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"""Qwen2MoE model configuration"""

from typing import Optional

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


logger = logging.get_logger(__name__)


class Qwen2MoeConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Qwen2MoeModel`]. It is used to instantiate a
    Qwen2MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of [Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B).

    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 151936):
            Vocabulary size of the Qwen2MoE model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Qwen2MoeModel`]
        hidden_size (`int`, *optional*, defaults to 2048):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 5632):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 24):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*, defaults to 16):
            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 `32`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 32768):
            The maximum sequence length that this model might ever be used with.
        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-06):
            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`.
        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_sliding_window (`bool`, *optional*, defaults to `False`):
            Whether to use sliding window attention.
        sliding_window (`int`, *optional*, defaults to 4096):
            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
        max_window_layers (`int`, *optional*, defaults to 28):
            The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
            additional layer afterwards will use SWA (Sliding Window Attention).
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        decoder_sparse_step (`int`, *optional*, defaults to 1):
            The frequency of the MoE layer.
        moe_intermediate_size (`int`, *optional*, defaults to 1408):
            Intermediate size of the routed expert.
        shared_expert_intermediate_size (`int`, *optional*, defaults to 5632):
            Intermediate size of the shared expert.
        num_experts_per_tok (`int`, *optional*, defaults to 4):
            Number of selected experts.
        num_experts (`int`, *optional*, defaults to 60):
            Number of routed experts.
        norm_topk_prob (`bool`, *optional*, defaults to `False`):
            Whether to normalize the topk probabilities.
        output_router_logits (`bool`, *optional*, defaults to `False`):
            Whether or not the router logits should be returned by the model. Enabling this will also
            allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
            The aux loss factor for the total loss.
        mlp_only_layers (`list[int]`, *optional*, defaults to `[]`):
            Indicate which layers use Qwen2MoeMLP rather than Qwen2MoeSparseMoeBlock
            The list contains layer index, from 0 to num_layers-1 if we have num_layers layers
            If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether to add a bias to the queries, keys and values.
        layer_types (`dict[int, str]`, *optional*): a dictionarry that explicitly maps layer index with
            the attention type. The attention type is one of `sliding_attention`, `full_attention`.
    ```python
    >>> from transformers import Qwen2MoeModel, Qwen2MoeConfig

    >>> # Initializing a Qwen2MoE style configuration
    >>> configuration = Qwen2MoeConfig()

    >>> # Initializing a model from the Qwen1.5-MoE-A2.7B" style configuration
    >>> model = Qwen2MoeModel(configuration)

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

    model_type = "qwen2_moe"
    keys_to_ignore_at_inference = ["past_key_values"]

    # Default tensor parallel plan for base model `Qwen2Moe`
    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"]),
    }

    def __init__(
        self,
        vocab_size: Optional[int] = 151936,
        hidden_size: Optional[int] = 2048,
        intermediate_size: Optional[int] = 5632,
        num_hidden_layers: Optional[int] = 24,
        num_attention_heads: Optional[int] = 16,
        num_key_value_heads: Optional[int] = 16,
        hidden_act: Optional[str] = "silu",
        max_position_embeddings: Optional[int] = 32768,
        initializer_range: Optional[float] = 0.02,
        rms_norm_eps: Optional[int] = 1e-6,
        use_cache: Optional[bool] = True,
        tie_word_embeddings: Optional[bool] = False,
        rope_parameters: Optional[RopeParameters | dict[RopeParameters]] = None,
        use_sliding_window: Optional[bool] = False,
        sliding_window: Optional[int] = 4096,
        max_window_layers: Optional[int] = 28,
        attention_dropout: Optional[float] = 0.0,
        decoder_sparse_step: Optional[int] = 1,
        moe_intermediate_size: Optional[int] = 1408,
        shared_expert_intermediate_size: Optional[int] = 5632,
        num_experts_per_tok: Optional[int] = 4,
        num_experts: Optional[int] = 60,
        norm_topk_prob: Optional[bool] = False,
        output_router_logits: Optional[bool] = False,
        router_aux_loss_coef: Optional[float] = 0.001,
        mlp_only_layers: Optional[bool] = None,
        qkv_bias: Optional[bool] = True,
        layer_types: Optional[list[str]] = None,
        **kwargs,
    ):
        self.layer_types = layer_types
        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.use_sliding_window = use_sliding_window
        self.sliding_window = sliding_window if use_sliding_window else 0
        self.max_window_layers = max_window_layers

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.attention_dropout = attention_dropout
        # 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

        # MoE arguments
        self.decoder_sparse_step = decoder_sparse_step
        self.moe_intermediate_size = moe_intermediate_size
        self.shared_expert_intermediate_size = shared_expert_intermediate_size
        self.num_experts_per_tok = num_experts_per_tok
        self.num_experts = num_experts
        self.norm_topk_prob = norm_topk_prob
        self.output_router_logits = output_router_logits
        self.router_aux_loss_coef = router_aux_loss_coef
        self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers
        self.qkv_bias = qkv_bias
        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention"
                if bool((i + 1) % 2) and i < self.max_window_layers and use_sliding_window
                else "full_attention"
                for i in range(self.num_hidden_layers)
            ]
        layer_type_validation(self.layer_types)

        # 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__(
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


__all__ = ["Qwen2MoeConfig"]
