class_docstr: Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
  are not all of the same length.
  
  Args:
      tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
          The tokenizer used for encoding the data.
      mlm (:obj:`bool`, `optional`, defaults to :obj:`True`):
          Whether or not to use masked language modeling. If set to :obj:`False`, the labels are the same as the
          inputs with the padding tokens ignored (by setting them to -100). Otherwise, the labels are -100 for
          non-masked tokens and the value to predict for the masked token.
      mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
          The probability with which to (randomly) mask tokens in the input, when :obj:`mlm` is set to :obj:`True`.
  
  .. note::
  
      For best performance, this data collator should be used with a dataset having items that are dictionaries or
      BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
      :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
      argument :obj:`return_special_tokens_mask=True`.
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code: def __post_init__(self):
      if self.mlm and self.tokenizer.mask_token is None:
          raise ValueError(
              "This tokenizer does not have a mask token which is necessary for masked language modeling. "
              "You should pass `mlm=False` to train on causal language modeling instead."
          )
  
imports: import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple
  import numpy
  from datasets import load_dataset
  from tqdm import tqdm
  import jax
  import jax.numpy
  from flax import jax_utils
  from flax.optim import Adam
  from flax.training import common_utils
  from flax.training.common_utils import get_metrics
  from jax.nn import log_softmax
  from modeling_flax_performer import FlaxPerformerForMaskedLM
  from transformers import MODEL_FOR_MASKED_LM_MAPPING, AutoTokenizer, BertConfig, FlaxBertForMaskedLM, HfArgumentParser, PreTrainedTokenizerBase, TensorType, TrainingArguments, is_tensorboard_available, set_seed
label_desc: Use image node_id 1 for calling the FlaxDataCollatorForLanguageModeling obj's underlying member method code with example usage: obj.__post_init__() without return types