import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig

class ChessConfig(PretrainedConfig):
    model_type = "chess_transformer"
    def __init__(self, vocab_size=1000, n_embd=128, n_layer=4, n_head=4, n_inner=512, n_ctx=256, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.n_embd, self.n_layer, self.n_head, self.n_inner, self.n_ctx = n_embd, n_layer, n_head, n_inner, n_ctx

class Block(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.ln1=nn.LayerNorm(config.n_embd)
        self.attn=nn.MultiheadAttention(config.n_embd,config.n_head,batch_first=True)
        self.ln2=nn.LayerNorm(config.n_embd)
        self.mlp=nn.Sequential(nn.Linear(config.n_embd,config.n_inner),nn.GELU(),nn.Linear(config.n_inner, config.n_embd))
    def forward(self, x, mask=None):
        attn_out,_=self.attn(self.ln1(x),self.ln1(x),self.ln1(x),attn_mask=mask,need_weights=False)
        return x+attn_out+self.mlp(self.ln2(x+attn_out))

class ChessForCausalLM(PreTrainedModel):
    config_class = ChessConfig
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.token_emb = nn.Embedding(config.vocab_size, config.n_embd)
        self.pos_emb = nn.Embedding(config.n_ctx, config.n_embd)
        self.blocks = nn.ModuleList([Block(config) for _ in range(config.n_layer)])
        self.ln_f = nn.LayerNorm(config.n_embd)
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.apply(self._init_weights)
    def _init_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Embedding)): torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
    def forward(self, input_ids, attention_mask=None, labels=None, **kwargs):
        B, T =input_ids.shape
        x = self.token_emb(input_ids)+self.pos_emb(torch.arange(T, device=input_ids.device))
        mask = torch.triu(torch.ones(T, T, device=input_ids.device) * float('-inf'), diagonal=1)
        for block in self.blocks: x = block(x, mask=mask)
        logits = self.lm_head(self.ln_f(x))
        loss = None
        if labels is not None:
            loss = nn.CrossEntropyLoss(ignore_index=-100)(logits[..., :-1, :].contiguous().view(-1, self.config.vocab_size), labels[..., 1:].contiguous().view(-1))
        return {"loss": loss, "logits": logits}