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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
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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="alibaba-nlp-community/gte-multilingual-base")
@strict
class GteConfig(PreTrainedConfig):
    r"""
    Examples:

    ```python
    >>> from transformers import GteConfig, GteModel

    >>> # Initializing a GTE alibaba-nlp-community/gte-multilingual-base style configuration
    >>> configuration = GteConfig()

    >>> # Initializing a model (with random weights) from the alibaba-nlp-community/gte-multilingual-base style configuration
    >>> model = GteModel(configuration)

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

    model_type = "gte"

    vocab_size: int = 250048
    hidden_size: int = 768
    num_hidden_layers: int = 12
    num_attention_heads: int = 12
    intermediate_size: int = 3072
    hidden_act: str = "gelu"
    hidden_dropout_prob: float | int = 0.1
    attention_probs_dropout_prob: float | int = 0.0
    max_position_embeddings: int = 8192
    type_vocab_size: int = 1
    initializer_range: float = 0.02
    layer_norm_eps: float = 1e-12
    pad_token_id: int | None = 1
    classifier_dropout: float | int | None = None
    bos_token_id: int | None = None
    eos_token_id: int | list[int] | None = None
    tie_word_embeddings: bool = True
    default_theta = 160_000.0
    rope_parameters: RopeParameters | dict | None = None


__all__ = ["GteConfig"]