# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors.
#
# 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
#
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# Lint as: python3
"""Tiny Shakespeare dataset."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import tensorflow.compat.v2 as tf
import tensorflow_datasets.public_api as tfds

_CITATION = """\
@misc{
  author={Karpathy, Andrej},
  title={char-rnn},
  year={2015},
  howpublished={\\url{https://github.com/karpathy/char-rnn}}
}"""

_DESCRIPTION = """\
40,000 lines of Shakespeare from a variety of Shakespeare's plays. \
Featured in Andrej Karpathy's blog post 'The Unreasonable Effectiveness of \
Recurrent Neural Networks': \
http://karpathy.github.io/2015/05/21/rnn-effectiveness/.

To use for e.g. character modelling:

```
d = tfds.load(name='tiny_shakespeare')['train']
d = d.map(lambda x: tf.strings.unicode_split(x['text'], 'UTF-8'))
# train split includes vocabulary for other splits
vocabulary = sorted(set(next(iter(d)).numpy()))
d = d.map(lambda x: {'cur_char': x[:-1], 'next_char': x[1:]})
d = d.unbatch()
seq_len = 100
batch_size = 2
d = d.batch(seq_len)
d = d.batch(batch_size)
```
"""


class TinyShakespeare(tfds.core.GeneratorBasedBuilder):
  """Tiny Shakespeare dataset builder."""

  VERSION = tfds.core.Version('1.0.0')

  def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({'text': tfds.features.Text()}),
        supervised_keys=None,
        homepage='https://github.com/karpathy/char-rnn/blob/master/data/tinyshakespeare/input.txt',
        citation=_CITATION,
    )

  def _split_generators(self, dl_manager):
    """Returns SplitGenerators."""
    download_path = dl_manager.download(
        'https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt'
    )
    if tf.io.gfile.isdir(download_path):
      # During testing the download manager mock gives us a directory
      txt_path = os.path.join(download_path, 'input.txt')
    else:
      txt_path = download_path
    with tf.io.gfile.GFile(txt_path, 'r') as f:
      text = f.read()

    # 90/5/5 split
    i = int(len(text) * 0.9)
    train_text, text = text[:i], text[i:]
    i = int(len(text) * 0.5)
    validation_text, text = text[:i], text[i:]
    test_text = text

    return [
        tfds.core.SplitGenerator(
            name=tfds.Split.TRAIN,
            # These kwargs will be passed to _generate_examples
            gen_kwargs={
                'split_key': 'train',
                'split_text': train_text
            },
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.VALIDATION,
            gen_kwargs={
                'split_key': 'validation',
                'split_text': validation_text
            },
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.TEST,
            gen_kwargs={
                'split_key': 'test',
                'split_text': test_text
            },
        ),
    ]

  def _generate_examples(self, split_key, split_text):
    """Yields examples."""
    data_key = split_key  # Should uniquely identify the thing yielded
    feature_dict = {'text': split_text}
    yield data_key, feature_dict