class_docstr: The basic Trainer for basic training and evaluation strategies in text generation systems.
  
  This class defines common functions for training and evaluation processes of most text generation system models,
  including `fit()`, `evaluate()`, `resume_checkpoint()` and some other features helpful for model training and
  evaluation.
  
  Generally speaking, this class can serve most text generation system models, If the training process of the model
  is to simply optimize a single loss without involving any complex training strategies, such as adversarial learning,
  pre-training and so on.
  
  Initializing the Trainer needs two parameters: `config` and `model`. `config` records the parameters' information
  for controlling training and evaluation, such as `learning_rate`, `epochs` and so on.
  More information can be found in [placeholder]. `model` is the instantiated object of a Model Class.
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docstr: Evaluate the model based on the `eval_data`.
  
  Args:
      eval_data (DataLoader): the eval data
      load_best_model (bool, optional): whether load the best model in the training process, default: True.
                                        It should be set True, if users want to test the model after training.
      model_file (str, optional): the saved model file, default: None. If users want to test the previously
                                  trained model file, they can set this parameter.
      is_valid: (default = False) True if evaluate during validation
  
  Returns:
      dict: eval result, key is the eval metric and value in the corresponding metric value
docstr_tok: ["Evaluate","the","model","based","on","the","`","eval_data","`",".","Args",":","eval_data","(","DataLoader",")",":","the","eval","data","load_best_model","(","bool",",","optional",")",":","whether","load","the","best","model","in","the","training","process",",","default",":","True",".","It","should","be","set","True",",","if","users","want","to","test","the","model","after","training",".","model_file","(","str",",","optional",")",":","the","saved","model","file",",","default",":","None",".","If","users","want","to","test","the","previously","trained","model","file",",","they","can","set","this","parameter",".","is_valid",":","(","default","=","False",")","True","if","evaluate","during","validation","Returns",":","dict",":","eval","result",",","key","is","the","eval","metric","and","value","in","the","corresponding","metric","value"]
code: def evaluate(
      self,
      eval_data: DataLoader,
      load_best_model: bool = True,
      is_valid: bool = False,
  ) -> Optional[dict]:
      r"""Evaluate the model based on the `eval_data`.
  
      Args:
          eval_data (DataLoader): the eval data
          load_best_model (bool, optional): whether load the best model in the training process, default: True.
                                            It should be set True, if users want to test the model after training.
          model_file (str, optional): the saved model file, default: None. If users want to test the previously
                                      trained model file, they can set this parameter.
          is_valid: (default = False) True if evaluate during validation
  
      Returns:
          dict: eval result, key is the eval metric and value in the corresponding metric value
      """
      if is_valid:
          load_best_model = False
  
      if load_best_model:
          checkpoint_dir = self.saved_model_filename + "_best"
          self.logger.info(
              "Loading model structure and parameters from {} ...".format(
                  checkpoint_dir
              )
          )
          self.accelerator.wait_for_everyone()
          unwrap_model = self.accelerator.unwrap_model(self.model)
          unwrap_model.from_pretrained(checkpoint_dir)
          unwrap_model.tokenizer.from_pretrained(checkpoint_dir)
  
      if not is_valid:
          self.model = self.accelerator.prepare(self.model)
  
      self.model.eval()
  
      if self.config["dataset"] == "multiwoz":
          self.evaluator.evaluators[0].load_data(
              "valid" if is_valid else "test"
          )
          turn_domains = self.evaluator.evaluators[0].turn_domains
  
      # generate
      generate_corpus = []
      eval_tqdm = (
          tqdm(eval_data, desc="generating", dynamic_ncols=True)
          if not self.disable_tqdm
          else eval_data
      )
      state = torch.default_generator.get_state()
      for i, batch_data in enumerate(eval_tqdm):
          if self.config["dataset"] != "multiwoz":
              generated = self.accelerator.unwrap_model(
                  self.model
              ).generate(batch_data, self.accelerator)
          else:
              batch_size = batch_data["source_ids"].size(0)
              idx_mask = torch.zeros(batch_size).to(self.device).bool()
              idx_mask[::3] = 1
              bs_batch = {}
              bs_batch["source_ids"] = batch_data["source_ids"][
                  idx_mask
              ]
              bs_batch["source_mask"] = batch_data["source_mask"][
                  idx_mask
              ]
              asrs_batch = {}
              asrs_batch["source_ids"] = batch_data["source_ids"][
                  ~idx_mask
              ]
              asrs_batch["source_mask"] = batch_data["source_mask"][
                  ~idx_mask
              ]
              bs_outputs = self.accelerator.unwrap_model(
                  self.model
              ).generate(bs_batch, self.accelerator)
  
              batch_size //= 3
              db_texts = [
                  self.evaluator.evaluators[0].span_db(bs, td)
                  for bs, td in zip(
                      bs_outputs,
                      turn_domains[
                          i * batch_size : (i + 1) * batch_size
                      ],
                  )
              ]
              db_ids = torch.tensor(
                  self.model.tokenizer.convert_tokens_to_ids(db_texts)
              ).long()
              db_ids = db_ids.repeat_interleave(2).to(self.device)
              db_idx = torch.eq(
                  asrs_batch["source_ids"],
                  self.model.tokenizer.convert_tokens_to_ids(
                      "[db_nores]"
                  ),
              )
              asrs_batch["source_ids"][db_idx] = db_ids
              asrs_outputs = self.accelerator.unwrap_model(
                  self.model
              ).generate(asrs_batch, self.accelerator)
              generated = sum(
                  [
                      [bs, aspn, rs]
                      for bs, aspn, rs in zip(
                          bs_outputs,
                          asrs_outputs[::2],
                          asrs_outputs[1::2],
                      )
                  ],
                  [],
              )
  
          generate_corpus.extend(generated)
      torch.default_generator.set_state(state)
      corpus_len = len(eval_data.dataset.target_text)
      reference_dataset = eval_data.dataset
      generate_corpus = generate_corpus[:corpus_len]
  
      if self.post_processing == "paraphrase":
          for i, gen in enumerate(generate_corpus):
              if gen.find("[SEP]") >= 0:
                  gen = gen.split("[SEP]")[1].strip()
              else:
                  last = max(gen.rfind("("), gen.rfind(")"))
                  last = gen.find(" ", last)
                  gen = gen[last:].strip()
              generate_corpus[i] = gen
  
      if self.accelerator.is_local_main_process:
          self.save_generated_text(generate_corpus, is_valid)
  
      result = self.evaluator.evaluate(
          generate_corpus, reference_dataset
      )
  
      return result
  
imports: import collections
  import os
  from accelerate.logging import get_logger
  from typing import Optional, Union, List, Dict
  import math
  import torch
  import torch.optim
  import transformers
  from accelerate import Accelerator
  from accelerate.utils import set_seed
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from textbox import Config
  from textbox.utils.dashboard import get_dashboard, Timestamp, EpochTracker
  from .scheduler import AbstractScheduler, InverseSquareRootScheduler, CosineScheduler, LinearScheduler, ConstantScheduler
  from torch.utils.data import DataLoader
  from ..evaluator import BaseEvaluator
  from ..model.abstract_model import AbstractModel
  from ..utils import serialized_save, init_seed
  from ..utils.enum_type import PLM_MODELS, RNN_MODELS
label_desc: Use image node_id 14 for calling the Trainer obj's underlying member method code with example usage: obj.evaluate(eval_data, load_best_model, is_valid) and returns: result