# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch

# This file collects all the relevant code that we covered thus far
# throughout Chapters 3-4, adapted to use Multi-Head Latent Attention (MLA).
# This file can be run as a standalone script.

import argparse
import time
import tiktoken
import torch
import torch.nn as nn


#####################################
# Multi-Head Latent Attention
#####################################
# The MLA code below is inspired by
# https://huggingface.co/bird-of-paradise/deepseek-mla


class MultiHeadLatentAttention(nn.Module):
    def __init__(self, d_in, d_out, dropout, num_heads,
                 qkv_bias=False, latent_dim=None):
        super().__init__()
        assert d_out % num_heads == 0, "d_out must be divisible by num_heads"

        self.d_out = d_out
        self.num_heads = num_heads
        self.head_dim = d_out // num_heads
        self.latent_dim = latent_dim if latent_dim is not None else max(16, d_out // 8)

        # Projections
        self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)              # per-head Q
        self.W_DKV = nn.Linear(d_in, self.latent_dim, bias=qkv_bias)    # down to latent C
        self.W_UK = nn.Linear(self.latent_dim, d_out, bias=qkv_bias)   # latent -> per-head K
        self.W_UV = nn.Linear(self.latent_dim, d_out, bias=qkv_bias)   # latent -> per-head V

        self.out_proj = nn.Linear(d_out, d_out)
        self.dropout = nn.Dropout(dropout)

        ####################################################
        # Latent-KV cache
        self.register_buffer("cache_c_kv", None, persistent=False)
        self.ptr_current_pos = 0
        ####################################################

    def reset_cache(self):
        self.cache_c_kv = None
        self.ptr_current_pos = 0

    @staticmethod
    def _reshape_to_heads(x, num_heads, head_dim):
        # (b, T, d_out) -> (b, num_heads, T, head_dim)
        bsz, num_tokens, _ = x.shape
        return x.view(bsz, num_tokens, num_heads, head_dim).transpose(1, 2).contiguous()

    def forward(self, x, use_cache=False):
        b, num_tokens, _ = x.shape
        num_heads = self.num_heads
        head_dim = self.head_dim

        # 1) Project to queries (per-token, per-head) and new latent chunk
        queries_all = self.W_query(x)  # (b, T, d_out)
        latent_new = self.W_DKV(x)  # (b, T, latent_dim)

        # 2) Update latent cache and choose latent sequence to up-project
        if use_cache:
            if self.cache_c_kv is None:
                latent_total = latent_new
            else:
                latent_total = torch.cat([self.cache_c_kv, latent_new], dim=1)
            self.cache_c_kv = latent_total
        else:
            latent_total = latent_new

        # 3) Up-project latent to per-head keys/values (then split into heads)
        keys_all = self.W_UK(latent_total)   # (b, T_k_total, d_out)
        values_all = self.W_UV(latent_total)   # (b, T_k_total, d_out)

        # 4) Reshape to heads
        queries = self._reshape_to_heads(queries_all, num_heads, head_dim)
        keys = self._reshape_to_heads(keys_all, num_heads, head_dim)
        values = self._reshape_to_heads(values_all, num_heads, head_dim)

        # 5) Scaled dot-product attention with causal mask
        attn_scores = torch.matmul(queries, keys.transpose(-2, -1))

        num_tokens_Q = queries.shape[-2]
        num_tokens_K = keys.shape[-2]
        device = queries.device
        if use_cache:
            q_positions = torch.arange(
                self.ptr_current_pos,
                self.ptr_current_pos + num_tokens_Q,
                device=device,
                dtype=torch.long,
            )
            self.ptr_current_pos += num_tokens_Q
        else:
            q_positions = torch.arange(num_tokens_Q, device=device, dtype=torch.long)
            self.ptr_current_pos = 0
        k_positions = torch.arange(num_tokens_K, device=device, dtype=torch.long)
        mask_bool = q_positions.unsqueeze(-1) < k_positions.unsqueeze(0)

        # Use the mask to fill attention scores
        attn_scores.masked_fill_(mask_bool, -torch.inf)

        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
        attn_weights = self.dropout(attn_weights)

        # Shape: (b, num_tokens, num_heads, head_dim)
        context_vec = (attn_weights @ values).transpose(1, 2)

        # Combine heads, where self.d_out = self.num_heads * self.head_dim
        context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out)
        context_vec = self.out_proj(context_vec)  # optional projection

        return context_vec


class LayerNorm(nn.Module):
    def __init__(self, emb_dim):
        super().__init__()
        self.eps = 1e-5
        self.scale = nn.Parameter(torch.ones(emb_dim))
        self.shift = nn.Parameter(torch.zeros(emb_dim))

    def forward(self, x):
        mean = x.mean(dim=-1, keepdim=True)
        var = x.var(dim=-1, keepdim=True, unbiased=False)
        norm_x = (x - mean) / torch.sqrt(var + self.eps)
        return self.scale * norm_x + self.shift


class GELU(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(
            torch.sqrt(torch.tensor(2.0 / torch.pi)) *
            (x + 0.044715 * torch.pow(x, 3))
        ))


class FeedForward(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
            GELU(),
            nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
        )

    def forward(self, x):
        return self.layers(x)


class TransformerBlock(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.att = MultiHeadLatentAttention(
            d_in=cfg["emb_dim"],
            d_out=cfg["emb_dim"],
            num_heads=cfg["n_heads"],
            dropout=cfg["drop_rate"],
            qkv_bias=cfg["qkv_bias"],
            latent_dim=cfg["latent_dim"])

        self.ff = FeedForward(cfg)
        self.norm1 = LayerNorm(cfg["emb_dim"])
        self.norm2 = LayerNorm(cfg["emb_dim"])
        self.drop_shortcut = nn.Dropout(cfg["drop_rate"])

    def forward(self, x, use_cache=False):
        # Shortcut connection for attention block
        shortcut = x
        x = self.norm1(x)

        # x = self.att(x)   # Shape [batch_size, num_tokens, emb_size]
        ####################################################
        #  KV cache-related
        x = self.att(x, use_cache=use_cache)
        ####################################################

        x = self.drop_shortcut(x)
        x = x + shortcut  # Add the original input back

        # Shortcut connection for feed-forward block
        shortcut = x
        x = self.norm2(x)
        x = self.ff(x)
        x = self.drop_shortcut(x)
        x = x + shortcut  # Add the original input back

        return x


class GPTModel(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
        self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
        self.drop_emb = nn.Dropout(cfg["drop_rate"])

        # self.trf_blocks = nn.Sequential(
        #    *[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
        ####################################################
        #  KV cache-related
        self.trf_blocks = nn.ModuleList(
            [TransformerBlock(cfg) for _ in range(cfg["n_layers"])])

        self.current_pos = 0
        ####################################################

        self.final_norm = LayerNorm(cfg["emb_dim"])
        self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)

    def forward(self, in_idx, use_cache=False):
        batch_size, seq_len = in_idx.shape
        tok_embeds = self.tok_emb(in_idx)

        # pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))

        ####################################################
        #  KV cache-related
        if use_cache:
            pos_ids = torch.arange(self.current_pos, self.current_pos + seq_len, device=in_idx.device, dtype=torch.long)
            self.current_pos += seq_len
        else:
            pos_ids = torch.arange(0, seq_len, device=in_idx.device, dtype=torch.long)
        pos_embeds = self.pos_emb(pos_ids).unsqueeze(0)
        ####################################################

        x = tok_embeds + pos_embeds  # Shape [batch_size, num_tokens, emb_size]
        x = self.drop_emb(x)

        # x = self.trf_blocks(x)
        ####################################################
        #  KV cache-related
        for blk in self.trf_blocks:
            x = blk(x, use_cache=use_cache)
        ####################################################

        x = self.final_norm(x)
        logits = self.out_head(x)
        return logits

    ####################################################
    #  KV cache-related
    def reset_kv_cache(self):
        for blk in self.trf_blocks:
            blk.att.reset_cache()
        self.current_pos = 0
    ####################################################


def generate_text_simple_cached(model, idx, max_new_tokens,
                                context_size=None, use_cache=True):
    model.eval()
    ctx_len = context_size or model.pos_emb.num_embeddings

    with torch.no_grad():
        if use_cache:
            # Init cache with full prompt
            model.reset_kv_cache()
            logits = model(idx[:, -ctx_len:], use_cache=True)

            for _ in range(max_new_tokens):
                # a) pick the token with the highest log-probability (greedy sampling)
                next_idx = logits[:, -1].argmax(dim=-1, keepdim=True)
                # b) append it to the running sequence
                idx = torch.cat([idx, next_idx], dim=1)
                # c) feed model only the new token
                logits = model(next_idx, use_cache=True)
        else:
            for _ in range(max_new_tokens):
                logits = model(idx[:, -ctx_len:], use_cache=False)
                next_idx = logits[:, -1].argmax(dim=-1, keepdim=True)
                idx = torch.cat([idx, next_idx], dim=1)

    return idx


def main():
    parser = argparse.ArgumentParser(description="Run GPT with standard multi-head attention.")
    parser.add_argument("--emb_dim", type=int, default=768, help="Model embedding dimension.")
    parser.add_argument("--n_heads", type=int, default=12, help="Number of attention heads.")
    parser.add_argument("--n_layers", type=int, default=12, help="Number of transformer blocks.")
    parser.add_argument("--max_new_tokens", type=int, default=200, help="Number of tokens to generate.")
    parser.add_argument("--latent_dim", type=int, default=None,
                        help="Latent dim for MLA (default: d_out//8)")

    args = parser.parse_args()

    start_context = "Hello, I am"
    tokenizer = tiktoken.get_encoding("gpt2")
    encoded = tokenizer.encode(start_context)

    GPT_CONFIG_124M = {
        "vocab_size": 50257,        # Vocabulary size
        "context_length": args.max_new_tokens + len(encoded),
        "emb_dim": args.emb_dim,    # Embedding dimension
        "n_heads": args.n_heads,    # Number of attention heads
        "n_layers": args.n_layers,  # Number of layers
        "drop_rate": 0.0,           # Dropout rate
        "qkv_bias": False,          # Query-Key-Value bias
        "latent_dim": args.latent_dim,
    }
    torch.manual_seed(123)
    model = GPTModel(GPT_CONFIG_124M)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device, dtype=torch.bfloat16)
    model.eval()  # disable dropout

    encoded_tensor = torch.tensor(encoded, device=device).unsqueeze(0)
    print(f"\n{50*'='}\n{22*' '}IN\n{50*'='}")
    print("\nInput text:", start_context)
    print("Encoded input text:", encoded)
    print("encoded_tensor.shape:", encoded_tensor.shape)

    if torch.cuda.is_available():
        torch.cuda.synchronize()
    start = time.time()

    token_ids = generate_text_simple_cached(
        model=model,
        idx=encoded_tensor,
        max_new_tokens=args.max_new_tokens,
    )

    if torch.cuda.is_available():
        torch.cuda.synchronize()
    total_time = time.time() - start

    decoded_text = tokenizer.decode(token_ids.squeeze(0).tolist())

    print(f"\n\n{50*'='}\n{22*' '}OUT\n{50*'='}")
    print("\nOutput:", token_ids)
    print("Output length:", len(token_ids[0]))
    print("Output text:", decoded_text)

    print(f"\nTime: {total_time:.2f} sec")
    print(f"{int(len(token_ids[0])/total_time)} tokens/sec")
    if torch.cuda.is_available():
        max_mem_bytes = torch.cuda.max_memory_allocated()
        max_mem_gb = max_mem_bytes / (1024 ** 3)
        print(f"Max memory allocated: {max_mem_gb:.2f} GB")


if __name__ == "__main__":
    main()