# 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
