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#           This file was automatically generated from src/transformers/models/eurobert/modular_eurobert.py.
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#                          modular_eurobert.py file directly. One of our CI enforces this.
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# Copyright 2025 Nicolas Boizard, Duarte M. Alves, Hippolyte Gisserot-Boukhlef and the EuroBert 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, strict
from ...modeling_rope_utils import RopeParameters
from ...utils import auto_docstring


@auto_docstring(checkpoint="EuroBERT/EuroBERT-210m")
@strict
class EuroBertConfig(PreTrainedConfig):
    r"""
    mask_token_id (`int`, *optional*, defaults to 128002):
        Mask token id.
    classifier_pooling (`str`, *optional*, defaults to `"late"`):
        The pooling strategy to use for the classifier. Can be one of ['bos', 'mean', 'late'].

    ```python
    >>> from transformers import EuroBertModel, EuroBertConfig

    >>> # Initializing a EuroBert eurobert-base style configuration
    >>> configuration = EuroBertConfig()

    >>> # Initializing a model from the eurobert-base style configuration
    >>> model = EuroBertModel(configuration)

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

    model_type = "eurobert"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `EuroBertModel`
    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 = 128256
    hidden_size: int = 768
    intermediate_size: int = 3072
    num_hidden_layers: int = 12
    num_attention_heads: int = 12
    num_key_value_heads: int | None = None
    hidden_act: str = "silu"
    max_position_embeddings: int = 8192
    initializer_range: float = 0.02
    rms_norm_eps: float = 1e-05
    use_cache: bool = True
    pad_token_id: int | None = 128001
    bos_token_id: int | None = 128000
    eos_token_id: int | list[int] | None = 128001
    pretraining_tp: int = 1
    tie_word_embeddings: bool = False
    rope_parameters: RopeParameters | dict | None = None
    attention_bias: bool = False
    attention_dropout: int | float = 0.0
    mlp_bias: bool = False
    head_dim: int | None = None
    mask_token_id: int = 128002
    classifier_pooling: str = "late"

    def __post_init__(self, **kwargs):
        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads
        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__ = ["EuroBertConfig"]
