import torch
import torch.nn as nn
import tqdm
from torch.nn import functional as F
from  core.layers import Block

class GPTLanguageModel(nn.Module):

    def __init__(self, vocab_size, n_embd, block_size, n_head, n_layer, dropout, device, name = "GPT"):
        super().__init__()
        self.name = name
        self.block_size = block_size
        self.device = device
        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
        self.position_embedding_table = nn.Embedding(block_size, n_embd)
        self.blocks = nn.Sequential(*[Block(n_embd, n_head, block_size, dropout) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd)
        self.lm_head = nn.Linear(n_embd, vocab_size)
        self.apply(self._init_weights)
        self.history = {}
        self.vocab_size = vocab_size

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            nn.init.normal_(module.weight, mean=0.0, std=0.02)

    def forward(self, idx, targets=None):
        B, T = idx.shape

        assert torch.all(idx < self.vocab_size), f"Input indices must be less than vocab_size ({self.vocab_size})"
        assert T <= self.block_size, f"Input sequence length ({T}) must be <= block_size ({self.block_size})"

        tok_emb = self.token_embedding_table(idx)
        pos_emb = self.position_embedding_table(torch.arange(T, device=idx.device))
        x = tok_emb + pos_emb
        x = self.blocks(x)
        x = self.ln_f(x)
        logits = self.lm_head(x)

        if targets is None:
            loss = None
        else:
            B, T, C = logits.shape
            logits = logits.view(B * T, C)
            targets = targets.view(B * T)
            loss = F.cross_entropy(logits, targets)

        return logits, loss

    def generate(self, idx, max_new_tokens, max_seq_length=200, temperature=1.0):
        for _ in range(max_new_tokens):
            if idx.size(1) > max_seq_length:
                idx = idx[:, -max_seq_length:]
            idx_cond = idx[:, -self.block_size:]
            logits, _ = self(idx_cond)
            logits = logits[:, -1, :] / temperature
            probs = F.softmax(logits, dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
            idx = torch.cat((idx, idx_next), dim=1)
            yield idx