# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""TODO: Add a description here."""


import csv
import json
import os

import numpy as np
from pathlib import Path
from tfrecord.reader import tfrecord_loader
import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLs = {
    'pretraining': "https://huggingface.co/great-new-dataset-first_domain.zip",
    # 'second_domain': "https://huggingface.co/great-new-dataset-second_domain.zip",
}


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class WikipediaBERT128(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="pretraining", version=VERSION, description="This part of my dataset covers a first domain"),
    ]


    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        print(self.config.name)
        features = datasets.Features(
            {
                "input_ids": datasets.Sequence(datasets.Value("int64")),
                "attention_mask": datasets.Sequence(datasets.Value("int64")),
                "token_type_ids": datasets.Sequence(datasets.Value("int64")),
                "labels": datasets.Sequence(datasets.Value("int64")),
                "next_sentence_label": datasets.Value("int64"),
                # These are the features of your dataset like images, labels ...
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "data_files": self.config.data_files["train"],
                },
            ),
        ]

    def _generate_examples(self, data_files):
        """ Yields examples as (key, example) tuples. """
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.

        # Convert from tfrecord files
        TFRECORD_KEYS = (           # Torch Model Keys
            'input_ids',            # input_ids                  : tokens after masking
            'input_mask',           # attention_mask             : 1 if padding token, 0 otherwise
            'segment_ids',          # token_type_ids             : sentence 0 or 1
            'masked_lm_positions',  # masked_lm_positions        : position of masked tokens in input_ids
            'masked_lm_ids',        # masked_lm_labels=None      : label of masked tokens with padding as 0.
            'next_sentence_labels'  # next_sentence_label=None   : 1 if next sentence, 0 otherwise
        )
        highest_id_ = -1
        for rec in data_files:
            reader = tfrecord_loader(rec, None, list(TFRECORD_KEYS))
            for id_, d in enumerate(reader, start=highest_id_+1):
                highest_id_ = id_
                input_ids = d["input_ids"]
                labels = np.ones_like(input_ids) * -100
                masked_lm_positions = d["masked_lm_positions"]
                masked_lm_labels = d["masked_lm_ids"]
                masked_lm_positions_ = masked_lm_positions[masked_lm_positions != 0]
                masked_lm_labels_ = masked_lm_labels[:len(masked_lm_positions_)]
                labels[masked_lm_positions_] = masked_lm_labels_

                yield id_, {
                    "input_ids": input_ids,
                    "attention_mask": d["input_mask"],
                    "token_type_ids": d["segment_ids"],
                    "labels": labels,
                    "next_sentence_label": d["next_sentence_labels"]
                }