# GPT-3 Paper
# add cosing delay
import os
import math
import time
import inspect
from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F


class CausalSelfAttention(nn.Module):

    def __init__(self, config):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        # key, query, value projections for all heads, but 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.NANGPT_SCALE_INIT = 1
        # regularization
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        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_embd)
        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
        # nh is "number of heads", hs is "head size", and C (number of channels) = nh * hs
        # e.g. in GPT-2 (124M), n_head=12, hs=64, so nh*hs=C=768 channels in the Transformer
        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)

        # att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
        # att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))
        # att = F.softmax(att, dim=-1)
        # y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)

        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)  # Flash attention

        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")
        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
        self.c_proj.NANOGPT_SCALE_INIT = 1

    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 = (
        50304  # number of tokens: 50,000 BPE merges + 256 bytes tokens + 1 <|endoftext|> token
    )
    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
        self.transformer.wte.weight = self.lm_head.weight

        # weight initialization
        self.apply(self._init_weights)

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            std = 0.02
            if hasattr(module, "NANGPT_SCALE_INIT"):
                std *= (2 * self.config.n_layer) ** -0.5
            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)

    def forward(self, idx, targets=None):
        # idx is 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 posisition embeddings
        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)  # shape (T)
        pos_emb = self.transformer.wpe(pos)  # position embeddings of shape (T, n_embd)
        tok_emb = self.transformer.wte(idx)  # token embeddings of shape (B, T, n_embd)
        x = tok_emb + pos_emb
        # forward the blocks of the transformer
        for block in self.transformer.h:
            x = block(x)
        # forward the final layernorm and the classifier
        x = self.transformer.ln_f(x)
        logits = self.lm_head(x)  # (B, T, vocab_size)
        loss = None
        if targets is not None:
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
        return logits, loss

    @classmethod
    def from_pretrained(cls, model_type):
        """Loads pretrained GPT-2 model weights from huggingface"""
        assert model_type in {"gpt2", "gpt2-medium", "gpt2-large", "gpt2-xl"}
        from transformers import GPT2LMHeadModel

        print("loading weights from pretrained gpt: %s" % model_type)

        # n_layer, n_head and n_embd are determined from model_type
        config_args = {
            "gpt2": dict(n_layer=12, n_head=12, n_embd=768),  # 124M params
            "gpt2-medium": dict(n_layer=24, n_head=16, n_embd=1024),  # 350M params
            "gpt2-large": dict(n_layer=36, n_head=20, n_embd=1280),  # 774M params
            "gpt2-xl": dict(n_layer=48, n_head=25, n_embd=1600),  # 1558M params
        }[model_type]
        config_args["vocab_size"] = 50257  # always 50257 for GPT model checkpoints
        config_args["block_size"] = 1024  # always 1024 for GPT model checkpoints
        # create a from-scratch initialized minGPT model
        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 / buffer, not a param

        # init a huggingface/transformers model
        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
        sd_hf = model_hf.state_dict()

        # copy while ensuring all of the parameters are aligned and match in names and shapes
        sd_keys_hf = sd_hf.keys()
        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, device_type):
        # start with all of the candidate parameters (that require grad)
        param_dict = {pn: p for pn, p in self.named_parameters()}
        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"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters"
        )
        print(
            f"num 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"

        print(f"using fused AdamW: {use_fused}")
        optimizer = torch.optim.AdamW(
            optim_groups, lr=learning_rate, betas=(0.9, 0.95), eps=1e-8, fused=use_fused
        )
        return optimizer