text: import argparse
  from typing import Callable
  from typing import Collection
  from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Tuple
  
  import numpy as np
  import torch
  from typeguard import check_argument_types
  from typeguard import check_return_type
  
  from espnet2.enh.decoder.abs_decoder import AbsDecoder
  from espnet2.enh.decoder.conv_decoder import ConvDecoder
  from espnet2.enh.decoder.null_decoder import NullDecoder
  from espnet2.enh.decoder.stft_decoder import STFTDecoder
  from espnet2.enh.encoder.abs_encoder import AbsEncoder
  from espnet2.enh.encoder.conv_encoder import ConvEncoder
  from espnet2.enh.encoder.null_encoder import NullEncoder
  from espnet2.enh.encoder.stft_encoder import STFTEncoder
  from espnet2.enh.espnet_model import ESPnetEnhancementModel
  from espnet2.enh.loss.criterions.abs_loss import AbsEnhLoss
  from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainL1
  from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainMSE
  from espnet2.enh.loss.criterions.time_domain import CISDRLoss
  from espnet2.enh.loss.criterions.time_domain import HubertDFLoss
  from espnet2.enh.loss.criterions.time_domain import SISNRLoss
  from espnet2.enh.loss.criterions.time_domain import SNRLoss
  from espnet2.enh.loss.criterions.time_domain import STOILoss
  from espnet2.enh.loss.criterions.time_domain import Wav2vec2DFLoss
  from espnet2.enh.loss.wrappers.abs_wrapper import AbsLossWrapper
  from espnet2.enh.loss.wrappers.fixed_order import FixedOrderSolver
  from espnet2.enh.loss.wrappers.pit_solver import PITSolver
  from espnet2.enh.separator.abs_separator import AbsSeparator
  from espnet2.enh.separator.asteroid_models import AsteroidModel_Converter
  from espnet2.enh.separator.conformer_separator import ConformerSeparator
  from espnet2.enh.separator.dprnn_separator import DPRNNSeparator
  from espnet2.enh.separator.neural_beamformer import NeuralBeamformer
  from espnet2.enh.separator.rnn_separator import RNNSeparator
  from espnet2.enh.separator.tcn_separator import TCNSeparator
  from espnet2.enh.separator.transformer_separator import TransformerSeparator
  from espnet2.tasks.abs_task import AbsTask
  from espnet2.torch_utils.initialize import initialize
  from espnet2.train.class_choices import ClassChoices
  from espnet2.train.collate_fn import CommonCollateFn
  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 str2bool
  from espnet2.utils.types import str_or_none
  
  encoder_choices = ClassChoices(
      name="encoder",
      classes=dict(stft=STFTEncoder, conv=ConvEncoder, same=NullEncoder),
      type_check=AbsEncoder,
      default="stft",
  )
  
  separator_choices = ClassChoices(
      name="separator",
      classes=dict(
          rnn=RNNSeparator,
          tcn=TCNSeparator,
          dprnn=DPRNNSeparator,
          transformer=TransformerSeparator,
          conformer=ConformerSeparator,
          wpe_beamformer=NeuralBeamformer,
          asteroid=AsteroidModel_Converter,
      ),
      type_check=AbsSeparator,
      default="rnn",
  )
  
  decoder_choices = ClassChoices(
      name="decoder",
      classes=dict(stft=STFTDecoder, conv=ConvDecoder, same=NullDecoder),
      type_check=AbsDecoder,
      default="stft",
  )
  
  loss_wrapper_choices = ClassChoices(
      name="loss_wrappers",
      classes=dict(pit=PITSolver, fixed_order=FixedOrderSolver),
      type_check=AbsLossWrapper,
      default=None,
  )
  
  criterion_choices = ClassChoices(
      name="criterions",
      classes=dict(
          snr=SNRLoss,
          ci_sdr=CISDRLoss,
          stoi=STOILoss,
          w2v2_df=Wav2vec2DFLoss,
          hubert_df=HubertDFLoss,
          si_snr=SISNRLoss,
          mse=FrequencyDomainMSE,
          l1=FrequencyDomainL1,
      ),
      type_check=AbsEnhLoss,
      default=None,
  )
  
  MAX_REFERENCE_NUM = 100
  
  
  class EnhancementTask(AbsTask):
      # If you need more than one optimizers, change this value
      num_optimizers: int = 1
  
      class_choices_list = [
          # --encoder and --encoder_conf
          encoder_choices,
          # --separator and --separator_conf
          separator_choices,
          # --decoder and --decoder_conf
          decoder_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")
  
          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(
              "--model_conf",
              action=NestedDictAction,
              default=get_default_kwargs(ESPnetEnhancementModel),
              help="The keyword arguments for model class.",
          )
  
          group.add_argument(
              "--criterions",
              action=NestedDictAction,
              default=[
                  {
                      "name": "si_snr",
                      "conf": {},
                      "wrapper": "fixed_order",
                      "wrapper_conf": {},
                  },
              ],
              help="The criterions binded with the loss wrappers.",
          )
  
          group = parser.add_argument_group(description="Preprocess related")
          group.add_argument(
              "--use_preprocessor",
              type=str2bool,
              default=False,
              help="Apply preprocessing to data or not",
          )
  
          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=0)
  
      @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()
          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_mix", "speech_ref1")
          else:
              # Recognition mode
              retval = ("speech_mix",)
          return retval
  
      @classmethod
      def optional_data_names(
          cls, train: bool = True, inference: bool = False
      ) -> Tuple[str, ...]:
          retval = ["dereverb_ref{}".format(n) for n in range(1, MAX_REFERENCE_NUM + 1)]
          retval += ["speech_ref{}".format(n) for n in range(2, MAX_REFERENCE_NUM + 1)]
          retval += ["noise_ref{}".format(n) for n in range(1, MAX_REFERENCE_NUM + 1)]
          retval = tuple(retval)
          assert check_return_type(retval)
          return retval
  
      @classmethod
      def build_model(cls, args: argparse.Namespace) -> ESPnetEnhancementModel:
          assert check_argument_types()
  
          encoder = encoder_choices.get_class(args.encoder)(**args.encoder_conf)
          separator = separator_choices.get_class(args.separator)(
              encoder.output_dim, **args.separator_conf
          )
          decoder = decoder_choices.get_class(args.decoder)(**args.decoder_conf)
  
          loss_wrappers = []
          for ctr in args.criterions:
              criterion = criterion_choices.get_class(ctr["name"])(**ctr["conf"])
              loss_wrapper = loss_wrapper_choices.get_class(ctr["wrapper"])(
                  criterion=criterion, **ctr["wrapper_conf"]
              )
              loss_wrappers.append(loss_wrapper)
  
          # 1. Build model
          model = ESPnetEnhancementModel(
              encoder=encoder,
              separator=separator,
              decoder=decoder,
              loss_wrappers=loss_wrappers,
              **args.model_conf
          )
  
          # FIXME(kamo): Should be done in model?
          # 2. Initialize
          if args.init is not None:
              initialize(model, args.init)
  
          assert check_return_type(model)
          return model
  