import glob
import json
import os
from io import BytesIO

import more_itertools
import pandas as pd

import datasets
from datasets import Dataset, DatasetDict, DatasetInfo, Features, Sequence, Value,load_dataset
from datasets.fingerprint import Hasher
import pickle
from datasets import ClassLabel, Dataset, DatasetDict, interleave_datasets, load_dataset,get_dataset_split_names


def to_dict_element(el, cols):
    bucked_fields = more_itertools.bucket(cols, key=lambda x: x.split(".")[0])
    final_dict = {}
    for parent_name in set(x.split(".")[0] for x in cols):

        fields = [y.split(".")[-1] for y in list(bucked_fields[parent_name])]
        if len(fields) == 1 and fields[0] == parent_name:
            final_dict[parent_name] = el[fields[0]]
        else:
            parent_list = []
            zipped_fields = list(zip(*[el[f"{parent_name}.{child}"] for child in fields]))
            for x in zipped_fields:
                parent_list.append({k: v for k, v in zip(fields, x)})
            final_dict[parent_name] = parent_list
    return final_dict


logger = datasets.logging.get_logger(__name__)


_CITATION = """ """

_DESCRIPTION = """ """
base_features = {"source": Value(dtype="string"),
                "meta":{
                        "id":Value(dtype="string"),
                        "qid":Value(dtype="string"),
                        "question":Value(dtype="string"),
                        "title":Value(dtype="string"),
                        "text":Value(dtype="string"),
                        }
}


def get_config_splits(path):
    return {config:datasets.get_dataset_split_names(path,config) 
        for config in datasets.get_dataset_config_names(path)}


reranking_mapped_features = Features({**base_features,"target": Value(dtype="string"),})

inference_mapped_features = Features(base_features)



class MappedMultitaskConfig(datasets.BuilderConfig):
    """BuilderConfig for MappedMultitaskDPR."""

    def __init__(self, features=None, retriever=None,feature_format=None, **kwargs):

        super(MappedMultitaskConfig, self).__init__(**kwargs)
        self.features = features
        self.retriever = retriever
        self.feature_format = feature_format


class MappedMultitask(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        MappedMultitaskConfig(
            name="reranking_bm25",
            version=datasets.Version("1.0.1", ""),
            description="MappedMultitask dataset in DPR format with the bm25 retrieval results",
            features=reranking_mapped_features,
            retriever="bm25",
            feature_format="reranking",
        ),
            MappedMultitaskConfig(
            name="reranking_dprnq",
            version=datasets.Version("1.0.1", ""),
            description="MappedMultitask dataset in DPR format with the bm25 retrieval results",
            features=reranking_mapped_features,
            retriever="dprnq",
            feature_format="reranking",
        ),
    ]

    def _info(self):
        self.features = self.config.features
        self.retriever = self.config.retriever
        self.feature_format = self.config.feature_format
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=self.config.features,
            supervised_keys=None,
            homepage="",
            citation=_CITATION,

        )

    def _split_generators(self, dl_manager):

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"split": "train"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"split": "validation"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"split": "test"},
            ),
        ]

    def _prepare_split(self, split_generator, **kwargs):
        self.info.features = self.config.features
        super()._prepare_split(split_generator, **kwargs)

    def _generate_examples(self, split):
        """This function returns the examples in the raw (text) form."""
        dataset_list = []
        qampari = load_dataset("iohadrubin/mapped_qampari", self.info.config_name)
        if split in get_config_splits("iohadrubin/mapped_qampari")[self.info.config_name] and split in qampari:
            dataset_list.append(qampari[split].flatten())
        
        nq = load_dataset("iohadrubin/mapped_nq", self.info.config_name)
        if split in get_config_splits("iohadrubin/mapped_nq")[self.info.config_name] and split in nq:
            dataset_list.append(nq[split].flatten())        
        flattened_dataset = interleave_datasets(datasets=dataset_list).flatten()
        
        for i,element in enumerate(flattened_dataset):
            new_element = dict(source=element['source'],target=element['target'])
            new_element['meta'] = dict(id=element['meta.id'],
                                        qid=element['meta.qid'],
                                        question=element['meta.question'],
                                        title=element['meta.title'],
                                        text=element['meta.text'],
                                        )
            yield i, new_element