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# Copyright 2026 NAVER CLOUD Corp. and The HuggingFace Inc. 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
# limitations under the License.
from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...modeling_rope_utils import RopeParameters
from ...utils import auto_docstring


@auto_docstring(checkpoint="naver-hyperclovax/HyperCLOVAX-SEED-Think-14B")
@strict
class HyperCLOVAXConfig(PreTrainedConfig):
    r"""
    embedding_multiplier (`float`, *optional*, defaults to `1.0`):
        Scaling factor applied to the token embedding outputs. Used in MuP to control the
        scale of the embedding activations.
    logits_scaling (`float`, *optional*, defaults to `1.0`):
        Scaling factor **multiplied** to the final logits before loss computation or sampling.
        Used in MuP to ensure consistent output scale across model sizes. Note: unlike
        [`GraniteConfig`], this is a multiplier, not a divisor.
    residual_multiplier (`float`, *optional*, defaults to `1.0`):
        Scaling factor applied to each sub-layer output before adding to the residual stream.
        Used in Maximal Update Parametrization (MuP) to stabilize training across model sizes.
    attention_multiplier (`float`, *optional*, defaults to `head_dim ** -0.5`):
        Scaling factor applied to attention logits before softmax, replacing the standard
        `1 / sqrt(head_dim)` scaling. Set explicitly for MuP-based training; when `None`,
        defaults to the standard value.
    use_post_norm (`bool`, *optional*, defaults to `True`):
        Whether to apply an extra RMSNorm after each sub-layer output (Peri-Layer Normalization).

    ```python
    >>> from transformers import HyperCLOVAXModel, HyperCLOVAXConfig

    >>> # Initializing a HyperCLOVAX style configuration
    >>> configuration = HyperCLOVAXConfig()

    >>> # Initializing a model from the configuration
    >>> model = HyperCLOVAXModel(configuration)

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

    model_type = "hyperclovax"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `HyperCLOVAXModel`
    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.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    vocab_size: int = 32000
    hidden_size: int = 4096
    intermediate_size: int = 11008
    num_hidden_layers: int = 32
    num_attention_heads: int = 32
    num_key_value_heads: int | None = None
    hidden_act: str = "silu"
    max_position_embeddings: int = 2048
    initializer_range: float = 0.02
    rms_norm_eps: float = 1e-6
    use_cache: bool = True
    pad_token_id: int | None = None
    bos_token_id: int | None = 1
    eos_token_id: int | list[int] | None = 2
    tie_word_embeddings: bool = False
    rope_parameters: RopeParameters | dict | None = None
    attention_bias: bool = False
    attention_dropout: float | int = 0.0
    mlp_bias: bool = False
    embedding_multiplier: float | int = 1.0
    logits_scaling: float | int = 1.0
    residual_multiplier: float | int = 1.0

    # MuP scaling factors: None means "resolve to the mathematically equivalent default".
    attention_multiplier: float | None = None

    head_dim: int | None = None

    # Peri-Layer Normalization
    use_post_norm: bool = True

    def __post_init__(
        self,
        **kwargs,
    ):
        if self.head_dim is None:
            self.head_dim = self.hidden_size // self.num_attention_heads
        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads

        super().__post_init__(**kwargs)

        # Resolve None MuP values to their mathematically equivalent defaults.
        if self.attention_multiplier is None:
            self.attention_multiplier = self.head_dim**-0.5

    def validate_architecture(self):
        """Validates that `hidden_size` is divisible by `num_attention_heads`."""
        if self.hidden_size % self.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
                f"heads ({self.num_attention_heads})."
            )


__all__ = ["HyperCLOVAXConfig"]
