from transformers.configuration_utils import PretrainedConfig # Copied from transformers.models.m2m_100.configuration_m2m_100.M2M100Config->IndicTrans class RotaryIndicTransConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`IT2Model`]. It is used to instantiate an IT2 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 IT2 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 50265): Vocabulary size of the IT2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`IT2Model`] or d_model (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *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. 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. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. max_position_embeddings (`int`, *optional*, defaults to 1024): 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). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. 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. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). ```""" model_type = "RotaryIndicTrans" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model", } def __init__( self, encoder_vocab_size=None, decoder_vocab_size=None, encoder_embed_dim=512, decoder_embed_dim=512, encoder_layers=6, encoder_ffn_dim=2048, encoder_attention_heads=8, decoder_layers=6, decoder_ffn_dim=2048, decoder_attention_heads=8, encoder_layerdrop=0.00, decoder_layerdrop=0.00, use_cache=True, is_encoder_decoder=True, activation_function="relu", encoder_normalize_before=False, decoder_normalize_before=False, layernorm_embedding=False, share_decoder_input_output_embed=False, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, scale_embedding=True, decoder_start_token_id=2, pad_token_id=1, bos_token_id=0, eos_token_id=2, attn_implementation="eager", rope_args={"theta": 10000}, **kwargs, ): self.encoder_vocab_size = encoder_vocab_size self.decoder_vocab_size = decoder_vocab_size self.encoder_normalize_before = encoder_normalize_before self.decoder_normalize_before = decoder_normalize_before self.layernorm_embedding = layernorm_embedding self.encoder_embed_dim = encoder_embed_dim self.decoder_embed_dim = decoder_embed_dim self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads 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.rope_args = rope_args self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding self.share_decoder_input_output_embed = share_decoder_input_output_embed self.attn_implementation = attn_implementation 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, **kwargs, )