import os
from datasets import DatasetBuilder, SplitGenerator, DownloadConfig, load_dataset, DownloadManager
from rdflib import Graph, URIRef, Literal, BNode
from rdflib.namespace import RDF, RDFS, OWL, XSD, Namespace

SCHEMA = Namespace('http://schema.org/')

YAGO = Namespace('http://yago-knowledge.org/resource/')

class YAGO45DatasetBuilder(DatasetBuilder):
    VERSION = "1.0.0"

    taxonomy = Graph()
    
    def _info(self):
        # Define dataset metadata and features
        return {
            "features": {
                "subject": "string",
                "predicate": "string",
                "object": "string"
            },
            "homepage": "https://yago-knowledge.org/",
            "license": "CC BY 3.0",
            "citation": "@article{suchanek2023integrating,title={Integrating the Wikidata Taxonomy into YAGO},author={Suchanek, Fabian M and Alam, Mehwish and Bonald, Thomas and Paris, Pierre-Henri and Soria, Jules},journal={arXiv preprint arXiv:2308.11884},year={2023}}"
        }
    
    def _split_generators(self, dl_manager):
        # Download and extract the dataset
        # Define splits for each chunk of your dataset.
        
        # Download and extract the dataset files
        dl_manager.download_config = DownloadConfig(cache_dir=os.path.abspath("raw"))
        dl_manager.download_and_extract(["raw/facts.tar.gz", "raw/yago-taxonomy.ttl"])
        
        # Load yago-taxonomy.ttl file in every process
        self.taxonomy.parse(os.path.join(dl_manager.manual_dir, 'yago-taxonomy.ttl'), format='turtle')
        
        # Extract prefix mappings 
        prefix_mappings = {prefix: namespace for prefix, namespace in self.taxonomy.namespaces()}
        
        # Define splits for each chunk
        chunk_paths = [os.path.join(dl_manager.manual_dir, chunk) for chunk in os.listdir(dl_manager.manual_dir) if chunk.endswith('.nt')]
        return [SplitGenerator(name="train", gen_kwargs={'chunk_paths': chunk_paths, 'prefix_mappings': prefix_mappings})]
    
    def _generate_examples(self, chunk_paths, prefix_mappings):
        # Load the chunks into an rdflib graph
        # Yield individual triples from the graph
        for chunk_path in chunk_paths:
            graph = Graph()
            for prefix, namespace in prefix_mappings.items():
                graph.bind(prefix, namespace)
            graph.parse(chunk_path, format='nt')
            
            # Yield individual triples from the graph
            for i, (subject, predicate, object_) in enumerate(graph):
                yield i, {
                    'subject': str(subject),
                    'predicate': str(predicate),
                    'object': str(object_)
                }