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

class ChessConfig(PretrainedConfig):
    model_type = "chess_lm"
    def __init__(
        self,
        vocab_size=1200,
        n_positions=256,
        n_embd=128,
        n_layer=4,
        n_head=4,
        n_ctx=256,
        tie_word_embeddings=True,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.n_positions = n_positions
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.n_ctx = n_ctx
        self.tie_word_embeddings = tie_word_embeddings
        super().__init__(**kwargs)

class ChessForCausalLM(PreTrainedModel):
    config_class = ChessConfig

    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.token_embedding = nn.Embedding(config.vocab_size, config.n_embd)
        self.position_embedding = nn.Embedding(config.n_positions, config.n_embd)
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=config.n_embd, nhead=config.n_head, dim_feedforward=config.n_embd * 4,
            batch_first=True, norm_first=True
        )
        self.blocks = nn.TransformerEncoder(encoder_layer, num_layers=config.n_layer)
        self.ln_f = nn.LayerNorm(config.n_embd)
        self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        if config.tie_word_embeddings:
            self.head.weight = self.token_embedding.weight
        self.post_init()

    def get_input_embeddings(self): return self.token_embedding
    def set_input_embeddings(self, value): self.token_embedding = value

    def forward(self, input_ids, labels=None, **kwargs):
        B, T = input_ids.shape
        tok_emb = self.token_embedding(input_ids)
        pos_emb = self.position_embedding(torch.arange(T, device=input_ids.device))
        x = tok_emb + pos_emb
        mask = torch.triu(torch.ones(T, T, device=input_ids.device) * float('-inf'), diagonal=1)
        x = self.blocks(x, mask=mask, is_causal=True)
        x = self.ln_f(x)
        logits = self.head(x)
        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
        return (loss, logits) if loss is not None else logits

def print_parameter_budget(config):
    print(f"Model params: Check")