#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Thanks to Abdelrahman Mohamed and Wei-Ning Hsu's help in this implementation,
# Their origial Hubert work is in:
#     Paper: https://arxiv.org/pdf/2106.07447.pdf
#     Code in Fairseq: https://github.com/pytorch/fairseq/tree/master/examples/hubert
import argparse
import logging
from typing import Callable, Collection, Dict, List, Optional, Tuple

import numpy as np
import torch
from typeguard import check_argument_types, check_return_type

from espnet2.asr.encoder.abs_encoder import AbsEncoder
from espnet2.asr.encoder.hubert_encoder import (  # noqa: H301
    FairseqHubertPretrainEncoder,
)
from espnet2.asr.frontend.abs_frontend import AbsFrontend
from espnet2.asr.frontend.default import DefaultFrontend
from espnet2.asr.frontend.windowing import SlidingWindow
from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
from espnet2.asr.preencoder.sinc import LightweightSincConvs
from espnet2.asr.specaug.abs_specaug import AbsSpecAug
from espnet2.asr.specaug.specaug import SpecAug
from espnet2.hubert.espnet_model import HubertPretrainModel
from espnet2.layers.abs_normalize import AbsNormalize
from espnet2.layers.global_mvn import GlobalMVN
from espnet2.layers.utterance_mvn import UtteranceMVN
from espnet2.tasks.abs_task import AbsTask
from espnet2.text.phoneme_tokenizer import g2p_choices
from espnet2.torch_utils.initialize import initialize
from espnet2.train.class_choices import ClassChoices
from espnet2.train.collate_fn import CommonCollateFn
from espnet2.train.preprocessor import CommonPreprocessor
from espnet2.train.trainer import Trainer
from espnet2.utils.get_default_kwargs import get_default_kwargs
from espnet2.utils.nested_dict_action import NestedDictAction
from espnet2.utils.types import float_or_none, int_or_none, str2bool, str_or_none

frontend_choices = ClassChoices(
    name="frontend",
    classes=dict(default=DefaultFrontend, sliding_window=SlidingWindow),
    type_check=AbsFrontend,
    default="default",
)
specaug_choices = ClassChoices(
    name="specaug",
    classes=dict(specaug=SpecAug),
    type_check=AbsSpecAug,
    default=None,
    optional=True,
)
normalize_choices = ClassChoices(
    "normalize",
    classes=dict(
        global_mvn=GlobalMVN,
        utterance_mvn=UtteranceMVN,
    ),
    type_check=AbsNormalize,
    default="utterance_mvn",
    optional=True,
)
preencoder_choices = ClassChoices(
    name="preencoder",
    classes=dict(
        sinc=LightweightSincConvs,
    ),
    type_check=AbsPreEncoder,
    default=None,
    optional=True,
)
encoder_choices = ClassChoices(
    "encoder",
    classes=dict(
        hubert_pretrain=FairseqHubertPretrainEncoder,
    ),
    type_check=AbsEncoder,
    default="hubert_pretrain",
)


class HubertTask(AbsTask):
    # If you need more than one optimizers, change this value
    num_optimizers: int = 1

    # Add variable objects configurations
    class_choices_list = [
        # --frontend and --frontend_conf
        frontend_choices,
        # --specaug and --specaug_conf
        specaug_choices,
        # --normalize and --normalize_conf
        normalize_choices,
        # --preencoder and --preencoder_conf
        preencoder_choices,
        # --encoder and --encoder_conf
        encoder_choices,
    ]

    # If you need to modify train() or eval() procedures, change Trainer class here
    trainer = Trainer

    @classmethod
    def add_task_arguments(cls, parser: argparse.ArgumentParser):
        group = parser.add_argument_group(description="Task related")

        # NOTE(kamo): add_arguments(..., required=True) can't be used
        # to provide --print_config mode. Instead of it, do as
        required = parser.get_default("required")
        required += ["token_list"]

        group.add_argument(
            "--token_list",
            type=str_or_none,
            default=None,
            help="A text mapping int-id to token",
        )
        group.add_argument(
            "--init",
            type=lambda x: str_or_none(x.lower()),
            default=None,
            help="The initialization method",
            choices=[
                "chainer",
                "xavier_uniform",
                "xavier_normal",
                "kaiming_uniform",
                "kaiming_normal",
                None,
            ],
        )

        group.add_argument(
            "--input_size",
            type=int_or_none,
            default=None,
            help="The number of input dimension of the feature",
        )

        group.add_argument(
            "--model_conf",
            action=NestedDictAction,
            default=get_default_kwargs(HubertPretrainModel),
            help="The keyword arguments for model class.",
        )

        group = parser.add_argument_group(description="Preprocess related")
        group.add_argument(
            "--use_preprocessor",
            type=str2bool,
            default=True,
            help="Apply preprocessing to data or not",
        )
        group.add_argument(
            "--token_type",
            type=str,
            default="bpe",
            choices=["bpe", "char", "word", "phn"],
            help="The text will be tokenized " "in the specified level token",
        )
        group.add_argument(
            "--bpemodel",
            type=str_or_none,
            default=None,
            help="The model file of sentencepiece",
        )
        group.add_argument(
            "--non_linguistic_symbols",
            type=str_or_none,
            help="non_linguistic_symbols file path",
        )
        group.add_argument(
            "--cleaner",
            type=str_or_none,
            choices=[None, "tacotron", "jaconv", "vietnamese"],
            default=None,
            help="Apply text cleaning",
        )
        group.add_argument(
            "--g2p",
            type=str_or_none,
            choices=g2p_choices,
            default=None,
            help="Specify g2p method if --token_type=phn",
        )
        group.add_argument(
            "--speech_volume_normalize",
            type=float_or_none,
            default=None,
            help="Scale the maximum amplitude to the given value.",
        )
        group.add_argument(
            "--rir_scp",
            type=str_or_none,
            default=None,
            help="The file path of rir scp file.",
        )
        group.add_argument(
            "--rir_apply_prob",
            type=float,
            default=1.0,
            help="THe probability for applying RIR convolution.",
        )
        group.add_argument(
            "--noise_scp",
            type=str_or_none,
            default=None,
            help="The file path of noise scp file.",
        )
        group.add_argument(
            "--noise_apply_prob",
            type=float,
            default=1.0,
            help="The probability applying Noise adding.",
        )
        group.add_argument(
            "--noise_db_range",
            type=str,
            default="13_15",
            help="The range of noise decibel level.",
        )
        parser.add_argument(
            "--pred_masked_weight",
            type=float,
            default=1.0,
            help="weight for predictive loss for masked frames",
        )
        parser.add_argument(
            "--pred_nomask_weight",
            type=float,
            default=0.0,
            help="weight for predictive loss for unmasked frames",
        )
        parser.add_argument(
            "--loss_weights",
            type=float,
            default=0.0,
            help="weights for additional loss terms (not first one)",
        )
        parser.add_argument(
            "--hubert_dict",
            type=str,
            default="./dict.txt",
            help="word-based target dictionary for Hubert pretraining stage",
        )

        for class_choices in cls.class_choices_list:
            # Append --<name> and --<name>_conf.
            # e.g. --encoder and --encoder_conf
            class_choices.add_arguments(group)

    @classmethod
    def build_collate_fn(
        cls, args: argparse.Namespace, train: bool
    ) -> Callable[
        [Collection[Tuple[str, Dict[str, np.ndarray]]]],
        Tuple[List[str], Dict[str, torch.Tensor]],
    ]:
        assert check_argument_types()
        return CommonCollateFn(float_pad_value=0.0, int_pad_value=-1)

    @classmethod
    def build_preprocess_fn(
        cls, args: argparse.Namespace, train: bool
    ) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
        assert check_argument_types()
        if args.use_preprocessor:
            retval = CommonPreprocessor(
                train=train,
                token_type=args.token_type,
                token_list=args.token_list,
                bpemodel=args.bpemodel,
                non_linguistic_symbols=args.non_linguistic_symbols,
                text_cleaner=args.cleaner,
                g2p_type=args.g2p,
                # NOTE(kamo): Check attribute existence for backward compatibility
                rir_scp=args.rir_scp if hasattr(args, "rir_scp") else None,
                rir_apply_prob=args.rir_apply_prob
                if hasattr(args, "rir_apply_prob")
                else 1.0,
                noise_scp=args.noise_scp if hasattr(args, "noise_scp") else None,
                noise_apply_prob=args.noise_apply_prob
                if hasattr(args, "noise_apply_prob")
                else 1.0,
                noise_db_range=args.noise_db_range
                if hasattr(args, "noise_db_range")
                else "13_15",
                short_noise_thres=args.short_noise_thres
                if hasattr(args, "short_noise_thres")
                else 0.5,
                speech_volume_normalize=args.speech_volume_normalize
                if hasattr(args, "rir_scp")
                else None,
            )
        else:
            retval = None
        assert check_return_type(retval)
        return retval

    @classmethod
    def required_data_names(
        cls, train: bool = True, inference: bool = False
    ) -> Tuple[str, ...]:
        if not inference:
            retval = ("speech", "text")
        else:
            # Recognition mode
            retval = ("speech",)
        return retval

    @classmethod
    def optional_data_names(
        cls, train: bool = True, inference: bool = False
    ) -> Tuple[str, ...]:
        retval = ()
        assert check_return_type(retval)
        return retval

    @classmethod
    def build_model(cls, args: argparse.Namespace) -> HubertPretrainModel:
        assert check_argument_types()
        if isinstance(args.token_list, str):
            with open(args.token_list, encoding="utf-8") as f:
                token_list = [line.rstrip() for line in f]
            # Overwriting token_list to keep it as "portable".
            args.token_list = list(token_list)
        elif isinstance(args.token_list, (tuple, list)):
            token_list = list(args.token_list)
        else:
            raise RuntimeError("token_list must be str or list")
        vocab_size = len(token_list)
        logging.info(f"Vocabulary size: {vocab_size }")

        # 1. frontend
        if args.input_size is None:
            # Extract features in the model
            frontend_class = frontend_choices.get_class(args.frontend)
            frontend = frontend_class(**args.frontend_conf)
            input_size = frontend.output_size()
        else:
            # Give features from data-loader
            args.frontend = None
            args.frontend_conf = {}
            frontend = None
            input_size = args.input_size

        # 2. Data augmentation for spectrogram
        if args.specaug is not None:
            specaug_class = specaug_choices.get_class(args.specaug)
            specaug = specaug_class(**args.specaug_conf)
        else:
            specaug = None

        # 3. Normalization layer
        if args.normalize is not None:
            normalize_class = normalize_choices.get_class(args.normalize)
            normalize = normalize_class(**args.normalize_conf)
        else:
            normalize = None

        # 4. Pre-encoder input block
        # NOTE(kan-bayashi): Use getattr to keep the compatibility
        if getattr(args, "preencoder", None) is not None:
            preencoder_class = preencoder_choices.get_class(args.preencoder)
            preencoder = preencoder_class(**args.preencoder_conf)
            input_size = preencoder.output_size()
        else:
            preencoder = None

        # 4. Encoder
        encoder_class = encoder_choices.get_class(args.encoder)
        encoder = encoder_class(
            input_size=input_size,
            use_amp=args.use_amp,
            hubert_dict=args.hubert_dict,
            **args.encoder_conf,
        )

        # 8. Build model
        model = HubertPretrainModel(
            vocab_size=vocab_size,
            frontend=frontend,
            specaug=specaug,
            normalize=normalize,
            preencoder=preencoder,
            encoder=encoder,
            token_list=token_list,
            **args.model_conf,
        )

        # 9. Initialize
        if args.init is not None:
            initialize(model, args.init)

        assert check_return_type(model)
        return model