from typing import Optional, Union
from transformers import PreTrainedTokenizer, PreTrainedModel, PretrainedConfig, GenerationMixin
from transformers.modeling_outputs import BaseModelOutput, CausalLMOutput
from tokenizers.models import BPE
from tokenizers import Tokenizer
import torch.nn as nn
import torch
import os.path

class ZZJRabbitConfig(PretrainedConfig):
    model_type = "zzjrabbit"

    def __init__(self, num_layers: int = 6, vocab_size: int = 10000, hidden_size: int = 256, **kwargs):
        self.num_layers = num_layers
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        super().__init__(**kwargs)

class ZZJRabbitLayer(nn.Module):
    def __init__(self, config: ZZJRabbitConfig):
        super().__init__()
        self.attn = nn.MultiheadAttention(config.hidden_size, 8, 0.1, batch_first=True)
        self.l1 = nn.Linear(config.hidden_size, config.hidden_size)
        self.l2 = nn.Linear(config.hidden_size, config.hidden_size)
        self.activate = nn.ReLU()
        self.norm = nn.RMSNorm(config.hidden_size)

    def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        key_padding_mask = None
        attn_mask = None
        if self.training:
            attn_mask = torch.gt(torch.triu(torch.ones(q.size(-2), q.size(-2), device=q.device), 1), 0)
        if attention_mask is not None:
            key_padding_mask = torch.lt(attention_mask, 1)
        attn = self.attn(
            q, k, v,
            key_padding_mask=key_padding_mask,
            attn_mask=attn_mask,
            is_causal=True
        )[0]
        q = self.norm(q + attn)
        o = self.l1(q)
        o = self.activate(o)
        o = self.l2(o)
        return self.norm(q + o)

class ZZJRabbitModel(PreTrainedModel):
    config_class = ZZJRabbitConfig

    def __init__(self, config: ZZJRabbitConfig, **kwargs):
        super().__init__(config, **kwargs)
        self.config = config
        self.emb = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layers = nn.ModuleList([ZZJRabbitLayer(config) for _ in range(config.num_layers)])

    def forward(self, input_ids: torch.Tensor, return_dict: Optional[bool] = None, attention_mask: Optional[torch.Tensor] = None, **kwargs):
        emb = self.emb(input_ids)
        res = emb
        for l in self.layers:
            res = l(res, res, res, attention_mask)
        if not return_dict:
            return (res,)
        else:
            return BaseModelOutput(res)


class ZZJRabbitModelForCausalLM(PreTrainedModel, GenerationMixin):
    config_class = ZZJRabbitConfig
    def __init__(self, config, **kwargs):
        super().__init__(config, **kwargs)
        self.model = ZZJRabbitModel(config, **kwargs)
        self.l = nn.Linear(config.hidden_size, config.vocab_size)

    def forward(self, input_ids: torch.Tensor, return_dict: Optional[bool] = None, labels: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs):
        # print(input_ids, return_dict, labels, attention_mask, logits_to_keep, kwargs)
        hidden = self.model(input_ids=input_ids, attention_mask=attention_mask)[0]
        logits = self.l(hidden[:, slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep, :])
        if labels is not None:
            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
            print(loss)
        if not return_dict:
            return (loss, logits) if labels is not None else (logits,)
        else:
            return CausalLMOutput(logits=logits, loss=loss) if labels is not None else CausalLMOutput(logits=logits)

    @classmethod
    def can_generate(cls):
        return True

    def prepare_inputs_for_generation(self, input_ids, **kwargs):
        return {"input_ids": input_ids}

class ZZJRabbitTokenizer(PreTrainedTokenizer):
    vocab_files_names = {"tokenizers_file": "tokenizer.json"}

    def __init__(self, tokenizers_file, **kwargs):
        self.internal = Tokenizer.from_file(tokenizers_file)
        super().__init__(**kwargs)

    def get_vocab(self):
        return {self.internal.id_to_token(i): i for i in range(self.vocab_size)}

    def tokenize(self, text, **kwargs):
        return self.internal.encode(text).tokens

    def convert_tokens_to_ids(self, tokens):
        return self.internal.token_to_id(tokens) if isinstance(tokens, str) else [self.internal.token_to_id(t) for t in tokens]

    def decode(self, tokens, skip_special_tokens=True, **kwargs):
        if isinstance(tokens, torch.Tensor):
            tokens = tokens.tolist()
        return self.internal.decode(tokens, skip_special_tokens=skip_special_tokens)

    @property
    def vocab_size(self):
        return self.internal.get_vocab_size()

    def save_vocabulary(self, path, *args, **kwargs) -> tuple[str]:
        p = os.path.join(path, "tokenizer.json")
        self.internal.save(p)
        return (p,)