from dataclasses import dataclass
import torch
from torch.nn import functional as F
import inspect
from transformers import GPT2LMHeadModel

import torch.nn as nn

#------------------------------------------

class CausalSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        # Projection of key, query, value for all heads in a batch
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
        # Output projection
        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
        self.c_proj.NANOGPT_SCALE_INIT = True

        # Regularization
        self.n_head = config.n_head
        self.n_embd = config.n_embd

        # Mask for attention
        self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
                                    .view(1, 1, config.block_size, config.block_size))


    def forward(self, x):
        B, T, C = x.size() # batch_size, sequence_length, embedding dimensionality (n_embed)
        # Calculate query, key, values for all heads in batch and move head forward to be the batch dim
        qkv = self.c_attn(x)
        q, k, v = qkv.split(self.n_embd, dim=2)
        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)

        # Attention (materializes the large (T,T) matrix for all the queries and keys)
        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)

        y = y.transpose(1, 2).contiguous().view(B, T, C) # Re-assemble all head outputs side by side

        # Output projection
        y = self.c_proj(y)
        return y

class MLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
        self.gelu = nn.GELU(approximate='tanh') # Approximation for historic reasons
        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
        self.c_proj.NANOGPT_SCALE_INIT = True

    def forward(self, x):
        x = self.c_fc(x)
        x = self.gelu(x)
        x = self.c_proj(x)
        return x

class Block(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.ln_1 = nn.LayerNorm(config.n_embd)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = nn.LayerNorm(config.n_embd)
        self.mlp = MLP(config)

    def forward(self, x):
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x

@dataclass
class GPTConfig:
    block_size: int = 1024  # Max sequence length
    vocab_size: int = 50257 # Number of tokens
    n_layer: int = 12 # Number of layers
    n_head: int = 12 # Number of heads
    n_embd: int = 768 # Embedding dimension

class GPT(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.transformer = nn.ModuleDict(dict(
            wte = nn.Embedding(config.vocab_size, config.n_embd),
            wpe = nn.Embedding(config.block_size, config.n_embd),
            h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
            ln_f = nn.LayerNorm(config.n_embd),
        ))
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

        # Weight sharing scheme
        self.transformer.wte.weight = self.lm_head.weight

        # Initialization
        self.apply(self._init_weights)

    def _init_weights(self, module):

        if isinstance(module, nn.Linear):
            std = 0.02
            if hasattr(module, 'NANOGPT_SCALE_INIT'):
                std = (2 * self.config.n_layer) ** -0.5 # 2 times as each layer has attention and MLP
            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) # Word embedding will be initialized twice

    def forward(self, idx, target = None):
        # idx of shape (B, T)
        B, T = idx.size()
        assert T <= self.config.block_size, f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}"
        # Forward the token and position embedding
        pos = torch.arange(0, T, dtype = torch.long, device=idx.device) # (T)
        pos_emb = self.transformer.wpe(pos) # (T, C)
        tok_emb = self.transformer.wte(idx) # (B, T, C)
        x = tok_emb + pos_emb # (B, T, C)
        # Forward the block for transformer
        for block in self.transformer.h:
            x = block(x)
        # Forward the final layer norm and classifier
        x = self.transformer.ln_f(x)
        logits = self.lm_head(x) # (B, T, vocab_size)
        # Compute the loss
        loss = None
        if target is not None:
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), target.view(-1))
        return logits, loss

    @classmethod
    def from_pretrained(cls, model_type):
        """ Loads pretrained GPT2 model from HuggingFace """
        assert model_type in {"gpt2", "gpt2-medium", "gpt2-large", "gpt2-xl"}
        print(f"Loading {model_type} model...")

        config_args = {
            "gpt2": dict(n_layer = 12, n_head = 12, n_embd = 768), # 124M
            "gpt2-medium": dict(n_layer = 24, n_head = 16, n_embd = 1024), # 350M
            "gpt2-large": dict(n_layer = 36, n_head = 20, n_embd = 1280), # 774M
            "gpt2-xl": dict(n_layer = 48, n_head = 25, n_embd = 1600), # 1558M
        }[model_type]

        config_args["vocab_size"] = 50257 # Always for GPT2 checkpoints
        config_args["block_size"] = 1024 # Always for GPT2 checkpoints

        config = GPTConfig(**config_args)
        model = GPT(config)
        sd = model.state_dict()
        sd_keys = sd.keys()

        sd_keys = [k for k in sd_keys if not k.endswith(".attn.bias")] # Discard this mask

        # Initialize Hugging Face model
        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
        sd_hf = model_hf.state_dict()
        sd_keys_hf = sd_hf.keys()

        # Copy while ensuring all of the parameters are aligned and match in names and types
        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith(".attn.masked_bias")] # Ignore these, just a buffer
        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith(".attn.bias")] # Same, just the mask (buffer)
        transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']

        # Basically the OpenAI checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
        # This means that we have to transpose these weights when we import them
        assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
        for k in sd_keys_hf:
            if any(k.endswith(w) for w in transposed):
                # Special treatment for the Conv1D weights we need to transpose
                assert sd_hf[k].shape[::-1] == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k].t())
            else:
                # Vanilla copy over the other parameters
                assert sd_hf[k].shape == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k])

        return model

    def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
        # Start with all of the candidate parameters
        param_dict = {pn: p for pn, p in self.named_parameters()}
        # Filter out those that do not require grad
        param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
        # Create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
        # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
        decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
        optim_groups = [
            {'params': decay_params, 'weight_decay': weight_decay},
            {'params': nodecay_params, 'weight_decay': 0.0}
        ]
        num_decay_params = sum(p.numel() for p in decay_params)
        num_nodecay_params = sum(p.numel() for p in nodecay_params)
        print(f"Number of decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
        print(f"Number of non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
        # Create AdamW optimizer and use the fused version if it is available
        fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
        use_fused = fused_available and device_type == 'cuda'
        extra_args = dict(fused=True) if use_fused else dict()
        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)
        print(f"Using fused AdamW: {use_fused}")

        return optimizer

    @torch.no_grad()
    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
        """
        Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete
        the sequence max_new_tokens times, feeding the predictions back into the model each time.
        Most likely you'll want to make sure to be in model.eval() mode of operation for this.
        """
        for _ in range(max_new_tokens):
            # If the sequence context is growing too long, crop it at block_size
            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
            # Forward the model to get the logits for the index in the sequence
            logits, _ = self(idx_cond)
            # Pluck the logits at the final step and scale by desired temperature
            logits = logits[:, -1, :] / temperature
            # Optionally crop the logits to only the top k options
            if top_k is not None:
                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits[logits < v[:, [-1]]] = -float('Inf')
            # Apply softmax to convert logits to (normalized) probabilities
            probs = F.softmax(logits, dim=-1)
            # Sample from the distribution
            idx_next = torch.multinomial(probs, num_samples=1)
            # Append sampled index to the running sequence and continue
            idx = torch.cat((idx, idx_next), dim=1)

        return idx