# -*- coding: utf-8 -*-

import warnings
from typing import Dict, Optional, Literal

from transformers.configuration_utils import PretrainedConfig


class DeltaNetConfig(PretrainedConfig):

    model_type = 'delta_net'
    keys_to_ignore_at_inference = ['past_key_values']

    def __init__(
        self,
        attn_mode: str = "chunk",
        hidden_size: int = 2048,
        expand_k: float = 1.0,
        expand_v: float = 1.0,
        use_gate: bool = False,
        use_short_conv: bool = True,
        conv_size: int = 4,
        use_beta: bool = True,
        use_output_norm: bool = True,
        num_heads: int = 16,
        qk_norm: str = 'l2',
        qk_activation: str = 'silu',
        max_position_embeddings: int = 2048,
        hidden_ratio: Optional[int] = 4,
        intermediate_size: Optional[int] = None,
        hidden_act: str = "swish",
        num_hidden_layers: int = 24,
        norm_eps: float = 1e-6,
        attn: Optional[Dict] = None,
        use_cache: bool = True,
        pad_token_id: Optional[int] = None,
        bos_token_id: int = 1,
        eos_token_id: int = 2,
        tie_word_embeddings: bool = False,
        initializer_range: float = 0.02,
        fuse_norm: bool = True,
        fuse_swiglu: bool = True,
        fuse_cross_entropy: bool = True,
        fuse_linear_cross_entropy: bool = False,
        use_l2warp: bool = False,
        vocab_size: int = 32000,
        position_embedding_type: Literal["nope", "rope", "selective_rope"] | None = None,
        rope_skip_conv: bool = False,
        temp_type: str = "rope",
        temp_theta: float = 500000,
        temp_max: float = 1.0,
        temp_grad: bool = False,
        use_low_rank_phi_proj: bool = False,
        phi_proj_rank: int = 32,
        phi_conv_activation: str | None = None,
        **kwargs
    ):
        self.attn_mode = attn_mode
        self.hidden_size = hidden_size
        self.expand_k = expand_k
        self.expand_v = expand_v
        self.use_gate = use_gate
        self.use_short_conv = use_short_conv
        self.conv_size = conv_size
        self.use_beta = use_beta
        self.use_output_norm = use_output_norm
        self.num_heads = num_heads
        self.qk_norm = qk_norm
        self.qk_activation = qk_activation
        self.max_position_embeddings = max_position_embeddings

        self.hidden_ratio = hidden_ratio
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.num_hidden_layers = num_hidden_layers
        self.norm_eps = norm_eps
        self.attn = attn
        self.use_cache = use_cache
        self.initializer_range = initializer_range
        self.fuse_norm = fuse_norm
        self.fuse_swiglu = fuse_swiglu
        self.fuse_cross_entropy = fuse_cross_entropy
        self.fuse_linear_cross_entropy = fuse_linear_cross_entropy
        self.use_l2warp = use_l2warp
        self.vocab_size = vocab_size

        self.position_embedding_type = position_embedding_type
        self.rope_skip_conv = rope_skip_conv
        self.temp_type = temp_type
        self.temp_theta = temp_theta
        self.temp_max = temp_max
        self.temp_grad = temp_grad
        self.use_low_rank_phi_proj = use_low_rank_phi_proj
        self.phi_proj_rank = phi_proj_rank
        self.phi_conv_activation = phi_conv_activation

        if fuse_cross_entropy and fuse_linear_cross_entropy:
            raise ValueError(
                "`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time."
            )
        if fuse_linear_cross_entropy:
            warnings.warn(
                "`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency "
                "at the potential cost of reduced precision. "
                "If you observe issues like loss divergence, consider disabling this setting."
            )

        if attn is not None:
            if not isinstance(attn, Dict):
                raise ValueError("attn must be a dictionary")
            if 'layers' not in attn:
                raise ValueError("Layer indices must be provided to initialize hybrid attention layers")
            if 'num_heads' not in attn:
                raise ValueError("Number of heads must be provided to initialize hybrid attention layers")
            attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads'])
            attn['qkv_bias'] = attn.get('qkv_bias', False)
            attn['window_size'] = attn.get('window_size', None)
            attn['rope_theta'] = attn.get('rope_theta', 10000.)

        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,
        )
