import csv
import json
import os
import pandas as pd
import datasets
import pickle
#import cohort

_DESCRIPTION = """\
Dataset for mimic4 data, by default for the Mortality task.
Available tasks are: Mortality, Length of Stay, Readmission, Phenotype.
The data is extracted from the mimic4 database using this pipeline: 'https://github.com/healthylaife/MIMIC-IV-Data-Pipeline/tree/main'
mimic path should have this form : 
"""

_HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset"
_CITATION = "https://proceedings.mlr.press/v193/gupta22a.html"
_GITHUB = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline/tree/main"

class Mimic4DatasetConfig(datasets.BuilderConfig):
    """BuilderConfig for Mimic4Dataset."""

    def __init__(
        self,
        #config,
        **kwargs,
    ):
        super().__init__(**kwargs)
        
        #self.config = config
        #cohort.task_cohort(self.task,self.mimic_path)

        
class Mimic4Dataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        Mimic4DatasetConfig(
            name="Phenotype",
            version=VERSION,
            data_dir=os.path.abspath("./data/dict/cohort_icu_readmission_30_I50"),
            description="Dataset for mimic4 Phenotype task",
            mimic_path = None
        ),
        Mimic4DatasetConfig(
            name="Readmission",
            version=VERSION,
            data_dir=os.path.abspath("./data/dict"),
            description="Dataset for mimic4 Readmission task",
        ),
        Mimic4DatasetConfig(
            name="Length of Stay",
            version=VERSION,
            data_dir=os.path.abspath("./data/dict"),
            description="Dataset for mimic4 Length of Stay task",
        ),
        Mimic4DatasetConfig(
            name="Mortality",
            version=VERSION,
            data_dir=os.path.abspath("./data/dict"),
            description="Dataset for mimic4 Mortality task",
        ),
    ]

    DEFAULT_CONFIG_NAME = "Mortality"

    def _info(self):
        

        features = datasets.Features(
            {
                "gender": datasets.Value("string"),
                "ethnicity": datasets.Value("string"),
                "age": datasets.Value("int32"),
                "COND": datasets.Sequence(datasets.Value("string")),
                "MEDS": datasets.Sequence(
                    {
                        "signal" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) },
                        "rate" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) },
                        "amount" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) }
                    }),
                "PROC": datasets.Sequence(
                        {datasets.Value("int32") : datasets.Sequence(datasets.Value("int32"))}
                    ),
                "CHART": datasets.Sequence(
                    {
                        "signal" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) },
                        "val" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) }
                    }),
                "OUT": datasets.Sequence(
                        {datasets.Value("int32") : datasets.Sequence(datasets.Value("int32"))}
                    ),
                "label": datasets.ClassLabel(names=["0", "1"]),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            github=_GITHUB,
        )

    def _split_generators(self, dl_manager):
        data_dir = self.config.data_dir + "/dataDic"
        mimic=self.mimic_path
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir}),
        ]


    def _generate_examples(self, filepath):
        with open(filepath, 'rb') as fp:
            dataDic = pickle.load(fp)
        for hid, data in dataDic.items():
            proc_features = data['Proc']
            chart_features = data['Chart']
            meds_features = data['Med']
            out_features = data['Out']
            cond_features = data['Cond']['fids']
            eth= data['ethnicity']
            age = data['age']
            gender = data['gender']
            label = data['label']

            yield hid, {
                "gender" : gender,
                "ethnicity" : eth,
                "age" : age,
                "MEDS" : {
                    "signal" : meds_features['signal'],
                    "rate" : meds_features['rate'],
                    "amount" : meds_features['amount']
                },
                "PROC" : proc_features,
                "CHART" : {
                    "signal" : chart_features['signal'],
                    "val" : chart_features['val']
                },
                "OUT" : out_features,
                "COND" : cond_features,
                "label" : label
            }