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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Loads dataset for the tagging (e.g., NER/POS) task."""
import dataclasses
from typing import Mapping, Optional

import tensorflow as tf, tf_keras
from official.common import dataset_fn
from official.core import config_definitions as cfg
from official.core import input_reader
from official.nlp.data import data_loader
from official.nlp.data import data_loader_factory


@dataclasses.dataclass
class TaggingDataConfig(cfg.DataConfig):
  """Data config for tagging (tasks/tagging)."""
  is_training: bool = True
  seq_length: int = 128
  include_sentence_id: bool = False
  file_type: str = 'tfrecord'


@data_loader_factory.register_data_loader_cls(TaggingDataConfig)
class TaggingDataLoader(data_loader.DataLoader):
  """A class to load dataset for tagging (e.g., NER and POS) task."""

  def __init__(self, params: TaggingDataConfig):
    self._params = params
    self._seq_length = params.seq_length
    self._include_sentence_id = params.include_sentence_id

  def _decode(self, record: tf.Tensor):
    """Decodes a serialized tf.Example."""
    name_to_features = {
        'input_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
        'input_mask': tf.io.FixedLenFeature([self._seq_length], tf.int64),
        'segment_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
        'label_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
    }
    if self._include_sentence_id:
      name_to_features['sentence_id'] = tf.io.FixedLenFeature([], tf.int64)
      name_to_features['sub_sentence_id'] = tf.io.FixedLenFeature([], tf.int64)

    example = tf.io.parse_single_example(record, name_to_features)

    # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
    # So cast all int64 to int32.
    for name in example:
      t = example[name]
      if t.dtype == tf.int64:
        t = tf.cast(t, tf.int32)
      example[name] = t

    return example

  def _parse(self, record: Mapping[str, tf.Tensor]):
    """Parses raw tensors into a dict of tensors to be consumed by the model."""
    x = {
        'input_word_ids': record['input_ids'],
        'input_mask': record['input_mask'],
        'input_type_ids': record['segment_ids']
    }
    if self._include_sentence_id:
      x['sentence_id'] = record['sentence_id']
      x['sub_sentence_id'] = record['sub_sentence_id']

    y = record['label_ids']
    return (x, y)

  def load(self, input_context: Optional[tf.distribute.InputContext] = None):
    """Returns a tf.dataset.Dataset."""
    reader = input_reader.InputReader(
        params=self._params,
        dataset_fn=dataset_fn.pick_dataset_fn(self._params.file_type),
        decoder_fn=self._decode,
        parser_fn=self._parse)
    return reader.read(input_context)