# Copyright (c) 2021 PaddlePaddle 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 argparse
import ast
import os
import re
from collections import OrderedDict
from typing import List
from typing import Optional
from typing import Union

import paddle

from ...s2t.utils.dynamic_import import dynamic_import
from ..executor import BaseExecutor
from ..log import logger
from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper

__all__ = ['TextExecutor']

pretrained_models = {
    # The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
    # e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
    # Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
    # "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
    "ernie_linear_p7_wudao-punc-zh": {
        'url':
        'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p7_wudao-punc-zh.tar.gz',
        'md5':
        '12283e2ddde1797c5d1e57036b512746',
        'cfg_path':
        'ckpt/model_config.json',
        'ckpt_path':
        'ckpt/model_state.pdparams',
        'vocab_file':
        'punc_vocab.txt',
    },
    "ernie_linear_p3_wudao-punc-zh": {
        'url':
        'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_wudao-punc-zh.tar.gz',
        'md5':
        '448eb2fdf85b6a997e7e652e80c51dd2',
        'cfg_path':
        'ckpt/model_config.json',
        'ckpt_path':
        'ckpt/model_state.pdparams',
        'vocab_file':
        'punc_vocab.txt',
    },
}

model_alias = {
    "ernie_linear_p7": "paddlespeech.text.models:ErnieLinear",
    "ernie_linear_p3": "paddlespeech.text.models:ErnieLinear",
}

tokenizer_alias = {
    "ernie_linear_p7": "paddlenlp.transformers:ErnieTokenizer",
    "ernie_linear_p3": "paddlenlp.transformers:ErnieTokenizer",
}


@cli_register(name='paddlespeech.text', description='Text infer command.')
class TextExecutor(BaseExecutor):
    def __init__(self):
        super(TextExecutor, self).__init__()

        self.parser = argparse.ArgumentParser(
            prog='paddlespeech.text', add_help=True)
        self.parser.add_argument(
            '--input', type=str, default=None, help='Input text.')
        self.parser.add_argument(
            '--task',
            type=str,
            default='punc',
            choices=['punc'],
            help='Choose text task.')
        self.parser.add_argument(
            '--model',
            type=str,
            default='ernie_linear_p7_wudao',
            choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
            help='Choose model type of text task.')
        self.parser.add_argument(
            '--lang',
            type=str,
            default='zh',
            choices=['zh', 'en'],
            help='Choose model language.')
        self.parser.add_argument(
            '--config',
            type=str,
            default=None,
            help='Config of cls task. Use deault config when it is None.')
        self.parser.add_argument(
            '--ckpt_path',
            type=str,
            default=None,
            help='Checkpoint file of model.')
        self.parser.add_argument(
            '--punc_vocab',
            type=str,
            default=None,
            help='Vocabulary file of punctuation restoration task.')
        self.parser.add_argument(
            '--device',
            type=str,
            default=paddle.get_device(),
            help='Choose device to execute model inference.')
        self.parser.add_argument(
            '--job_dump_result',
            type=ast.literal_eval,
            default=False,
            help='Save job result into file.')

    def _get_pretrained_path(self, tag: str) -> os.PathLike:
        """
            Download and returns pretrained resources path of current task.
        """
        support_models = list(pretrained_models.keys())
        assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
            tag, '\n\t\t'.join(support_models))

        res_path = os.path.join(MODEL_HOME, tag)
        decompressed_path = download_and_decompress(pretrained_models[tag],
                                                    res_path)
        decompressed_path = os.path.abspath(decompressed_path)
        logger.info(
            'Use pretrained model stored in: {}'.format(decompressed_path))

        return decompressed_path

    def _init_from_path(self,
                        task: str='punc',
                        model_type: str='ernie_linear_p7_wudao',
                        lang: str='zh',
                        cfg_path: Optional[os.PathLike]=None,
                        ckpt_path: Optional[os.PathLike]=None,
                        vocab_file: Optional[os.PathLike]=None):
        """
            Init model and other resources from a specific path.
        """
        if hasattr(self, 'model'):
            logger.info('Model had been initialized.')
            return

        self.task = task

        if cfg_path is None or ckpt_path is None or vocab_file is None:
            tag = '-'.join([model_type, task, lang])
            self.res_path = self._get_pretrained_path(tag)
            self.cfg_path = os.path.join(self.res_path,
                                         pretrained_models[tag]['cfg_path'])
            self.ckpt_path = os.path.join(self.res_path,
                                          pretrained_models[tag]['ckpt_path'])
            self.vocab_file = os.path.join(self.res_path,
                                           pretrained_models[tag]['vocab_file'])
        else:
            self.cfg_path = os.path.abspath(cfg_path)
            self.ckpt_path = os.path.abspath(ckpt_path)
            self.vocab_file = os.path.abspath(vocab_file)

        model_name = model_type[:model_type.rindex('_')]
        if self.task == 'punc':
            # punc list
            self._punc_list = []
            with open(self.vocab_file, 'r') as f:
                for line in f:
                    self._punc_list.append(line.strip())

            # model
            model_class = dynamic_import(model_name, model_alias)
            tokenizer_class = dynamic_import(model_name, tokenizer_alias)
            self.model = model_class(
                cfg_path=self.cfg_path, ckpt_path=self.ckpt_path)
            self.tokenizer = tokenizer_class.from_pretrained('ernie-1.0')
        else:
            raise NotImplementedError

        self.model.eval()

    def _clean_text(self, text):
        text = text.lower()
        text = re.sub('[^A-Za-z0-9\u4e00-\u9fa5]', '', text)
        text = re.sub(f'[{"".join([p for p in self._punc_list][1:])}]', '',
                      text)
        return text

    def preprocess(self, text: Union[str, os.PathLike]):
        """
            Input preprocess and return paddle.Tensor stored in self.input.
            Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
        """
        if self.task == 'punc':
            clean_text = self._clean_text(text)
            assert len(clean_text) > 0, f'Invalid input string: {text}'

            tokenized_input = self.tokenizer(
                list(clean_text), return_length=True, is_split_into_words=True)

            self._inputs['input_ids'] = tokenized_input['input_ids']
            self._inputs['seg_ids'] = tokenized_input['token_type_ids']
            self._inputs['seq_len'] = tokenized_input['seq_len']
        else:
            raise NotImplementedError

    @paddle.no_grad()
    def infer(self):
        """
            Model inference and result stored in self.output.
        """
        if self.task == 'punc':
            input_ids = paddle.to_tensor(self._inputs['input_ids']).unsqueeze(0)
            seg_ids = paddle.to_tensor(self._inputs['seg_ids']).unsqueeze(0)
            logits, _ = self.model(input_ids, seg_ids)
            preds = paddle.argmax(logits, axis=-1).squeeze(0)

            self._outputs['preds'] = preds
        else:
            raise NotImplementedError

    def postprocess(self) -> Union[str, os.PathLike]:
        """
            Output postprocess and return human-readable results such as texts and audio files.
        """
        if self.task == 'punc':
            input_ids = self._inputs['input_ids']
            seq_len = self._inputs['seq_len']
            preds = self._outputs['preds']

            tokens = self.tokenizer.convert_ids_to_tokens(
                input_ids[1:seq_len - 1])
            labels = preds[1:seq_len - 1].tolist()
            assert len(tokens) == len(labels)

            text = ''
            for t, l in zip(tokens, labels):
                text += t
                if l != 0:  # Non punc.
                    text += self._punc_list[l]

            return text
        else:
            raise NotImplementedError

    def execute(self, argv: List[str]) -> bool:
        """
            Command line entry.
        """
        parser_args = self.parser.parse_args(argv)

        task = parser_args.task
        model_type = parser_args.model
        lang = parser_args.lang
        cfg_path = parser_args.config
        ckpt_path = parser_args.ckpt_path
        punc_vocab = parser_args.punc_vocab
        device = parser_args.device
        job_dump_result = parser_args.job_dump_result

        task_source = self.get_task_source(parser_args.input)
        task_results = OrderedDict()
        has_exceptions = False

        for id_, input_ in task_source.items():
            try:
                res = self(input_, task, model_type, lang, cfg_path, ckpt_path,
                           punc_vocab, device)
                task_results[id_] = res
            except Exception as e:
                has_exceptions = True
                task_results[id_] = f'{e.__class__.__name__}: {e}'

        self.process_task_results(parser_args.input, task_results,
                                  job_dump_result)

        if has_exceptions:
            return False
        else:
            return True

    @stats_wrapper
    def __call__(
            self,
            text: str,
            task: str='punc',
            model: str='ernie_linear_p7_wudao',
            lang: str='zh',
            config: Optional[os.PathLike]=None,
            ckpt_path: Optional[os.PathLike]=None,
            punc_vocab: Optional[os.PathLike]=None,
            device: str=paddle.get_device(), ):
        """
            Python API to call an executor.
        """
        paddle.set_device(device)
        self._init_from_path(task, model, lang, config, ckpt_path, punc_vocab)
        self.preprocess(text)
        self.infer()
        res = self.postprocess()  # Retrieve result of text task.

        return res