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from ...configuration_utils import PretrainedConfig
from ...modeling_rope_utils import RopeParameters


class NanoChatConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`NanoChatModel`]. It is used to instantiate a
    NanoChat 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 [karpathy/nanochat-d32](https://huggingface.co/karpathy/nanochat-d32).

    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 50304):
            Vocabulary size of the NanoChat model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`NanoChatModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 8192):
            Dimension of the MLP representations. If `None`, it will be computed based on the model architecture.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 6):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
            `num_attention_heads`.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with.
        hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`):
            The non-linear activation function (function or string) in the decoder.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionaty should contain
            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
            with longer `max_position_embeddings`.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        final_logit_softcapping (`float`, *optional*, defaults to 15.0):
            scaling factor when applying tanh softcapping on the logits.
        attention_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, and value projection layers during self-attention.
        bos_token_id (`int`, *optional*, defaults to 0):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 1):
            End of stream token id.
        pad_token_id (`int`, *optional*, defaults to 1):
            Padding token id.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings

    ```python
    >>> from transformers import NanoChatModel, NanoChatConfig

    >>> # Initializing a NanoChat style configuration
    >>> configuration = NanoChatConfig()

    >>> # Initializing a model from the NanoChat style configuration
    >>> model = NanoChatModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "nanochat"
    keys_to_ignore_at_inference = ["past_key_values"]

    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.fc1": "colwise",
        "layers.*.mlp.fc2": "rowwise",
    }

    def __init__(
        self,
        vocab_size: int = 50304,
        hidden_size: int = 768,
        intermediate_size: int | None = 8192,
        num_hidden_layers: int = 12,
        num_attention_heads: int = 6,
        num_key_value_heads: int | None = None,
        max_position_embeddings: int = 2048,
        hidden_act: str = "relu2",
        attention_dropout: float = 0.0,
        rms_norm_eps: float = 1e-6,
        initializer_range: float = 0.02,
        rope_parameters: RopeParameters | dict | None = None,
        use_cache: bool = True,
        final_logit_softcapping: float | None = 15.0,
        attention_bias: bool = False,
        bos_token_id: int = 0,
        eos_token_id: int = 1,
        pad_token_id: int = 1,
        tie_word_embeddings: bool = False,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads

        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.max_position_embeddings = max_position_embeddings
        self.hidden_act = hidden_act
        self.attention_dropout = attention_dropout
        self.rms_norm_eps = rms_norm_eps
        self.initializer_range = initializer_range
        self.use_cache = use_cache
        self.final_logit_softcapping = final_logit_softcapping
        self.attention_bias = attention_bias
        self.rope_parameters = rope_parameters

        super().__init__(
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            pad_token_id=pad_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


__all__ = ["NanoChatConfig"]
