from transformers import PretrainedConfig


class PhiPrunedConfig(PretrainedConfig):
    model_type = "phi"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
            self,
            vocab_size=51200,
            hidden_size=2048,
            intermediate_size=8192,
            num_hidden_layers=24,
            num_attention_heads=32,
            num_key_value_heads=None,
            resid_pdrop=0.0,
            embd_pdrop=0.0,
            attention_dropout=0.0,
            hidden_act="gelu_new",
            max_position_embeddings=2048,
            initializer_range=0.02,
            layer_norm_eps=1e-5,
            use_cache=True,
            tie_word_embeddings=False,
            rope_theta=10000.0,
            rope_scaling=None,
            partial_rotary_factor=0.5,
            qk_layernorm=False,
            bos_token_id=1,
            eos_token_id=2,
            begin_pruned_layer=None,
            end_pruned_layer=None,
            **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads

        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.resid_pdrop = resid_pdrop
        self.embd_pdrop = embd_pdrop
        self.attention_dropout = attention_dropout
        self.hidden_act = hidden_act
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.partial_rotary_factor = partial_rotary_factor
        self.qk_layernorm = qk_layernorm
        self._rope_scaling_validation()

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

    def _rope_scaling_validation(self):
        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
            raise ValueError(
                "`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
            )
        rope_scaling_type = self.rope_scaling.get("type", None)
        rope_scaling_factor = self.rope_scaling.get("factor", None)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
            raise ValueError(
                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
            )
        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")