# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2021 deepset GmbH. 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 os
import json
import sys
import argparse
import re
from tqdm import tqdm

import paddle
from paddlenlp import Taskflow
from pipelines.nodes.base import BaseComponent
from paddlenlp.utils.env import PPNLP_HOME
from paddlenlp.taskflow.utils import download_file
from paddle.dataset.common import md5file


class AnswerExtractor(BaseComponent):
    """
    Answer Extractor based on Universal Information Extraction. 
    """
    resource_files_names = {
        "model_state": "model_state.pdparams",
        "model_config": "model_config.json",
        "vocab_file": "vocab.txt",
        "special_tokens_map": "special_tokens_map.json",
        "tokenizer_config": "tokenizer_config.json"
    }

    resource_files_urls = {
        "uie-base-answer-extractor": {
            "model_state": [
                "https://bj.bcebos.com/paddlenlp/pipelines/answer_generator/uie-base-answer-extractor/uie-base-answer-extractor-v1/model_state.pdparams",
                "c8619f631a0c20434199840d34bb8b8c"
            ],
            "model_config": [
                "https://bj.bcebos.com/paddlenlp/pipelines/answer_generator/uie-base-answer-extractor/uie-base-answer-extractor-v1/model_config.json",
                "74f033ab874a1acddb3aec9b9c4d9cde"
            ],
            "vocab_file": [
                "https://bj.bcebos.com/paddlenlp/pipelines/answer_generator/uie-base-answer-extractor/uie-base-answer-extractor-v1/vocab.txt",
                "1c1c1f4fd93c5bed3b4eebec4de976a8"
            ],
            "special_tokens_map": [
                "https://bj.bcebos.com/paddlenlp/pipelines/answer_generator/uie-base-answer-extractor/uie-base-answer-extractor-v1/special_tokens_map.json",
                "8b3fb1023167bb4ab9d70708eb05f6ec"
            ],
            "tokenizer_config": [
                "https://bj.bcebos.com/paddlenlp/pipelines/answer_generator/uie-base-answer-extractor/uie-base-answer-extractor-v1/tokenizer_config.json",
                "3e623b57084882fd73e17f544bdda47d"
            ]
        },
    }

    return_no_answers: bool
    outgoing_edges = 1
    query_count = 0
    query_time = 0

    def __init__(self,
                 model='uie-base-answer-extractor',
                 schema=['答案'],
                 task_path=None,
                 device="gpu",
                 batch_size=64,
                 position_prob=0.01,
                 max_answer_candidates=5):
        paddle.set_device(device)
        self.model = model
        self._from_taskflow = False
        self._custom_model = False
        if task_path:
            self._task_path = task_path
            self._custom_model = True
        else:
            if model in ["uie-base"]:
                self._task_path = None
                self._from_taskflow = True
            else:
                self._task_path = os.path.join(
                    PPNLP_HOME, "pipelines", "unsupervised_question_answering",
                    self.model)
                self._check_task_files()
        self.batch_size = batch_size
        self.max_answer_candidates = max_answer_candidates
        self.schema = schema
        self.answer_generator = Taskflow(
            "information_extraction",
            model=self.model if self._from_taskflow else "uie-base",
            schema=schema,
            task_path=self._task_path,
            batch_size=batch_size,
            position_prob=position_prob)

    def _check_task_files(self):
        """
        Check files required by the task.
        """
        for file_id, file_name in self.resource_files_names.items():
            path = os.path.join(self._task_path, file_name)
            url = self.resource_files_urls[self.model][file_id][0]
            md5 = self.resource_files_urls[self.model][file_id][1]

            downloaded = True
            if not os.path.exists(path):
                downloaded = False
            else:
                if not self._custom_model:
                    if os.path.exists(path):
                        # Check whether the file is updated
                        if not md5file(path) == md5:
                            downloaded = False
                            if file_id == "model_state":
                                self._param_updated = True
                    else:
                        downloaded = False
            if not downloaded:
                download_file(self._task_path, file_name, url, md5)

    def answer_generation_from_paragraphs(self,
                                          paragraphs,
                                          batch_size=16,
                                          model=None,
                                          max_answer_candidates=5,
                                          schema=None,
                                          wf=None):
        """Generate answer from given paragraphs."""
        result = []
        buffer = []
        i = 0
        len_paragraphs = len(paragraphs)
        for paragraph_tobe in tqdm(paragraphs):
            buffer.append(paragraph_tobe)
            if len(buffer) == batch_size or (i + 1) == len_paragraphs:
                predicts = model(buffer)
                paragraph_list = buffer
                buffer = []
                for predict_dict, paragraph in zip(predicts, paragraph_list):
                    answers = []
                    probabilitys = []
                    for prompt in schema:
                        if prompt in predict_dict:
                            answer_dicts = predict_dict[prompt]
                            answers += [
                                answer_dict['text']
                                for answer_dict in answer_dicts
                            ]
                            probabilitys += [
                                answer_dict['probability']
                                for answer_dict in answer_dicts
                            ]
                        else:
                            answers += []
                            probabilitys += []
                    candidates = sorted(list(
                        set([(a, p) for a, p in zip(answers, probabilitys)])),
                                        key=lambda x: -x[1])
                    if len(candidates) > max_answer_candidates:
                        candidates = candidates[:max_answer_candidates]
                    outdict = {
                        'context': paragraph,
                        'answer_candidates': candidates,
                    }
                    if wf:
                        wf.write(json.dumps(outdict, ensure_ascii=False) + "\n")
                    result.append(outdict)
            i += 1
        return result

    def run(self, meta):
        print('createing synthetic answers...')
        synthetic_context_answer_pairs = self.answer_generation_from_paragraphs(
            meta,
            batch_size=self.batch_size,
            model=self.answer_generator,
            max_answer_candidates=self.max_answer_candidates,
            schema=self.schema,
            wf=None)
        results = {"ca_pairs": synthetic_context_answer_pairs}
        return results, "output_1"
