#!/usr/bin/env python3
import argparse
import logging
from pathlib import Path
import sys
from typing import Optional
from typing import Sequence
from typing import Tuple
from typing import Union
from typing import Any
from typing import List

import numpy as np
import torch
from typeguard import check_argument_types

from funasr_local.datasets.preprocessor import CodeMixTokenizerCommonPreprocessor
from funasr_local.utils.cli_utils import get_commandline_args
from funasr_local.tasks.punctuation import PunctuationTask
from funasr_local.torch_utils.device_funcs import to_device
from funasr_local.torch_utils.forward_adaptor import ForwardAdaptor
from funasr_local.torch_utils.set_all_random_seed import set_all_random_seed
from funasr_local.utils import config_argparse
from funasr_local.utils.types import str2triple_str
from funasr_local.utils.types import str_or_none
from funasr_local.datasets.preprocessor import split_to_mini_sentence


class Text2Punc:

    def __init__(
        self,
        train_config: Optional[str],
        model_file: Optional[str],
        device: str = "cpu",
        dtype: str = "float32",
    ):
        #  Build Model
        model, train_args = PunctuationTask.build_model_from_file(train_config, model_file, device)
        self.device = device
        # Wrape model to make model.nll() data-parallel
        self.wrapped_model = ForwardAdaptor(model, "inference")
        self.wrapped_model.to(dtype=getattr(torch, dtype)).to(device=device).eval()
        # logging.info(f"Model:\n{model}")
        self.punc_list = train_args.punc_list
        self.period = 0
        for i in range(len(self.punc_list)):
            if self.punc_list[i] == ",":
                self.punc_list[i] = "，"
            elif self.punc_list[i] == "?":
                self.punc_list[i] = "？"
            elif self.punc_list[i] == "。":
                self.period = i
        self.preprocessor = CodeMixTokenizerCommonPreprocessor(
            train=False,
            token_type=train_args.token_type,
            token_list=train_args.token_list,
            bpemodel=train_args.bpemodel,
            text_cleaner=train_args.cleaner,
            g2p_type=train_args.g2p,
            text_name="text",
            non_linguistic_symbols=train_args.non_linguistic_symbols,
        )

    @torch.no_grad()
    def __call__(self, text: Union[list, str], split_size=20):
        data = {"text": text}
        result = self.preprocessor(data=data, uid="12938712838719")
        split_text = self.preprocessor.pop_split_text_data(result)
        mini_sentences = split_to_mini_sentence(split_text, split_size)
        mini_sentences_id = split_to_mini_sentence(data["text"], split_size)
        assert len(mini_sentences) == len(mini_sentences_id)
        cache_sent = []
        cache_sent_id = torch.from_numpy(np.array([], dtype='int32'))
        new_mini_sentence = ""
        new_mini_sentence_punc = []
        cache_pop_trigger_limit = 200
        for mini_sentence_i in range(len(mini_sentences)):
            mini_sentence = mini_sentences[mini_sentence_i]
            mini_sentence_id = mini_sentences_id[mini_sentence_i]
            mini_sentence = cache_sent + mini_sentence
            mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0)
            data = {
                "text": torch.unsqueeze(torch.from_numpy(mini_sentence_id), 0),
                "text_lengths": torch.from_numpy(np.array([len(mini_sentence_id)], dtype='int32')),
            }
            data = to_device(data, self.device)
            y, _ = self.wrapped_model(**data)
            _, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
            punctuations = indices
            if indices.size()[0] != 1:
                punctuations = torch.squeeze(indices)
            assert punctuations.size()[0] == len(mini_sentence)

            # Search for the last Period/QuestionMark as cache
            if mini_sentence_i < len(mini_sentences) - 1:
                sentenceEnd = -1
                last_comma_index = -1
                for i in range(len(punctuations) - 2, 1, -1):
                    if self.punc_list[punctuations[i]] == "。" or self.punc_list[punctuations[i]] == "？":
                        sentenceEnd = i
                        break
                    if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，":
                        last_comma_index = i

                if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
                    # The sentence it too long, cut off at a comma.
                    sentenceEnd = last_comma_index
                    punctuations[sentenceEnd] = self.period
                cache_sent = mini_sentence[sentenceEnd + 1:]
                cache_sent_id = mini_sentence_id[sentenceEnd + 1:]
                mini_sentence = mini_sentence[0:sentenceEnd + 1]
                punctuations = punctuations[0:sentenceEnd + 1]

            # if len(punctuations) == 0:
            #    continue

            punctuations_np = punctuations.cpu().numpy()
            new_mini_sentence_punc += [int(x) for x in punctuations_np]
            words_with_punc = []
            for i in range(len(mini_sentence)):
                if i > 0:
                    if len(mini_sentence[i][0].encode()) == 1 and len(mini_sentence[i - 1][0].encode()) == 1:
                        mini_sentence[i] = " " + mini_sentence[i]
                words_with_punc.append(mini_sentence[i])
                if self.punc_list[punctuations[i]] != "_":
                    words_with_punc.append(self.punc_list[punctuations[i]])
            new_mini_sentence += "".join(words_with_punc)
            # Add Period for the end of the sentence
            new_mini_sentence_out = new_mini_sentence
            new_mini_sentence_punc_out = new_mini_sentence_punc
            if mini_sentence_i == len(mini_sentences) - 1:
                if new_mini_sentence[-1] == "，" or new_mini_sentence[-1] == "、":
                    new_mini_sentence_out = new_mini_sentence[:-1] + "。"
                    new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
                elif new_mini_sentence[-1] != "。" and new_mini_sentence[-1] != "？":
                    new_mini_sentence_out = new_mini_sentence + "。"
                    new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
        return new_mini_sentence_out, new_mini_sentence_punc_out


def inference(
    batch_size: int,
    dtype: str,
    ngpu: int,
    seed: int,
    num_workers: int,
    output_dir: str,
    log_level: Union[int, str],
    train_config: Optional[str],
    model_file: Optional[str],
    key_file: Optional[str] = None,
    data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
    raw_inputs: Union[List[Any], bytes, str] = None,
    cache: List[Any] = None,
    param_dict: dict = None,
    **kwargs,
):
    inference_pipeline = inference_modelscope(
        output_dir=output_dir,
        batch_size=batch_size,
        dtype=dtype,
        ngpu=ngpu,
        seed=seed,
        num_workers=num_workers,
        log_level=log_level,
        key_file=key_file,
        train_config=train_config,
        model_file=model_file,
        param_dict=param_dict,
        **kwargs,
    )
    return inference_pipeline(data_path_and_name_and_type, raw_inputs)


def inference_modelscope(
    batch_size: int,
    dtype: str,
    ngpu: int,
    seed: int,
    num_workers: int,
    log_level: Union[int, str],
    key_file: Optional[str],
    train_config: Optional[str],
    model_file: Optional[str],
    output_dir: Optional[str] = None,
    param_dict: dict = None,
    **kwargs,
):
    assert check_argument_types()
    logging.basicConfig(
        level=log_level,
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )

    if ngpu >= 1 and torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"

    # 1. Set random-seed
    set_all_random_seed(seed)
    text2punc = Text2Punc(train_config, model_file, device)

    def _forward(
        data_path_and_name_and_type,
        raw_inputs: Union[List[Any], bytes, str] = None,
        output_dir_v2: Optional[str] = None,
        cache: List[Any] = None,
        param_dict: dict = None,
    ):
        results = []
        split_size = 20

        if raw_inputs != None:
            line = raw_inputs.strip()
            key = "demo"
            if line == "":
                item = {'key': key, 'value': ""}
                results.append(item)
                return results
            result, _ = text2punc(line)
            item = {'key': key, 'value': result}
            results.append(item)
            return results

        for inference_text, _, _ in data_path_and_name_and_type:
            with open(inference_text, "r", encoding="utf-8") as fin:
                for line in fin:
                    line = line.strip()
                    segs = line.split("\t")
                    if len(segs) != 2:
                        continue
                    key = segs[0]
                    if len(segs[1]) == 0:
                        continue
                    result, _ = text2punc(segs[1])
                    item = {'key': key, 'value': result}
                    results.append(item)
        output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
        if output_path != None:
            output_file_name = "infer.out"
            Path(output_path).mkdir(parents=True, exist_ok=True)
            output_file_path = (Path(output_path) / output_file_name).absolute()
            with open(output_file_path, "w", encoding="utf-8") as fout:
                for item_i in results:
                    key_out = item_i["key"]
                    value_out = item_i["value"]
                    fout.write(f"{key_out}\t{value_out}\n")
        return results

    return _forward


def get_parser():
    parser = config_argparse.ArgumentParser(
        description="Punctuation inference",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )

    parser.add_argument(
        "--log_level",
        type=lambda x: x.upper(),
        default="INFO",
        choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
        help="The verbose level of logging",
    )

    parser.add_argument("--output_dir", type=str, required=False)
    parser.add_argument(
        "--ngpu",
        type=int,
        default=0,
        help="The number of gpus. 0 indicates CPU mode",
    )
    parser.add_argument("--seed", type=int, default=0, help="Random seed")
    parser.add_argument(
        "--dtype",
        default="float32",
        choices=["float16", "float32", "float64"],
        help="Data type",
    )
    parser.add_argument(
        "--num_workers",
        type=int,
        default=1,
        help="The number of workers used for DataLoader",
    )
    parser.add_argument(
        "--batch_size",
        type=int,
        default=1,
        help="The batch size for inference",
    )

    group = parser.add_argument_group("Input data related")
    group.add_argument("--data_path_and_name_and_type", type=str2triple_str, action="append", required=False)
    group.add_argument("--raw_inputs", type=str, required=False)
    group.add_argument("--cache", type=list, required=False)
    group.add_argument("--param_dict", type=dict, required=False)
    group.add_argument("--key_file", type=str_or_none)

    group = parser.add_argument_group("The model configuration related")
    group.add_argument("--train_config", type=str)
    group.add_argument("--model_file", type=str)

    return parser


def main(cmd=None):
    print(get_commandline_args(), file=sys.stderr)
    parser = get_parser()
    args = parser.parse_args(cmd)
    kwargs = vars(args)
    # kwargs.pop("config", None)
    inference(**kwargs)


if __name__ == "__main__":
    main()
