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"""PyTorch Arcee model."""

from typing import Optional

from transformers.utils import auto_docstring, logging

from ...modeling_rope_utils import RopeParameters
from ..llama.configuration_llama import LlamaConfig
from ..llama.modeling_llama import (
    LlamaForCausalLM,
    LlamaForQuestionAnswering,
    LlamaForSequenceClassification,
    LlamaForTokenClassification,
)
from ..nemotron.modeling_nemotron import NemotronMLP


logger = logging.get_logger(__name__)


class ArceeConfig(LlamaConfig):
    r"""
    This is the configuration class to store the configuration of a [`ArceeModel`]. It is used to instantiate an Arcee
    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 AFM-4.5B-Base.

    Pre-trained weights are available at
    [arcee-ai/AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B)
    and were used to build the examples below.

    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 32000):
            Vocabulary size of the Arcee model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`ArceeModel`]
        hidden_size (`int`, *optional*, defaults to 2560):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 18432):
            Dimension of the MLP representations.
        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 decoder.
        num_key_value_heads (`int`, *optional*):
            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 checkout [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 `"relu2"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 4096):
            The maximum sequence length that this model might ever be used with. AFM-4.5B-Base supports up to 16384 tokens.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms 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`.
        pad_token_id (`int`, *optional*):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 128000):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 128001):
            End of stream token id.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        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`.
        attention_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        mlp_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
        head_dim (`int`, *optional*):
            The attention head dimension. If None, it will default to hidden_size // num_attention_heads

    ```python
    >>> from transformers import ArceeModel, ArceeConfig

    >>> # Initializing an Arcee AFM-4.5B-Base style configuration
    >>> configuration = ArceeConfig()

    >>> # Initializing a model from the AFM-4.5B-Base style configuration
    >>> model = ArceeModel(configuration)

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

    model_type = "arcee"
    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.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }

    def __init__(
        self,
        vocab_size: Optional[int] = 32000,
        hidden_size: Optional[int] = 2560,
        intermediate_size: Optional[int] = 18432,
        num_hidden_layers: Optional[int] = 32,
        num_attention_heads: Optional[int] = 32,
        num_key_value_heads: Optional[int] = None,
        hidden_act: Optional[str] = "relu2",
        max_position_embeddings: Optional[int] = 4096,
        initializer_range: Optional[float] = 0.02,
        rms_norm_eps: Optional[int] = 1e-5,
        use_cache: Optional[bool] = True,
        pad_token_id: Optional[int] = None,
        bos_token_id: Optional[int] = 128000,
        eos_token_id: Optional[int] = 128001,
        tie_word_embeddings: Optional[bool] = False,
        rope_parameters: Optional[RopeParameters | dict[RopeParameters]] = None,
        attention_bias: Optional[bool] = False,
        attention_dropout: Optional[float] = 0.0,
        mlp_bias: Optional[bool] = False,
        head_dim: Optional[int] = None,
        **kwargs,
    ):
        super().__init__(
            vocab_size=vocab_size,
            hidden_size=hidden_size,
            intermediate_size=intermediate_size,
            num_hidden_layers=num_hidden_layers,
            num_attention_heads=num_attention_heads,
            num_key_value_heads=num_key_value_heads,
            hidden_act=hidden_act,
            max_position_embeddings=max_position_embeddings,
            initializer_range=initializer_range,
            rms_norm_eps=rms_norm_eps,
            use_cache=use_cache,
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            rope_parameters=rope_parameters,
            attention_bias=attention_bias,
            attention_dropout=attention_dropout,
            mlp_bias=mlp_bias,
            head_dim=head_dim,
            **kwargs,
        )

        del self.pretraining_tp


class ArceeMLP(NemotronMLP):
    pass


@auto_docstring(checkpoint="arcee-ai/AFM-4.5B")
class ArceeForCausalLM(LlamaForCausalLM):
    pass


@auto_docstring(checkpoint="arcee-ai/AFM-4.5B")
class ArceeForSequenceClassification(LlamaForSequenceClassification):
    pass


@auto_docstring(checkpoint="arcee-ai/AFM-4.5B")
class ArceeForQuestionAnswering(LlamaForQuestionAnswering):
    pass


@auto_docstring(checkpoint="arcee-ai/AFM-4.5B")
class ArceeForTokenClassification(LlamaForTokenClassification):
    pass


__all__ = [
    "ArceeConfig",
    "ArceeForCausalLM",
    "ArceeForQuestionAnswering",
    "ArceeForSequenceClassification",
    "ArceeForTokenClassification",
    "ArceeModel",  # noqa: F822
    "ArceePreTrainedModel",  # noqa: F822
]
