class_docstr: Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
  methods for loading/downloading/saving configurations.
  
  <Tip>
  
  A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to
  initialize a model does **not** load the model weights. It only affects the model's configuration.
  
  </Tip>
  
  Class attributes (overridden by derived classes):
  
  - **model_type** (`str`) -- An identifier for the model type, serialized into the JSON file, and used to recreate
    the correct object in [`~transformers.AutoConfig`].
  - **is_composition** (`bool`) -- Whether the config class is composed of multiple sub-configs. In this case the
    config has to be initialized from two or more configs of type [`~transformers.PretrainedConfig`] like:
    [`~transformers.EncoderDecoderConfig`] or [`~RagConfig`].
  - **keys_to_ignore_at_inference** (`List[str]`) -- A list of keys to ignore by default when looking at dictionary
    outputs of the model during inference.
  - **attribute_map** (`Dict[str, str]`) -- A dict that maps model specific attribute names to the standardized
    naming of attributes.
  
  Common attributes (present in all subclasses):
  
  - **vocab_size** (`int`) -- The number of tokens in the vocabulary, which is also the first dimension of the
    embeddings matrix (this attribute may be missing for models that don't have a text modality like ViT).
  - **hidden_size** (`int`) -- The hidden size of the model.
  - **num_attention_heads** (`int`) -- The number of attention heads used in the multi-head attention layers of the
    model.
  - **num_hidden_layers** (`int`) -- The number of blocks in the model.
  
  Arg:
      name_or_path (`str`, *optional*, defaults to `""`):
          Store the string that was passed to [`PreTrainedModel.from_pretrained`] or
          [`TFPreTrainedModel.from_pretrained`] as `pretrained_model_name_or_path` if the configuration was created
          with such a method.
      output_hidden_states (`bool`, *optional*, defaults to `False`):
          Whether or not the model should return all hidden-states.
      output_attentions (`bool`, *optional*, defaults to `False`):
          Whether or not the model should returns all attentions.
      return_dict (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return a [`~transformers.utils.ModelOutput`] instead of a plain tuple.
      is_encoder_decoder (`bool`, *optional*, defaults to `False`):
          Whether the model is used as an encoder/decoder or not.
      is_decoder (`bool`, *optional*, defaults to `False`):
          Whether the model is used as decoder or not (in which case it's used as an encoder).
      cross_attention_hidden_size** (`bool`, *optional*):
          The hidden size of the cross-attention layer in case the model is used as a decoder in an encoder-decoder
          setting and the cross-attention hidden dimension differs from `self.config.hidden_size`.
      add_cross_attention (`bool`, *optional*, defaults to `False`):
          Whether cross-attention layers should be added to the model. Note, this option is only relevant for models
          that can be used as decoder models within the [`EncoderDecoderModel`] class, which consists of all models
          in `AUTO_MODELS_FOR_CAUSAL_LM`.
      tie_encoder_decoder (`bool`, *optional*, defaults to `False`):
          Whether all encoder weights should be tied to their equivalent decoder weights. This requires the encoder
          and decoder model to have the exact same parameter names.
      prune_heads (`Dict[int, List[int]]`, *optional*, defaults to `{}`):
          Pruned heads of the model. The keys are the selected layer indices and the associated values, the list of
          heads to prune in said layer.
  
          For instance `{1: [0, 2], 2: [2, 3]}` will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
      chunk_size_feed_forward (`int`, *optional*, defaults to `0`):
          The chunk size of all feed forward layers in the residual attention blocks. A chunk size of `0` means that
          the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes `n` <
          sequence_length embeddings at a time. For more information on feed forward chunking, see [How does Feed
          Forward Chunking work?](../glossary.html#feed-forward-chunking).
  
      > Parameters for sequence generation
  
      max_length (`int`, *optional*, defaults to 20):
          Maximum length that will be used by default in the `generate` method of the model.
      min_length (`int`, *optional*, defaults to 10):
          Minimum length that will be used by default in the `generate` method of the model.
      do_sample (`bool`, *optional*, defaults to `False`):
          Flag that will be used by default in the `generate` method of the model. Whether or not to use sampling ;
          use greedy decoding otherwise.
      early_stopping (`bool`, *optional*, defaults to `False`):
          Flag that will be used by default in the `generate` method of the model. Whether to stop the beam search
          when at least `num_beams` sentences are finished per batch or not.
      num_beams (`int`, *optional*, defaults to 1):
          Number of beams for beam search that will be used by default in the `generate` method of the model. 1 means
          no beam search.
      num_beam_groups (`int`, *optional*, defaults to 1):
          Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams
          that will be used by default in the `generate` method of the model. 1 means no group beam search.
      diversity_penalty (`float`, *optional*, defaults to 0.0):
          Value to control diversity for group beam search. that will be used by default in the `generate` method of
          the model. 0 means no diversity penalty. The higher the penalty, the more diverse are the outputs.
      temperature (`float`, *optional*, defaults to 1):
          The value used to module the next token probabilities that will be used by default in the `generate` method
          of the model. Must be strictly positive.
      top_k (`int`, *optional*, defaults to 50):
          Number of highest probability vocabulary tokens to keep for top-k-filtering that will be used by default in
          the `generate` method of the model.
      top_p (`float`, *optional*, defaults to 1):
          Value that will be used by default in the `generate` method of the model for `top_p`. If set to float < 1,
          only the most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.
      repetition_penalty (`float`, *optional*, defaults to 1):
          Parameter for repetition penalty that will be used by default in the `generate` method of the model. 1.0
          means no penalty.
      length_penalty (`float`, *optional*, defaults to 1):
          Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to
          the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
          likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while
          `length_penalty` < 0.0 encourages shorter sequences.
      no_repeat_ngram_size (`int`, *optional*, defaults to 0) -- Value that will be used by default in the
          `generate` method of the model for `no_repeat_ngram_size`. If set to int > 0, all ngrams of that size can
          only occur once.
      encoder_no_repeat_ngram_size (`int`, *optional*, defaults to 0) -- Value that will be used by
          default in the `generate` method of the model for `encoder_no_repeat_ngram_size`. If set to int > 0, all
          ngrams of that size that occur in the `encoder_input_ids` cannot occur in the `decoder_input_ids`.
      bad_words_ids (`List[int]`, *optional*):
          List of token ids that are not allowed to be generated that will be used by default in the `generate`
          method of the model. In order to get the tokens of the words that should not appear in the generated text,
          use `tokenizer.encode(bad_word, add_prefix_space=True)`.
      num_return_sequences (`int`, *optional*, defaults to 1):
          Number of independently computed returned sequences for each element in the batch that will be used by
          default in the `generate` method of the model.
      output_scores (`bool`, *optional*, defaults to `False`):
          Whether the model should return the logits when used for generation.
      return_dict_in_generate (`bool`, *optional*, defaults to `False`):
          Whether the model should return a [`~transformers.utils.ModelOutput`] instead of a `torch.LongTensor`.
      forced_bos_token_id (`int`, *optional*):
          The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful for
          multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be the target
          language token.
      forced_eos_token_id (`int`, *optional*):
          The id of the token to force as the last generated token when `max_length` is reached.
      remove_invalid_values (`bool`, *optional*):
          Whether to remove possible _nan_ and _inf_ outputs of the model to prevent the generation method to crash.
          Note that using `remove_invalid_values` can slow down generation.
  
      > Parameters for fine-tuning tasks
  
      architectures (`List[str]`, *optional*):
          Model architectures that can be used with the model pretrained weights.
      finetuning_task (`str`, *optional*):
          Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow
          or PyTorch) checkpoint.
      id2label (`Dict[int, str]`, *optional*):
          A map from index (for instance prediction index, or target index) to label.
      label2id (`Dict[str, int]`, *optional*): A map from label to index for the model.
      num_labels (`int`, *optional*):
          Number of labels to use in the last layer added to the model, typically for a classification task.
      task_specific_params (`Dict[str, Any]`, *optional*):
          Additional keyword arguments to store for the current task.
      problem_type (`str`, *optional*):
          Problem type for `XxxForSequenceClassification` models. Can be one of `"regression"`,
          `"single_label_classification"` or `"multi_label_classification"`.
  
      > Parameters linked to the tokenizer
  
      tokenizer_class (`str`, *optional*):
          The name of the associated tokenizer class to use (if none is set, will use the tokenizer associated to the
          model by default).
      prefix (`str`, *optional*):
          A specific prompt that should be added at the beginning of each text before calling the model.
      bos_token_id (`int`, *optional*): The id of the _beginning-of-stream_ token.
      pad_token_id (`int`, *optional*): The id of the _padding_ token.
      eos_token_id (`int`, *optional*): The id of the _end-of-stream_ token.
      decoder_start_token_id (`int`, *optional*):
          If an encoder-decoder model starts decoding with a different token than _bos_, the id of that token.
      sep_token_id (`int`, *optional*): The id of the _separation_ token.
  
      > PyTorch specific parameters
  
      torchscript (`bool`, *optional*, defaults to `False`):
          Whether or not the model should be used with Torchscript.
      tie_word_embeddings (`bool`, *optional*, defaults to `True`):
          Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
          model has a output word embedding layer.
      torch_dtype (`str`, *optional*):
          The `dtype` of the weights. This attribute can be used to initialize the model to a non-default `dtype`
          (which is normally `float32`) and thus allow for optimal storage allocation. For example, if the saved
          model is `float16`, ideally we want to load it back using the minimal amount of memory needed to load
          `float16` weights. Since the config object is stored in plain text, this attribute contains just the
          floating type string without the `torch.` prefix. For example, for `torch.float16` ``torch_dtype` is the
          `"float16"` string.
  
          This attribute is currently not being used during model loading time, but this may change in the future
          versions. But we can already start preparing for the future by saving the dtype with save_pretrained.
  
      > TensorFlow specific parameters
  
      use_bfloat16 (`bool`, *optional*, defaults to `False`):
          Whether or not the model should use BFloat16 scalars (only used by some TensorFlow models).
      tf_legacy_loss (`bool`, *optional*, defaults to `False`):
          Whether the model should use legacy TensorFlow losses. Legacy losses have variable output shapes and may
          not be XLA-compatible. This option is here for backward compatibility and will be removed in Transformers
          v5.
code: def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
      with open(json_file, "r", encoding="utf-8") as reader:
          text = reader.read()
      return json.loads(text)
  
imports: import copy
  import json
  import os
  import re
  import warnings
  from typing import Any, Dict, List, Optional, Tuple, Union
  from packaging import version
  from .None import __version__
  from .dynamic_module_utils import custom_object_save
  from .utils import CONFIG_NAME, PushToHubMixin, cached_file, copy_func, download_url, extract_commit_hash, is_remote_url, is_torch_available, logging
label_desc: Use image node_id 15 for calling the PretrainedConfig obj's underlying member method code with example usage: obj._dict_from_json_file(cls, json_file) and returns: json