"""
Run BERT on SQuAD 1.1
code reference Google AI research offical BERT code
"""

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

import os
import csv
import tokenization
import numpy as np


#flags = tf.flags
#FLAGS = flags.FLAGS

F_max_seq_length = 128


class TF_BERTDataSet():
    """
    feature params:
    self.unique_id = unique_id
    self.example_index = example_index
    self.doc_span_index = doc_span_index
    self.tokens = tokens
    self.token_to_orig_map = token_to_orig_map
    self.token_is_max_context = token_is_max_context
    self.input_ids = input_ids #id 6
    self.input_mask = input_mask # id7
    self.segment_ids = segment_ids # id 8
    self.start_position = start_position
    self.end_position = end_position
    self.is_impossible = is_impossible

    """
    def __init__(self, data_dir, vocab_file, do_lower_case):
        tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file,
                                           do_lower_case=do_lower_case)
        eval_examples = get_dev_examples(data_dir)
        self.input_ids, self.input_mask, self.segment_ids, self.labels = convert_examples_to_features(
            examples=eval_examples,
            label_list=["0", "1"],
            max_seq_length=F_max_seq_length,
            tokenizer=tokenizer)
            
    def __getitem__(self, idx):
        input_ids_data = self.input_ids[idx]
        input_mask_data = self.input_mask[idx]
        segment_ids_data = self.segment_ids[idx]
        if len(input_ids_data) < F_max_seq_length:
            input_ids_data += [0] * (F_max_seq_length - len(input_ids_data))
            input_mask_data += [0] * (F_max_seq_length - len(input_mask_data))
            segment_ids_data += [0] * (F_max_seq_length - len(segment_ids_data))
        return (np.array(input_ids_data).astype('int32'), 
                np.array(segment_ids_data).astype('int32'), 
                np.array(input_mask_data).astype('int32')), self.labels[idx]

    def __len__(self):
        return len(self.input_ids)

### BERTDataset ###
class InputExample(object):
    """A single training/test example for simple sequence classification."""
    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.

    Args:
      guid: Unique id for the example.
      text_a: string. The untokenized text of the first sequence. For single
        sequence tasks, only this sequence must be specified.
      text_b: (Optional) string. The untokenized text of the second sequence.
        Only must be specified for sequence pair tasks.
      label: (Optional) string. The label of the example. This should be
        specified for train and dev examples, but not for test examples.
    """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label


class InputFeatures(object):
    """A single set of features of data."""
    def __init__(self,
                 input_ids,
                 input_mask,
                 segment_ids,
                 label_id,
                 is_real_example=True):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id
        self.is_real_example = is_real_example


def _read_tsv(input_file, quotechar=None):
    """Reads a tab separated value file."""
    with open(input_file, "r") as f:
        reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
        lines = []
        for line in reader:
            lines.append(line)
        return lines


def _create_examples(lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
        if i == 0:
            continue
        guid = "%s-%s" % (set_type, i)
        text_a = tokenization.convert_to_unicode(line[3])
        text_b = tokenization.convert_to_unicode(line[4])
        if set_type == "test":
            label = "0"
        else:
            label = tokenization.convert_to_unicode(line[0])
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples


def get_dev_examples(data_dir):
    """See base class."""
    return _create_examples(_read_tsv(os.path.join(data_dir, "dev.tsv")),
                            "dev")


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()


def convert_single_example(example, label_list, max_seq_length, tokenizer):
    """Converts a single `InputExample` into a single `InputFeatures`."""

    label_map = {}
    for (i, label) in enumerate(label_list):
        label_map[label] = i

    tokens_a = tokenizer.tokenize(example.text_a)
    tokens_b = None
    if example.text_b:
        tokens_b = tokenizer.tokenize(example.text_b)

    if tokens_b:
        # Modifies `tokens_a` and `tokens_b` in place so that the total
        # length is less than the specified length.
        # Account for [CLS], [SEP], [SEP] with "- 3"
        _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
    else:
        # Account for [CLS] and [SEP] with "- 2"
        if len(tokens_a) > max_seq_length - 2:
            tokens_a = tokens_a[0:(max_seq_length - 2)]

    # The convention in BERT is:
    # (a) For sequence pairs:
    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
    #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
    # (b) For single sequences:
    #  tokens:   [CLS] the dog is hairy . [SEP]
    #  type_ids: 0     0   0   0  0     0 0
    #
    # Where "type_ids" are used to indicate whether this is the first
    # sequence or the second sequence. The embedding vectors for `type=0` and
    # `type=1` were learned during pre-training and are added to the wordpiece
    # embedding vector (and position vector). This is not *strictly* necessary
    # since the [SEP] token unambiguously separates the sequences, but it makes
    # it easier for the model to learn the concept of sequences.
    #
    # For classification tasks, the first vector (corresponding to [CLS]) is
    # used as the "sentence vector". Note that this only makes sense because
    # the entire model is fine-tuned.
    tokens = []
    segment_ids = []
    tokens.append("[CLS]")
    segment_ids.append(0)
    for token in tokens_a:
        tokens.append(token)
        segment_ids.append(0)
    tokens.append("[SEP]")
    segment_ids.append(0)

    if tokens_b:
        for token in tokens_b:
            tokens.append(token)
            segment_ids.append(1)
        tokens.append("[SEP]")
        segment_ids.append(1)

    input_ids = tokenizer.convert_tokens_to_ids(tokens)

    # The mask has 1 for real tokens and 0 for padding tokens. Only real
    # tokens are attended to.
    input_mask = [1] * len(input_ids)

    # Zero-pad up to the sequence length.
    while len(input_ids) < max_seq_length:
        input_ids.append(0)
        input_mask.append(0)
        segment_ids.append(0)

    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length

    label_id = label_map[example.label]
    feature = InputFeatures(input_ids=input_ids,
                            input_mask=input_mask,
                            segment_ids=segment_ids,
                            label_id=label_id,
                            is_real_example=True)
    return feature


def convert_examples_to_features(examples, label_list, max_seq_length,
                                 tokenizer):
    """Convert a set of `InputExample`s to numpy array."""
    input_ids_list = []
    input_mask_list = []
    segment_ids_list = []
    labels = []

    for example in examples:
        feature = convert_single_example(example, label_list, max_seq_length,
                                         tokenizer)
        input_ids_list.append(feature.input_ids)
        input_mask_list.append(feature.input_mask)
        segment_ids_list.append(feature.segment_ids)
        labels.append(feature.label_id)

    return np.array(input_ids_list, dtype="int32"), \
            np.array(input_mask_list, dtype="int32"), \
            np.array(segment_ids_list, dtype="int32"), \
            np.array(labels, dtype="int32")
