"""Implementation of the paper:

LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
https://arxiv.org/abs/2304.15010

Port for Lit-GPT
"""
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Type

import torch
import torch.nn as nn
from typing_extensions import Self

import lit_gpt
from lit_gpt.adapter import GPT as BaseModel
from lit_gpt.adapter import Block as BaseBlock
from lit_gpt.adapter import CausalSelfAttention as BaseCausalSelfAttention
from lit_gpt.adapter import Config as BaseConfig
from lit_gpt.model import KVCache
from lit_gpt.utils import map_old_state_dict_weights


@dataclass
class Config(BaseConfig):
    @property
    def mlp_class(self) -> Type:
        return getattr(lit_gpt.adapter_v2, self._mlp_class)


def adapter_filter(key: str, value: Any) -> bool:
    adapter_substrings = (
        # regular adapter v1 parameters
        "adapter_wte",
        "gating_factor",
        # adapter v2: new bias and scale used in Linear
        "adapter_scale",
        "adapter_bias",
        # adapter v2: Norm parameters are now trainable
        "norm_1",
        "norm_2",
        "ln_f",
    )
    return any(s in key for s in adapter_substrings)


class AdapterV2Linear(torch.nn.Module):
    def __init__(self, in_features: int, out_features: int, **kwargs) -> None:
        super().__init__()
        self.linear = torch.nn.Linear(in_features, out_features, **kwargs)
        self.adapter_bias = torch.nn.Parameter(torch.zeros(out_features), requires_grad=False)
        self.adapter_scale = torch.nn.Parameter(torch.ones(out_features), requires_grad=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.adapter_scale * (self.linear(x) + self.adapter_bias)

    def reset_parameters(self) -> None:
        nn.init.zeros_(self.adapter_bias)
        nn.init.ones_(self.adapter_scale)


class GPT(BaseModel):
    def __init__(self, config: Config) -> None:
        # Skip the parent class __init__ altogether and replace it to avoid useless allocations
        nn.Module.__init__(self)
        assert config.padded_vocab_size is not None
        self.config = config

        self.lm_head = AdapterV2Linear(config.n_embd, config.padded_vocab_size, bias=False)
        self.transformer = nn.ModuleDict(
            dict(
                wte=nn.Embedding(config.padded_vocab_size, config.n_embd),
                h=nn.ModuleList(Block(config, i) for i in range(config.n_layer)),
                ln_f=config.norm_class(config.n_embd, eps=config.norm_eps),
            )
        )
        self.max_seq_length = self.config.block_size
        self.mask_cache: Optional[torch.Tensor] = None

    @classmethod
    def from_name(cls, name: str, **kwargs: Any) -> Self:
        return cls(Config.from_name(name, **kwargs))

    def _init_weights(self, module: nn.Module) -> None:
        """Meant to be used with `gpt.apply(gpt._init_weights)`. Unused method left for completeness."""
        super()._init_weights(module)
        if isinstance(module, AdapterV2Linear):
            module.reset_parameters()

    def _load_from_state_dict(self, state_dict: Dict, prefix: str, *args: Any, **kwargs: Any) -> None:
        """For compatibility with base checkpoints."""
        mapping = {"lm_head.weight": "lm_head.linear.weight"}
        state_dict = map_old_state_dict_weights(state_dict, mapping, prefix)
        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)


class Block(BaseBlock):
    """The implementation is identical to `lit_gpt.model.Block` with the exception that
    we replace the attention layer where adaption is implemented."""

    def __init__(self, config: Config, block_idx: int) -> None:
        # Skip the parent class __init__ altogether and replace it to avoid useless allocations
        nn.Module.__init__(self)
        self.norm_1 = config.norm_class(config.n_embd, eps=config.norm_eps)
        self.attn = CausalSelfAttention(config, block_idx)
        if not config.shared_attention_norm:
            self.norm_2 = config.norm_class(config.n_embd, eps=config.norm_eps)
        self.mlp = config.mlp_class(config)

        self.config = config


class CausalSelfAttention(BaseCausalSelfAttention):
    """A modification of `lit_gpt.adapter.CausalSelfAttention` that uses the Adapter V2 Linear class"""

    def __init__(self, config: Config, block_idx: int) -> None:
        # Skip the parent class __init__ altogether and replace it to avoid useless allocations
        nn.Module.__init__(self)
        shape = (config.n_head + 2 * config.n_query_groups) * config.head_size
        # key, query, value projections for all heads, but in a batch
        self.attn = AdapterV2Linear(in_features=config.n_embd, out_features=shape, bias=config.bias)
        # output projection
        self.proj = AdapterV2Linear(config.n_embd, config.n_embd, bias=config.bias)
        # disabled by default
        self.kv_cache: Optional[KVCache] = None

        if block_idx >= config.adapter_start_layer:
            # adapter embedding layer
            self.adapter_wte = nn.Embedding(config.adapter_prompt_length, config.n_embd)
            # gate for adaption
            self.gating_factor = torch.nn.Parameter(torch.zeros(1, 1, config.n_head, 1))
            # kv cache for inference
            self.adapter_kv_cache: Optional[Tuple[torch.Tensor, torch.Tensor]] = None
        self.block_idx = block_idx

        self.config = config

    def _load_from_state_dict(self, state_dict: Dict, prefix: str, *args: Any, **kwargs: Any) -> None:
        """For compatibility with base checkpoints."""
        mapping = {
            "attn.weight": "attn.linear.weight",
            "attn.bias": "attn.linear.bias",
            "proj.weight": "proj.linear.weight",
            "proj.bias": "proj.linear.bias",
        }
        state_dict = map_old_state_dict_weights(state_dict, mapping, prefix)
        # For compatibility with older checkpoints
        if (key := prefix + "gating_factor") in state_dict and state_dict[key].size(1) == self.config.n_head:
            state_dict[key] = state_dict[key].permute(0, 2, 1, 3)
        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)


class GptNeoxMLP(lit_gpt.model.GptNeoxMLP):
    def __init__(self, config: Config) -> None:
        nn.Module.__init__(self)
        self.fc = AdapterV2Linear(config.n_embd, config.intermediate_size, bias=config.bias)
        self.proj = AdapterV2Linear(config.intermediate_size, config.n_embd, bias=config.bias)

    def _load_from_state_dict(self, state_dict: Dict, prefix: str, *args: Any, **kwargs: Any) -> None:
        """For compatibility with base checkpoints."""
        mapping = {
            "fc.weight": "fc.linear.weight",
            "fc.bias": "fc.linear.bias",
            "proj.weight": "proj.linear.weight",
            "proj.bias": "proj.linear.bias",
        }
        state_dict = map_old_state_dict_weights(state_dict, mapping, prefix)
        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)


class LLaMAMLP(lit_gpt.model.LLaMAMLP):
    def __init__(self, config: Config) -> None:
        nn.Module.__init__(self)
        self.fc_1 = AdapterV2Linear(config.n_embd, config.intermediate_size, bias=config.bias)
        self.fc_2 = AdapterV2Linear(config.n_embd, config.intermediate_size, bias=config.bias)
        self.proj = AdapterV2Linear(config.intermediate_size, config.n_embd, bias=config.bias)

    def _load_from_state_dict(self, state_dict: Dict, prefix: str, *args: Any, **kwargs: Any) -> None:
        """For compatibility with base checkpoints."""
        mapping = {
            "fc_1.weight": "fc_1.linear.weight",
            "fc_1.bias": "fc_1.linear.bias",
            "fc_2.weight": "fc_2.linear.weight",
            "fc_2.bias": "fc_2.linear.bias",
            "proj.weight": "proj.linear.weight",
            "proj.bias": "proj.linear.bias",
        }
        state_dict = map_old_state_dict_weights(state_dict, mapping, prefix)
        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)


def mark_only_adapter_v2_as_trainable(model: GPT) -> None:
    """Sets requires_grad=False for all non-adapter weights"""
    for name, param in model.named_parameters():
        print(name)
        param.requires_grad = adapter_filter(name, param)
    assert(0)
