#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
#           This file was automatically generated from src/transformers/models/deepseek_v2/modular_deepseek_v2.py.
#               Do NOT edit this file manually as any edits will be overwritten by the generation of
#             the file from the modular. If any change should be done, please apply the change to the
#                          modular_deepseek_v2.py file directly. One of our CI enforces this.
#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# Copyright 2025 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="deepseek-ai/DeepSeek-V2-Lite")
@strict
class DeepseekV2Config(PreTrainedConfig):
    r"""
    first_k_dense_replace (`int`, *optional*, defaults to 0):
        Number of dense layers in the shallow layers before switching to MoE layers.
    n_group (`int`, *optional*):
        Number of groups for routed experts.
    topk_method (`str`, *optional*, defaults to `"greedy"`):
        The method used for selecting top-k experts in the routed gate mechanism.

    Example:

    ```python
    >>> from transformers import DeepseekV2Model, DeepseekV2Config
    >>> # Initializing a DeepSeek-V2 style configuration
    >>> configuration = DeepseekV2Config()
    >>> # Accessing the model configuration
    >>> model = DeepseekV2Model(configuration)
    >>> print(model.config)
    ```
    """

    model_type = "deepseek_v2"
    keys_to_ignore_at_inference = ["past_key_values"]

    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "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"]),
    }

    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
    pretraining_tp: int | None = 1
    tie_word_embeddings: bool = False
    rope_parameters: RopeParameters | dict | None = None
    attention_bias: bool = False
    attention_dropout: float | None = 0.0
    mlp_bias: bool = False
    head_dim: int | None = None
    base_model_ep_plan = {
        "layers.*.mlp.gate": "ep_router",
        "layers.*.mlp.experts.gate_up_proj": "grouped_gemm",
        "layers.*.mlp.experts.down_proj": "grouped_gemm",
        "layers.*.mlp.experts": "moe_tp_experts",
    }
    attribute_map = {
        "num_experts": "n_routed_experts",
    }
    first_k_dense_replace: int = 0
    kv_lora_rank: int = 512
    q_lora_rank: int | None = 1536
    n_group: int | None = None
    n_routed_experts: int = 64
    n_shared_experts: int = 2
    qk_nope_head_dim: int = 128
    qk_rope_head_dim: int = 64
    routed_scaling_factor: float = 1.0
    topk_group: int | None = None
    topk_method: str | None = "greedy"
    norm_topk_prob: bool | None = False
    v_head_dim: int = 128
    num_experts_per_tok: int | None = None
    moe_intermediate_size: int = 1407

    def __post_init__(self, **kwargs):
        self.head_dim = self.qk_rope_head_dim
        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)

    def validate_architecture(self):
        """Part of `@strict`-powered validation. Validates the architecture of the config."""
        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__ = ["DeepseekV2Config"]
