#!/usr/bin/python

import datasets

import pyarrow as pa
import pyarrow.parquet as pq

BASE_DATASET = "ejschwartz/oo-method-test"

class OOMethodTestDataset(datasets.ArrowBasedBuilder):

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="combined",
            version=datasets.Version("1.0.0"),
            description="All data files combined",
        ),
        datasets.BuilderConfig(
            name="byrow",
            version=datasets.Version("1.0.0"),
            description="Split by example (dumb)",
        ),
        datasets.BuilderConfig(
            name="byfuncname",
            version=datasets.Version("1.0.0"),
            description="Split by function name",
        )

    ]

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def _info(self):
        return datasets.DatasetInfo()

    def _split_generators(self, dl_manager):
        ds = datasets.load_dataset(BASE_DATASET)

        #print(files)
        #print(downloaded_files)

        if self.config.name == "combined":

            return [
                datasets.SplitGenerator(
                    name="combined",
                    gen_kwargs={
                        "ds": ds['combined'],
                    },
                ),
            ]
        
        elif self.config.name == "byrow":

            ds = ds['combined'].train_test_split(test_size=0.1, seed=42)
            #print(ds)

            return [
                datasets.SplitGenerator(
                    name="train",
                    gen_kwargs={
                        "ds": ds['train'],
                    },
                ),
                datasets.SplitGenerator(
                    name="test",
                    gen_kwargs={
                        "ds": ds['test'],
                    },
                ),

            ]
        
        elif self.config.name == "byfuncname":

            ds = ds['combined']

            unique_names = ds.unique('Name')
            nameds = datasets.Dataset.from_dict({'Name': unique_names})

            name_split = nameds.train_test_split(test_size=0.1, seed=42)
            #print(name_split)

            train_name = name_split['train']['Name']
            test_name = name_split['test']['Name']

            return [
                datasets.SplitGenerator(
                    name="train",
                    gen_kwargs={
                        "ds": ds.filter(lambda r: r['Name'] in train_name),
                    },
                ),
                datasets.SplitGenerator(
                    name="test",
                    gen_kwargs={
                        "ds": ds.filter(lambda r: r['Name'] in test_name),
                    },
                ),

            ]

        else:
            assert False
    
    def _generate_tables(self, ds):

        # Converting to pandas is silly, but the old version of datasets doesn't
        # seem to have a way to convert to Arrow?
        for i, batch in enumerate(ds.to_pandas(batched=True)):
            yield i, pa.Table.from_pandas(batch)