasta file
	sequence, description = str(record.seq), record.description
	regex_match = prog.match(description)

	if regex_match is not None:

	# get chromosome
	chromosome = regex_match[1]

	# continue if the chromosome belongs to this split
	if continue_loop(split=split, chromosome=chromosome):

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

	# split into chunks
	num_chunks = (seq_length - 2 * _OVERLAP) // chunk_length
	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,
	'chromosome': chromosome,
	'start_pos': start_pos,
	'end_pos': end_pos
	}
	key += 1