from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


logger = logging.get_logger(__name__)



class CodeFuseCGESmallConfig(PretrainedConfig):
    model_type = "phi3"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=32064,
        hidden_size=3072,
        intermediate_size=8192,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=None,
        resid_pdrop=0.0,
        embd_pdrop=0.0,
        attention_dropout=0.0,
        hidden_act="silu",
        max_position_embeddings=4096,
        original_max_position_embeddings=4096,
        initializer_range=0.02,
        rms_norm_eps=1e-5,
        use_cache=True,
        tie_word_embeddings=False,
        rope_theta=10000.0,
        rope_scaling=None,
        bos_token_id=1,
        eos_token_id=32000,
        pad_token_id=32000,
        sliding_window=None,
        embedding_method="pma",
        inf_seq_length=1024,
        padding_side="right",
        compress_dim=1024,
        keep_max_layer=32,
        pma_num_heads=32,
        pma_ln=True,
        pma_norm=False,
        pma_norm_mode="post_normal",
        **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.original_max_position_embeddings = original_max_position_embeddings
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self._rope_scaling_adjustment()
        self._rope_scaling_validation()
        self.sliding_window = sliding_window

        self.embedding_method = embedding_method
        self.inf_seq_length = inf_seq_length
        self.padding_side = padding_side
        self.compress_dim = compress_dim
        self.keep_max_layer = keep_max_layer
        self.pma_num_heads = pma_num_heads
        self.pma_ln = pma_ln
        self.pma_norm = pma_norm
        self.pma_norm_mode = pma_norm_mode

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

    def _rope_scaling_adjustment(self):
        """
        Adjust the `type` of the `rope_scaling` configuration for backward compatibility.
        """
        if self.rope_scaling is None:
            return

        rope_scaling_type = self.rope_scaling.get("type", None)

        # For backward compatibility if previous version used "su" or "yarn"
        if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:
            self.rope_scaling["type"] = "longrope"

    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) != 3:
            raise ValueError(
                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
                f"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)
        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)}"
            )