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#           This file was automatically generated from examples/modular-transformers/modular_my_new_model2.py.
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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
from ...utils.type_validators import interval


@auto_docstring(checkpoint="meta-my_new_model2/MyNewModel2-2-7b-hf")
@strict
class MyNewModel2Config(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
    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 Gemma-7B.
    e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)
    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 256000):
            Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GemmaModel`]
    ```python
    >>> from transformers import GemmaModel, GemmaConfig
    >>> # Initializing a Gemma gemma-7b style configuration
    >>> configuration = GemmaConfig()
    >>> # Initializing a model from the gemma-7b style configuration
    >>> model = GemmaModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "my_new_model2"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `MyNewModel2Model`
    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 = interval(min=0.0, max=1.0)(default=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: int | float | None = 0.0
    mlp_bias: bool = False
    head_dim: int | None = None

    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)

    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})."
            )