import os
import json
import re
import pandas as pd
import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@article{jolma2010multiplexed,
  title={Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities},
  author={Jolma, Arttu and Kivioja, Teemu and Toivonen, Jarkko and Cheng, Lu and Wei, Gonghong and Enge, Martin and \
  Taipale, Mikko and Vaquerizas, Juan M and Yan, Jian and Sillanp{\"a}{\"a}, Mikko J and others},
  journal={Genome research},
  volume={20},
  number={6},
  pages={861--873},
  year={2010},
  publisher={Cold Spring Harbor Lab}
}
"""

_DESCRIPTION = """\
PRJEB3289
https://www.ebi.ac.uk/ena/browser/view/PRJEB3289
Data that has been generated by HT-SELEX experiments (see Jolma et al. 2010. PMID: 20378718 for description of method) \
that has been now used to generate transcription factor binding specificity models for most of the high confidence \
human transcription factors. Sequence data is composed of reads generated with Illumina Genome Analyzer IIX and \
HiSeq2000 instruments. Samples are composed of single read sequencing of synthetic DNA fragments with a fixed length \
randomized region or samples derived from such a initial library by selection with a sequence specific DNA binding \
protein. Originally multiple samples with different "barcode" tag sequences were run on the same Illumina sequencing \
lane but the released files have been already de-multiplexed, and the constant regions and "barcodes" of each sequence \
have been cut out of the sequencing reads to facilitate the use of data. Some of the files are composed of reads from \
multiple different sequencing lanes and due to this each of the names of the individual reads have been edited to show \
the flowcell and lane that was used to generate it. Barcodes and oligonucleotide designs are indicated in the names of \
individual entries. Depending of the selection ligand design, the sequences in each of these fastq-files are either \
14, 20, 30 or 40 bases long and had different flanking regions in both sides of the sequence. Each run entry is named \
in either of the following ways: Example 1) "BCL6B_DBD_AC_TGCGGG20NGA_1", where name is composed of following fields \
ProteinName_CloneType_Batch_BarcodeDesign_SelectionCycle. This experiment used barcode ligand TGCGGG20NGA, where both \
of the variable flanking constant regions are indicated as they were on the original sequence-reads. This ligand has \
been selected for one round of HT-SELEX using recombinant protein that contained the DNA binding domain of \
human transcription factor BCL6B. It also tells that the experiment was performed on batch of experiments named as "AC".\
 Example 2) 0_TGCGGG20NGA_0 where name is composed of (zero)_BarcodeDesign_(zero) These sequences have been generated \
 from sequencing of the initial non-selected pool. Same initial pools have been used in multiple experiments that were \
 on different batches, thus for example this background sequence pool is the shared background for all of the following \
 samples. BCL6B_DBD_AC_TGCGGG20NGA_1, ZNF784_full_AE_TGCGGG20NGA_3, DLX6_DBD_Y_TGCGGG20NGA_4 and MSX2_DBD_W_TGCGGG20NGA_2
"""

_URL = "ftp://ftp.sra.ebi.ac.uk/vol1/run/"
"ftp://ftp.sra.ebi.ac.uk/vol1/run/ERR173/ERR173154/CTCF_full_AJ_TAGCGA20NGCT_1.fastq.gz"
# _FORWARD_PRIMER = "TAATACGACTCACTATAGGGAGCAGGAGAGAGGTCAGATG"
# _REVERSE_PRIMER = "CCTATGCGTGCTAGTGTGA"
# _DESIGN_LENGTH = 30


config = datasets.load_dataset(path="thewall/deepbindweight", split="all")
info = pd.read_excel(config['selex'][0], index_col=0)

_URLS = {}
_DESIGN_LENGTH = {}
pattern = re.compile("(\d+)")
for idx, row in info.iterrows():
    sra_id = idx
    file = row["file"]
    _URLS[sra_id] = "/".join([_URL, sra_id[:6], sra_id, file])
    _DESIGN_LENGTH[sra_id] = int(pattern.search(row["Ligand"]).group(0))


class JolmaUniqueConfig(datasets.BuilderConfig):
    def __init__(self, url, sra_id="ERR173157", length_match=True, filter_N=True, design_length=None, file=None, **kwargs):
        super(JolmaUniqueConfig, self).__init__(**kwargs)
        self.url = url
        self.sra_id = sra_id
        self.length_match = length_match
        self.design_length = design_length
        self.filter_N = filter_N
        self.file = file


class JolmaUnique(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        JolmaUniqueConfig(name=key, url=_URLS[key], design_length=_DESIGN_LENGTH[key], file=info.loc[key]['file']) for key in _URLS
    ]

    DEFAULT_CONFIG_NAME = "ERR173157"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("int32"),
                    "identifier": datasets.Value("string"),
                    "seq": datasets.Value("string"),
                    "quality": datasets.Value("string"),
                    "count": datasets.Value("int32"),
                }
            ),
            homepage="https://www.ebi.ac.uk/ena/browser/view/PRJEB3289",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = None
        if getattr(self.config, "data_dir") is not None:
            # downloaded_files = os.path.join(self.config.data_dir, self.config.sra_id, self.config.file)
            downloaded_files = dl_manager.extract(os.path.join(self.config.data_dir, self.config.sra_id, self.config.file))
            logger.info(f"Load from {downloaded_files}")
        if downloaded_files is None or not os.path.exists(downloaded_files):
            logger.info(f"Download from {self.config.url}")
            downloaded_files = dl_manager.download_and_extract(self.config.url)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files}),
        ]

    def filter_fn(self, example):
        seq = example["seq"]
        if self.config.length_match and len(seq)!=self.config.design_length:
            return False
        if self.config.filter_N and "N" in seq:
            return False
        return True

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            data = []
            ans = {}
            for i, line in enumerate(f):
                if line.startswith("@") and i%4==0:
                    ans["identifier"] = line[1:].strip()
                elif i%4==1:
                    ans["seq"]  = line.strip()
                elif i%4==3:
                    ans["quality"] = line.strip()
                    if self.filter_fn(ans):
                        ans['id'] = len(data)
                        data.append(ans)
                    ans = {}
            data = pd.DataFrame(data)
            groups = data.groupby("seq")
            for idx, (s, group) in enumerate(groups):
                yield idx, {"id": idx,
                            "identifier": f"{self.config.name}:{group['id'].iloc[0]}",
                            "seq": group.iloc[0]['seq'],
                            "count": len(group),
                            'quality': group.iloc[0]['quality']}


if __name__=="__main__":
    from datasets import load_dataset
    dataset = load_dataset("jolma_unique.py", name="ERR173157", split="all")
    # dataset = load_dataset("jolma.py", name="ERR173157", split="all", data_dir="/root/autodl-fs/ERP001824-461")