from transformers.modeling_utils import PretrainedConfig

class RasphiConfig(PretrainedConfig):
    model_type = "rasphi"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=32064,
        hidden_size=4096,
        intermediate_size=6400,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=8,
        hidden_act="silu",
        max_position_embeddings=4096 * 32,
        initializer_range=0.02,
        rms_norm_eps=1e-5,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=1,
        eos_token_id=2,
        tie_word_embeddings=False,
        rope_theta=1e6,
        rope_scaling=None,
        sliding_window=None,
        attention_dropout=0.0,
        num_experts_per_tok=2,
        num_local_experts=16,
        output_router_logits=False,
        router_aux_loss_coef=0.001,
        router_jitter_noise=0.01,
        input_jitter_noise=0.0,
        attention_bias=False,
        lm_head_bias=False,
        # Rasphi specific configurations
        num_reasoning_experts=8,  # Number of experts dedicated to reasoning stream
        num_content_experts=8,    # Number of experts dedicated to content stream
        reasoning_hidden_size=2048,  # Hidden size for reasoning stream
        content_hidden_size=2048,    # Hidden size for content stream
        stream_interaction="attention",  # How the two streams interact: "attention", "mlp", or "both"
        **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.sliding_window = sliding_window
        self.attention_bias = attention_bias
        self.lm_head_bias = lm_head_bias
        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.rope_theta = rope_theta
        self.attention_dropout = attention_dropout
        self.num_experts_per_tok = num_experts_per_tok
        self.num_local_experts = num_local_experts
        self.output_router_logits = output_router_logits
        self.router_aux_loss_coef = router_aux_loss_coef
        self.router_jitter_noise = router_jitter_noise
        self.input_jitter_noise = input_jitter_noise
        self.rope_scaling = rope_scaling
        self._rope_scaling_validation()

        # Rasphi specific configurations
        self.num_reasoning_experts = num_reasoning_experts
        self.num_content_experts = num_content_experts
        self.reasoning_hidden_size = reasoning_hidden_size
        self.content_hidden_size = content_hidden_size
        self.stream_interaction = stream_interaction

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

    def _rope_scaling_validation(self):
        """
        Validate the `rope_scaling` configuration.
        """
        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 6:
            raise ValueError(
                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor`, `long_factor`, "
                f"`short_mscale`, `long_mscale` and `original_max_position_embeddings`, got {self.rope_scaling}"
            )
        rope_scaling_type = self.rope_scaling.get("type", None)
        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
        rope_scaling_short_mscale = self.rope_scaling.get("short_mscale", None)
        rope_scaling_long_mscale = self.rope_scaling.get("long_mscale", None)
        original_max_position_embeddings = self.rope_scaling.get("original_max_position_embeddings", None)
        if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
            raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
        if not (
            isinstance(rope_scaling_short_factor, list)
            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
        ):
            raise ValueError(
                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
            )
        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
            raise ValueError(
                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
            )
        if not (
            isinstance(rope_scaling_long_factor, list)
            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
        ):
            raise ValueError(
                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
            )
        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
            raise ValueError(
                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
            )
        if not isinstance(rope_scaling_short_mscale, (int, float)):
            raise ValueError(
                f"`rope_scaling`'s short_mscale field must be a number, got {rope_scaling_short_mscale}"
            )
        if not isinstance(rope_scaling_long_mscale, (int, float)):
            raise ValueError(
                f"`rope_scaling`'s long_mscale field must be a number, got {rope_scaling_long_mscale}"
            )
        if not isinstance(original_max_position_embeddings, int):
            raise ValueError(
                f"`rope_scaling`'s original_max_position_embeddings field must be an integer, got {original_max_position_embeddings}"
            )