import math
import json
import re
from copy import deepcopy
from pathlib import Path
from dataclasses import dataclass
from typing import Callable

import torch
import torch.nn.functional as F
from torch import nn
from torch.utils.checkpoint import checkpoint

import xformers.ops as xops

from huggingface_hub import PyTorchModelHubMixin

from open_lm.attention import get_attn_func, xformers_attn, torch_attn
from open_lm.norms import get_norm_class
from open_lm.positional_embedding.head_rotary import HeadRotaryWithCast
from open_lm.positional_embedding.rotary import RotaryWithCast
from open_lm.positional_embedding.llama_rotary import LLaMARotaryWithCast
from open_lm.positional_embedding.none import identity_with_cast

from open_lm.file_utils import pt_load

# from open_lm.moe.mixture_of_experts import MoE
try:
    from megablocks.layers.moe import MoE
    from megablocks.layers.arguments import Arguments as MoEArgs
except ImportError:
    MoE = None
    MoEArgs = None

try:  # optional import
    from mamba_ssm import MambaLMHeadModel
except ImportError:
    MambaLMHeadModel = None

# from openclip
_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
_MODEL_CONFIGS = {}  # directory (model_name: config) of model architecture configs


def _natural_key(string_):
    return [int(s) if s.isdigit() else s for s in re.split(r"(\d+)", string_.lower())]


def _rescan_model_configs(model_config_paths=None):
    global _MODEL_CONFIGS

    config_iter = None
    if model_config_paths is not None:
        config_iter = [
            Path(model_config_paths),
        ]
    else:
        config_iter = _MODEL_CONFIG_PATHS

    config_ext = (".json",)
    config_files = []
    for config_path in config_iter:
        if config_path.is_file() and config_path.suffix in config_ext:
            config_files.append(Path(config_path))
        elif config_path.is_dir():
            for ext in config_ext:
                config_files.extend(config_path.glob(f"*{ext}"))

    for cf in config_files:
        with open(cf, "r") as f:
            model_cfg = json.load(f)
            _MODEL_CONFIGS[cf.stem] = model_cfg

    _MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))}


_rescan_model_configs()  # initial populate of model config registry


# args and default params follow llama (except with LayerNorm instead of RmsNorm)
@dataclass
class Params:
    dim: int = 512
    n_layers: int = 8
    n_heads: int = 8
    vocab_size: int = -1
    norm_eps: float = 1e-5
    seq_len: int = 2048
    post_embed_norm: bool = False
    weight_tying: bool = False
    norm_type: nn.Module = nn.LayerNorm
    attn_func: Callable = xformers_attn if torch.cuda.is_available() else torch_attn
    apply_qk_norm: bool = False
    moe_loss_weight: float = 0.1
    moe_capacity_factor: float = 1.25
    moe_expert_model_parallelism: bool = False
    moe_weight_parallelism: bool = False
    moe_num_experts: int = 8
    moe_top_k: int = 2
    moe_freq: int = 0
    positional_embedding_type: str = "rotary"
    ffn_type: str = "swiglu"


def get_pos_embed(args: Params):
    head_dim = args.dim // args.n_heads
    if args.positional_embedding_type == "rotary":
        return RotaryWithCast(head_dim, args.seq_len)
    elif args.positional_embedding_type == "llama_rotary":
        return LLaMARotaryWithCast(head_dim, args.n_heads, args.seq_len)
    elif args.positional_embedding_type == "head_rotary":
        return HeadRotaryWithCast(head_dim, args.seq_len)
    elif args.positional_embedding_type == "none":
        return identity_with_cast
    else:
        raise RuntimeError(f"Unknown positional embedding type {args.positional_embedding_type}")


class CustomAttn(nn.Module):
    def __init__(self, layer_id, args: Params):
        super().__init__()
        self.n_heads = args.n_heads
        self.head_dim = args.dim // args.n_heads
        self.in_proj = nn.Linear(args.dim, 3 * args.n_heads * self.head_dim, bias=False)
        self.out_proj = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
        self.pos_embed = get_pos_embed(args)
        self.attn_fn = args.attn_func
        self.apply_qk_norm = args.apply_qk_norm

        # initialize norm layers for queries and keys if needed
        self.q_norm = (
            args.norm_type(
                args.n_heads * self.head_dim,
                eps=args.norm_eps,
            )
            if self.apply_qk_norm
            else nn.Identity()
        )
        self.k_norm = (
            args.norm_type(
                args.n_heads * self.head_dim,
                eps=args.norm_eps,
            )
            if self.apply_qk_norm
            else nn.Identity()
        )

        self.layer_id = layer_id
        self.dim = args.dim
        self.reset_parameters()

    def reset_parameters(self):
        # initialize weights by trunc_normal(1/sqrt(fan_in))
        std = 1.0 / math.sqrt(self.dim)
        torch.nn.init.trunc_normal_(self.in_proj.weight, std=std, a=-3 * std, b=3 * std)
        # scale init by depth as in https://arxiv.org/abs/1908.11365 -- worked slightly better.
        std = std / math.sqrt(2 * (self.layer_id + 1))
        torch.nn.init.trunc_normal_(self.out_proj.weight, std=std, a=-3 * std, b=3 * std)

    def forward(self, x: torch.Tensor, is_causal=True, past_key_value=None, use_cache=False, attention_mask=None):
        batchsize, q_len, _ = x.shape
        queries, keys, vals = self.in_proj(x).chunk(3, dim=-1)

        queries = self.q_norm(queries)
        keys = self.k_norm(keys)

        queries = queries.view(batchsize, q_len, self.n_heads, self.head_dim)
        keys = keys.view(batchsize, q_len, self.n_heads, self.head_dim)
        vals = vals.view(batchsize, q_len, self.n_heads, self.head_dim)

        past_length = 0 if past_key_value is None else past_key_value[0].shape[1]
        queries, keys, vals = self.pos_embed(queries, keys, vals, offset=past_length)

        if past_key_value is not None and use_cache:
            keys = torch.cat([past_key_value[0], keys], dim=1)
            vals = torch.cat([past_key_value[1], vals], dim=1)

        if use_cache:
            past_key_value = [keys, vals]

        output = self.attn_fn(
            queries,
            keys,
            vals,
            is_causal=is_causal,
            attention_mask=attention_mask,
        )

        output = output.view(batchsize, q_len, -1)

        return self.out_proj(output), past_key_value


class GemmaMLP(nn.Module):
    """Google's Gemma model MLP (aka GeGLU).

    Modified from https://github.com/google/gemma_pytorch/blob/01062c9ef4cf89ac0c985b25a734164ede017d0b/gemma/model.py#L182-L201
    """

    def __init__(self, dim: int, hidden_dim: int, layer_id: int):
        super().__init__()
        self.dim = dim
        self.hidden_dim = hidden_dim
        self.gate_proj = nn.Linear(dim, hidden_dim)
        self.up_proj = nn.Linear(dim, hidden_dim)
        self.down_proj = nn.Linear(hidden_dim, dim)
        self._layer_id = layer_id

    def forward(self, x):
        gate = self.gate_proj(x)
        gate = F.gelu(gate)
        up = self.up_proj(x)
        fuse = gate * up
        outputs = self.down_proj(fuse)
        return outputs

    def reset_parameters(self):
        std = 1.0 / math.sqrt(self.dim)
        torch.nn.init.trunc_normal_(self.gate_proj.weight, std=std, a=-3 * std, b=3 * std)
        torch.nn.init.trunc_normal_(self.up_proj.weight, std=std, a=-3 * std, b=3 * std)

        std = 1.0 / math.sqrt(self.hidden_dim)
        std = std / math.sqrt(2 * (self._layer_id + 1))
        torch.nn.init.trunc_normal_(self.down_proj.weight, std=std, a=-3 * std, b=3 * std)


# Same as pseudocode provided from xformers SwiGLU
# https://github.com/facebookresearch/xformers
class SwiGLUTorch(nn.Module):
    def __init__(self, in_dim, hidden_dim, out_dim, bias=True):
        super().__init__()
        self.w12 = nn.Linear(in_dim, 2 * hidden_dim, bias=bias)
        self.w3 = nn.Linear(hidden_dim, out_dim, bias=bias)

    def forward(self, x):
        gate, x = self.w12(x).chunk(2, dim=-1)
        x = F.silu(gate) * x
        return self.w3(x)


class Block(nn.Module):
    def __init__(self, layer_id, args: Params):
        super().__init__()
        self.n_heads = args.n_heads
        self.dim = args.dim

        self.head_dim = args.dim // args.n_heads
        self.attention = CustomAttn(layer_id, args)
        self._ffn_type = args.ffn_type
        if args.ffn_type == "swiglu":
            # this follows llama / lit llama -- go to multiple of 256
            self.hidden_dim = 256 * ((int(2 * 4 * args.dim / 3) + 256 - 1) // 256)
            self.feed_forward = xops.SwiGLU(args.dim, self.hidden_dim, args.dim, bias=False)
        elif args.ffn_type == "swiglu_torch":
            # this follows llama / lit llama -- go to multiple of 256
            self.hidden_dim = 256 * ((int(2 * 4 * args.dim / 3) + 256 - 1) // 256)
            self.feed_forward = SwiGLUTorch(args.dim, self.hidden_dim, args.dim, bias=False)
        elif args.ffn_type == "gelu":
            # Follows mosaic mpt7b, but without a bias.
            self.hidden_dim = args.dim * 4
            self._ff_w1 = nn.Linear(args.dim, self.hidden_dim, bias=False)
            self._ff_w2 = nn.Linear(self.hidden_dim, args.dim, bias=False)
            self.feed_forward = nn.Sequential(self._ff_w1, nn.GELU(approximate="none"), self._ff_w2)
        elif args.ffn_type == "gemma_geglu":
            # this follows llama / lit llama -- go to multiple of 256
            self.hidden_dim = 256 * ((int(2 * 4 * args.dim / 3) + 256 - 1) // 256)
            self.feed_forward = GemmaMLP(args.dim, self.hidden_dim, layer_id)
        elif args.ffn_type == "moe":
            moe_args = MoEArgs(
                hidden_size=args.dim,
                ffn_hidden_size=args.dim * 4,
                moe_num_experts=args.moe_num_experts,
                moe_weight_parallelism=args.moe_weight_parallelism,
                moe_expert_model_parallelism=args.moe_expert_model_parallelism,
                moe_top_k=args.moe_top_k,
                moe_capacity_factor=args.moe_capacity_factor,
                moe_loss_weight=args.moe_loss_weight,
                device=torch.cuda.current_device(),
                bf16=False,
                fp16=False,
            )
            self.feed_forward = MoE(moe_args)

        self.layer_id = layer_id
        self.attention_norm = args.norm_type(
            args.dim,
            eps=args.norm_eps,
        )
        self.ffn_norm = args.norm_type(
            args.dim,
            eps=args.norm_eps,
        )
        self.attention.seq_len = args.seq_len
        self.reset_parameters()

    def reset_parameters(self):
        if self._ffn_type == "swiglu" or self._ffn_type == "swiglu_torch":
            # initialize weights trunc_normal(1/sqrt(fan_in))
            std = 1.0 / math.sqrt(self.dim)
            torch.nn.init.trunc_normal_(self.feed_forward.w12.weight, std=std, a=-3 * std, b=3 * std)
            # scale init by depth as in https://arxiv.org/abs/1908.11365 -- worked slightly better.
            std = 1.0 / math.sqrt(self.hidden_dim)
            std = std / math.sqrt(2 * (self.layer_id + 1))
            torch.nn.init.trunc_normal_(self.feed_forward.w3.weight, std=std, a=-3 * std, b=3 * std)
        elif self._ffn_type == "gelu":
            std = 1.0 / math.sqrt(self.dim)
            torch.nn.init.trunc_normal_(self._ff_w1.weight, std=std, a=-3 * std, b=3 * std)

            std = 1.0 / math.sqrt(self.hidden_dim)
            std = std / math.sqrt(2 * (self.layer_id + 1))
            torch.nn.init.trunc_normal_(self._ff_w2.weight, std=std, a=-3 * std, b=3 * std)

    def forward(self, x, past_key_value=None, use_cache=False, attention_mask=None):
        h, past_key_value = self.attention(
            self.attention_norm(x),
            is_causal=True,
            past_key_value=past_key_value,
            use_cache=use_cache,
            attention_mask=attention_mask,
        )
        h = x + h
        if self._ffn_type == "moe":
            ffn_out, _ = self.feed_forward(self.ffn_norm(h))
        else:
            ffn_out = self.feed_forward(self.ffn_norm(h))
        out = h + ffn_out
        return out, past_key_value


class Transformer(nn.Module, PyTorchModelHubMixin):
    def __init__(self, params):
        super().__init__()
        # for convenience we often share param names with llama
        self.params = params
        self.dim = params.dim
        self.vocab_size = params.vocab_size
        self.n_layers = params.n_layers
        self.moe_num_experts = params.moe_num_experts
        self.seq_len = params.seq_len
        self.post_embed_norm = (
            params.norm_type(
                params.dim,
                eps=params.norm_eps,
            )
            if params.post_embed_norm
            else nn.Identity()
        )
        self.weight_tying = params.weight_tying

        self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)

        self.layers = torch.nn.ModuleList()
        ffn_type_ = params.ffn_type
        for layer_id in range(params.n_layers):
            if params.moe_freq > 0 and layer_id % params.moe_freq == 0:
                params.ffn_type = "moe"
            else:
                params.ffn_type = ffn_type_
            self.layers.append(Block(layer_id, params))

        # get class for normalization layers
        self.norm = params.norm_type(
            params.dim,
            eps=params.norm_eps,
        )
        self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
        if self.weight_tying:
            self.tok_embeddings.weight = self.output.weight
        self.grad_checkpointing = False
        self.reset_parameters()

    def reset_parameters(self):
        # initialize weight 1/sqrt(dim)
        # this is 1/fan_in for output, as is default, and Maciej Kilian tried another option
        # for the embed layer (from RWKV paper) but this was better.
        std = 1.0 / math.sqrt(self.params.dim)
        torch.nn.init.trunc_normal_(self.output.weight, std=std, a=-3 * std, b=3 * std)
        torch.nn.init.trunc_normal_(self.tok_embeddings.weight, std=std, a=-3 * std, b=3 * std)

    @torch.jit.ignore
    def set_grad_checkpointing(self, enable=True):
        self.grad_checkpointing = enable

    def forward(self, input_ids=None, inputs_embeds=None, past_key_values=None, use_cache=False, attention_mask=None):
        """
        Args:
            input
            past_key_values
            use_cache (bool)
            attention_mask (torch.Tensor): Shape (batch_size, sequence_len), indicates tokens that should not be
                attended to. attention_mask[s, i] = False indicates that token i should not be attended to by any other
                token for sequence s.
        """
        if input_ids is not None:
            x = self.tok_embeddings(input_ids)
        elif inputs_embeds is not None:
            x = inputs_embeds
        else:
            raise ValueError("Either input_ids or inputs_embeds must be provided.")

        x = self.post_embed_norm(x)

        if past_key_values is None:
            past_key_values = [None] * self.n_layers
        elif isinstance(past_key_values, tuple):
            past_key_values = list(past_key_values)
        for i, layer in enumerate(self.layers):
            if self.grad_checkpointing:
                x, past_key_values[i] = checkpoint(layer, x, past_key_values[i], use_cache, attention_mask)
            else:
                x, past_key_values[i] = layer(x, past_key_values[i], use_cache=use_cache, attention_mask=attention_mask)
        if past_key_values[0] is None:
            past_key_values = None
        x = self.norm(x)
        output = self.output(x)
        # follow llama in casting this to float.
        return output.float(), x, past_key_values

    def get_input_embeddings(self):
        return self.tok_embeddings

    def get_output_embeddings(self):
        return self.output


def create_params(args):
    cfg = None

    if args.model.endswith(".json"):
        _rescan_model_configs(model_config_paths=args.model)
        args.model = Path(args.model).stem

    if args.model in _MODEL_CONFIGS:
        cfg = deepcopy(_MODEL_CONFIGS[args.model])
    else:
        raise ValueError("Pass a pre-defined open_lm model name or a json config")

    # Note: here all the parameters should come from the config file
    # but for retro-compatibility, we add new model parameters to the args (with a default value that matches the old version)
    # These args are managed separately by the argparser
    # If a parameter is in the model config, regardless of the args, we use the config parameters
    # If a parameter is not in the model config, we use the args parameter

    if "mamba" in args.model:
        return {
            "d_model": cfg["d_model"],
            "n_layer": cfg["n_layer"],
            "vocab_size": cfg["vocab_size"],
            "seq_len": cfg["seq_len"],
        }
    else:
        return Params(
            dim=cfg["hidden_dim"],
            n_layers=cfg["n_layers"],
            n_heads=cfg["n_heads"],
            seq_len=cfg["seq_len"],
            vocab_size=cfg["vocab_size"],
            post_embed_norm=cfg["post_embed_norm"],
            weight_tying=cfg["weight_tying"],
            norm_type=get_norm_class(cfg.get("model_norm", args.model_norm)),
            attn_func=get_attn_func(
                args.attn_name, args.attn_activation, args.attn_seq_scalar, args.attn_seq_scalar_alpha
            ),
            apply_qk_norm=cfg.get("qk_norm", args.qk_norm),
            positional_embedding_type=cfg.get("positional_embedding_type", args.positional_embedding_type),
            ffn_type=cfg.get("ffn_type", args.ffn_type),
            moe_num_experts=cfg.get("moe_num_experts", args.moe_num_experts),
            moe_loss_weight=cfg.get("moe_loss_weight", args.moe_loss_weight),
            moe_expert_model_parallelism=cfg.get("moe_expert_model_parallelism", args.moe_expert_model_parallelism),
            moe_weight_parallelism=cfg.get("moe_weight_parallelism", args.moe_weight_parallelism),
            moe_capacity_factor=cfg.get("moe_capacity_factor", args.moe_capacity_factor),
            moe_freq=cfg.get("moe_freq", args.moe_freq),
            moe_top_k=cfg.get("moe_top_k", args.moe_top_k),
        )


class Mamba(nn.Module):
    # Experimental architecture, please "pip install mamba-ssm"
    # https://arxiv.org/abs/2312.00752
    def __init__(self, params):
        if MambaLMHeadModel is None:
            raise ImportError(
                "MambaLMHeadModel is not available. Please install the 'mamba_ssm' package by running 'pip install mamba-ssm'."
            )

        super().__init__()
        self.seq_len = params.pop("seq_len")
        self.vocab_size = params["vocab_size"]

        self.model = MambaLMHeadModel(**params)

    def reset_parameters(self):
        return

    def forward(self, x):
        out = self.model(x).logits
        return out, None, None


def create_model(args):
    if "mamba" in args.model:
        model = Mamba(create_params(args))
        return model
    else:
        model = Transformer(create_params(args))
        return model
        
        
def create_classif_model(args):
    model = Transformer(create_params(args))
    
    if args.classif_model_path is not None:
        checkpoint = pt_load(args.classif_model_path, map_location="cpu")
        model.load_state_dict(checkpoint["state_dict"])
    
    dim = model.output.in_features
    model.output = nn.Linear(dim, args.num_classes, bias = False)
        
    return model
    
    
def test_classif_model(args):
    model = Transformer(create_params(args))
    
    dim = model.output.in_features
    model.output = nn.Linear(dim, args.num_classes, bias = False)
    
    checkpoint = pt_load(args.classif_model_path, map_location="cpu")
    model.load_state_dict(checkpoint["state_dict"])
    
    return model    


def test_perplexity_model(args):
    model = Transformer(create_params(args))
    
    checkpoint = pt_load(args.classif_model_path, map_location="cpu")
    model.load_state_dict(checkpoint["state_dict"])
    
    return model  


