# Copyright 2020 The HuggingFace Datasets Authors.
# Copyright 2023 Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, and 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)
        
        """
        Set default configs
        """
        if name is None:
            name = 'parity'
        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)
        self.sampler = dataset_map[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, 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.n_states = data_config['n_states']
        self.T = self.data_config['length']

    def f(self, x):
        """
        Get output sequence given an input seq
        """
        raise NotImplementedError()

    def sample(self):
        raise NotImplementedError()


class ParitySampler(AutomatonSampler):
    def __init__(self, data_config):
        super(ParitySampler, self).__init__(data_config)
        self.name = 'parity'
        self.data_config = data_config

    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)


class FlipflopSampler(AutomatonSampler):
    def __init__(self, data_config):
        super(FlipflopSampler, self).__init__(data_config)
        self.name = 'parity'
        self.data_config = data_config

        self.n_actions = self.n_states + 1
        self.transition = np.array([list(range(self.n_actions))] + [[i+1]*self.n_actions for i in range(self.n_states)]).T

    def f(self, x):
        state, states = 0, []
        for action in x:
          state = self.transition[state, action]
          states += state,
        return np.array(states)

    def sample(self):
        rand = np.random.uniform(size=self.T)
        nonzero_pos = (rand < 0.5).astype(np.int64)
        writes = np.random.choice(range(1, self.n_states+1), size=self.T)
        x = writes * nonzero_pos
        return x, self.f(x)


dataset_map = {
  'parity': ParitySampler,
  'flipflop': FlipflopSampler,
  # TODO: more datasets
  }