# -*- coding: utf-8 -*-
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang

from __future__ import annotations

import warnings
from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple

import torch
import torch.nn as nn
from einops import rearrange
from torch.nn import functional as F

from fla.layers.utils import get_unpad_data, index_first_axis, pad_input
from fla.modules import (
    FusedRMSNormGated,
    RMSNorm,
    ShortConvolution,
    SelectiveRoPE,
    RotaryEmbedding,
)
from fla.ops.delta_rule import chunk_delta_rule, fused_recurrent_delta_rule

if TYPE_CHECKING:
    from transformers.processing_utils import Unpack

    from fla.models.utils import Cache


def elu_p1(x):
    return (F.elu(x, 1.0, False) + 1.0).to(x)


def sum_norm(x):
    return (x / x.sum(-1, keepdim=True)).to(x)


class DeltaNet(nn.Module):
    r"""
    The layer implementaion for [Parallelizing Linear Transformers with the Delta Rule over Sequence Length](https://arxiv.org/abs/2406.06484).  # noqa:
    DeltaNet was originally proposed in [Linear Transformers Are Secretly Fast Weight Programmers](https://arxiv.org/abs/2102.11174). # noqa

    Args:
        mode (str, Optional):
            Which DeltaNet kernel to use.
            Currently available: `chunk`, `fused_recurrent`, and `fused_chunk`.
            Default: `chunk`.
        hidden_size (int, Optional):
            The hidden size of the input. Default: 1024.
        expand_k (float, Optional):
            The expansion ratio for the key dim. Default: 1.0.
        expand_v (float, Optional):
            The expansion ratio for the value dim. Default: 1.0.
        num_heads (int, Optional):
            The number of heads. Default: 4.
        use_beta (bool, Optional):
            Whether to use beta. Default: `True`.
        use_gate (bool, Optional):
            Whether to use output gate. Default: `False`.
        use_short_conv (bool, Optional):
            Whether to use short convolutions. Default: `True`.
        conv_size (int, Optional):
            The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4.
        conv_bias (bool, Optional):
            Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`.
        allow_neg_eigval (bool, Optional):
            Allow negative eigenvalues. Default: `False`. If set to `True`, the beta will be multiplied by 2.
            See reference: [Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues](https://arxiv.org/abs/2411.12537)
        layer_idx (int, Optional):
            The index of the layer. Default: None.
        norm_eps (float, Optional):
            The epsilon value for the layernorm/rmsnorm layer. Default: 1e-5.
        qk_activation (str, Optional):
            The activation function for the query and key. Default: `silu`.
        qk_norm (str, Optional):
            The normalization method for the query and key. Default: `l2`.
    """

    def __init__(
        self,
        mode: str = "chunk",
        d_model: int = None,
        hidden_size: int = 1024,
        expand_k: float = 1.0,
        expand_v: float = 1.0,
        num_heads: int = 4,
        use_beta: bool = True,
        use_gate: bool = False,
        use_short_conv: bool = True,
        conv_size: int = 4,
        conv_bias: bool = False,
        allow_neg_eigval: bool = False,
        layer_idx: int = None,
        qk_activation: str = "silu",
        qk_norm: str = "l2",
        norm_eps: float = 1e-5,
        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,
        max_position_embeddings: int = 2048,
        phi_proj_rank: int = 32,
        phi_conv_activation: str | None = None,
        use_low_rank_phi_proj: bool = False,
        **kwargs,
    ) -> DeltaNet:
        super().__init__()

        self.mode = mode
        self.qk_activation = qk_activation
        self.qk_norm = qk_norm

        assert self.qk_activation in ["silu", "relu", "elu", "identity"]
        assert self.qk_norm in ["l2", "sum"]

        if d_model is not None:
            hidden_size = d_model
        self.hidden_size = hidden_size
        self.expand_k = expand_k
        self.expand_v = expand_v
        self.num_heads = num_heads
        self.use_gate = use_gate
        self.use_short_conv = use_short_conv
        self.conv_size = conv_size
        self.conv_bias = conv_bias
        self.allow_neg_eigval = allow_neg_eigval
        self.position_embedding_type = position_embedding_type
        self.key_dim = int(hidden_size * expand_k)
        self.value_dim = int(hidden_size * expand_v)
        self.head_k_dim = self.key_dim // num_heads
        self.head_v_dim = self.value_dim // num_heads
        self.layer_idx = layer_idx

        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.phi_proj_rank = phi_proj_rank
        self.phi_conv_activation = phi_conv_activation
        self.use_low_rank_phi_proj = use_low_rank_phi_proj

        self.max_position_embeddings = max_position_embeddings

        if mode == "fused_chunk":
            raise NotImplementedError(
                "fused_chunk_delta_rule is now deprecated. Please use `chunk_delta_rule` instead."
            )
        assert mode in ["chunk", "fused_recurrent"], f"Not supported mode `{mode}`."
        assert self.key_dim % num_heads == 0, (
            f"key dim must be divisible by num_heads of {num_heads}"
        )
        assert self.value_dim % num_heads == 0, (
            f"value dim must be divisible by num_heads of {num_heads}"
        )

        self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
        self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
        self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False)

        self.use_beta = use_beta
        if self.use_beta:
            self.b_proj = nn.Linear(hidden_size, self.num_heads, bias=False)
        if use_short_conv:
            self.conv_size = conv_size
            self.q_conv1d = ShortConvolution(
                hidden_size=self.key_dim,
                kernel_size=conv_size,
                bias=conv_bias,
                activation="silu" if qk_activation == "silu" else None,
            )
            self.k_conv1d = ShortConvolution(
                hidden_size=self.key_dim,
                kernel_size=conv_size,
                bias=conv_bias,
                activation="silu" if qk_activation == "silu" else None,
            )
            self.v_conv1d = ShortConvolution(
                hidden_size=self.value_dim,
                kernel_size=conv_size,
                bias=conv_bias,
                activation="silu",
            )
        else:
            warnings.warn(
                "ShortConvolution is crucial to the performance. "
                "Do not turn it off, i.e., setting `use_short_conv=False` unless you know what you are doing."
            )
        if use_gate:
            self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
            self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps)
        else:
            self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps)

        self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False)

        match self.position_embedding_type:
            case "selective_rope":
                self.rotary = SelectiveRoPE(
                    num_heads=self.num_heads,
                    head_dim=self.head_k_dim,
                    skip_conv=self.rope_skip_conv,
                    temp_type=self.temp_type,
                    temp_theta=self.temp_theta,
                    temp_max=self.temp_max,
                    temp_grad=self.temp_grad,
                    use_low_rank_phi_proj=self.use_low_rank_phi_proj,
                    phi_proj_rank=self.phi_proj_rank,
                    phi_conv_activation=self.phi_conv_activation,
                    is_softmax=True,
                )
            case "rope":
                self.rotary = RotaryEmbedding(dim=self.head_k_dim, base=temp_theta)
            case "nope" | None:
                self.rotary = None
            case _:
                raise ValueError(
                    f"Position embedding scheme {self.position_embedding_type} not supported."
                )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Cache] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
        **kwargs: Unpack[Dict],
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
        if attention_mask is not None:
            assert len(attention_mask.shape) == 2, (
                "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] "
                "for padding purposes (0 indicating padding). "
                "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed."
            )

        batch_size, q_len, _ = hidden_states.shape
        # change to inference mode.
        mode = "fused_recurrent" if q_len <= 64 else self.mode

        last_state = None
        if past_key_values is not None and len(past_key_values) > self.layer_idx:
            last_state = past_key_values[self.layer_idx]

        cu_seqlens = kwargs.get("cu_seqlens", None)
        if attention_mask is not None:
            indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:])
            hidden_states = index_first_axis(
                rearrange(hidden_states, "b s ... -> (b s) ..."), indices
            ).unsqueeze(0)

        if self.use_short_conv:
            conv_state_q, conv_state_k, conv_state_v = None, None, None
            if last_state is not None:
                conv_state_q, conv_state_k, conv_state_v = last_state["conv_state"]
            q, conv_state_q = self.q_conv1d(
                x=self.q_proj(hidden_states),
                cache=conv_state_q,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens,
            )
            k, conv_state_k = self.k_conv1d(
                x=self.k_proj(hidden_states),
                cache=conv_state_k,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens,
            )
            v, conv_state_v = self.v_conv1d(
                x=self.v_proj(hidden_states),
                cache=conv_state_v,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens,
            )
        else:
            q = self.q_proj(hidden_states)
            k = self.k_proj(hidden_states)
            if self.qk_activation == "silu":
                q, k = F.silu(q), F.silu(k)
            v = F.silu(self.v_proj(hidden_states))

        q, k = map(
            lambda x: rearrange(x, "... (h d) -> ... h d", d=self.head_k_dim), (q, k)
        )
        v = rearrange(v, "... (h d) -> ... h d", d=self.head_v_dim)
        if self.qk_activation != "silu":
            if self.qk_activation == "relu":
                q, k = q.relu(), k.relu()
            elif self.qk_activation == "elu":
                q, k = elu_p1(q), elu_p1(k)
            elif self.qk_activation != "identity":
                raise NotImplementedError

        if self.qk_norm == "sum":
            q = sum_norm(q).to(q)
            k = sum_norm(k).to(k)

        if self.use_beta:
            beta = self.b_proj(hidden_states).sigmoid()
        else:
            beta = torch.ones_like(q[..., 0])

        if self.allow_neg_eigval:
            beta = beta * 2.0

        if self.rotary is not None:
            if self.position_embedding_type == "selective_rope":
                conv_rotary_state = None
                if last_state is not None and not self.rope_skip_conv:
                    conv_rotary_state = last_state.get("rotary_state", None)
                q, k, conv_rotary_state = self.rotary(
                    q=q,
                    k=k,
                    inputs=hidden_states,
                    output_final_state=use_cache,
                    cache=conv_rotary_state,
                    cu_seqlens=cu_seqlens,
                )
            else:
                q, k = self.rotary(
                    q, k, cu_seqlens=cu_seqlens, max_seqlen=self.max_position_embeddings
                )

        recurrent_state = (
            last_state["recurrent_state"] if last_state is not None else None
        )
        if mode == "fused_recurrent":
            o, recurrent_state = fused_recurrent_delta_rule(
                q=q,
                k=k,
                v=v,
                beta=beta,
                initial_state=recurrent_state,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens,
                use_qk_l2norm_in_kernel=True if self.qk_norm == "l2" else False,
            )
        elif mode == "chunk":
            o, recurrent_state = chunk_delta_rule(
                q=q,
                k=k,
                v=v,
                beta=beta,
                initial_state=recurrent_state,
                output_final_state=use_cache,
                cu_seqlens=cu_seqlens,
                use_qk_l2norm_in_kernel=True if self.qk_norm == "l2" else False,
            )
        else:
            raise NotImplementedError(f"Not supported mode `{mode}`.")

        if past_key_values is not None:
            past_key_values.update(
                recurrent_state=recurrent_state,
                conv_state=(conv_state_q, conv_state_k, conv_state_v)
                if self.use_short_conv
                else None,
                rotary_state=conv_rotary_state
                if self.position_embedding_type == "selective_rope"
                and not self.rope_skip_conv
                else None,
                layer_idx=self.layer_idx,
                offset=q_len,
            )

        if self.use_gate:
            g = rearrange(
                self.g_proj(hidden_states), "... (h d) -> ... h d", d=self.head_v_dim
            )
            o = self.o_norm(o, g)
        else:
            o = self.o_norm(o)
        o = rearrange(o, "b t h d -> b t (h d)")
        o = self.o_proj(o)
        if attention_mask is not None:
            o = pad_input(o.squeeze(0), indices, batch_size, q_len)

        return o, None, past_key_values
