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"""StableLM model configuration"""

from typing import Optional

from ...configuration_utils import PreTrainedConfig
from ...modeling_rope_utils import RopeParameters, rope_config_validation, standardize_rope_params
from ...utils import logging


logger = logging.get_logger(__name__)


class StableLmConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`~StableLmModel`].
    It is used to instantiate an StableLM 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 StableLM [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) architecture.

    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 50304):
            Vocabulary size of the StableLM model. Defines the number of different tokens that
            can be represented by the `inputs_ids` passed when calling [`StableLmModel`].
        intermediate_size (`int`, *optional*, defaults to 6912):
            Dimension of the MLP representations.
        hidden_size (`int`, *optional*, defaults to 2560):
            Number of hidden layers in the Transformer decoder.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*, defaults to 32):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
            `num_attention_heads`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string).
        max_position_embeddings (`int`, *optional*, defaults to 4096):
            The maximum sequence length that this model might ever be used with.
            Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing
             all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions
            (not used by all models). Only relevant if `config.is_decoder=True`.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionaty should contain
            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
            with longer `max_position_embeddings`.
        use_qkv_bias (`bool`, *optional*, defaults to `False`):
            Whether or not the model should use bias for qkv layers.
        qk_layernorm (`bool`, *optional*, defaults to `False`):
            Whether or not to normalize, per head, the Queries and Keys after projecting the hidden states.
        use_parallel_residual (`bool`, *optional*, defaults to `False`):
            Whether to use a "parallel" formulation in each Transformer layer, which can provide a slight training
            speedup at large scales.
        hidden_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio after applying the MLP to the hidden states.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        partial_rotary_factor (`float`, *optional*, defaults to 0.25):
            Percentage of the query and keys which will have rotary embedding.
        bos_token_id (int, *optional*, defaults to 0):
            The id of the `BOS` token in the vocabulary.
        eos_token_id (int, *optional*, defaults to 0):
            The id of the `EOS` token in the vocabulary.

    Example:

    ```python
    >>> from transformers import StableLmModel, StableLmConfig

    >>> # Initializing a StableLM stablelm-3b style configuration
    >>> configuration = StableLmConfig()
    ```"""

    model_type = "stablelm"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size: Optional[int] = 50304,
        intermediate_size: Optional[int] = 6912,
        hidden_size: Optional[int] = 2560,
        num_hidden_layers: Optional[int] = 32,
        num_attention_heads: Optional[int] = 32,
        num_key_value_heads: Optional[int] = 32,
        hidden_act: Optional[str] = "silu",
        max_position_embeddings: Optional[int] = 4096,
        initializer_range: Optional[float] = 0.02,
        layer_norm_eps: Optional[float] = 1.0e-5,
        use_cache: Optional[bool] = True,
        tie_word_embeddings: Optional[bool] = False,
        rope_parameters: Optional[RopeParameters | dict[RopeParameters]] = None,
        use_qkv_bias: Optional[bool] = False,
        qk_layernorm: Optional[bool] = False,
        use_parallel_residual: Optional[bool] = False,
        hidden_dropout: Optional[float] = 0.0,
        attention_dropout: Optional[float] = 0.0,
        partial_rotary_factor: Optional[float] = 0.25,
        bos_token_id: Optional[int] = 0,
        eos_token_id: Optional[int] = 0,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings

        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act

        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.use_cache = use_cache
        self.use_qkv_bias = use_qkv_bias
        self.qk_layernorm = qk_layernorm
        self.use_parallel_residual = use_parallel_residual
        self.hidden_dropout = hidden_dropout
        self.attention_dropout = attention_dropout
        self.partial_rotary_factor = partial_rotary_factor
        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
        rope_scaling = kwargs.pop("rope_scaling", None)
        self.rope_parameters = rope_scaling or rope_parameters

        # Validate the correctness of rotary position embeddings parameters
        rope_theta = kwargs.get("rope_theta", 10000.0)
        standardize_rope_params(self, rope_theta=rope_theta)
        rope_config_validation(self)

        super().__init__(
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


__all__ = ["StableLmConfig"]
