from typing import Dict, Optional
import datasets
from Bio import SeqIO
import os
import gzip
import wget
import logging
import tqdm
from collections import defaultdict
import tempfile

_CITATION = """\
@article{the2019rnacentral,
    title={RNAcentral: a hub of information for non-coding RNA sequences},
    author={The RNAcentral Consortium},
    journal={Nucleic acids research},
    volume={47},
    number={D1},
    pages={D221--D229},
    year={2019},
    publisher={Oxford University Press}
}
"""

_DESCRIPTION = """\
RNAcentral corpus for pre-training, containing sequences and basic metadata.
"""

class RNACentralDownloader:
    """Helper class to manage RNAcentral downloads"""
    BASE_URL = "https://ftp.ebi.ac.uk/pub/databases/RNAcentral/current_release"
    
    @staticmethod
    def download_file(url: str, output_dir: str) -> str:
        """Download a file with progress bar and return local path"""
        os.makedirs(output_dir, exist_ok=True)
        output_path = os.path.join(output_dir, os.path.basename(url))
        
        if os.path.exists(output_path):
            logging.info(f"File already exists: {output_path}")
            return output_path
            
        logging.info(f"Downloading {url} to {output_path}")
        wget.download(url, output_path)
        print()  # New line after wget progress bar
        return output_path

class RNACentralMinimalConfig(datasets.BuilderConfig):
    """BuilderConfig for RNAcentral minimal corpus."""
    def __init__(
        self,
        *args,
        min_length: int = 10,
        max_length: int = 10000,
        max_sequences: Optional[int] = None,
        include_metadata: bool = True,
        cache_dir: Optional[str] = None,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        self.min_length = min_length
        self.max_length = max_length
        self.max_sequences = max_sequences
        self.include_metadata = include_metadata
        self.cache_dir = cache_dir or tempfile.gettempdir()

class RNACentralMinimalCorpus(datasets.GeneratorBasedBuilder):
    """Minimal RNAcentral corpus for pre-training."""
    
    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIG_CLASS = RNACentralMinimalConfig
    DEFAULT_CONFIG_NAME = "default"
    
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.config = self.config or self.BUILDER_CONFIG_CLASS(
            name=self.DEFAULT_CONFIG_NAME,
            min_length=10,
            max_length=1000,
            max_sequences=1000000,
            include_metadata=True
        )
        self.downloader = RNACentralDownloader()
    
    def _info(self):
        features = {
            "id": datasets.Value("string"),
            "sequence": datasets.Value("string"),
            "length": datasets.Value("int32"),
        }
        
        if self.config.include_metadata:
            features.update({
                "rna_type": datasets.Value("string"),
                "rfam_family": datasets.Value("string"),
            })
        
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(features),
            homepage="https://rnacentral.org/",
            license="CC0",
            citation=_CITATION,
        )

    def _load_minimal_rfam(self, filepath: str) -> Dict[str, str]:
        """Load minimal Rfam annotations (family only)."""
        rfam_families = {}
        with gzip.open(filepath, 'rt') as f:
            next(f)
            for line in f:
                fields = line.strip().split('\t')
                if len(fields) >= 3:
                    rna_id = fields[0]
                    family = fields[2]
                    rfam_families[rna_id] = family
        return rfam_families

    def _split_generators(self, dl_manager: datasets.DownloadManager):
        # Direct download using wget instead of datasets downloader
        sequences_url = f"{self.downloader.BASE_URL}/sequences/rnacentral_active.fasta.gz"
        sequences_path = self.downloader.download_file(sequences_url, self.config.cache_dir)
        
        rfam_families = {}
        if self.config.include_metadata:
            rfam_url = f"{self.downloader.BASE_URL}/rfam/rfam_annotations.tsv.gz"
            rfam_path = self.downloader.download_file(rfam_url, self.config.cache_dir)
            rfam_families = self._load_minimal_rfam(rfam_path)
    
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": sequences_path,
                    "rfam_families": rfam_families,
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": sequences_path,
                    "rfam_families": rfam_families,
                    "split": "validation",
                },
            ),
        ]

    def _generate_examples(self, filepath: str, rfam_families: Dict[str, str], split: str):
        """Generate examples with size and content restrictions."""
        processed_count = 0
        split_start = 0.0 if split == "train" else 0.9
        split_end = 0.9 if split == "train" else 1.0
        
        with gzip.open(filepath, 'rt') as handle:
            for idx, record in enumerate(SeqIO.parse(handle, 'fasta')):
                if self.config.max_sequences and processed_count >= self.config.max_sequences:
                    break
                
                if not (split_start <= (idx % 10) / 10 < split_end):
                    continue
                
                sequence = str(record.seq)
                length = len(sequence)
                
                if length < self.config.min_length or length > self.config.max_length:
                    continue
                
                rna_id = record.id.split('|')[0]
                
                example = {
                    "id": rna_id,
                    "sequence": sequence,
                    "length": length,
                }

                if self.config.include_metadata:
                    description_parts = record.description.split('|')
                    rna_type = "unknown"
                    for part in description_parts:
                        if "RNA_type:" in part:
                            rna_type = part.split(':')[1].strip()
                            break
                    
                    example.update({
                        "rna_type": rna_type,
                        "rfam_family": rfam_families.get(rna_id, "unknown"),
                    })
                
                yield processed_count, example
                processed_count += 1