# Copyright 2020 The HuggingFace Datasets Authors.
# Copyright 2023 Cyril Zhang.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import csv
import json
import os

import datasets
import numpy as np


_CITATION = """\
"""

_DESCRIPTION = """\
Online dataset mockup.
"""

_HOMEPAGE = ""

_LICENSE = ""

_URLS = {}

class MockupDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("0.0.0")
    BUILDER_CONFIGS = []
    
    def __init__(self, name=None, data_config={}, **kwargs):
        super().__init__(**kwargs)
        
        if 'length' not in data_config:
            data_config['length'] = 20
        if 'size' not in data_config:
            data_config['size'] = 100

        self.data_config = data_config
        self.sampler = AutomatonSampler(name, data_config)

    def _info(self):
        features = datasets.Features(
            {
                "x": datasets.Sequence(datasets.Value("int32"), length=-1),
                "y": datasets.Sequence(datasets.Value("int32"), length=-1)
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "split": "train",
                },
            )
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, split):
        for i in range(self.data_config['size']):
            x, y = self.sampler.sample()
            yield i, {
                "x": x,
                "y": y
            }

class AutomatonSampler:
    def __init__(self, name, data_config):
        self.name = name
        self.data_config = data_config

        if 'seed' in self.data_config:
            self.np_rng = np.random.default_rng(self.data_config['seed'])
        else:
            self.np_rng = np.random.default_rng()

        self.T = self.data_config['length']

    def f(self, x):
        return np.cumsum(x) % 2

    def sample(self):
        x = self.np_rng.binomial(1,0.5,size=self.T)
        return x, self.f(x)