# coding=utf-8
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"""JetMoe model configuration"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)


class JetMoeConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`JetMoeModel`]. It is used to instantiate a
    JetMoe model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a configuration of the JetMoe-4B.

    [jetmoe/jetmoe-8b](https://huggingface.co/jetmoe/jetmoe-8b)

    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 32000):
            Vocabulary size of the JetMoe model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`JetMoeModel`]
        hidden_size (`int`, *optional*, defaults to 2048):
            Dimension of the hidden representations.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_key_value_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each key and value in the Transformer encoder.
        kv_channels (`int`, *optional*, defaults to 128):
            Defines the number of channels for the key and value tensors.
        intermediate_size (`int`, *optional*, defaults to 5632):
            Dimension of the MLP representations.
        max_position_embeddings (`int`, *optional*, defaults to 4096):
            The maximum sequence length that this model might ever be used with. JetMoe's attention allows sequence of
            up to 4096 tokens.
        activation_function (`string`, *optional*, defaults to `"silu"`):
            Defines the activation function for MLP experts.
        num_local_experts (`int`, *optional*, defaults to 8):
            Defines the number of experts in the MoE and MoA.
        num_experts_per_tok (`int, *optional*, defaults to 2):
            The number of experts to route per-token and for MoE and MoA.
        output_router_logits (`bool`, *optional*, defaults to `False`):
            Whether or not the router logits should be returned by the model. Enabling this will also
            allow the model to output the auxiliary loss.
        aux_loss_coef (`float`, *optional*, defaults to 0.01):
            The coefficient for the auxiliary loss.
        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`.
        bos_token_id (`int`, *optional*, defaults to 1):
            The id of the "beginning-of-sequence" token.
        eos_token_id (`int`, *optional*, defaults to 2):
            The id of the "end-of-sequence" token.
        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
            Whether the model's input and output word embeddings should be tied.
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        initializer_range (`float`, *optional*, defaults to 0.01):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.

    ```python
    >>> from transformers import JetMoeModel, JetMoeConfig

    >>> # Initializing a JetMoe 4B style configuration
    >>> configuration = JetMoeConfig()

    >>> # Initializing a model from the JetMoe 4B style configuration
    >>> model = JetMoeModel(configuration)

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

    model_type = "jetmoe"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {"head_dim": "kv_channels"}

    def __init__(
        self,
        vocab_size=32000,
        hidden_size=2048,
        num_hidden_layers=12,
        num_key_value_heads=16,
        kv_channels=128,
        intermediate_size=5632,
        max_position_embeddings=4096,
        activation_function="silu",
        num_local_experts=8,
        num_experts_per_tok=2,
        output_router_logits=False,
        aux_loss_coef=0.01,
        use_cache=True,
        bos_token_id=1,
        eos_token_id=2,
        tie_word_embeddings=True,
        rope_theta=10000.0,
        rms_norm_eps=1e-6,
        initializer_range=0.01,
        attention_dropout=0.0,
        **kwargs,
    ):
        if num_experts_per_tok > num_local_experts:
            raise ValueError("`num_experts_per_tok` must be less than or equal to `num_local_experts`")
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_key_value_heads * num_experts_per_tok
        self.num_key_value_heads = num_key_value_heads
        self.kv_channels = kv_channels
        self.intermediate_size = intermediate_size
        self.max_position_embeddings = max_position_embeddings
        self.activation_function = activation_function
        self.num_local_experts = num_local_experts
        self.num_experts_per_tok = num_experts_per_tok
        self.output_router_logits = output_router_logits
        self.aux_loss_coef = aux_loss_coef
        self.use_cache = use_cache
        self.initializer_range = initializer_range
        self.attention_dropout = attention_dropout

        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id

        self.rope_theta = rope_theta
        self.rms_norm_eps = rms_norm_eps

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


__all__ = ["JetMoeConfig"]
