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# Copyright 2025 the MiniMax AI Team and HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from ...configuration_utils import PreTrainedConfig
from ...modeling_rope_utils import RopeParameters


class MiniMaxM2Config(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MiniMaxM2Model`]. It is used to instantiate an
    MiniMaxM2 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 MiniMaxM2.

    [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2)

    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 (`Optional`, *optional*, defaults to 200064):
            Vocabulary size of the MiniMaxM2 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`MiniMaxM2Model`]
        hidden_size (`Optional`, *optional*, defaults to 3072):
            Dimension of the hidden representations.
        intermediate_size (`Optional`, *optional*, defaults to 1536):
            Dimension of the MLP representations.
        num_hidden_layers (`Optional`, *optional*, defaults to 62):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`Optional`, *optional*, defaults to 48):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`Optional`, *optional*, defaults to 8):
            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 `8`.
        head_dim (`Optional`, *optional*, defaults to 128):
            The attention head dimension.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`Optional`, *optional*, defaults to 196608):
            The maximum sequence length that this model might ever be used with. MiniMaxM2's sliding window attention
            allows sequence of up to 196608 tokens.
        initializer_range (`Optional`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`Optional`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        use_cache (`Optional`, *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`.
        pad_token_id (`Optional`, *optional*):
            The id of the padding token.
        bos_token_id (`Optional`, *optional*, defaults to 200034):
            The id of the "beginning-of-sequence" token.
        eos_token_id (`Optional`, *optional*, defaults to 200020):
            The id of the "end-of-sequence" token.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.
        attention_dropout (`Optional`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        num_experts_per_tok (`Optional`, *optional*, defaults to 8):
            The number of experts to route per-token, can be also interpreted as the `top-k` routing
            parameter
        num_local_experts (`Optional`, *optional*, defaults to 256):
            Number of experts per Sparse MLP layer.
        output_router_logits (`Optional`, *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. See [here]() for more details
        router_aux_loss_coef (`Optional`, *optional*, defaults to 0.001):
            The aux loss factor for the total loss.
        router_jitter_noise (`Optional`, *optional*, defaults to 0.0):
            Amount of noise to add to the router.
        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`.

    ```python
    >>> from transformers import MiniMaxM2Model, MiniMaxM2Config

    >>> # Initializing a MiniMaxM2 style configuration
    >>> configuration = MiniMaxM2Config()

    >>> # Initializing a model from the MiniMaxM2 style configuration
    >>> model = MiniMaxM2Model(configuration)

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

    model_type = "minimax_m2"
    keys_to_ignore_at_inference = ["past_key_values"]
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise_rep",
        "layers.*.self_attn.k_proj": "colwise_rep",
        "layers.*.self_attn.v_proj": "colwise_rep",
        "layers.*.self_attn.o_proj": "rowwise_rep",
        "layers.*.mlp.gate": "colwise_rep",  # we need to replicate here to correctly route experts
        "layers.*.mlp.experts.gate_up_proj": "local_rowwise",
        "layers.*.mlp.experts.down_proj": "local_rowwise",
        "layers.*.mlp.experts": "gather",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }
    attribute_map = {
        "num_experts": "num_local_experts",
    }
    default_theta = 5000000.0

    def __init__(
        self,
        vocab_size: int | None = 200064,
        hidden_size: int | None = 3072,
        intermediate_size: int | None = 1536,
        num_hidden_layers: int | None = 62,
        num_attention_heads: int | None = 48,
        num_key_value_heads: int | None = 8,
        head_dim: int | None = 128,
        hidden_act: str | None = "silu",
        max_position_embeddings: int | None = 196608,
        initializer_range: float | None = 0.02,
        rms_norm_eps: int | None = 1e-06,
        use_cache: bool | None = True,
        pad_token_id: int | None = None,
        bos_token_id: int | None = 200034,
        eos_token_id: int | None = 200020,
        tie_word_embeddings: bool | None = False,
        attention_dropout: float | None = 0.0,
        num_experts_per_tok: int | None = 8,
        num_local_experts: int | None = 256,
        output_router_logits: bool | None = False,
        router_aux_loss_coef: float | None = 0.001,
        router_jitter_noise: float | None = 0.0,
        rope_parameters: RopeParameters | dict[RopeParameters] | None = None,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.attention_dropout = attention_dropout
        self.head_dim = head_dim
        self.rope_parameters = rope_parameters

        self.num_experts_per_tok = num_experts_per_tok
        self.num_local_experts = num_local_experts
        self.output_router_logits = output_router_logits
        self.router_aux_loss_coef = router_aux_loss_coef
        self.router_jitter_noise = router_jitter_noise

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

    def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation=None, **kwargs):
        rope_scaling = kwargs.pop("rope_scaling", None)
        self.rope_parameters = rope_scaling or self.rope_parameters
        self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else {}

        # Standardize and validate the correctness of rotary position embeddings parameters
        # Model uses non-standard naming for rope params, overwrite!
        self.rope_parameters.setdefault("rope_theta", self.default_theta)
        self.rope_parameters["partial_rotary_factor"] = (
            kwargs.pop("rotary_dim", self.head_dim // 2) / self.head_dim
        )  # Default to `0.5`
        self.standardize_rope_params()

        if ignore_keys_at_rope_validation is None:
            ignore_keys_at_rope_validation = {"partial_rotary_factor"}
        else:
            ignore_keys_at_rope_validation |= {"partial_rotary_factor"}

        self.validate_rope(ignore_keys=ignore_keys_at_rope_validation)
        return kwargs


__all__ = ["MiniMaxM2Config"]
