class_docstr: This is the configuration class to store the configuration of a [`WhisperModel`]. It is used to instantiate a
  Whisper model according to the specified arguments, defining the model architecture. Instantiating a configuration
  with the defaults will yield a similar configuration to that of the Whisper
  [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  
  Args:
      vocab_size (`int`, *optional*, defaults to 51865):
          Vocabulary size of the Whisper model. Defines the number of different tokens that can be represented by the
          `decoder_input_ids` passed when calling [`WhisperModel`]
      num_mel_bins (`int`, *optional*, defaults to 80):
          Number of mel features used per input features. Should correspond to the value used in the
          `WhisperProcessor` class.
      encoder_layers (`int`, *optional*, defaults to 6):
          Number of encoder layers.
      decoder_layers (`int`, *optional*, defaults to 6):
          Number of decoder layers.
      encoder_attention_heads (`int`, *optional*, defaults to 4):
          Number of attention heads for each attention layer in the Transformer encoder.
      decoder_attention_heads (`int`, *optional*, defaults to 4):
          Number of attention heads for each attention layer in the Transformer decoder.
      encoder_ffn_dim (`int`, *optional*, defaults to 1536):
          Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
      decoder_ffn_dim (`int`, *optional*, defaults to 1536):
          Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
      encoder_layerdrop (`float`, *optional*, defaults to 0.0):
          The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
          for more details.
      decoder_layerdrop (`float`, *optional*, defaults to 0.0):
          The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
          for more details.
      decoder_start_token_id (`int`, *optional*, defaults to 50257):
          Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids`
          are provided to the `generate` function. It is used to guide the model`s generation process depending on
          the task.
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return the last key/values attentions (not used by all models).
      is_encoder_decoder (`bool`, *optional*, defaults to `True`):
          Whether the model is used as an encoder/decoder or not.
      activation_function (`str`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"silu"` and `"gelu_new"` are supported.
      d_model (`int`, *optional*, defaults to 256):
          Dimensionality of the layers.
      dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      attention_dropout (`float`, *optional*, defaults to 0.0):
          The dropout ratio for the attention probabilities.
      activation_dropout (`float`, *optional*, defaults to 0.0):
          The dropout ratio for activations inside the fully connected layer.
      init_std (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      scale_embedding (`bool`, *optional*, defaults to False):
          Scale embeddings by diving by sqrt(d_model).
      max_source_positions (`int`, *optional*, defaults to 1500):
          The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
      max_target_positions (`int`, *optional*, defaults to 448):
          The maximum sequence length that this model might ever be used with. Typically set this to something large
          just in case (e.g., 512 or 1024 or 2048).
      pad_token_id (`int`, *optional*, defaults to 50256):
          Padding token id.
      bos_token_id (`int`, *optional*, defaults to 50256):
          Begin of stream token id.
      eos_token_id (`int`, *optional*, defaults to 50257):
          End of stream token id.
      tie_word_embeddings (`bool`, *optional*, defaults to `True`):
          Whether to tie input and output embeddings.
      suppress_tokens (`List[int]`, *optional*):
          A list containing the non-speech tokens that will be used by the logit processor in the `generate`
          function. NON_SPEECH_TOKENS and NON_SPEECH_TOKENS_MULTI each correspond to the `english-only` and the
          `multilingual` model.
      begin_suppress_tokens (`List[int]`, *optional*, defaults to `[220,50256]`):
          A list containing tokens that will be supressed at the beginning of the sampling process. Initialized as
          the token for `" "` (`blank_token_id`) and the `eos_token_id`
  
  
  Example:
  
  ```python
  >>> from transformers import WhisperConfig, WhisperModel
  
  >>> # Initializing a Whisper tiny style configuration
  >>> configuration = WhisperConfig()
  
  >>> # Initializing a model (with random weights) from the tiny style configuration
  >>> model = WhisperModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      vocab_size=51865,
      num_mel_bins=80,
      encoder_layers=6,
      encoder_attention_heads=4,
      decoder_layers=6,
      decoder_attention_heads=4,
      decoder_ffn_dim=1536,
      encoder_ffn_dim=1536,
      encoder_layerdrop=0.0,
      decoder_layerdrop=0.0,
      decoder_start_token_id=50257,
      use_cache=True,
      is_encoder_decoder=True,
      activation_function="gelu",
      d_model=256,
      dropout=0.0,
      attention_dropout=0.0,
      activation_dropout=0.0,
      init_std=0.02,
      scale_embedding=False,
      max_source_positions=1500,
      max_target_positions=448,
      pad_token_id=50256,
      bos_token_id=50257,
      eos_token_id=50256,
      tie_word_embeddings=True,
      suppress_tokens=None,
      begin_suppress_tokens=[220, 50256],
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.num_mel_bins = num_mel_bins
      self.d_model = d_model
      self.encoder_layers = encoder_layers
      self.encoder_attention_heads = encoder_attention_heads
      self.decoder_layers = decoder_layers
      self.decoder_attention_heads = decoder_attention_heads
      self.decoder_ffn_dim = decoder_ffn_dim
      self.encoder_ffn_dim = encoder_ffn_dim
      self.dropout = dropout
      self.attention_dropout = attention_dropout
      self.activation_dropout = activation_dropout
      self.activation_function = activation_function
      self.init_std = init_std
      self.encoder_layerdrop = encoder_layerdrop
      self.decoder_layerdrop = decoder_layerdrop
      self.use_cache = use_cache
      self.num_hidden_layers = encoder_layers
      self.scale_embedding = (
          scale_embedding  # scale factor will be sqrt(d_model) if True
      )
      self.tie_word_embeddings = tie_word_embeddings
      self.max_source_positions = max_source_positions
      self.max_target_positions = max_target_positions
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          is_encoder_decoder=is_encoder_decoder,
          decoder_start_token_id=decoder_start_token_id,
          tie_word_embeddings=tie_word_embeddings,
          suppress_tokens=suppress_tokens,
          begin_suppress_tokens=begin_suppress_tokens,
          **kwargs,
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/whisper/configuration_whisper.py
imports: from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxSeq2SeqConfigWithPast
  from ...utils import logging
label_desc: Use image node_id 1 to create a new WhisperConfig object from inherited base classes: PretrainedConfig with example: obj = WhisperConfig(vocab_size, num_mel_bins, encoder_layers, encoder_attention_heads, decoder_layers, decoder_attention_heads, decoder_ffn_dim, encoder_ffn_dim, encoder_layerdrop, decoder_layerdrop, decoder_start_token_id, use_cache, is_encoder_decoder, activation_function, d_model, dropout, attention_dropout, activation_dropout, init_std, scale_embedding, max_source_positions, max_target_positions, pad_token_id, bos_token_id, eos_token_id, tie_word_embeddings, suppress_tokens, begin_suppress_tokens)