text: # Copyright 2019 The Forte Authors. All Rights Reserved.
  #
  # 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 logging
  import os
  from typing import Dict, List, Tuple, Optional
  
  import texar.torch as tx
  import torch
  
  from forte.common.configuration import Config
  from forte.common.resources import Resources
  from forte.data.data_pack import DataPack
  from forte.data.span import Span
  from forte.data.types import DataRequest
  from forte.models.srl.model import LabeledSpanGraphNetwork
  from forte.processors.base.batch_processor import FixedSizeBatchProcessor
  from ft.onto.base_ontology import (
      Token, Sentence, PredicateLink, PredicateMention, PredicateArgument)
  
  logger = logging.getLogger(__name__)
  
  __all__ = [
      "SRLPredictor",
  ]
  
  Prediction = List[
      Tuple[Span, List[Tuple[Span, str]]]
  ]
  
  
  class SRLPredictor(FixedSizeBatchProcessor):
      """
      An Semantic Role labeler trained according to `He, Luheng, et al.
      "Jointly predicting predicates and arguments in neural semantic role
      labeling." <https://aclweb.org/anthology/P18-2058>`_.
      """
  
      word_vocab: tx.data.Vocab
      char_vocab: tx.data.Vocab
      model: LabeledSpanGraphNetwork
  
      def __init__(self):
          super().__init__()
          self.device = torch.device(
              torch.cuda.current_device() if torch.cuda.is_available() else 'cpu')
  
      def initialize(self,
                     resources: Resources,
                     configs: Optional[Config]):
          super().initialize(resources, configs)
  
          model_dir = configs.storage_path if configs is not None else None
          logger.info("restoring SRL model from %s", model_dir)
  
          # initialize the batcher
          if configs:
              self.batcher.initialize(configs.batcher)
  
          self.word_vocab = tx.data.Vocab(
              os.path.join(model_dir, "embeddings/word_vocab.english.txt"))
          self.char_vocab = tx.data.Vocab(
              os.path.join(model_dir, "embeddings/char_vocab.english.txt"))
          model_hparams = LabeledSpanGraphNetwork.default_hparams()
          model_hparams["context_embeddings"]["path"] = os.path.join(
              model_dir, model_hparams["context_embeddings"]["path"])
          model_hparams["head_embeddings"]["path"] = os.path.join(
              model_dir, model_hparams["head_embeddings"]["path"])
          self.model = LabeledSpanGraphNetwork(
              self.word_vocab, self.char_vocab, model_hparams)
          self.model.load_state_dict(torch.load(
              os.path.join(model_dir, "pretrained/model.pt"),
              map_location=self.device))
          self.model.eval()
  
      @staticmethod
      def _define_context():
          return Sentence
  
      @staticmethod
      def _define_input_info() -> DataRequest:
          input_info: DataRequest = {Token: []}
          return input_info
  
      def predict(self, data_batch: Dict) -> Dict[str, List[Prediction]]:
          text: List[List[str]] = [
              sentence.tolist() for sentence in data_batch["Token"]["text"]]
          text_ids, length = tx.data.padded_batch([
              self.word_vocab.map_tokens_to_ids_py(sentence)
              for sentence in text])
          text_ids = torch.from_numpy(text_ids).to(device=self.device)
          length = torch.tensor(length, dtype=torch.long, device=self.device)
          batch_size = len(text)
          batch = tx.data.Batch(batch_size, text=text, text_ids=text_ids,
                                length=length, srl=[[]] * batch_size)
          self.model = self.model.to(self.device)
          batch_srl_spans = self.model.decode(batch)
  
          # Convert predictions into annotations.
          batch_predictions: List[Prediction] = []
          for idx, srl_spans in enumerate(batch_srl_spans):
              word_spans = data_batch["Token"]["span"][idx]
              predictions: Prediction = []
              for pred_idx, pred_args in srl_spans.items():
                  begin, end = word_spans[pred_idx]
                  # TODO cannot create annotation here.
                  # Need to convert from Numpy numbers to int.
                  pred_span = Span(begin.item(), end.item())
                  arguments = []
                  for arg in pred_args:
                      begin = word_spans[arg.start][0].item()
                      end = word_spans[arg.end][1].item()
                      arg_annotation = Span(begin, end)
                      arguments.append((arg_annotation, arg.label))
                  predictions.append((pred_span, arguments))
              batch_predictions.append(predictions)
          return {"predictions": batch_predictions}
  
      def pack(self, data_pack: DataPack,
               inputs: Dict[str, List[Prediction]]) -> None:
          batch_predictions = inputs["predictions"]
          for predictions in batch_predictions:
              for pred_span, arg_result in predictions:
  
                  pred = PredicateMention(data_pack, pred_span.begin,
                                          pred_span.end)
  
                  for arg_span, label in arg_result:
                      arg = PredicateArgument(
                          data_pack, arg_span.begin, arg_span.end
                      )
                      link = PredicateLink(data_pack, pred, arg)
                      link.arg_type = label
  
      @classmethod
      def default_configs(cls):
          """
          This defines a basic config structure
          :return:
          """
          configs = super().default_configs()
          configs.update({
              'storage_path': None,
              "batcher": {
                  "batch_size": 4
              }
          })
          return configs
  