class_docstr: Construct a Whisper tokenizer.
  
  This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
  the superclass for more information regarding such methods.
  
  Args:
      vocab_file (`str`):
          Path to the vocabulary file.
      merges_file (`str`):
          Path to the merges file.
      normalizer_file (`str`, *optional*):
          Path to the normalizer_file file.
      errors (`str`, *optional*, defaults to `"replace"`):
          Paradigm to follow when decoding bytes to UTF-8. See
          [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
      unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
          The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
          token instead.
      bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
          The beginning of sequence token. The `decoder_start_token_id` is used to set the first token as
          `"<|startoftranscript|>"` when generating.
      eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
          The end of sequence token.
      pad_token (`str`, *optional*):
          The token used for padding, for example when batching sequences of different lengths.
      add_prefix_space (`bool`, *optional*, defaults to `False`):
          Whether or not to add an initial space to the input. This allows to treat the leading word just as any
          other word.
      language (`str`, *optional*):
          The language of the transcription text. The corresponding language id token is appended to the start of the
          sequence for multilingual speech recognition and speech translation tasks, e.g. for Spanish the token
          `"<|es|>"` is appended to the start of sequence. This should be used for multilingual fine-tuning only.
      task (`str`, *optional*):
          Task identifier to append at the start of sequence (if any). This should be used for mulitlingual
          fine-tuning, with `"transcribe"` for speech recognition and `"translate"` for speech translation.
      predict_timestamps (`bool`, *optional*, defaults to `False`):
          Whether to omit the `<|notimestamps|>` token at the start of the sequence.
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docstr: A simple chat template that ignores role information and just concatenates messages with EOS tokens.
docstr_tok: ["A","simple","chat","template","that","ignores","role","information","and","just","concatenates","messages","with","EOS","tokens","."]
code: def default_chat_template(self):
      """
      A simple chat template that ignores role information and just concatenates messages with EOS tokens.
      """
      logger.warning_once(
          "\nNo chat template is defined for this tokenizer - using the default template "
          f"for the {self.__class__.__name__} class. If the default is not appropriate for "
          "your model, please set `tokenizer.chat_template` to an appropriate template. "
          "See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
      )
      return (
          "{% for message in messages %}"
          "{{ message.content }}{{ eos_token }}"
          "{% endfor %}"
      )
  
imports: import json
  import os
  from functools import lru_cache
  from typing import List, Optional, Tuple, Union
  import numpy
  import regex
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  from .english_normalizer import BasicTextNormalizer, EnglishTextNormalizer
label_desc: Use image node_id 24 for calling the WhisperTokenizer obj's underlying member method code with example usage: obj.default_chat_template() and returns: str