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# Copyright 2026 the 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
# limitations under the License.


from ...configuration_utils import PreTrainedConfig, layer_type_validation
from ...modeling_rope_utils import RopeParameters


class GlmMoeDsaConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`GlmMoeDsaModel`]. It is used to instantiate a
    GLM-5 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 GLM-5.
    e.g. [zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5)
    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 154880):
            Vocabulary size of the model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GlmMoeDsaModel`].
        hidden_size (`int`, *optional*, defaults to 6144):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 12288):
            Dimension of the dense MLP representations.
        moe_intermediate_size (`int`, *optional*, defaults to 2048):
            Dimension of the MoE expert representations.
        num_hidden_layers (`int`, *optional*, defaults to 78):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 64):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*, defaults to 64):
            Number of key-value heads for Grouped Query Attention. If equal to `num_attention_heads`, uses MHA.
        n_shared_experts (`int`, *optional*, defaults to 1):
            Number of shared experts in MoE layers.
        n_routed_experts (`int`, *optional*, defaults to 256):
            Number of routed experts in MoE layers.
        routed_scaling_factor (`float`, *optional*, defaults to 2.5):
            Scaling factor for routed experts.
        kv_lora_rank (`int`, *optional*, defaults to 512):
            Rank of the LoRA matrices for key and value projections (MLA).
        q_lora_rank (`int`, *optional*, defaults to 2048):
            Rank of the LoRA matrices for query projections (MLA).
        qk_rope_head_dim (`int`, *optional*, defaults to 64):
            Dimension of the query/key heads that use rotary position embeddings.
        qk_nope_head_dim (`int`, *optional*, defaults to 192):
            Dimension of the query/key heads that don't use rotary position embeddings.
        v_head_dim (`int`, *optional*, defaults to 256):
            Dimension of the value heads.
        n_group (`int`, *optional*, defaults to 1):
            Number of groups for routed experts.
        topk_group (`int`, *optional*, defaults to 1):
            Number of selected groups for each token.
        num_experts_per_tok (`int`, *optional*, defaults to 8):
            Number of experts selected per token.
        norm_topk_prob (`bool`, *optional*, defaults to `True`):
            Whether to normalize the weights of the routed experts.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 202752):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions.
        pad_token_id (`int`, *optional*):
            Padding token id.
        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.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings.
        rope_parameters (`RopeParameters`, *optional*):
            Configuration parameters for the RoPE embeddings, including `rope_theta` and optional scaling parameters.
        rope_interleave (`bool`, *optional*, defaults to `True`):
            Whether to interleave the rotary position embeddings.
        mlp_layer_types (`list`, *optional*):
            MLP type pattern for each layer (`"dense"` or `"sparse"`). Defaults to 3 dense + rest sparse.
        attention_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        index_topk (`int`, *optional*, defaults to 2048):
            Number of top tokens selected by the indexer for sparse attention.
        index_head_dim (`int`, *optional*, defaults to 128):
            Head dimension for the indexer projections (DSA).
        index_n_heads (`int | None`, *optional*, defaults to 32):
            Number of heads for the indexer projections (DSA).
        indexer_rope_interleave (`bool`, *optional*, defaults to `True`):
            Whether the indexer uses interleaved rotary position embeddings.


    ```python
    >>> from transformers import GlmMoeDsaConfig, GlmMoeDsaModel

    >>> # Initializing a GLM-MoE-DSA configuration
    >>> configuration = GlmMoeDsaConfig()

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

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

    model_type = "glm_moe_dsa"
    keys_to_ignore_at_inference = ["past_key_values"]

    base_model_tp_plan = {
        "layers.*.self_attn.q_b_proj": "colwise",
        "layers.*.self_attn.kv_a_proj_with_mqa": "mla_kv_a_proj",
        "layers.*.self_attn.kv_b_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.experts.gate_up_proj": "packed_colwise",
        "layers.*.mlp.experts.down_proj": "rowwise",
        "layers.*.mlp.experts": "moe_tp_experts",
        "layers.*.mlp.shared_experts.gate_proj": "colwise",
        "layers.*.mlp.shared_experts.up_proj": "colwise",
        "layers.*.mlp.shared_experts.down_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"]),
    }
    attribute_map = {
        "num_local_experts": "n_routed_experts",
    }

    def __init__(
        self,
        vocab_size: int | None = 154880,
        hidden_size: int | None = 6144,
        intermediate_size: int | None = 12288,
        moe_intermediate_size: int | None = 2048,
        num_hidden_layers: int | None = 78,
        num_attention_heads: int | None = 64,
        num_key_value_heads: int | None = 64,
        n_shared_experts: int | None = 1,
        n_routed_experts: int | None = 256,
        routed_scaling_factor: float | None = 2.5,
        kv_lora_rank: int | None = 512,
        q_lora_rank: int | None = 2048,
        qk_rope_head_dim: int | None = 64,
        qk_nope_head_dim: int | None = 192,
        v_head_dim: int | None = 256,
        n_group: int | None = 1,
        topk_group: int | None = 1,
        num_experts_per_tok: int | None = 8,
        norm_topk_prob: bool | None = True,
        hidden_act: str | None = "silu",
        max_position_embeddings: int | None = 202752,
        initializer_range: float | None = 0.02,
        rms_norm_eps: float | None = 1e-5,
        use_cache: bool | None = True,
        pad_token_id: int | None = None,
        bos_token_id: int | None = 0,
        eos_token_id: int | None = 1,
        tie_word_embeddings: bool | None = False,
        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
        mlp_layer_types=None,
        attention_bias: bool | None = False,
        attention_dropout: float | None = 0.0,
        index_topk: int | None = 2048,
        index_head_dim: int | None = 128,
        index_n_heads: int | None = 32,
        **kwargs,
    ):
        # Model dimensions
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.moe_intermediate_size = moe_intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.max_position_embeddings = max_position_embeddings

        # Attention dimensions (MLA)
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads
        self.kv_lora_rank = kv_lora_rank
        self.q_lora_rank = q_lora_rank
        self.qk_rope_head_dim = qk_rope_head_dim
        self.qk_nope_head_dim = qk_nope_head_dim
        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
        self.v_head_dim = v_head_dim
        self.head_dim = qk_rope_head_dim

        # MoE parameters
        self.n_shared_experts = n_shared_experts
        self.n_routed_experts = n_routed_experts
        self.routed_scaling_factor = routed_scaling_factor
        self.n_group = n_group
        self.topk_group = topk_group
        self.num_experts_per_tok = num_experts_per_tok
        self.norm_topk_prob = norm_topk_prob

        # MLP layer types: first 3 dense, rest sparse
        self.mlp_layer_types = mlp_layer_types
        if self.mlp_layer_types is None:
            self.mlp_layer_types = ["dense"] * min(3, num_hidden_layers) + ["sparse"] * (num_hidden_layers - 3)
        layer_type_validation(self.mlp_layer_types, self.num_hidden_layers, attention=False)

        # Indexer (DSA) parameters
        self.index_topk = index_topk
        self.index_head_dim = index_head_dim
        self.index_n_heads = index_n_heads

        # General config
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.rope_parameters = rope_parameters

        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,
        )


__all__ = ["GlmMoeDsaConfig"]
