class_docstr: Construct a SeamlessM4T tokenizer.
  
  Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
  [SentencePiece](https://github.com/google/sentencepiece).
  
  The tokenization method is `<language code> <tokens> <eos>` for source language documents, and `<eos> <language
  code> <tokens> <eos>` for target language documents.
  
  Examples:
  
  ```python
  >>> from transformers import SeamlessM4TTokenizer
  
  >>> tokenizer = SeamlessM4TTokenizer.from_pretrained(
  ...     "facebook/hf-seamless-m4t-medium", src_lang="eng", tgt_lang="fra"
  ... )
  >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
  >>> expected_translation_french = "Le chef de l'ONU affirme qu'il n'y a pas de solution militaire en Syrie."
  >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_french, return_tensors="pt")
  ```
  
  Args:
      vocab_file (`str`):
          Path to the vocabulary file.
      bos_token (`str`, *optional*, defaults to `"<s>"`):
          The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
  
          <Tip>
  
          When building a sequence using special tokens, this is not the token that is used for the beginning of
          sequence. The token used is the `cls_token`.
  
          </Tip>
  
      eos_token (`str`, *optional*, defaults to `"</s>"`):
          The end of sequence token.
  
          <Tip>
  
          When building a sequence using special tokens, this is not the token that is used for the end of sequence.
          The token used is the `sep_token`.
  
          </Tip>
  
      sep_token (`str`, *optional*, defaults to `"</s>"`):
          The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
          sequence classification or for a text and a question for question answering. It is also used as the last
          token of a sequence built with special tokens.
      cls_token (`str`, *optional*, defaults to `"<s>"`):
          The classifier token which is used when doing sequence classification (classification of the whole sequence
          instead of per-token classification). It is the first token of the sequence when built with special tokens.
      unk_token (`str`, *optional*, defaults to `"<unk>"`):
          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.
      pad_token (`str`, *optional*, defaults to `"<pad>"`):
          The token used for padding, for example when batching sequences of different lengths.
      tokenizer_file (`str`, *optional*):
          The path to a tokenizer file to use instead of the vocab file.
      src_lang (`str`, *optional*, defaults to `"eng"`):
          The language to use as source language for translation.
      tgt_lang (`str`, *optional*, defaults to `"fra"`):
          The language to use as target language for translation.
      sp_model_kwargs (`Dict[str, Any]`, *optional*):
          Additional keyword arguments to pass to the model initialization.
      additional_special_tokens (tuple or list of `str` or `tokenizers.AddedToken`, *optional*):
          A tuple or a list of additional special tokens. Can be used to specify the list of languages that will be
          supported by the tokenizer.
code: def src_lang(self, new_src_lang: str) -> None:
      if "__" not in new_src_lang:
          self._src_lang = f"__{new_src_lang}__"
      else:
          self._src_lang = new_src_lang
      self.set_src_lang_special_tokens(self._src_lang)
  
imports: import os
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple, Union
  import sentencepiece
  from ...convert_slow_tokenizer import import_protobuf
  from ...tokenization_utils import BatchEncoding, PreTokenizedInput, PreTrainedTokenizer, TextInput
  from ...tokenization_utils_base import AddedToken
  from ...utils import PaddingStrategy, logging
label_desc: Use image node_id 7 for calling the SeamlessM4TTokenizer obj's underlying member method code with example usage: obj.src_lang(new_src_lang) without return types