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

from __future__ import annotations

from typing import Optional, Tuple

import torch
import torch.nn as nn
from einops import rearrange, repeat
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache

from fla.modules import FusedRMSNormSwishGate, RMSNorm, ShortConvolution
from fla.modules.rotary import RotaryEmbedding
from fla.ops.retention import (chunk_retention, fused_chunk_retention,
                               fused_recurrent_retention, parallel_retention)


class MultiScaleRetention(nn.Module):
    r"""
    The layer implementaion for [Retentive Network: A Successor to Transformer for Large Language Models](https://arxiv.org/pdf/2307.08621.pdf).  # noqa

    Args:
        mode (str, Optional):
            Which Retention kernel to use.
            Currently available: `chunk`, `fused_recurrent`, `parallel`, and `fused_chunk`.
            Default: `fused_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: 2.0.
        num_heads (int, Optional):
            The number of heads. Default: 8.
        num_kv_heads (int, Optional):
            The number of key/value heads, used for MQA. Default: None.
        feature_map (str, Optional):
            Feature map function applied to queries/keys. Default: None.
        use_short_conv (bool, Optional):
            Whether to use short convolutions. Default: `False`.
        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`.
        share_conv_kernel (bool, Optional):
            Whether to apply convolutions berfore q/k/v mapping, only taking effects when `use_short_conv`. Default: `True`.
        use_output_gate (bool, Optional):
            Whether to use output gate. Default: `True`.
        gate_fn (str, Optional):
            The activation function for the output gate. Default: `swish`.
        elementwise_affine (bool, Optional):
            If `True`, applies elementwise affine to LayerNorm with learnable parameters. Default: `True`.
        norm_eps (float, Optional):
            The epsilon value for the layernorm/rmsnorm layer. Default: 1e-5.
        fuse_norm (bool, Optional):
            Whether to fuse the norm and the output gate for better memory footprint. Default: `True`.
        layer_idx (int, Optional):
            The index of the layer. Default: None.
    """

    def __init__(
        self,
        mode: str = 'fused_chunk',
        hidden_size: int = 1024,
        expand_k: float = 1.0,
        expand_v: float = 2.0,
        num_heads: int = 8,
        num_kv_heads: Optional[int] = None,
        feature_map: Optional[str] = None,
        use_short_conv: bool = False,
        conv_size: int = 4,
        conv_bias: bool = False,
        share_conv_kernel: bool = True,
        use_output_gate: bool = True,
        gate_fn: str = 'swish',
        elementwise_affine: Optional[bool] = True,
        norm_eps: float = 1e-5,
        fuse_norm: bool = True,
        layer_idx: int = None,
        **kwargs
    ) -> MultiScaleRetention:
        super().__init__()

        self.mode = mode
        self.hidden_size = hidden_size
        self.expand_k = expand_k
        self.expand_v = expand_v
        self.num_heads = num_heads
        self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads
        self.num_kv_groups = self.num_heads // self.num_kv_heads
        self.feature_map_fn = ACT2FN[feature_map] if feature_map is not None else None

        self.use_short_conv = use_short_conv
        self.conv_size = conv_size
        self.conv_bias = conv_bias
        self.share_conv_kernel = share_conv_kernel
        self.use_output_gate = use_output_gate

        self.key_dim = int(hidden_size * expand_k)
        self.value_dim = int(hidden_size * expand_v)
        self.key_dim_per_group = self.key_dim // self.num_kv_groups
        self.value_dim_per_group = self.value_dim // self.num_kv_groups
        self.layer_idx = layer_idx

        assert mode in ['chunk', 'fused_chunk', 'parallel', 'fused_recurrent'], f"Not suppoerted 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.head_qk_dim = self.key_dim // num_heads
        self.head_v_dim = self.value_dim // num_heads

        self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
        self.k_proj = nn.Linear(hidden_size, self.key_dim_per_group, bias=False)
        self.v_proj = nn.Linear(hidden_size, self.value_dim_per_group, bias=False)
        if self.use_output_gate:
            self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False)

        if use_short_conv:
            self.conv_size = conv_size
            if share_conv_kernel:
                self.h_conv1d = ShortConvolution(hidden_size, conv_size, activation='silu')
            else:
                self.q_conv1d = ShortConvolution(self.key_dim, conv_size, activation='silu')
                self.k_conv1d = ShortConvolution(self.key_dim_per_group, conv_size, activation='silu')
                self.v_conv1d = ShortConvolution(self.value_dim_per_group, conv_size, activation='silu')

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

        if gate_fn == 'swish' and fuse_norm and use_output_gate:
            self.g_norm_swish_gate = FusedRMSNormSwishGate(self.head_v_dim, elementwise_affine, norm_eps)
            self.fuse_norm_and_gate = True
        else:
            self.fuse_norm_and_gate = False
            self.g_norm = RMSNorm(self.head_v_dim, elementwise_affine, norm_eps)
            self.gate_fn = ACT2FN[gate_fn]

        # TODO: fix this issue
        # https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/ops/triton/rotary.py#L180
        # Ideally, we would want to support arbitrary d_head_qk
        assert self.head_qk_dim <= 256, "head_qk_dim must be less than or equal to 256"
        self.rotary = RotaryEmbedding(dim=self.head_qk_dim)

        self.apply(self._initialize_weights)

    def _initialize_weights(self, module: nn.Module):
        if getattr(module, "_is_hf_initialized", False):
            return
        if isinstance(module, nn.Linear):
            nn.init.xavier_uniform_(module.weight, gain=2 ** -2.5)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        module._is_hf_initialized = True

    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
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
        # launching the triton kernel for just one token will actually be slower
        mode = 'fused_recurrent' if hidden_states.shape[1] == 1 else self.mode

        last_state = past_key_values[self.layer_idx] if use_cache else None
        if self.use_short_conv:
            conv_state = last_state[0] if use_cache else None
            if self.share_conv_kernel:
                # conv state is updated inplace
                hidden_states = self.h_conv1d(hidden_states, attention_mask, conv_state)
                q = self.q_proj(hidden_states)
                k = self.k_proj(hidden_states)
                v = self.v_proj(hidden_states)
            else:
                conv_state_q = last_state[0] if use_cache else None
                conv_state_k = last_state[1] if use_cache else None
                conv_state_v = last_state[2] if use_cache else None
                q = self.q_proj(hidden_states)
                k = self.k_proj(hidden_states)
                v = self.v_proj(hidden_states)
                q = self.q_conv1d(q, attention_mask, conv_state_q)
                k = self.k_conv1d(k, attention_mask, conv_state_k)
                v = self.v_conv1d(v, attention_mask, conv_state_v)
        else:
            q = self.q_proj(hidden_states)
            k = self.k_proj(hidden_states)
            v = self.v_proj(hidden_states)

        # dealing with left-padding
        if attention_mask is not None:
            v = v.mul_(attention_mask.unsqueeze(-1))
        q = rearrange(q, '... (h d) -> ... h d', h=self.num_heads)
        k = rearrange(k, '... (h d) -> ... h d', h=self.num_kv_heads)
        if self.feature_map_fn is not None:
            q, k = map(self.feature_map_fn, (q, k))

        seqlen_offset, max_seqlen = 0, None
        if past_key_values is not None:
            seqlen_offset = past_key_values.get_seq_length(self.layer_idx)
            max_seqlen = q.shape[1] + seqlen_offset
        if attention_mask is not None:
            # to deliminate the offsets of padding tokens
            seqlen_offset = seqlen_offset + attention_mask.sum(-1) - attention_mask.shape[-1]
            max_seqlen = q.shape[1] + max(seqlen_offset)
        q, k = self.rotary(q, k, seqlen_offset, max_seqlen)
        q = q.transpose(1, 2)
        if self.num_kv_groups > 1:
            k = repeat(k, 'b t h d -> b (h g) t d', h=self.num_kv_heads, g=self.num_kv_groups)
            v = repeat(v, 'b t (h d) -> b (h g) t d', h=self.num_kv_heads, g=self.num_kv_groups)
        else:
            k, v = rearrange(k, 'b t h d -> b h t d'), rearrange(v, 'b t (h d) -> b h t d', h=self.num_kv_heads)

        state = last_state[-1] if use_cache else None
        if mode == 'chunk':
            o, recurrent_state = chunk_retention(q, k, v, initial_state=state, output_final_state=use_cache)
        elif mode == 'fused_chunk':
            o, recurrent_state = fused_chunk_retention(q, k, v, initial_state=state, output_final_state=use_cache)
        elif mode == 'parallel':
            o, recurrent_state = parallel_retention(q, k, v, initial_state=state, output_final_state=use_cache)
        elif mode == 'fused_recurrent':
            o, recurrent_state = fused_recurrent_retention(q, k, v, initial_state=state, output_final_state=use_cache)
        else:
            raise NotImplementedError(f"Not supported mode `{mode}`.")

        if past_key_values is not None:
            if self.use_short_conv:
                if self.share_conv_kernel:
                    last_state = (conv_state, recurrent_state)
                else:
                    last_state = (conv_state_q, conv_state_k, conv_state_v, recurrent_state)
            else:
                last_state = (recurrent_state,)
            past_key_values.update(last_state, self.layer_idx, q.shape[2])

        o = rearrange(o, 'b h l d -> b l h d')
        if self.use_output_gate:
            g = self.g_proj(hidden_states)
            if self.fuse_norm_and_gate:
                g = rearrange(g, 'b l (h d) -> b l h d', h=self.num_heads)
                o = self.g_norm_swish_gate(o, g)
                o = rearrange(o, 'b l h d -> b l (h d)')
            else:
                o = rearrange(self.g_norm(o), 'b l h d -> b l (h d)')
                o = o * self.gate_fn(g)
        else:
            o = rearrange(self.g_norm(o), 'b l h d -> b l (h d)')
        o = self.o_proj(o)

        return o, None, past_key_values

    def init_state(self, batch_size: int) -> Tuple[torch.Tensor]:
        param = next(self.parameters())
        state = tuple()
        if self.use_short_conv:
            if self.share_conv_kernel:
                state += (param.new_zeros(batch_size, self.hidden_size, self.conv_size),)
            else:
                state += (param.new_zeros(batch_size, self.key_dim, self.conv_size),
                          param.new_zeros(batch_size, self.key_dim, self.conv_size),
                          param.new_zeros(batch_size, self.value_dim, self.conv_size))
        state += (param.new_zeros(batch_size, self.num_heads, self.head_qk_dim, self.head_v_dim),)
        return state

    def state_size(self, **kwargs) -> int:
        state_size = self.key_dim * self.head_v_dim
        for module in self.children():
            if isinstance(module, ShortConvolution):
                state_size += module.state_size
        return state_size